EMPLOYEE PERCEPTIONS OF ORGANISATIONAL COMMITMENT…

EMPLOYEE PERCEPTIONS OF ORGANISATIONAL COMMITMENT, JOBSATISFACTION AND TURNOVER INTENTIONS IN A POST-MERGER

INSTITUTION

by

ADAM MARTIN

DISSERTATION

submitted in fulfilment of the requirements for the degree

MAGISTER COMMERCII

in

HUMAN RESOURCE MANAGEMENT

in the

FACULTY OF MANAGEMENT

at the

UNIVERSITY OF JOHANNESBURG

Supervisor: Professor Gert RoodtJULY 2007

EMPLOYEE PERCEPTIONS OF ORGANISATIONAL COMMITMENT, JOB SATISFACTIONAND TURNOVER INTENTIONS IN A POST-MERGER INSTITUTION

i

STATEMENT

I certify that the dissertation submitted by me for the degree Master of Commerce

(Human Resource Management) at the University of Johannesburg is my

independent work and has not been submitted by me for a degree at another

university.

__________________

ADAM MARTIN

JULY 2007


ii

DECLARATION OF ADHERENCE: ETHICS IN RESEARCH

I, the undersigned, hereby declare that:

1. the content of this document is my own work; and

2. I have adhered to the ethical obligations and principles of research ethics,

as prescribed by the faculty’s guidelines for ethics research, during all

phases of the research process.

_________________________ NAME OF PRINCIPAL RESEARCHER

_________________________ SIGNATURE

_________________________ PLACE

_________________________ DATE


iii

ACKNOWLEDGEMENTS

I would like to extend my gratitude and appreciation to the following people for

making this dissertation possible:

To my mother, Caroline, for providing me with the opportunity to study further.

Your love, support and encouragement throughout this endeavour always

reminded both of us how important it was. There are not enough words in the

world to express my love and gratitude.

My supervisor, Professor Gert Roodt, for allowing me the complete freedom to

pursue this study; to work on my own initiative; and for showing confidence in my

abilities. Your professionalism, expertise and exceptional turnaround time have

been well appreciated.

My brother, Daniel, Boetie, for your understanding, attitude and humour. Your

carefree stance reminded me not to take myself too seriously always and that I,

too, need to take a break sometimes. And although short, your fortnightly visits

were always appreciated.

To Freddy Labutte, thank you for your friendship, company and tireless support

in helping create the stability at home that nurtured my writings and allowed me

to work without any hindrances.

To my previous manager, Riëtte Eiselen; you provided the essential crux to my

thinking in the initial phases of my study and made me realise the enormity of the

work that lay ahead. Your guidance during my initial stages made me truly

appreciate and understand what it felt like to undertake independent research.


iv

To my previous colleagues at STATKON; Anneli Hardy, Robert Crawford, and

David Venter; your support, advice and friendship were a rewarding experience.

To Everd Jacobs, it is now my turn to thank you for your advice and contribution.

Your attention to detail and insight provided additional, and appreciated, value to

my study.

To all my friends, whose numerous invitations I continually had to turn down,

thank you for your understanding and support.

And to the University of Johannesburg for granting me the permission to pursue

this study and the faceless employees who completed my survey. Without you

all, my study would not have borne fruition.


v

ABSTRACT

EMPLOYEE PERCEPTIONS OF ORGANISATIONAL COMMITMENT, JOBSATISFACTION AND TURNOVER INTENTIONS IN A POST-MERGER

INSTITUTION

by

ADAM MARTIN

SUPERVISOR: Professor Gert RoodtDEPARTMENT: Department of Human Resource Management

Faculty of ManagementUniversity of Johannesburg

DEGREE: M.Com.DATE: July 2007

A merger can be considered both a phenomenological and significant life event

for an organisation and its employees, and how people cope with and respond to

a merger has a direct impact on the institutional performance in the short to

medium term. It is within this context that post-merger perceptions of a tertiary

institution were gauged.

Restructuring in any organisation is characterised by uncertainty, high levels of

anxiety, low levels of morale, and tardy job performance, as well as high levels of

absenteeism and staff turnover, all of which potentially impact on productivity and

performance. Notably, the global phenomenon of transformation of higher

education, taking place in most countries in the world, is an undeniable fact.


vi

The abolition of apartheid and the post-1994 aftermath period have seen South

Africa undergoing tremendous transformation in its political, economic, social and

technological environments. As part of the social environment, education, too,

will be subjected to the restructuring and transition resulting in the new

characterisation of the country and its people. Mergers are taking place between

teacher-training colleges and technical colleges, as well as between universities

and technikons. In South Africa to date, mergers have been limited mainly to the

federal absorption of smaller, specialist institutions into universities; however

larger and more unitary mergers have been advocated.

Few notable studies have investigated the commitment perceptions of the

employees (and the associated selected work constructs of job satisfaction and

turnover intentions) who feel the full impact of these restructurings in a South

African context. This subsequently results in a dearth of knowledge on the

context of South African mergers and acquisitions of tertiary institutions. Human

capital element in the form of teacher / facilitator / lecturer in educational

institutions (knowledge intensive organisations) is much more important than in

other organisations. In light of the recent restructuring of the institution in

question, no attempt has yet been made to gauge the levels of organisational

commitment amongst its employees. It is within this context that the research

problem emerges: What are the employee perceptions of job satisfaction,

organisational commitment, and turnover intentions in a post-merger tertiary

institution, and how are these variables related?

Job satisfaction was determined as a pleasurable or positive emotional state

resulting from the appraisal of one s job or job experiences. A global approach

was adopted, whereby job satisfaction is explained as a single, overall feeling

toward one s job.

Organisational commitment was defined as a cognitive predisposition towards a

particular focus, insofar as this focus has the potential to satisfy needs, realise


vii

values, and achieve goals, and was subsequently addressed through a

motivational approach. The state of commitment is not only separated from its

antecedent and consequential conditions and behaviours, but also from its

related affective and conative components that are also present in other widely

used constructs, such as job satisfaction and turnover intentions respectively.

Turnover intentions, approached as being mental decisions intervening between

an individual s attitudes regarding a job and the stay or leave decision, were

addressed as a planned behaviour. This is a result from the argument that

behavioural intention is a good predictor of actual behaviour, in this case actual

turnover. Turnover behaviour is a multistage process that includes attitudinal,

decisional, and behavioural components. Furthermore the turnover process is

initially stimulated by the thought of quitting, which ultimately will result in the

actual process of either staying or leaving.

The instance of a merger or acquisition normally results in, amongst others, lack

of commitment, job dissatisfaction, increased labour turnover and absenteeism

rates (even at managerial level), lowered work goals, uncertainty, and employee

theft or acts of sabotage. The relationships established between the three

selected work constructs, primarily in terms of mergers and acquisitions, suggest

that a positive relationship exists between job satisfaction and organisational

commitment, whilst also yielding a negative relationship with turnover intentions.

The research approach could be described as a non-experimental and cross-

sectional field survey, the data as primary data, and data analysis as ex post

facto and correlational. The non-probability (convenience) sample consisted of

367 employees of a South African tertiary instituition. The completion of the

electronic questionnaires was personally administered and anonymously

handled.


viii

Job satisfaction was assessed by the Minnesota Satisfaction Questionnaire

(MSQ20). The MSQ20 measures 20 different job-related items and can be sub-

categorised into extrinsic and intrinsic satisfaction. The end factor analystic result

revealed the need to remove three items. Commitment was addressed through

the Organisational Commitment Questionnaire which consisted of 18 items,

measuring different foci of commitment, namely work, career, occupational and

organisational. Diagnostic analyses indicated the need to remove three items.

Turnover intentions were measured by an unpublished 15 item questionnaire.

The diagnostic analyses warranted the removel of two items.

The analyses followed a two phase procedure. The intial phase included all

diagnostic testing of the measuring instruments in order to determine the

reliabilty and validty of the measuring instruments for subsequent testing

purposes of the study. The tests utilised were basic descriptives, factor (first and

second order) and reliability analyses and normality testing. The latter phase

described the inferential section of the sample, whereby statistics are used either

to infer the truth or falsify hypotheses / research objectives. The tests carried out

consisted of t-tests and ANOVA, correlations, structural equation modelling, two-

way ANOVA and lastly a stepwise linear regression. Fifteen predefined models

were investigated whereupon the most parsimonious model was selected.

In applying the stepwise linear regression for the prediction of turnover intentions,

the model was determined by entering all the variables simultaneously into the

regression equation. The variables determined for the inclusion on the regression

were based on the results from the inferenital testing phase. The final result

yielded a prediction of 47% of the variance in turnover intentions. The final (most

parsimonious) model determined for turnover intentions indicated as being

significantly predicted by: job satisfaction, tenure, and a combination of job

satisfaction and organisational commitment. Contrary to popular belief,

commitment does not correlate more strongly than satisfaction does with


ix

turnover intentions. This indicates that withdrawal entails a rejection of the job

rather than of the organisation.

Turnover intentions of tertiary employees can be actively managed through the

manipulation of the contextual variables of organisational commitment and job

satisfaction. The resulting predictive model can be regarded as an important tool

for management and the Human Resource Department in effectively planning

talent retention strategies focusing on its controllable dimensions. Since this

model was developed based on internal components, possible strategies can be

derived from this model to prevent turnover intentions.


x

TABLE OF CONTENTS

STATEMENT ......................................................................................................... i

DECLARATION OF ADHERENCE: ETHICS IN RESEARCH .............................. ii

ACKNOWLEDGEMENTS ................................................................................... iii

ABSTRACT .......................................................................................................... v

TABLE OF CONTENTS ....................................................................................... x

LIST OF TABLES.............................................................................................. xvi

LIST OF FIGURES ...........................................................................................xxiv

ANNEXURES ................................................................................................... xxv

CHAPTER 1: INTRODUCING THE PROBLEM

1.1 Introduction.............................................................................................1

1.2 Background of the Problem ....................................................................1

1.3 Motivation and Rationale for the Study...................................................3

1.4 Problem Statement .................................................................................6

1.5 Proposed Value-Add of Research ........................................................11

1.5.1 Proposed Methodological Value ...........................................................12

1.5.2 Proposed Theoretical Value .................................................................12

1.5.3 Proposed Practical Value .....................................................................13

1.6 Outline of Remaining Chapters.............................................................13

1.7 Synthesis ..............................................................................................14

CHAPTER 2: LITERATURE REVIEW

2.1 Introduction...........................................................................................15

2.2 Theoretical Objectives ..........................................................................15

2.3 Defining the Key Concepts ...................................................................16

2.3.1 Job Satisfaction .............................................................................16


xi

2.3.2 Organisational Commitment ..........................................................18

2.3.3 Turnover Intentions........................................................................19

2.4 Job Satisfaction ....................................................................................20

2.4.1 Theoretical Framework of Job Satisfaction...........................................20

2.4.2 Job Satisfaction Dimensions.................................................................23

2.5 Organisational Commitment .................................................................25

2.5.1 Theoretical Framework of Organisational Commitment.................25

2.5.2 Approaches to the Study of Commitment ......................................29

2.5.3 Commitment Foci ..........................................................................32

2.5.4 A Linkage Motivational Model........................................................35

2.6 Turnover Intentions...............................................................................37

2.6.1 Turnover Intentions as Planned Behaviour....................................37

2.6.2 Turnover Cognition Types .............................................................39

2.7 Outcomes of a Merger or Acquisition....................................................40

2.8 Relationships between the Key Concepts ............................................44

2.9 Background Factors Related to Key Concepts .....................................46

2.9.1 Age................................................................................................47

2.9.2 Tenure ...........................................................................................49

2.9.3 Gender ..........................................................................................50

2.9.4 Race..............................................................................................52

2.9.5 Marital Status ................................................................................54

2.9.6 Highest Academic Qualification.....................................................55

2.10 Synthesis ..............................................................................................57

CHAPTER 3: RESEARCH DESIGN AND METHODOLOGY

3.1 Introduction...........................................................................................59

3.2 Empirical Research Objectives.............................................................59

3.3 Research Approach..............................................................................61

3.3.1 Qualitative versus Quantitative Research......................................63

3.3.2 Experimental versus Non-Experimental Research ........................65


xii

3.3.3 Primary versus Secondary Data....................................................68

3.3.4 Self-Administered versus Others...................................................69

3.4 Research Methodology.........................................................................70

3.4.1 Participants / Sample.....................................................................71

3.4.2 Research Procedure......................................................................89

3.4.3 Measuring Instruments ..................................................................92

3.4.4 Statistical Analysis.........................................................................99

3.5 Synthesis ............................................................................................113

CHAPTER 4: RESULTS OF THE STUDY

4.1 Introduction.........................................................................................114

4.2 Empirical Research Objectives...........................................................116

4.3 Basic Descriptive Statistics.................................................................118

4.3.1 Demographics .............................................................................118

4.3.2 Descriptive Statistics of the Minnesota Satisfaction

Questionnaire (MSQ20)...............................................................118

4.3.3 Descriptive Statistics of the Organisational Commitment

Questionnaire (OCQ)...................................................................120

4.3.4 Descriptive Statistics of the Intentions to Stay Questionnaire

(ISQ)............................................................................................122

4.3.5 Summary of Descriptive Statistics of the Total Scores ................124

4.4 Results of the Factor Analysis ............................................................125

4.4.1 The Minnesota Satisfaction Questionnaire (MSQ20)...................125

4.4.2 The Organisational Commitment Questionnaire (OCQ) ..............132

4.4.3 The Intentions to Stay Questionnaire (ISQ).................................139

4.5 Results of the Reliability Analyses......................................................144

4.5.1 Job Satisfaction Iterative Item Reliability Analysis.......................144

4.5.2 Organisational Commitment Iterative Item Reliability Analysis....146

4.5.3 Turnover Intentions Iterative Item Reliability Analysis .................147

4.6 Kolmogorov-Smirnoz Test for Normality of Overall Factors................148


xiii

4.7 Inferential Testing (ANOVA, t-tests)....................................................149

4.7.1 The Minnesota Satisfaction Questionnaire (MSQ20)...................151

4.7.2 The Organisational Commitment Questionnaire (OCQ) ..............159

4.7.3 Intentions to Stay Questionnaire (ISQ)........................................171

4.8 Intercorrelations of Constructs............................................................182

4.9 Structural Equation Modelling.............................................................183

4.10 Two-Way Analysis of Variance ...........................................................189

4.10.1 Age versus Gender......................................................................189

4.10.2 Age versus Race .........................................................................191

4.10.3 Age versus Martial Status............................................................193

4.10.4 Age versus Highest Academic Qualification ................................195

4.10.5 Age versus Tenure ......................................................................197

4.10.6 Gender versus Race....................................................................199

4.10.7 Gender versus Marital Status ......................................................201

4.10.8 Gender versus Highest Academic Qualification ..........................203

4.10.9 Gender versus Tenure.................................................................205

4.10.10 Race versus Marital Status..........................................................207

4.10.11 Race versus Highest Academic Qualification ..............................209

4.10.12 Race versus Tenure ....................................................................210

4.10.13 Marital Status versus Highest Academic Qualification.................212

4.10.14 Marital Status versus Tenure.......................................................214

4.10.15 Highest Academic Qualification versus Tenure ...........................216

4.11 Stepwise Linear Regression...............................................................218

4.11.1 Indicator Variables.......................................................................219

4.11.2 Model #7 with Demographic Variables ........................................221

4.11.3 Model #10 with Demographic Variables ......................................222

4.11.4 Model #12 with Demographic Variables ......................................224

4.11.5 Model Comparisons.....................................................................226

4.12 Synthesis ............................................................................................228


xiv

CHAPTER 5: DISCUSSION AND INTERPRETATION

5.1 Introduction.........................................................................................231

5.2 Literature Survey ................................................................................231

5.2.1 Review of the Theoretical Research Objectives ..........................231

5.2.2 Results of the Literature Survey ..................................................232

5.3 Key Empirical Findings .......................................................................247

5.3.1 Basic Descriptives .......................................................................247

5.3.2 Factor Analysis............................................................................248

5.3.3 Reliability Analysis.......................................................................252

5.3.4 Normality Testing.........................................................................253

5.3.5 ANOVA and t-tests ......................................................................253

5.3.6 Correlations .................................................................................255

5.3.7 Structural Equation Modelling......................................................255

5.3.8 Two-Way Analysis of Variance....................................................257

5.3.9 Stepwise Linear Regression........................................................258

5.4 The Empirical Study............................................................................259

5.4.1 Review of the Empirical Research Objectives .............................259

5.4.2 Addressing the Empirical Research Objectives...........................261

5.5 Synthesis ............................................................................................268

CHAPTER 6: CONCLUSION

6.1 Introduction.........................................................................................270

6.2 Overview of the Chapters ...................................................................270

6.3 Key Findings.......................................................................................275

6.3.1 Theoretical Key Findings.............................................................277

6.3.2 Practical Key Findings.................................................................279

6.3.3 Methodological Key Findings.......................................................282

6.4 Recommendations..............................................................................284

6.4.1 Theoretical Recommendations....................................................284


xv

6.4.2 Practical Recommendations........................................................285

6.4.3 Methodological Recommendations..............................................285

6.5 Value-Add...........................................................................................286

6.5.1 Theoretical Value-Add.................................................................286

6.5.2 Practical Value-Add.....................................................................287

6.5.3 Methodological Value-Add...........................................................288

6.6 Limitations and Suggestions for Future Research ..............................289

6.7 Synthesis ............................................................................................293

LIST OF REFERENCES...................................................................................294


xvi

LIST OF TABLES

Table 3.1: Difference Between Quantitative and Qualitative Research..........63

Table 3.2: Differences Between Experimental and Non-Experimental

Research .......................................................................................66

Table 3.3: Differences Between Primary and Secondary Data.......................68

Table 3.4: Key Used for Demographics Questions.........................................73

Table 3.5: Bias Analysis of Age......................................................................75

Table 3.6: Bias Analysis of Gender ................................................................76

Table 3.7: Bias Analysis of Race....................................................................76

Table 3.8: Bias Analysis of Home Language..................................................77

Table 3.9: Bias Analysis of Home Language Recategorised..........................78

Table 3.10: Bias Analysis of Marital Status ......................................................79

Table 3.11: Bias Analysis of Marital Status Recategorised ..............................80

Table 3.12: Bias Analysis of Campus...............................................................81

Table 3.13: Bias Analysis of Job Status ...........................................................82

Table 3.14: Bias Analysis of Conditions of Service ..........................................82

Table 3.15: Demographic Information of the Respondents ..............................84

Table 3.16: Dropout Rate per Each Section.....................................................88

Table 3.17: Interpretation of the Correlation Coefficient .................................107

Table 4.1: Descriptive Statistics of the MSQ20 ............................................119

Table 4.2: Descriptive Statistics of the OCQ ................................................121

Table 4.3: Descriptive Statistics of the ISQ ..................................................123

Table 4.4: Descriptive Statistics of the Overall Dimensions..........................124

Table 4.5: KMO and Bartlett’s Test of the Item Intercorrelation Matrix of

the MSQ20 ..................................................................................126

Table 4.6 : Communalities and Unit MSA of the MSQ20...............................127

Table 4.7: Eigenvalues of the Unreduced Item Intercorrelation Matrix of

the MSQ20 ..................................................................................128

Table 4.8: Rotated and Sorted Factor Matrix of the MSQ20 ........................129


xvii

Table 4.9: KMO and Bartlett’s Test of the Sub-Score Intercorrelation

Matrix of the MSQ20....................................................................130

Table 4.10: Communalities and Sub-Score MSA of the MSQ20 ....................131

Table 4.11: Eigenvalues of the Unreduced Sub-Score Intercorrelation

Matrix of the MSQ20....................................................................131

Table 4.12: Factor Matrix of the MSQ20 ........................................................132


the OCQ ......................................................................................133

Table 4.14: Communalities and Unit MSA of the OCQ...................................134


the OCQ ......................................................................................135

Table 4.16: Rotated and Sorted Factor Matrix of the OCQ ............................136

Table 4.17: KMO and Bartlett’s Test of the Sub-Score Intercorrelation

Matrix of the OCQ........................................................................137

Table 4.18: Communalities and Sub-Score MSA of the OCQ ........................137

Table 4.19: Eigenvalues of the Unreduced Sub-Score Intercorrelation

Matrix of the OCQ........................................................................138

Table 4.20: Factor Matrix of the OCQ ............................................................139


the ISQ ........................................................................................140

Table 4.22: Communalities and Unit MSA of the ISQ.....................................141


the ISQ ........................................................................................142

Table 4.24: Rotated and Sorted Factor Matrix of the ISQ ..............................143

Table 4.25: Iterative Item Reliability Analysis of the MSQ20 ..........................145

Table 4.26: Iterative Item Reliability Analysis of the OCQ..............................146

Table 4.27: Iterative Item Reliability Analysis of the ISQ................................147

Table 4.28: Kolmogorov-Smirnov Test for Normality......................................148

Table 4.29: Recoded Demographic Information of the Respondents.............150

Table 4.30: Descriptive Statistics of the Age Groups for the MSQ20 .............152


xviii

Table 4.31: Levene’s Test of Homogeneity of Variance of Different Age

Categories for the MSQ20...........................................................153

Table 4.32: ANOVA - Comparison between Different Age Categories for

the MSQ20 ..................................................................................153

Table 4.33: Descriptive Statistics of the Gender Groups for the MSQ20........154

Table 4.34: Independent Samples t-test for the Equality of Means of the

Gender Groups for the MSQ20 ...................................................154

Table 4.35: Descriptive Statistics of the Race Groups for the MSQ20 ...........155


Race Groups for the MSQ20 .......................................................155

Table 4.37: Descriptive Statistics of the Marital Status Groups for the

MSQ20 ........................................................................................156


Marital Status Groups for the MSQ20..........................................156

Table 4.39: Descriptive Statistics of the Highest Academic Qualification

Groups for the MSQ20 ................................................................157

Table 4.40: Levene’s Test of Homogeneity of Variance of Different

Highest Academic Qualification Categories for the MSQ20 ........157

Table 4.41: ANOVA - Comparison between Different Highest Academic

Qualification Categories for the MSQ20 ......................................158

Table 4.42: Descriptive Statistics of the Tenure Groups for the MSQ20 ........158


Tenure Categories for the MSQ20...............................................159

Table 4.44: ANOVA - Comparison between Different Tenure Categories

for the MSQ20 .............................................................................159

Table 4.45: Descriptive Statistics of the Age Groups for the OCQ .................160


Categories for the OCQ...............................................................161


the OCQ ......................................................................................161


xix

Table 4.48: Post-Hoc Test - Comparison between Different Age


Table 4.49: Descriptive Statistics of the Gender Groups for the OCQ............163


Gender Groups for the OCQ .......................................................163

Table 4.51: Descriptive Statistics of the Race Groups for the OCQ ...............164


Race Groups for the OCQ...........................................................164


OCQ ............................................................................................166


Marital Status Groups for the OCQ..............................................166


Groups for the OCQ ....................................................................167


Highest Academic Qualification Categories for the OCQ ............167


Qualification Categories for the OCQ ..........................................168



Table 4.59: Descriptive Statistics of the Tenure Groups for the OCQ ............170


Tenure Categories for the OCQ...................................................170


for the OCQ .................................................................................170

Table 4.62: Descriptive Statistics of the Age Groups for the ISQ ...................172


Categories for the ISQ.................................................................172


the ISQ ........................................................................................173


xx



Table 4.66: Descriptive Statistics of the Gender Groups for the ISQ..............175


Gender Groups for the ISQ .........................................................175

Table 4.68: Descriptive Statistics of the Race Groups for the ISQ .................176


Race Groups for the ISQ.............................................................176


ISQ ..............................................................................................177


Marital Status Groups for the ISQ................................................177


Groups for the ISQ ......................................................................178


Highest Academic Qualification Categories for the ISQ ..............178


Qualification Categories for the ISQ ............................................179

Table 4.75: Descriptive Statistics of the Tenure Groups for the ISQ ..............179


Tenure Categories for the ISQ ....................................................180


for the ISQ...................................................................................180

Table 4.78: Post-Hoc Test - Comparison between Different Tenure


Table 4.79: Intercorrelations between the Different Work Constructs ............182

Table 4.80: Structural Equation Modelling Outcome Summary ......................186

Table 4.81: Descriptive Statistics of the Age and Gender Groups for the

ISQ ..............................................................................................190

Table 4.82: Two-Way ANOVA - Comparison between Different Age and

Gender Categories for the ISQ....................................................191


xxi

Table 4.83: Descriptive Statistics of the Age and Race Groups for the

ISQ ..............................................................................................192


Race Categories for the ISQ .......................................................193

Table 4.85: Descriptive Statistics of the Age and Martial Status Groups

for the ISQ...................................................................................194


Marital Status Categories for the ISQ..........................................195

Table 4.87: Descriptive Statistics of the Age and Highest Academic

Qualification Groups for the ISQ..................................................196


Highest Academic Qualification (HAQ) Categories for the

ISQ ..............................................................................................197

Table 4.89: Descriptive Statistics of the Age and Tenure Groups for the

ISQ ..............................................................................................198



Table 4.91: Descriptive Statistics of the Gender and Race Groups for the

ISQ ..............................................................................................200

Table 4.92: Two-Way ANOVA - Comparison between Different Gender

and Race Categories for the ISQ.................................................200

Table 4.93: Descriptive Statistics of the Gender and Marital Status



and Marital Status Categories for the ISQ...................................203

Table 4.95: Descriptive Statistics of the Gender and Highest Academic



and Highest Academic Qualification (HAQ) Categories for

the ISQ ........................................................................................205


xxii

Table 4.97: Descriptive Statistics of the Gender and Tenure Groups for

the ISQ ........................................................................................206


and Tenure Categories for the ISQ..............................................207

Table 4.99: Descriptive Statistics of the Race and Marital Status Groups

for the ISQ...................................................................................208

Table 4.100: Two-Way ANOVA - Comparison between Different Race and

Marital Status Categories for the ISQ..........................................208

Table 4.101: Descriptive Statistics of the Race and Highest Academic



Highest Academic Qualification (HAQ) Categories for the

ISQ ..............................................................................................210

Table 4.103: Descriptive Statistics of the Race and Tenure Groups for the

ISQ ..............................................................................................211



Table 4.105: Descriptive Statistics of the Marital Status and Highest

Academic Qualification Groups for the ISQ.................................213

Table 4.106: Two-Way ANOVA - Comparison between Different Marital

Status and Highest Academic Qualification (HAQ)


Table 4.107: Descriptive Statistics of the Marital Status and Tenure


Table 4.108: Two-Way ANOVA - Comparison between Different Marital

Status and Tenure Categories for the ISQ ..................................216


and Tenure Groups for the ISQ...................................................217

Table 4.110: Two-Way ANOVA - Comparison between Different Highest

Academic Qualification (HAQ) and Tenure Categories for the

ISQ ..............................................................................................218


xxiii

Table 4.111: Age of Younger than 40 years Indicator Variable ........................219

Table 4.112: Tenure of 6 to 10 Years Indicator Variable...................................220

Table 4.113: White / Male | Black / Female Indicator Variable .........................220

Table 4.114: Model Summary of Model #7.......................................................221

Table 4.115: Coefficients and Colinearity Diagnostics of Model #7..................222

Table 4.116: Model Summary of Model #10.....................................................223

Table 4.117: Coefficients and Colinearity Diagnostics of Model #10................224

Table 4.118: Model Summary of Model #12.....................................................225

Table 4.119: Coefficients and Colinearity Diagnostics of Model #12................226

Table 4.120: Comparison of the Models...........................................................227

Table 5.1: Summary of Background Variables against Job Satisfaction ......242

Table 5.2: Summary of Background Variables against Organisational

Commitment ................................................................................244

Table 5.3: Summary of Background Variables against Turnover

Intentions.....................................................................................246

Table 5.4: Summary of Testing between Background Variables And

Instruments..................................................................................254

Table 5.5: Summary of Correlations between Instruments...........................255

Table 5.6: Structural Equation Modelling Outcome Summary ......................257

Table 5.7: Summary of Two-Way ANOVA Testing.......................................258

Table 5.8: Comparison of the Models...........................................................259

Table 6.1: Limitations of Study and Suggestions for Future Research.........290


xxiv

LIST OF FIGURES

Figure 1.1: The 15 Hypothesised Models ........................................................10

Figure 2.1: The Link between Cognition, Affect, Conation and Manifest

Behaviour (Adapted from Fishbein & Ajzen, 1975)........................36

Figure 2.2: Sequence of Turnover Cognitions (Adapted from Mobley,

1977) .............................................................................................39

Figure 3.1: Outline of Steps in the Research Approach...................................62

Figure 3.2: Outline of Steps in the Research Methodology .............................70

Figure 3.3: Statistical Flow Chart Process.....................................................100

Figure 4.1: Statistical Flow Chart Process.....................................................115

Figure 4.2: Mean Values of Organisational Commitment for Each Age

Category......................................................................................162

Figure 4.3: Mean Values of Organisational Commitment for Each Race

Category......................................................................................165

Figure 4.4: Mean Values of Organisational Commitment for Each

Highest Academic Qualification Category ...................................169

Figure 4.5: Mean Values of Turnover Intentions for Each Age Category.......174

Figure 4.6: Mean Values of Turnover Intentions for Each Tenure

Category......................................................................................181

Figure 4.7: Intercorrelations between the Different Work Constructs ............183

Figure 4.8: Path Analysis to Determine the Best Fit Model ...........................185

Figure 4.9: Selected Hypothesised Models ...................................................188

Figure 4.10: Mean Values of Turnover Intentions for the Interaction

between the Gender and Race Groups. ......................................201

Figure 4.11: Final Turnover Intentions Model ..................................................227



Figure 5.3: Final Turnover Intentions Model ..................................................267

Figure 6.1: Chapter Process Sequence.........................................................271


xxv

ANNEXURES

Annexure A: Permission for Study ....................................................................324

Annexure B: Introduction ..................................................................................325

Annexure C: Instructions and Demographic Questionnaire ..............................326

Annexure D: Job Satisfaction Questionnaire ....................................................330

Annexure E: Organisational Commitment Questionnaire..................................332

Annexure F: Intentions to Stay Questionnaire ..................................................334


1

1 CHAPTER 1: INTRODUCING THE PROBLEM

1.1 Introduction

Chapter 1 introduces the problem of the study. More specifically, the following

areas will be outlined: background of the problem, motivation and rationale for

the study, the problem statement, proposed value-add of the research and an

outline of the remaining chapters.

1.2 Background of the Problem

Fourie (1999) stated that the global phenomenon of transformation of higher

education, taking place in most countries in the world, is an undeniable fact.

Green and Hayward (1997, p. 3) argued that “(a)ltough higher education is often

seen as slow to change or downright resistant, it has undergone rapid

transformation throughout the world in the last 25 years and may be in a period

of unprecedented change.”

The abolition of apartheid and the post-1994 aftermath period have seen South

Africa undergoing tremendous transformation in its political, economic, social and

technological environments (Bainbridge, 1996). As part of the social

environment, education, too, is destined for the restructuring and transition

characterising the country and its people. The primary and secondary school

sectors were restructured and changed accordingly whereby those learners and

educators who were not in the past allowed to attend or teach at particular

schools were now granted admission into these schools (Arnolds & Boshoff,

2004). Unlike the above-mentioned procedures for the primary and secondary

school sectors, tertiary education institutions were subjected to a more complex

and challenging restructuring process in the form of mergers. Mergers are taking


2

place between teacher-training colleges and technical colleges, as well as

between universities and technikons. The merging of these institutions is

prescribed and guided by the Higher Education Act 101 of 1997 (South Africa,

1997) which indicates that the merging of public higher education institutions will

be implemented including respective subdivisions. Notably though, the Council of

Higher Education Report regarding the restructuring of higher education

indicated that no institutions would close – but rather that the number of

institutions would be reduced through mergers rather than closure (Bisseker,

2000).

In South Africa to date, mergers have been limited mainly to the federal

absorption of smaller, specialist institutions into universities. However larger and

more unitary mergers have been advocated in order to address two particular

problems of the apartheid legacy – the disadvantages experienced by historically

black universities in the form of declining enrolments and bankruptcies1; and the

staff profiles of the former traditionally white Afrikaans universities which still do

not closely reflect racial distribution (Reddy, 1998). Notably these issues

experienced by black universities reduced their efficiency and effectiveness at

competing on a global level (Subotzky, 1997). The crisis at the predominantly

black institutions came to light as South Africa considered ways to cut costs and

avoid the duplication of courses and curriculum; thus the need to restructure its

post-secondary education system. Another notable reason for the necessity was

that under apartheid, many institutions were established to educate only

members of specific racial groups (Vergani, 1999). Brill and Worth (1997)

indicated that change processes must begin with a clearly defined goal2 and in

February 2001, the National Plan for Higher Education (2001, p. 68) stipulated

1 Of the sharp declines experienced, one university reported that its student enrolment dropped

from that of 15 000 students in 1995 to 10 000 in 1998 and then a further decline to 5 500 in 1999

(Vergani, 1999), while other universities had large, unexplained budget deficits.2 They further add that the change effort is not a single event that begins and ends in a single

year, but a highly complicated process.


3

that the Government would work to change the ‘institutional landscape’ through

mergers. The goal thereof is “To build new institutional and organisational forms

and new institutional identities and cultures as integral components of a single

coordinated national higher education system.”

Fourie (1999, p. 276) further argued within this context of institutional change

process that “(t)he transformation of higher education is not only a

comprehensive (i.e. encompassing) process, but also a radical one (i.e. going to

the roots)”.

1.3 Motivation and Rationale for the Study

Restructuring in any organisation is characterised by uncertainty, high levels of

anxiety, low levels of morale, and tardy job performance, as well as high levels of

absenteeism and staff turnover, all of which potentially impact on productivity and

performance. The transition from the old structure to the new can be a time of

hope and exhilaration on the one hand, whilst a time of uncertainty, risk, and loss

on the other (Gersick, 1991).

The recent, politically inspired [see: Higher Education Act 101 of 1997 (South

Africa, 1997); National Plan for Higher Education (2001)] restructuring has now

been extended to that of the tertiary environment, notably in the form of mergers

between technikons and universities. Few have investigated the commitment

perceptions of the employees (and the associated work constructs) who feel the

full impact of these restructurings in a South African context. Jansen (2002)

investigated, amongst others issues, the merger effects felt by the staff members

(i.e. administrative staff versus academic staff versus technical staff). Arnolds

and Boshoff (2004) have undertaken to investigate, longitudinally, the

development of organisational commitment in a restructuring organisation. Their

paper discusses the first phase of such a process.


4

Arnolds and Boshoff (2004, p. 2) validly pointed out that the human capital

element in the form of teacher / facilitator / lecturer in educational institutions

(knowledge intensive organisations) is far more important than in other

organisations, “…as the development, transfer and reception of knowledge

cannot be achieved without the inputs of the educators…” Bourdieu (1986)

observed that intellectual capital in academic institutions is a sought-after

commodity and is recognised as invaluable. Viewed through Bourdieu’s

perspective, organisational members’ specialised knowledge functions as

intellectual capital to the degree that other members recognise it as valuable. To

support this notion, the question may be asked whether professors at universities

could earn higher salaries in the private sector. However, many choose to stay.

Why? Some would go as far as to argue that the inherent culture and values in

universities are in direct conflict with the culture that is necessary for effective

knowledge sharing, but many academic staff consider knowledge to be

proprietary and as a source of differentiation, reputation building and academic

prowess and power (Wind & Main, cited in Rowley, 2003). Arnolds and Boshoff

(2004) added that only through the academic and support staff can the vision,

mission, and goals of tertiary institutions hope to be achieved. Thus, it is

essential that educators and supporting staff be highly committed to their tasks

as well as their institution, if quality outputs are to be achieved. Boshoff and

Arnolds (1995), for example, found that the relationship between teacher’s and

administrative personnel’s job performance and intent to resign was significantly

and positively influenced by their professional and organisational commitment. It

is therefore important that attention be given to the organisational commitment of

staff members of educational institutions that are undergoing, or have

undergone, restructuring.

Education institutions in the past have had stable workforces and reasonably

high levels of organisational commitment among their staff members. On a

historical basis too, working in a higher education institution has been considered

relatively stress-free and highly satisfying (Willie & Stecklein, 1982). This was


5

primarily due to the lack to structural changes that needed to take place, as

historically, South African educational institutions were regarded as protected

institutions. Apart from the normal turnover of staff, few educational institutions

have ever been required to merge with other educational institutions. Recently

these institutions have been called upon aggressively to merge with other

institutions, and, to make matters worse, the different types of educational

institutions have never anticipated the merger of a technikon with a university, as

these institutions serve different markets (Arnolds & Boshoff, 2004).

Fourie (1999) contended that academic staff will have to make paradigm shifts,

adapt, and approach their professional endeavours in new and innovative ways.

This is the result of the precipitous change process, as almost daily there are

new issues and shifts of emphasis that dominate the higher education debate.

A merger can be considered as being both a phenomenological and significant

life event for the organisation and its employees (Sinetar, 1981), and how people

cope with and respond to a merger has a direct impact on the institutional

performance in the short to medium term. DeConinck and Stilwell (2004)

concentrated on job satisfaction and organisational commitment as antecedents

for turnover intentions whereby significant relationships were indicated.

The question now arises as to the extent to which these new changes in the

aftermath of the restructuring have altered the perceptions of the employees

dealing with the relevant work constructs within these institutions. This study will

address the current state of perceptions regarding employees’ job satisfaction

levels, organisational commitment, and turnover intentions in the post-merger

phase.

In light of the recent restructuring of the institution in question, no attempt has yet

been made to gauge the levels of organisational commitment amongst its

employees. The newly merged institution formed a unitary structure, whereby


6

former participating, culturally incompatible, institutions [namely a Technikon

(centralised managerial system) and a University (decentralised managerial

system)] are no longer recognised as such and there is now a single governing

body, a single CEO and a single set of structures for governance (Harman &

Harman, 2003).

Management has yet to determine how committed its employees are on an

official basis i.e. a structured survey administered by its employees. Thus, the

researcher has sought the need to address this problem in which there is a lack

of information conveyed to management about its employees’ levels of

organisational commitment (amongst other work constructs).

Alongside in determining these commitment levels, a scale for turnover intentions

(positively addressed as ‘intentions to stay’) will be administered as well as a

standardised job satisfaction scale, thus providing management with more

accessible information and closing the gap in the knowledge of the perceptions of

its employees.

On a wider scale, there is a dearth of empirical research on the merging of

tertiary institutions in South Africa. This too is considered a motivational factor for

the research to be conducted.

1.4 Problem Statement

The research objectives are set out below.

Primary Research Objective: What are the employee perceptions of job

satisfaction, organisational commitment, and turnover intentions in a post-merger

tertiary institution and how are these variables related?


7

Secondary Research Objective #1: What are the perceptions of employees’

(academic, administrative and support staff) job satisfaction within the institution

across all campuses?

Secondary Research Objective #2: What are the perceptions of employees’

(academic, administrative and support staff) organisational commitment within

the institution across all campuses?

Secondary Research Objective #3: What is the employees’ (academic,

administrative and support staff) level of turnover intentions within the institution

across all campuses?

Secondary Research Objective #4: What are the measured relationships or

associations between these scales within the institution across all campuses?

Within this question, a ‘best-fitting’ model will be determined. Figure 1.1

highlights the hypothesised models below (note, no sub-scales are indicated for

purposes of simplicity).

Model #1

Model #2

TurnoverIntentions

OrganisationalCommitment

JobSatisfaction


TurnoverIntentions

JobSatisfaction


8

Model #3

Model #4

Model #5

Model #6

Model #7

JobSatisfaction


TurnoverIntentions

TurnoverIntentions

JobSatisfaction


JobSatisfaction

TurnoverIntentions



JobSatisfaction

TurnoverIntentions

JobSatisfaction

TurnoverIntentions



9

Model #8

Model #9

Model #10

Model #11

TurnoverIntentions


JobSatisfaction


TurnoverIntentions

JobSatisfaction

JobSatisfaction


TurnoverIntentions

TurnoverIntentions

JobSatisfaction



10

Model #12

Model #13

Model #14

Model #15

Figure 1.1: The 15 Hypothesised Models

Secondary Research Objective #5: What relationships exist between the attained

biographical variables and the three individual scales (work constructs)? The


JobSatisfaction

TurnoverIntentions

JobSatisfaction


TurnoverIntentions

TurnoverIntentions

JobSatisfaction


JobSatisfaction

TurnoverIntentions



11

selected biographical variables are: Age, Tenure, Gender, Race, Marital Status,

and Highest Academic Qualification.

Secondary Research Objective #6: What relationships exist between the

selected work construct (to be determined through the best model fit vetting) and

the interactions between the attained biographical variables? The selected

biographical variables are: Age, Tenure, Gender, Race, Marital Status, and

Highest Academic Qualification.

Secondary Research Objective #7: What relationships exist between the attained

biographical variables, the interactions, and the three scales within the ‘best-fit’

model of the proposed models from Secondary Research Objective #4?

Formalised stating of both the Empirical and Theoretical research objectives will

be addressed in Chapters 2 and 3.

1.5 Proposed Value-Add of Research

Although organisations may take years to adjust to the impact of a merger, Marks

and Mirvis (1992) note that one to two years following a merger is a “good time to

take a company’s pulse” (p. 76). A comprehensive assessment during this period

can reveal how a company has emerged from the combination and how ready it

is to achieve future goals. Hogan and Overmyer-Day (1994) echo these thoughts

by indicating that there appears to be a developing consensus towards a two-

year period when deciding on the appropriate period over which to make the

measurement of whether the merger / acquisition was a success.

The merger between the University and Technikon in question took place 21

months ago officially to form the new higher education institution (at the time of


12

writing this dissertation). Thus looking at the above commentary, the timing of the

survey is more than adequate to gauge the current ‘pulse’ of the organisation.

The value of the research, in a broad South African context, is to determine the

extent of these work relationships (based on the present situation) and how they

relate to the mentioned constructs. Ultimately, the researcher intends to gauge

these associations within the newly merged institution.

Considering the above paragraph, the value of the research will be

conceptualised in more specific terms, namely: the methodological, theoretical,

and practical perspectives:

1.5.1 Proposed Methodological Value

The research procedure will be carried out by the utilisation of an electronic

means of distributing the questionnaire through a web survey. This enables the

research to be focused and controlled through this channel.

1.5.2 Proposed Theoretical Value

This work will contribute to the developing body of knowledge on the

interrelationship among work-related constructs, namely that in a newly merged

institution. This will be achieved by conducting further research and empirical

testing of standardised work questionnaires.

Harman and Harman (2003) note that, while mergers notably are frequently

disruptive, strongly contested and costly in both human and financial terms, they

have the potential to produce substantial longer-term benefits. This however

could only be accurately determined at a later stage, indicating the need for

continued research. As is similar to the study Arnolds and Boshoff (2004), this


13

research can be considered a stepping-stone for potentially such a longitudinal

study of the merged institution.

1.5.3 Proposed Practical Value

This study aims to take snapshot profile of the perceptions of its employees, thus

aiding management in terms of its human resources endeavours by potentially

highlighting possible problem areas within the institution, thereby presenting the

opportunity to influence perceptions through both direct and indirect means.

1.6 Outline of Remaining Chapters

The remaining chapters address the following key topics relevant to this research

and these are set out below.

In Chapter 2, the key concepts of the study with regard to job satisfaction,

turnover intentions and organisational commitment are defined and discussed.

Special emphasis is placed on the relationship between these concepts and

background variables as well as the current level of published research in these

areas.

In Chapter 3, attention will be given to the empirical study, whereby the research

design and methodology of the study will be discussed in detail.

In Chapter 4, the results of the study will be introduced to the reader as well as

the analysis thereof.

Chapter 5 engages the reader in a discussion and interpretation of the results.


14

Lastly, in Chapter 6, the conclusion and recommendations of the study, drawn up

by the author, will be discussed. Subsequent steps for future research are also

considered.

1.7 Synthesis

There is a dearth of knowledge, save for a handful of studies, on the context of

South African mergers and acquisitions of tertiary institutions. The human

element, in the form of intellectual capital, is the most sought-after commodity in

tertiary institutions; and hence the importance placed on the needs of its

employees. This study aims to contribute to this body of knowledge on a post-

merger level, especially in the context of current employee perceptions relating to

organisational commitment, job satisfaction and turnover intentions. It hopes to

achieve this by utilising and enhancing standardised questionnaires and by

employing both basic and advanced statistical procedures. Through these

processes, managerial practices can be aided in determining problem areas and

how these can be accordingly addressed.

Given the objectives introduced above, in the next chapter, Chapter 2, the key

concepts of the study with regard to job satisfaction, turnover intentions and

organisational commitment, are defined and discussed in depth.


15

2 CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

The previous chapter outlined the context and the aims of the study. Chapter 2

now presents an overview of the literature relating to the study. The theoretical

objectives are outlined. Thereafter the chapter follows the sequence of the

theoretical objectives. The key concepts of each construct are defined and a

theoretical overview of each is provided.

The current status of research, regarding the relationships between key concepts

in the hypothesised models as well as the biographical variables, is explored.

The chapter is then concluded with a synthesis.

Next, the theoretical objectives of the study are outlined.

2.2 Theoretical Objectives

2.2.1 Define the key concepts of the study, namely that of job satisfaction,

organisational commitment, and turnover intentions (with some emphasis

on the positive ‘spin’ by asking about the intentions to stay of the

respondents).

2.2.2 Describe job satisfaction with the emphasis on a theoretical framework of

the concept and the dimensions of job satisfaction.

2.2.3 Describe organisational commitment with the emphasis on a theoretical

framework of the concept, approaches to study commitment (incorporating


16

the behavioural, attitudinal and motivational approaches), commitment foci

and a linkage motivational model of organisational commitment.

2.2.4 Describe turnover intentions emphasising that these are described as a

planned behaviour, and the different types of turnover cognitions.

2.2.5 Describe the outcomes of a merger or acquisition.

2.2.6 Describe the empirical evidence of the relationships between the key

variables mentioned.

2.2.7 Describe the empirical evidence of the background factors (antecedents)

of job satisfaction, organisational commitment, and turnover intentions.

The selected variables are age, gender, tenure, marital status, highest

academic qualification, and race.

In the following section a brief conceptual clarification of each concept used in

this study will be explored.

2.3 Defining the Key Concepts

2.3.1 Job Satisfaction

Schneider and Snyder (1975) described job satisfaction as a personal evaluation

of the conditions present in the one’s job, or the outcomes thereof that arise as a

result of possessing a job. Other researchers seem to agree with this insofar as

job satisfaction thus involving an individual’s perception and evaluation of his /

her job, but it is added that this perception is influenced by the person’s unique

circumstances, such as values, needs and expectations (Sempane, Rieger, &


17

Roodt, 2002). People will therefore evaluate their jobs on the basis of factors,

which they perceive as being of importance to them.

Job satisfaction is, stemming from cognitive processes, a generalised affective

work orientation towards one’s present job and employer (Lincoln & Kalleberg,

1990). Smith, Kendall and Hulin (1969, p. 37) define job satisfaction as

“persistent feelings towards discriminable aspects of the job situation” and say

that “these feelings are thought to be associated with perceived differences

between what is expected and what is experienced in relation to the alternatives

available in given situation”.

An established and popular conceptualisation, used in this study, is the intrinsic-

extrinsic distinction which is sought as a potential source of satisfaction or

dissatisfaction (Weiss, Dawis, England, & Lofquist, 1967). Intrinsic satisfaction is

derived from performing the work and consequently experiencing feelings of

accomplishment, self-actualisation, and identification with the task. Extrinsic

satisfaction is derived from the rewards bestowed upon an individual by peers,

supervisors or the organisation, and can take the form of recognition,

compensation and advancement. More so, Weiss et al. (1967) identified various

extrinsic factors (e.g. supervision, compensation, company policies and

practices) and intrinsic factors (e.g. activity, variety, responsibility). The intrinsic

factors are thought to measure satisfaction with intrinsic reinforcement factors,

whilst the extrinsic factors are external to the job.

The above stems from the assumption that each person seeks to achieve and

maintain correspondence with his or her environment. Association with the

environment at work can be described in terms of the work environment fulfilling

the requirements of the individual (satisfaction), and the individual fulfilling the

requirements of this environment (satisfactoriness) (Cook, Hepworth, Wall &

Warr, 1981).


18

Job satisfaction is therefore, for the purpose of this study, defined as:

“A pleasurable or positive emotional state resulting from the appraisal of one’s

job or job experiences” (Locke, 1976, p. 1300).

The above discussion has achieved theoretical objective 2.2.1. The next section

will provide a more in-depth discussion on the concepts that were introduced

above.

2.3.2 Organisational Commitment

Organisational commitment is defined as “the relative strength of an individual’s

identification with and involvement in a particular organisation” (Mowday, Porter,

& Steers, 1982, p. 27). It is commonly characterised by three factors:

(1) identifying with an organisation and its goals and values (identification);

(2) a strong desire to maintain investment with the organisation (loyalty); and

(3) willingness to work extra hard on behalf of the organisation (involvement).

Organisational commitment is important and can be inferred from the expression

of individuals’ beliefs, opinions, and actions.

Building upon this definition, organisational commitment can also be viewed as

either the internalisation of an organisation’s values or identification of the

organisation’s culture including its values, norms, and beliefs (O’Reilly &

Chatman, 1986). Oliver (1990) notes that an individual’s attitudes and beliefs

may not only be determinants of behaviour, but also the consequences of it.

Organisational commitment has important ramifications for both the individual

and the organisation as a whole.

Roodt (2004a) advocated a much-needed common basis for comparing the

different commitment foci. Thus, a motivational approach was adopted, which


19

included the potential for satisfying salient needs, the realisation of salient values

and the achievement of salient goals.

For this study, organisational commitment will be defined as:

“…a cognitive predisposition towards a particular focus, insofar this focus has the

potential to satisfy needs, realise values, and achieve goals” (Roodt, 2004a, p.

85).

2.3.3 Turnover Intentions

Gauging turnover intentions are partly the primary aim of the study; however, in

the actual questionnaire distributed, intentions to stay were measured. Turnover

intention has sought greater audience with both academic and managerial

attention than that of intentions to stay over the years. It has been realised that

“many important topics in the field are intrinsically negative”, with intention to

turnover one of the many constructs following suit (Turner, Barling, & Zacharatos,

2002, p. 725). Turner et al. (2002) further added that “failing to recognise the

positive aspects of work in our research is also inappropriate” (p. 715). Seligman

and Csikszentmihalyi (2000) identified one of the several possible reasons that

positive psychology has not attracted attention as much as the negative form

thereof. Namely, that negative emotions and experiences are considered the

more urgent of the two and thus override the positive ones. Henry (2004) noted

that although much organisational practice and research is negatively orientated,

many organisational interventions take a more positive orientation. It is

suggested that organisations could be accused of inclining toward naïve positivity

in their acceptance of organisational interventions as curative. Thus, although the

questionnaire utilised deals with intentions to stay, the theory and subsequent

discussion will delve further into turnover intentions.


20

Turnover behaviour is a multistage process that includes attitudinal, decisional,

and behavioural components. Furthermore, many studies have rested on the

belief that turnover is an individual choice behavioural pattern based on the

conceptualisation that it is a psychological response (Lum, Kervin, Clark, Reid, &

Sirola, 1998; Mobley, Griffeth, Hand, & Meglino, 1979).

Anticipated turnover is the degree to which an organisational member believes

he or she would terminate his or her position at some unspecified time in the

future (Hinshaw, Smeltzer, & Atwood, 1987). Jones (2000) defined intention to

turnover as an individual’s desire not to continue membership of a particular

organisation.

For the sake of this study, the following definition (which can synonymously be

used for both intentions to stay or leave, or intention to turnover) will be utilised:

“…mental decisions intervening between an individual’s attitudes regarding a job

and the stay or leave decision” (Sager, Griffeth, & Hom, 1998, p. 255).

2.4 Job Satisfaction

2.4.1 Theoretical Framework of Job Satisfaction

Job satisfaction is a topic of wide interest both to people who work in

organisations and people who study organisations. It is a most frequently studied

variable in organisational behaviour research, and also a central variable in both

research about and theory of organisational phenomena. The traditional model of

job satisfaction focuses on all the feelings that an individual has about his / her

job. However, what makes a job satisfying or dissatisfying does not depend only

on the nature of the job, but also on the expectations that individuals have of

what their jobs should provide (Lu, While, & Barriball, 2004).


21

Most employers realise that the optimal functioning of their organisations

depends in part on the level of job satisfaction of employees (Rothman &

Coetzer, 2002). Employees’ full potential is needed on all levels in organisations,

which stresses the importance of being satisfied. Motivational and job satisfaction

theories have provided a strong basis in understanding human behaviour in

organisations. Leading theorists like Maslow (1943; 1954) and Herzberg and

Mausner (1959) emphasised the importance of the fulfilment of various needs of

employees that will determine their behaviour in organisations.

Maslow (1943) developed a sound motivational theory assuming that people are

continuously in a motivational state, but the nature of the motivation is fluctuating

and complex. Human beings rarely reach a state of complete satisfaction, except

for a short time. As one desire becomes satisfied, another arises to take its

place, and as this desire becomes satisfied, another replaces it. Maslow (1943)

therefore postulated a hierarchy ranging from lower to higher order needs. The

physiological needs refer to basic needs (including food, water, and air); the

safety needs to be protected (including freedom from physical threats and harm

as well as economic security); belongingness and love needs, the social need of

approval and recognition; the esteem needs for mastery and achievement; and

the self-fulfilling needs to realise one’s full potential for continual self-

development.

People experience a greater sense of wholeness and fullness when they are able

to satisfy their higher order growth needs. Survival needs are often referred to as

extrinsic needs (e.g. compensation and working conditions), while higher order

needs are referred to as intrinsic needs (e.g. recognition and achievement).

Maslow’s (1943) theory is of particular importance as it is postulated in this study

that the satisfaction of needs will lead to firmer staying intentions. Satisfying job-

related needs may lead to higher commitment and ultimately stronger staying

intentions.


22

Herzberg and Mausner (1959) formulated the two-factor theory of job satisfaction

and postulated that satisfaction and dissatisfaction were two separate and

sometimes unrelated phenomena. Extrinsic factors were named ‘hygiene’ factors

and were claimed to involve primarily the context in which the job was performed.

They were found to be ‘dissatisfiers’ and included: salary, supervision, company

policies, administration, interpersonal relations and working conditions. Intrinsic

factors were named ‘motivators’ and were believed to involve mainly aspects of

the job itself. These were found to be job ‘satisfiers’ and included: advancement,

responsibility, recognition, work itself and achievement. The hygiene factors

operate only to frustrate or fulfil one’s physical needs, while the motivators serve

to fulfil or frustrate one’s growth need. Herzberg and Mausner (1959) concluded

that only the fulfilment of the motivators could lead to positive satisfaction on the

job, and that the fulfilment of the hygiene factors could prevent dissatisfaction,

but could not contribute to positive satisfaction. Once again, although the

satisfaction of hygiene factors is also of importance to retain talent, it is

postulated in this study that the satisfaction of motivators may lead to job

satisfaction and greater staying intentions of the intuition’s employees.

According to Robbins, Odendaal and Roodt (2003) and Spector (2003), there

have been two approaches to the study of job satisfaction – the global approach

and the facet approach. The global approach explains job satisfaction as a

single, overall feeling towards a job, while the facet approach suggests that there

are different facets or different aspects of the jobs, such as rewards (pay or fringe

benefits), other people on the job (supervisors or co-workers), job conditions,

communication, security, promotion opportunities, and the nature of the work

itself. It is believed that the job facet approach permits a more complete picture of

job satisfaction and an individual typically has different levels of satisfaction with

regard to the various facets. An example may be an employee who is very

dissatisfied with pay and fringe benefits, but at the same time may be very

satisfied with the nature of the work or the supervisors (Spector, 2003). Coster


23

(1992), in a South African study, found that job facet satisfaction is more strongly

related to specific job domains than overall to job satisfaction.

2.4.2 Job Satisfaction Dimensions

Locke (1976) explains that, for researchers to understand job attitudes, they

need to understand job dimensions, which are complex and interrelated in

nature. Therefore, a thorough understanding of job attitudes requires that the job

be analysed in terms of its constituent elements. He mentioned that the common

dimensions of job satisfaction include: work, pay, promotions, recognition,

benefits, working conditions, supervision, co-workers, company, and

management. The evaluation of the different aspects of the job by employees is

of a subjective nature, and people will reflect different levels of satisfaction

around the same factors. A wide range of dimensions were used previously to

measure job satisfaction. Based on the review of the most popular job

satisfaction instruments, Spector (1997) summarised the following facets of job

satisfaction as being the most frequently referred to: appreciation,

communication, co-workers, fringe benefits, job conditions, the nature of the work

itself, the nature of the organisation itself, the organisation’s policies and

procedures, pay, personal growth, promotion opportunities, recognition, security,

and supervision.

Jones (2000) reported a variety of variables have been used to determine the

various sources of job satisfaction. These can be viewed in terms of:

(1) characteristics or nature of the job itself – examples are participation in

decision making, authority to initiate independent actions;

(2) characteristics of the organisation – examples include salary, benefits,

and opportunities for promotion; and

(3) values – this includes when viewing one’s work as an important aspect

of one’s life and that it defines the person. Alternatively others will


24

consider it a means to meet other needs so that the work itself is not

intrinsically satisfying.

One model of particular interest is the Price-Mueller model of job satisfaction,

organisational commitment and intention to turnover (Price & Mueller, 1981). This

model assumes that employees value certain conditions of work and if these

conditions are found in the workplace, employees will be more satisfied and

committed and less likely to leave the organisation. The Price-Mueller model

identifies three dependent variables and the causal relationships between them.

Job satisfaction has been shown to affect organisational commitment positively,

which in turn, negatively affects intent to turnover or leave the organisation. Thus

employees, who are satisfied with their jobs become more committed to the

organisation’s goals and are less likely to leave. The various independent

variables that can be identified are:

(1) opportunity – the availability of alternative jobs in the organisation’s

environment;

(2) routinisation – the degree to which a job is repetitive, with high

routinisation signifying a high degree of repetitiveness;

(3) participation – the degree of power an individual exercise concerning

performance of the job;

(4) instrumental communication – the degree to which information about the

job is transmitted by an organisation to its members;

(5) integration – the degree to which an individual has close friends among

organisational members;

(6) pay – refers to money and its equivalents, such as fringe benefits, which

individuals receive for their services to the organisation;

(7) distributive justice – the degree to which rewards and punishments are

related to performance inputs into the organisation;

(8) promotional opportunity – the degree of potential vertical occupational

mobility within an organisation;


25

(9) professionalism – the degree of dedication to occupational standards of

performance; the greater the dedication to occupational standards, the

greater the professionalism;

(10) general training – the degree to which the occupational socialisation of

an individual results in the ability to increase the productivity of diverse

organisations; and

(11) kinship responsibility – the degree of an individual’s obligations to

relatives in the community in which the employer is located.

The popular conceptualisation of the intrinsic-extrinsic definition of job

satisfaction used by Weiss et al. (1967) is of particular relevance in this study.

Intrinsic satisfaction is derived from performing the work and consequently

experiencing feelings of accomplishment, self-actualisation, and identity with the

task. Extrinsic satisfaction is derived from the rewards bestowed upon an

individual by peers, supervisors or the organisation, and can take the form of

recognition, compensation and advancement. In the complete ‘list’ that was

identified included the following factors: activity, independence, variety, social

status, supervision – human relations, supervision – technical, moral values,

security, social services, authority, ability utilisation, company policies and

practices, compensation, advancement, responsibility, creativity, working

conditions, co-workers, recognition and achievement.

2.5 Organisational Commitment

2.5.1 Theoretical Framework of Organisational Commitment

Organisational commitment has a long history, and has been the subject of a

great deal of research and empirical attention both as a consequence and an

antecedent of other work-related variables of interest. The psychological bond

between employee and employer, in terms of consequences and antecedents, is


26

an important correlate of work-related attitudes and behaviours, namely: several

personal variables, role states, and aspects of work environment ranging from

job characteristics to dimensions of organisational structure, as well as,

predicting employees’ absenteeism, performance, turnover, and other behaviours

(Mathieu & Zajac, 1990). Recent research (Fleming, 2000a, 2000b; Harter,

2000a, 2000b) has identified organisational commitment as an important variable

predicting organisational performance and even that of national economies

(Roodt, 2004a). Benkhoff (1997) found that employee commitment is significantly

related to the financial success of bank branches of a particular German bank.

Even though the topic of organisational commitment has been extensively

researched, there is little agreement on the conceptualisation of this construct.

Morrow (1983) identified no fewer than 30 different forms of commitment

measures and their formulators. At least five different foci (excluding the

combined dimensions of commitment) were identified:

• value or personal focus (i.e. Protestant work ethic endorsement,

conventional ethic, work ethic);

• career focus (i.e. career commitment, career salience, commitment to a

profession);

• job focus (i.e. job involvement, job orientation, job attachment, ego-

involvement, work as a central life interest);

• organisation focus (i.e. organisational commitment, organisational

identification); and

• union focus (i.e. union commitment, various attitudes toward union

scales).

This list is far from comprehensive or complete, since many additional measures

were developed after the work of Morrow (1983).

Organisational commitment has been defined and measured in several different

fashions. Despite the lack of consensus on the various definitions and

measurements, a common theme is shared across all these deviations, that is,


27

organisational commitment is considered to be a bond or linkage of the individual

to the organisation. Mowday et al. (1982) introduced the concept of

organisational commitment being viewed in terms of ‘linkages’ which portray the

relative intensity of an employee’s identification with, and involvement, in his / her

employer organisation. Baruch (1998) suggests that organisational commitment

concerns itself with the mutuality in the employer-employee relationship, as the

bilateral nature of most relationships is a precondition for any commitment. If the

organisation gives up its commitment to its employees or the employees perceive

a lack of commitment by their company, there is no solid or stable basis for the

commitment relationship. Thus commitment cannot be only one-sided if the

organisation’s goals are to be achieved.

Mowday et al. (1982) argued that progress in socialisation leads to deepened

commitment. Accordingly, the formation of a sense of commitment to the

organisation is a process that may evolve with the different socialisation stages:

(1) an anticipation or pre-entry stage – the period when any job alternatives

and the formation of initial expectation toward the future employer lay the

potential groundwork for the development of later organisational

commitment;

(2) an early employment stage – the period during the first few months of

employment when the worker’s socialisation into the job ideally facilitates

the development of organisational linkages; and

(3) a phase of middle and late career stages – when the continued

development of organisational commitment and eventual entrenchment

occurs.

Entrenchment is an important concept within this framework because an

employee who has developed a high degree of commitment is capable of

assuming more challenging assignments, working more autonomously and

productively, and maintains an increased number of social involvements with

others within the organisation (Jones, 2000).


28

The strength of an individual’s work commitment consists of at least five

elements, namely: job involvement, organisational commitment, career

commitment, work values / involvement, and union commitment (Morrow, 1983).

Commitment has evolved as a wide range of ‘types’ (e.g. engagement,

attachment, commitment, involvement) within a wide spectrum of foci (e.g. work,

job, career, profession / occupation, organisation, union), while categories

studying toward commitment varied between behavioural, attitudinal and

motivational within three broad research streams through sociological, industrial /

organisational psychology and health psychology (Roodt, 2004a). Johnson

(1973) emphasised that on the social science front; one frequently encountered

the usage of the concept alienation and its obverse, involvement. Kanungo

(1979) took these bipolar states as being the same phenomena and chose to

discuss the nature of alienation and involvement within a work-related context.

Roodt, Bester and Boshoff (1994a) argued in the same vein that the degree of

work involvement could be described as a bipolar continuum, ranging from work

alienation to extreme (excessive) work involvement.

Morrow (1983) highlighted that growth in the commitment related concepts has

not been accompanied by careful segmentation of commitment’s theoretical

domain in terms of the intended meaning of each concept or the concepts’

relations between one another. O’Reilly and Chatman (1986) indicated that this

lack of consensus has manifested itself into remarkable variations of how

commitment is defined and measured. Thus in this field, research is marred by

redundant concepts, and the epistemic correlations of instruments are now under

suspicion. As a result, research is characterised by concept redundancy and

concept contamination. Roodt (2004a) defined concept redundancy in this

context as the use of related variables that largely overlap in meaning, e.g. work

involvement and work commitment. Concept contamination occurs when a

variable contains a large proportion of shared or common content with other

‘unrelated’ variables, e.g. morale and work involvement. This results in poor

theory building and development with regard to employee commitment.


29

It is accordingly necessary to outline the concept of commitment for the purpose

of this study. To do this, a short overview on the different approaches to the

study of commitment must first be described.

2.5.2 Approaches to the Study of Commitment

Different theoretical approaches were followed within each of the research

streams and these can be categorised into three broad groups. These divergent

schools of thought on the subject of commitment are divided into the behavioural,

the attitudinal, and the motivational school.

2.5.2.1 Behavioural Approach

Mowday et al. (1982) described behavioural commitment as the process by

which individuals become locked into a certain organisation and how they deal

with this problem. Lodhal and Kejner (1965), as well as Becker (1960), identified

a number of commitment behaviours in the work context. Terms, such as

‘investments’ and ‘side bets’, were used to describe some form of commitment

behaviours. Becker’s (1960) Side-Bet Theory is based on the costs that an

employee associates with leaving an organisation. Thus, according to this

conceptualisation, employees with ‘high costs’ engage in certain behaviours, not

because it is the right thing to do, but because they believe that they will derive

some reward, or minimise some cost, from doing so. Such an example (one of

many) could be the seniority and ‘connections’ one would lose in one’s current

position if one moved positions. This approach, however, did not distinguish

between the antecedents, the state of commitment itself and the consequences.

According to Roodt (2004a), the behavioural approach is particularly problematic,

because behaviour is multi-deterministic; i.e. predictors related to a particular

behaviour can also predict other behaviours. Antecedent and consequential

behaviours of commitment can also be related to other determinants or ensuing


30

conditions such as job satisfaction, morale or the intentions to stay or leave. The

state of commitment in the behavioural approach is, therefore, not precisely

defined.

2.5.2.2 Attitudinal Approach

The attitudinal approach to organisational commitment draws on the contribution

of several researchers. For instance, Kanter (1968) considered commitment as

being “the willingness of social actors to give energy and loyalty to the social

system” (p. 499) and as “the attaching of an individual’s fund of affectivity and

emotion to the group” (p. 507). Etzioni (1961) proposed the concept of moral

commitment based on an individual’s internalisation of norms and identification

with the authority. In a similar vein, Mowday, Steers and Porter (1979) defined

organisational commitment as “the relative strength of an individual’s

identification with and involvement in an organization” (p. 226). They

characterised organisational commitment by three factors:

(1) a strong belief in and acceptance of the organisation’s goals and values;

(2) a willingness to exert considerable efforts on behalf of the organisation;

and

(3) a strong desire to maintain membership in the organisation.

More recently, Meyer and Allen (1991) have attempted to integrate the

behavioural and attitudinal perspectives of organisational commitment. They

proposed a three-component conceptualisation: affective commitment

continuance commitment, and normative commitment.

• Affective commitment refers to the employee’s emotional attachment to,

identification with, and involvement, in the organisation. Employees with

strong affective commitment remain with the company because they want

to do so.


31

• Continuance commitment refers to an awareness of the costs associated

with leaving the organisation. Employees with strong continuance

commitment remain in the company because they need to do so.

• Normative commitment reflects a feeling of obligation to continue

employment. Strong commitment in this situation is where employees feel

they ought to remain with the company.

These three components correspond with some attitude theories and contain a

cognitive (normative), an affective (emotional), and a conative (continuance)

element.

The attitudinal approach, of the three schools of thought, to commitment currently

dominates the research literature, but has some limitations, according to Roodt

(2004a). Firstly, the commitment construct is conceptualised as being multi-

dimensional which poses problems in predictive models and, from a conceptual

perspective, does not meet the criteria for parsimony, clarity and precision; and

secondly, it includes an affective as well as conative component which creates a

conceptual overlap with job attitudes such as job satisfaction and job intentions

(such as the intention to leave or stay) respectively or moral and / or normative

commitment such as work values.

2.5.2.3 Motivational Approach

The motivational approach is a third school of thought that has recently emerged

in an attempt to integrate the diverse perspectives encountered and also to

overcome the most important limitations of the other two approaches discussed

above (Roodt, 2004a). The motivational approach was proposed by Kanungo

(1982a) and variations thereof were used, amongst others, by Harter (2000a);

Lefkowitz, Somers and Weinberg (1984); Misra, Kanungo, Rosenstiel and

Stuhler (1985); and Roodt (1997).


32

This approach focuses only on the state of commitment (cognitive predisposition)

in a particular focus. The state of commitment is not only separated from its

antecedent and consequential conditions and behaviours, but also from its

related affective and conative components that are present in other widely used

constructs, such as job satisfaction and turnover intentions respectively. The

degree of commitment is operationalised as an individual’s cognitive assessment

of the potential of the commitment focus to satisfy salient needs, realise salient

values and achieve salient objectives and is therefore categorised as a

motivational approach. As this approach is of particular relevance in this study, a

discussion of the different commitment foci is mandatory. Thereafter, the

discussion of commitment as a cognitive predisposition will be outlined in a

linkage motivational model as suggested by Fishbein and Ajzen (1975).

2.5.3 Commitment Foci

Shore, Newton and Thornton (1990) advocated that it is first useful to investigate

how important the focus of attitudes (job versus organisation) is, before

comparing attitudes such as satisfaction and commitment. According to Ajzen

and Fishbein (1977), attitudinal and behavioural entities may be defined by four

different elements: the action, the target at which the action is directed, the

context in which the action is performed, and the time at which the action is

performed, or any combination of elements. Shore et al. (1990) say that this

suggests that attitudes with different targets are distinct, and therefore distinction

between different foci is also necessary. For example, they suggested that a

distinction must be made between organisation and job foci. Organisational

attitudes may reflect more general employment policies and practices, especially

compared with other potential employers. By contrast, job attitudes may reflect

the type of work, tasks, and immediate supervision experienced by the employee

on the job. Thus, an employee may feel quite positively about the job because of


33

the immediate experience of the job, but feel negatively toward the organisation

due to policies such as pay scales or promotion.

These positive or negative feelings about jobs and organisations should then

contribute to more specific attitudes such as job satisfaction or organisational

commitment. That is, feelings of liking or disliking your job (satisfaction) can be

distinguished from feelings of attachment to the job (commitment), though these

attitudes should be related since they have the same focus (Shore et al., 1990).

Furthermore, since a job exists within an organisational context, it is anticipated

that attitudes towards either the job or organisation will be related.

Shore et al. (1990) sum up by adding that though the four work attitudes are

seen as distinct from one another; the focus (or target) of the attitudes is viewed

as having an impact on attitude formation that is distinct from the type of attitude

(satisfaction or commitment).

Meyer and Allen (1997) acknowledged that to understand commitment at work,

distinctions must be understood with respect to both the focus of commitment

and the nature of commitment; in addition it must be recognised that commitment

can take multiple forms, each of which can be focused on multiple entities,

including the work group, the immediate supervisor, top management, peers,

customers, the occupation or profession, and the union. This draws upon their

earlier comment that what is of major concern is the lack of consensus in

construct definitions (Meyer & Allen, 1991). Research suggests that employees

experience several different commitments to the goals and values of multiple

groups, and that, where two individuals may be committed to ‘the organisation’,

the focus of the two commitments may be entirely different (Mester, Visser,

Roodt, & Kellerman, 2003; Roodt, 1997). Roodt (1997) found a weak, albeit

significant, positive correlation between the two fairly independent foci of ‘union

commitment’ and ‘organisational-related commitment / involvement’ which was


34

described to the differences in the goals, ideologies, and values of the two

organisations (i.e. the work organisation and the union).

Roodt (1997) found that when six foci (work, job, career, profession, organisation

and union), were operationalised on the same theoretical basis, as a cognitive

predisposition, five of the foci were significantly correlated and only the focus

‘union’ emerged as a separate focus after scores were factor analysed. Roodt

(1997) concluded that a distinction between different work-related foci is only of

theoretical importance if the same theoretical base is used in operationalising the

different foci. Thus the question needs to be posed seriously as to whether it

serves a purpose to distinguish between the different work-related foci, except

perhaps to obtain a better understanding of the dynamics of organisational

commitment or the relative importance of each focus. This supports the

suggestion of Shore et al. (1990) that distinguished between organisation and job

foci as their results supported their theoretical model which suggested that job

and organisation attitudes related differently to job and organisational

behavioural intention. Similarly, there was a difference between ‘work’ and ‘union’

foci as observed by Roodt (1997). This is consistent with the views of Morrow,

Eastman and Elroy (1991), who also raised concerns as to whether raters were

able to distinguish clearly between the different foci or concepts, as it was

discovered that naïve raters demonstrated more redundancy than raters familiar

with the concepts and measures. In the South African context, individuals do not

easily distinguish between the foci of work, job, occupation, career and

organisation (Roodt, 1997). Thus, for purposes of this research, the concept of

employee commitment will include all the above foci.

In research (Morrow & McElroy, 1986; Randall & Cote, 1991) where different

commitment foci operationalised on different bases were used, results frequently

indicated that the different foci are significantly correlated and thus share some

common variance. Mathieu and Zajac (1990) also investigated and reported

significant relationships between organisational commitment and a range of other


35

commitment / foci which involved, namely, job involvement; occupational and

professional commitment; union commitment; and Protestant work-ethic. It also

appears in some instances (Lefkowitz et al., 1984) that some commitment foci

were closely correlated with other non-commitment work variables such as

‘intrinsic motivation’, ‘need-satisfaction’, ‘extrinsic motivation’ and ‘non-salient

needs’ which Roodt (2004a) highlighted as additional support for the motivational

approach.

According to Roodt (2004a), it seems that researchers, who have reported

construct and discriminant validity in the instruments used, have capitalised on

the effect of using different theoretical foundations and measures (this can also

be referred to as hetero-method variance). Thus, comparative studies on

different commitment foci should rather be conducted on a comparable or a

similar theoretical basis. It can accordingly be argued that a more parsimonious

approach in the use of work-related commitment foci is needed.

2.5.4 A Linkage Motivational Model

It seems as if the golden thread running through all the definitions of commitment

is the potential for a particular focus to satisfy salient needs. A motivational

approach, which also includes the realisation of salient values and the

achievement of salient goals, seems to be more appropriate to study

commitment, as suggested by Roodt (2004a). The psychological activities of

human beings are often divided into four categories, namely cognition (beliefs),

affect (attitudes / emotions), conation (intentions), and manifest behaviour

(Fishbein & Ajzen, 1975). These psychological activities are distinguishable, but

related components (Figure 2.1). Affect has a cognitive origin and is related to a

range of possible behavioural intentions. There is a direct link between the three

components and manifest behaviour.


36

Figure 2.1: The Link between Cognition, Affect, Conation and ManifestBehaviour (Adapted from Fishbein & Ajzen, 1975)

According to Roodt (2004a), this theoretical model provides a basis for

distinguishing between related concepts such as work commitment and work

satisfaction. Based on this widely accepted distinction, commitment is defined as

a cognitive predisposition (a belief state), based on the subjective assessment of

the potential to satisfy salient needs or the worthiness of the commitment (Brown,

1996). Job satisfaction, on the other hand, is defined as a multifaceted composite

of emotions (affect) towards job related objects (Coster, 1992). Turnover

intentions can again be defined as conation, based on the content of cognition

and affect.

According to Roodt (2004a), the state of commitment not only needs to be clearly

differentiated from its antecedent and consequential conditions or behaviours,

but also in terms of its psychological state, i.e. whether it is cognition, affect,

conation, or all three. In the latter case it would alleviate problems in

differentiating the state of commitment, from job satisfaction (affect), or turnover

intentions (conation). Roodt (2004a) suggested that the only logical way it

seemed, would be to attempt a theoretical integration on a meta-theoretical level.

Cognition:Beliefs andassumptionsabout X1,2,3,..k

Conation:Intentionsabout X

1,2,3,..k

Affect:Attitudes about X

Key: Influence Feedback

ManifestBehaviour:Behaviourtowards X1,2,3,..k


37

This integration was subsequently illustrated [see: Roodt, 2004a], however

commentary thereon is beyond the scope of this dissertation.

Kanungo (1982a) proposed a model based on a motivational approach where

socialisation processes result in salient needs (in work and non-work spheres)

which are followed by instrumental behaviours and attitudes. The potential of

these behaviours and attitudes to satisfy salient needs are then evaluated which

result in commitment and alienation and their resulting behaviours. The limitation

of this model is, according to Roodt (1991), that the conflict potential between

different life roles and the resulting struggle to establish equilibrium between

these roles are not fully reflected in this model. The role of defence mechanisms

(as an important motivational mechanism) in the aforementioned process is also

not mentioned. Nor is the inclusion of salient values and needs addressed in

Kanungo’s (1982a) model (Roodt, 2004a).

2.6 Turnover Intentions

As discussed in 2.3.1, the actual work construct used in this study is turnover

intentions; however intentions to stay was addressed in the questionnaire. Due to

the conceptual similarity of the two, the theory that can be applied to turnover

intentions can also be applied to that of intentions to stay.

2.6.1 Turnover Intentions as Planned Behaviour

The theory of planned behaviour (Ajzen, 1991), suggests that behavioural

intention is a good predictor of actual behaviour. In a study by Lum et al. (1998),

‘turnover intent’ was chosen over actual ‘turnover’. This approach drew upon a

number of recent studies that have assessed the role of intentions in predicting

and understanding turnover, whereby intentions are statements about specific

behaviours of interest. Such studies have successfully demonstrated that


38

behavioural turnover intentions is consistently correlated with turnover (Fox &

Fallon, 2003; Mobley, 1982). There is considerable support for the notion that

intention to quit-stay is probably the most important and immediate individual-

level antecedent and predictor of turnover decisions (Chiu & Francesco, 2003;

Fox & Fallon, 2003; Mobley, 1982; Slate & Vogel, 1997; Steel & Ovalle, 1984;

and Tett & Meyer, 1993). Through a model predicting overt behaviour from verbal

predictors, Fishbein (1967) demonstrated that behavioural intentions to stay or

leave are related to turnover. Newman (1974) found support of this model in the

field, thus adding to the consistency of intentions relating to turnover.

Furthermore, Mobley, Horner and Hollingsworth (1978) reported moderate to

strong correlations between intention to quit, intention to job search and thinking

of quitting, and actual turnover, among hospital employees. Their study found a

correlation of 0.49 between intention to quit and actual turnover within one year.

Shields and Ward (2001) reported that quitting intentions were the strongest

predictor of actual turnover, with 79% of nurses in a longitudinal study reporting

an intention to quit and doing so within one year. Steel and Ovalle (1984)

reported, in a large number of studies between 1965 and 1983, a correlation

coefficient of 0.50 between quitting intentions and actual turnover. Hom and Hulin

(1981) attained a correlation as high as 0.71 between the intention to quit

(‘reenlistment intention’) and ultimate turnover (‘reenlistment’) in a survey of Army

Guardsmen. Tett and Meyer (1993) in their meta-analysis, identified (with a

correlation of 0.45) turnover intention / withdrawal cognitions as their strongest

predictor of turnover. An added benefit cited for using turnover intentions as

opposed to actual turnover is that this intention is under more individual control

than turnover (Shore & Martin, 1989). Mobley et al. (1979) highlight that

intentions offer a worthy explanation of behaviour since it encompasses the

individual’s perception and evaluation of job alternatives. This availability of

alternative jobs in employee turnover models has long been recognised.


39

2.6.2 Turnover Cognition Types

As indicated above, the immediate precursor of behaviour is thought to be

intentions, and therefore the best predictor of turnover should be intention to

turnover. The relationship between turnover and intention should be more

convincing the more precise the intention statement and the closer in time the

measurement of the intention and behaviour (Mobley et al., 1979). Furthermore,

understanding the turnover process more intricately must be facilitated by

including intentions and subsequently evaluating their precursors.

Mobley (1977) suggested that there are several other possible thought processes

of interest to add in the withdrawal decision (the decision to quit a job), namely

intention to search and intention to quit. More specifically though, the thought

process of actual turnover is to stimulate thoughts of quitting, leading to an

evaluation of the expected utility of search and the cost of quitting, intention to

search for alternatives, the search for alternatives, the evaluation of alternatives,

intention to quit / stay, and then finally the withdrawal decision and behaviour of

actually quitting or staying .

Mobley’s (1977) simplified sequence of turnover cognitions:

Figure 2.2: Sequence of Turnover Cognitions (Adapted from Mobley, 1977)

Thinking of quitting

Intention to quit /stay

Intention to searchfor alternatives

Turnover /Stay


40

2.7 Outcomes of a Merger or Acquisition

A growing body of literature indicates that mergers and acquisitions can be a

traumatic event in the lives of individuals (Morrison & Robinson, 1997), and

organisations (Ashkenas & Francis, 2000; Lubatkin, 1983). The turbulence

associated with mergers, in turn, is often associated with declines in

organisational commitment and job satisfaction by some employees (Newman &

Krzystofiak, 1993; Schweiger & DiNisi, 1991), which can be very costly to firms.

Mirvis and Lawler (1977) reported that a decline of one-half standard deviation in

job satisfaction for a bank employee sample was associated with a cost of more

than $17,000 per employee. The costs were primarily a result of increased

absenteeism / turnover and decreased performance. If these costs are adjusted

for inflation over time (and of course converting them to Rands), it becomes

evident why troubled mergers or acquisitions might quickly erode some of the

hoped for financial gains. Poorly integrated cultures can also be problematic, as

noted by Walter (1985) who estimated that the cost of culture collisions resulting

from poor integration may be as high as 25 to 30 percent of the acquired

company’s performance in most cases.

Loyalty and productivity may be lost and replaced by distrust, low morale,

reduced productivity or organisational effectiveness, stress, illness, accidents,

conflicts, lack of commitment, job dissatisfaction, increased labour turnover (even

at management level) and absenteeism rates, lowered work goals, uncertainty,

and employee theft or acts of sabotage are all found results of mergers

(Altendorf, 1986; Cartwright & Cooper, 1994; Ernst & Young, 1994; Fink, 1988;

Hogan & Overmyer-Day, 1994; Meeks, 1977; Sinetar, 1981; and Walsh, 1988).

These penultimate or intermediate variables are not only outcomes of the merger

process itself, but in turn exert influence over the same process and the

attainment of the organisation’s objectives.


41

Several studies have shown that employees’ organisational commitment, job

satisfaction and turnover intentions have been negatively affected as a result of a

merger or an acquisition or the announcement of one (Armstrong-Stassen,

Cameron, Mantler & Horsburgh, 2001; Bastien, 1987; Buono, Bowditch & Lewis,

1985; Covin, Sightler, Kolenko & Tudor, 1996; Davy, Kinicki, Kilroy & Scheck,

1988; Jones, 2000; Weber, Lubatkin & Schweiger, 1994; and Zhu, May &

Rosenfield, 2004).

Bastien (1987) performed a qualitative study over three separate incidents of

mergers and acquisitions ranging from a genuine merger to an actual acquisition.

Outcomes included a general increase in turnover intentions with some

employees following through with those intentions. Also job satisfaction and

commitment to the organisational deteriorated for an array of reasons including

lack of communication within the institutions and initial perceptions of the

merging partners.

Buono et al. (1985) found in their study of a merger between two mutual savings

banks that the lack of communication experienced contributed to employees’

negative feelings towards the merger. These negative feelings towards the

merger in turn negatively affected their organisational commitment and job

satisfaction.

Weber et al. (1994) after collecting data from 36 firms, found that corporate

cultural differences in mergers were negatively associated with organisational

commitment of the acquired top managers, and that this commitment explained

subsequent turnover for years after the merger.

Davy et al. (1989) found that there was a decrease in both organisational

commitment and job satisfaction during and after an acquisition due to the

uncertainty surrounding a company’s acquisition. The employees felt that they

had no control over the acquisition and their work. Davy et al. (1989) argue that


42

in an attempt to regain that control, employees started to withdraw from the

organisation that they felt was responsible for their lack of control.

Armstrong-Stassen et al. (2001) found that, compared with the pre-amalgamation

period, nurses in both the acquiring and the acquired hospitals reported a

significant decrease in job satisfaction, organisational commitment, and

organisational trust, and a significant increase in turnover intentions of a hospital

amalgamation in Canada.

Covin et al. (1996) indicated a significant difference in merger satisfaction both

within and between the acquiring firm and acquired firm employees of a large

manufacturing firm. The level of individual satisfaction with the merger was also

found to be closely associated with, amongst others, turnover intent.

Jones (2000), on determining the effect of a merger between three hospitals,

found that none of the nurses at any of the hospitals displayed a very strong

commitment to either their own employing hospital or to the healthcare system.

Job satisfaction was found to be fairly consistent between hospitals, whilst the

scores of Intention to Turnover Scale indicated that nurses at all three hospitals

had no strong feelings about either staying at their present hospital or leaving.

There was a slight intention by the nurses at each hospital to leave their job but

the notion of a lateral transfer with the same or higher pay at a different hospital

or healthcare system did not appear to influence this variable.

Zhu et al. (2004) found that within a merger between two Chinese Internet

companies post-merger job satisfaction was lower than pre-merger satisfaction

for both groups on four of the five Job Descriptive Index dimensions (satisfaction

with work, supervisor, co-workers, promotions, and pay); the exception was for

co-worker satisfaction.


43

From the above there seems to be a consensus as to the outcomes of a merger

or amalgamation and, focusing on the primary constructs in question, all are

negatively affected by such a process. Job satisfaction is reduced; organisation

commitment is diminished; and turnover intentions levels are increased.

However, some researchers still indicate that there is a dearth of knowledge of

relationships between mentioned constructs and the causes thereof.

A review of literature by Cartwright and Cooper (1990) concluded that the human

merger literature was fragmented, eclectic, and essentially composed of

hypothetical speculation and anecdotal articles. Notably, much of the research

carried out has been US dominated (Sommer, Bae, & Luthans, 1996). The

review of Cartwright and Cooper (1990) highlighted the paucity of research

studies in the area generally and the notable lack of empirical research. The

scarcity of the research was attributed to two major obstacles which

psychological research has faced (Cartwright & Cooper, 1990, p. 67):

(1) a lack or recognition that mergers are essentially a human activity and,

as such, that psychology has a legitimate interest and useful contribution

to make; and

(2) the problem of complexity, and the inherent methodological difficulties

this complexity presents for human merger research.

Singh (1999) indicated an alternative approach need be adopted due to the

absence of research on line employees, therefore data must be examined from

line employees or individual contributors, rather than only managers of the

merging or acquired companies.

Jones (2000) and Armstrong-Stassen et al. (2001) asserted that studies were

lacking and pointed to the effect that organisational change, such as mergers

and restructuring in the healthcare environment, had on these variables.


44

There is seemingly a lack of studies that have prioritised the effects of a merger

within a tertiary environment. More notably; very little has been addressed within

a South African context, save for Jansen (2002), and Arnolds and Boshoff

(2004).

The above review indicated that work has been done on mergers and

acquisitions, however within a tertiary environment and more particular within the

South African tertiary environment, there is a dearth of research.

2.8 Relationships between the Key Concepts

Job satisfaction and organisational commitment are commonly viewed as

intervening variables in the turnover process (Shore et al., 1990). Their results

highlighted a causal link between organisational commitment and turnover

intentions, whilst the path analysis was found to be significant, with the inclusion

of a path between job satisfaction and turnover intentions.

Organisational commitment and job satisfaction are viewed as an essential

component of turnover models because their empirical relationship with voluntary

turnover has been established through numerous meta-analyses (e.g. Cohen,

1993; Lee, Carswell, & Allen, 2000; Mathieu & Zajac, 1990; Meyer, Stanley,

Herscovitch, & Topolnytsky, 2002; Steel & Ovalle, 1984; Tett & Meyer, 1993; and

Yin & Yang, 2002).

Cohen (1993) found a significant relationship between organisational

commitment and turnover, but indicated that the relationship either grows or

wanes, given the tenure of the respondent.

Lee et al. (2000) found support for the two best predictors of organisational

turnover intentions, namely: job satisfaction and organisational commitment.


45

Their meta-analytic considerations yielded correlation values of -0.538 for

organisational commitment and -0.581 for job satisfaction.

Mathieu and Zajac (1990) examined the antecedents, correlates, and

consequences of organisational commitment. The study yielded uniformly

positive correlations between job satisfaction and organisational commitment as

none of the 95% confidence intervals included zero. The need to develop

stronger theoretical foundations was addressed, given the uncertainty whether

job satisfaction precedes organisational commitment or vice versa. As an

antecedent, organisational commitment has been used on numerous occasions

to predict withdrawal behaviour and in this analysis yielded a large correlation

value of -0.464.

Meyer et al. (2002), in their meta-analysis, determined relationships between the

three forms of commitment (affective, continuance, and normative) and their

subsequent variables identified as antecedents, correlates, and consequences.

The results, amongst others, yielded significant relationships between job

satisfaction (hypothesised correlate) and turnover intentions (hypothesised

consequence).

Steel and Ovalle (1984) found significant correlations in their meta-analysis,

yielding correlation values, again turnover intentions, for organisational

commitment (-0.38) and job satisfaction (-0.28).

Tett and Meyer (1993) found that job satisfaction and commitment each

contribute independently to the prediction of turnover intentions / withdrawal

cognitions, where these intentions / cognitions were more strongly predicted by

job satisfaction than commitment.


46

Yin and Yang (2002) in their meta-analysis of Taiwanese nurses found

organisational commitment and job satisfaction to be significant predictors of

turnover intentions.

Attitudinal constructs have come to be accepted as reliable predictors of attrition.

Mobley (1982) summarised the causes and correlates of employee turnover. It

was found that individual variables that bear a ‘consistent’ relationship to

employee turnover are, amongst others, overall job satisfaction and

organisational commitment.

2.9 Background Factors Related to Key Concepts

While many studies have adopted the environmental approach when determining

causes of organisational commitment, job satisfaction and intentions to stay /

leave, personal attributes do also play a role. Other researchers have also

supported this notion as the discussion below shows.

The variables listed below have been identified in the relevant literature namely

(adapted from the actual questionnaire).

• Please indicate your age group.

• How many complete years have you been working at the [university’s

name]?

• What is your gender?

• What is your race?

• What is your marital status?

• What is your highest academic qualification?


47

2.9.1 Age

2.9.1.1 Age and Satisfaction

Many investigations have been carried out over the past four decades, with

contradictory results, which have left the true nature of the relationship between

age and job satisfaction unresolved, but still, age may be a contributing factor in

the experience of job satisfaction. Empirical research endeavours have found a

U-shaped relationship (Clark, Oswald & Warr, 1996; Handyside, 1961; and

Herzberg, Mausner, Peterson, & Capwell, 1957), revealing that the employee’s

job satisfaction decreased initially and then increased with age. A positive linear

relationship between employee age and job satisfaction was also found and in

this case the employees became more satisfied with their job as their

chronological age progressed (Brush, Moch, & Pooyan, 1987; Gechman &

Wiener, 1975; Ingersoll et al., 2002; Herrera, 2003; Koch & Steers, 1978; Oswald

& Gardner, 2001; Shields & Ward, 2001; and Warr, 1992). Muchinsky (1978)

found a negative linear relationship between age and job satisfaction. An inverted

U-shaped or inverted J-shaped relationship was observed by Saleh and Otis

(1964) and Oswald (2002), while no significant relationship was found by

Chambers (1999); Ronen (1978); and White and Spector (1987).

2.9.1.2 Age and Commitment

There are contradictory findings in the relevant literature about the relationship

between age and commitment. Some studies found no relationship between age

and commitment, as was supported by Batlis (1978); Gechman and Wiener

(1975); Kanungo (1982b); Knoop (1986); Mannheim (1975); Müller and Roodt

(1998); Roodt (1992) and Roodt, Bester, and Boshoff (1993). Other researchers

have found that commitment has been related positively to age (Angle & Perry,


48

1981; Arnold & Feldman, 1982; Cohen & Lowenberg, 1990; DeCotiis &

Summers, 1987; Dornstein & Matalon, 1989; Hrebiniak, 1974; Hrebiniak & Alutto,

1972; Ingersoll, Olsan, Drew-Cates, Vinney, & Davies, 2002; Jones, James, &

Bruni, 1975; Kacmar & Carlson, 1999; Lee, 1971; Lodahl & Kejner, 1965; Lok &

Crawford, 1999; Luthans, Baack, & Taylor, 1987; Mathieu & Zajac, 1990;

McKelvey & Sekaran, 1977; Morris & Sherman, 1981; Newton & Keenan, 1983;

Rabinowitz, Hall, & Goodale, 1977; Saal, 1978, 1981; Schwyhart & Smith, 1972;

Sekaran & Mowday, 1981; Sheldon, 1971; Steers, 1977; and Van Rooyen,

1981). Research has also indicated that there is a positive relationship between

age and affective commitment (Ferris & Aranya, 1983; Harrell, 1990; Meyer &

Allen, 1984; and Reilly & Orsak, 1991).

2.9.1.3 Age and Turnover

Research results dealing with this relationship are seemingly consistent. Jacobs

(2005) found that professional nurses aged 50 and older are significantly less

intentional on quitting than professional nurses in the age categories of 40-49

years and 30-39 years. Federico, Federico, and Lundquist (1976) found that the

younger the age of the employee at application for the organisation, the higher

the turnover association. Mangione (1973), in a diverse occupational sample,

encountered a significant chi-square value entailing that the younger the age of

the employee, the higher the turnover association. Porter, Steers, Mowday, and

Boulian (1974) found that stayers are significantly older than leavers, while

Lambert, Hogan, and Barton (2001) experienced a significant positive correlation

between age and intentions to stay. Chiu and Francesco (2003), Marsh and

Mannari (1977), Mobley et al. (1978), and Waters, Roach, and Waters (1976) all

encountered statistically significant negative correlations, with turnover ranging

from -0.220 to -0.270. Hellriegel and White (1973) however, in their sample of

certified public accountants, reportedly found no consistent statistical differences.


49

Yin and Yang (2002), too, in their meta-analysis of nurses, found no statistical

differences.

2.9.2 Tenure

2.9.2.1 Tenure and Satisfaction

Job satisfaction followed a U-shaped relationship with respect to tenure in current

position (Shields & Ward, 2001). Cano and Miller (1992), however, found no

relation between years of experience and overall job satisfaction among

agricultural education teachers. Similar results - that job satisfaction and years of

experience indicated no relationship - were found by Bedeian, Farris, and

Kacmar (1992); Bertz and Judge (1994); O’Reilly and Roberts (1975); and Ma,

Samuels and Alexander (2003). However, research by Chambers (1999);

Gechman and Wiener (1975); Herrera (2003); and Koch and Steers (1978)

indicated that overall job satisfaction increased as the years of experience

increased.

2.9.2.2 Tenure and Commitment

The findings of Hackett, Bycio, and Hausdorf (1994) support a positive

relationship between tenure and affective and continuance commitment. Cohen

and Lowenberg (1990); Buchanan (1974); DeCotiis and Summers (1987); Gould

and Werbel (1983); Grusky (1966); Hall, Schneider, and Nygren (1970);

Hrebiniak (1974); Hrebiniak and Alutto (1972); Lee (1971); Luthans et al. (1987);

March and Simon (1958); Meyer and Allen (1984); Mowday et al. (1979; 1982);

Sheldon (1971); and Welsch and La Van (1981) all reported, that the longer

employees worked in an organisation, the higher their levels of commitment.

However, contradictory to those results, Roodt (1992) conducted a study in


50

South Africa at an academic institution and found no significant relationship

between tenure and organisational commitment. Ferris and Aranya (1983);

Gechman and Wiener (1975); Knoop (1986); Lok and Crawford (1999); McFarlin

and Sweeney (1992); Reilly and Orsak (1991); and Schwyhart and Smith (1972)

also found no meaningful relationship between tenure and organisational

commitment.

2.9.2.3 Tenure and Turnover

Lum et al. (1998) found a statistically significant positive correlation between

tenure and turnover intentions and likewise Jacobs (2005) found similar results,

namely that professional nurses with 11 years and more tenure are statistically

significantly more inclined to quit than professional nurses with fewer years of

service. Chiu and Francesco (2003); Mobley et al. (1978); and Waters et al.

(1976), however, all encountered significant negative correlations ranging

between -0.250 to -0.30. Similar results were found with Lambert et al. (2001)

experienced a significant positive correlation with tenure and intentions to stay.

Also, Mangione (1973) encountered a significant chi-square value whereby lower

tenure is associated with higher turnover. Yin and Yang (2002), however,

reported and nonsignificant correlation between turnover intentions and tenure.

2.9.3 Gender

2.9.3.1 Gender and Satisfaction

Gender has been found to be a significant predictor of job satisfaction

(Handyside, 1961). A number of empirical studies on job satisfaction have

suggested that female workers have lower levels of job satisfaction than their

male counterparts, because male officials dominate most of the public


51

organisations (Bedeian et al., 1992; Buzawa, 1984; and Herrera, 2003). Cano

and Miller (1992) found that male and female agriculture teachers in Ohio were

satisfied with their jobs and that they did not differ significantly in terms of their

overall job satisfaction scores. Meta-analytic studies involving multiple samples

and thousands of employees have failed to find gender differences (Brush et al.,

1987; Witt & Nye, 1992). Greenhaus, Parasuraman, and Wormley (1990) also

did not find significant gender differences, even though the racial distribution was

not the same in their sample for both genders. Koch and Steers (1978) in their

study of public employees also found no difference, where men were generally in

the managerial positions and women mainly in the clerical jobs. They suggested

that women were happier with the lower pay and responsibility than men and

therefore their expectations were lower. Interestingly, Smart and Ethington

(1987) found that women employed in gender equitable jobs expressed more

satisfaction with the intrinsic and overall nature of jobs than did women in female-

dominated occupations. Smart, Elton and McLaughlin (1986) found that gender-

specific differences are apparent in terms of extrinsic (males only) and overall

(females only) job satisfaction.

2.9.3.2 Gender and Commitment

There are contradictory research findings with regard to gender and commitment.

Some studies that were conducted on gender found women to be more

committed than men (Angle & Perry, 1981; Gould, 1975; Grusky, 1966; Hrebiniak

& Alutto, 1972; Mathieu & Zajac, 1990; and Saal, 1978). Mathieu and Hamel

(1989) support this in their study on professional employees. While others found

men remain more committed to continue with their work than women (Cohen &

Lowenberg, 1990; Ferris & Aranya, 1983; Lacy, Bokemeier, & Shepard, 1983).

Graddick and Farr (1983) also found in their research that men are more

committed to the organisation than their female colleagues. Other researchers

found that gender was not related to commitment (Aven, Parker, & McEvoy,


52

1993; Blau & Boal, 1989; DeCotiis & Summers, 1987; Gould & Werbel, 1983;

Kacmar & Carlson, 1999; Kanungo, 1982b; Knoop, 1986; and McFarlin &

Sweeney, 1992). On the South African front, Roodt (1992) found a significant

relationship between gender and commitment.

2.9.3.3 Gender and Turnover

In their study of nurses, Lum et al. (1998) found gender had little or no impact on

turnover intentions. Lambert et al. (2001), too, found, in their national sample of

US workers, no significant relationship with gender and turnover intentions as

well as did Mangione (1973) who found no result. Porter et al. (1974) in their

longitudinal study of psychiatric technician trainees found the same. Marsh and

Mannari (1977) however, in their sample of Japanese electrical company

employees, encountered a negative correlation whereby women had higher


2.9.4 Race

2.9.4.1 Race and Satisfaction

A recent trend in the composition of the workforce in South Africa and other

countries is that it is becoming more diverse or multicultural. Some studies have

proven that blacks have slightly lower satisfaction (Greenhaus et al., 1990; Tuch

& Martin, 1991) although Brush et al. (1987) reported no racial differences in a

meta-analysis of 21 studies. Shields and Ward (2001) also found that Asians and

blacks reported lower overall job satisfaction than the omitted category of whites.

Vallabh and Donald (2001), however, indicated in their study that blacks reported

higher job satisfaction levels than whites.


53

2.9.4.2 Race and Commitment

In a study conducted by Vallabh and Donald (2001) on 30 black and white middle

managers, they found that the white group had higher levels of commitment than

the black group. Some of the reasons could be that blacks still experience issues

of racism and hostility in the workplace (Matuna, 1996; Wood, 1995). Another

reason could be that blacks do not allow themselves to become committed,

because if a new job offer arises, then it is easier to break away from the

organisation (Vallabh & Donald, 2001). The ‘job-hopping’ phenomenon (Primos,

1994; Qunta, 1995; and Sibanda, 1995), where black managers are short in

supply and high in demand, also contributes to this lower commitment (Vallabh &

Donald, 2001). Furthermore, the white managers were possibly more committed

to the organisation due to a lack of job offers (Vallabh & Donald, 2001). Other

studies could find no significant differences across racial-ethnic groups (Angle &

Perry, 1981).

2.9.4.3 Race and Turnover

Lambert et al. (2001) indicted that race is a poor and inconsistent variable to be

used as a predictor of turnover; however, Jacobs (2005) found that African

professional nurses are significantly more inclined to quit than their coloured or

white counterparts. Vallabh and Donald (2001) found that far more black

managers were seriously considering leaving their current positions than their

white counterparts.


54

2.9.5 Marital Status

2.9.5.1 Marital Status and Satisfaction

Cetin (2006) found that there was no difference between the job satisfaction

levels of the academics according to the marital status variable. Chambers

(1999), too, found inconclusive results with a study dealing with managerial and

executive respondents. The same can be said for Gechman and Wiener (1975)

in their study of female elementary (primary) school teachers. Shields and Ward

(2001) interestingly found that being married had positive effects on the

employees’ overall job satisfaction.

2.9.5.2 Marital Status and Commitment

Marital status has been shown to be related to commitment (Hrebiniak & Alutto,

1972; Mathieu & Hamel, 1989; Mathieu & Zajac, 1990; and Meyer & Allen, 1988)

because married people have greater financial responsibilities towards their

family and this increases their need to stay (Hrebiniak & Alutto, 1972; Kacmar &

Carlson, 1999; Kanungo, Misra, & Dayal, 1975; Knoop, 1986; and Mathieu &

Zajac, 1990). Other studies, however, have indicated no relationship between

marital status and commitment (Blau & Boal, 1989; Cohen & Lowenberg, 1990;

Ferris & Aranya, 1983; Gechman & Wiener, 1975; Kanungo, 1982b; Lodahl &

Kejner, 1965; Rabinowitz et al., 1977; Roodt et al., 1993; and Saal, 1978, 1981).

2.9.5.3 Marital Status and Turnover

Lambert et al. (2001) concluded that the variable marital status adds little value

given its record of being a poor and inconsistent predictor of turnover. This is


55

echoed by the results attained by both Lum et al. (1998) and Jacobs (2005). Lum

et al. (1998) found that the marital status of the respondent had little or no impact

on turnover intentions and Jacobs (2005) found no significant differences in

mean scores between the different marital categories and intention to turnover.

Waters et al. (1976) also encountered no significant associations. Yin and Yang

(2002) however, reported a significant finding in their meta-analysis of nurses,

with married respondents reporting higher staying intentions, as did Federico et

al. (1976) in their study of voluntary turnover of women.

2.9.6 Highest Academic Qualification

2.9.6.1 Highest Academic Qualification and Satisfaction

Oswald and Gardner (2001) found that Britons with university degrees reported

the lowest levels of satisfaction at work, as did Oswald (2002), who found that

average job satisfaction scores decline with education and the highest level of

job satisfaction were gained by people with no qualifications. Higher levels of

qualification were associated with significantly lower levels of job satisfaction for

nurses (Shields & Ward, 2001), as was also found by Koch and Steers (1978)

and Kramer (1974). Crewson (1997) commented that those who are highly

educated have greater expectations and therefore are more difficult to satisfy

than those less educated. Griffin, Dunbar and McGill (1978) and Herrera (2003),

on the other hand, found that workers with higher educational levels tend to be

more satisfied with their job than workers with lower educational levels. Ingersoll

et al. (2002) found that nurses with masters degrees were significantly more

satisfied than baccalaureate-prepared nurses and nurses prepared at less than

the baccalaureate level. Jayaratne (1993) and Burk (1985) found that the

increased levels of education influenced job satisfaction positively.


56

2.9.6.2 Highest Academic Qualification and Commitment

There are conflicting findings with regard to commitment and education.

Education is inversely (negatively) related to commitment (Angle & Perry, 1981;

Cohen & Lowenberg, 1990; Dornstein & Matalon, 1989; Koch & Steers, 1978;

Mathieu & Hamel, 1989; Mathieu & Zajac, 1990; Meyer & Allen, 1988; Morris &

Sherman, 1981; Mowday et al., 1982; Ruh, White, & Wood, 1975; Saal, 1978,

1981; Sekaran & Mowday, 1981; and Steers, 1977). However, Lacy et al. (1983)

found that more highly educated people were more committed to work. Likewise

Grusky (1966); Knoop (1986); Lee (1971); Mannheim (1975); Newton and

Keenan (1983) and Siegel and Ruh (1973) also found a positive relationship

between education and commitment. Lok and Crawford (1999) found a near zero

relationship between education and commitment. The zero relationship was also

viewed by DeCotiis and Summers (1987); Ferris and Aranya (1983); Gould and

Werbel (1983); Ingersoll et al. (2002); Jones et al. (1975); Kanungo (1982b)

Luthans et al. (1987); Rabinowitz et al. (1977); and Welsch and La Van (1981) all

of whom found no relationship between education and work commitment. Steers

and Spencer (1977) claim to have found results within this particular relationship

to be inconsistent.

2.9.6.3 Highest Academic Qualification and Turnover

The assorted research results prove inconclusive. Lum et al. (1998) and Shields

and Ward (2001) found a significant positive correlation between education and

turnover intentions, as did Chiu and Francesco (2003) who achieved a significant

positive correlation of 0.2 in their sample of Chinese managers. Yin and Yang

(2002), too, achieved a significant correlation. Jacobs (2005), on the other hand,

found no significant differences in mean scores between the different educational

level categories and intention to turnover. Lambert et al. (2001); Hellriegel and

White (1973); Mangione (1973); and Porter et al. (1974) all found no consistent


57

significant relationship between education and turnover intentions. Other

research, such as Federico et al. (1976), found that the high educational level of

the respondent was associated with shorter tenure.

2.10 Synthesis

The emphasis of this chapter was to provide a literature overview of the concepts

of this study. The key concepts, namely organisational commitment, job

satisfaction and intentions to stay / turnover were defined. Thereafter a

theoretical framework for each concept was provided.

The relationship between job satisfaction, organisational commitment and

turnover intentions is theoretically and empirically well established, where

following the merger or acquisition, job satisfaction is reduced; organisation

commitment is lowered; and turnover intentions levels are increased. This

indicates the positive association between organisational commitment and job

satisfaction, while both having a negative relationship with turnover intentions.

However it was highlighted that in South African literature more can be done,

especially in a merger and acquisition context. From the theoretical overview, it is

clear that organisational commitment and job satisfaction are regarded as

important predictors of organisational outcomes, such as turnover intentions.

While there is reasonable consensus about the domain of job satisfaction and

turnover intentions, the study of organisational commitment is characterised by

concept redundancy and contamination.

Research revealed the bivariate relationship between biographic variables

(gender, race, age, tenure, marital status, and highest academic qualification)

and the work constructs (organisational commitment, job satisfaction, and

turnover intentions) is well documented; however in some cases results proved

to be contradictory.


58

The next chapter will outline the design of the empirical part of the study namely,

the research design and methodology of the study.


59

3 CHAPTER 3: RESEARCH DESIGN AND METHODOLOGY

3.1 Introduction

In the previous chapter a review of the literature on organisational commitment,

job satisfaction and turnover intentions was provided. The key concept of each of

these constructs was defined and a theoretical overview highlighted. The current

status of research, regarding the relationships between key concepts in the

hypothesised models, as well as relationships of the biographical variables, was

also explored.

In this chapter, the focus will be on the research approach and research

methodology. This includes the research design, target population, research

procedure, measuring instruments, and the statistical procedures used in the

analysis of the sample.

3.2 Empirical Research Objectives

The primary research objective of the study is to investigate the relationships

between employee perceptions of organisational commitment, job satisfaction,

and turnover intentions within a post-merger tertiary institution.

The research objectives at the secondary level are listed below.

Research Objective #1: Determine what the perceptions of employees’

(academic, administrative and support staff) job

satisfaction are within the institution across all

campuses.


60


(academic, administrative and support staff)

organisational commitment are within the institution

across all campuses.

Research Objective #3: Determine what the employees’ (academic,

administrative and support staff) level of turnover

intentions is within the institution across all campuses.

Research Objective #4: Determine what the measured relationships or

associations between these scales are within the

institution across all campuses. Within this objective a

‘best-fitting’ model will be determined.

Research Objective #5: Determine what relationships exist between the

attained biographical variables and the three individual

scales (work constructs). The selected biographical

variables to be utilised are: Age, Tenure, Gender, Race,

Marital Status, and Highest Academic Qualification.

Research Objective #6: Determine what relationship exists between the

selected dependent work construct (to be determined

through the best model fit vetting) and the interactions

between the attained biographical variables. The

selected biographical variables are: Age, Tenure,

Gender, Race, Marital Status, and Highest Academic

Qualification.


61


attained biographical variables, interactions thereof,

and the three scales within the ‘best-fit’ model of the

proposed models from Research Objective #4.

It is against this background that the following research design will be discussed.

3.3 Research Approach

A research design is a plan or blueprint of how the research is to be conducted

(Mouton, 2001). It reflects the type of study undertaken to provide acceptable

answers to the research problem. Research designs are invented to enable the

researcher to answer research objectives as validly, objectively, accurately, and

economically as possible. Adequately planned and executed design helps greatly

in permitting one to rely on both one’s observations and inferences.

This research was carefully designed and its design has the characteristics listed

below.

• It falls within the quantitative research paradigm.

• It is of the non-experimental kind.

• It is retrospective (ex post facto) in nature.

• It is based on primary data.

It follows now why the selected research design was applicable to the current

study.

The strengths of this design are that, if the following has been properly and

carefully implemented namely: appropriate sampling design; high measurement

reliability from proper questionnaire construction and high construct validity from

proper controls, then the potential exists to generalise to large populations

(Mouton, 2001). Researchers using this design should, however, be careful of


62

sampling error, questionnaire error, high refusal and non-response rate, data

capturing errors, and the inappropriate selection of statistical techniques

(Mouton, 2001). These issues were addressed when the analysis was carried out

and will be discussed in subsequent sections.

The rationale for the different types of research designs will be discussed briefly

(see Figure 3.1 below), thus enabling the research paradigm followed in this

study to be contextualised. The bolded paths indicate the approach adopted.

Figure 3.1: Outline of Steps in the Research Approach

Research Approach

QualitativeQuantitative

Experimental

Non-Experimental

Secondary Data

Primary Data

Observation

Self-Administered Focus Groups

Interviews

X

X

X

X


63

3.3.1 Qualitative versus Quantitative Research

Presently there are two well-known and recognised traditions associated with

empirical research, namely the quantitative paradigm and the qualitative

paradigm (Schurink & Schurink, 2001). The difference between quantitative and

qualitative research is based on different research paradigms. According to these

authors the quantitative paradigm is based on positivism, which takes scientific

explanation to be nomothetic (i.e. relating to the discovery of universal laws). Its

main aims are to measure the social world objectively and to test hypotheses /

research objectives (i.e. whether the predictive generalisation of the theory holds

true), based on testing a theory composed of variables, measured with numbers,

and analysed with statistical procedures. By contrast, the qualitative paradigm

stems from an anti-positivistic, interpretative approach, is holistic in nature and

aims at understanding social life and the meaning that people attach to everyday

life.

The ontology, epistemology and methodical differences in the characteristics of

quantitative vs. qualitative research are illustrated in Table 3.1.

TABLE 3.1DIFFERENCE BETWEEN QUANTITATIVE AND QUALITATIVE RESEARCH

Quantitative Qualitative

Behaviour can be explained in causal

deterministic ways and people can be

manipulated and controlled.

Behaviour is intentional and creative

and it can be explained but not

predicted.

Objective – researcher seen as

detached from the object that one

studies.

Subjective – because interaction takes

place with the subject (object of

investigation).


64


Questions / hypotheses / objectives are

stated and subjected to empirical

testing to verify them.

Dialectical and interpretative.

Sample size is large. Sample size is small.

Depends on the use of numbers and

measurements.

Does not depend on the use of

numbers or measurements.

Focuses on phenomena that can be

explained by numbers and statistics.

Focuses on phenomena that cannot be

explained adequately with statistics.

The researcher needs to play a more

prominent role in the data gathering

process.

The researcher is unobtrusive or a

participating observer.

The researcher experiences subjects

on a secondary level through the

interpretation of numbers and

measurement.

The researcher encounters the

subjects through a firsthand

experience.

Amount of information from each

respondent varies.

Amount of information from each

respondent is substantial.

Has a structured data collection

process.

The data collection process is semi-

structured. Processes are naturalistic,

participatory and interpretative in

nature.


65


Needs a set plan for the completion of

research.

Is very flexible and changes as the

data and circumstances change.

One of the main focuses is to test

hypotheses / objectives.

The researcher can develop new

hypotheses during the research

process.

Tries to establish causal relationships. Generates hunches.

Degree of replicability is high. Degree of replicability is low.

[Adapted from Babbie and Mouton (2001); Kerlinger and Lee (2000); McDaniel

and Gates (2006); Schurink and Schurink, 2001; and Struwig and Stead (2001).]

As highlighted in Table 3.1, for the sake of this research and that best served the

purpose of this study, a quantitative research paradigm was adopted. This was

primarily selected due to the need to address given, and already determined,

hypotheses / research objectives and from which causal relationships (and the

strengths as such) can be established from the large population size selected.

Following that, all intended reporting will be based on established questionnaires

(i.e. measurements) whereby statistical procedures are carried out. This study

stems from others studies in similar circumstances, and hence the need for

replicability.

3.3.2 Experimental versus Non-Experimental Research

According to Kerlinger and Lee (2000), non-experimental, cross-sectional, field

survey research (or more accurately ex post facto research) is a systematic

empirical inquiry in which the researcher does not have direct control of


66

independent variables in the sense of being able to manipulate them, because

their manifestations have already occurred, or because they are inherently not

manipulable. Inferences about relations between variables are made, without

direct intervention, from the associated variation of independent and dependant

variables. All experimental research, on the other hand, has one characteristic in

common, the intervention. The unit of analysis, which could be individuals or an

organisation, has to be exposed to something to which they would otherwise not

have been subjected (Welman & Kruger, 2001).

Table 3.2 highlights the differences found between experimental and non-

experimental research.

TABLE 3.2DIFFERENCES BETWEEN EXPERIMENTAL AND NON-EXPERIMENTAL RESEARCH

Experimental Non-Experimental

Has an intervention as common factor. Intervention not planned.

Unit of analysis (individuals or

organisation) exposed to something

that would normally not occur.

Absence of the assignment of a unit of

analysis to groups.

Normal research conditions require

rigorous standards.

Research can be done where

experimental conditions are not

possible.

High cost of experiments. Affordable costs in comparison to

experimental designs.


67

Experimental Non-Experimental

The research has control over the

independent variable in the levels to be

applied in the research.

Researcher does not have direct

control over the independent variables

due to a given manifestation or

because inherently the variables are

not manipulated.

The random assignment of units of

analysis to groups, which means that

the assignment of a unit of analysis is

done in a randomised manner.

Researcher does not have direct

control over the independent variables

and hence the luxury of randomness

cannot be afforded.

Causality more easily identifiable. Causality factors not readily identified.

Due to the exact nature of experimental

setting, interpretation is made clearer.

The risk of improper interpretation.

Examples of experimental research are

laboratory experiments. The reason for

choosing a laboratory experiment, as a

method, is to test relations under ‘pure’

conditions.

Examples of non-experimental

research are field surveys. The reason

for choosing a field survey, as a

method, is to test relations in real social

structures or in life situations.

[Adapted from Cohen, Manion and Morrison (2000); Kerlinger and Lee (2000);

McDaniel and Gates (2006); Struwig and Stead (2001); and Welman and Kruger

(2001).]

According to Kerlinger and Lee (2000), despite the weaknesses, much non-

experimental research must be done in the social sciences, because many of the

research problems in the social sciences lend themselves to controlled inquiry of

the non-experimental kind, a facet which is also holds true for this study. Non-


68

experimental is the most adequate given the need to address life situations

through a field survey, and due to the population size, cost implications also

dictated the choice of a non-experimental research design.

3.3.3 Primary versus Secondary Data

Primary data are the original data obtained from a direct observation of the

phenomenon under investigation or are collected personally, whilst secondary

data are information collected by individuals or organisations other than the

researcher (Struwig & Stead, 2001; Welman & Kruger, 2001).

Table 3.3 illustrates the differences found between primary and secondary data.

TABLE 3.3DIFFERENCES BETWEEN PRIMARY AND SECONDARY DATA

Primary Secondary

Primary data is gathered from direct

observation or data personally

collected.

Secondary data collected by people /

institutions other than the researcher.

Method of collection is through

interviews, personal or telephone calls,

focus groups, observation, self-

administrated questionnaires.

Method of gathered data is the re-

analysis of existing data.

Written or oral account of a direct

witness of, or participant, in an event.

Second-hand information about an

event that has not been personally

witnessed by the researcher.


69

Primary Secondary

Data collected for a specific purpose. Data collected by someone else for

another purpose therefore there may

be a lack of relevance.

Costs are incurred on a first time basis. The use of secondary data saves time

and money.

Full control is provided in working with

primary data.

Inaccuracy may occur because of a

number of potential sources or error

such as gathering and processing the

data.

Data accessed for the first time. Accessibility to the data may be

problematic.

[Adapted from McDaniel and Gates (2006); Mouton (2001); Struwig and Stead

(2001); and Welman and Kruger (2001).]

Although it is seen above that secondary data has its ‘resource’ benefits in terms

of cost and effort concerned with attaining the data, the primary data approach

was adopted by this study, given the nature of the premise: that the institution in

question is experiencing these circumstances (i.e. a merger) for the first time.

3.3.4 Self-Administered versus Others

Support of utilising a self-administered Internet survey will be highlight in the next

section under Research Procedure.


70

The above section focused on the research design. The next few sections will

focus on the research methodology.

3.4 Research Methodology

The process outlined below in Figure 3.2 was applied toward achieving the

stipulated research objectives. Before the commencement of the actual analysis,

where the research objectives will be directly addressed, there is a need to carry

out ‘diagnostic’ procedures. These procedures involve entailing the correct and

adequate selection of the population and subsequently the sample. The research

procedure details support for the manner in which the data was assimilated. The

measuring instruments argue the need for the particular questionnaire and the

questionnaire’s validity and reliability as such. Lastly, an outline of all intended

statistical procedures will be shown where the theoretical ‘rules of thumb’ will be

indicated. Figure 3.2 below illustrates the outline to be followed.

Figure 3.2: Outline of Steps in the Research Methodology

Research Methodology

Measuring Instruments

Research Procedure

Participants / Sample

Statistical Analysis


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3.4.1 Participants / Sample

Sampling is the process of selecting observations (Babbie & Mouton, 1998). A

sample is a special subset of a population observed in order to make inferences

about the nature of the total population (Babbie & Mouton, 1998).

Sampling is described by Mouton (1996) as a research strategy to study objects

or phenomena as representative examples of a larger population of similar

objects or phenomena. Representativeness is the underlying epistemic criterion

of a ‘valid’, unbiased sample. According to Mouton (1996), it is important to

distinguish between the target population and the sampling frame. The target

population refers to the population to which one wishes to generalise, while the

sampling frame (unit of analysis) refers to the set of cases from which the sample

will actually be selected.

A good sampling procedure fulfils two criteria: the sample should be

representative, in that the total population, the observations and the significant

relationships between them are carefully defined; and the sample should be

adequate, allowing for sufficient confidence to exist in the stability of its

characteristics (Goode & Hatt, 1952, cited in Chorn, 1987).

The study at hand addresses both the need for a representative sample through

a bias analysis of the particular demographic variables; and the adequacy of the

sample size. The study achieved 367 responses, which is later reflected upon in

subsequent sections.

3.4.1.1 Sampling Framework

The two main sampling categorisations are probability sampling and non-

probability sampling. Probability sampling provides a way of selecting


72

representative samples from large, known populations. It samples in which every

element of the population has a known, non-zero likelihood of section (McDaniel

& Gates, 2006). Probability sampling methods make it possible to estimate the

amount of sampling error that can be expected in any given sample (Babbie &

Mouton, 1998). Non-probability sampling, by contrast, risks introducing selection

bias into the sample. This occurs since it samples specific elements from the

population that are selected in a non-random manner (McDaniel & Gates, 2006).

The researcher is well aware of the hazard of potential selection bias given its

non-probability sampling framework. This is discussed further later in the chapter.

Convenience or opportunity sampling was used in the context of a non-

experimental research design. The sample focused on those respondents willing

to participate in the research. While the advantage of the approach lies in its

inclusion of those willing to participate, the disadvantage is that the results are

not representative of the wider population. The generalisation of the results is

therefore minimised (Cohen et al., 2000).

The bias analysis carried out is indicated below. The population data were made

available for the following variables and thus provided the opportunity to

determine whether the sample was representative of the intended population.




• What do you consider your predominant home language?


• At which campus of the [university's name] do you predominantly work?

• What is your current job status?

• Under what conditions of service are you employed at [university's name]?

All questions have been shortened on account of space requirements. The

following key found in Table 3.4 is used.


73

TABLE 3.4KEY USED FOR DEMOGRAPHICS QUESTIONS

Question Shortened Form

Please indicate your age group. Age

What is your gender? Gender

What is your race? Race

What do you consider your predominant home language? Home Language

What is your marital status? Marital Status

What is your highest academic qualification? Highest Academic

Qualification

At which campus of the [university's name] do you

predominantly work?

Campus

How many complete years have you been working at the

[university's name] (including the former institutions prior to

the merger)?

Tenure

What is your current job status? Job Status

Under what conditions of service are you employed at

[university's name]?

Conditions of

Service

The bias results are indicated initially for age below in Table 3.5. All results below

are based on the population data available at the time the survey took place.

Population data variables are categorised in a different manner and subsequently

the questionnaires categories had to be altered for this particular analysis to

allow for parsimonious testing. Total values deviate at times due to missing

values encountered from some respondents completing the demographic

questions.

The chi-square test will be used. This statistic compares the actual cell

frequencies (of the sample) to an expected cell frequency (of the population). If

the p-value is found to be less than 0.05, then the demographic variable at hand

is said to be unrepresentative of the population. A conservative rule for the use of


74

the chi-square test requires that most cells have expected values greater than 5.

If more than 20% of the cells have expected values less than 5, categories

should then be combined if the intended new combinations are logical (Noru is,

2005).

The following abbreviations have been used in the analysis below:

• Observed Number Obs. No.

• Expected Number Exp. No.

• Residual Res.

• Chi-Square 2

• Degrees of Freedom df

3.4.1.1.1 Bias Analysis of Age

Table 3.5 presents the outcome of the bias analysis dealing with age. No rules

are violated, as 0% of the cells have an expected value less than 5. Scrutiny of

the p-value indicates that there is no significant difference at the 95% level of

significance, with the p-value at 0.141, ensuring that the sample is representative

of the population based on this demographic variable.


75

TABLE 3.5BIAS ANALYSIS OF AGE

Categories Obs. No. Exp. No. Res. 2 df p-value

Younger than 25 11 7.5 3.5

25 – 29 41 30.7 10.3

30 – 34 63 51.9 11.1

35 – 39 53 57.5 -4.5

40 – 44 56 57.5 -1.5

45 – 49 58 56.7 1.3

50 – 54 35 43.2 -8.2

55 – 59 25 34.2 -9.2

60 or Older 19 21.7 -2.7

Total 361

12.236 8 0.141

0 CELLS (.0%) HAVE EXPECTED FREQUENCIES LESS THAN 5. THE MINIMUM EXPECTED

CELL FREQUENCY IS 7.5.

3.4.1.1.2 Bias Analysis of Gender

Table 3.6 presents the outcome of the bias analysis dealing with gender. No

rules are violated, as 0% of the cells have an expected value less than 5.

Scrutiny of the p-value indicates that there is significant difference at the 95%

level of significance, with the p-value at 0.000, signifying that the sample is not

representative of the population based on this demographic variable. A closer

look reveals that the sample included a higher representation of females than

required, and conversely a lower representation of males.


76

TABLE 3.6BIAS ANALYSIS OF GENDER


Male 133 186.7 -53.7

Female 224 170.3 53.7

Total 357

32.406 1 0.000



3.4.1.1.3 Bias Analysis of Race

Table 3.7 presents the outcome of the bias analysis dealing with race. No rules

are violated, as 0% of the cells have an expected value less than 5. Scrutiny of

the p-value indicates that there is a significant difference at the 95% level of

significance, with the p-value at 0.000, signifying that the sample is not


look reveals that the sample included a higher representation of whites than

required, with a lower representation of blacks. All other racial groups were

sufficiently represented.

TABLE 3.7BIAS ANALYSIS OF RACE

Categories Obs. No. Exp. No. Res. 2 Df p-value

African 78 137.3 -59.3

White 231 183.1 47.9

Coloured 28 18.0 10.0

Indian / Asian 16 14.6 1.4

Total 353

43.896 3 0.000




77

3.4.1.1.4 Bias Analysis of Home Language

Table 3.8 presents the outcome of the bias analysis dealing with home language.

The representation rule is violated as 33.3% of the cells have an expected value

of less than 5. Thus categories require to be combined, yielding new categories,

namely:

• Nguni (isiZulu, isiXhosa, siSwati (Swazi), isiNdebele);

• Sotho (SeSotho (Southern Sotho), Sepedi (Northern Sotho), SeTswana);

and

• Other South African (TshiVenda, xiTsonga);

TABLE 3.8

BIAS ANALYSIS OF HOME LANGUAGE


Afrikaans 191 113.8 77.2

English 93 172.9 -79.9

isiZulu 18 6.0 12.0

isiXhosa 8 2.1 5.9

Swazi 1 .4 .6

SeSotho 10 6.9 3.1

Sepedi 9 17.6 -8.6

SeTswana 14 5.9 8.1

TshiVenda 3 3.0 .0

xiTsonga 9 5.6 3.4

Other African 2 26.4 -24.4

Other European 3 .4 2.6

Total 361

187.178 11 0.000

4 CELLS (33.3%) HAVE EXPECTED FREQUENCIES LESS THAN 5. THE MINIMUM EXPECTED



78

Table 3.9 presents the outcome of the bias analysis dealing with the

recategorised variable of home language. No rules are violated, as 14.3% of the

cells have an expected value less than 5. Scrutiny of the p-value indicates that

there is a significant difference at the 95% level of significance, with the p-value

at 0.000, signifying that the sample is not representative of the population based

on this demographic variable. A closer look reveals that the sample included a

higher representation of Afrikaans-speaking respondents than required, together

with a less than required representation of English-speaking respondents. ‘Other

African’ was also identified has having a poor representation. The remaining

categories yielded satisfactory results.

TABLE 3.9BIAS ANALYSIS OF HOME LANGUAGE RECATEGORISED


Afrikaans 191 113.8 77.2

English 93 172.9 -79.9

Nguni 27 8.5 18.5

Sotho 33 30.4 2.6

Other South African 12 8.6 3.4

Other African 2 26.4 -24.4

Other European 3 .4 2.6

Total 361

170.577 6 0.000


CELL FREQUENCY IS .4.

3.4.1.1.5 Bias Analysis of Marital Status

Table 3.10 presents the outcome of the bias analysis dealing with marital status.

The representation rule is violated as 25% of the cells have an expected value of


79

less than 5. Thus categories require to be combined, yielding a new category,

namely:

• Other (Divorced or separated, Widowed).

TABLE 3.10

BIAS ANALYSIS OF MARITAL STATUS


Not married (single) 90 111.4 -21.4

Married or cohabitating 232 215.4 16.6

Divorced or separated 29 26.4 2.6

Widowed 7 4.8 2.2

Total 358

6.621 3 0.085



Table 3.11 presents the outcome of the bias analysis dealing with the

recategorised variable of marital status. No rules are violated, as 0% of the cells

have an expected value less than 5. Scrutiny of the p-value indicates that there is

a significant difference at the 95% level of significance, with the p-value at 0.047,

signifying that the sample is not representative of the population based on this

demographic variable. A closer look reveals that the sample included a higher

representation of those respondents indicating ‘Married or cohabitating’, but

fewer of those indicating ‘Not married (single)’. The ‘Other’ category was



80

TABLE 3.11BIAS ANALYSIS OF MARITAL STATUS RECATEGORISED


Not married (single) 90 111.4 -21.4


Other 36 31.2 4.8

Total 358

6.114 2 0.047



3.4.1.1.6 Bias Analysis of Campus

Table 3.12 presents the outcome of the bias analysis dealing with campus. No


Scrutiny of the p-value indicates that there is a significant difference at the 95%



look reveals that the sample included a higher representation of Campus B than

required, but a lower representation of Campus C. All other campus groups were



81

TABLE 3.12BIAS ANALYSIS OF CAMPUS


Campus A 42 39.2 2.8

Campus B 244 198.7 45.3

Campus C 51 97.3 -46.3

Campus D 15 15.8 -.8

Campus E 8 9.1 -1.1

Total 360

32.720 4 0.000



3.4.1.1.7 Bias Analysis of Job Status

Table 3.13 presents the outcome of the bias analysis dealing with job status. No


Scrutiny of the p-value indicates that there is a significant difference at the 95%



look reveals that the sample included a higher representation of permanent staff

member than required, but conversely a lower representation of ‘Other’ (contract,

temporary) staff members.


82

TABLE 3.13BIAS ANALYSIS OF JOB STATUS


Permanent 316 235.5 80.5

Other(contract/temporary) 45 125.5 -80.5

Total 361

79.085 1 0.000



3.4.1.1.8 Bias Analysis of Conditions of Service

Table 3.14 presents the outcome of the bias analysis dealing with conditions of

service. No rules are violated, as 0% of the cells have an expected value less

than 5. Scrutiny of the p-value indicates that there is no significant difference at

the 95% level of significance, with the p-value at 0.059, ensuring that the sample

is representative of the population based on this demographic variable.

TABLE 3.14

BIAS ANALYSIS OF CONDITIONS OF SERVICE


Academic / Research staff 145 127.9 17.1

Administrative staff 214 231.1 -17.1

Total 359

3.556 1 0.059



Based on the above results, the sample data assimilated is only representative of

the population in terms of the age and conditions of service. This is considered a

limitation of the study and will be noted and acknowledged accordingly.


83

3.4.1.2 Target Population and Sample

The target population can be described as all academic / research, support and

administrative personnel of the recently merged tertiary institution (i.e. all white-

collar workers) who are presently in possession of a valid work email address,

irrespective of their current employment contract within the organisation (i.e.

permanent, contract or temporary). The unit of analysis is each employee

regardless of their status within the respective departments and across all the

relevant campuses. Since the nature of the study targets each employee’s

commitment to the organisation as a whole (as well as their job satisfaction and

turnover intentions), each employee would be treated independent of position he

or she holds. No sample was actually selected from the overall population, as the

researcher contacted all relevant personnel (i.e. the entire population); thus the

sample made up those who responded to the survey given the voluntary nature

of the survey. Altogether 2279 emails were sent out to potential respondents of

whom 367 responded, entailing a response rate of 16%. McDaniel and Gates

(2006) indicate that 21% is the current standard of surveys of today, thus,

although the response rate achieved in the study is below par, the researcher

found the size of the data adequate to perform the required statistical analyses.

Participant anonymity was maintained throughout the questionnaire. At no time

during the research were the respondents required to divulge any kind of

information whereby they could be identified by the researcher. The anonymity

was intended to enhance the honesty of the responses given.

The details of the participants (demographics) are provided in below in Table

3.15. Note that the same key as provided in Table 3.4 will be used.


84

TABLE 3.15DEMOGRAPHIC INFORMATION OF THE RESPONDENTS

Demographic Information Respondents Percentage

AgeYounger than 25 11 3.0

25 – 29 41 11.2

30 – 34 63 17.2

35 – 39 53 14.4

40 – 44 56 15.3

45 – 49 58 15.8

50 – 54 35 9.5

55 – 59 25 6.8

60 or Older 19 5.2

Missing 6 1.6

Total 367 100

GenderMale 133 36.2

Female 224 61.0

Missing 10 2.7

Total 367 100

Race

African 78 21.3

White 231 62.9

Coloured 28 7.6

Indian 14 3.8

Asian 2 0.5

Missing 14 3.8

Total 367 100


85


Home LanguageAfrikaans 191 52.0

English 93 25.3

isiZulu 18 4.9

isiXhosa 8 2.2

Swazi 1 0.3

SeSotho 10 2.7

Sepedi 9 2.5

SeTswana 14 3.8

TshiVenda 3 0.8

xiTsonga 9 2.5

Other African 2 0.5

Other European 3 0.8

Missing 6 1.6

Total 367 100

Marital StatusNot married (single) 90 24.5

Married or cohabitating 232 63.2

Divorced or separated 29 7.9

Widowed 7 1.9

Missing 9 2.5

Total 367 100

Job StatusPermanent 316 86.1

Contract 36 9.8

Temporary 8 2.2

Other (please specify) 1 0.3

Missing 6 1.6

Total 367 100


86


Highest Academic QualificationLess than Grade 12 7 1.9

Grade 12 / Matric 58 15.8

Post-school certificate or diploma 73 19.9

Bachelors degree 43 11.7

Honours degree 52 14.2

Masters degree 62 16.9

Doctorate 64 17.4

Missing 8 2.2

Total 367 100

CampusCampus A 42 11.4

Campus B 244 66.5

Campus C 51 13.9

Campus D 15 4.1

Campus E 8 2.2

Missing 7 1.9

Total 367 100

TenureLess than one year 21 5.7

1 – 5 years 137 37.3

6 – 10 years 80 21.8

11 – 15 years 41 11.2

16 – 20 years 49 13.4

21 – 25 years 19 5.2

26 – 30 years 7 1.9

More than 30 years 6 1.6

Missing 7 1.9

Total 367 100


87


Conditions of Service

Academic / Research staff 145 39.5

Administrative staff 161 43.9

Support staff 49 13.4

Other (please specify) 4 1.1

Missing 8 2.2

Total 367 100

The important trends from Table 3.5 are summarised below.

The respondents were predominantly Afrikaans-speaking, white, female,

primarily between the ages of 30 to 49, had one to five years of work experience,

married, and in possession of a postgraduate degree. Most of the respondents

originated from Campus B, were primarily administrative staff, and their job

status was described as permanent.

Respondents who fully completed each section are indicated below in Table 3.16

where it can be clearly seen that the demographics section favoured the largest

response rate of the four sections. For the remaining three sections there is

consistency found in the percentage completeness ranging from roughly 82% to

86%. The fully completed questionnaire yielded a 70% response rate. Although

this value is less than desirable, all respondents (367) were still included in the

actual analysis, as each response is still of value.


88

TABLE 3.16DROPOUT RATE PER EACH SECTION

Questionnaire Section Respondents Percentage

Demographic VariablesComplete 339 92.4

Missing 28 7.6

Total 367 100

Job Satisfaction

Complete 316 86.1

Missing 51 13.9

Total 367 100

Organisational Commitment

Complete 302 82.3

Missing 65 17.7

Total 367 100

Intentions to Stay

Complete 310 84.5

Missing 57 15.5

Total 367 100

Entire Questionnaire

Complete 256 69.8

Missing 111 30.2

Total 367 100

The next section focuses on the research procedure.


89

3.4.2 Research Procedure

3.4.2.1 Obtaining Permission

Permission to carry out the research was obtained from the Vice-Chancellor of

the university. A memorandum was issued to the Vice-Chancellor’s office with

signatures in support of the research obtained from the Vice-Dean of the Faculty

of Management, the Supervisor of the researcher, and the researcher himself

(see Annexure A).

3.4.2.2 Internet Survey

The Internet provides opportunities to conduct surveys more efficiently and

effectively than the traditional means of pen and paper. One of the primary

reasons for this manner of distributing the survey was due to the new physical

structure of the merger institution (i.e. campuses that are geographically unique).

Zhang (2000) highlighted both the advantages and disadvantages of conducting

web-based surveys. Compared to a conventional mail survey, the advantages of

Internet-based surveys can be summarised as set out below.

• The research costs for sending questionnaires and coding data are

relatively low for Internet-based surveys.

• Internet-based surveys usually have a short turnaround time.

• They easily reach potential respondents in geographically remote areas.

• When a research topic is of a sensitive nature, Internet-based surveys

offer a means of reaching a group that is normally difficult to identify or

access, such a drug dealers or gay, lesbian and bisexual university

students.

• They offer a means of surveying large groups of individuals efficiently.


90

• They may increase respondents’ motivation to participate by providing a

dynamic / interactive survey process.

• They may reduce errors caused by transcription and coding. In Internet-

based surveys, most responses are in electronic format and have been

pre-coded.

• Target respondents can complete the survey at their convenience.

However, that is not to say that Internet-based surveys can also be without their

disadvantages. Zhang (2000) indicated that potential problems and concerns

unique to Internet-based surveys include the points listed below.

• Biased sample and biased return: respondents may most likely be those

who have the skills to use the survey tools and also accept and feel

comfortable with Internet surveys.

• Access to the Internet and survey: individuals in a population or sample

may not have equal access to the Internet.

• Comfort with the Internet survey format: whenever researchers offered

multiple options for receiving and / or replying to surveys, some

respondents chose to use the conventional means of completion –

completing surveys on paper.

• Effect of self-selection in Internet-based surveys: most Internet-based

surveys depend on self-selected respondents. Anderson and Gansneder

(1995) found that respondents, who were more likely to respond, made

use of the computer system more often and more frequently than non-

respondents.

• Validity of respondents: survey messages are very likely to reach

unintended individuals.

• Multiple responses from the same respondent: participants can easily

submit their replies many times, consequently making the overall results

over-representative of these respondents.


91

The researcher attempted to address most concerns dealing with the above

stated disadvantages.

• Biased sample and biased return this was a disadvantage the

researcher was prepared to accept.

• Access to the Internet and survey given the network of the university, all

respondents had access to the Internet from their offices.

• Comfort with the Internet survey format although the Internet-based

survey was strongly encouraged, it was indicated that a paper format of

the questionnaire would be provided to those who felt comfortable with

this method. Some did request this manner, however these respondents

made up only 2% of the total sample (eight requested paper based forms

of the survey).

• Effect of self-selection in Internet-based surveys as with sample

biasness, this was a disadvantage the researcher was prepared to accept.

• Validity of respondents since all email addresses of the potential

respondents were on the organisation’s database, it was assumed that the

validity of the potential respondents need not be questioned.

• Multiple responses from the same respondent cookies were enabled in

the survey. Cookies are small text files that a website puts on one’s

computer to store a variety of information and in this case they recorded

the fact that a respondent completed a survey, thus eliminating duplicates.

A database of all potential staff email addresses was constructed from relevant

sources, and subsequently an email was sent to all respondents notifying them of

the survey. The researcher wrote a letter of introduction (see Annexure B) to the

respondents explaining the rationale of the survey and emphasising the

importance of their contribution to the study. Within the email was a link directing

the potential respondents to the survey. All instructions were made clear. Some

respondents did indicate that they would prefer a hard copy of the questionnaire

(i.e. paper-based) and this was addressed. All capturing of data took place


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electronically, highlighting one of the advantages of conducting a survey in this

manner.

3.4.2.3 Bias in the Sample

Given the potential diversity of the respondents represented and the different

campuses participating in the study, it is worthwhile considering the possibility of

bias manifesting in the sample. Consequently, the generalisations made from the

research propositions should be treated with caution.

The researcher is well aware of the potential bias, and a bias analysis (where

possible) was carried out on the background variables to determine what

sections of the population were misrepresented.

3.4.3 Measuring Instruments

Respondents completed the following sections: Demographic details; Minnesota

Satisfaction Questionnaire (MSQ20) (Weiss et al., 1967); the Organisational

Commitment Questionnaire (OCQ) (Roodt, 1997); and Intentions to Stay

Questionnaire (ISQ) (unpublished questionnaire by Roodt, 2004b). However, it is

essential first to discuss the concepts of the reliability and validity of the research

instruments used in this research. The next section will pay attention to this.

3.4.3.1 Reliability and Validity

In order to establish the reliability and validity of each research instrument, it is

necessary firstly, to clarify these concepts and secondly, to relate them to the

research in question.


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According to Hair, Black, Babin, Anderson, and Tatham (2006) reliability is

considered an assessment of the degree of consistency between multiple

measurements of a variable. It is a measurement concept that represents the

consistency with which an instrument measures a given performance or

behaviour. A measurement instrument that is reliable will provide consistent

results when a given individual is measured repeatedly under near-identical

conditions. The diagnostic measure used is the reliability coefficient that

assesses the consistency of the entire scale, namely Cronbach’s Alpha, which is

the most widely used measure. The generally agreed upon lower limit for

Cronbach’s Alpha is 0.70, although this may decrease to 0.60 in exploratory

research (Hair et al., 2006; Robinson, Shaver, & Wrightman, 1991a; and

Robinson, Shaver, & Wrightman, 1991b).

Validity, on the other hand, is a measurement concept that is concerned with the

degree to which a measurement instrument actually measures what it purports to

measure. Hair et al. (2006) show that validity is present in many forms and the

five most widely accepted forms of validity are convergent, discriminant,

nomological, content, and construct validity which are discussed below.

• Convergent validity assesses the degree to which two measures of the

same concept are correlated. This will be determined through a factor

analysis for each instrument.

• Discriminant validity is the degree to which two conceptually similar

concepts are distinct. This was argued both in the previous and current

chapter and thus the researcher is satisfied with the level of discriminant

validity of the three constructs.

• Nomological validity refers to the degree that the summated scales of

each construct make accurate predictions of the other concepts in a

theoretically based model. Theoretical relationships were established in

the previous chapter, and these are tested on a practical level as

described in the following chapter.


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• Content validity (or face validity) subjectively assesses the

correspondence between the individual items and the concept. The

objective is to ensure that the selection of scale items extends past merely

empirical issues to include also theoretical and practical considerations.

All measurement instruments have already been constructed and

subsequently tested based on these terms; thus the researcher is satisfied

with the level of content validity.

• Construct validity is the extent to which a set of measured variables

actually represent the theoretical latent constructs they are designed to

measure. This was investigated by means of factor analysis. Factor

analysis is a particularly useful as a tool for examining the validity of tests

or the measurement characteristic of attitude scales. It will now be

discussed further under the statistical analyses to be carried out.

Next, all four sections of the questionnaire will be discussed.

3.4.3.2 Demographic Section

The demographic questionnaire was constructed in order to obtain relevant

background data about the respondents. The questions asked were:




• What do you consider your predominant home language?



• At which campus of the [university's name] do you predominantly work?

• How many complete years have you been working at the [university's

name] (including the former institutions prior to the merger)?

• What is your current job status?


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• Under what conditions of service are you employed at [university's name]?

As indicated, participant anonymity was maintained throughout the questionnaire

in order to enhance the honesty of the responses given. See Annexure C for this

particular section.

3.4.3.3 Minnesota Satisfaction Questionnaire (MSQ20) (Weiss et al., 1967)

(1) Rationale for Inclusion: The Minnesota Satisfaction Questionnaire (MSQ)

(Weiss et al., 1967) assessed the level of Job Satisfaction amongst the

employees (see Annexure D). The MSQ is designed to measure an

employee's satisfaction with his or her job. It provides more specific

information on the aspects of a job that an individual finds rewarding, than

do more general measures of job satisfaction. The questionnaire is

constructed around the theory that each person seeks to achieve and

maintain correspondence with his or her environment. Correspondence

with the environment at work can be described in terms of the individual

fulfilling the requirements of this environment (satisfactoriness), and the

work environment fulfilling the requirements of the individual (satisfaction).

The short form of the MSQ will be used, namely the MSQ20. This form

consists of 20 items from the long-form MSQ (consisting of 100 items) that

best represent each of the 20 scales. Factor analysis of the 20 items

results in two factors, namely, Intrinsic and Extrinsic Satisfaction. Thus the

purpose of the MSQ20 is to determine the degree of job satisfaction in

characteristics associated with the task itself (intrinsic satisfaction), in non-

task characteristics of the job (extrinsic satisfaction) and in overall job

satisfaction (total satisfaction) (Weiss et al., 1967). Spector (1997)

commented that most researchers who use the short form combine all the


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items into a single total score, or compute extrinsic and intrinsic

satisfaction subscales from the subsets of items.

(2) Composition of the Instrument: The MSQ20 measures 20 different items

from the long-form questionnaire. The questionnaire was modified in this

study to modernise the phrasing of questions to be more closely related to

the respondents. The questionnaire measures the following satisfaction

domains. Those of intrinsic nature are – activity, independence, variety,

social status, moral values, security, social service, authority, ability

utilisation, responsibility, creativity, and achievement. Those of extrinsic

nature are – supervision-human relations, supervision-technical, company

policies and practices, compensation, advancement, working conditions,

co-workers and recognition. The questions consisted of a five-point

intensity response scale situated at the polar ends. An example of an item

is: “How well do co-workers get along with each other in your present

job?” (“not well at all” 1-low intensity to “extremely well” 5-high intensity).

(3) Reliability and Validity: The questionnaire has been widely administered

and many researchers report acceptable levels of reliability. Sempane et

al. (2002) achieved a Cronbach’s Alpha of 0.9169 on a sample of

government welfare employees in South Africa. Jacobs (2005) yielded a

coefficient of 0.886 in a study of nurses in South Africa. On the sub-scale

level, Ivancevich (in Cook et al., 1981) reported alpha coefficients of 0.80

and 0.84 for the intrinsic and extrinsic satisfaction sub-scales respectively

in a study of machinists and technicians. Pierce, Dunham and Blackburn

(in Cook et al., 1981) recorded alpha coefficients of 0.88 for the intrinsic

satisfaction sub-scale and 0.84 for the extrinsic satisfaction sub-scale.

Therefore the Minnesota Satisfaction Questionnaire (MSQ20) appears to

yield a sound measure of overall job satisfaction (Cook et al., 1981).


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3.4.3.4 Organisational Commitment Questionnaire (OCQ) (Roodt, 1997)

(1) Rationale for Inclusion: A motivational approach was adopted to study

commitment within this context (as discussed, the other relatively well-

known approaches are attitudinal and behavioural). This entails needs,

values, and goals that can all be regarded as motivational constructs

(Roodt, 2004a). The Organisational Commitment Questionnaire (OCQ)

developed by Roodt (1997) will thus be used to measure organisational

commitment. Furthermore, this questionnaire has been selected, due to

the predictive model of turnover intentions in this study, as it is based on

the assumption that university employees who satisfy their higher order

needs (measured through job satisfaction), will be inclined to stay. The

original questionnaire was modified from 38 items to that of 18. The

reduction came as a result of selecting those items of the original 38 that

illustrated the highest metric item total correlation per each section from

Roodt’s original 1997 study. Scrutiny of the reduced 18 items indicated

that the content validity still covered all the foci concerned.

(2) Composition of the Instrument: This questionnaire consists of 18 items

(see Annexure E), each with a five-point intensity response scale

anchored at the polar ends. The foci of the questionnaire consist of work,

job, career, occupation, and organisation i.e. all organisationally related,

as no distinction between these foci need be made (Roodt 1997, 2004a).

An example of an item is: “How many of your interests are outside this

organisation?” (“no interests” 1-low intensity to “all interests” 5-high

intensity).

(3) Reliability and Validity: The reliability of the questionnaire can be gauged

through a handful of successful implementations it has undergone.

Reliable Cronbach’s Alpha values of 0.914 (Roodt, 1997); 0.94 (Storm &


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Roodt, 2001); 0.91 (Pretorius & Roodt, 2004); 0.926 (Jacobs, 2005) and

0.88 on a shortened version (Janse van Rensburg, 2004) were all

reported. As discussed in the previous chapter, the organisational

commitment questionnaire was compiled through a process of factor

analysis resulting in a clear distinction between work related and union

foci (Roodt, 1997). This is a clear indication of the construct validity of the

instrument.

3.4.3.5 Intentions to Stay Questionnaire (ISQ) (Unpublished Questionnaire,Roodt, 2004b)

(1) Rationale for Inclusion: The measure of turnover intentions (see Annexure

F) will be addressed by an unpublished questionnaire developed by Roodt

(2004b). As indicated in the previous chapter, although the questionnaire

deals with the intentions to stay, the theory and findings still hold valid for

turnover intentions. Although turnover intentions is thoroughly covered in

the literature, there is still a need to validate scales formally to represent

turnover cognitions (Sager et al., 1998). The motivation for using this

questionnaire is that most instruments in the literature measure turnover

intentions on only a relatively small number of items. Various researchers

have either applied a single item scale (Guimaraes, 1997; Lambert et al.,

2001) with obvious metric limitations, while a few other studies have used

more than three items per instrument (Becker, 1992; Fox & Fallon, 2003;

Lum, et al., 1998).

(2) Composition of the Instrument: The questionnaire is made up of 15 items

that are measured on a five-point intensity response scale anchored at the

polar ends. An example of an item is: “How often are your personal values

at work compromised?” (“never” 1-low intensity to “always” 5-high

intensity).


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(3) Reliability and Validity: The reliability of the questionnaire is relatively

unknown for this instrument. Jacobs (2005) reported a 0.913 Cronbach’s

Alpha coefficient. Both a factor and reliability analysis were carried out

during the analysis to determine the instrument’s reliability and validity on

the sample.

3.4.4 Statistical Analysis

The statistical analysis selected form a logical thought process for addressing the

research objectives with the final objective formulating a best fit model in

predicting the desired outcome variable. In this section, the statistical analyses

employed in the analysis of the data of the study will be described. SPSS 14.0

and AMOS 6 (SPSS Inc., 2005a) were utilised by the researcher in attaining the

findings. The statistical analysis consisted of two broad phases. The first phase

consisted of the descriptive statistical analysis describing the sample at hand.

The second phase consisted of the inferential testing. There now follows a brief

overview of the flow chart process of the statistical methods that were employed

as part of this study. Thereafter each section will be discussed separately:


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Figure 3.3: Statistical Flow Chart Process

Phase I

Phase I details the description of the sample. Descriptive statistics simply

describe what the data are showing. They provide the researcher with a ‘bird’s

eye’ view of how the data looks. The main focus of the first phase of the data

analysis is to provide proof that the measuring instruments and variables are

reliable and valid for the purpose of the study.

Phase I

Basic Descriptives

Factor Analyses

Reliability Analyses

Phase II

Correlations

ANOVA andt-tests

Structural EquationModelling

Two-Way Analysisof Variance

Stepwise LinearRegression

Normality Testing


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3.4.4.1 Basic Descriptives

The descriptive statistics discussed below were used in the analysis.

• The Mean is calculated by summing the values of a variable for all

observations and then dividing by the number of observations (Noru is,

2005). This describes the central tendency of the data.

• The Variance is calculated by finding the squared difference between an

observation and the mean, summing for all cases and then dividing by the

number of observations minus 1 (Noru is, 2005). It shows the relation that

a set of scores has to the mean of the sample. This describes the

dispersion of the data.

• The Standard Deviation is calculated as the square root of the variance

(Noru is, 2005). This describes the dispersion of the data. Since Standard

Deviation is a direct form of Variance, it will be used in place of the latter

when reporting.

• The Median is considered another measure of central tendency. It is the

middle value when observations are ordered from the smallest to the

largest (Noru is, 2005).

• Skewness is a measure of symmetry of a distribution; in most instances

the comparison is made to a normal distribution (Hair et al., 2006).

Schepers (undated) emphasises those variables with a skewness higher

than 2 should be avoided.

• Kurtosis is a measure of the peakedness or flatness of a distribution when

compared with the normal distribution (Hair et al., 2006). Leptokurtosis is

normally associated with low reliabilities and should be avoided at all

costs. Indices as high as 7 are rather extreme and signify very low

reliabilities (Schepers, undated).


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3.4.4.2 Factor Analysis

This technique was incorporated to assist in establishing the reliability and

validity of the measuring instruments used in the study. Hair et al. (2006)

describe factor analysis as an interdependence technique, whose primary

purpose is to define the underlying structure among the variables in the analysis.

The general purpose of factor analytic techniques is to find a way to condense

(summarise) the information in a number of original variables into a smaller set of

new, composite dimensions or variates (factors) with the smallest loss of

information. Noru is (2005) further adds that it is a statistical technique used to

identify a relatively small number of factors that explain observed correlations

between variables.

The interpretation and labelling of the outcome factors is a subjective process. To

enable a meaningful interpretation, certain guidelines would be appropriate as

postulated by Hair et al. (2006). These are discussed below.

• Factor analysis should most often be performed on metric variables. In the

case of the study, the 5-point Likert scale is appropriate.

• Strive to have at least five variables for each proposed factor. All

dimensions in this study are more than sufficiently above this level.

• The sample must have more observations than variables; whilst the

minimum absolute sample size should be 50 observations. The total

number achieved for the sample was 367.

• Maximise the number of observations per variable, with a minimum of five

and at least 10 observations per variable. The largest construct consists of

20 items (MSQ20), thus with a sample size of 367, this rule is comfortably

met.

• A statistically significant Bartlett’s test of sphericity (p-value < 0.05)

indicates that sufficient correlations exist between the variables to

proceed.


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• Measure of sampling adequacy (MSA) values must exceed 0.50 for both

the overall test and each individual variable; variables with values less

than 0.50 should be omitted from the factor analysis one at a time, with

the smallest being omitted each time. Although 0.50 is considered the

bare minimum, Hair et al. (2006) describe that particular cut-off point as

‘miserable’. Thus a stronger cut-off point of 0.6 will be enforced in the

factor analyses of all questionnaires.

• Several stopping criteria need to be used to determine the initial number

of factors to retain:

o factors with eigenvalues greater than 1.0 (unity);

o enough factors to meet a specified percentage of variance

explained, usually 60% or higher; and

o a predetermined number of factors based on research objectives

and / or prior research. This particular rule will only be enforced if

there is any uncertainty concerning the structure resulting from the

above two rules.

• A common rule of thumb is that each factor should have at least three

factors that load highly on it Noru is (2005). Should this not be the case

the factor would then be considered undefined.

• Choosing an extraction method is discussed below.

o The defining characteristic that distinguishes between the two

factor analytic models is that in principal components analysis, it is

assumed that all variability in an item should be used in the

analysis, while in principal factors analysis, only the variability in an

item that it has in common with the other items is used. In most

cases, these two methods usually yield very similar results.

However, principal components analysis is often preferred as a

method for data reduction, while principal factors analysis is often

preferred when the goal of the analysis is to detect structure.

Although data reduction is one of the aims of the factor analysis in

this study, a more pertinent aim is to determine if any underlying


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clear structure is present within the data per each questionnaire.

Thus for the purposes of the study Principal Axis Factoring will be

adopted.

• Choosing factor rotation methods is discussed below.

o Orthogonal rotation methods are the most widely used rotational

methods and the preferred method when the research goal is data

reduction to either a smaller number of variables, or a set of

uncorrelated measures for subsequent use in other multivariate

techniques.

o Oblique rotation methods are best suited to the goal of obtaining

several theoretically meaningful factors or constructs, because few

constructs in the real world are uncorrelated. For purposes of this

study, all factor analyses utilised the oblique rotation method.

• Although factor loadings of ±0.30 to ±0.40 are accepted has the bare

minimum, values greater than ±0.50 are generally considered necessary

for practical purposes.

• Variables should generally have extracted communalities of greater than

0.50 to be retained in the analysis. However values as low as 0.30 are

generally accepted.

3.4.4.3 Reliability Analysis

The validity and reliability of the measuring instruments utilised in the research

was determined through both factor (discussed above) and reliability analyses

(discussed theoretically earlier in the chapter). To recap, the diagnostic measure

used is the reliability coefficient that assesses the consistency of the entire scale,

namely Cronbach’s Alpha, which is the most widely used measure. The generally

agreed upon lower limit for Cronbach’s Alpha is 0.70, although it may decrease

to 0.60 in exploratory research (Hair et al., 2006).


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3.4.4.4 Normality Testing

All intended statistical procedures assume that the distribution of all variables is

normal, thus there is the need to address if normality is present. Hair et al. (2006)

advocate that the most fundamental assumption in multivariate and univariate

analysis is normality. Furthermore, if the variation from the normal distribution is

sufficiently large, all resulting statistical tests are invalid, because normality is

required to use both the F and t statistics. Normality is the degree to which the

distribution of the sample data corresponds to a normal distribution. Hair et al.

(2006) define a normal distribution where scores on the variable are clustered

around the mean in a symmetrical, unimodal pattern known as the bell-shaped,

or normal, curve. The Kolmogorov-Smirnov test will be used to test for normality.

It calculates the level of significance of the differences from a normal distribution.

If the p-value is found to be less than 0.05, then the variable in question does not

conform to normality. Normality tests will be carried out on all attained final

dimensions to ensure that the further testing does not violate any assumptions.

Phase II

Phase II describes the inferential section of the sample, where statistics are used

to either infer the truth or falsify a hypothesis (or stated research objective). This

section is used to address the majority of the hypotheses / research objectives

set out in Chapter 1.

3.4.4.5 ANOVA and Independent Samples t-test

These tests were utilised to determine whether any of the background variables

specified have a statistical relationship with the work constructs in the laid out

research objectives. The Independent Samples t-test (also know as the two-

sample t test) compares the means of one variable for two groups of cases


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(SPSS Inc, 2005a). This test is commonly used for comparisons between groups

of only two categories, such as gender. The One-Way ANOVA procedure

produces a one-way analysis of variance for a quantitative dependent variable by

a single factor (independent) variable. Analysis of variance is used to test the

hypothesis that several means are equal. This technique is an extension of the

Independent t-test (SPSS Inc, 2005a). Such staple examples of three category

variables include that of race or tenure. If the p-value is found to be less than

0.05, then the independent variable in question does have a significant

relationship with the factor at hand.

A result that is statistically significant at conventional levels may not be practically

significant as judged by the magnitude of the effect; or a result that may be

perceived as ‘nonsignificant’ may have practical importance (Rosenthal, Rosnow

& Rubin, 2000). Interpreting empirical data on the basis of significance tests only,

may lead to misrepresentation of the findings; hence the need to describe more

fully the measure of association between the independent and dependent

variables. This entails the use of the coefficient of association. The coefficient of

association (or effect size) was statistically investigated by finding the Eta value.

The researcher used these values to ascertain the practical significance of the

contextual factors with the independent variables as per the questionnaire.

According to Rosenthal et al. (2000), an Eta value of less than 0.1 (0.0 – 0.09)

indicates that the independent variable had a negligible effect on the construct in

question. An Eta value of 0.1 – 0.29 shows a small effect size, 0.3 – 0.49 a

medium effect size, and values above 0.50 a large effect size. The reader can

gauge for him / herself the importance of the independent variables and the

strength of the association of each variable.


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3.4.4.6 Correlations

This procedure will assist in addressing Research Objective #4. Correlation

analysis is the analysis of the degree to which changes in one variable are

associated with changes in another (McDaniel & Gates, 2006). It is a measure of

the relation between two or more variables. Correlation coefficients can range

from -1.00 to +1.00. The value of -1.00 represents a perfect negative correlation,

while a value of +1.00 represents a perfect positive correlation. A value of 0.00

represents a lack of correlation. Correlations will be utilised initially to determine

the zero-order correlation between the job satisfaction, organisational

commitment and turnover intentions, before proceeding to the Structural

Equation Modelling. The most commonly used measurement is the Pearson

product-moment correlation, which is a measure of linear association between

two variables. The correlation coefficient may be interpreted as follows (see

Table 3.17).

TABLE 3.17

INTERPRETATION OF THE CORRELATION COEFFICIENT

Correlation Coefficient Interpretation

-1.0 to -0.8 High

-0.8 to -0.6 Substantial

-0.6 to -0.4 Medium

-0.4 to -0.2 Low

-0.2 to 0.2 Very Low

0.2 to 0.4 Low

0.4 to 0.6 Medium

0.6 to 0.8 Substantial

0.8 to 1.0 High


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A considerable amount of caution must be taken when interpreting correlation

coefficients, because they give no indication of the direction of causality. This

causality is based on two reasons:

• the third variable problem – in any bivariate correlation, causality between

two variables cannot be assumed because there may be other measured

or unmeasured variables affecting the results; and

• the direction of causality – correlation coefficients indicate nothing about

which variable causes the other to change.

3.4.4.7 Structural Equation Modelling

This is utilised in the attainment of a best fitting model between all considered

work constructs. Hair et al. (2006) describe Structural Equation Modelling (SEM)

as a technique that allows separate relationships for each of the dependent

variables. It is characterised by a basic component known either as the structural

or the path model, which relates independent to dependent variables. Hair et al.

(2006) further add that in such situations, theory and prior experience enable the

researcher to distinguish which independent variables predict each dependent

variable. In this analysis, SEM was utilised to determine firstly, which

hypothesised models hold statistically and secondly, which model was the best

fitting.

Hair et al. (2006) recommended a few data considerations on account of missing

values and sample size when working with SEM. These appear below.

• Regarding missing values, pairwise deletion of missing cases (all-

available approach) is a good alternative for handling missing data (rather

than calculating the missing data artificially) when the amount of missing

data is less than 10% and the sample size is about 250 or more. There is

a caveat however; when the missing data becomes very high (15% or

more), SEM may not be appropriate. This study employed pairwise


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deletion. Although the total number of incomplete questionnaire (as shown

earlier) was 30% this is considered a limitation of the study and will be

noted and acknowledged accordingly by the researcher.

• In dealing with sample sizes, SEM models containing five or fewer

constructs (in this case the study utilised three), each with more than three

items (in the study these original underlying items ranged in number from

15 to 20), and with higher communalities (0.6 or higher), can be

adequately estimated with samples as small as 100 – 150.

Model evaluation is one of the most unsettled and difficult issues connected with

structural modelling, as dozens of statistics, besides the value of the discrepancy

function at its minimum, have been proposed as measures of the merit for a

model. Hair et al. (2006) contend that, as a guideline for establishing whether a fit

is acceptable or unacceptable, multiple indices need be reported. However, a

researcher need not report all available indices because of the redundancy

among them. Furthermore, it is added that to assess a fit the following types of

indices need to be represented:

• one absolute fit index – for this the research selected the Relative Chi-

Square Measurement ( 2 / df);

• one incremental fit index – the Comparative Fit Index (CFI) was selected;

• one goodness-of-fit index – here the researcher selected the Goodness-

of-fit Index (GFI); and

• one badness-of-fit index – the Root Mean Square Error of Approximation

(RMSEA) was chosen.

These indices will now be discussed further below.

• The Relative Chi-Square Measurement is a fit based on the minimum

value of the discrepancy. For every estimation criterion the ratio should be

close to 1 for correct models. It is suggested a ratio of approximately five

or less ‘as beginning to be reasonable.’ However, 2 to degrees of freedom

(df) ratios in the range of 2 to 1 or 3 to 1 are indicative of an acceptable fit


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between the hypothetical model and the sample data (SPSS Inc, 2005b).

Naudé and Rothman (2004) indicate that a value smaller than 2 indicates

an acceptable fit. These criteria, often referred to as ‘subjective’ or

‘practical’ indices of fit, are typically used as addition to the 2 statistic.

• The Comparative Fit Index is an incremental fit index that is normed so

that values range between 0 and 1, with the higher values indicating a

better fit. Because the CFI has many desirable properties including its

relative, but not complete, insensitivity to model complexity, it is among

the most widely used indices. CFI values less than 0.90 are not usually

associated with a model that fits well (Hair et al., 2006). Naudé and

Rothman (2004) concur that critical values for good model fit have been

recommended for the CFI to be acceptable above the 0.90 level.

• The Goodness-of-fit Index indicates the relative amount of variance and

co-variance in the sample predicted by estimates of the population. Its

value usually varies between 0 and 1 with values higher than 0.90

indicating good model fit with the data (Naudé & Rothman, 2004). Hair et

al. (2006) agree that GFI values of greater than 0.90 are considered good.

• The Root Mean Square Error of Approximation provides an indication of

the overall amount of error in the hypothesised model-data fit, relative to

the number of estimated parameters (complexity) in the model. Naudé and

Rothman (2004) recommend that acceptable levels of the RMSEA should

be 0.05 or less and should not exceed 0.08. Furthermore it is argued that

a model with a RMSEA of above 0.1 should not be employed (SPSS Inc,

2005b). Hair et al. (2006) indicate that the lower RMSEA values indicate a

better fit, contrast to other indices where higher values produce a better fit

and that values below 0.1 are acceptable for most models.


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3.4.4.8 Two-Way Analysis of Variance

Also known as a Two-Way Factorial Design, this allows the researcher to

examine the effects of two independent variables rather than using only a single

variable in the ANOVA. The only concern of this procedure is to identify

interaction effects between the independent variables in predicting the dependent

variable. This higher order technique will allow the analysis to delve deeper into

the prediction of the selected dependent variable. In factorial designs, it is the

joint effects of two variables in addition to the individual main effects. This means

that the difference between groups on one variable varies depending on the level

of the second variable (Hair et al., 2006).

Hair et al. (2006) emphasis that statistical testing indicating that interaction is

non-significant denotes the independent effects of the treatments. Independence

in factorial designs means that the effect of one variable is the same for each

level of the other variable and that the main effects can be interpreted directly.

3.4.4.9 Stepwise Linear Regression

The final procedure to be carried out will determine the best fitting model

incorporating both the selected work constructs and the relevant demographic

variables that have loaded significantly on the dependent variable. Linear

Regression estimates the coefficients of the linear equation, involving one or

more independent variables that best predict the value of the dependent variable.

The decision about the selection of independent and dependent variables will be

the product of the Structural Equation Modelling. The Stepwise estimation

technique will be used. This method of selecting variables for inclusion in the

regression model starts by selecting the best predictor of the dependent variable.

Additional independent variables are then selected in terms of the incremental

explanatory power they can add to the regression model. Independent variables


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are added as long as their partial correlation coefficients are statistically

significant. A general rule for the ratio of observations to independent variables is

5 to 1, although the desired level is between 15 to 20 observations for each

independent variable. However, if a stepwise procedure is used, the

recommended level increases to 50 to 1 (Hair et al., 2006).

Model comparison will be determined through the comparison of the Adjusted

Coefficient of Determination (Adjusted R2), which is more commonly known as

the Adjusted R-Square. In its standard form the Coefficient of Determination (R2)

measures the proportion of the variance of the dependent variable about its

mean that is explained by the independent, or predictor, variables. The

coefficient can vary between 0 and 1. The greater the explanatory power of the

regression equation, the better the prediction of the dependent variable. The

Adjusted Coefficient of Determination takes into account the number of

independent variables included in the regression equation and the sample size.

Although the addition of independent variables will always cause the coefficient

of determination to rise, the adjusted coefficient of determination may fall if the

added independent variables have little explanatory power and / or if the degrees

of freedom become too small.

A caveat of note is collinearity (any single independent variable that is highly

correlated with other independent variable). This impact reduces any single

independent variable's predictive power by the extent to which it is associated

with the other independent variables. As collinearity increases, the unique

variance explained by each independent variable decreases and the shared

prediction percentage rises i.e. it becomes increasingly more difficult to add

unique explanatory prediction from additional variables. Collinearity is measured

through the statistics listed below.

• The first is Tolerance – commonly used as a measure of collinearity. As

the Tolerance value diminishes, the variable is more highly predicted by

the other independent variables. A common cutoff threshold is a


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Tolerance value of 0.10.

• The second is the Variance Inflation Factor (VIF) – the inverse of

Tolerance. Large VIF values indicate a high degree of colinearity.

Inversing the common cutoff point of Tolerance, a value of 10 or more

indicates high collinearity.

• And lastly, the Condition Index is measure of relative amount of variance

associated with an eigenvalue, so a large condition index indicates a high

degree of collinearity. The threshold value should usually be in a range of

up to 30.

3.5 Synthesis

In this chapter, the research design was outlined. The research approach and

research methodology were discussed against the background of the stated

research objectives. The optimum research approach selected can be described

as quantitative and non-experimental with the usage of primary data as the

design of analysis. This approach was selected based on the stated research

objectives. The research methodology referred to the target population and

research procedure, which resulted in a sampling process whereby a self-

administered electronic survey was utilised. The research methodology

continued with the measuring instruments where satisfactory rationale and

theoretically sound reliability and validity were provided. Lastly, the statistical

procedures were laid out, highlighting the path chosen to achieve the research

objectives in the analysis of the data.

The next chapter will discuss the research findings of the study.


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4 CHAPTER 4: RESULTS OF THE STUDY

4.1 Introduction

In the previous chapter, the research design was outlined, and the research

approach and research methodology was discussed. The research approach

was described as quantitative and non-experimental, with the usage of primary

data as the design of analysis. The research methodology referred to the target

population, research procedure, measuring instruments, and the statistical

procedures used in the analysis of the data. The present chapter deals with the

results of the research objectives addressed by the research design.

In this chapter, the results of the various procedures (indicated in the statistical

flow chart process below) are documented and the most significant observations

made. Figure 4.1 depicts the processes to be followed.


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Figure 4.1: Statistical Flow Chart Process

The first phase of the analysis comprises the initial diagnostic testing whereby

statistical reliability and validity are determined. In this, results of the descriptives,

factor analyses (both first and second levels), reliability analyses (iterative item

analyses) and normality will be addressed. The main focus of the first phase of

the data analysis is to provide proof that the measuring instruments and variables

were reliable and valid for the purpose of the study.

In the second phase, the results will be described by referring to the objectives of

the study, namely to end with a best-fitting predictive model incorporating

Phase I

Basic Descriptives

Factor Analyses

Reliability Analyses

Phase II

Correlations

ANOVA andt-tests

Structural EquationModelling

Two-Way Analysisof Variance

Stepwise LinearRegression

Normality Testing


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significant demographic variables. This will be addressed by means of the

process of inferential testing (ANOVA and t-tests), Correlations, Structural

Equation Modelling (SEM), Two-Way Analysis of Variance and finally, a Stepwise

Linear Regression. The main focus of the second phase is to explore the

relationships between sets of key variables in the initial theoretical model in order

to present a final predictive model of the selected dependent variable attained

from the SEM.

The empirical research objectives will be referred to next.

4.2 Empirical Research Objectives



and turnover intentions within a post-merger tertiary institution.

The research objectives at the secondary level are set out below.


(academic, administrative and support staff) job

satisfaction are within the institution across all

campuses.


(academic, administrative and support staff)

organisational commitment are within the institution



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Research Objective #3: Determine what the employees’ (academic,

administrative and support staff) level of turnover

intentions is within the institution across all campuses.

Research Objective #4: Determine what the measured relationships or

associations between these scales are within the

institution across all campuses. Within this objective a

‘best-fitting’ model will be determined.


attained biographical variables and the three individual

scales (work constructs). The selected biographical

variables to be utilised are: Age, Tenure, Gender, Race,



selected dependent work construct (to be determined

through the best model fit vetting) and the interactions

between the attained biographical variables. The

selected biographical variables are: Age, Tenure,

Gender, Race, Marital Status, and Highest Academic

Qualification.


attained biographical variables, interactions thereof,

and the three scales within the ‘best-fit’ model of the

proposed models from Research Objective #4.

All research objectives will be answered in their entirety in Chapter 5, however

Chapter 4 will concern itself with the procedures and techniques required to

enable the addressing of each research objective. The next section will focus on


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the first phase of the statistical procedure that will be followed. Phase I will

include the descriptive statistics, factor analyses, reliability analyses, and

normality testing.

PHASE I

4.3 Basic Descriptive Statistics

The following categories of descriptive statistics to be discussed are set out

below.

4.3.1 Demographics

This involves basic descriptives of the sample at hand. The demographics

section was discussed in the previous chapter.

4.3.2 Descriptive Statistics of the Minnesota Satisfaction Questionnaire(MSQ20)

Depicted in the 20 items below are the means, standard deviations, medians,

skewness and kurtosis for each item. Note that only simplified names are

provided in order to save space. The full questions relating to Job Satisfaction

can be found in Annexure D. (Note: Standard Deviation SD)


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TABLE 4.1DESCRIPTIVE STATISTICS OF THE MSQ20

Item Valid Missing Mean Median SD Skewness Kurtosis

QB1 342 25 4.526 5 0.657 -1.189 0.747

QB2 340 27 4.044 4 1.079 -1.110 0.633

QB3 339 28 3.732 4 1.089 -0.738 0.041

QB4 340 27 3.300 3 1.268 -0.221 -1.030

QB5 339 28 3.442 4 1.363 -0.492 -0.951

QB6 339 28 3.324 4 1.348 -0.417 -1.006

QB7 338 29 4.047 4 1.123 -1.155 0.608

QB8 339 28 4.000 4 1.112 -1.091 0.490

QB9 340 27 3.812 4 1.131 -0.780 -0.090

QB10 337 30 3.136 3 1.217 -0.283 -0.755

QB11 337 30 3.593 4 1.117 -0.629 -0.238

QB12 333 34 3.228 3 1.096 -0.062 -0.647

QB13 339 28 2.280 2 1.121 0.370 -0.908

QB14 339 28 2.395 2 1.188 0.432 -0.702

QB15 337 30 3.421 4 1.080 -0.700 -0.161

QB16 340 27 3.526 4 1.183 -0.670 -0.378

QB17 338 29 3.130 3 1.119 -0.323 -0.634

QB18 337 30 3.561 4 1.048 -0.607 -0.091

QB19 339 28 2.850 3 1.254 -0.012 -1.104

QB20 337 30 3.475 4 1.012 -0.486 -0.169

From the above frequency table it can be see that the majority of the questions

have a negative skewness indicating that the questions were favourably

answered i.e. a positive inclination towards Job Satisfaction. This is further

supported by the fact that the majority of the questions experience higher than

average mean values. Since the Likert scale is divided into five categories, the

middle category (“3”) indicates a neutral response to the question. The majority

of the items in this case scored higher than “3”, suggesting an overall positive


120

inclination to Job Satisfaction. This is again further strengthened by the

calculated median values. Both skewness and kurtosis values were found to be

within acceptable ranges.

Question 1 “How busy are you kept in your present job?” scored the highest, with

a mean value of 4.526, whilst Question 13 “How satisfied are you that the pay

you receive reflects the amount of effort you put into your job?” scored the lowest

with 2.280.

4.3.3 Descriptive Statistics of the Organisational CommitmentQuestionnaire (OCQ)



provided in order to save space. The full questions relating to Organisational

Commitment can be found in Annexure E. (Note: Standard Deviation SD)


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TABLE 4.2DESCRIPTIVE STATISTICS OF THE OCQ


QC1 325 42 4.575 5 0.701 -2.052 5.644

QC2 325 42 4.028 4 0.967 -0.901 0.539

QC3 325 42 4.338 4 0.713 -1.010 1.399

QC4 323 44 4.161 4 0.841 -0.974 0.895

QC5 324 43 4.247 4 0.771 -1.026 1.476

QC6 326 41 4.531 5 0.640 -1.248 1.350

QC7 327 40 4.281 4 0.814 -1.039 0.767

QC8 326 41 4.202 4 0.892 -1.142 1.188

QC9 323 44 4.201 4 0.780 -0.724 0.026

QC10 327 40 4.061 4 0.830 -0.859 0.968

QC11 324 43 4.130 4 0.838 -0.821 0.519

QC12 327 40 4.187 4 0.817 -0.899 0.771

QC13 323 44 3.709 4 1.135 -0.423 -0.902

QC14 321 46 3.511 4 1.132 -0.294 -0.868

QC15 325 42 3.375 4 1.142 -0.348 -0.682

QC16 325 42 3.751 4 1.134 -0.686 -0.347

QC17 324 43 2.914 3 1.013 -0.131 -0.502

QC18 324 43 3.901 4 0.895 -0.716 0.398

From the above frequency table it can be see that the majority of the questions

have a negative skewness indicating that the questions were favourably

answered i.e. a positive inclination towards Organisational Commitment. This is

further supported by the fact that the majority of the questions experience higher

than average mean values. Since the Likert scale is divided into five categories,

the middle category (“3”) indicates a neutral response to the question. The

majority of the items in this case scored higher than “3”, suggesting an overall

positive inclination to Organisational Commitment. This is again further


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strengthened by the calculated median values. Both skewness and kurtosis

values were found to be within acceptable ranges.

Question 1 “To what extent should everyone have a feeling of pride in work?”

scored the highest with a mean value of 4.575, whilst Question 17 “How many of

your interests are outside of this organisation?” scored the lowest with 2.914.

However, Question 17 has been identified as a ‘negatively’ phrased question and

thus consequently should be inverted to be in line with that of the remaining

questions. Inverting it thus allows it to score a mean value of 3.086, still making

this question the least positively answered item. With the inversion, it is

interesting to note that all questions had a mean value of over “3”.

4.3.4 Descriptive Statistics of the Intentions to Stay Questionnaire (ISQ)



provided in order to save space. The full questions relating to Turnover Intentions

can be found in Annexure F. (Note: Standard Deviation SD)


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TABLE 4.3DESCRIPTIVE STATISTICS OF THE ISQ


QD1 325 42 2.772 3 1.337 0.111 -1.173

QD2 325 42 2.566 3 1.317 0.357 -0.982

QD3 326 41 3.239 3 1.100 -0.109 -0.827

QD4 324 43 3.096 3 1.232 -0.123 -0.936

QD5 323 44 2.486 2 1.099 0.474 -0.455

QD6 323 44 3.003 3 1.299 0.123 -1.130

QD7 323 44 2.700 2 1.466 0.275 -1.339

QD8 323 44 2.715 3 1.131 0.241 -0.702

QD9 321 46 2.766 3 1.453 0.191 -1.350

QD10 325 42 3.545 4 1.432 -0.515 -1.110

QD11 325 42 2.997 3 1.357 -0.039 -1.209

QD12 326 41 3.025 3 1.142 0.002 -0.878

QD13 324 43 2.997 3 1.341 -0.002 -1.140

QD14 325 42 2.631 3 1.374 0.328 -1.121

QD15 326 41 2.190 2 1.280 0.836 -0.436

From the above frequency table it can be seen that the majority of the questions

have a close to zero skewness revealing the neutrality of the questions towards

the items i.e. a neutral inclination towards Turnover Intentions. This is further

supported by the fact that the majority of the questions experience similar mean

values to that of the average. Since the Likert scale is divided into five

categories, the middle category (“3”) indicates a neutral response to the question.

The majority of the items had scores very close to the “3” category, suggesting

an overall neutral inclination to Turnover Intentions. This is again further

strengthened by the calculated median values. Both skewness and kurtosis

values were found to be within acceptable ranges.


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Question 10 “To what extent do other responsibilities prevent you from quitting

your job?” scored the highest, with a mean value of 3.545, whilst Question 15

“How frequently do you scan the Internet in search of alternative job

opportunities?” scored the lowest with 2.190. Question 3 “To what extent is your

current job satisfying your personal needs?” has been identified as a ‘negatively’

phrased question and thus consequently should be inverted to be in line with that

of the remaining questions. Inverting it thus allows it to score a mean value of

2.761.

4.3.5 Summary of Descriptive Statistics of the Total Scores

Depicted below are the means, standard deviations, medians, skewness and

kurtosis for each total questionnaire. (Note: Standard Deviation SD; Skewness

Skew.)

TABLE 4.4DESCRIPTIVE STATISTICS OF THE OVERALL DIMENSIONS

Items Valid Mean Median SD Skew. Kurtosis

Job Satisfaction 316 3.321 3.353 0.713 -0.518 -0.124

Organisational Commitment 302 4.026 4.067 0.563 -0.479 0.291

Turnover Intentions 310 2.831 2.808 0.872 0.188 -0.668

From the above frequency table it can be seen that Organisational Commitment

had the most positive response from the sample, with one full category above the

average value. Job Satisfaction scored just above the average category value of

“3”, while Turnover Intentions scored the ‘lowest’ with 2.83. Of the three

questionnaires, positive sentiments from the ISQ would have been scored lower

i.e. less likelihood of wanting to turnover. Thus since its value is below “3” it

indicates that there is a positive sentiment inherent in the overall response. This

is further supported as Turnover Intentions was the only construct to be indicated


125

as having a positive skewness. Thus on an overall level, all three dimensions

shared a positive outcome.

4.4 Results of the Factor Analysis

As discussed; one of the objectives of factor analysis is to reduce the number of

variables to a smaller number of dimensions which explain what is common

among the original set of variables. The results of the different sections of the

Minnesota Satisfaction Questionnaire (MSQ20), the Organisational Commitment

Questionnaire (OCQ) and the Intentions to Stay Questionnaire (ISQ) will be

discussed separately.

Each questionnaire was factor analysed according to the procedure as described

in the previous chapter. This procedure includes first and second level factor

analysis. All the calculations were done by means of the SPSS Version 14 for

Windows program of SPSS International. The details of the results follow.

4.4.1 The Minnesota Satisfaction Questionnaire (MSQ20)

4.4.1.1 First Order Factor Analysis

Following the rules and procedures laid out in the previous chapter in order to

determine the sampling adequacy and sphericity of the item intercorrelation

matrix, the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA)

and Bartlett’s Test of Sphericity were respectively conducted on the item

intercorrelation matrix of the instrument. Several important points are repeated

below.

• A statistically significant Bartlett’s Test of Sphericity (p-value < 0.05)

indicates that sufficient correlations exist among the variables to proceed.


126

• Measure of Sampling Adequacy (MSA) values, it was decided, must

exceed 0.60 for both the overall test and each individual variable;

variables with values less than 0.60 should be omitted from the factor

analysis one at a time, with the smallest being omitted each time.


0.50 to be retained in the analysis, however values as low as 0.30 are

generally accepted.

Question 9 “To what extent do you have the chance to do things for other people

in your present job?” was omitted from further analysis, as its communality value

was calculated to be 0.168, as was Question 7 “How satisfied are you that you

do not do things that go against your conscience?”, as its communality value was

calculated to be 0.230.The final results for the first order analysis are reported in

Table 4.5.

TABLE 4.5

KMO AND BARTLETT’S TEST OF THE ITEM INTERCORRELATION MATRIX OF THE MSQ20

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.881

Approximate Chi-Square 2499.562

Degrees of Freedom 153

Bartlett's Test of Sphericity

p-value 0.000

From the above it can be clearly seen that there are sufficient correlations (p-

value < 0.05) between the variables and the KMO MSA overall value is

sufficiently high to proceed further with the analysis (must exceed 0.60). It is

concluded that the matrix is suitable for further factor analysis.

The communalities and unit MSA of the first order factor analysis are depicted

below in Table 4.6.


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TABLE 4.6COMMUNALITIES AND UNIT MSA OF THE MSQ20

Item Initial Extraction MSA

QB1 0.237 0.303 0.635

QB2 0.256 0.290 0.872

QB3 0.439 0.502 0.950

QB4 0.525 0.565 0.913

QB5 0.820 0.888 0.806

QB6 0.811 0.865 0.817

QB8 0.327 0.399 0.894

QB10 0.296 0.312 0.905

QB11 0.555 0.542 0.883

QB12 0.279 0.278 0.926

QB13 0.248 0.317 0.874

QB14 0.280 0.340 0.919

QB15 0.619 0.622 0.885

QB16 0.642 0.648 0.880

QB17 0.466 0.456 0.904

QB18 0.270 0.280 0.927

QB19 0.544 0.519 0.896

QB20 0.518 0.520 0.902

In line with the stipulated restrictions, all extracted communalities are above 0.3,

indicating that a suitable amount of variance in each variable is accounted for.

This is only true because two questions were removed due to lower than

accepted communalities. An examination of the MSA values, based on the

individual unit level, reveals that all values fall above the required 0.6 level.

The use of two stopping criteria to determine the initial number of factors to retain

was used, namely:

• factors with eigenvalues greater than 1.0 (unity); and


128

• enough factors to meet a specified percentage of variance explained,

usually 60% or higher.

Thus the results of four factors were initially extracted in line with the above. The

cumulative percentage explained in this case was not exactly at the required

60%, however, due to its close proximity of 59% it was gauged as sufficient to

continue the analysis. The eigenvalues of the unreduced item intercorrelation

matrix are provided in Table 4.7.

TABLE 4.7

EIGENVALUES OF THE UNREDUCED ITEM INTERCORRELATION MATRIX OF THE MSQ20

Initial EigenvaluesFactor

Total % of Variance Cumulative %

1 6.655 36.971 36.971

2 1.712 9.509 46.480

3 1.204 6.691 53.171

4 1.107 6.148 59.320

Although four factors were extracted in the first order factor analysis, the large

difference between the first and second factor in terms of their eigenvalues

indicates that essentially there is only one overall factor present in the data for

Job Satisfaction.

The attained factor matrix was rotated using the oblique rotation and sorted

accordingly to enable easier interpretation of the underlying factors. See Table

4.8. As indicated previously, only those factors with loadings higher than 0.3

were retained for the analysis.


129

TABLE 4.8ROTATED AND SORTED FACTOR MATRIX OF THE MSQ20

FactorItem

1 2 3 4

QB3 0.607

QB2 0.597

QB20 0.574 -0.149

QB16 0.556 -0.316 0.108

QB15 0.506 -0.163 -0.351

QB11 0.420 -0.203 0.322

QB5 -0.966

QB6 -0.945

QB19 0.210 -0.603

QB4 0.295 -0.344 0.229 0.287

QB1 -0.537

QB14 0.555

QB13 0.120 0.236 0.487

QB17 0.140 -0.196 0.462

QB8 -0.383 0.436

QB12 -0.270 0.360

QB10 0.164 -0.292 0.357

QB18 0.116 -0.199 0.150 0.319

It can be seen from Table 4.8 that Factor 3 has only the one item loading, which

accordingly makes it non-determined. It has been pointed out that a factor should

consist of at least three items in it to make it meaningful. However, this factor will

still be retained for the second order factor analysis. All the other three factors

are regarded to have sufficient representation.


130

4.4.1.2 Second Order Factor Analysis

Sub-scores were calculated for the above four extracted factors. Again, the same

procedure was followed whereby the Kaiser-Meyer-Olkin (KMO) Measure of

Sampling Adequacy (MSA) and Bartlett’s Test of Sphericity were respectively

conducted on the item intercorrelation matrix of the sub-scores.

Question 1, forming Factor 3, “How busy are you kept in your present job?” was

omitted from further analysis as its MSA value was calculated to be 0.510. The

final results for the second order analysis are reported in Table 4.9.

TABLE 4.9KMO AND BARTLETT’S TEST OF THE SUB-SCORE INTERCORRELATION MATRIX OF THE

MSQ20





p-value 0.000


value < 0.05) between the variables, and the KMO MSA overall value is suitably

high enough to proceed further with the analysis (must exceed 0.60). It is


The communalities and unit MSA of the second order factor analysis are

depicted below in Table 4.10.


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TABLE 4.10:COMMUNALITIES AND SUB-SCORE MSA OF THE MSQ20

Factor Initial Extraction MSA

Factor 1 0.453 0.597 0.716

Factor 2 0.424 0.549 0.740

Factor 4 0.480 0.656 0.695

In line with the stipulated restrictions all extracted communalities are above 0.3,

thus indicating that a suitable amount of variance in each variable is accounted

for. An examination of the MSA values, based on the factor unit level, reveals

that all values fall above the required 0.6 level.

The use of two stopping criteria determined that only one overall factor need be

retained. The cumulative percentage explained in this case was above that of the

required 60%, at 73%. The eigenvalue of the unreduced item intercorrelation

matrix is provided in Table 4.11.

TABLE 4.11EIGENVALUES OF THE UNREDUCED SUB-SCORE INTERCORRELATION MATRIX OF THE

MSQ20



1 2.200 73.328 73.328

Thus, one overall factor was extracted for Job Satisfaction. This was anticipated

from the first order factor analysis due to the large difference encountered

between the first and second factors in terms of their eigenvalues.

The attained factor matrix cannot subsequently be rotated, as only one factor

was extracted. See Table 4.12. In line with the procedures set out all factor

loadings are above the 0.3 level.


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TABLE 4.12FACTOR MATRIX OF THE MSQ20

FactorItem

1

Factor 4 0.810

Factor 1 0.773

Factor 2 0.741

Thus Job Satisfaction will be represented in subsequent analyses by one factor.

This concludes the factor analysis results of the Minnesota Job Satisfaction

Questionnaire (MSQ20). The results of the factor analysis of the Organisational

Commitment Questionnaire (OCQ) will be discussed next.

4.4.2 The Organisational Commitment Questionnaire (OCQ)






intercorrelation matrix of the instrument. Several important points are repeated

below.




exceed 0.60 for both the overall test and each individual variable;

variables with values less than 0.60 should be omitted from the factor

analysis one at a time, with the smallest being omitted each time.


133


0.50 to be retained in the analysis. However, values as low as 0.30 are

generally accepted.

Question 1 “To what extent should everyone have a feeling of pride in work?”

was omitted from further analysis, as its MSA value was found to be 0.586. The

inverted Question 17 “How many of your interests are outside of this

organisation?” was omitted from further analysis, as its communality value was

calculated to be 0.117, as was Question 2 “To what extent do you consider your

work to be a means to other important ends?” as its communality value was

calculated to be 0.160. The final results for the first order analysis are reported in

Table 4.13.

TABLE 4.13KMO AND BARTLETT’S TEST OF THE ITEM INTERCORRELATION MATRIX OF THE OCQ





p-value 0.000


value < 0.05) between the variables and the KMO MSA overall value is suitably

high enough to proceed further with the analysis (must exceed 0.60). It is



below in Table 4.14.


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TABLE 4.14COMMUNALITIES AND UNIT MSA OF THE OCQ


QC3 0.610 0.606 0.852

QC4 0.394 0.398 0.933

QC5 0.702 0.909 0.844

QC6 0.509 0.481 0.892

QC7 0.482 0.559 0.895

QC8 0.482 0.516 0.879

QC9 0.593 0.697 0.911

QC10 0.642 0.963 0.794

QC11 0.672 0.658 0.822

QC12 0.364 0.337 0.926

QC13 0.546 0.682 0.873

QC14 0.553 0.629 0.840

QC15 0.443 0.486 0.844

QC16 0.381 0.322 0.835

QC18 0.435 0.445 0.938



This is only true because two questions were removed due to lower than

accepted communalities, and one based on its low MSA value. Inspection of the

MSA values, based on the individual unit level, reveals that all values fall above

the required 0.6 level.


was used, namely:





135

Thus the results of four factors were initially extracted in line with the above. The

cumulative percentage explained in this case was above that of the required

60%, at 67%. The eigenvalues of the unreduced item intercorrelation matrix are

provided in Table 4.15.

TABLE 4.15EIGENVALUES OF THE UNREDUCED ITEM INTERCORRELATION MATRIX OF THE OCQ



1 6.169 41.127 41.127

2 1.681 11.204 52.331

3 1.274 8.492 60.823

4 1.001 6.670 67.493

Although four factors were extracted in the first order factor analysis, the large

difference between the first and second factor in terms of their eigenvalues

indicates that essentially there is only one overall factor present in the data for

Organisation Commitment.






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TABLE 4.16ROTATED AND SORTED FACTOR MATRIX OF THE OCQ

FactorItem

1 2 3 4

QC7 0.750

QC9 0.721 -0.101 0.213

QC8 0.670 0.127

QC13 0.796 -0.119

QC14 0.780 0.122

QC15 0.689 0.179

QC16 0.486 -0.195

QC10 0.958 -0.147

QC11 0.292 0.163 0.569

QC12 0.214 0.138 0.319

QC5 -0.933

QC3 0.153 -0.715

QC18 0.261 -0.459

QC6 0.328 -0.432

QC4 0.333 -0.370

It can be seen from Table 4.16 that all extracted factors have three or more items

representing them. It has been pointed out that a factor should consist of at least

three items in it to make it meaningful. All four factors are regarded as having

sufficient representation.


Sub-scores were calculated for the above four extracted factors. Again the same

procedure was followed whereby the Kaiser-Meyer-Olkin (KMO) Measure of


137

Sampling Adequacy (MSA) and Bartlett’s Test of Sphericity were respectively

conducted on the item intercorrelation matrix of the sub-scores.

No questions were dropped during the second round. The final results for the

second order analysis are reported in Table 4.17.

TABLE 4.17

KMO AND BARTLETT’S TEST OF THE SUB-SCORE INTERCORRELATION MATRIX OF THE

OCQ





p-value 0.000


value < 0.05) between the variables and the KMO MSA overall value is suitably

high to proceed further with the analysis (must exceed 0.60). It is concluded that

the matrix is suitable for further factor analysis.

The communalities and unit MSA of the second order factor analysis are

depicted below in Table 4.18.

TABLE 4.18COMMUNALITIES AND SUB-SCORE MSA OF THE OCQ

Factor Initial Extraction MSA

Factor 1 0.454 0.589 0.761

Factor 2 0.263 0.319 0.842

Factor 3 0.423 0.540 0.779

Factor 4 0.443 0.591 0.776


138

In line with the stipulated restrictions, all extracted communalities are above 0.3,

indicating that a suitable amount of variance in each variable is accounted for. An

examination of the MSA values, based on the factor unit level, reveals that all

values fall above the required 0.6 level.

The use of two stopping criteria determined that only one overall factor need be

retained. The cumulative percentage explained in this case was above that of the

required 60%, at 63%. The eigenvalue of the unreduced item intercorrelation

matrix is provided in Table 4.19.

TABLE 4.19

EIGENVALUES OF THE UNREDUCED SUB-SCORE INTERCORRELATION MATRIX OF THE

OCQ



1 2.510 62.752 62.752

Thus, one overall factor was extracted for Organisational Commitment. This was

anticipated from the first order factor analysis due to the large difference

encountered between the first and second factor in terms of their eigenvalues.

The attained factor matrix cannot subsequently be rotated, as only one factor

was extracted. See Table 4.20. In line with the procedures set out all factor

loadings are above the 0.3 level.


139

TABLE 4.20FACTOR MATRIX OF THE OCQ

FactorItem

1

Factor 4 0.769

Factor 1 0.768

Factor 3 0.735

Factor 2 0.565

Thus Organisational Commitment will be represented in subsequent analyses by

one factor. This concludes the factor analysis results of the Organisational

Commitment Questionnaire (OCQ). The results of the factor analysis of the final

construct Intentions to Stay Questionnaire (ISQ) will be discussed next.

4.4.3 The Intentions to Stay Questionnaire (ISQ)






intercorrelation matrix of the instrument. Some important points are repeated

below.




exceed 0.60 for both the overall test and each individual; variables with


140

values less than 0.60 should be omitted from the factor analysis one at a

time, with the smallest being omitted each time.


0.50 to be retained in the analysis. However values as low as 0.30 are

generally accepted.

Question 11 “To what extent do the benefits associated with your current job

prevent you from quitting your job?” was omitted from further analysis, as its

MSA value was found to be 0.552. Question 5 “How often are your personal

values at work compromised?” was omitted from further analysis, as its

communality value was calculated to be 0.117. The final results for the first order

analysis are reported in Table 4.21.

TABLE 4.21KMO AND BARTLETT’S TEST OF THE ITEM INTERCORRELATION MATRIX OF THE ISQ





p-value 0.000

From the above it can be clearly seen that sufficient correlations (p-value < 0.05)

exist between the variables and the KMO MSA overall value is suitably high

enough to proceed further with the analysis (must exceed 0.60). It is concluded

that the matrix is suitable for further factor analysis.


below in Table 4.22. Note, as discussed in the basic descriptives section,

Question 3 “To what extent is your current job satisfying your personal needs?”

was identified as a ‘negatively’ phrased question and was subsequently inverted

(“I”).


141

TABLE 4.22:COMMUNALITIES AND UNIT MSA OF THE ISQ


QD1 0.669 0.696 0.918

QD2 0.644 0.708 0.892

IQD3 0.441 0.419 0.935

QD4 0.285 0.301 0.918

QD6 0.651 0.682 0.915

QD7 0.404 0.371 0.917

QD8 0.308 0.322 0.951

QD9 0.356 0.361 0.917

QD10 0.319 0.285 0.894

QD12 0.540 0.540 0.847

QD13 0.609 0.654 0.876

QD14 0.305 0.268 0.875

QD15 0.580 0.655 0.885



This is only true because one question was removed due to lower than accepted

communality, and one based on its low MSA value. Inspection of the MSA

values, based on the individual unit level, reveals that all values fall above the

required 0.6 level.


was used, namely:




Thus the results of only two factors were initially extracted in line with the above.

The cumulative percentage explained in this case was not exactly at the required


142

60%, however as it was close at 56% it was regarded as being sufficient to

continue the analysis. The eigenvalues of the unreduced item intercorrelation

matrix are provided in Table 4.23.

TABLE 4.23EIGENVALUES OF THE UNREDUCED ITEM INTERCORRELATION MATRIX OF THE ISQ



1 5.924 45.569 45.569

2 1.295 9.961 55.530

Although two factors were extracted in the first order factor analysis, the large

difference between the first and second factor in terms of the eigenvalues

indicates that essentially there is only one overall factor prevalent in the data for

Turnover Intentions.






143

TABLE 4.24ROTATED AND SORTED FACTOR MATRIX OF THE ISQ

FactorItem

1 2

QD13 0.804

QD12 0.779

QD4 0.574

QD14 0.529

QD10 0.509

QD8 0.474 -0.129

QD7 0.428 -0.235

QD15 -0.100 -0.872

QD2 -0.845

QD6 0.239 -0.649

QD9 -0.608

QD1 0.325 -0.584

IQD3 0.338 -0.372

It can be seen from Table 4.24 that all extracted factors have three or more items

representing them. It was pointed out that a factor should consist of at least three

items in it to make it meaningful. Both factors are regarded as having sufficient

representation.


To carry out a second order factor analysis on only two factors is considered

redundant. When the primary aim of the factor analysis technique is to reduce

the data, naturally the two factors will join as one. Also, given the initial

observation based on the eigenvalues on the first order factor analysis, one

factor is deemed appropriate as representation of Turnover Intentions.


144

Thus Turnover Intentions will be presented in subsequent analyses by one factor.

This concludes the factor analysis results of the Intentions to Stay Questionnaire

(ISQ). The reliability analysis results of all three constructs will now be

addressed.

4.5 Results of the Reliability Analyses

As discussed, reliability is considered to be an assessment of the degree of

consistency between multiple measurements of a variable. A measurement

instrument that is reliable will provide consistent results when a given individual is

measured repeatedly under near-identical conditions. The diagnostic measure

used is the reliability coefficient that assesses the consistency of the entire scale,

namely Cronbach’s Alpha, which is the most widely used measure. Cronbach’s

Alpha values will now be provided for all three overall constructs. The generally

agreed upon lower limit for Cronbach’s Alpha is 0.70, although it may decrease

to 0.60 in exploratory research (Hair et al., 2006).

4.5.1 Job Satisfaction Iterative Item Reliability Analysis

The result obtained from the iterative reliability analysis of the MSQ20 yielded a

Cronbach’s Alpha of 0.898 based on 17 items, indicating an acceptable reliability.

All Corrected Item-Total Correlations are above 0.3 indicting sufficient correlation

of each item with the overall factor. It can also been seen that removal of any

question will not improve on the already attained Cronbach’s Alpha. See Table

4.25.


145

TABLE 4.25ITERATIVE ITEM RELIABILITY ANALYSIS OF THE MSQ20

Item Item-Total Correlation Cronbach's Alpha if Item Deleted

QB3 0.607 0.891

QB2 0.357 0.898

QB20 0.628 0.890

QB16 0.655 0.889

QB15 0.645 0.890

QB11 0.648 0.889

QB5 0.673 0.888

QB6 0.673 0.888

QB19 0.608 0.890

QB4 0.654 0.889

QB14 0.459 0.896

QB13 0.385 0.898

QB17 0.613 0.890

QB8 0.455 0.895

QB12 0.456 0.895

QB10 0.416 0.897

QB18 0.452 0.895

NUMBER OF ITEMS = 17CRONBACH’S ALPHA = 0.898

This concludes the reliability analysis results of the Minnesota Job Satisfaction

Questionnaire (MSQ20). The results of the reliability analysis of the

Organisational Commitment Questionnaire (OCQ) will be discussed next.


146

4.5.2 Organisational Commitment Iterative Item Reliability Analysis

The result obtained from the iterative reliability analysis of the OCQ yielded a





4.26.

TABLE 4.26ITERATIVE ITEM RELIABILITY ANALYSIS OF THE OCQ


QC7 0.550 0.881

QC9 0.635 0.878

QC8 0.580 0.880

QC13 0.602 0.879

QC14 0.577 0.881

QC15 0.460 0.887

QC16 0.453 0.887

QC10 0.544 0.882

QC11 0.644 0.878

QC12 0.520 0.883

QC5 0.632 0.879

QC3 0.557 0.882

QC18 0.597 0.879

QC6 0.598 0.881

QC4 0.543 0.882



147

This concludes the reliability analysis results of the Organisational Commitment

Questionnaire (OCQ). The results of the reliability analysis of the final construct

Intentions to Stay Questionnaire (ISQ) will be discussed next.

4.5.3 Turnover Intentions Iterative Item Reliability Analysis

The result obtained from the iterative reliability analysis of the ISQ yielded a





4.27.

TABLE 4.27

ITERATIVE ITEM RELIABILITY ANALYSIS OF THE ISQ


QD13 0.691 0.882

QD12 0.602 0.887

QD4 0.466 0.893

QD14 0.454 0.894

QD10 0.486 0.893

QD8 0.522 0.890

QD7 0.573 0.888

QD15 0.637 0.885

QD2 0.695 0.882

QD6 0.756 0.879

QD9 0.500 0.892

QD1 0.774 0.878

IQD3 0.606 0.887



148

This concludes the reliability analysis results of the Intentions to Stay

Questionnaire (ISQ).

The Cronbach’s Alpha coefficients of all the sections indicate that the overall

scales have an accepted reliability and can consistently measure the particular

dimensions of the magnitude they are designed to measure. In other words, the

measuring instruments are capable of consistently reflecting the same underlying

constructs. Furthermore, this consistency indicates a high degree of homogeneity

between the each questionnaire’s items. The normality each overall dimension

will now be addressed.

4.6 Kolmogorov-Smirnoz Test for Normality of Overall Factors

In order to determine the normality of the overall factors obtained in the factor

analysis, the Kolmogorov-Smirnov test was executed. From Table 4.28 it can be

seen that all factors conform to normality.

TABLE 4.28KOLMOGOROV-SMIRNOV TEST FOR NORMALITY

Dimension Kolmogorov-Smirnov Z p-value

Job Satisfaction 1.332 0.058

Organisational Commitment 0.798 0.547

Turnover Intentions 1.114 0.167

The Kolmogorov-Smirnov test determines if the distribution adheres to a normal

distribution. The null hypothesis of the test assumes the variable at hand (in this

case the three work constructs) is normally distributed. If the p-value is found to

be less than 0.05, the hypothesis will be rejected, and thus one cannot conclude

that the variable is normally distributed. From Table 4.28 it is concluded that the

Job Satisfaction, Organisational Commitment and Turnover Intentions


149

dimensions are all normally distributed (all p-values larger than 0.05) and thus

are suitable for parametric statistical procedures.

This concludes the first phase of results. The second phase, starting with the

inferential testing, will now be reported.

PHASE II

4.7 Inferential Testing (ANOVA, t-tests)

In order to address the secondary research objective detailing whether a

relationship exists between the selected biographical variables and the three

individual scales, inferential testing will be carried out. Depending on the nature

of the biographical variable at hand i.e. the number of categories present, either

ANOVA or the Independent Samples t-test will be used.

However, before the inferential testing can take place, recoding or recategorising

is required of some of the selected demographic variables, in a similar way to the

recategorising encountered in the previous chapter during the bias analysis. This

is in order to improve on cell representation, so that no bias can be inherent in

the analyses due to lack of cell representation. Of the six selected demographic

variables, the five listed below needed to be recoded.





• How many complete years have you been working at the [university's

name] (including the former institutions prior to the merger)?


150

The recoded demographic variables are presented in Table 4.29. Note that

Gender has been included to indicate all variables that will be used in the

inferential testing.

TABLE 4.29RECODED DEMOGRAPHIC INFORMATION OF THE RESPONDENTS

Demographic Information Respondents %

(R) AgeYounger than 30 52 14.2

30 – 34 63 17.2

35 – 39 53 14.4

40 – 44 56 15.3

45 – 49 58 15.8

50 or Older 79 21.5

Missing 6 1.6

Total 367 100

Gender

Male 133 36.2

Female 224 61.0

Missing 10 2.7

Total 367 100

(R) RaceBlack (African, Coloured, Indian) 120 32.7

White 231 62.9

Missing 16 4.4

Total 367 100

(R) Marital StatusSingle (Not married, Divorced / Separated, Widowed) 126 34.3

Married or cohabitating 232 63.2

Missing 9 2.5

Total 367 100


151

Demographic Information Respondents %

(R) Highest Academic Qualification

Grade 12 / Matric or less 65 17.7

Post-school certificate or diploma 73 19.9

Bachelors degree 43 11.7

Honours degree 52 14.2

Masters degree 62 16.9

Doctorate 64 17.4

Missing 8 2.2

Total 367 100

(R) TenureLess than 6 years 158 43.1

6 – 10 years 80 21.8

More than 10 years 122 33.2

Missing 7 1.9

Total 367 100

The results of the different sections of the Minnesota Satisfaction Questionnaire

(MSQ20), the Organisational Commitment Questionnaire (OCQ) and the

Intentions to Stay Questionnaire (ISQ) will be discussed separately. As indicated

previously, six biographical variables were selected and these will all be included

in the bivariate inferential testing, namely: Age, Gender, Race, Marital Status,

Highest Academic Qualification, and Tenure.

4.7.1 The Minnesota Satisfaction Questionnaire (MSQ20)

The following abbreviations have been used throughout this section:

• Degrees of Freedom df;

• Mean Square MS; and

• F Statistic F Stat.


152

4.7.1.1 Age

The descriptive statistics are depicted below for the different age categories in

the MSQ20 in Table 4.30. Three hundred and fourteen respondents were

suitable candidates for the testing, namely those who answered all MSQ20

related questions and the biographical Age question. Of the 314 respondents, 43

were younger than 30; 53 were between the ages of 30 and 34; 48 were between

the ages of 35 and 39; 55 were between the ages of 40 and 44; 50 were between

the ages of 45 and 49; and 65 were either 50 years or older.

TABLE 4.30DESCRIPTIVE STATISTICS OF THE AGE GROUPS FOR THE MSQ20

Category Number Mean Standard Deviation

Younger than 30 43 3.282 0.774

30 – 34 53 3.132 0.658

35 – 39 48 3.347 0.660

40 – 44 55 3.368 0.736

45 – 49 50 3.349 0.779

50 or Older 65 3.439 0.641

Total 314 3.325 0.707

The results of Levene’s Test for Equality of Homogeneity of Variance are

depicted in Table 4.31. From Table 4.31 it is clear that the error variance is equal

across the different age categories for the MSQ20 (p-value > 0.05).


153

TABLE 4.31LEVENE’S TEST OF HOMOGENEITY OF VARIANCE OF DIFFERENT AGE CATEGORIES FOR

THE MSQ20

Levene Statistic Degrees of Freedom 1 Degrees of Freedom 2 p-value

0.869 5 308 0.502

The results of the test of between-subject effects are depicted in Table 4.32.

From the ANOVA it is clear that there are no significant differences in mean

scores between the different age groups for Job Satisfaction (p-value > 0.05).

The coefficient of association depicts a small effect size of 0.140 (ranged

between 0.1 and 0.29).

TABLE 4.32ANOVA: COMPARISON BETWEEN DIFFERENT AGE CATEGORIES FOR THE MSQ20

Sum of Squares df MS F Stat. p-value Eta

Between Groups 3.050 5 0.610 1.224 0.297 0.140

Within Groups 153.477 308 0.498

Total 156.527 313

4.7.1.2 Gender

The descriptive statistics are depicted below for the different gender categories in

the MSQ20 in Table 4.33. Three hundred and eleven respondents were suitable

candidates for the testing, namely those who answered all MSQ20 related

questions and the biographical Gender question. Of the 311 respondents, 119

were male, and 192 were female.


154

TABLE 4.33DESCRIPTIVE STATISTICS OF THE GENDER GROUPS FOR THE MSQ20


Male 119 3.348 0.721

Female 192 3.317 0.703

Total 311 3.329 0.709

The results of the t-test are depicted in Table 4.34. From the Independent

Samples t-test it is clear that there are no significant differences in mean scores

between the different gender categories for Job Satisfaction (p-value > 0.05). The

coefficient of association shows a negligible effect size of 0.021 (ranged between

0.0 and 0.09).

TABLE 4.34INDEPENDENT SAMPLES T-TEST FOR THE EQUALITY OF MEANS OF THE GENDER

GROUPS FOR THE MSQ20

t Statistic Degrees of Freedom p-value Eta

0.371 309 0.711 0.021

4.7.1.3 Race

The descriptive statistics are set out below for the different race categories in the

MSQ20 in Table 4.35. Three hundred and five respondents were suitable


questions and the biographical Race question. Of the 305 respondents, 98 were

black, and 207 were white.


155

TABLE 4.35DESCRIPTIVE STATISTICS OF THE RACE GROUPS FOR THE MSQ20


Black 98 3.214 0.801

White 207 3.370 0.653

Total 305 3.320 0.706

The results of the t-test are presented in Table 4.36. From the Independent


between the different race categories for Job Satisfaction (p-value > 0.05). The

coefficient of association depicts a small effect size of 0.103 (ranged between 0.1

and 0.29).

TABLE 4.36INDEPENDENT SAMPLES T-TEST FOR THE EQUALITY OF MEANS OF THE RACE GROUPS

FOR THE MSQ20


-1.682 160.124 0.095 0.103

4.7.1.4 Marital Status

The descriptive statistics are given below for the different race categories in the

MSQ20 in Table 4.37. Three hundred and twelve respondents were suitable


questions and the biographical Marital Status question. Of the 312 respondents,

108 were single, and 204 were either married or cohabitating.


156

TABLE 4.37DESCRIPTIVE STATISTICS OF THE MARITAL STATUS GROUPS FOR THE MSQ20


Single 108 3.330 0.7110


Total 312 3.327 0.7088



between the different marital status categories for Job Satisfaction (p-value >

0.05). The coefficient of association reveals a negligible effect size of 0.004

(ranged between 0.0 and 0.09).

TABLE 4.38INDEPENDENT SAMPLES T-TEST FOR THE EQUALITY OF MEANS OF THE MARITAL

STATUS GROUPS FOR THE MSQ20


0.077 310.000 0.938 0.004

4.7.1.5 Highest Academic Qualification

The descriptive statistics are depicted below for the different highest academic

qualification categories in the MSQ20 in Table 4.39. Three hundred and twelve

respondents were suitable candidates for the testing, namely those who

answered all MSQ20 related questions and the biographical Highest Academic

Qualification question. Of the 312 respondents, 51 had either Grade 12 / Matric

or lower; 59 had a post-school certificate or diploma; 37 had a bachelors degree;

47 had an honours degree; 58 had a masters degree; and 60 a doctorate.


157

TABLE 4.39DESCRIPTIVE STATISTICS OF THE HIGHEST ACADEMIC QUALIFICATION GROUPS FOR

THE MSQ20


Grade 12 / Matric or less 51 3.397 0.796

Post-school certificate or diploma 59 3.318 0.740

Bachelors degree 37 3.320 0.742

Honours degree 47 3.214 0.717

Masters degree 58 3.408 0.642

Doctorate 60 3.306 0.616

Total 312 3.330 0.704



across the different highest academic qualification categories for the MSQ20 (p-

value > 0.05).

TABLE 4.40LEVENE’S TEST OF HOMOGENEITY OF VARIANCE OF DIFFERENT HIGHEST ACADEMIC

QUALIFICATION CATEGORIES FOR THE MSQ20


1.233 5 306 0.293

The results of the test of between-subject effects appear in Table 4.41. From the

ANOVA it is clear that there are no significant differences in mean scores

between the different highest academic qualification groups for Job Satisfaction

(p-value > 0.05). The coefficient of association depicts a negligible effect size of

0.090 (ranged between 0.0 and 0.09).


158

TABLE 4.41ANOVA: COMPARISON BETWEEN DIFFERENT HIGHEST ACADEMIC QUALIFICATION

CATEGORIES FOR THE MSQ20


Between Groups 1.257 5 0.251 0.503 0.774 0.090

Within Groups 152.828 306 0.499

Total 154.085 311

4.7.1.6 Tenure

The descriptive statistics are set out below for the different tenure categories in

the MSQ20 in Table 4.42. Three hundred and thirteen respondents were suitable


questions and the biographical Tenure question. Of the 313 respondents, 133

were for less than six years; 73 between six to 10 years; and 107 for more than

10 years.

TABLE 4.42DESCRIPTIVE STATISTICS OF THE TENURE GROUPS FOR THE MSQ20


Less than 6 years 133 3.391 0.666

6 – 10 years 73 3.198 0.818

More than 10 years 107 3.327 0.673

Total 313 3.324 0.708



across the different tenure categories for the MSQ20 (p-value > 0.05).


159

TABLE 4.43LEVENE’S TEST OF HOMOGENEITY OF VARIANCE OF DIFFERENT TENURE CATEGORIES

FOR THE MSQ20


2.238 2 310 0.108

The results of the test of between-subject effects are presented in Table 4.44.


scores between the different tenure groups for Job Satisfaction (p-value > 0.05).

The coefficient of association reveals a small effect size of 0.106 (ranged


TABLE 4.44ANOVA: COMPARISON BETWEEN DIFFERENT TENURE CATEGORIES FOR THE MSQ20


Between Groups 1.760 2 0.880 1.763 0.173 0.106

Within Groups 154.725 310 0.499

Total 156.485 312

This concludes the inferential testing section on the Minnesota Satisfaction

Questionnaire. The results of the inferential testing of the Organisational

Commitment Questionnaire will be discussed next.

4.7.2 The Organisational Commitment Questionnaire (OCQ)






160

4.7.2.1 Age


the OCQ in Table 4.45. Three hundred respondents were suitable candidates for

the testing, namely those who answered all Organisational Commitment related

questions and the biographical Age question. Of the 300 respondents, 41 were

younger than 30; 51 were between the ages of 30 and 34; 44 were between the

ages of 35 and 39; 51 were between the ages of 40 and 44; 48 were between the

ages of 45 and 49; and 65 were either 50 years or older.

TABLE 4.45DESCRIPTIVE STATISTICS OF THE AGE GROUPS FOR THE OCQ


Younger than 30 41 3.842 0.623

30 – 34 51 3.878 0.576

35 – 39 44 4.064 0.509

40 – 44 51 4.052 0.504

45 – 49 48 4.079 0.574

50 or Older 65 4.198 0.479

Total 300 4.032 0.551


presented in Table 4.46. From Table 4.46 it is clear that the error variance is

equal across the different age categories for the OCQ (p-value > 0.05). Hence

the Scheffé test is used in the post-hoc multiple comparisons to further compare

means per each category level.


161


THE OCQ


1.191 5 294 0.314


From the ANOVA it is clear that there are significant differences between the

mean scores between the different age groups for Organisational Commitment

(p-value < 0.05). The coefficient of association depicts a small effect size of


TABLE 4.47ANOVA: COMPARISON BETWEEN DIFFERENT AGE CATEGORIES FOR THE OCQ


Between Groups 4.640 5 0.928 3.170 0.008 0.226

Within Groups 86.065 294 0.293

Total 90.706 299

The results of the Scheffé test appear in Table 4.48. It can be seen that the older

the respondent, the stronger (or more positive) the commitment to the

organisational. This is seen through the significant differences (at the 10% level

of significance) found between respondents 50 years or older and those aged

younger than 30 and those between the ages of 30 to 34. (Note: Younger than

30 < 30; 50 or Older 50 +)


162

TABLE 4.48POST-HOC TEST: COMPARISON BETWEEN DIFFERENT AGE CATEGORIES FOR THE OCQ

< 30 30 – 34 35 – 39 40 – 44 45 – 49 50 +

< 30 1.000 0.616 0.635 0.517 *0.05730 – 34 1.000 0.736 0.756 0.638 *0.080

35 – 39 0.616 0.736 1.000 1.000 0.899

40 – 44 0.635 0.756 1.000 1.000 0.839

45 – 49 0.517 0.638 1.000 1.000 0.931

50 + *0.057 *0.080 0.899 0.839 0.931

*The mean difference is significant at the .10 level.

Figure 4.2 below highlights the trend encountered of commitment to the

organisation becoming stronger as age increases.

3.8

4.0

4.2

Younger than 30 30 – 34 35 – 39 40 – 44 45 – 49 50 or Older(R) Please indicate your age group

Org

anis

atio

nal C

omm

itmen

t Mea

n Va

lues

Figure 4.2: Mean Values of Organisational Commitment for Each AgeCategory


163

4.7.2.2 Gender

The descriptive statistics are set out below for the different gender categories in

the OCQ in Table 4.49. Two hundred and ninety eight respondents were suitable

candidates for the testing, namely those who answered all Organisational

Commitment related questions and the biographical Gender question. Of the 298

respondents, 110 were male, and 188 were female.

TABLE 4.49

DESCRIPTIVE STATISTICS OF THE GENDER GROUPS FOR THE OCQ


Male 110 3.955 0.545

Female 188 4.075 0.554

Total 298 4.031 0.553



between the different gender categories for Organisational Commitment (p-value

> 0.05). The coefficient of association reveals a small effect size of 0.105 (ranged



GROUPS FOR THE OCQ


-1.817 296 0.070 0.105


164

4.7.2.3 Race

The descriptive statistics are depicted below for the different race categories in

the OCQ in Table 4.51. Two hundred and ninety one respondents were suitable


Commitment related questions and the biographical Race question. Of the 291

respondents, 91 were black, and 200 were white.

TABLE 4.51

DESCRIPTIVE STATISTICS OF THE RACE GROUPS FOR THE OCQ


Black 91 4.195 0.572

White 200 3.964 0.522

Total 291 4.036 0.548


Samples t-test it is clear that there are significant differences in mean scores

between the different race categories for Organisational Commitment (p-value <

0.05). The coefficient of association shows a small effect size of 0.196 (ranged



FOR THE OCQ


3.392 289 0.001 0.196

It can be seen, from the descriptive statistics in Table 4.51, that the black

respondents from the sample are more positive toward the commitment of the

organisational than the white respondents. Figure 4.3 below highlights the


165

significant difference encountered between the race categories and levels of

commitment to the organisation.

3.8

4.0

4.2

4.4

Black WhiteWhat is your race?

Org

anis

atio

nal C

omm

itmen

t Mea

n Va

lues

Figure 4.3: Mean Values of Organisational Commitment for Each RaceCategory



the OCQ in Table 4.53. Two hundred and ninety eight respondents were suitable


Commitment related questions and the biographical Marital Status question. Of

the 298 respondents, 100 were single, and 198 were either married or

cohabitating.


166

TABLE 4.53DESCRIPTIVE STATISTICS OF THE MARITAL STATUS GROUPS FOR THE OCQ


Single 100 4.067 0.548


Total 298 4.031 0.550

The results of the t-test are given in Table 4.54. From the Independent Samples

t-test it is clear that there are no significant differences in mean scores between

the different marital status categories for Organisation Commitment (p-value >

0.05). The coefficient of association depicts a negligible effect size of 0.046



STATUS GROUPS FOR THE OCQ


0.797 296 0.426 0.046



qualification categories in the OCQ in Table 4.55. Two hundred and ninety eight


answered all Organisational Commitment related questions and the biographical

Highest Academic Qualification question. Of the 298 respondents, 47 had either

Grade 12 / Matric or lower; 56 had a post-school certificate or diploma; 34 had a

bachelors degree; 47 had an honours degree; 54 had a masters degree; and 60

a doctorate.


167


THE OCQ







Doctorate 60 3.956 0.434

Total 298 4.036 0.546



across the different highest academic qualification categories for the OCQ (p-

value > 0.05). Hence the Scheffé test is used in the post-hoc multiple

comparisons to further compare means per each category level.


QUALIFICATION CATEGORIES FOR THE OCQ


1.961 5 292 0.084

The results of the test of between-subject effects are set out in Table 4.57. From

the ANOVA it is clear that there are significant differences in mean scores

between the different highest academic qualification groups for Organisational

Commitment (p-value < 0.05). The coefficient of association depicts a negligible

effect size of 0.233 (ranged between 0.1 and 0.29).


168


CATEGORIES FOR THE OCQ


Between Groups 4.786 5 0.957 3.341 0.006 0.233


Total 88.444 297

The results of the Scheffé test appear in Table 4.58. It can be seen that the

higher the educational level of the respondent, the weaker (or more negative) the

commitment to the organisational. This is made clear by the significant

differences (at the 10% level of significance) found between respondents who

hold a post-school certificate or diploma and those who hold an honours degree

and doctorate.

TABLE 4.58POST-HOC TEST: COMPARISON BETWEEN DIFFERENT HIGHEST ACADEMIC

QUALIFICATION CATEGORIES FOR THE OCQ

G P B H M D

Grade 12 / Matric or less (G) 0.831 0.982 0.504 0.948 0.836

Post-school certificate (P) 0.831 0.434 *0.024 0.227 *0.097Bachelors degree (B) 0.982 0.434 0.952 1.000 0.999

Honours degree (H) 0.504 *0.024 0.952 0.949 0.989

Masters degree (M) 0.948 0.227 1.000 0.949 1.000

Doctorate (D) 0.836 *0.097 0.999 0.989 1.000


Figure 4.4 below highlights the trend encountered of commitment to the

organisation becoming weaker as the highest academic qualification increases.


169

3.6

3.8

4.0

4.2

4.4

Grade 12/Matric orless

Post-schoolcertificate or

diploma

Bachelors degree Honours degree Masters degree Doctorate

(R) What is your highest academic qualification?

Org

anis

atio

nal C

omm

itmen

t Mea

n Va

lues

Figure 4.4: Mean Values of Organisational Commitment for Each HighestAcademic Qualification Category

4.7.2.6 Tenure

The descriptive statistics are depicted below for the different tenure categories in

the OCQ in Table 4.59. Two hundred and ninety nine respondents were suitable


Commitment related questions and the biographical Tenure question. Of the 299

respondents, 129 were for less than six years; 67 between six to 10 years; and

103 for more than 10 years.


170

TABLE 4.59DESCRIPTIVE STATISTICS OF THE TENURE GROUPS FOR THE OCQ


Less than 6 years 129 4.012 0.554

6 – 10 years 67 3.998 0.599

More than 10 years 103 4.075 0.518

Total 299 4.031 0.552



across the different tenure categories for the OCQ (p-value > 0.05).

TABLE 4.60

LEVENE’S TEST OF HOMOGENEITY OF VARIANCE OF DIFFERENT TENURE CATEGORIES

FOR THE OCQ


0.203 2 296 0.817

The results of the test of between-subject effects are presented in Table 4.61.


scores between the different tenure groups for Organisational Commitment (p-

value > 0.05). The coefficient of association depicts a negligible effect size of


TABLE 4.61ANOVA: COMPARISON BETWEEN DIFFERENT TENURE CATEGORIES FOR THE OCQ


Between Groups 0.318 2 0.159 0.520 0.595 0.059


Total 90.650 298


171

This concludes the inferential testing section on the Organisational Commitment

Questionnaire. The results of the inferential testing of the Intentions to Stay

Questionnaire will be discussed next.

4.7.3 Intentions to Stay Questionnaire (ISQ)





4.7.3.1 Age


the ISQ in Table 4.62. Three hundred and five respondents were suitable

candidates for the testing, namely those who answered all Turnover Intentions

related questions and the biographical Age question. Of the 305 respondents, 40

were younger than 30; 52 were between the ages of 30 and 34; 45 were between

the ages of 35 and 39; 53 were between the ages of 40 and 44; 48 were between

the ages of 45 and 49; and 67 were either 50 years or older.


172

TABLE 4.62DESCRIPTIVE STATISTICS OF THE AGE GROUPS FOR THE ISQ


Younger than 30 40 2.935 0.876

30 – 34 52 3.093 0.874

35 – 39 45 2.798 0.772

40 – 44 53 2.910 0.954

45 – 49 48 2.745 0.785

50 or Older 67 2.572 0.856

Total 305 2.828 0.868



across the different age categories for the ISQ (p-value > 0.05). Hence the

Scheffé test is used in the post-hoc multiple comparisons to further compare



THE ISQ


1.101 5 299 0.360

The results of the test of between-subject effects are given in Table 4.64. From

the ANOVA it is clear that there are significant differences between the mean

scores between the different age groups for Turnover Intentions (p-value < 0.05).

The coefficient of association depicts a small effect size of 0.201 (ranged



173

TABLE 4.64ANOVA: COMPARISON BETWEEN DIFFERENT AGE CATEGORIES FOR THE ISQ


Between Groups 9.236 5 1.847 2.515 0.030 0.201

Within Groups 219.637 299 0.735

Total 228.873 304

The results of the Scheffé test are presented in Table 4.65. It can be seen that

the older the respondent, the higher the intentions of staying (i.e. lower likelihood

of leaving). This is seen through the significant difference (at the 10% level of

significance) found between respondents 50 years or older and those between

the ages of 30 to 34. (Note: Younger than 30 < 30; 50 or Older 50 +)

TABLE 4.65POST-HOC TEST: COMPARISON BETWEEN DIFFERENT AGE CATEGORIES FOR THE ISQ

< 30 30 – 34 35 – 39 40 – 44 45 – 49 50 +

< 30 0.978 0.991 1.000 0.957 0.483

30 – 34 0.978 0.722 0.945 0.534 *0.05835 – 39 0.991 0.722 0.995 1.000 0.865

40 – 44 1.000 0.945 0.995 0.968 0.467

45 – 49 0.957 0.534 1.000 0.968 0.950

50 + 0.483 *0.058 0.865 0.467 0.950


Figure 4.5 below highlights the trend encountered of intentions to turnover

decreasing in likelihood as age increases.


174

2.4

2.6

2.8

3.0

3.2

Younger than 30 30 – 34 35 – 39 40 – 44 45 – 49 50 or Older(R) Please indicate your age group

Turn

over

Inte

ntio

ns M

ean

Valu

es

Figure 4.5: Mean Values of Turnover Intentions for Each Age Category

4.7.3.2 Gender

The descriptive statistics are depicted below for the different gender categories in

the ISQ in Table 4.66. Three hundred and three respondents were suitable


related questions and the biographical Gender question. Of the 303 respondents,

108 were male, and 195 were female.


175

TABLE 4.66DESCRIPTIVE STATISTICS OF THE GENDER GROUPS FOR THE ISQ


Male 108 2.804 0.851

Female 195 2.833 0.879

Total 303 2.823 0.868

The results of the t-test appear in Table 4.67. From the Independent Samples t-

test it is clear that there are no significant differences in mean scores between

the different gender categories for Turnover Intentions (p-value > 0.05). The

coefficient of association depicts a negligible effect size of 0.016 (ranged



GROUPS FOR THE ISQ


-0.274 301 0.784 0.016

4.7.3.3 Race


the ISQ in Table 4.68. Two hundred and ninety six respondents were suitable


related questions and the biographical Race question. Of the 296 respondents,

93 were black, and 203 were white.


176

TABLE 4.68DESCRIPTIVE STATISTICS OF THE RACE GROUPS FOR THE ISQ


Black 93 2.884 0.914

White 203 2.825 0.838

Total 296 2.843 0.861

The results of the t-test are set out in Table 4.69. From the Independent Samples

t-test it is clear that there are no significant differences in mean scores between

the different race categories for Turnover Intentions (p-value > 0.05). The




FOR THE ISQ


0.552 294 0.581 0.032


The descriptive statistics are shown below for the different marital status

categories in the ISQ in Table 4.70. Three hundred and three respondents were

suitable candidates for the testing, namely those who answered all Turnover

Intentions related questions and the biographical Marital Status question. Of the

303 respondents, 104 were single, and 199 were either married or cohabitating.


177

TABLE 4.70DESCRIPTIVE STATISTICS OF THE MARITAL STATUS GROUPS FOR THE ISQ


Single 104 2.924 0.844


Total 303 2.831 0.869



between the different marital status categories for Turnover Intentions (p-value >

0.05). The coefficient of association reveals a negligible effect size of 0.077



STATUS GROUPS FOR THE ISQ


1.339 301 0.182 0.077



qualification categories in the ISQ in Table 4.72. Three hundred and three


answered all Turnover Intentions related questions and the biographical Highest

Academic Qualification question. Of the 303 respondents, 50 had either Grade

12 / Matric or lower; 57 had a post-school certificate or diploma; 37 had a


a doctorate.


178


THE ISQ







Doctorate 58 2.879 0.864

Total 303 2.822 0.865



across the different highest academic qualification categories for the ISQ (p-

value > 0.05).


QUALIFICATION CATEGORIES FOR THE ISQ


0.816 5 297 0.539



scores between the different highest academic qualification groups for Turnover

Intentions (p-value > 0.05). The coefficient of association shows a small effect

size of 0.147 (ranged between 0.1 and 0.29).


179


CATEGORIES FOR THE ISQ


Between Groups 4.862 5 0.972 1.305 0.262 0.147

Within Groups 221.301 297 0.745

Total 226.163 302

4.7.3.6 Tenure

The descriptive statistics are set out below for the different tenure categories in

the ISQ in Table 4.75. Three hundred and four respondents were suitable


related questions and the biographical Tenure question. Of the 304 respondents,

131 were for less than six years; 69 between six to 10 years; and 104 for more

than 10 years.

TABLE 4.75DESCRIPTIVE STATISTICS OF THE TENURE GROUPS FOR THE ISQ


Less than 6 years 131 2.711 0.811

6 – 10 years 69 3.117 0.945

More than 10 years 104 2.782 0.852

Total 304 2.827 0.869



across the different tenure categories for the ISQ (p-value > 0.05). Hence the

Scheffé test is used in the post-hoc multiple comparisons to further compare



180

TABLE 4.76LEVENE’S TEST OF HOMOGENEITY OF VARIANCE OF DIFFERENT TENURE CATEGORIES

FOR THE ISQ


1.779 2 301 0.171


ANOVA it is clear that there are significant differences between the mean scores

between the different tenure groups for Turnover Intentions (p-value < 0.05). The


and 0.29).

TABLE 4.77ANOVA: COMPARISON BETWEEN DIFFERENT TENURE CATEGORIES FOR THE ISQ


Between Groups 7.777 2 3.889 5.294 0.005 0.184

Within Groups 221.087 301 0.735

Total 228.864 303

The results of the Scheffé test are depicted in Table 4.78. It can be seen that a

negative U-shaped relationship is indicated. This is seen through the significant

difference (at the 10% level of significance) found between respondents who

have been with the organisation for six to 10 years as opposed to those who

have either worked less (less than six years) or more (more than 10 years).


181

TABLE 4.78POST-HOC TEST: COMPARISON BETWEEN DIFFERENT TENURE CATEGORIES FOR THE

ISQ

Less than 6 years 6 – 10 years More than 10 years

Less than 6 years *0.007 0.821

6 – 10 years *0.007 *0.043More than 10 years 0.821 *0.043*The mean difference is significant at the .10 level.

Figure 4.6 below highlights the inverted U-trend encountered of Turnover

Intentions that increases initially as tenure increases, and then decreases once a

peak is reached.

2.6

2.8

3.0

3.2

Less than 6 years 6 – 10 years More than 10 years

(R) How many complete years have you been working at the [university's name] (including the former institutionsprior to the merger)?

Turn

over

Inte

ntio

ns M

ean

Valu

es

Figure 4.6: Mean Values of Turnover Intentions for Each Tenure Category


182

This concludes the inferential testing section on the Intentions to Stay

Questionnaire.

The results of the correlations between each of the work constructs will be

discussed.

4.8 Intercorrelations of Constructs

The results of the intercorrelation of the finalised work constructs (second order

factors) of the different instruments are depicted in Table 4.79.

TABLE 4.79INTERCORRELATIONS BETWEEN THE DIFFERENT WORK CONSTRUCTS

JS OC TI

Job Satisfaction (JS) *.408 *-.689

Organisational Commitment (OC) *.408 *-.396Turnover Intentions (TI) *-.689 *-.396*Correlation is significant at the 0.01 level.

Examining the correlations of Job Satisfaction, Organisational Commitment and

Turnover Intentions, both positive and negative correlations were found. All work

constructs yielded significant correlations ranging from low to substantial in their

interpretations. The strongest correlation encountered was that between

Turnover Intentions against Job Satisfaction, producing a substantial correlation,

whereby 47% of the variance can be accounted for. The lowest, albeit still

significant and interpreted as a low correlation, is that between Turnover

Intentions and Organisational Commitment, whereby 16% of the variance can be

explained.


183

To illustrate the above intercorrelation matrix visually, see Figure 4.7. As can be

seen from Figure 4.7, there are significant correlations between all the attained

dependent variables.

Figure 4.7: Intercorrelations between the Different Work Constructs

In the next section, the Structural Equation Modelling will be discussed.

4.9 Structural Equation Modelling

Analysis of the proposed models commenced by proceeding to calculate the

relevant indices as indicated in the previous chapter. As mentioned, to assess a

fit the types of indices listed below need to be represented.

• One absolute fit index – for this the researcher selected the Relative Chi-

Square Measurement ( 2 / df).

• One incremental fit index – the Comparative Fit Index (CFI) was selected.

• One goodness-of-fit index – here the researcher selected the Goodness-

of-fit Index (GFI).

• One badness-of-fit index – the Root Mean Square Error of Approximation

(RMSEA) was chosen.

The objective of the model fitting is to determine which model, of the 15

specified, has the best fit and thereafter which model’s dependent variable has

JobSatisfaction

Intention toStay


0.408 -0.689

-0.396


184

the largest amount of variance explained. Figure 4.8 indicates the path analysis

used to address the research objective. Note that interaction arrows have been

omitted.

The key described below is used.

• Error Terms (e1 – e48) – These are used as the prediction of the

dependent variable will not be perfect, and hence the model requires the

inclusion of an error term. The error terms represent not only random

fluctuations in the predicted variable due to measurement error, but also a

composite of other variables on which the predicted variable may depend,

that was not measured in the study. This error term is essential because

the path diagram is supposed to show all variables that affect the

predicted variable.

• Rectangles (QB’s, QC’s, and QD’s) – These are used to represent the

observed variables i.e. all actual asked items from each questionnaire.

• Ellipse – This is used to represent the unobserved variables i.e. all created

work constructs.

• Single-Headed Arrow – This is used to represent a path from one variable

to the other i.e. a typical linear dependency.

• Double-Headed Arrow – Although not indicated in the diagram below, it

will be discussed for completeness sake. This is used to represent the

covariance between two variables. The rule is to assume a correlation or

covariance of zero whenever arrows do not connect variables. The

double-headed arrow was utilised in those models where a correlation

was indicated between work constructs (Models 7, 8, and 9).


185

TurnoverIntentions

QD13

e1

1

1

QD12

e21

QD4

e31

QD14

e41

QD10

e51

QD8

e61

QD7

e71

QD15

e81

QD2

e91

QD6

e101

QD9

e111

QD1

e121

IQD3

e131

JobSatisfaction

QB3 e14

QB2 e15

QB20 e16

QB16 e17

QB15 e18

QB11 e19

QB5 e20

QB6 e21

QB19 e22

QB4 e23

QB14 e24

QB13 e25

QB17 e26

1

1

1

1

1

1

1

1

1

1

1

1

1

1

QB8 e271

QB12 e281

QB10 e291

QB18 e301


QC18e33

QC3e34

QC5e35

QC12e36

QC11e37

QC10e38

QC16e39

QC15e40

QC14e41

QC13e42

QC8e43

QC9e44

QC7e45

1

1

1

1

1

1

1

1

1

1

1

1

1

1

QC6e321

QC4e311

e48

1

e46

1

e47

1

Figure 4.8: Path Analysis to Determine the Best Fit Model


186

Table 4.80 displays the comparison between each model hypothesised. Due to

space restriction, the following abbreviations will be used in the table:

• 2 = Relative Chi-Square Measurement ( 2 / df);

• CFI = Comparative Fit Index;

• GFI = Goodness-of-fit Index;

• RMSEA = Root Mean Square Error of Approximation;

• JS = Job Satisfaction;

• OC = Organisational Commitment; and

• TI = Turnover Intentions.

TABLE 4.80STRUCTURAL EQUATION MODELLING OUTCOME SUMMARY

Model 2 CFI GFI RMSEA JS OC TI

1 3.748 .650 .649 .092 .176

2 3.592 .669 .662 .089 .154

3 3.594 .669 .662 .089 .548

4 3.592 .669 .662 .089 .545

5 3.594 .669 .662 .089 .151

6 3.748 .650 .649 .092 .167

7 3.591 .670 .662 .089 .5548 3.591 .670 .662 .089 .166

9 3.591 .670 .662 .089 .550

10 3.591 .670 .662 .089 .55411 3.591 .670 .662 .089 .166

12 3.591 .670 .662 .089 .55413 3.591 .670 .662 .089 .550

14 3.591 .670 .662 .089 .166

15 3.591 .670 .662 .089 .550


187

Values under the indices have already been addressed in terms of what

constitutes a good fit. The objective of the SEM, in addressing the secondary

objectives, is to ascertain which of the 15 models has the best fit, and not to

improve on the structure. Looking at Table 4.80, it can clearly be seen that

Relative Chi-Square Measurement has an acceptable fit as it falls under the ratio

of 5 to 1. Root Mean Square Error of Approximation is within reason, as it falls

just out of the preferable level of 0.08. However, both the Comparative Fit Index

and Goodness-of-fit Index yielded poor fits, with their respective values lying

between 0.67 and 0.66 respectively, which is lower than the acceptable levels of

0.9. In terms of testing the models and not intending to improve, the researcher is

satisfied with the levels attained. In comparing the above model, based on the

four indices, very little is garnered in ascertaining which model has the best fit. In

terms of the best fit, models 7 to 15 all fall into the top category (highlighted in

grey).

Values below the work constructs in Table 4.80 reveal the Estimated Squared

Multiple Correlations, namely the amount of variance that the predictors of a

particular dependent variable can explain. From the above, the models with the

variance most explained are through the prediction of Turnover Intentions with a

value of 55.4% (Models 7, 10, and 12). Closely followed is Job Satisfaction

where the highest value is that of 55%. However, Organisational Commitment

yielded low levels of variance, as compared to the other work constructs, with its

highest variance explained being that of 16.6%.

Thus Model 7, 10, and 12 were selected for further analysis. Figure 4.9 revisits

the selected models visually.


188

Model #7

Model #10

Model #12

Figure 4.9: Selected Hypothesised Models

This concludes the Structural Equation Modelling section. Next, the results of the

interaction effects of the demographic variables in predicting Turnover Intentions

will be presented.

JobSatisfaction

TurnoverIntentions


0.554


TurnoverIntentions

JobSatisfaction

0.554

JobSatisfaction

TurnoverIntentions


0.554


189

4.10 Two-Way Analysis of Variance

As seen in the Inferential Testing section, the only demographic variables that

had a significant impact on Turnover Intentions were Age and Tenure. This

section will now address the interaction effects of the demographics variable on

each other in predicting Turnover Intentions. This is to gauge on a secondary

level the role the demographic variables play.

The following abbreviations have been used:

• Degree of Freedom df;

• Mean Square MS;

• F Statistic F Stat.; and

• Type III Sum of Squares TIII SOS.

4.10.1 Age versus Gender

The descriptive statistics are presented (only the physical number present in

each category in indicated) below for the different age and gender categories in



related questions and the biographical Age and Gender questions. Of the 303

respondents, Age was divided into groups, where 40 were younger than 30; 52

between 30 to 34 years; 45 between 35 to 39; 53 between 40 to 44; 46 between

45 to 49; and 67 for 50 years or older. Gender was divided, where 108 were male

and 195 were female.


190

TABLE 4.81DESCRIPTIVE STATISTICS OF THE AGE AND GENDER GROUPS FOR THE ISQ

Variable Category Number

Younger than 30 40

30 – 34 52

35 – 39 45

40 – 44 53

45 – 49 46

(R) Please indicate your age group.

50 or Older 67

Male 108What is your gender?

Female 195

Total 303


From the Two-Way ANOVA it is clear that there are no significant differences

between the mean scores of the interaction between the different age and

gender groups for Turnover Intentions (p-value > 0.05). The concern of this

section is only to ascertain if interactions have any influence on the dependent

variable and will thus be the only scrutinised aspect of the test (see highlighted

row). The coefficient of association shows a small effect size of 0.107 (ranged



191

TABLE 4.82TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT AGE AND GENDER


Source TIII SOS df MS F Stat. p-value Eta

Corrected Model 12.065 11 1.097 1.481 0.138 0.230

Intercept 2079.379 1 2079.379 2807.731 0.000 0.952

Age 6.774 5 1.355 1.829 0.107 0.175

Gender 0.006 1 0.006 .008 0.929 0.005

Interaction 2.495 5 0.499 .674 0.644 0.107

Error 215.512 291 0.741

Total 2641.503 303

Corrected Total 227.577 302

DEPENDENT VARIABLE: TURNOVER INTENTIONS

4.10.2 Age versus Race

The descriptive statistics are depicted (only the physical number present in each

category in indicated) below for the different age and race categories in the ISQ

in Table 4.83. Two hundred and ninety six respondents were suitable candidates

for the testing, namely those who answered all Turnover Intentions related

questions and the biographical Age and Race questions. Of the 296



45 to 49; and 61 for 50 years or older. Race was divided, where 93 were black

and 203 were white.


192

TABLE 4.83DESCRIPTIVE STATISTICS OF THE AGE AND RACE GROUPS FOR THE ISQ


Younger than 30 40

30 – 34 51

35 – 39 44

40 – 44 52

45 – 49 48


50 or Older 61

Black 93(R) What is your race?

White 203

Total 296


From the Two-Way ANOVA, it is clear that there are no significant differences

between the mean scores of the interaction between the different age and race

groups for Turnover Intentions (p-value > 0.05). The concern of this section is

only to ascertain if interactions have any influence on the dependent variable and

will thus be the only scrutinised aspect of the test (see highlighted row). The

coefficient of association reveals a small effect size of 0.129 (ranged between 0.1

and 0.29).


193

TABLE 4.84TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT AGE AND RACE CATEGORIES

FOR THE ISQ


Corrected Model 10.231 11 0.930 1.266 0.244 0.216

Intercept 1919.795 1 1919.795 2613.709 0.000 0.950

Age 5.920 5 1.184 1.612 0.157 0.166

Race 0.003 1 0.003 0.005 0.946 0.004

Interaction 3.532 5 0.706 0.962 0.442 0.129

Error 208.601 284 0.735

Total 2611.793 296



4.10.3 Age versus Martial Status


category in indicated) below for the different age and marital status categories in



related questions and the biographical Age and Marital Status questions. Of the

303 respondents, Age was divided into groups, where 40 were younger than 30;

52 between 30 to 34 years; 45 between 35 to 39; 53 between 40 to 44; 48

between 45 to 49; and 65 for 50 years or older. Marital Status was divided, where

104 were single and 199 were married or cohabiting.


194

TABLE 4.85DESCRIPTIVE STATISTICS OF THE AGE AND MARTIAL STATUS GROUPS FOR THE ISQ


Younger than 30 40

30 – 34 52

35 – 39 45

40 – 44 53

45 – 49 48


50 or Older 65

Single 104(R) What is your marital status?

Married or cohabitating 199

Total 303



between the mean scores of the interaction between the different age and marital

status groups for Turnover Intentions (p-value > 0.05). The concern of this



row). The coefficient of association shows a negligible effect size of 0.084



195

TABLE 4.86TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT AGE AND MARITAL STATUS



Corrected Model 11.061 11 1.006 1.347 0.198 0.220

Intercept 2082.998 1 2082.998 2791.002 0.000 0.952

Age 7.053 5 1.411 1.890 0.096 0.177

Marital Status 0.675 1 0.675 0.905 0.342 0.056

Interaction 1.538 5 0.308 0.412 0.840 0.084

Error 217.181 291 0.746

Total 2657.391 303



4.10.4 Age versus Highest Academic Qualification

The descriptive statistics are set out (only the physical number present in each

category in indicated) below for the different age and highest academic

qualification categories in the ISQ in Table 4.87. Two hundred and ninety six


answered all Turnover Intentions related questions and the biographical Age and

Highest Academic Qualification questions. Of the 303 respondents, Age was

divided into groups, where 40 were younger than 30; 51 between 30 to 34 years;

45 between 35 to 39; 53 between 40 to 44; 48 between 45 to 49; and 66 for 50

years or older. Highest Academic Qualification was divided into groups, where 50

had either Grade 12 / Matric or lower; 57 had a post-school certificate or diploma;

37 had a bachelors degree; 44 had an honours degree; 57 had a masters

degree; and 58 a doctorate.


196

TABLE 4.87DESCRIPTIVE STATISTICS OF THE AGE AND HIGHEST ACADEMIC QUALIFICATION

GROUPS FOR THE ISQ


Younger than 30 40

30 – 34 51

35 – 39 45

40 – 44 53

45 – 49 48

(R) Please indicate your agegroup.

50 or Older 66

Grade 12 / Matric or less 50

Post-school certificate or diploma 57

Bachelors degree 37

Honours degree 44

Masters degree 57

(R) What is your highestacademic qualification?

Doctorate 58

Total 303



between the mean scores of the interaction between the different age and

highest academic qualification groups for Turnover Intentions (p-value > 0.05).

The concern of this section is only to ascertain if interactions have any influence

on the dependent variable and will thus be the only scrutinised aspect of the test

(see highlighted row). The coefficient of association reveals a small effect size of



197

TABLE 4.88TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT AGE AND HIGHEST ACADEMIC

QUALIFICATION (HAQ) CATEGORIES FOR THE ISQ


Corrected Model 29.727 35 0.849 1.154 0.261 0.363

Intercept 1953.819 1 1953.819 2655.674 0.000 0.953

Age 7.828 5 1.566 2.128 0.062 0.196

HAQ 5.362 5 1.072 1.458 0.204 0.163

Interaction 15.592 25 0.624 0.848 0.678 0.271

Error 196.436 267 0.736

Total 2638.787 303



4.10.5 Age versus Tenure

The descriptive statistics are presented (only the physical number present in

each category in indicated) below for the different age and tenure categories in

the ISQ in Table 4.89. Three hundred and four respondents were suitable


related questions and the biographical Age and Tenure questions. Of the 304



45 to 49; and 67 for 50 years or older. Tenure was divided into groups, where

131 had less than six years experience; 69 between six to 10 years; and 104

more than 10 years experience.


198

TABLE 4.89DESCRIPTIVE STATISTICS OF THE AGE AND TENURE GROUPS FOR THE ISQ


Younger than 30 40

30 – 34 51

35 – 39 45

40 – 44 53

45 – 49 48


50 or Older 67

Less than 6 years 131

6 – 10 years 69

(R) How many complete years have you

been working at the [university's name](including the former institutions)? More than 10 years 104

Total 304


the Two-Way ANOVA it is clear that there are no significant differences between

the mean scores of the interaction between the different age and tenure groups

for Turnover Intentions (p-value > 0.05). The concern of this section is only to

ascertain if interactions have any influence on the dependent variable and will

thus be the only scrutinised aspect of the test (see highlighted row). The


and 0.29).


199

TABLE 4.90TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT AGE AND TENURE



Corrected Model 33.042 16 2.065 3.027 0.000 0.380

Intercept 843.993 1 843.993 1236.970 0.000 0.901

Age 6.659 5 1.332 1.952 0.086 0.181

Tenure 5.382 2 2.691 3.944 0.020 0.164

Interaction 11.739 9 1.304 1.912 0.050 0.238

Error 195.822 287 0.682

Total 2659.148 304



4.10.6 Gender versus Race


category in indicated) below for the different gender and race categories in the

ISQ in Table 4.91. Two hundred and ninety four respondents were suitable


related questions and the biographical Gender and Race questions. Of the 294

respondents, Gender was divided, where 103 respondents were male and 191

were female. Race was divided, where 92 respondents were black and 202 were

white.


200

TABLE 4.91DESCRIPTIVE STATISTICS OF THE GENDER AND RACE GROUPS FOR THE ISQ



Female 191


White 202

Total 294

The results of the test of between-subject effects are shown in Table 4.92. From

the Two-Way ANOVA it is clear that there are significant differences between the

mean scores of the interaction between the different gender and race groups for

Turnover Intentions (p-value < 0.05). The concern of this section is only to

ascertain if interactions have any influence on the dependent variable and will

thus be the only scrutinised aspect of the test (see highlighted row). The


and 0.29).

TABLE 4.92TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT GENDER AND RACE



Corrected Model 3.115 3 1.038 1.404 0.242 0.120

Intercept 1808.771 1 1808.771 2445.780 0.000 0.946

Gender 0.424 1 0.424 0.573 0.450 0.044

Race 0.014 1 0.014 0.019 0.890 0.008

Interaction 2.902 1 2.902 3.924 0.049 0.116

Error 214.469 290 0.740

Total 2585.604 294




201

Figure 4.10 below highlights the significant differences found. It can be seen that

white males and black females score higher (i.e. are more negative) than their

counterparts among the black males and white females.

2.6

2.7

2.8

2.9

3.0

Male Female

Turn

over

Inte

ntio

n M

ean

Valu

es

Black White

Figure 4.10: Mean Values of Turnover Intentions for the Interaction betweenthe Gender and Race Groups.

4.10.7 Gender versus Marital Status


category in indicated) below for the different gender and martial status categories

in the ISQ in Table 4.93. Three hundred and one respondents were suitable


related questions and the biographical Gender and Marital Status questions. Of

the 301 respondents, Gender was divided, where 107 respondents were male


202

and 194 were female. Marital Status was divided, where 103 respondents were

single and 198 were either married or cohabitating.

TABLE 4.93DESCRIPTIVE STATISTICS OF THE GENDER AND MARITAL STATUS GROUPS FOR THE

ISQ



Female 194



Total 301



the mean scores of the interaction between the different gender and marital

status groups for Turnover Intentions (p-value > 0.05). The concern of this



row). The coefficient of association depicts a negligible effect size of 0.027



203

TABLE 4.94TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT GENDER AND MARITAL

STATUS CATEGORIES FOR THE ISQ


Corrected Model 1.453 3 0.484 0.638 0.591 0.080

Intercept 1983.819 1 1983.819 2612.784 0.000 0.948

Gender 0.098 1 0.098 0.129 0.720 0.021

Marital Status 0.878 1 0.878 1.156 0.283 0.062

Interaction 0.166 1 0.166 0.219 0.640 0.027

Error 225.504 297 0.759

Total 2631.201 301



4.10.8 Gender versus Highest Academic Qualification


category in indicated) below for the different gender and highest academic

qualification categories in the ISQ in Table 4.95. Three hundred and one


answered all Turnover Intentions related questions and the biographical Gender

and Highest Academic Qualification questions. Of the 301 respondents, Gender

was divided, where 106 respondents were male and 195 were female. Highest

Academic Qualification was divided, where 49 had either Grade 12 / Matric or

lower; 57 had a post-school certificate or diploma; 37 had a bachelors degree; 44

had an honours degree; 57 had a masters degree; and 57 a doctorate.


204

TABLE 4.95DESCRIPTIVE STATISTICS OF THE GENDER AND HIGHEST ACADEMIC QUALIFICATION

GROUPS FOR THE ISQ



Female 195



Bachelors degree 37

Honours degree 44

Masters degree 57


Doctorate 57

Total 301


Two-Way ANOVA it is clear that there are no significant differences between the

mean scores of the interaction between the different gender and highest

academic qualification groups for Turnover Intentions (p-value > 0.05). The

concern of this section is only to ascertain if interactions have any influence on

the dependent variable and will thus be the only scrutinised aspect of the test

(see highlighted row). The coefficient of association depicts a small effect size of



205

TABLE 4.96TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT GENDER AND HIGHEST

ACADEMIC QUALIFICATION (HAQ) CATEGORIES FOR THE ISQ


Corrected Model 8.576 11 0.780 1.042 0.409 0.195

Intercept 1725.590 1 1725.590 2305.878 0.000 0.943

Gender 0.800 1 0.800 1.070 0.302 0.061

HAQ 3.735 5 0.747 0.998 0.419 0.130

Interaction 2.712 5 0.542 0.725 0.605 0.111

Error 216.271 289 0.748

Total 2612.598 301



4.10.9 Gender versus Tenure


category in indicated) below for the different gender and tenure categories in the

ISQ in Table 4.97. Three hundred and two respondents were suitable candidates

for the testing, namely those who answered all Turnover Intentions related

questions and the biographical Gender and Tenure questions. Of the 302

respondents, Gender was divided, where 107 respondents were male and 195

were female. Tenure was divided, where 130 had less than six years experience;

68 between six to 10 years; and 104 more than 10 years experience.


206

TABLE 4.97DESCRIPTIVE STATISTICS OF THE GENDER AND TENURE GROUPS FOR THE ISQ



Female 195


6 – 10 years 68



Total 302



between the mean scores of the interaction between the different gender and

tenure groups for Turnover Intentions (p-value > 0.05). The concern of this






207

TABLE 4.98TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT GENDER AND TENURE



Corrected Model 9.931 5 1.986 2.701 0.021 0.209

Intercept 2096.749 1 2096.749 2851.731 0.000 0.952

Gender 0.326 1 0.326 0.443 0.506 0.039

Tenure 5.090 2 2.545 3.462 0.033 0.151

Interaction 2.383 2 1.192 1.621 0.200 0.104

Error 217.635 296 0.735

Total 2632.959 302



4.10.10 Race versus Marital Status


category in indicated) below for the different race and marital status categories in

the ISQ in Table 4.99. Two hundred and ninety four respondents were suitable


related questions and the biographical Race and Marital Status questions. Of the

294 respondents, Race was divided, where 92 respondents were black and 202

were white. Marital status was divided, where 102 respondents were single and

192 were either married or cohabitating.


208

TABLE 4.99DESCRIPTIVE STATISTICS OF THE RACE AND MARITAL STATUS GROUPS FOR THE ISQ



White 202



Total 294

The results of the test of between-subject effects are given in Table 4.100. From


the mean scores of the interaction between the different race and marital status

groups for Turnover Intentions (p-value > 0.05). The concern of this section is

only to ascertain if interactions have any influence on the dependent variable and

will thus be the only scrutinised aspect of the test (see highlighted row). The



TABLE 4.100TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT RACE AND MARITAL STATUS



Corrected Model 2.533 3 0.844 1.135 0.335 0.108

Intercept 1967.531 1 1967.531 2646.090 0.000 0.949

Race 0.014 1 0.014 0.019 0.890 0.008

Marital Status 0.753 1 0.753 1.013 0.315 0.059

Interaction 0.857 1 0.857 1.152 0.284 0.063

Error 215.633 290 0.744

Total 2601.491 294




209

4.10.11 Race versus Highest Academic Qualification


category in indicated) below for the different race and highest academic

qualification categories in the ISQ in Table 4.101. Two hundred and ninety four


answered all Turnover Intentions related questions and the biographical Race

and Highest Academic Qualification questions. Of the 294 respondents, Race

was divided, where 93 respondents were black and 201 were white. Highest

Academic Qualification was divided, where 49 had either Grade 12 / Matric or

lower; 57 had a post-school certificate or diploma; 35 had a bachelors degree; 42

had an honours degree; 56 had a masters degree; and 55 a doctorate.

TABLE 4.101DESCRIPTIVE STATISTICS OF THE RACE AND HIGHEST ACADEMIC QUALIFICATION

GROUPS FOR THE ISQ



White 201



Bachelors degree 35

Honours degree 42

Masters degree 56

(R) What is your highest

academic qualification?

Doctorate 55

Total 294



between the mean scores of the interaction between the different race and

highest academic qualification groups for Turnover Intentions (p-value > 0.05).


210

The concern of this section is only to ascertain if interactions have any influence

on the dependent variable and will thus be the only scrutinised aspect of the test

(see highlighted row). The coefficient of association reveals a small effect size of


TABLE 4.102TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT RACE AND HIGHEST

ACADEMIC QUALIFICATION (HAQ) CATEGORIES FOR THE ISQ


Corrected Model 10.757 11 0.978 1.342 0.201 0.223

Intercept 1717.790 1 1717.790 2358.163 0.000 0.945

Race 0.132 1 0.132 0.182 0.670 0.025

HAQ 4.552 5 0.910 1.250 0.286 0.147

Interaction 4.214 5 0.843 1.157 0.331 0.142

Error 205.421 282 0.728

Total 2582.888 294



4.10.12 Race versus Tenure


category in indicated) below for the different race and tenure categories in the

ISQ in Table 4.103. Two hundred and ninety five respondents were suitable


related questions and the biographical Race and Tenure questions. Of the 295

respondents, Race was divided, where 93 respondents were black and 202 were

white. Tenure was divided, where 128 had less than six years experience; 67

between six to 10 years; and 100 more than 10 years.


211

TABLE 4.103DESCRIPTIVE STATISTICS OF THE RACE AND TENURE GROUPS FOR THE ISQ



White 202


6 – 10 years 67

(R) How many complete years have youbeen working at the [university's name](including the former institutions)? More than 10 years 100

Total 295



between the mean scores of the interaction between the different race and

tenure groups for Turnover Intentions (p-value > 0.05). The concern of this






212

TABLE 4.104TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT RACE AND TENURE



Corrected Model 9.252 5 1.850 2.552 0.028 0.206

Intercept 1587.223 1 1587.223 2188.761 .000 0.940

Race 0.391 1 0.391 0.539 0.463 0.043

Tenure 7.971 2 3.985 5.496 0.005 0.191

Interaction 2.560 2 1.280 1.765 0.173 0.110

Error 209.574 289 0.725

Total 2603.249 295



4.10.13 Marital Status versus Highest Academic Qualification


category in indicated) below for the different marital status and highest academic

qualification categories in the ISQ in Table 4.105. Three hundred and one


answered all Turnover Intentions related questions and the biographical Marital

Status and Highest Academic Qualification questions. Of the 301 respondents,

Marital Status, was divided where 103 respondents were single and 198 married

or cohabitating. Highest Academic Qualification was divided, where 50 had either

Grade 12 / Matric or lower; 57 had a post-school certificate or diploma; 37 had a


a doctorate.


213

TABLE 4.105DESCRIPTIVE STATISTICS OF THE MARITAL STATUS AND HIGHEST ACADEMIC

QUALIFICATION GROUPS FOR THE ISQ


Single 103(R) What is your maritalstatus? Married or cohabitating 198



Bachelors degree 37

Honours degree 44

Masters degree 56


Doctorate 57

Total 301



between the mean scores of the interaction between the different marital status

and highest academic qualification groups for Turnover Intentions (p-value >

0.05). The concern of this section is only to ascertain if interactions have any

influence on the dependent variable and will thus be the only scrutinised aspect

of the test (see highlighted row). The coefficient of association depicts a small



214

TABLE 4.106TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT MARITAL STATUS AND

HIGHEST ACADEMIC QUALIFICATION (HAQ) CATEGORIES FOR THE ISQ


Corrected Model 10.580 11 0.962 1.293 0.228 0.217

Intercept 2131.081 1 2131.081 2865.023 0.000 0.953

Marital Status 1.554 1 1.554 2.089 0.149 0.085

HAQ 4.358 5 0.872 1.172 0.323 0.141

Interaction 4.231 5 0.846 1.138 0.340 0.139

Error 214.966 289 0.744

Total 2628.485 301



4.10.14 Marital Status versus Tenure

The descriptive statistics appear (only the physical number present in each

category in indicated) below for the different marital status and tenure categories

in the ISQ in Table 4.107. Three hundred and two respondents were suitable


related questions and the biographical Marital Status and Tenure questions. Of

the 302 respondents, Marital Status, was divided where 104 respondents were

single and 198 married or cohabitating. Tenure was divided, where 130 had less

than six years experience; 69 between six to 10 years; and 103 more than 10

years.


215

TABLE 4.107DESCRIPTIVE STATISTICS OF THE MARITAL STATUS AND TENURE GROUPS FOR THE ISQ





6 – 10 years 69



Total 302



between the mean scores of the interaction between the different marital status

and tenure groups for Turnover Intentions (p-value > 0.05). The concern of this



row). The coefficient of association depicts a negligible effect size of 0.053



216

TABLE 4.108TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT MARITAL STATUS AND

TENURE CATEGORIES FOR THE ISQ


Corrected Model 10.602 5 2.120 2.884 0.015 0.216

Intercept 1978.997 1 1978.997 2691.623 0.000 0.949

Marital Status 2.910 1 2.910 3.958 0.048 0.115

Tenure 8.895 2 4.447 6.049 0.003 0.198

Interaction 0.608 2 0.304 0.414 0.662 0.053

Error 217.632 296 0.735

Total 2648.846 302



4.10.15 Highest Academic Qualification versus Tenure


category in indicated) below for the different highest academic qualification and

tenure categories in the ISQ in Table 4.109. Three hundred and two respondents

were suitable candidates for the testing, namely those who answered all

Turnover Intentions related questions and the biographical Highest Academic

Qualification and Tenure questions. Of the 302 respondents, Highest Academic

Qualification was divided, where 50 had either Grade 12 / Matric or lower; 57 had

a post-school certificate or diploma; 37 had a bachelors degree; 44 had an

honours degree; 56 had a masters degree; and 58 a doctorate. Tenure was

divided, where 131 had less than six years experience; 68 between six to 10

years; and 103 more than 10 years.


217

TABLE 4.109DESCRIPTIVE STATISTICS OF THE HIGHEST ACADEMIC QUALIFICATION AND TENURE

GROUPS FOR THE ISQ




Bachelors degree 37

Honours degree 44

Masters degree 56


Doctorate 58


6 – 10 years 68

(R) How many complete years

have you been working at the[university's name]? More than 10 years 103

Total 302

The results of the test of between-subject effects are set out in Table 4.110.


between the mean scores of the interaction between the different highest

academic qualification and tenure groups for Turnover Intentions (p-value >

0.05). The concern of this section is only to ascertain if interactions have any

influence on the dependent variable and will thus be the only scrutinised aspect

of the test (see highlighted row). The coefficient of association depicts a small



218

TABLE 4.110TWO-WAY ANOVA: COMPARISON BETWEEN DIFFERENT HIGHEST ACADEMIC

QUALIFICATION (HAQ) AND TENURE CATEGORIES FOR THE ISQ


Corrected Model 23.704 17 1.394 1.956 0.014 0.324

Intercept 2118.985 1 2118.985 2972.555 0.000 0.955

HAQ 3.979 5 0.796 1.116 0.352 0.139

Tenure 6.675 2 3.338 4.682 0.010 0.179

Interaction 11.714 10 1.171 1.643 0.094 0.234

Error 202.449 284 0.713

Total 2630.243 302



This concludes the Two-Way ANOVA of the interaction between all demographic

variables in predicting the dependent variable, namely, Turnover Intentions.

The Stepwise Linear Regression will now be addressed.

4.11 Stepwise Linear Regression

The purpose of the linear regression analysis is to determine the independent

and interactive role of demographic variables in explaining the variance in

Turnover Intentions. Here all independent variables, namely the work constructs

adopted (suggested by the Structural Equation Modelling), the individual

contribution from each demographic variables and the interaction from the

demographic variables, will be regressed on the dependent variable Turnover

Intentions.


219

4.11.1 Indicator Variables

Given the nature of linear regression, there was first a need to form indicator

variables from the categories forming the demographic variables. As stipulated

previously, the minimum of observations to variables is 50:1 since the Stepwise

estimation method will be utilised. As indicated in the previous chapter the

complete data set (i.e. all questions were completed) comprised of 256 cases,

therefore there is sufficient space for five independent variables. Given that the

work construct variables occupy two slots, conservatively there is only space for

three remaining variables which in this case will be taken up by, Age, Tenure,

and the interaction of Race versus Gender.

Below in Tables 4.111 to 4.113 are the newly created indicator variables from the

original demographic variables. The creation of the indicator variables emerged

from the scrutiny of the graphical displays in earlier parts of the chapter whereby

logical patterns dictated the creation of the indicators.

Table 4.111 highlights the created Age indicator. Since the recoded form, used in

the inferential testing, suggested a linear trend that the older the respondent, the

higher the intentions of staying (i.e. lower likelihood of leaving). The marked

categories, therefore, were those who were younger than the age of 40, while the

unmarked were those who were either 40 years of age or older.

TABLE 4.111AGE OF YOUNGER THAN 40 YEARS INDICATOR VARIABLE

Category Frequency Percent

Unmarked 193 53.5

Marked 168 46.5

Total 361 100


220

Table 4.112 highlights the created Tenure indicator. Since the recoded form,

used in the inferential testing, suggested a difference between those respondents

who have been with the organisational of six to 10 years as compared to those

who have either worked less (less than six years) or more (more than 10 years)

forming a U-trend. The marked categories, therefore, were those who have been

with the organisational for six to 10 years, while the unmarked were those who

have either worked less (less than six years), or more (more than 10 years), than

the indicated years.

TABLE 4.112TENURE OF 6 TO 10 YEARS INDICATOR VARIABLE


Unmarked 280 77.8

Marked 80 22.2

Total 360 100

Table 4.113 highlights the created Race versus Gender indicator. The Two-Way

ANOVA suggested a difference in that those respondents who are either white

and male or black and female score higher (i.e. are more negative) than their

counterparts who are either black and male, or white and female. The marked

categories, therefore, were those who are either white and male, or black and

female, while the unmarked categories were those who formed the black and

male, or white and female categories.

TABLE 4.113WHITE / MALE | BLACK / FEMALE INDICATOR VARIABLE


Unmarked 187 53.9

Marked 160 46.1

Total 347 100.0


221

This concludes the formation of the indicator variables. Each model will now be

handled separately, starting with model #7.

4.11.2 Model #7 with Demographic Variables

The results of the Stepwise Linear Regression are laid out below. Two tables are

pivotal in determining the fit and acceptability of the model. The initial table

depicts the variables entered and the fit of the model where the R-squared and

Adjusted R-squared are presented. The second indicates the extent of multi-

colinearity present in the model and the parameter estimates (coefficients) for

each of the independent variables.

As can clearly been seen from Table 4.114, through the Stepwise estimation

technique, only the combination of Organisation Commitment and Job

Satisfaction and the Tenure Indicator are found to be significant, resulting in a

final model predicting 44.5% of variance in Turnover Intentions.

TABLE 4.114MODEL SUMMARY OF MODEL #7

Model Variables Entered R2 Adjusted R2

1 Organisational Commitment * Job Satisfaction 0.432 0.430

2 Tenure of 6 - 10 years 0.449 0.445


Table 4.115 indicates that colinearity statistics are within an acceptable range for

the final model (Model 2). Tolerance levels are above the 0.1 level, while,

conversely, Variance Inflation Factor levels are below the level of 10. The

Condition Index is situated below 30. Parameter estimates indicate that the

combination of Organisational Commitment and Job Satisfaction has a negative


222

impact on Turnover Intentions, while falling into the 6 to 10 years level of Tenure

has a positive impact.


• Unstandardised Beta Coefficients Beta;

• t Statistic t Stat.;

• Variance Inflation Factor VIF;

• Tolerance Tol.;

• Condition Index Cond.;

• Organisational Commitment OC;

• Job Satisfaction JS; and

• Tenure of 6 – 10 years Tenure.

TABLE 4.115COEFFICIENTS AND COLINEARITY DIAGNOSTICS OF MODEL #7

Colinearity StatisticsModel Variable Beta t Stat. p-value

Tol. VIF Cond.

(Constant) 4.777 33.191 0.0001

OC * JS -0.144 -14.038 0.000 1.000 1.000

7.130

(Constant) 4.704 32.560 0.000

OC * JS -0.143 -14.126 0.000 .999 1.001

2

Tenure 0.258 2.808 0.005 .999 1.001

7.779


Model #10 will now be addressed.


The results of the Stepwise Linear Regression are set out below. Two tables are

pivotal in determining the fit and acceptability of the model. The initial table


223







Satisfaction, Job Satisfaction itself, and the Tenure Indicator are found to be

significant, resulting in a final model predicting 47% of variance in Turnover

Intentions.



1 Job Satisfaction 0.443 0.440


3 Tenure of 6 - 10 years 0.476 0.470

Dependent Variable: Turnover Intentions






impact on Turnover Intentions; Job Satisfaction itself a negative impact too, while

falling into the six to 10 years level of Tenure has a positive impact.






224

• Tolerance Tol.;





TABLE 4.117

COEFFICIENTS AND COLINEARITY DIAGNOSTICS OF MODEL #10


Tol. VIF Cond.

(Constant) 5.570 28.597 0.0001 JS -0.822 -14.338 0.000 1.000 1.000

9.828

(Constant) 5.339 25.699 0.000

JS -0.479 -3.671 0.000 0.188 5.328

2

OC * JS -0.067 -2.917 0.004 0.188 5.328

24.630

(Constant) 5.255 25.343 0.000

JS -0.468 -3.631 0.000 0.187 5.333

OC * JS -0.068 -2.993 0.003 0.188 5.329

3

Tenure 0.248 2.760 0.006 0.998 1.002

25.860


Model #12 will now be addressed.


The results of the Stepwise Linear Regression are presented below. Two tables

are pivotal in determining the fit and acceptability of the model. The initial table




225





Satisfaction, Organisational Commitment itself, and the Tenure Indicator are

found to be significant, resulting in a final model predicting 46.6% of variance in

Turnover Intentions.




2 Organisational Commitment 0.455 0.451

3 Tenure of 6 - 10 years 0.472 0.466







impact on Turnover Intentions, Organisational Commitment itself has a positive

impact, while falling into the six to 10 years level of Tenure has a positive impact.





• Tolerance Tol.;



226




TABLE 4.119COEFFICIENTS AND COLINEARITY DIAGNOSTICS OF MODEL #12


Tol. VIF Cond.

(Constant) 4.777 33.191 0.0001

OC * JS -0.144 -14.038 0.000 1.000 1.000

7.130

(Constant) 3.891 12.848 0.000

OC * JS -0.180 -12.056 0.000 0.451 2.219

2

OC 0.344 3.309 0.001 0.451 2.219

23.212

(Constant) 3.819 12.739 0.000

OC * JS -0.179 -12.153 0.000 0.450 2.220

OC 0.343 3.349 0.001 0.451 2.219

3

Tenure 0.257 2.857 0.005 0.999 1.001

24.373


The summation and decision of the best fitting model will be discussed below.

4.11.5 Model Comparisons

Table 4.120 highlights the most pertinent statistic in determining which model has

the best prediction of the dependent variable. Since all the models discussed

conformed to rules initially set out in the previous chapter, all can thus be

compared. The Adjusted R-Square will be utilised, given the nature of the test

statistic allowing for model comparison. As highlighted in Table 4.120, Model #10

has the best fit and thus is selected as the best fitting model.


227

TABLE 4.120COMPARISON OF THE MODELS

Model Number of Variables Entered Adjusted R2

#7 Two 0.445

#10 Three 0.470

#12 Three 0.466

Thus the final equation achieved in the predicting of Turnover Intentions can be

represented as follows:

Figure 4.11 presents the final model visually together with the relevant parameter

estimates. Note that Organisational Commitment and Job Satisfaction have

already been combined for the sake of simplified representation.

Figure 4.11: Final Turnover Intentions Model

OrganisationalCommitment * Job

Satisfaction -0.068

Job Satisfaction

Tenure of 6 - 10years

Turnover Intentions-0.468

0.248

Turnover Intentions = 5.255

– (0.468 * Job Satisfaction)

– (0.068 * Organisational Commitment * Job Satisfaction)

+ (0.248 * Tenure of 6 - 10 years).

Adjusted R-Square = 0.470


228

4.12 Synthesis

A synthesis of the results with regard to Phase I and Phase II will be provided

next.

Phase I

The procedures described below were carried out and, together with the main

results, can be summarised as discussed below.

Basic Descriptives are primarily used to provide the researcher with a ‘bird’s eye’

view of the data at hand. The results yielded indicate that all questions fell within

the satisfactory levels of skewness and kurtosis. Average and median values

emphasised that there is a positive sentiment towards both Organisational

Commitment and Job Satisfaction, while a neutral feeling exists toward Turnover

Intentions.

Factor Analyses – This technique was incorporated to assist in establishing the

reliability and validity of the measuring instruments used in the study. The

procedure assisted in improving on already established instruments catering for

the sample at hand. All analyses, with the exclusions of weak items, yielded a

single second order factor.

Reliability Analyses – Further assisted in establishing the reliability and validity of

the measuring instruments used in the study, all Cronbach’s Alpha values were

found to exceed the level of 0.7 ranging from 0.888 to 0.898.

Normality Testing is used to determine if normality is present in all variables used

for testing purposes. All selected procedures assume that normality is present

and hence the need to test it accordingly. The results indicated that all three

attained variables are observed to follow a normal distribution.


229

Phase II

The procedures described below were carried out and, together with the main

results, can be summarised below.

ANOVA and t-tests – These tests were utilised to determine if any of the

background variables specified has a statistical relationship with the work

constructs in the stated research objectives. Results indicated that statistical

differences were found in a handful of the tests. Results proved to concur with

those which were theoretically discussed in chapter 2.

Correlations are used to determine the degree to which changes in one variable

are associated with changes in another. It was found that all attained constructs

had a significant level of association. The strongest correlation was found

between Turnover Intentions and Job Satisfaction.

Structural Equation Modelling – This is utilised in the attainment of a best fitting

model between all considered work constructs. In this analysis, SEM was utilised

to determine firstly, which hypothesised models hold statistically and secondly,

which model was the best fitting. The results indicated that models proved the

strongest with Turnover Intentions regarded as the dependent variable. Three

models resulted from this procedure.

Two-Way Analysis of Variance – This allowed the researcher to examine the

effects of two independent variables where the only concern of this procedure is

to identify interaction effects between the independent variables in predicting the

dependent variable. The result yielded only one interaction of interest which was

that between race and gender.


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Stepwise Linear Regression – The final procedure carried out determined the

best fitting model incorporating both the work constructs selected and the

relevant demographic variables that have loaded significantly on the dependent

variable. The final model attained consisted of the predicted, Turnover Intentions,

being significantly predicted by, Job Satisfaction, Tenure, and a combination of

Job Satisfaction and Organisational Commitment. The Adjusted R-Square value

was 0.470.

In the next chapter, Chapter 5, the results will be discussed and interpreted.


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5 CHAPTER 5: DISCUSSION AND INTERPRETATION

5.1 Introduction

In the previous chapter, the results of all various statistical procedures that were

carried out were documented and observations were made. The results of the

basic descriptives, factor analyses, reliability analyses, normality testing, and

inferential statistics such as ANOVA and t-tests, correlations, structural equation

modelling, two-way ANOVA, and stepwise regression were presented.

The focus of this chapter is on how the objectives of the study, both theoretical

and empirical, were addressed. It also aims to discuss and interpret the key

statistical findings of the empirical study.

5.2 Literature Survey

5.2.1 Review of the Theoretical Research Objectives

The following theoretical objectives are set out below.

(1) Define the key concepts of the study, namely job satisfaction,

organisational commitment and turnover intentions (with some emphasis

on the positive ‘spin’ by asking intentions to stay of the respondents).

(2) Describe job satisfaction with the emphasis on a theoretical framework of



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(3) Describe organisational commitment with the emphasis on a theoretical




(4) Describe turnover intentions with emphasis on it as being categorised as a

planned behaviour and different types of turnover cognitions.

(5) Describe the outcomes of a merger or acquisition.

(6) Describe the empirical evidence of the relationships between the key


(7) Describe the empirical evidence of the background factors (antecedents)

of organisational commitment, job satisfaction and turnover intentions. The

selected variables are age, gender, tenure, marital status, highest

academic qualification and race.

5.2.2 Results of the Literature Survey

The theoretical objectives of 2.2.1 – 2.2.7 as initially laid out in Chapter 2 will be

reviewed. The most pertinent findings of the literature survey will presented by:

• defining each concept as described in theoretical research objective 2.2.1;

• providing a short review of the theoretical development of each concept as

indicated in theoretical research objectives 2.2.2 – 2.2.4;

• highlighting the outcomes of a merger or acquisition; research objective

2.2.5 primarily deals with the chosen work constructs;

• providing an overview of the relationships between the said concepts,

existing in mergers and acquisitions; research objective 2.2.6 is seen as


233

the pivotal focus of the study given the nature of its empirical objectives;

and

• lastly, providing an overview of the relationships between background

variable and the selected work constructs. This is in line with the stated

research objective 2.2.7.

The key concepts will be introduced in the next sections.

5.2.2.1 Defining the Key Concepts

The following three concepts, job satisfaction, organisational commitment, and

turnover intentions will be introduced below:

5.2.2.1.1 Job Satisfaction

Job satisfaction is a very popular work construct given the number of studies and

definitions associated with it. What is agreed is that, stemming from cognitive

processes, job satisfaction is a generalised affective work orientation towards

one’s present job and employer (Lincoln & Kalleberg, 1990). An established and

popular conceptualisation, used in this study, is the intrinsic-extrinsic distinction

which addresses the potential sources of satisfaction or dissatisfaction (Weiss et

al., 1967). This stems from the assumption that each person seeks to achieve

and maintain correspondence with his or her environment. Furthermore, this

association with the environment at work can be described in terms of the work

environment fulfilling the requirements of the individual (satisfaction) and the

individual fulfilling the requirements of this environment (satisfactoriness) (Cook

et al., 1981). This study defined job satisfaction as a pleasurable or positive

emotional state that results from the appraisal of one’s job or job experiences

(Locke, 1976, p. 1300).


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5.2.2.1.2 Organisational Commitment

A review of the literature indicated that organisational commitment has been

addressed by a number of researchers with the result yielding a plethora of

definitions. Roodt (2004a) adopted a motivational approach, thereby allowing for

the inclusion of the potential to satisfy salient needs, the realisation of salient

values and the achievement of salient goals. Thus organisational commitment,

for the purpose of this study, can be defined as a cognitive predisposition

towards a particular focus, insofar as this focus has the potential to satisfy needs,

realise values, and achieve goals (Roodt, 2004a, p. 85).

5.2.2.1.3 Turnover Intentions

Turnover behaviour is a multistage process that includes attitudinal, decisional,

and behavioural components. Furthermore, many studies have rested on the

belief that turnover is an individual choice behavioural pattern based on the

conceptualisation that it is a psychological response (Lum et al., 1998; Mobley et

al., 1979). This study assumed a positive approach toward turnover intentions by

measuring respondents’ intentions to stay, as such positive psychology has seen

support in recent times (Henry, 2004; Seligman & Csikszentmihalyi, 2000; Turner

et al., 2002). For the sake of this study, turnover intentions is seen as a mental

decision (conation) intervening between an individual’s attitudes (affect)

regarding a job and his / her subsequent behaviour to either stay or leave (Sager

et al., 1998, p. 255).

The theoretical development of these constructs will be discussed in the section

below.


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5.2.2.2 Review of the Theoretical Development of the Constructs

The theoretical development of the three constructs is presented in the sections

below.


Job satisfaction is a frequently studied variable in organisational behaviour

research, and also a central variable in both research and theory of

organisational phenomena. Leading theorists such as Maslow (1943; 1954) and

Herzberg and Mausner (1959) have emphasised the importance of the fulfilment

of various needs of employees that will determine their behaviour in

organisations.

Maslow (1943) postulated a hierarchy ranging from lower to higher order needs.

Lower order needs, such as survival needs, are often referred to as extrinsic

needs (e.g. compensation and working conditions), while higher order needs are

referred to as intrinsic needs (e.g. recognition and achievement). Herzberg and

Mausner (1959) formulated the two-factor theory of job satisfaction and

postulated that satisfaction and dissatisfaction were two separate and sometimes

unrelated phenomena. Extrinsic factors were named ‘hygiene’ factors and were

claimed to involve primarily the context in which the job was performed. Intrinsic

factors were named ‘motivators’ and were believed to involve mainly aspects of

the job itself.

A review of the literature indicates that a wide range of dimensions have

previously been used to measure job satisfaction. Many researchers have opted

for different methods of measuring job satisfaction. Locke (1976) explains, that

for researchers to understand job attitudes, they need to understand job

dimensions, which are complex and interrelated in nature. He mentioned


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common dimensions of job satisfaction such as work, pay and promotions.

Spector (1997) adopted a multifaceted approach to job satisfaction including

facets such as appreciation, communication and fringe benefits. One model of

particular interest was the Price-Mueller model (Price & Mueller, 1981), which

assumed that employees value certain conditions of work and if these conditions

are found in the workplace, employees will be more satisfied and committed and

less likely to leave the organisation.

Of particular relevance to this study was the utilisation of the intrinsic-extrinsic

definition of job satisfaction used by Weiss et al. (1967): intrinsic satisfaction was

derived from performing the work and consequently experiencing feelings of

accomplishment, self-actualisation, and identity with the task. Extrinsic

satisfaction was derived from the rewards bestowed upon an individual by peers,

supervisors or the organisation, and can take the form of recognition,

compensation, advancement, and so forth.


Organisational commitment has a long history, and has been the subject of a

great deal of research and empirical attention both as a consequence and an

antecedent of other work-related variables of interest. Commitment has evolved

as a wide range of ‘types’ (e.g. engagement, attachment, commitment,

involvement) within a wide spectrum of foci (e.g. work, job, career, profession /

occupation, organisation, union), while studying towards commitment varied

between the categories of behavioural, attitudinal and motivational within three

broad research streams through sociological, industrial / organisational

psychology and health psychology (Roodt, 2004a). Despite the lack of

consensus on the various definitions, conceptualisations and measurements, a

common theme is shared across all these deviations, that is, organisational


237

commitment is considered to be a bond or linkage of the individual to the

organisation.

Morrow (1983) highlighted that growth in the commitment related concepts has

not been accompanied by careful segmentation of commitment’s theoretical

domain in terms of the intended meaning of each concept or the concepts’

relations among themselves. Roodt (2004a) subsequently realised that research

was characterised by concept redundancy and concept contamination. Concept

redundancy was defined in this context as the use of related variables that

largely overlap in meaning, e.g. work involvement and work commitment.

Concept contamination occurs when a variable contains a large proportion of

shared or common content with other ‘unrelated’ variables, e.g. morale and work

involvement (Roodt, 2004a).

Organisational commitment, in this study, is viewed as a unidimensional

construct. This stems from Roodt’s (1997) proposal of a unidimensional manner

of measuring commitment by distinguishing between different commitment foci.

Furthermore, Roodt (1997) concluded that a distinction between different work-

related foci is only of theoretical importance if the same theoretical base is used

in operationalising the different foci. Thus, the question needs to be seriously

posed as to whether it serves a purpose to distinguish between the different

work-related foci, except maybe to obtain a better understanding of the dynamics

of organisational commitment or the relative importance of each foci. This was

supported, amongst others, by Shore et al. (1990) as they advocated that these

attitudes should be related due to their focus being the same.

It seems as if the golden thread running through all the definitions of commitment

is the potential for a particular focus to satisfy salient needs. A motivational

approach (as opposed to the attitudinal and behavioural approaches), which also

includes the realisation of salient values and the achievement of salient goals, as

suggested by Roodt (2004a), seems to be more appropriate to study


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commitment. This approach only focuses on the state of commitment (cognitive

predisposition) in a particular focus. The state of commitment is not only

separated from its antecedent and consequential conditions and behaviours, but

also from its related affective and conative components that are also present in

other widely used constructs, such as job satisfaction and turnover intentions

respectively.


The theory of planned behaviour (Ajzen, 1991), suggests that behavioural

intention is a good predictor of actual behaviour. Studies (such as Fox & Fallon,

2003; Mobley et al., 1978; Hom & Hulin, 1981; Mobley, 1982; Newman, 1974;

Shields & Ward, 2001; and Tett & Meyer, 1993) have successfully demonstrated

that behavioural turnover intentions is consistently seen with moderate to strong

correlations with turnover, substantiating the notion of Ajzen (1991). There is

considerable support for the notion that intention to quit-stay is probably the most

important and immediate individual-level antecedent and predictor of turnover

decisions (as seen in the work of Chiu & Francesco, 2003; Fox & Fallon, 2003;

Mobley, 1982; Slate & Vogel, 1997; Steel & Ovalle, 1984; and Tett & Meyer,

1993).

As indicated above, the immediate precursor of behaviour is thought to be

intentions, and therefore the best predictor of turnover should be intention to

turnover. However, Mobley (1977) has suggested that there are several other

possible turnover cognition types of interest to add in the withdrawal decision (the

decision to quit a job), highlighting notions such as thinking of quitting, followed

by the intention to search for alternatives.


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5.2.2.3 Outcomes of a Merger or Acquisition

A growing body of literature indicates that the turbulence caused by mergers and

acquisitions can be a traumatic event in the lives of individuals (Morrison &

Robinson, 1997), and organisations (Ashkenas & Francis, 2000; Lubatkin, 1983).

Several studies have shown that employees’ organisational commitment, job

satisfaction and turnover intentions have been negatively affected as a result of a

merger or an acquisition or even the announcement of one (Armstrong-Stassen

et al., 2001; Bastien, 1987; Buono et al., 1985; Covin et al., 1996; Davy et al.,

1988; Jones, 2000; Weber et al., 1994; and Zhu et al., 2004), which can be very

costly to firms.

From the above there seems to be a consensus as to the outcomes of a merger

or amalgamation and, focusing on the primary constructs in question, all are

negatively affected by such a process. Job satisfaction is reduced; organisation

commitment is lowered; and turnover intentions levels are increased. There was

an indication that knowledge of relationships between mentioned constructs and

the causes thereof is still lacking (Armstrong-Stassen et al., 2001; Cartwright &

Cooper, 1990; Jones, 2000; Singh, 1999). This is generally more the case on the

South African front where research in this context is meagre, save for Jansen

(2002) and Arnolds and Boshoff (2004).

5.2.2.4 Relationships between Key Concepts

Numerous studies have continually shown the effect of both job satisfaction and

organisational commitment on turnover intentions. Organisational commitment

and job satisfaction are viewed as an essential component of turnover models

because their empirical relationship with voluntary turnover has been established

through numerous meta-analyses, in which a negative relationship with turnover

intentions has continually been illustrated (Cohen, 1993; Lee et al., 2000;


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Mathieu & Zajac, 1990; Meyer et al., 2002; Steel & Ovalle, 1984; Tett & Meyer,

1993; and Yin & Yang, 2002). The greater the job satisfaction, the less the

likelihood that the individual will leave the organisation, and, the higher

commitment levels of the employee, the lower the predicted turnover intentions.

Organisational commitment and job satisfaction were proved to correlate with

one another yielding a positive association.

5.2.2.5 Background Factors Related to Key Concepts


Many investigations have been done over the past four decades, with

contradictory results, which have left the true nature of the relationship between

age and job satisfaction unresolved, still age may be a contributing factor in the

experience of job satisfaction. Empirical research endeavours have found a U-

shaped relationship (examples are: Clark et al., 1996; Handyside, 1961; and

Herzberg et al., 1957). A positive linear relationship has also been found

between employee age and job satisfaction and in this case the employee

became more satisfied with their job as their chronological age progressed (as

mentioned in Ingersoll et al., 2002; Herrera, 2003; Oswald & Gardner, 2001; and

Shields & Ward, 2001). A negative linear relationship between age and job

satisfaction has also been found by Muchinsky (1978), while an inverted U-

shaped or inverted J-shaped relationship was found by Saleh and Otis (1964)

and Oswald (2002). Cases of no significant relationship have also been observed

(Chambers, 1999; White & Spector, 1987).

Job satisfaction was seen to follow a U-shaped relationship with respect to

tenure in current position (Shields & Ward, 2001), while no relationship has also

been indicated with years of experience (such as Bedeian et al., 1992; Bertz &

Judge, 1994; and Ma et al., 2003). However, research has also shown that


241

overall job satisfaction increased as the years of experience increased (examples

are Chambers, 1999; and Herrera, 2003).

A number of empirical studies have found female workers to have lower levels of

job satisfaction than their male counterparts as it was argued that male officials

dominate most of the public organisations (such as Bedeian et al., 1992; Herrera,

2003). Another study found that gender did not feature significantly in terms of

overall job satisfaction scores (examples are: Brush et al., 1987; Cano & Miller,

1992; and Witt & Nye, 1992).

A meta-analysis of 21 studies reported no racial differences (Brush et al., 1987),

whilst a recent study found that Asians and blacks reported lower overall job

satisfaction than the omitted category of whites (Greenhaus et al., 1990; Tuch &

Martin, 1991). Another study, however, indicated that blacks reported higher job

satisfaction levels than whites (Vallabh & Donald, 2001).

Inconclusive results concerning marital status were found in regard to a study of

managerial and executive respondents (Chambers, 1999) as well as academics

(Cetin, 2006). Another study interestingly found that being married had positive

effects on an employee’s overall job satisfaction (Shields & Ward, 2001).

Higher levels of academic qualification were associated with significantly lower

levels of job satisfaction was found in a handful of studies (such as Oswald,

2002; Oswald & Gardner, 2001; and Shields & Ward, 2001). Conversely, it was

also found that workers with higher educational levels tend to be more satisfied

with their job than workers with lower educational levels (examples are: Griffin et

al., 1978; Herrera, 2003; and Jayaratne, 1993).

The results are summarised in Table 5.1 below.


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TABLE 5.1SUMMARY OF BACKGROUND VARIABLES AGAINST JOB SATISFACTION

Variable Outcome

Age No relationship

Positively related

Negatively related

Negative U-shape found

Positive U-shape found

J-Shape found

Tenure No relationship

U-shape found

Positively related

Gender Male more satisfied than females

No relationship

Race Blacks more satisfied than whites

Blacks / Asians less satisfied than whites

No relationship

Marital Status Married respondents more satisfied

No relationship

Highest Academic Qualification Positively related

Negatively related


There are contradictory findings in the relevant literature about the relationship

between age and commitment. While some studies have found no relationship

between age and commitment (such as Gechman & Wiener, 1975; Kanungo,

1982b; Knoop, 1986; and Müller & Roodt, 1998), others have found that

commitment has been positively related to age (including Cohen & Lowenberg,

1990; Ingersoll et al., 2002; Lok & Crawford, 1999; and Mathieu & Zajac, 1990).


243

There has been support of a positive relationship with tenure and affective and

continuance commitment (Hackett et al., 1994). Other studies have found that

the longer employees worked in an organisation, the higher their levels of

commitment (such as Cohen & Lowenberg, 1990; DeCotiis & Summers, 1987;

Luthans et al., 1987; and Meyer & Allen, 1984). However, no meaningful

relationship was found between tenure and organisational commitment (including

Lok & Crawford, 1999; McFarlin & Sweeney, 1992; Reilly & Orsak, 1991; and

Roodt, 1992).

Conflicting results are also present with gender. Some studies that were

conducted on gender found women to be more committed than men (such as

Angle & Perry, 1981; Gould, 1975; Hrebiniak & Alutto, 1972; and Mathieu &

Zajac, 1990), while others found that men are more committed to the

organisation than their female colleagues (including Cohen & Lowenberg, 1990;

Ferris & Aranya, 1983; and Lacy et al., 1983). Other researchers found that

gender was not related at all to commitment (examples are: Aven et al., 1993;

Kacmar & Carlson, 1999; and McFarlin & Sweeney, 1992).

As regards to race: whites have been indicated to have higher levels of

commitment than their black counterparts (Vallabh & Donald, 2001).

Studies have indicated that married people have greater financial responsibilities

towards their family and this increases the need to stay i.e. they have higher

levels of commitment that single status people (such as Mathieu & Hamel, 1989;

Mathieu & Zajac, 1990; and Meyer & Allen, 1988). Other studies, however, have

shown no relationship between marital status and commitment (examples are:

Blau & Boal, 1989; Cohen & Lowenberg, 1990; Ferris & Aranya, 1983; Kanungo,

1982b; Roodt et al., 1993; and Saal, 1978, 1981).

There are conflicting findings with regard to commitment and academic

qualification. Education was found to be inversely (negatively) related to


244

commitment (including Cohen & Lowenberg, 1990; Dornstein & Matalon, 1989;

Mathieu & Hamel, 1989; Mathieu & Zajac, 1990; and Meyer & Allen, 1988), while

others found the opposite with higher educated people indicating more

commitment to work (such as Knoop, 1986; Mannheim, 1975; Newton & Keenan,

1983; and Siegel & Ruh, 1973). A near zero relationship between education and

commitment has also been found (examples are: DeCotiis & Summers, 1987;

Ingersoll et al., 2002; and Luthans et al., 1987).


TABLE 5.2

SUMMARY OF BACKGROUND VARIABLES AGAINST ORGANISATIONAL COMMITMENT

Variable Outcome

Age No relationship

Positively related

Tenure No relationship

Positively related

Gender Females more committed than males

Male more committed than females

No relationship

Race Whites more committed than blacks

Marital Status Married respondents more committed

No relationship


Negatively related

No relationship


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Research results dealing with the relationship with age are seemingly consistent.

Results continually indicate that the younger the age at application for the

organisation, the higher the turnover association (such as Chiu & Francesco,

2003; Federico et al., 1976; Jacobs, 2005; and Porter et al., 1974). It has been

reported (Hellriegel & White, 1973), however, that no consistent statistical

differences were found, as observed by Yin and Yang (2002) in a recent meta-

analysis.

Conflicting results merge in terms of tenure. Studies support the finding of a

statistically significant positive correlation between tenure and turnover intentions

(Jacobs, 2005; Lum et al., 1998); while significant negative correlations have also

been encountered (examples are: Chiu & Francesco, 2003; Mobley et al., 1978;

and Waters et al., 1976).

Many studies have reported that no significant relationship exists between

gender and turnover intentions (such as Lambert et al., 2001; Lum et al., 1998).

This was also the case found in a longitudinal study (Porter et al., 1974). Another

study, however, reported a negative correlation whereby women had higher

turnover intentions (Marsh & Mannari, 1977).

Race has been indicated as a poor and inconsistent variable when used as a

predictor of turnover (Lambert et al., 2001), but recently it was found that African

professional nurses are significantly more inclined to quit than their coloured or

white counterparts (Jacobs, 2005). Also it has been found that far more black

managers were seriously considering leaving their current positions than their

white counterparts (Vallabh & Donald, 2001).


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Conflicting results in terms of marital status form two stems of thought. The first

stem indicates that no significant differences were found in mean scores between

the different marital categories and intention to turnover (such as Jacobs, 2005;

and Lum et al., 1998). The second stem reports significant evidence that married

respondents are less likely to defect (Federico et al., 1976; Yin & Yang, 2002).

The assorted research results of the highest academic qualification prove wholly

inconclusive. It has been found that a significant positive correlation exists with

education (Lum et al., 1998; Shields & Ward, 2001). Some studies, on the other

hand, find no significant differences in mean scores between the different

educational level categories and intention to turnover (Jacobs, 2005; Lambert et

al., 2001). Seemingly, other studies have found that a high educational level of

the respondent was associated with lower tenure (Federico et al., 1976).


TABLE 5.3SUMMARY OF BACKGROUND VARIABLES AGAINST TURNOVER INTENTIONS

Variable Outcome

Age No relationship

Positively related

Tenure Positively related

Negatively related

Gender Females have higher turnover intentions

No relationship

Race Blacks have higher turnover intentions

Marital Status Married respondents lower turnover intentions

No relationship


Negatively related

No relationship


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5.3 Key Empirical Findings

The key empirical findings will be discussed in two broad phases. The first phase

consisted of the descriptive statistical analysis describing the sample at hand.

The second phase consisted of the inferential testing.

Phase I

Phase I details the description of the sample. Descriptive statistics simply

describe what the data are showing. They provide the researcher with a ‘bird’s

eye’ view of how the data looks. The main focus of the first phase of the data

analysis is to provide proof that the measuring instruments and variables were

reliable and valid for the purpose of the study.

5.3.1 Basic Descriptives

This section was primarily used to provide the researcher with a ‘bird’s eye’ view

of the data at hand. The results indicated that all questions fell within the required

levels of skewness and kurtosis (namely 2 for skewness and 7 for kurtosis

respectively). Average and median values highlighted that there is a positive

sentiment towards both Organisational Commitment and Job Satisfaction within

the organisation, while a neutral feeling emerged toward Turnover Intentions.

This came to light as the Likert scale was utilised, where “3” is indicative of a

neutral feeling. Organisational Commitment and Job Satisfaction were seen to

show scores of 4.026 and 3.321 respectively, whilst Turnover Intentions yielded

an average of 2.831.


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5.3.2 Factor Analysis

This technique was incorporated to assist in establishing the reliability and

validity (namely construct and convergent validity) of the measuring instruments

used in the study. The procedure assisted in improving on the already

established instruments catering for the sample at hand. Both first and second

level factor analyses were carried out in which all first order factor analyses

yielded one second order factor. Validity was initially discussed as a

measurement concept that is concerned with the degree to which a

measurement instrument actually measures what it purports to measure. Hair et

al. (2006) show that validity is present in many forms and the five most widely

accepted forms of validity are convergent, discriminant, nomological, content,

and construct validity and will therefore be subsequently discussed per each

measuring instrument utilised.

5.3.2.1 Job Satisfaction

• Convergent validity assesses the degree to which two measures of the

same concept are correlated (Hair et al., 2006). Factor analysis

determined that all but three items (namely Questions 1, 7 and 9) were

found to correlate sufficiently indicating a satisfactory convergent validity

for Job Satisfaction.

• Discriminant validity is the degree to which two conceptually similar

concepts are distinct (Hair et al., 2006). The sub-scale level was

comprised of two distinct scales in terms of intrinsic and extrinsic job

satisfaction which was further broken down into 20 aspects of job

satisfaction (such as activity, independence, compensation, and

advancement). The researcher is satisfied with the level of discriminant

validity offered by the respective construct. This was argued in earlier

chapters whereby it was illustrated that the theoretical construction of job


249

satisfaction was conceptually distinct (different facets of job satisfaction)

but still yielding sufficient similarity in addressing global job satisfaction.

• Nomological validity refers to the degree that the summated scales of

each construct make accurate predictions of the other concepts in a

theoretically based model (Hair et al., 2006). Theoretical relationships

were established in the previous chapter, and were upheld practically in

the analytical chapter. All relationships were found to be statistically

significant in nature.

• Content validity (or face validity) subjectively assesses the

correspondence between the individual items and the concept (Hair et al.,

2006). The objective is to ensure that the selection of scale items extends

past just empirical issues to include also theoretical and practical

considerations. All measurement instruments have already been

constructed and subsequently tested based on these terms. Previous

studies indicated that all considerations were incorporated, thus the

researcher is satisfied with the level of content validity. This was

subsequently supported by the high Cronbach’s Alpha value achieved of

0.898.

• Construct validity is the extent to which a set of measured variables

actually represent the theoretical latent constructs they are designed to

measure (Hair et al., 2006). This was investigated by means of factor

analysis. The results thereof indicated that only one second order factor

emerged, indicating a satisfactory level of construct validity. This was

subsequently supported by the fact that achieved factor loadings ranged

from 0.319 to 0.966 for the first order factor analysis and from 0.741 to

0.810 for the second order.


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5.3.2.2 Organisational Commitment

• Convergent validity – Factor analysis determined that all but three items

(namely Questions 1, 2 and 17) were found to correlate sufficiently

indicating a satisfactory convergent validity for Organisational

Commitment.

• Discriminant validity – This stems from Roodt’s (1997) proposal that

different foci of commitment are distinct and theoretically important,

however for the current study, organisational commitment can be viewed

as a unidimensional construct. The researcher is satisfied with the level of

discriminant validity offered by the respective construct.

• Nomological validity – Theoretical relationships were established in the

previous chapter, and were upheld practically in the analytical chapter. All

relationships were found to be statistically significant in nature.

• Content validity (or face validity) – The objective is to ensure that the

selection of scale items extends past just empirical issues to include also

theoretical and practical considerations. All measurement instruments

have already been constructed and subsequently tested based on these

terms. Previous studies indicated that all considerations were

incorporated, thus the researcher is satisfied with the level of content

validity. This was subsequently supported by the high Cronbach’s Alpha

value achieved of 0.888.

• Construct validity – This was investigated by means of factor analysis. The

results thereof indicated that only one second order factor emerged,

indicating a satisfactory level of construct validity. This was subsequently

supported by the fact that achieved factor loadings ranged from 0.319 to

0.958 for the first order factor analysis and from 0.565 to 0.769 for the

second order.


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5.3.2.3 Turnover Intentions

• Convergent validity – Factor analysis determined that all but two items

(namely Questions 11 and 5) were found to correlate sufficiently indicating

a satisfactory convergent validity for Turnover Intentions.

• Discriminant validity – The motivation to use this questionnaire is that

most instruments in literature measure turnover intentions on only a

relatively small number of items, however this study opted for more items

which were distinct in nature to gauge turnover cognition accurately. The

researcher is satisfied with the level of discriminant validity offered by the

respective construct.

• Nomological validity – Theoretical relationships were established in the

previous chapter, and were upheld practically in the analytical chapter. All

relationships were found to be statistically significant in nature.

• Content validity (or face validity) – The objective is to ensure that the

selection of scale items extends past just empirical issues to include also

theoretical and practical considerations. All measurement instruments

have already been constructed and subsequently tested based on these

terms. Previous studies indicated that all considerations were

incorporated, thus the researcher is satisfied with the level of content

validity. This was subsequently supported by the high Cronbach’s Alpha

value achieved of 0.895.

• Construct validity – This was investigated by means of factor analysis. The

results thereof indicated that only one second order factor emerged,

indicating a satisfactory level of construct validity. This was subsequently

supported by the fact that achieved factor loadings ranged from 0.372 to

0.872 for the first order factor analysis. To carry out a second order factor

analysis on only two factors is considered redundant.


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5.3.3 Reliability Analysis

Used in conjunction with factor analysis, this test assists in establishing the

reliability and validity of the measuring instruments used in the study. To recap,

the diagnostic measure used is the reliability coefficient that assesses the

consistency of the entire scale, namely Cronbach’s Alpha, which is the most

widely used measure. The generally accepted upon lower limit for Cronbach’s

Alpha is 0.70 (Hair et al., 2006; Robinson et al., 1991a; and Robinson et al.,

1991b). The results for each instrument will now be highlighted.

5.3.3.1 Job Satisfaction

The questionnaire has been widely administered and many report acceptable

levels of reliability. Studies such as that of Sempane et al. (2002) achieved a

Cronbach’s Alpha of 0.9169 on a sample of government welfare employees in

South Africa, while Jacobs (2005) yielded a coefficient of 0.886 in a study of

nurses in South Africa. This study was found to be no different, yielding a

Cronbach’s Alpha of 0.898.

5.3.3.2 Organisational Commitment

The reliability of the questionnaire can be gauged through a handful of successful

implementations which it has undergone. Reliable Cronbach’s Alpha values of

0.914 (Roodt, 1997); 0.94 (Storm & Roodt, 2001); 0.91 (Pretorius & Roodt,

2004); 0.926 (Jacobs, 2005) and 0.88 on a shortened version (Janse van

Rensburg, 2004) have all been reported. This study was found to be no different,

yielding a Cronbach’s Alpha of 0.888.


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5.3.3.3 Turnover Intentions

The reliability of the questionnaire is relatively unknown for this instrument as it

has only reportedly been administered once before this study. Jacobs (2005)

reported a 0.913 Cronbach’s Alpha coefficient. This study maintains support of

the instrument utilised in which it yielded a Cronbach’s Alpha of 0.895.

5.3.4 Normality Testing

This was to determine whether normality was present in all variables used for

testing purposes. All selected procedures assume normality is present and hence

the need to test it accordingly. The results of the Kolmogorov-Smirnov test used

indicated that all three attained variables are considered to follow a normal

distribution. The following statistically nonsignificant p-values resulted:

• Job Satisfaction 0.058;

• Organisational Commitment 0.547; and

• Turnover Intentions 0.167.

Phase II

Phase II describes the inferential section of the sample, whereby statistics are

used either to infer the truth or falsify a hypothesis / research objective.

5.3.5 ANOVA and t-tests

These tests were utilised to determine where any of the background variables

specified has a statistical relationship with the work constructs in the laid out

research objectives. All results (and their respective coefficient of associations)


254

are depicted below in Table 5.4 with statistically significant relationships indicated

accordingly.

TABLE 5.4SUMMARY OF TESTING BETWEEN BACKGROUND VARIABLES AND INSTRUMENTS

Instrument Variable p-value Eta

Age 0.297 0.140

Gender 0.711 0.021

Race 0.095 0.103

Marital Status 0.938 0.004

Highest Academic Qualification 0.774 0.090

Job Satisfaction

Tenure 0.173 0.106

Age 0.008 0.226

Gender 0.070 0.105

Race 0.001 0.196



Organisational

Commitment

Tenure 0.595 0.059

Age 0.030 0.201

Gender 0.784 0.016

Race 0.581 0.032



Turnover Intentions

Tenure 0.005 0.184

As seen from the above, all highlighted Eta values are indicative of small

measures of association (ranged between 0.1 and 0.29).


255

5.3.6 Correlations

These are used to determine the degree to which changes in one variable are

associated with changes in another. It was found that all attained constructs had

a significant level of association with one another. The correlations can be

accordingly interpreted in Table 5.5.

TABLE 5.5SUMMARY OF CORRELATIONS BETWEEN INSTRUMENTS

Relationship Coefficient Interpretation

Job Satisfaction / Organisational Commitment 0.408 Medium

Job Satisfaction / Turnover Intentions -0.689 Substantial

Organisational Commitment / Turnover Intentions -0.396 Low

The highest correlation was found between Turnover Intentions and Job

Satisfaction.

5.3.7 Structural Equation Modelling

This technique is utilised to attain a best fitting model between all considered

work constructs. In this analysis, SEM was utilised to determine firstly, which

hypothesised models held statistically and secondly, which model was the best

fitting. The results indicated that models proved the strongest with Turnover

Intentions determined as the dependent variable. This was due to the most

variance being explained in the prediction of Turnover Intentions with a value of

55.4% (Models 7, 10, and 12). This is closely followed by Job Satisfaction with

the highest value being that of 55%. However Organisational Commitment

yielded low levels of variance, as compared to the other work constructs,


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explained with highest value being that of 16.6%. Three models of Turnover

Intentions resulted from this procedure and are depicted below in Figure 5.1:

Model #7

Model #10

Model #12


Table 5.6 presents the respective fit indices of the three above models. Due to

space restriction, the following abbreviations will be used in the table:

• 2 = Relative Chi-Square Measurement ( 2 / df);

• CFI = Comparative Fit Index;

• GFI = Goodness-of-fit Index;

JobSatisfaction

TurnoverIntentions


0.554


TurnoverIntentions

JobSatisfaction

0.554

JobSatisfaction

TurnoverIntentions


0.554


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• RMSEA = Root Mean Square Error of Approximation; and

• TI = Turnover Intentions.

TABLE 5.6

STRUCTURAL EQUATION MODELLING OUTCOME SUMMARY

Model 2 CFI GFI RMSEA TI

#7 3.591 .670 .662 .089 .554

#10 3.591 .670 .662 .089 .554

#12 3.591 .670 .662 .089 .554

The above summary shows that the Relative Chi-Square Measurement has an

acceptable fit as it falls under the ratio of 5 to 1. Root Mean Square Error of

Approximation is within reason, as it falls just outside the preferable level of 0.08.

However, both the Comparative Fit Index and Goodness-of-fit Index yielded poor

fits, with their respective values lying between 0.67 and 0.66, which is lower than

the acceptable level of 0.9.

5.3.8 Two-Way Analysis of Variance

This allowed the researcher to examine the effects of two independent variables

whereby the only concern of this procedure is to identify interaction effects

between the independent variables in predicting the dependent variable, namely

in this case Turnover Intentions. Summarised below in Table 5.7 are the

outcomes of all interactions with Turnover Intentions (with respective p-values

and measure of associations):


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TABLE 5.7SUMMARY OF TWO-WAY ANOVA TESTING

Interaction p-value Eta

Age / Gender 0.664 0.107

Age / Race 0.442 0.129

Age / Marital Status 0.840 0.084

Age / Highest Academic Qualification 0.678 0.217

Age / Tenure 0.050 0.238

Gender / Race 0.049 0.116

Gender / Marital Status 0.640 0.027

Gender / Highest Academic Qualification 0.605 0.111

Gender / Tenure 0.200 0.104

Race / Marital Status 0.284 0.063

Race / Highest Academic Qualification 0.331 0.142

Race / Tenure 0.173 0.110

Marital Status / Highest Academic Qualification 0.340 0.139

Marital Status / Tenure 0.662 0.053

Highest Academic Qualification / Tenure 0.094 0.234

The result yielded only one interaction of interest which was that between race

and gender.

5.3.9 Stepwise Linear Regression

The final procedure carried out determined the best fitting model incorporating

both the work constructs selected and the relevant demographic variables that

have loaded significantly on the dependent variable. Here all independent

variables, namely the form the work constructs adopt (suggested by the

Structural Equation Modelling), the individual contribution from each

demographic variables and the interaction from the demographic variables, were


259

regressed on the dependent variable Turnover Intentions. A comparison of each

model (7, 10, and 12) is depicted in Table 5.8 below:

TABLE 5.8COMPARISON OF THE MODELS

Model Number of Variables Entered Adjusted R Square

#7 Two 0.445

#10 Three 0.470

#12 Three 0.466

Thus, model 10 was selected as the final model attained consisted of the

predicted, Turnover Intentions, being significantly predicted by Job Satisfaction,

Tenure, and a combination of Job Satisfaction and Organisational Commitment.

The final equation achieved in the predicting of Turnover Intentions can be

represented below.

5.4 The Empirical Study

5.4.1 Review of the Empirical Research Objectives



and turnover intentions in a post-merger tertiary institution.

Turnover Intentions = 5.255

– (0.468 * Job Satisfaction)

– (0.068 * Organisational Commitment * Job Satisfaction)

+ (0.248 * Tenure of 6 - 10 years)


260

The secondary level research objectives are set out below.

(1) Determine what the perceptions of employees’ (academic, administrative

and support staff) job satisfaction are within the institution across all

campuses.


and support staff) organisational commitment are within the institution


(3) Determine what the employees’ (academic, administrative and support

staff) level of turnover intentions is within the institution across all

campuses.

(4) Determine what the measured relationships or associations between

these scales are within the institution across all campuses. Within this

objective a ‘best-fitting’ model will be determined.

(5) Determine what relationships exist between the attained biographical

variables and the three individual scales (work constructs). The selected

biographical variables to be utilised are: Age, Tenure, Gender, Race,


(6) Determine what relationship exists between the selected dependent work

construct (to be determined through the best model fit vetting) and the

interactions between the attained biographical variables. The selected

biographical variables are: Age, Tenure, Gender, Race, Marital Status,

and Highest Academic Qualification.


261


variables, interactions thereof, and the three scales within the ‘best-fit’

model of the proposed models from Research Objective (4).

5.4.2 Addressing the Empirical Research Objectives

Stemming from the above discussion of the key empirical findings, all seven

empirical research objectives will now be addressed.

(1) Determine what the perceptions of employees’ (academic,administrative and support staff) job satisfaction are within theinstitution across all campuses.

At the item level it was seen that the majority of the questions have a negative

skewness indicating the questions were favourably answered i.e. a positive

inclination towards their job satisfaction. This is further supported by the fact that

the majority of the questions experience higher than average (“3”) mean values.

On the overall level, Job Satisfaction had a positive response from the sample

yielding an average of 3.321 (the higher the value, the more positive). Due to this

study’s post-hoc nature (i.e. a dipstick measurement), no theoretical insight can

be provided of the overall score on this construct.

(2) Determine what the perceptions of employees’ (academic,administrative and support staff) organisational commitment arewithin the institution across all campuses.

At the item level it was seen that the majority of the questions have a negative

skewness indicating the questions were favourably answered i.e. a positive

inclination towards organisational commitment. This is further supported by the

fact that the majority of the questions experience higher than average (“3”) mean


262

values. On the overall level, Organisational Commitment had the most positive

response of the instruments from the sample yielding an average of 4.026 (the

higher the value, the more positive). Due to this study’s post-hoc nature (i.e. a

dipstick measurement), no theoretical insight can be provided on the overall

score of this construct.

(3) Determine what the employees’ (academic, administrative and

support staff) level of turnover intentions is within the institutionacross all campuses.

At the item level it was seen that the majority of the questions have a close to

zero skewness indicating the neutrality of the questions towards the items i.e. a

neutral inclination towards turnover intentions. This is further supported by the

fact that the majority of the questions experience similar mean (“3”) values to that

of the average. On the overall level, Turnover Intentions had a positive response

from the sample yielding an average of 2.831 (the lower the value, the more

positive). Thus since its value is below “3” it indicates that there is a positive

sentiment inherit in the overall response. Due to this study’s post-hoc nature (i.e.

a dipstick measurement), no theoretical insight can be provided on the overall

score of this construct.

(4) Determine what the measured relationships or associations betweenthese scales are within the institution across all campuses. Within

this objective a ‘best-fitting’ model will be determined.

Intercorrelations of the three attained work constructs all yielded statistically

significant relationships. The strongest correlation was found between Turnover

Intentions and Job Satisfaction valued at -0.689. The remaining correlations had

coefficients of 0.408 (Job Satisfaction / Organisational Commitment) and -0.396

(Organisational Commitment / Turnover Intentions).


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Current research indicated that Organisational Commitment has a higher

correlation than Job Satisfaction with Turnover Intentions. This was seen with

Lee et al. (2000) yielding correlation values of -0.538 for organisational

commitment and -0.581 for job satisfaction, while Steel and Ovalle (1984) had

values for organisational commitment of -0.38 and of job satisfaction -0.28.

Hence the results of this current study indicate otherwise as Job Satisfaction

yielded a higher correlation than that of Organisational Commitment.

From this analysis, Structural Equation Modelling was carried to determine the

‘best-fitting’ model. The results indicated that models proved the strongest with

Turnover Intentions being identified as the dependent variable. This was due to

the most variance being explained in the prediction of Turnover Intentions with a

value of 55.4% (Models 7, 10, and 12). Thus from this particular analysis the

‘best-fitting’ model was shared between three initially hypothesised models:

Model #7

Model #10


TurnoverIntentions

JobSatisfaction

JobSatisfaction

TurnoverIntentions



264

Model #12


(5) Determine what relationships exist between the attained biographicalvariables and the three individual scales (work constructs). Theselected biographical variables to be utilised are: Age, Tenure,

Gender, Race, Marital Status, and Highest Academic Qualification.

The results of the inferential testing between the instruments and background

variables yielded significant relationships which are discussed below:

Organisational Commitment against:

• Age The results indicated a trend emerging that as age increases, so

does one’s commitment to the organisation. This is in line with numerous

other studies (such as Angle & Perry, 1981; Arnold & Feldman, 1982;

Cohen & Lowenberg, 1990; DeCotiis & Summers, 1987; and Dornstein &

Matalon, 1989). These results reveal a basis given the nature of the

institution whereby job opportunities ‘diminish’ as staff become older and

more specialised in their respective fields.

• Race It was seen that black respondents from the sample are more

positive towards commitment to the organisational than white

respondents. This contradicts previous findings showing that the inverse

relationship exists. This may be due to the year of the previous studies.

South Africa is continually changing, weeding out the inequalities of the

past, and thus with the merger at hand, black staff will feel more

committed to the changes than their counterparts who may feel

JobSatisfaction

TurnoverIntentions



265

intimidated because, historically, South African educational institutions

were regarded as protected institutions.

• Highest Academic Qualification It was found that Organisational

Commitment decreases as the level of education increases. This is in line

with numerous studies (such as Mathieu & Zajac, 1990; Meyer & Allen,

1988; Morris & Sherman, 1981; and Mowday et al., 1982). The fact that a

higher academic qualification results in more job opportunities could be

the rationalisation behind this result. Commitment may not be a

psychological feeling but rather, in this case, the confidence one has

about finding alternative work. Thus the commitment to the organisation

will diminish as less ‘dependence’ is placed on the organisation.

Turnover Intentions against:

• Age The results indicate that, as age, increases intentions to stay are

improved. This is in line with numerous studies such as Jacobs (2005);

Lambert et al., (2001); and Porter et al., (1974) amongst others. An older

respondent does not necessarily equate to a more qualified one. This

relationship holds true given the previous results of Highest Academic

Qualification against Organisational Commitment. Therefore, older

respondents have more invested within their organisation and hence their

intention on staying longer.

• Tenure The results indicate that an inverted U-trend is encountered

where Turnover Intentions increases initially as tenure increases, and then

decreases once a peak is reached. The peak in this case is six – 10 years.

This contradicts previous findings where either a linear positive or

negative relationship was determined. In this case Turnover Intentions

would be the most positive for both the less and the very experienced

(work wise) respondents, whilst those with experience in-between the two

illustrate a less positive inclination toward Turnover Intentions. This may

be due to the fact that the new employees experiencing their twilight years

there and are also naïve about the organisational as a complete whole,


266

while those who are very experienced are attached to the organisation

after investing many years of service in it. Those in the six – 10 years

category feel that they have sufficiently experienced the organisation and

thus feel a need to change.

(6) Identify what exists between the selected dependent work construct(to be determined through the best model fit vetting) and the

interactions between the attained biographical variables. Theselected biographical variables are: Age, Tenure, Gender, Race,Marital Status, and Highest Academic Qualification.

Only one interaction was found (where the dependent variable was Turnover

Intentions) to be statistically significant at the 5%, namely: that between race and

gender. It was found that that white males and black females score higher (i.e.

are more negative) than their counterparts among the black males and white

females. This is because black females (under government regulations) are very

sought after in the workplace (more so than black males and white females) and

this drives their turnover intentions into the negative. On the other side of the

spectrum, white males were previously (and in some instances still are) the

dominant role players in the workplace and this drive and focus is still maintained

today, thereby reducing their staying intentions.


variables, interactions thereof, and the three scales within the ‘best-fit’ model of the proposed models from Research Objective (4).

The last phase of the research incorporated a ‘one pot’ solution whereby results

attained from various previous procedures, namely: the correlations, the

Structural Equation Modelling, inferential testing, and Two-Way ANOVA, would

all be incorporated into a Stepwise Linear Regression model. The final predictive

model of Turnover Intentions is displayed in Figure 5.3 below:


267

Figure 5.3: Final Turnover Intentions Model

As can be seen from Figure 5.3, the following variables emerged as significantly

influencing Turnover Intentions:

• the interaction of Organisational Commitment and Job Satisfaction having

a negative influence;

• Job Satisfaction in its entirety influencing the dependent variables

negatively; and

• the indicator tenure variables (6 – 10 years) exerting a positive influence

on Turnover Intentions.

The resulting Adjusted R-Square was 0.470.

This is in line with what Tett and Meyer (1993) discovered contrary to popular

belief about Job Satisfaction and Organisational Commitment. Contrary to

expectations, commitment does not correlate more strongly than satisfaction

does with Turnover Intentions. This indicates that withdrawal entails a rejection of

the job more than the organisation. This may be due to the historic nature of the

academic environment in which employees were given more job autonomy in

their positions. However of late administrative responsibilities and increasing

requirements related to student output have changed what the job used to entail.

OrganisationalCommitment * Job

Satisfaction -0.068

Job Satisfaction

Tenure of 6 - 10years

Turnover Intentions-0.468

0.248

Adjusted R-Square = 0.470


268

Thus, although the university has changed in terms of structure recently, change

in job responsibilities has been continually changing and hence the greater

impact of withdrawal cognitions.

5.5 Synthesis

To conclude the discussion and interpretation of this chapter, the following

literature and empirical objectives were addressed.

• Literature Objectives

The relationships between job satisfaction, organisational commitment, and

turnover intentions were theoretically and empirically well established. A positive

association between organisational commitment and job satisfaction was

highlighted, while both were found to exhibit a negative relationship with turnover

intentions. From the theoretical objectives, it is clear that organisational

commitment and job satisfaction are regarded as important predictors of

organisational outcomes, such as turnover intentions.

• Empirical Objectives

Through the key findings of the empirical study, all the stated research objectives

were addressed. Through the many procedures carried out, it was determined

that a high standard of validity and reliability was maintained throughout the

study, resulting in one factor representing each unique work dimension. All three

dimensions were found to have a significant measure of association with one

another and through this, a model was determined. Background variables aided

in the model construction, contributing in greater variance explained of the

dependent variable (determined to be Turnover Intentions). The final model

attained consisted of the predicted, Turnover Intentions, being significantly

predicted by, Job Satisfaction, Tenure, and a combination of Job Satisfaction and


269

Organisational Commitment. This is indicates that withdrawal entails a rejection

of the job rather than of the organisation.

Chapter 6 will first present a brief summary of the research and then the

recommendation and limitations of the study.


270

6 CHAPTER 6: CONCLUSION

6.1 Introduction

The focus of the previous chapter addressed the objectives of the study, both

theoretically and empirically, and also discussed and interpreted the key


The focus of this chapter is to provide a short summary of the broad research

process, with the emphasis on the most important conclusions and

recommendations. The limitations of the study, recommendations for further

study, the value of the study and the final conclusion will be provided.

A summary of the study will be provided next.

6.2 Overview of the Chapters

A summary of the sequence of chapters is presented in Figure 6.1.


271

Figure 6.1: Chapter Process Sequence

Focusing briefly on each chapter will provide a broad synthesis of the entire

study.

Chapter 1

There is a dearth of knowledge, save for a handful of studies, on the context of

South African mergers and acquisitions of tertiary institutions. The human

element, in the form of intellectual capital, is the most sought-after commodity in

tertiary institutions, and hence the importance placed on the needs of its

Chapter 1Defining the purpose of the study

Chapter 2Overview of literature provided linked to literature objectives

Chapter 3Discussion of research design and research methodology

Chapter 4Reporting of empirical results

Chapter 5Discussion and interpretation of literature and empirical objectives

Chapter 6Conclusion of study


272

employees. Chapter 1 presented the purpose of the study and the problem

statement, which was to determine what current employee perceptions are of

organisational commitment, job satisfaction, and turnover intentions in a post-

merger tertiary institution and how these variables are related. It proposed to

achieve this by utilising and enhancing standardised questionnaires and by

employing both basic and advanced statistical procedures. The following areas

were also outlined: the background of the problem; the motivation and rationale

for the study; the problem statement; proposed value-add of the research; and an

outline of the remaining chapters.

Chapter 2

Chapter 2 presented a literature overview of the study structured in terms of the

stated literature review objectives. The emphasis of this chapter was to provide a

literature overview of the concepts of this study. The key concepts, namely

organisational commitment, job satisfaction and intentions to stay / turnover were

defined. Thereafter a theoretical framework for each concept was provided.

The current status of research into the relationships of job satisfaction,

organisational commitment, and turnover intentions was found to be theoretically

and empirically well established, where the aftermath of a merger or acquisition

resulted in job satisfaction being reduced; organisation commitment diminishing;

and turnover intentions levels increasing. This revealed the positive association

between organisational commitment and job satisfaction, while both these have a

negative relationship with turnover intentions. However it was emphasised that in

South African literature more could be done, especially in a merger and

acquisition context. From the theoretical overview, it was clear that organisational

commitment and job satisfaction are regarded as important predictors of

organisational outcomes, such as turnover intentions. While there is reasonable

consensus about the domain of job satisfaction and turnover intentions, the study


273

of organisational commitment is characterised by concept redundancy and

contamination.

Research indicated the bivariate relationship between biographic variables

(gender, race, age, tenure, marital status, and highest academic qualification)

and the work constructs (organisational commitment, job satisfaction, and

turnover intentions) is well documented. However, in some cases, results proved

to be contradictory.

Chapter 3

Chapter 3 outlined the research design. The research approach and research

methodology were discussed against the background of the stated research

objectives. The research approach deemed best to select was described as

quantitative and non-experimental with the usage of primary data. This approach

was selected based on the stated research objectives. The research

methodology referred to the target population and research procedure, which

resulted in a sampling process whereby a self-administered electronic survey

was utilised. The research methodology continued with the measuring

instruments where rationale and sound theory were provided, while also

addressing the reliability and validity of the instruments. Lastly, the statistical

procedures were laid out highlighting the intended path selected to achieve the

research objectives in the analysis of the data.

Chapter 4

Chapter 4, split into two phases, yielded the results of the various statistical

procedures that were documented while the most significant observations were

made.


274

The first phase of the analysis consisted of the initial diagnostic testing whereby

statistical reliability and validity were determined. In this, the results of the

descriptives, factor analyses (both first and second levels), reliability analyses

(iterative item analyses) and normality testing were addressed. The main focus of

the first phase of the data analysis was to confirm that the measuring instruments

and variables were reliable and valid for purposes of the study.

In the second phase, the results were described by referring to the objectives of

the study (revisited below), namely to end with a best-fitting predictive model

incorporating significant demographic variables. This was addressed through the

process of inferential testing (ANOVA and t-tests), Correlations, Structural

Equation Modelling (SEM), Two-Way Analysis of Variance and finally, a Stepwise

Linear Regression. The main focus of the second phase was to explore

relationships between sets of key variables in the initial theoretical model in order

to present a final predictive model of the selected dependent variable obtained

from the SEM.

Chapter 5

Chapter 5 focused on how the objectives of the study, both theoretical and

empirical, were addressed, and also aimed to discuss and interpret the key


The literature objectives resulted in the relationships between organisational

commitment, job satisfaction and turnover intentions being determined as

theoretically and empirically well established. A positive association between

organisational commitment and job satisfaction was highlighted, while both were

found to exhibit a negative relationship with turnover intentions. From the

theoretical objectives, it was clear that organisational commitment and job

satisfaction are regarded as important predictors of organisational outcomes,

such as turnover intentions.


275

Through the key findings of the empirical study, all the stated empirical research

objectives were addressed. Through the many procedures carried out it was

determined that a high standard of validity and reliability was maintained

throughout the study. This resulted in one factor representing each unique work

dimension. All three dimensions were found to have a significant measure of

association with one another, and through this a model was determined.

Background variables aided in the model construction contributing greater

variance explained of the dependent variable (identified as being Turnover

Intentions). The final model attained consisted of the predicted, Turnover

Intentions, being significantly predicted by, Job Satisfaction, Tenure, and a

combination of Job Satisfaction and Organisational Commitment. This is

indicative that withdrawal entails a rejection of the job, more than of the

organisation.

6.3 Key Findings

The objectives of both the literature review and empirical research are laid out

below.

The theoretical objectives are listed below.

(1) Define the key concepts of the study, namely that of job satisfaction,

organisational commitment, and turnover intentions (with some emphasis

on the positive ‘spin’ by asking intentions to stay of the respondents).

(2) Describe job satisfaction with the emphasis on a theoretical framework of



276

(3) Describe organisational commitment with the emphasis on a theoretical




(4) Describe turnover intentions with emphasis on it as being categorised as a

planned behaviour and the different types of turnover cognitions.

(5) Describe the outcomes of a merger or acquisition.

(6) Describe the empirical evidence of the relationships between the key


(7) Describe the empirical evidence of the background factors (antecedents)

of job satisfaction, organisational commitment, and turnover intentions.

The selected variables are age, gender, tenure, marital status, highest

academic qualification, and race.

The research objectives are listed below.


and support staff) job satisfaction are within the institution across all

campuses.


and support staff) organisational commitment are within the institution


(3) Determine what the employees’ (academic, administrative and support

staff) level of turnover intentions is within the institution across all

campuses.


277

(4) Determine what the measured relationships or associations between

these scales are within the institution across all campuses. Within this

objective a ‘best-fitting’ model will be determined.


variables and the three individual scales (work constructs). The selected

biographical variables to be utilised are: Age, Tenure, Gender, Race,


(6) Identify what exists between the selected dependent work construct (to be

determined through the best model fit vetting) and the interactions

between the attained biographical variables. The selected biographical

variables are: Age, Tenure, Gender, Race, Marital Status, and Highest

Academic Qualification.


variables, interactions thereof, and the three scales within the ‘best-fit’

model of the proposed models from Research Objective (4).

The findings of this study, categorised on a theoretical, practical, and

methodological level, are based on the objectives attainment.

6.3.1 Theoretical Key Findings

(1) The results of bivariate analyses indicate that background variables could

be used for compiling profiles of job satisfaction, organisational

commitment, and turnover intentions. All findings contributed to the

theoretical pool of results previously attained.


278

(2) In the case where multivariate approaches were used in the prediction of

Turnover Intentions, selected personal variables were included, and the

most parsimonious predictive model was established. However, this model

can only be interpreted if one considers all inter-relationships between all

the predictor variables.

(3) A comprehensive literature study was conducted on the following topics:

• job satisfaction;

• organisational commitment; and

• turnover intentions

(4) The relevant literature regarding organisational commitment included an

indication of the research and the empirical attention, both as a

consequence and an antecedent of other work-related variables of

interest, it has received. As a result, Roodt (2004a) subsequently realised

that commitment research was marred by concept redundancy and

concept contamination. In order to clarify the concept, a motivational

approach, which included the realisation of salient values and the

achievement of salient goals, was proposed by Roodt (2004a) to study

commitment.

(5) The concept of job satisfaction was found to be theoretically well defined

and based on sound theoretical models through leading theorists of

Maslow (1943) and Herzberg and Mausner (1957) who provide strong

motivational and job satisfaction theories in understanding human

behaviour in organisations.

(6) Consensus exists regarding the theoretical development of turnover

intentions as one of planned behaviour. There was considerable support

where behavioural intention is shown as a good predictor of actual


279

behaviour. Furthermore, an overview indicated several other possible

turnover cognition types of interest in the withdrawal decision.

(7) The theoretical contribution of this study is that all the results found

contribute to the field of study, as it was clear from the literature that there

is a dearth of studies of this type in South Africa.

(8) In the empirical part of the study, these variables were correlated to

determine the statistical significance with other factors mentioned by

means of a multivariate approach. The statistical process explored in this

study has not been applied in the South African post-merger tertiary

environment.

(9) As turnover intentions form one of the real issues in the South African

education sector and in the world, these results may be useful in any

continuing for future studies in the merger and acquisition context.

6.3.2 Practical Key Findings

(1) The tertiary environment (under a post-merger context) should be aware

of the predictors of turnover intentions in order to address their staff’s

needs. It was found that certain personal variables predict the degree of


(2) It is clear that the independent variables, selected interaction and

demographic variables contributed significantly in predicting Turnover

Intentions (47% of the variance). Job Satisfaction emerged as the most

important predictor of Turnover Intentions, where interaction of

Organisational Commitment and Job Satisfaction had less (although

significant) influence, and likewise with the indicator variable of Tenure.


280

(3) Contrary to expectations, commitment does not correlate more strongly

than satisfaction does with turnover intentions. This indicates that

withdrawal entails a rejection of the job rather than of the organisation.

This may be due to the historic nature of the academic environment in

which employees were given more job autonomy in their positions.

However of late administrative responsibilities and increasing

requirements related to student output have changed what the job used to

entail. Thus, although the university has changed in terms of structure

recently, change in job responsibilities has been continually changing and

hence the greater impact of withdrawal cognitions.

(4) Selected personal variables predicted Turnover Intentions, and the

different biographical variables influencing the level of Turnover Intentions

could be used for future management of employees.

(5) Organisational commitment had a significant relationship with the age of

the respondent where results indicated a trend emerging in which as age

increases, so does one’s commitment to the organisation. These results

have a rational basis, given the nature of the institution, where job

opportunities ‘diminish’ as staff become older and more specialised in their

respective fields.

(6) Organisational Commitment had a significant relationship with the race of

the respondent where it was seen that black respondents from the sample

are more positive about the commitment to the organisational than the

white respondents. South Africa is continually changing, weeding out the

inequalities of the past, and thus with the merger at hand, black staff will

feel more committed to the changes than their white counterparts who

may feel intimidated, particularly as historically, South African educational

institutions were regarded as protected institutions.


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(7) Organisational Commitment had a significant relationship with the highest

academic qualification of the respondent where it was found that

Organisational Commitment decreased as the level of education

increased. A higher academic qualification results in more job

opportunities could be the rationalisation behind this result. Commitment

may not be a psychological predisposition, but rather in this case the

confidence one has about finding alternative work. Thus the commitment

to the organisation will be lowered as less ‘dependence’ is placed on the

organisation.

(8) Turnover Intentions had a significant relationship with the age of the

respondent, as the results indicated that as age increased, intentions to

stay are improved. An older respondent does not necessarily equate to a

more qualified one and hence the fact that this relationship holds value.

Older respondents place more investment within an organisation and

hence their intention on staying longer.

(9) Turnover Intentions had a significant relationship with the tenure of the

respondent as the results indicated that an inverted U-trend is

encountered where Turnover Intentions increased initially as tenure

increased, and then decreased once a peak is reached. The peak in this

case is six – 10 years. This may be due to the fact that the new

employees are experiencing their twilight years there and are also naïve

about the organisational as a complete whole, while those who are very

experienced are attached to the organisation after investing many years of

service in it. Those in the six – 10 years category feel that they have

experienced the organisation sufficiently and thus feel a need to change.

(10) An interaction relationship between race and gender was found (whereby

the dependent variable was Turnover Intentions). It can be seen that white

males and black females score higher (i.e. are more negative) than the


282

black males and white females. This is because black females (under

government regulations) are very sought after in the workplace (more so

than black males and white females) and this drives their Turnover

Intentions into the negative. On the other side of the spectrum, white

males were previously (and in some instances still are) the dominant role

players in the workplace and this drive and focus is still maintained today,

thereby reducing their intentions to stay.

(11) A predictive empirical multivariate model of subjective perception of

turnover intentions has been developed and can be applied in the tertiary

environment.

6.3.3 Methodological Key Findings

(1) The fact that all instruments were self-completion questionnaires could

have enhanced the obtained intercorrelations based on mono-method

variance. The results should therefore be interpreted with caution.

(2) Both bivariate and multivariate statistical techniques were used to test

hypotheses (inherently implied within each of the inferential techniques)

and ultimately develop the most parsimonious model for Turnover

Intentions through the Stepwise Linear Regression.

(3) Previous studies suggested that bivariate analyses have restrictive

predictive validity. The findings in this study suggest that the outcomes of

the bivariate analyses alone can provide a distorted picture of the

relationships between sets of key variables. A multivariate approach would

overcome this distortion and would be more applicable. By using

multivariate statistical predictive models such as ANOVA, Structural

Equation Modelling and Stepwise Linear Regression, this study went


283

further than the bivariate analysis. This model takes a more holistic

approach by including a wider range of variables and thus yielding a more

parsimonious predictive model.

(4) The research approach could be described as a non-experimental, cross-

sectional, field survey research (or more accurately ex post facto and

correlational research). Its main aims are to measure and test hypotheses

/ research objectives objectively to evaluate preconceived theoretical

models, as well as ultimately answering the primary research objective

stated in Chapter 1.

(5) The target population can be described as all academic / research,

support and administrative personnel of the recently merged tertiary

institution. The unit of analysis is each employee regardless of his / her

status within the respective departments and across all the relevant

campuses. This enabled the researcher to achieve a diverse offering in

terms of the employees of the institution.

(6) Convenient non-probability sampling was used in the context of a non-

experimental research design and was regarded as unavoidable for

practical reasons. Nevertheless, the sampling procedure was conducted

as inclusively as possible.

(7) The research procedure to allow the respondent to complete the

questionnaire online ensured a satisfactory response to the

questionnaires, and although it was not without problems such as online

access and the technical skill of respondents, it yielded the desired

results.

(8) Three questionnaires were administrated in this study. The questionnaires

were selected according to their operational definitions of each desired


284

concept. Organisational Commitment Questionnaire (Roodt, 1997);

Minnesota Satisfaction Questionnaire (MSQ20) (Weiss et al., 1967); and

the Intentions to Stay Questionnaire (Roodt, 2004b) were selected from

previous research. All instruments were factor analysed to ascertain the

study’s validity and an iterative item analysis yielded high Cronbach’s

Alpha reliability coefficients and could be of value for similar studies.

(9) The most parsimonious model was chosen and developed at the end to

predict subjectively experienced turnover intentions in a post-merger

tertiary education environment in South Africa.

(10) Little research on an integrated, holistic multivariate model has been

conducted in the South African post-merger tertiary environment context,

and it is important to appreciate the model’s components to understand

turnover intentions in this environment.

6.4 Recommendations

The recommendations are made from a theoretical, practical, and methodological

perspective.

6.4.1 Theoretical Recommendations

(1) The motivational approach towards the study of organisational

commitment of Roodt (2004a) is supported given its sound theoretical

foundation for operationalising commitment as a cognitive predisposition

towards a particular focus. This stems from the observations of Morrow

(1983) and O’Reilly and Chatman (1986) and the quantified result of

Roodt (2004a) whereby commitment research is characterised by concept

redundancy and concept contamination.


285

(2) Some researchers have defined commitment on a multi-dimensional

basis. This may provide problems with statistical analyses. Roodt’s (1997)

unidimensional view of organisational commitment is thus supported,

given its rationale whereby the distinction between different work-related

foci is only of theoretical importance.

6.4.2 Practical Recommendations

(1) Management has been provided with the necessary means to improve

perceived turnover intentions. In the light of the model created by the

study, methods of manipulating turnover intentions in this post-merger

institution have been provided. In the light of this study, it is

recommended that the employees’ needs be addressed primarily in terms

of their job satisfaction.

6.4.3 Methodological Recommendations

(1) Internet surveys are not without their flaws, primarily in terms of bias from

responses from only those who are most likely have the skills to use the

survey tool and those respondents who are comfortable with the electronic

nature of the survey. Although the study addressed most concerns dealing

with Internet survey problems, future recommendations would incorporate

a way of discovering how many of the intended respondents actually feel

comfortable with the online format and presentation. This would

substantially reduce bias in that problematic potential respondents could

be addressed accordingly.

(2) The instruments, although established, were required to be altered to

accommodate the current sample, thus, the scales used in this study

should be further refined to improve their reliability and validity.


286

(3) It is recommended that multivariate studies should continually be

conducted in future research, as this will provide a more holistic picture

than the bivariate models, which can be used on a more explorative basis.

Bivariate testing is pivotal in determining initial relationships. However,

thereafter the research must incorporate multivariate procedures.

(4) This study should be broadened across private and public tertiary

institutions that find themselves in a post-merger context in order to

improve its statistical validity.

6.5 Value-Add

The value of the study is presented in terms of the contribution made from a

theoretical, practical, and methodological perspective.

6.5.1 Theoretical Value-Add

(1) A comprehensive overview of the theoretical development of

organisational commitment, job satisfaction and turnover intentions was

provided.

(2) Relationships between organisational commitment, job satisfaction and

turnover intentions were addressed theoretically through the illustration of

previous research findings and further strengthened by the results of the

study.

(3) There was a comprehensive comparison between organisational-related

constructs (namely organisational commitment, job satisfaction and

turnover intentions) and background data.


287

(4) An understanding of the relationships within the context of the model

developed offered further insight into the ways in which organisational

commitment and job satisfaction influence turnover intentions, and

ultimately turnover, of employees within a post-merger tertiary

environment.

(5) Since much debate exists regarding the concept of organisational

commitment, this research has contributed by further strengthening of

Roodt’s (1997) argument concerning the unidimensional nature of

commitment.

(6) Job satisfaction, against current perceptions, was seen as more pivotal in

the prediction of turnover intentions than was organisational commitment.

This strengthens the argument of Tett and Meyer (1993) where withdrawal

entails a rejection of the job rather than of the organisation.

6.5.2 Practical Value-Add

(1) An integrative and predictive model of turnover intentions was developed

and can be regarded as an important tool for predicting employee turnover

intentions in a post-merger tertiary institution.

(2) Since this model focused primarily on internal components, possible

strategies could be derived from this model to prevent turnover intentions

from increasing. Such strategies include focusing on the primary predictor

of turnover intentions, namely job satisfaction. Poor, negative scoring

questions indicated that pay and growth potential are lacklustre. Pay

related questions have the tendency to score low in many a questionnaire

and addressing it normally constrains resources; however indicating the

prospect for future job growth, with an actual plan, spurs on the employee


288

and results in greater job satisfaction. Knowing that by working hard, a

goal (in the sense of growth) can be achieved will motivate the employee.

Lack of praise also scored low, and this is a simple managerial tool

whereby the direct supervisor or manager simply needs to recognise the

work done by those they preside over. The predictive model which results

can be regarded as an important tool for management and the Human

Resource Department in effectively planning talent retention strategies

focusing on the model’s controllable dimensions.

(3) The perceptions relating to job satisfaction played a significant role in the

turnover intentions of employees and should therefore be addressed

carefully.

(4) The research findings shed light on the general perceptions about job

satisfaction, organisational commitment and turnover intentions in South

Africa.

(5) Management was assisted by the provision of a snapshot profile of current

perceptions of: commitment to the organisation, satisfaction with the job,

and intention to turnover.

6.5.3 Methodological Value-Add

(1) In the model developed, all concerned variables were simultaneously

entered into a statistical equation in the Stepwise Linear Regression. This

approach went beyond a bivariate analysis by using a multivariate

statistical approach. This model takes a more holistic approach by

including a wider range of variables, but then yielding a more

parsimonious predictive model.


289

(2) The systematic approach use of Structural Equation Modelling, Two-Way

ANOVA and a Stepwise Linear Regression to address the research

objective made it possible to enter various kinds of variables

simultaneously into an equation to determine turnover intentions.

(3) All three instruments utilised were further improved upon given the sample

and research context at hand. This highlights the fact that questionnaire

development needs to be continually addressed given the social

environment in which the research is carried out. Furthermore, all

instruments yielded high construct validity in the study.

(4) Give the dearth of research on this subject there is little research on an

integrated, holistic multivariate model in the South African context. It is

thus important to appreciate the model’s components to understand

turnover intentions of post-merger tertiary employees in a South African

environment.

6.6 Limitations and Suggestions for Future Research

Table 6.1 presents the following limitations (accompanied by suggestions) which

need to be considered in this study.


290

TABLE 6.1LIMITATIONS OF STUDY AND SUGGESTIONS FOR FUTURE RESEARCH

Limitation Suggestion

Only one institution was utilised in the

study, thus limiting the generalisation to

other such institutions.

Other tertiary institutions need to be

incorporated in order to compare the

results and to generalise a predictive

model of the present study.

Due to the size of the present study,

other work-related variables such as

organisational citizen behaviour,

organisational culture and knowledge

sharing were not included as

independent variables.

By including these variables, a more

holistic picture would be possible and

would add some insight into

understanding the subjective

experience of turnover intentions.

Participants in the research study

completed both the questionnaires for

the independent and the dependent

variables, which could have enhanced

the obtained intercorrelations owing to

mono-method variance.

Although more complex, peer rating

would have garnered a greater

scientific element than the imposed self

rating system.

Since the study’s sampling was

described as convenient, only a once-

off perceptions gauge was acquired.

Longitudinal studies of this nature

would be of large value especially in

terms of turnover intentions whereby

the intentions may result in actual

behaviour.


291


Although a generally understood

limitation, the sampling method applied

in this study was unavoidable and

limited in scientific terms.

The approach was a census study and

all respondents had an equal choice of

participation and as such is still

regarded as scientific. However, a

more experimental approach to social

studies would throw new light on such

studies.

The Structural Equation Model yielded

poor Comparative Fit and Goodness-

of-fit Indices.

The primary objective of the study was

to determine which of the

predetermined models achieved the

best fit. And thus the objective was

addressed. However, improvement on

the indices indicates a examination of

the instruments used is required.

Alternatively a different sample may

yield a new range of fits.

The turnover model developed in this

study focused only on internal

dimensions.

External forces such as job

opportunities, which theoretically were

described as important in turnover

cognitions, need to be added.

This study targeted a sampling frame

of 367 respondents of whom 256

completed the questionnaires fully

completely (a rate of 70%). This finding

is consistent with flaws of Internet

surveying.

Determine how many of the intended

respondents actually feel comfortable

with the online format, presentation and

tools.


292


The theoretical model only

incorporated three work-related

dimensions.

More turnover models should be

developed, with different concepts

entered into the equation, or a

refinement of the current model.

Given the findings of the study, there is

still a large amount of unexplained

variance (53%).

More work-related variables, as well as

external variables, need to be included.

The turnover model was only in the

context of a tertiary environment.

Although not necessarily a limitation, it

is brought to light as the education

sector is vastly different from other

sectors.

The turnover model proposed in this

study could be empirically tested in

other sectors such as banking, mining

and health.

Altogether 2279 emails were sent out

to potential respondents to which there

was a response of 367, producing a

response rate of 16%.

Determine if all emails actually reach

their intended targets, rather than

placing implicit trust in databases.

Email databases are continually

changing and thus a more proactive

involvement would be required.

Bias was discovered in the study.

While this is rarely tested for in most

studies, it improves the validity of the

results of the study.

Bias analyses must be mandatory in all

studies where there is access to the

population data. Addressing bias itself;

a more proactive involvement is

required when receiving the results

enabling the researcher to address

those areas where bias has crept in.


293

6.7 Synthesis

Turnover intentions of tertiary employees can be actively managed through the

manipulation of the contextual variables of organisational commitment and job

satisfaction. The resulting predictive model can be regarded as an important tool

for management and the Human Resource Department in effectively planning

talent retention strategies focusing on the model’s controllable dimensions. Since

this model was developed based on internal components, possible strategies

could be derived from this model to prevent turnover intentions.

A final review of the research has indicated clearly that all the literature and

empirical objectives, as set out to be achieved in the beginning of the study are

met at the end of this research, thus resulting in the final integrated predictive

model for turnover intentions.

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ANNEXURE

324

ANNEXURE A: PERMISSION FOR STUDY

ANNEXURE

325

ANNEXURE B: INTRODUCTION

Dear Colleague

The merger between the former TWR and RAU, and also the incorporation of the East

Rand and Soweto campuses of Vista, have brought about many changes. Although

change is welcomed on the one hand, it could also be perceived as threatening on the

other. It is within this framework that I, a masters student in the Faculty of Management

and permanent employee of the UJ, am conducting a survey to explore various aspects

of employee perceptions within the merged institution.

I have been granted permission by management to conduct this survey. The aims are to

determine the extent to which employees at the merged institution, i.e. the University of

Johannesburg, are committed to their jobs, the extent to which they experience job

satisfaction, and their intentions to stay at the institution. Due to its sensitive nature, the

survey will be conducted anonymously and responses can therefore NOT be traced

back to any individual.

It is envisaged that the results of this survey will highlight possible problem areas within

the institution, and aid management in terms of its human resources endeavours. Your

participation in this survey is therefore of the utmost importance and you are kindly

requested to answer the following questionnaire which should take no longer than 30

minutes to complete.

Should you be interested in the outcome of this study, a summary report will be made

available on request. Do not hesitate to contact me or my supervisor should you have

any questions or comments with regard to this questionnaire or the nature of this

evaluation.

Regards

Adam Martin Professor Gert Roodt

Statistical Consultation Service Department of Human Resource Management

011 489 2703 011 489 2075

[email protected] [email protected]

ANNEXURE

326

ANNEXURE C: INSTRUCTIONS AND DEMOGRAPHIC QUESTIONNAIRE

UJ STAFF PERCEPTION SURVEY

Instructions:

This questionnaire contains a number of questions about the organisation in which you

work, i.e. the University of Johannesburg. Please read each question carefully and tick

the number corresponding to the response that most accurately represents your view.

There are no right or wrong answers to any opinion-related items (questions). You are

only requested to provide your frank and honest opinion.

The questionnaire contains four sections:

SECTION A: DEMOGRAPHIC DETAILS

SECTION B: JOB SATISFACTION

SECTION C: ORGANISATIONAL COMMITMENT

SECTION D: INTENTIONS TO STAY

Your time in completing this questionnaire is greatly appreciated.

ANNEXURE

327

SECTION A: DEMOGRAPHIC DETAILS

1. Please indicate your age group.

[in complete years]

Younger than 25 1

25 – 29 2

30 – 34 3

35 – 39 4

40 – 44 5

45 – 49 6

50 – 54 7

55 – 59 8

60 or Older 9

2. What is your gender?

Male 1 Female 2

3. What is your race?

African 1 White 2 Coloured 3 Indian 4 Asian 5

4. What is your highest academic qualification?

Less than Grade 12 1

Grade 12 / Matric 2


Bachelors degree 4

Honours degree 5

Masters degree 6

Doctorate 7

ANNEXURE

328

5. What do you consider your predominant home language?

[select only ONE language]

Afrikaans 1

English 2

isiZulu 3

isiXhosa 4

Swazi 5

isiNdebele 6

SeSotho 7

Sepedi 8

SeTswana 9

TshiVenda 10

xiTsonga 11

Other African 12

Other European 13

Other Asian 14

6. What is your marital status?

Not married (single) 1


Divorced or separated 3

Widowed 4

7. At which campus of the UJ do you predominantly work?

Auckland Park Bunting Road Campus 1

Auckland Park Kingsway Campus 2

Doornfontein Campus 3

Soweto Campus 4

East Rand Campus 5

ANNEXURE

329

8. How many complete years have you been working at the UJ (including theformer RAU, TWR or Vista, i.e. institutions prior to the merger)?

Less than one year 1

1 – 5 years 2

6 – 10 years 3

11 – 15 years 4

16 – 20 years 5

21 – 25 years 6

26 – 30 years 7

More than 30 years 8

9. What is your current job status?

Permanent 1

Contract 2

Temporary 3

Other (please specify) _____________________ 4

10. Under what conditions of service are you employed at UJ?

Academic / Research staff 1

Administrative staff 2

Support staff 3

Other (please specify) _____________________ 4

ANNEXURE

330

ANNEXURE D: JOB SATISFACTION QUESTIONNAIRE

SECTION B: JOB SATISFACTION

The following section relates to your feelings towards your work-related needs (job

satisfaction).

Please read each question and indicate your response using the scale provided in each

case:

1.How busy are you kept in your present

job?Not busy at all 1--2--3--4--5 Extremely busy

2.To what extent do you have the chance to

work on your own in your present job?To no extent 1--2--3--4--5

To a very largeextent

3.How satisfied are you with the task variety

in your present job?Not satisfied 1--2--3--4--5 Highly satisfied

4.To what extent do you feel that you are

valued in your present job?To no extent 1--2--3--4--5


5.How satisfied are you with your immediate

supervisor (superior) in your present job?Not satisfied 1--2--3--4--5 Highly satisfied

6.

How satisfied are you with your immediate

supervisor’s (superior’s) ability to make

effective decisions?

Not satisfied 1--2--3--4--5 Highly satisfied

7.How satisfied are you that you do not do

things that go against your conscience?Not satisfied 1--2--3--4--5 Highly satisfied

8.To what extent does your present job

provide steady employment?To no extent 1--2--3--4--5


9.

To what extent do you have the chance to

do things for other people in your present

job?

To no extent 1--2--3--4--5To a very large

extent

10.

How often do you have the opportunity in

your present job to be in a position of

authority and instruct other people what to

do?

Never 1--2--3--4--5 Always

11.To what extent does the current work you

do reflect your abilities?To no extent 1--2--3--4--5


12.To what extent are the organisation’s

policies put into practice?To no extent 1--2--3--4--5


ANNEXURE

331

13.

How satisfied are you that the pay you

receive reflects the amount of effort you

put into your job?

Not satisfied 1--2--3--4--5 Highly satisfied

14.To what extent are there opportunities for

advancement in your present job?To no extent 1--2--3--4--5


15.How much freedom is there in your

present job to use your own judgement?No freedom 1--2--3--4--5 Total freedom

16.

To what extent are you allowed to

experiment with your own methods of

doing the job?


extent

17.How satisfied are you with your work

conditions?Not satisfied 1--2--3--4--5 Highly satisfied

18.How well do co-workers get along with

each other in your present job?Not well at all 1--2--3--4--5 Extremely well

19.How often do you get praise for doing a

good job?Never 1--2--3--4--5 Always

20.How often do you experience a feeling of

accomplishment from your job?Never 1--2--3--4--5 Always

ANNEXURE

332

ANNEXURE E: ORGANISATIONAL COMMITMENT QUESTIONNAIRE

SECTION C: ORGANISATIONAL COMMITMENT

The following questions relate to your commitment towards the UJ, henceforth referred

to as the organisation.

In this section certain words are used. For purposes of clarity a short definition of each is

provided.

Career: a pattern of work-related experiences that transcends a person's life cycle

Job: the position you currently hold

Occupation: the vocation you are practising due to specialised training

Organisation: a group of people identified by a shared interest or purpose, i.e. the

university

Work: your activities at the UJ in general

Please read each question and indicate your response using the scale provided in each

case:

1.To what extent should everyone have a

feeling of pride in work?To no extent 1--2--3--4--5


2.

To what extent do you consider your

work to be a means to other important

ends?


extent

3.How much time and energy do you

willingly devote to work?None at all 1--2--3--4--5 All of it

4.Right now, how important an aspect of

your life is your job?

Not important atall

1--2--3--4--5Of critical

importance


willingly devote to your job?None at all 1--2--3--4--5 All of it

6. How much do you give in your job?I don’t giveanything

1--2--3--4--5I give

everything

7.How involved are you in your

occupation?Not involved at all 1--2--3--4--5

Extremelyinvolved

8.To what extent does your occupation

have special value to you?To no extent 1--2--3--4--5


ANNEXURE

333

9.How much do you give to your

occupation?Nothing at all 1--2--3--4--5

Everything Ihave

10.How much time do you willingly devote

to your career?None at all 1--2--3--4--5 All of my time

11.How much do you give of yourself in

your career?

I don’t giveanything

1--2--3--4--5I give

everything

12.To what extent does your career have

special personal value to you?To no extent 1--2--3--4--5


13.To what extent do you see yourself as

part of this organisation?To no extent 1--2--3--4--5


14.How involved are you personally in this

organisation?Not involved at all 1--2--3--4--5

Extremelyinvolved

15.To what degree is who you are related

to your involvement to this organisation?To no degree 1--2--3--4--5

To a very largedegree

16.

If you could choose again, to what

extent would you consider working for

this organisation?


extent

17.How many of your interests are outside

of this organisation?No interests 1--2--3--4--5 All interests


willingly devote to this organisation?None at all 1--2--3--4--5 All of it

ANNEXURE

334

ANNEXURE F: INTENTIONS TO STAY QUESTIONNAIRE

SECTION D: INTENTIONS TO STAY

The following section aims to ascertain the extent to which you intend to stay at the

organisation.

Please read each question and indicate your response using the scale provided for each

question:

DURING THE PAST 9 MONTHS…

1.How often have you considered leaving

your job?Never 1--2--3--4--5 Always

2.How frequently do you scan newspapers

in search of alternative job opportunities?Never 1--2--3--4--5 All the time

3.To what extent is your current job

satisfying your personal needs?To no extent 1--2--3--4--5


4.

How often are you frustrated when not

given the opportunity at work to achieve

your personal work-related goals?


5.How often are your personal values at

work compromised?Never 1--2--3--4--5 Always

6.

How often do you dream about getting

another job that will better suit your

personal needs?


7.

How likely are you to accept another job

at the same compensation level should it

be offered to you?

Highly unlikely 1--2--3--4--5 Highly likely

8.How often do you look forward to another

day at work?Always 1--2--3--4--5 Never

9.How often do you think about starting your

own business?Never 1--2--3--4--5 Always

10.To what extent do other responsibilities

prevent you from quitting your job?To no extent 1--2--3--4--5


ANNEXURE

335

11.

To what extent do the benefits associated

with your current job prevent you from

quitting your job?


extent

12.How frequently are you emotionally

agitated when arriving home after work?Never 1--2--3--4--5 All of the time

13.

To what extent does your current job have

a negative effect on your personal well-

being?


extent

14.To what extent does the “fear of the

unknown”, prevent you from quitting?To no extent 1--2--3--4--5


15.How frequently do you scan the internet in

search of alternative job opportunities?Never 1--2--3--4--5 All of the time

Thank you for taking the time to complete this survey.

Documents

EMPLOYEE PERCEPTIONS OF ORGANISATIONAL COMMITMENT…