Professor : Zhao FUQIANGRealized by: Nabil EYGUE Nationality: MOROCCONumber: A23Developement and management of human resources Mba class A

QUALITY HR-TQM MODEL IN SERVICE CONTEXT

Abstract

HRM issues in TQM implementation have been studied from several perspectives. The related areas include TQM pitfalls caused by people barriers, employee commitment, employee involvement, and people management. Derived from the relevant literature, this research is to discover a quality working climate that will explicate the conditions for an overall quality HR-TQM concept. This is the environment that this research assumes will enhance quality HRM activities to ensure performance excellence, which is also called the mediating factor. The testing and evaluating of the research framework are supported by data collected from questionnaire survey and case study interviews of Malaysian universities. Meanwhile, the self-assessment performance criteria from Malcolm Baldrige National Quality Awards were used to evaluate organizational performance. Our results largely support that there is a significant relationship between quality working climate and organisational performance.

Keywords: Quality working climate; Employee involvement; Employee commitment; Quality Human Resource Management; Organizational performance.

INTRODUCTION This paper emphasises on proving the fact that TQM has specific relationships with HRM in continuous improvement systems in either a manufacturing or a service environment. HRM is commonly practised as a part of the overall organisational quality planning, and implicitly intended for internal customer satisfaction, across organisational team working, training and career development, and quality of working life Furthermore, the coherent purpose of performance management in TQM and HRM is for systems enhancement, sustaining continuous organisational improvement, and also as one part of the recognition and reward process. Therefore, the exercise is not only for performance improvement in the quality system, but also to enhance the performance of the people.This study uses a quantitative approach founded on concrete empirical analysis to achieve its objectives, focusing on Malaysian universities involved in ISO 9000:2000 accreditation initiatives. Various appropriate statistical tests were conducted on 281 usable questionnaires to investigate the reliability and validity of the measurements of the variable construct. Using SPSS, a normality test of data distribution was conducted prior to further analysis, and the item-to-total Cronbachs alpha coefficients are acceptably high for every variable. The factor loading within items explaining each variable was validated using exploratory factor analysis. The proposition suggests that the soft or HR aspects stand as the fundamental issue of concern for organisational management in quality planning and creating a quality working climate to ensure successful expected performance.

HUMAN RESOURCE MANAGEMENT (HRM) ISSUES IN TQM IMPLEMENTATION

Wilkinson et al. (1992) claim that the hard and soft aspects in TQM programmes are interdependent elements. Similarly, this is supported by Rees (1995) who identified valid linkages between them. The hard aspect is referred to as generally quantifiable quality tools and techniques, such as total quality control, just-in-time production, six-sigma and zero defect performance measurement, and task-based team working. Meanwhile, the soft or more qualitative aspect of a TQM programme consists of the use of HRM policies and activities to generate employee commitment to quality, and the dissemination of management vision and ideology that may reinforce the maxims of quality working culture, attitudinal change, continuous improvement, and customer orientation.As the origin of quality management lies within the operation and production fields, manufacturing firms may tend to place emphasis on the hard and quantifiable measurement aspects. In contrast, service-oriented organizations, which have a greater degree of employee-customer interaction, should concentrate on the more qualitative and softer aspects of working culture, customer care and personal interactions. However, the managerial approach towards various aspects of soft issues in quality management must continue to search for more quantifiable measurement of performance outcomes. On the other hand, while modern management allows greater employee discretion, autonomy and empowerment as the benefits of a TQM programme, closer monitoring and tighter management control are still needed to go hand-in-hand. Miller and Cardy (2000) suggest that HRM need to respond in a creative way to TQM and reengineering in organisational changes. The research constructs used are staffing, training, performance appraisal, mentoring compensation, and social support. Furthermore, Dale and Cooper (1993) highlight the importance of concern for people issues in TQM. The HR activities that they relate to quality improvement processes are the role of the senior managers, motivating middle managers, training and education, team building, employee involvement at work, and handling people resistance in quality change management. The above evidence indicates significant relationships between HRM elements and TQM implementation. The following section elaborates on literature on HR issues, including people resistance, managerial roles, management commitment, and the behavioral and cultural issues in relation to TQM implementation.QUALITY HRM CLIMATE In this study, variable items that would form a research construct of quality working climate involving the employee involvement and commitment elements. Therefore, the scope of discussion will cover literature review related to employee involvement, employee commitment, and other perspectives of quality HRM in TQM implementation. Most of the literature selected is on the service organisation context, with a sample of some manufacturing and mixed (service and manufacturing) organisations also included. Employee Commitment in Quality InitiativesEmployee commitment to organizational initiatives is always important, as they are the people who are going to perform the quality mission in daily operations. They are also the front-liners who are directly interacting with customers. Customers evaluation and perception to determine whether they are satisfied with the service provided or not would be the main indicators of quality and excellent performance. Therefore, this section aims to present evidence from previous study on the significant impact of employee commitment to quality working environment. Most of the researches done in the selected literature are in a service-oriented context, including public and private organizations. The review is intended to identify the variables research constructs used in investigating employee commitment with respect to quality initiatives.The Hogan Personality Inventory (HPI) was used by Cran (1994) to assess the validity of employee longer-term attitudinal commitment and work performance in service-orientation organizations involved in a TQM initiative. Training effectiveness on personality change could lead to attitudinal change on work commitment to fulfill their job requirements. The personality attribute, which was used to study the relationships between personality and employee commitment, seems to be narrowly and intrinsically motivated. In contrast, some external factors, such as management support, leadership role and recognition, were discovered to have significant influence on the results. Meanwhile, Taylor (1995) defines commitment as more than accepting responsibility for an activity, or even being engaged in pursuit of a quality objective. It involved both attitudes and behaviours of all organisational members. Taylors research adopted the Mowday et al. (1982) constructs on attitudinal commitment, which include:1. Strong belief in and internalisation of the organisations goals and values.2. Preparedness to expend considerable effort on behalf of the organisation.3. Strong desire to maintain organisational membership.Meanwhile, Boshoff and Tait (1996) used organisational commitment as an intervening variable in a study on service quality. An empirical test was conducted on SERVQUAL model involving frontline employees own perception of service quality, and the service quality their supervisor believe they should deliver (Parasuraman et al., 1988). The hypothesised antecedent variables used under Gap 1 to affect organisation commitment are role conflict, role ambiguity, goal setting, upward communication, downward communication, and performance feedback. The hypothesised antecedent factors that will affect organisational commitment under Gap 3 are the initiating structure, job satisfaction, and teamwork. Figure 1: Hypothesised model of Boshoff and Tait

Role conflict Gap 1s hypothesised antecedents

Goal setting

Upward communication

Service qualityOrganizational commitment Gap 3s hypothesised antecedents

Job satisfactionInitiation of structureRole ambiguityDownward communicationPerformance feedback

Teamwork

Source: Boshoff and Tait (1996)Commitment in quality management programmes must include a recalibration of organization-wide thinking for high quality work methods combined with excellent results. Meanwhile, commitment to quality assurance is oriented towards the assurance of product and service quality based on quality techniques and guidelines for operational management to enhance internal processes. Quality management demands all organization members active commitment and participation in quality activities, and top management play a crucial role to attract their interest and motivate them to achieve a shared vision. Employees total commitment in quality management can only be achieved through the foundation of a quality culture of continuous improvement, cooperation and teamworking, and mutual respect between top management and other organizational members. Therefore, the literature finding suggests that employee commitment on quality performance would enhance organisational performance excellence. The above literature demonstrates that the frequent methods used to measure employee commitment are those introduced by Cook and Wall (1980) and Modway et al. (1982), Some of the common variables used to validate the employee commitment research construct are:1. Intrinsic and extrinsic motivation2. Employee quality attitude and perception or quality mindset. 3. Shared organisational quality goals values or mutual respect. 4. Performance feedback and recognition,5. Clear and acceptable quality purpose (communication is the main tool recommended)6. Participation and teamworking.These findings suggest that employee commitment cannot be evaluated merely from a narrow dimension, but it also requires external perspectives such as management support, quality systems and procedures, and team cooperation. The analysis highlights the linkages between HR-related CSF and employee commitment as the right condition to ensure successful TQM programmes. Clear quality vision, recognition and motivation, quality attitudes, congruent goals between management and employee, and effective communication were the familiar HR-related CSFs included and validated to describe employee commitment in the studies discussed earlier. Top management commitment to quality, employee deployment and staffing, training and education, effective communication to develop quality attitude and commitment, and people management to promote quality of working life or motivation were viewed and validated as the fundamental success factors in quality initiatives, either in service or in other organisational contexts. The significance of the findings provides the evidence that HR-related CSFs are very closely related to the construction of research in employee involvement and employee commitment in relation to TQM implementation. On the other hand, the employee involvement and commitment construct was also directly related to performance measurement (Coyle-Shapiro, 1999; Wong and Sohal, 2002). Relationship between Employee Involvement and Commitment

Most factors included in the HR aspect of this research have significant relationships that cause employees to be involved and/or committed in quality activities. Leadership roles have a significant part in creating a quality working environment and encouraging people involvement and commitment. Employees with customer-focus orientation certainly have the understanding that their involvement and also commitment to ensure customer satisfaction are important in quality management. Furthermore, the congruent objectives, and recognition and motivation aspects would make the employees willing to participate with full commitment, and also understand that their involvement is important to ensure successful quality initiatives for the benefit of the employee and also the organisation. As another example of quality initiative for the HE context, usually the staff member who has the skills and competence to perform a quality job, which directly stipulates his/her involvement, will be committed to prove that the team is able to achieve successful quality implementation. Therefore, in relation to HR-related CSFs in quality HRM, it is suggested that when people are involved in the quality initiatives, they would be committed as well to ensure the quality objectives are achieved. This condition creates a quality working climate that would lead to successful TQM implementation. It provides sound justification that quality working climate is the mediating factor for the research framework of this study. Moreover, this study intends to investigate the nature of relationship between employee involvement and commitment in the HE context. The relationships are illustrated in Figure 2, and the hypothesis derived from it is shown in Table 1. Figure 2: Employee Involvement and Commitment Relationships in Quality Initiatives

Quality Working Climate

Employee Involvement

Successful Quality Initiatives in HE

Employee Commitment

Table 1: Hypothesis on Relationship between Employee Involvement and Commitment

Research HypothesisEmployee involvement has a significantly close relationship to employee commitment in quality initiatives

Research HypothesisQuality HR working climate (combination of employee involvement and employee commitment elements) leads to successful quality initiatives in the HE institutions

Indicators for Organizational Performance Assessment The element of excellent performance is used to explain the successful quality initiatives in this research. The excellent performance indicators were identified based on the performance measurement criteria used in the MBNQA 1994 Excellence Award and 2003 Education Criteria model. The assessment criteria used are:1. Customer satisfaction results (or student learning results)2. Employee satisfaction results (or faculty and staff satisfaction results)3. Stakeholder-focused results4. Financial performance results5. Organizational performance results.This research will validate the elements of performance measurement in the HR-focused research in the context of quality initiatives in HE institutions. Arif and Smiley (2004) validated the MBNQA assessment to establish a continuous academic process improvement model for the University of Wisconsin, as the first HE institution to receive the excellent performance award in 2001. In the HRM perspective, Soltani et al. (2004) suggest that the MBNQA assessment criteria can be considered to maximise the effectiveness of HR performance evaluation in the TQM context. The comprehensive areas of measurement have made the MBNQA performance model relevant to various organisational contexts and sectors. However, adjustments and flexibility are necessary to adapt the model to different conditions of legislation, management policy, and socio-cultural requirements.Impact of HR-Related CSFs and Quality HR Climate on Quality Initiatives The overall scope of this research is focusing on providing empirical evidence that quality HRM practices would encourage quality working climate, and further on, lead to successful quality initiatives in HE institutions. The ten HR-related CSFs in quality initiatives are the factors identified, soundly based on the theoretical concepts and literature review, to be the important factors used in quality HRM practices and approaches relevant to the HE context. These factors are believed to create a quality HR climate and congenial working environment to expedite excellent performance. Moreover, the employees involvement and commitment are the appearance of a quality HR working climate where their quality and efforts would result in excellent outcomes. The expected result of those quality HRM practices and working climate is the successful quality initiatives, and in achieving organizational performance excellence.

Questionnaire Design for Quality Working ClimateEmployee commitment is another factor identified to be included as a part of the quality working climate element. This research has decided to adopt mainly the organisational commitment questionnaire (OCQ) introduced by Modway et al. (1982). Beside that, at least two items used are based on the attitudinal and behavioural approaches of commitment by Meyer and Allen (1997). They are the attitude towards customer complaints, and the effect of individual performance on team performance. The ten variable items included in the measurement of employee commitment variable. Questionnaire Design for Organizational PerformanceOrganisational performance is the dependent variable in this research framework. Five performance indicators were used, and they are adopted from the MBNQA 2003 Education Criteria for Excellence Performance award. Four of the seven self-assessment categories were included in this study. The Students, stakeholders, and market focus category was modified into two different indicators as Student learning results and Stakeholder-focused results. The other performance indicator variables used are the Faculty and staff satisfaction results, Financial performance results, and Organisational performance results. Details of items for measurement of the variable construct are shown in Table 3.

Table 3: Questionnaire items on performance indicators factorPerformance indicatorsItems of measurement

Student learning resultsStudent expectations

Student satisfaction

Student learning

Faculty and staff satisfaction resultsHarmony environment

Staff requirements fulfilled

Staff development opportunity

Organisational citizenship attitude

Stakeholder-focused resultsProcess designed to government expectations

Employer job requirements

Pride to local community

Minimise unemployment problem

Financial performance resultsFinancial support for teaching and research

Efficient budget plan

Able to generate income

Organisational performance resultsInitiative increases productivity

Improved students choice

Quality targets are achieved

It is very important to address the issues of language and measurement in questionnaire design to ensure that it will be useful to achieve the research objectives. Besides that, an attractive and neat questionnaire, with appropriate introduction, instructions, and a well-arrayed set of questions and answers alternatives will help the respondents to complete them (Sekaran, 2003). Introduction to the research, which is usually in the form of a cover letter, is essential to establish some rapport with the respondents, and motivate them to respond to the questionnaire, which will improve the response rate. Questionnaire survey was the main quantitative method approach, with statistical data analysis using SPSS, which includes descriptive statistical analysis, reliability analysis, correlation analysis, hypothesis testing, factor analysis, and structural equation modelling. Meanwhile non-scheduled structured interviewing technique was applied in the qualitative data collection, and 7 universities in Malaysia participated as respondents in the case study approach.Reliability Analysis for Quality Working ClimateFurther analysis was made of the mediating variable which is the quality working climate element. Two main factors included in this element are the employees involvement and employees commitment variables. Nine items were used in the questionnaire to measure the opinion on employees involvement, and ten items for the employees commitment variable. This indicates that all items included in this research are reliable to be considered for further analysis. Furthermore, on the overall reliability analysis, both employees involvement and employees commitment variables show high alpha coefficients of reliability, which are 0.8616, and 0.8871, respectively. These also indicate the variables used have a strong internal consistency, and would be reliable to be tested for further analysis and extended survey in any organisation. This is supported by the high overall alpha coefficient of all 19 items combined at 0.8967.Table 4: Reliability Analysis for Quality Working Climate

Variable/ items codingItems of measurementItem-to-total correlationAlpha if item deletedStandardisedCronbachs Alpha

EeInvEmployee involvement0.8616

B2aImprovement suggestion.6432.8407

B2bRoles in the initiative.6353.8418

B2cStaff own objectives.6072.8443

B2dAllowed to design product.5359.8515

B2eManagement response to suggestion.6480.8402

B2fStaff capable to solve problems.4972.8544

B2gMember of quality team.4993.8552

B2hQuality teamworking environment.6461.8406

B2iStaff suggest rewards and recognition.5893.8462

EeCmmtEmployee commitment0.8871

C1a Give effort to quality initiative.5883.8765

C1bEnjoy the working environment.6045.8767

C1cLoyal to department.6760.8701

C1dAccept any task in quality initiative.6518.8719

C1eSimilar own and departments values.6414.8730

C1fQuality initiative is important.6936.8689

C1gFeel accountable to complaint.7056.8682

C1hFeel responsible any failure.5900.8761

C1iDealt seriously with complaint.4884.8829

C1jOwn performance affects department.5706.8776

Analysis on all 19 items0.8967

Source: Analysis of survey data

Exploratory Factor Analysis Quality Working ClimateIt is useful to remind that main purpose of EFA process is achieved by looking for variables that correlate highly with a group of other variables, but correlate badly with variables thought to be outside that group (Field, 2000). Moreover, EFA requires particular conditions before the process can be successfully employed. One of the main requirements is to measure the variables using the data collected in interval scales. The quantitative survey of this research basically used a 5-point Likert scale questionnaire, which fulfils the requirement. Therefore, EFA is the way to provide clearer justification for any items included to form the measurement construct for the mediating variable. The other condition is that the sample size is also fulfilled, as the survey managed to collect 281 usable questionnaires (Hair et al., 1998). The results for the tests for these conditions (interval scale and sample size) are as below:1. Bartletts Test of SphericityA test was run on the 19 items of the mediating variables, and the results are shown in Table 5. The result for the Bartletts Test of Sphericity (BTS) was large at 2418.488, and associated with a very small value of significance (p = 0.000). Hence, this proves that the data were appropriate for EFA (Field, 2000).Table 5: Factor Analysis Output KMO and Bartletts Test on Mediating Variable

Kaiser-Meyer-Olkin Measure of Sampling Adequacy..884

Bartlett's Test of SphericityApprox. Chi-Square2418.488

df171

Sig..000

Source: Analysis of survey data2. Kaiser-Meyer-Olkin Measure of Sample Adequacy The Kaiser-Meyer-Olkin (KMO) is an indicator (between 0 and 1) for measurement of sample adequacy. A value close to 1 indicates that patterns of correlations among items are relatively compact, and so factor analysis should produce distinct and reliable factor loadings (Field, 2000). The value of KMO should be greater that 0.5 to indicate that the sample is adequate for EFA. The KMO result of the analysis in this study is 0.884 (see Table 5), and hence adequate and appropriate for EFA to yield reliable outcomes.Results of Principal Component Analysis Extraction The principal component analysis (PCA) is concerned with establishing which linear components exist within data, and how a particular variable might contribute to a component (Field, 2000). This means that PCA merely decomposes the original data into a set of linear variates. The factor extraction results of the employee involvement and employee commitment variables using PCA are shown in Table 6. An eigenvalue of 1.0 is used as the benchmark (by default in SPSS) in deciding the number of factors explained by all items in the analysis (Hair et al., 1998; Field, 2000). The initial or un-rotated solution identified 19 items, and two-component factors with eigenvalues of more that 1, accounting for 48.82% of the variance. Table 6: Principal Component Analysis Extraction ResultsTotal Variance Explained ComponentInitial EigenvaluesExtraction Sums of Squared Loadings

Total% of VarianceCumulative %Total% of VarianceCumulative %

Rescaled 14.01936.49036.4906.66335.07135.071

21.43112.99649.4862.61213.74748.818

3.7386.70156.1861.1105.84554.663

4.6245.66861.8541.1005.78860.451

5.5815.27267.126.8424.43164.882

Extraction Method: Principal Component Analysis.Source: Analysis of survey dataMeanwhile, the entire 19 items show acceptable communalities values retrieved from the PCA analysis, ranging between .364 and .836. Therefore, we can conclude that a degree of confidence in the factor solution in the data provided has been achieved (Hair et al., 1998).A loading of all items in the employee involvement and employee commitment variables was examined using factor analysis within the two factors extracted from the previous analysis. The Varimax technique (in SPSS) for rotated component analysis was used, with a cut-off point for interpretation of the factors at 0.35 or above (Hair et al., 1998; Field, 2000). The output for rotated factor loadings of the HR-related CSFs is summarised in Table 7.

Table 7: Rotated Component Matrix of Quality Working Climate Rescaled

Component

12

CMTloyal.763

CMTvalue.746

CMTacct.735

CMTimpt.724

CMTaccept.716

CMTw/place.674

CMTwilling.654

CMTrespons.640

CMTperf.597

CMTattd.531

INVt/work.722

INVrespond.720

INVsuggest.713

INVrewards.696

INVobj.679

INVroles.676

INVdesign.661

INVt/member.600

INVcapable.522

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a Rotation converged in 3 iterations.

Source: Analysis of survey data

The interpretation of the output is based on the factor loadings score of each item or variable. Factor loadings provided are the correlations of each item and the factor, which indicate the degree of correspondence between items and the factor (Hair et al., 1998). The next step of EFA is to redefine every factor loading, and review each item that yields low loading for further interpretation, and consider the possibility of renaming variables. According to the factor loading provided by the analysis in 3 iterations on the ten variables of HR-related CSFs which consist of 19 items, 2 variables fall nicely into the common components or factors, and therefore, do not need to reviewed and/or renamed, The results have proved to hold construct validity of the mediating variables, and have supported the theoretical concepts presented earlier. To satisfy the analysis criteria, the multivariate normality of the data was investigated by conducting the tests of normality, particularly skewness and kurtosis (Hair et al., 1998). The normality tests indicated no departure from normality. Therefore, the data fulfill the condition to proceed with analysis using the MLE method to estimate the model. Goodness-of-Fit of Structural ModelThe model (Figure 3) was run as a structural model, and the measure of goodness-of-fit of the data was achieved. A summary of the various goodness-of-fit statistics provided by the AMOS 5.0 output is shown in Table 8. Goodness-of-fit is a measure of the correspondence of the actual or observed input (covariance and correlation) matrix with that predicted from the proposed model (Hair et al., 1998). There is huge literature on testing the goodness-of-fit of the SEM solution. However, it seems that there is no consensus on which is the best approach. Hair et al. (1998) probably provide the most comprehensive discussion of the many issues involved. The chi-square value is very small, and that indicates a significant difference in the predicted and observed input matrices. This might be caused by the large sample size that has a significant effect of the model relationship. Thus the chi-square statistic (2) is quite sensitive in either way to both small and large sample sizes, and this index needs to be complemented with other measures of fit. Hair et al. (1998) claim that the 2 use is appropriate for sample sizes between 100 and 200, with the significance test becoming less reliable with sample sizes outside this range. As this study uses 281 samples, the significance results of the 2 statistic would be supported with other measures of fit.Table 8: Goodness-of-Fit Indices for Path Model2 statisticComparative Fit IndexCFINormed Fit IndexNFIRoot mean square error RMSEA

2 valuedfp- value

114.2330.001.001.00.0511

Hair et al. (1998) suggest that the Comparative Fit Index (CFI), one of the incremental fit measures, and Normed Fit Index (NFI), one of the parsimonious fit measures, should be the indices of choice when assessing the goodness-of-fit of a structural model, with an index greater than 0.90, is the proposed benchmark. Therefore, the measures of CFI at 1.00, and NFI at 1.00, were deemed to be acceptable. Meanwhile, the root mean square error of approximation (RMSEA) is another measure that attempts to correct the tendency of the 2 statistic to reject any specific model with a sufficiently large sample. The acceptable value of RMSEA is between .05 and .08 (Hair et al., 1998). Therefore, on the basis of the results obtained, we would probably assume that the model is goodness of fit.Testing Hypothesized Causal RelationshipsFigure 3 shows the path diagram with the parameter coefficients for the research model. It provides the estimated standardized estimates for the causal paths, and their level of significance. The latent variable e serves as the error term to absorb random variation in the predicted variable, and the systematic components for which no suitable predictors were provided. Figure 3: Results of Causal Path Analysis

.26**.62**HR-related CSFs.66**.32**Employee involvementExcellent performancee1 .25**Employee commitment.47**

The diagram shows that all observed inputs of causal path have significant relationships. Table 9 summarizes the direct and indirect correlation relationships with parameter estimates between variables, and the results of the hypothesis testing of significance at the 99% confidence level involving employee involvement and commitment as the mediating factor.

Table 9: Research Hypotheses Quality Working ClimateHypothesised relationshipsStandardised estimatesStandard error

HR-related CSFs Employee involvement.622**.018

HR-related CSFs Employee commitment.658**.016

Employee involvement Employee commitment.468**.018

HR-related CSFs Excellent performance.260**.071

Employee involvement Excellent performance.322**.049

Employee commitment Excellent performance.249**.09

** Indicates significant at 0.001CONCLUSIONThe statistical analysis using has successfully tested the research hypotheses developed to meet the objectives of quantitative analysis of this study. The first category of hypothesis testing was to validate the items used to represent the indicators for performance measurement as the dependent variable. The 10-iteration factor loading of EFA has given proof that the items were grouped satisfactorily into 5 factors, and this has complied with the performance measurement classifications recommended by the MBNQA 2003 Education Criteria (NIST, 2003). Meanwhile, the bivariate correlation analysis at the 99% confidence level was done, and all correlations indicate significant relationship between all variables. These results have proved that they were the significant indicators to measure organisational performance of HE institutions. Moreover, a multiple correlation test was done to evaluate the overall relationships of all HR-related CSFs variables as the predictor of excellent performance. Meanwhile, the path model analysis, or SEM, using AMOS version 5 has validated the interrelationships between the employee involvement and employee commitment variables which were identified as the mediating factor, and named quality working climate. The estimated coefficients of each parameter indicated strong relationship at the 95% confidence level between the two variables. The ANOVA to test the variation between means also provided a significant result to validate the quality working climate as the mediating variable. Finally, the SEM results also show significant relationships between the three HR-related CSFs, quality working climate, and organizational performance.

This evidence is on the same lines as the findings by Thiagarajan et al. (2001) and Wilkinson and Dale (2001) on the importance of integrating the soft and hard aspects in TQM, and McCabe and Knight (2000) on the role of quality people management to enhance performance in TQM. The relationships between HR-related CSFs and quality working climate were proved to be potent to signify the quality HRM conditions, and it caused significant impact on organizational performance.

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