Factors influencing sustainable performance in the

Factors influencing sustainable

performance in the construction industry in

South Africa

S Ngcengeni

orcid.org 0000-0003-3785-9081

Mini-dissertation accepted in partial fulfilment of the requirements

for the degree Master of Business Administration at the North-West

University

Supervisor: Dr JA Jordaan Graduation: May 2020

Student number: 24661147

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PREFACE AND ACKNOWLEDGEMENTS

It was one of the most critical educational obstacles I had to face to compose this study. This

research would not have been done without the support, dedication and encouragement of the

following people. I give my most heartfelt gratitude to you.

My supervisor, Dr Johan Jordaan, for the endurance while guiding and coaching me

through the study. From the bottom of my heart, I thank you Doctor.

My mum, Intombi ka-Mamsithwa, I hope this is your proud moment for inspiration and

hope. Although you never went to school, you always reminded me of how important

education is.

My wife, Meiki Ngcengeni, for your commitment and willingness to forgo my family time in

order to complete my studies. I know that successful completion of this MBA is a

milestone and a great achievemnt to you too.

My kids, Elam and Inam, for since they were born in 2018 and 2019 respectively, I have

been absent in their lives due to this MBA programme.

My friends, syndicate group, and many more to promote and facilitate robust discussions,

influenced by thoughts from the start of the study to completion.

I also have a manager, Danie Moller, who is always willing to lend a hand, offer

suggestions and motivation. Thank you for your understanding and support through my

journey on this MBA programme. When I think of you I always remember your words

“family first, studies second, and work follows”. But I always tried to ensure that studies

and work are forever balanced.

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ABSTRACT

The construction industry makes a major contribution to the overall economy's sustainable

development by achieving its basic development goals, including job creation, regeneration,

production and income generation. However, the industry has recently been lagging behind with

its performance, and not meeting the business objectives expected by its key stakeholders and

customers. In order to change the situation, it is important for a healthier and more efficient

construction industry to improve people's lives. Hence, this study sought to identify the main

factors that influence sustainable performance in the South African construction industry.

The data were collected through a structured questionnaire distributed randomly to 250 officers

of the CIDB and Electricity Supply Commission (Eskom) via an online survey. 133

questionnaires were returned, giving a response rate of 53%. From 133 responses, 116

questionnaires were fully completed and 17 were incomplete. From the incomplete survey, only

six could be used and this brought the total to 122 (49%) usable questionnaires to be used for

data analysis.

Data were analysed using the Social Sciences Statistical Package (SPSS), which included the

following analysis: descriptive analysis; correlation analysis; RII; Cronbach analysis. The key

factors affecting the sustainable performance of the South African construction industry were

identified as "quality of service and labour, security and internal customer (customer)

satisfaction, market effectiveness and competitiveness and profitability.” From the top five

factors, the first four are non-financial measures concerning customer service, work quality and

overall performance delivered by the business to their customers. The results of this study are

important for understanding the knowledge management critical success factors in the

construction industry. This knowledge may help improve industry performance and decision-

making.

KEY TERMS: Building construction, Competitive Advantages, Construction Industry, Economy

Performance, Global Growth, Knowledge Management, Sustainable Performance

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TABLE OF CONTENTS

PREFACE AND ACKNOWLEDGEMENTS .............................................................................. I

ABSTRACT ........................................................................................................................... II

CHAPTER 1: NATURE AND SCOPE OF THE STUDY ........................................................... 1

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

1.2 The construction industry in South Africa .................................................... 1

1.3 Problem statement ......................................................................................... 2

1.3.1 Background and setting of the problem ............................................................. 3

1.3.2 The motivation of the study ............................................................................... 4

1.4 The expected contribution of the study......................................................... 5

1.5 Research objective ......................................................................................... 5

1.5.1 Main objective .................................................................................................. 6

1.5.2 Specific objectives ............................................................................................ 6

1.6 Research questions ....................................................................................... 6

1.7 Research outline ............................................................................................ 7

1.7.1 Literature review............................................................................................... 7

1.7.2 Empirical study ................................................................................................. 8

1.7.2.1 Research design .............................................................................................. 8

1.7.2.2 Population ........................................................................................................ 8

1.7.2.3 Sample ............................................................................................................ 8

1.7.2.4 Data collection.................................................................................................. 8

1.7.2.5 Analysis of data ................................................................................................ 9

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1.8 Limitations of the study ................................................................................. 9

1.9 Ethical principles............................................................................................ 9

1.10 Summary ...................................................................................................... 10

CHAPTER 2: LITERATURE REVIEW................................................................................... 11

2.1 Introduction .................................................................................................. 11

2.2 Definition of performance and sustainability .............................................. 11

2.3 Performance measures in the construction industry.................................. 11

2.4 Financial performance measures ................................................................ 12

2.5 Non-financial measures ............................................................................... 13

2.6 Sustainable construction performance ....................................................... 13

2.7 Factors influencing sustainable performance ............................................. 14

2.7.1 Scope definition.............................................................................................. 15

2.7.2 Political and policy formulation factors............................................................. 16

2.7.3 Technical capacity (Capacity availability) ........................................................ 17

2.7.4 Knowledge management ................................................................................ 17

2.7.5 Material management/availability .................................................................... 19

2.7.6 Quality factors ................................................................................................ 20

2.7.7 Resource capacity .......................................................................................... 21

2.7.8 Technology transformation ............................................................................. 22

2.7.9 Regulatory factors .......................................................................................... 22

2.7.10 Job satisfaction .............................................................................................. 23

2.7.11 Business conditions (Market competition) ....................................................... 23

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2.7.12 Leadership ..................................................................................................... 24

2.7.13 Budget constraints/financial capacity............................................................... 24

2.7.14 Corruption ...................................................................................................... 25

2.7.15 Socio-cultural factors ...................................................................................... 25

2.8 The construction industry in South Africa .................................................. 26

2.8.1 Structure of the construction industry .............................................................. 26

2.8.2 Construction industry grading ......................................................................... 26

2.8.3 Doing business in the SA construction industry ............................................... 27

2.8.4 Contribution to gross domestic product (GDP)................................................. 28

2.9 Empirical studies.......................................................................................... 32

2.10 Chapter conclusion ...................................................................................... 43

CHAPTER 3: RESEARCH METHODOLOGY ....................................................................... 44

3.1 Introduction .................................................................................................. 44

3.2 Research design........................................................................................... 44

3.2.1 Purpose of the study....................................................................................... 45

3.2.2 Extent of the researcher interference with the study ........................................ 46

3.2.3 Study setting .................................................................................................. 46

3.2.4 Research design ............................................................................................ 46

3.3 Research approach ...................................................................................... 46

3.3.1 Quantitative and qualitative research .............................................................. 47

3.3.1.1 Qualitative research........................................................................................ 47

3.3.1.2 Quantitative research ..................................................................................... 48

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3.3.2 Selecting an applicable research .................................................................... 49

3.4 Research method ......................................................................................... 49

3.5 Measuring instrument (s) ............................................................................. 49

3.6 Research procedure ..................................................................................... 50

3.7 Research population .................................................................................... 50

3.8 Sampling procedure ..................................................................................... 52

3.9 Data analysis ................................................................................................ 53

3.10 Reliability and validity .................................................................................. 54


CHAPTER 4: PRESENTATION OF RESULTS AND FINDINGS ........................................... 55

4.1 Introduction .................................................................................................. 55

4.2 Descriptive statistics of variables................................................................ 56

4.2.1 Demographics ................................................................................................ 56

4.2.2 Descriptive statistics ....................................................................................... 61

4.2.3 Cronbach’s alpha ........................................................................................... 73

4.3 Correlation analysis ..................................................................................... 80

4.4 Relative importance index............................................................................ 83


CHAPTER 5: DISCUIONS, CONCLUSION AND RECOMMENDATIONS ............................. 91

5.1 Introduction .................................................................................................. 91

5.2 Discussion .................................................................................................... 91

5.2.1 Discussion pertaining to objective 1 ................................................................ 91

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5.2.2 Discussion pertaining to objective 2 ................................................................ 93

5.3 Conclusion ................................................................................................... 97

5.3.1 SWOT analysis .............................................................................................. 98

5.4 Recommendations ....................................................................................... 99

5.4.1 Corruption ...................................................................................................... 99

5.4.2 Budget constraints........................................................................................ 100

5.4.3 Policy development ...................................................................................... 100

5.5 Implementation........................................................................................... 100

5.6 Future research .......................................................................................... 101

REFERENCES ................................................................................................................... 102

ANNEXURES ..................................................................................................................... 110

Appendix 1-5-1: Variables ................................................................................................... 110

Appendix 1-5-2: VariableS analysis ..................................................................................... 114

Appendix 1-5-3: Relative importance index.......................................................................... 149

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LIST OF TABLES

Table 1-1: CIDB grading designation............................................................................. 2

Table 2-1: Summary of factors influencing sustainable performance in CI .................... 32

Table 3-1: Interpretation guideline ............................................................................... 53

Table 4-1: Descriptive statistics................................................................................... 61

Table 4-2: Cronbach’s alpha ....................................................................................... 74

Table 4-3: Correlation analysis.................................................................................... 80

Table 4-4: Relative importance index .......................................................................... 83

Table 5-1: Critical performance measures ................................................................... 91

Table 5-2: Critical factors influencing sustainable performance in the construction industry93

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LIST OF FIGURES

Figure 1-1: Construction process flow............................................................................. 3

Figure 2-1: Ease of doing business in SA ..................................................................... 27

Figure 2-2: Measuring quality and efficiency of the construction industry ....................... 28

Figure 2-3: Gross fixed capital formation (GFCF) in construction; 2010 Rand (million) ... 28

Figure 2-4: Capital expenditure for public sector (R billions) – 2010/2016 ...................... 29

Figure 2-5: Capital expenditure for private sector (R billions) – 2010/2016 .................... 30

Figure 3-1: The research design................................................................................... 44

Figure 4-1: Types of business ...................................................................................... 57

Figure 4-2: Turnover of the business ............................................................................ 58

Figure 4-3: Experience in the business ......................................................................... 59

Figure 4-4: Level of management ................................................................................. 60

Figure 4-5: Parent of the organisation........................................................................... 60

Figure 5-1: SWOT Analysis .......................................................................................... 98

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CHAPTER 1: NATURE AND SCOPE OF THE STUDY

1.1 Introduction

This chapter introduces various sections of the research, including the introduction to the study,

the problem statement and background to the problem that was investigated and what led to its

existence. This section also includes the motivation of the study and briefly introduces existing

literature related to the problem. It deals with research objectives, identifies a research design, a

selection of the most appropriate design approach, method, and identification of the targeted

population. Finally, this section also provides a description of the measuring instruments and

procedures used for statistical analysis.

In the following chapters, various factors influencing sustainable performance are identified from

prior studies. A survey was conducted within the chosen population to identify applicable factors

within the South African (SA) context. The results of the survey are presented and analysed,

using applicable statistical analysis tools. The implication of the findings and how to achieve

performance sustainability is discussed. The study also provides a framework for addressing the

factors identified which affect sustainable performance in the construction industry of South

Africa.

1.2 The construction industry in South Africa

The civil construction sector is a project-based industry which encompasses various firms in

temporary multidisciplinary organisations, to produce infrastructures, such as roads, building,

and factories (Kamara et al., 2002:55). The SA construction industry is diverse and fragmented,

with the construction of civil engineering and construction of buildings structures being the most

dominant (Windapo & Cattell, 2013:65). As in any other nation around the world, the industry

contributes significantly to the country's socio-economic development (Windapo & Cattell,

2013:65).

The Construction Industry Development Board (CIDB) Act is the statutory body established in

2000 with the aim of providing an integrated construction industry development strategy

(Construction Industry Development Board, 2014:2). This statutory body is also responsible for

the overall measure of the industry’s performance.

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In South Africa, contractors that are registered with CIDB are graded according to their ability to

perform works within classes of works in the construction sector. For the purpose of this study,

only construction companies that are classified as grade 2 to grade 9 are considered. The table

below provides a breakdown of the various designations with minimum and maximum tender

values that are applicable in terms of South African CIDB.

Table 1-1: CIDB grading designation

Source: CIDB Report, (2018)

1.3 Problem statement

The performance of the construction industry has recently deteriorated and continuously fails to

meet deadlines and budget limits, as expected by its key stakeholders and clients (Gunduz &

Yahya, 2015:77). Globally, the performance of the construction industry is anticipated to

develop considerably, whereas the local (SA) market regresses in terms of its performance (SA

Construction Industry, 2017:9). According to the report, a delay in planned completion of up to

20% longer and 80% over budget is obtained on South Africa’s construction projects, resulting

in the SA construction index trading at 69% lower than it was during the 2009 global financial

crisis.

In 2017, the South African construction industry went through a technical recession.

This resulted in a considerable decline in construction employment and economic

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growth (CIDB Construction Monitor-Employment, 2018:2). As a result of the industry’s

poor performance, Global Visionary Science Report Team (GVSRT) reported 240 000

jobs loses during the 2018 calendar year, and 91 civil contractors going into liquidation

in 2018 (Terblanche, 2019:4).

Moreover, GVSRT projects a construction performance decline of 1.10% within the next five

years (2017 - 2022), as compared to 4.2% in the Compound Annual Growth Rate (CAGR)

globally. This contraction will lead to a further economic decline, job loses, and a widening

income gap between the poor and richer. To turn things around, a healthier and better

performing construction industry is required in order to improve the lives of people. Hence, the

study is to to determine factors influencing sustainable performance in the construction industry.

1.3.1 Background and setting of the problem

Globally, the construction industry has been a focus of discourse on economic development for

several years (Hove & Banjo, 2018:2). The construction industry dates back as far as the

Palaeolithic Age between 12,000 and 40,000 B.C when people occupied caves or in built

structures that were nearest to the level of the ground (Zhou et al., 2015:338). Since these

times, the construction industry has been key in the development and transfer of technology

(Windapo & Cattell, 2013:65). Technology transfer has transformed the industry from the

ancient labour demand that produced comparatively simple designs and buildings into complex

systems and designs (Gunduz & Yahya, 2015:77).

Through this transformation, the industry has continued creating opportunities for enterprise

development and improved the lives of its users. The construction industry has been utilised by

the government not only to stimulate growth, but also to assist economic rescues from

recessions (Anon; IŞIk & AladaĞ, 2016; Zhang et al., 2017). An overview of the basic

construction process is given in figure 1-1 indicating the stages involved in construction works.

The process consists of four stages and each process is briefly discussed (Kamara et al.,

2002:55):

Figure 1-1: Construction process flow

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Source: Kamara et al. (2002)

Project conception – during this process, the need for the project is established and the

client develops user requirements and specifications (URS) which detail the set of

requirements.

Design of facility – the URS is put into a design perspective; the client prepares bid

documents and invites contractors to tender.

Construction of facility – following an award of the contract to a successful tenderer,

execution of the works takes place and the design is transformed into the real facility as

required.

Use of facility – the client takes over the facility from the contractor and puts it into

commercial operation for use.

1.3.2 The motivation of the study

Globally, relatively well-documented literature exists investigating factors influencing sustainable

performance in the construction industry. Authors such as Chang et al. (2018:1448); Durdyev et

al. (2018b:8); Elhaniash and Stevovic (2016:138); Elkhalifa (2016:197); Enshassi et al.

(2016:53); Luo et al. (2017:3222); Shahraki et al. (2018:72) have recently identified many critical

success factors and provided recommendations on how companies should address them.

Considering that setting strategies to address performance sustainability is more of a country-

specific than a one-size-fits-all (Elkhalifa, 2016:197), this study addresses factors relating to the

South African construction industry.

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Some of the factors identified in the literature have existed for some time. However, due to the

growing uncertainties in technology and development processes that result in the dynamic

construction industry in nature (Ranawat et al., 2018:10108), it could not be concluded that

these factors addressed and/or contributed to current issues of performance decline. Hence, the

investigation of the factors was believed to be a way of enhancing the adequacy of an

undertaking by construction professionals towards achieving the sustainable performance of the

construction industry. Also, this study was conducted to suggest a diversity of examined factors

and different assumptions made to address the nature of these factors.

1.4 The expected contribution of the study

Practical/Organisation

Although the results of construction activities show an adverse impact on the environment, the

provision of infrastructure is seen as a major benefit and improvement in the country's economy

(Emmanuel et al., 2014:929). Therefore, the researcher hopes to assist managers, employees,

and owners of construction companies to understand critical success factors and determine

which factors have more influence in construction performance. Moreover, this study aims at

improving the relevance of the company's policies to current organisation activities by assisting

policymakers to address performance indicators that are not being addressed.

Literature:

The study aims at filling gaps identified within the existing body of academic knowledge on

construction performance, particularly in the South African context. The conclusion reached in

this study is of great interest to both academics and professionals in perceiving and

understanding the construction industry and performance success factors, resulting in

sustainable growth, in particular in the South African context.

1.5 Research objective

The research objectives were divided into a general objective and specific objectives.

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1.5.1 Main objective

The primary objective is to determine critical factors influencing sustainable performance in the

construction industry in South Africa.

1.5.2 Specific objectives

The specific objectives of this research are:

To determine critical performance measures of the construction industry and to what

extent they correlated with the company's performance,

To investigate a comprehensive set of critical factors influencing sustainable performance

in the construction industry and to what extent these factors influenced the performance

of the company.

To recommend to the industry on what they must focus to address these factors

influencing sustainable performance.

1.6 Research questions

How effective are current performance measures in measuring the performance of the

construction companies?

What are the critical factors influencing sustainable performance in the construction

industry?

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1.7 Research outline

1.7.1 Literature review

Following the review of the literature, the researcher attempted to analyse theories available

within the body of knowledge on critical performance measures and factors influencing

sustainable performance in the construction industry. The review is restricted to published peer-

reviewed articles in the English language, with more than 75% published within the last 5 years

(2014 – 2019) in order to ensure suitability. The search strategy procedure contained search

terms, such as sustainable performance in the construction industry.

To gain access to more studies, search terms, such as success factors and critical success

factors were used in conjunction with the search strategy procedure and were tested and

adjusted until more accurate literature was obtained. This method helps in refining the results to

a more relevant set of publications (Sfakianaki, 2019:179). Also, to advance the quality of the

overall search, more individual searches were conducted for each database. Performance data

used in this study were obtained from public domain knowledge, such as official company

records or annual reports which are accessible from internet sources.

The sources that were consulted while conducting the literature review included:

Published journals.

Books.

Internet articles.

Theses and dissertations from previous students (North-West University).

Published reports on the construction industry i.e. SA Construction Report, published by

PricewaterhouseCoopers.

North-West University library (database).

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1.7.2 Empirical study

1.7.2.1 Research design

The research design used for this study is a quantitative survey design. The reason for selecting

this design method was the convenience it provided when gathering a lot of data within a limited

timeframe.

1.7.2.2 Population

The population for this study was the South African construction industry. Data collection was

done from role-players who are currently involved in the construction industry. This included

construction professionals dealing with construction projects in state-owned entities,

professional consulting firms, construction firms, and building engineering firms.

1.7.2.3 Sample

Construction companies who were involved in the day-to-day work of project execution and

management of businesses were identified from the CIDB site. Emails were sent to grade 3 to

grade 9 CIDB registered companies requesting participation from their construction profesionals

in the research. These professionals consisted of knowledgeable industry officials, construction,

and building and engineering officials. Using purposive and random sampling techniques, a

sample of 250 officials was used, from which, 116 respondents fully completed the survey while

17 returns were incomplete. From the incomplete survey, only six could be used and this

brought the total to 122 officials who formed part of the sample.

1.7.2.4 Data collection

Questionnaires for this study consisted of four sections which were developed, based on the

literature, and the sections are as follows:

Section A – demographic information,

Section B – company performance measures,

Section C – factors affecting sustainable performance

9

Section D – effectiveness of performance measures in sustainable performance.

Questionnaires were emailed through a survey link to the intended participants and email

reminders were sent weekly to participants who had not yet completed the survey. The survey

link was open for a period of six weeks before the information was exported to Excel, CSV, and

PDF files for statistical analysis.

1.7.2.5 Analysis of data

The Statistical Package for Social Sciences was used for coding, organising, and analysing

data obtained from questionnaires and steps used included descriptive analysis; correlation

analysis; relative importance index, Kruskal Wallis test, Cronbach alpha analysis, and

exploratory factor analysis (EFA)/Regression analysis. The findings of the research are

presented in chapter 4 of this study as tables, frequencies and percentages.

1.8 Limitations of the study

This study was limited to:

Construction professionals working for companies that are registered with the South

African Construction Industry Development Board (CIDB) as grade 2 to grade 9 and

whose core activities are construction. These companies also formed part of the CIDB

Small Medium Enterprise (SME) business conditions survey which is conducted

quarterly.

Companies with South African subsidiaries, in the case of international companies doing

business in South Africa.

1.9 Ethical principles

For the purpose of this study, various sources were required in order to provide detailed

analysis and proper recommendations on factors affecting sustainable performance and

development in South Africa's construction industry. Although the researcher did not intend to

access nor foresaw any sensitive information regarding the research, the following aspects

10

were addressed while reviewing sources and acquiring more information about the respective

companies or participants:

The purpose of the research was explained to the participants.

Participants were given the opportunity to decline participation.

Following submission of the survey, participants were allowed to withdraw their consent

at any time.

Prior to using any information received from voluntary participants, they were informed of

the researcher's intention before it could be published.

For publicly known information, such as newspapers and the Internet, the researcher did

not request permission prior to usage.

For any company archives that the researcher intended to use as a reference,

permission was requested from the concerned company.

Mentioning of individual's name and/or company name taking part in the survey was

prohibited. Instead, where applicable, participants were referred to as Mr.X1, Mr.X2 for a

male participant; Ms.Y1, Ms.Y2 for female participants. And in the case of companies;

Company X, Company Y was used.

Moreover, the aforementioned, the questionnaire/survey, was subject to scrutiny by the Faculty

of Economic and Management Sciences research ethics committee of the North-West

University and has received ethical clearance (Ethics approval number NWU-01326-19-A4). Also,

the researcher ensured the confidentiality of those elements considered as per point 6 and 7 of

Appendix B “Ethics informed consent form” of the North-West University.

1.10 Summary

This chapter discussed the following: introduction, background, problem statement, study

objectives, research methodology, study restriction and conclusion. The following chapter

provides a concise discussion of the literature.

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CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

In the previous chapter, the researcher delineated the introduction and the purpose of the study.

This chapter provides an insight into the dynamics of the civil construction industry, as well as a

review of the existing literature. This brief literature review is divided into sub-sections which are

discussed below, with the summary of the factors provided in section 2.5: Prior empirical

studies. The overview of the South African construction industry is provided in the third section

which provides a link between the global and the South African construction industry.

2.2 Definition of performance and sustainability

The concept of a performance measurement system (PMS) comes from the manufacturing

industry where it has shown great success, and it can be done either at the organisation level or

process level (Haponava & Al-Jibouri, 2011:140). This PMS was adopted by the construction

industry to assist in monitoring its performance status and requires consistent “collecting and

reporting of information about the inputs, efficiency and effectiveness of process or projects” in

a given operation (Sabone & Addo-Tenkorang, 2016:1493).

Performance

Performance is defined differently depending on the nature of the environment in which it is

used. Several authors have used the word “performance” in numerous studies and how it is

defined varies. In the construction industry, performance is defined as the ability to continually

improve the company’s efficiency or productivity in order to remain competitive in the market

(Horta et al., 2012:90; Hu & Liu, 2016:147).

Sustainability

Sustainability is defined as the development which "meets the needs of the present generation

without compromising the ability of future generations to meet their own needs" (Nguyen & Ye,

2015:390).

2.3 Performance measures in the construction industry

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The construction industry needs to evaluate its success or achievement in the long term,

looking at the internal and external constraints influencing its performance (Aladag & Isik

2016:11), and this is achieved through applying performance measures. Sibiya et al. (2015:3)

define performance measurement as the process of quantifying the efficiency and effectiveness

of actions. Considering that construction projects are unique and possess different objectives,

determining performance measures and their correlation to company performance is needed.

These measures help companies to evaluate their long term success, taking into consideration

the internal and external factors affecting their performances (Aladag & Isik, 2016:11).

Also, performance measurement assists companies to develop strategic direction and improve

their competitiveness within the industry (Ali et al., 2013:126). This is done through “regular

collecting and reporting of information about inputs, efficiency and effectiveness of process or

projects” (Sabone & Addo-Tenkorang, 2016:1493). To achieve construction industry

sustainability, a set of performance measures balancing financial and non-financial (operational)

performance should be an integral part of any company (Ali et al., 2013:130).

2.4 Financial performance measures

Generally, only financial performance measures were traditionally used to evaluate the

performance of the construction industry (Ali et al., 2013:125; Horta et al., 2012; Hu & Liu,

2016). More recently, profitability, sales growth, financial stability, and cash flow are the highly

ranked financial measures, with profitability found to be the most used and valued measure in

many studies (Aladag & Isik 2016:11; Ali et al., 2013:132; Bassioni et al., 2004:48; Costa et al.,

2004:11; Hu & Liu, 2016:156; Jung et al., 2018:12; Prasad et al., 2018:18). To the authors’

knowledge, these measures are indications of improvements or regressions achieved from the

leading indicators such as quality, and productivity of workers. And they also help executives of

companies to identify specific gaps and actions within the business, allocate them to employees

and evaluate employees on the execution of those actions (Ali et al., 2013:130). Profitability

refers to the company’s ability to generate profit within a given operating period (Prasad et al.,

2018).

Oladimeji and Aina (2018:123) posited that financial performance alone does not determine the

company’s well-being, instead, it should be partnered with a good organisational culture and

employee attributes. Factors such as an increase in the industry’s competitiveness, rise in

13

construction costs (including labour costs) and low labour productivity seriously affect the

company’s profitability (Chan & Martek, 2017:145). These factors have led to major criticism of

these financial measures, most notably, of “lagging indicators, inadequacy, narrow, and

reactive” (Ali et al., 2013; Bassioni et al., 2004; Isik & Aladag, 2016), and their inability to reflect

current value-creating actions which could result in unexpected losses to investors (Jung et al.,

2018:1).

According to Willis and Rankin (2011:20), financial measures indicate the results of

management actions already taken which is the major problem for the construction industry.

Although criticism has continued to rise, Thompson et al. (2017) argue that efficient

management of these measures provides the company with a competitive advantage when

compared to its rivals.

2.5 Non-financial measures

With traditional financial measures no longer sufficient to be solely used for measuring

construction performance, non-financial measures, such as timely completion of a project, client

relations, customer and client satisfaction, and employee productivity, could be used together

with the financial performance measures (Ali et al., 2013:130; Tripathi & Jha, 2018:1063).

According to Ali et al. (2013:130), these measures are heavily influenced by quality of service

and work rendered to customers. Measuring the performance of Guyana’s construction industry,

Willis and Rankin (2011:20) examined the non-financial measures that are perceived to have a

cause and influence relationship with the lagging measures.

The authors further found that these measures provide early warnings, which enable companies

to provide or seek solutions that will bring advantage to the affected lagging results. Like Ali et

al. (2013), they indicated that these measures are leading indicators, meaning they allow

managers of the companies to realise potential financial performance contraction before it

emerges. Hence, Tripathi and Jha (2018:1052) examined that it is crucial for construction

companies to familiarise themselves with critical performance measures to evaluate their

performance at both the project and organisational level.

2.6 Sustainable construction performance

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For the purpose of this study, the following are the concepts which have been adopted from

previous studies and used by the researcher in this study.

Sustainable construction denotes “the contribution of the construction sector to economic

prosperity, social well-being, and environmental sustainability” Ye et al. (2015:3),

Performance is defined as the ability to continually improve the company’s efficiency or

productivity in order to remain sustainable and competitive in the market (Horta et al.,

2012; Hu & Liu, 2016), and

Sustainability is defined as the development which "meets the needs of the present

generation without compromising the ability of future generations to meet their own

needs" (Nguyen & Ye, 2015:390).

Therefore, from the above concepts, the sustainability performance of the construction industry

denotes the comprehensive degree to which the construction industry supports sustainable

development.

2.7 Factors influencing sustainable performance

Generally, projects develop goals and objectives to be pursued in order to provide specific

benefits for society (Mavi & Standing, 2018:752). To the author’s knowledge, the success of the

project is measured in terms of its constraints such as time, cost, and quality. To achieve these

constraints; a clearly defined scope of work and effective site management during execution

(Gunduz & Yahya, 2018:76), and effective human resource management (Ranawat et al.,

2018), is required. Like IŞIk and AladaĞ (2016:506) indicated, a company’s performance is

determined by the success of the projects it executes which result in the overall performance of

the construction industry.

Globally, several studies have been conducted to investigate factors influencing sustainable

performance in the construction industry. Some researchers preferred to investigate critical

success factors or causes of failure in the construction industry. Critical success factors are

measures for evaluating project success which result in the sustainable performance of the

construction industry. These CSFs can either be quantitative or qualitative (Sfakianaki,

15

2019:176), and this makes it challenging to measure observations of current trends (Mavi &

Standing, 2018:761).

To achieve success in the construction industry, several underlying factors which influence the

performance should be considered. This provides an opportunity for construction managers to

have a proper allocation of resources addressing these factors. For better performance

sustainability in the construction industry, a diversity of factors from various categories is

important (Sfakianaki, 2019:189).

For discovering the most factors influencing sustainable performance in the construction

industry, a literature review was conducted which led to articles covering varius countries

globally. that related to this study. Table 2-1 provides a summary of the existing studies

reviewed, based on countries and authors. Although Lindhard and Larsen (2016:668)

postulated that different project participants’ experienced these factors differently, most of the

studies reviewed showed several similarities and those most common were chosen and

grouped into 15 groups: scope definition, political and policy formulation, technical capacity,

knowledge management, material management, quality, resource capacity, technology

transformation, regulatory, job satisfaction, business conditions, leadership, budget constraints,

corruption, and socio-cultural factors. The factors are briefly discussed below:

2.7.1 Scope definition

Bjørn et al. (2018:75) define scope definition as one of the phases (initiation phase) within the

project which “determines what product systems are to be assessed and how this assessment

should be placed”. Defining project scope involves collaboration of appropriate or relevant

stakeholders concerned right at the beginning of the project planning (Chaturvedi et al.,

2018:347), and this affords each stakeholder an opportunity to clearly define what need to be

done, and the expectations of the deliverables from each party involved (Gunduz & Yahya,

2015:76). Having a well-defined scope of work provides a clear vision and goal of the project, as

each party involved becomes fully aware of what is expected from them (Gunduz & Yahya,

2018:76; Tayeh et al., 2018:307).

According to Tayeh et al. (2018:307), executing a project without understanding its objectives is

an ingredient of project failure. According to the authors’ finding, the main reason for poor scope

16

definition is attributed to insufficient time given to designers to prepare designs and project

drawings. A clearly defined and well-resourced scope of work minimises variations to orders

and disputes during execution which result in major delays or an increase in project costs

(Gunduz & Yahya, 2018:76). Therefore, given sufficient time, more accurate designs could

avoid or minimise scope variations during execution and thus, result in sustainable performance

2.7.2 Political and policy formulation factors

Political factors refer to the involvement and commitment of the government in the formation

and implementation of legislation and policies that relate to the construction industry in order to

achieve sustainable performance (Ametepey et al., 2015:1686; Opoku et al., 2015b:163).

According to the authors' findings, having appropriate co-operative policies, strategies and

various policy documents could lead to the company’s sustainability. To address the issues of

sustainability, government has exerted pressure on construction companies to consider the

social and environmental impact of their works (Alotaibi et al., 2019:16). However, successful

implementation of policies requires consistency and stability in leadership and in politics, hence

Damoah and Kumi (2018); Larsen et al. (2015); and Mavi and Standing (2018) identified

political stability as one factor that is critical for the success of the projects.

Although Gunduz and Yahya (2018:76) most recently found that political conflicts and

instabilities have a minor effect on construction project success, in particular in the developed

countries, looking at emerging economies such as South Africa, in which construction projects

have become a highpoint of development, the effect of the political factor has emerged

significantly (Damoah & Kumi, 2018:4). The authors concluded that political conflicts are mostly

attributed to changes in government and corruption.

Another key political factor is the identification and understanding of key stakeholders in order to

ensure project success. Depending on the nature and type of projects executed, some of the

stakeholders are of a specific political alignment, and therefore, their actions could considerably

influence the project management success (Mavi & Standing, 2018:761). Alotaibi et al.

(2019:24) examined that companies should consider different perceptions and opinions of

stakeholders and formally incorporate them into their strategic decision-making process.

Therefore, the company’s ability to identify and respond to its stakeholder requirements during

the project initiation phase significantly contributes towards its success.

17

2.7.3 Technical capacity (Capacity availability)

Another body of research examined the impact the technical capacity (TC) has on construction

industry performance. Gill (2015:95) defines capacity as the “amount of available resources or

the output achievable to meet the operational challenges over a specified period of time”.

According to Mishra (2018:17), TC in the construction industry refers to different personnel,

such as engineers (technical advisor, senior/site engineer, supervisors) and project managers

who can be utilised in various positions within the industry. in developing countries such as

South Africa, the issue of a low qualified labour force continues emerging due to insufficient

training provided to the employees (Horta & Camanho, 2014:974).

Also, the rotation of workers to various companies affects company performance, and this could

be minimised by ensuring that a relevant TC in terms of experience, skills and knowledge is

maintained (Gunduz & Yahya, 2018:76; Senaratne & Gunawardane, 2015:18). Construction

companies are faced with a challenge of managing capacity as it requires a strong capital base

which makes it difficult for the companies to build additional capacity (Gill, 2015:95). Therefore,

companies are tested to have strong abilities in positioning existing resources in an effective

and efficient way in order to achieve sustainable performance.

Although Tayeh et al. (2018:310) found that the engineer’s capabilities and experience are the

most critical factors within TC that influence construction performance, Senaratne and

Gunawardane (2015:18) argued that a balanced design team and appropriate distribution of

roles to team members has a positive influence in project performance. This applies to both the

contractor and the client’s technical capacity (Gunduz & Yahya, 2015:76; Mat Isa et al.,

2015:18).

2.7.4 Knowledge management

Another influential factor in construction performance is knowledge management (KM). KM is

critical to the construction industry due to the industry’s diversified and changing nature of works

(Sfakianaki, 2019:189). Skyrme and Amidon (1997), cited by Kamara et al. (2002:54), defined

knowledge management (KM) as the “organizational optimization of knowledge to achieve

18

enhanced performance, increased value, competitive advantage, and return on investment,

through the use of various tools, processes, methods and techniques”.

With the construction industry mostly dependent on continuous innovation and improved

business performance, the need for strategic knowledge management is required in order to

gain a competitive advantage (Kamara et al., 2002:53). These changes rely heavily on

professionals to identify changes and develop a research framework for future investigation (Yu

& Yang, 2018:782). Therefore, companies should provide suitable training for employees with

relevant knowledge management systems to enhance their career prospects.

Developing KM strategies which focus on the reassignment of workers from one project to

another, the use of standards and best practice guides, and other activities such as post-project

reviews could improve the company’s performance (Kamara et al., 2002:63). The authors

further examined that these strategies could help in the capturing and sharing of lessons

learned from other executed projects. Having a deeper insight into understanding the basic

factors influencing construction performance could lead to improvement of consequence factors

(Chaturvedi et al., 2018:351).

Although some companies possess knowledge management systems, such as knowledge

worker system (KWS) and embedded strategies to improve their performance, Kazi (2005:25)

identified three major obstacles attributed to the establishment of a knowledge-sharing culture,

and these are briefly discussed below;

An unsupportive culture – sharing information with other workers minimises opportunities

for getting a promotion. Workers enjoy holding onto a degree of specialised knowledge

which varies from that of their colleagues.

Poor communication structures – company’s lack of effective mechanisms and processes

to inspire open communication has an effect on company performance.

Time constraints – insufficient time available to allow workers to engage in knowledge-

sharing activities. The research attributed this factor to more focus put on working

responsibilities rather than knowledge sharing.

19

KWS is the tool developed to assist knowledge workers in capturing and organising “activity

information, and help them learn, prioritize, and execute knowledge worker tasks more

efficiently and effectively” (Kamara et al., 2002:60). Most recently, a computational narrative

semi-fiction generation (CSNG) approach was developed which facilitates knowledge retention

and learning (Yeung et al., 2016:408). The authors realised the application of a CNSG approach

as highly effective in providing a realistic experience for individuals to gain lessons learned, and

assist workers to learn and remember important things and learning points from the narrative.

2.7.5 Material management/availability

Material availability has been identified by many authors, such as Durdyev et al. (2018c);

Larsen et al. (2015); Mavi and Standing (2018); and Ranawat et al. (2018) as other factors that

heavily influence construction industry performance. With 50-60% of the construction project

costs attributed to material usage (Prasad et al., 2018:15), efficient use and management of

material is critical to enhancing project performance.

Most recently, a study conducted by Durdyev et al. (2018c) to determine drivers and barriers of

the sustainable construction industry in Cambodia indicated a limited availability of material as

the most significant factor in construction works. Therefore, as a resource that is largely

consumed by the industry, its usage should be optimized in order to ensure sustainability of the

construction performance. A larger volume of material used in construction projects is attributed

to errors and omissions in the consultant material (Larsen et al., 2015:16). Therefore, using

more durable material could minimize material consumption while lengthening the lives of the

construction works (Durdyev et al., 2018c:14).

The material shortage was further contemplated by Zidane and Andersen (2018:663) in broader

research conducted on universal delay factors in the construction industry. Both the client and

the contractor’s ability to effectively co-ordinate material replenishment could positively

influence the project performance, resulting in on-time project delivery (Chaturvedi et al.,

2018:352; Tayeh et al., 2018:10). With regards to the South African construction industry, a

study conducted by Windapo and Cattell (2013:75) identified the increasing costs of building

material as a significant factor influencing the industry’s performance. The need for resource

shortages cannot be over-emphasized, and this requires all parties involved developing a

20

proper co-ordination plan with their respective manufacturers and suppliers to ensure timeous

availability of material and equipment.

2.7.6 Quality factors

Despite several studies conducted relating to quality in the past years, there is still no one clear

definition of quality. For this study, the following are the three definitions of quality used for

examining the influence quality has in construction performance.

“Meeting the expectations of the customer” (Larsen et al., 2015:3)

“Reduce rework or defects” (Larsen et al., 2015:3)

“A product or service free of deficiencies” (Rumane, 2017:6)

Broader research has been conducted to determine the effect of quality factors on construction

work; Senaratne and Gunawardane (2015:2) found these factors as having a significant

influence in construction performance. To the authors ’ findings, lack of collaboration among

parties during the design phase resulted in poor quality performance. The authors highlight the

importance of selecting a design team, adaptation of good team-working practices, and applying

a balanced team role as significant factors influencing performance of the construction industry.

The definition of team role is given as “how the individual fits into the team, not what particular

function he or she performs” (Senaratne & Gunawardane, 2015:2). Therefore, having good

teamwork practices in construction teams results in sustainable performance of the industry.

Some authors identified human resources (HR) capacity as another aspect to consider for

sustaining construction performance. Most recently, Mishra (2018) evaluated the impact HR

capacity has on Nepalese construction companies. The author found that the quality of HR

relies on the quality of the workforce the company employs, meaning, during recruitment for job

opportunities, the company should extend its search to a sufficiently large sample of capable

candidates through referrals or use of recruitment agencies. Retaining a workforce that is

capable of producing high-quality performance provides a competitive advantage to the

company (Sing et al., 2018:3).

Workforce retention ensures the transfer of experience and knowledge from the experienced

employees to inexperienced or newly qualified consultants (Larsen et al., 2015:16). Having a

21

capable workforce with relevant skills and knowledge minimises errors or omissions in tender

documents and construction works, and reduces the cost of reworks (Larsen et al., 2015;

Lindhard & Larsen, 2016; Mavi & Standing, 2018; and Ranawat et al., 2018). Another factor

attributed to poor tender documents is mainly lack of employee involvement and teamwork;

unavailability of a skilled workforce; and management not fully committed to quality

management (Larsen et al., 2015:668). The aforementioned factors contribute significantly to

the high failure rate of contracting enterprises (Windapo & Cattell, 2013:75). Without proper

controls or measures put in place to alleviate the increase in building material costs, the authors

posit that the performance of the construction industry will continue regressing.

2.7.7 Resource capacity

Evaluating the critical success factors and based on their impact and frequency they possess in

sustainable performance of the construction industry, two human resources (HR) factors were

identified; effective site management, and availability of appropriate personnel on project sites

(Gunduz & Yahya, 2018:76; Tayeh et al., 2018:10). These factors were deemed to be very

influential in helping to drive the project to be completed as planned. The issue of HR capacity

has been a major concern for many researchers globally. This requires significant attention to

achieving sustainable construction performance. According to Mishra (2018:17), “human

resource capacity is about ensuring that an organization has enough people with the necessary

skills to achieve its objectives”.

Sing et al. (2018:25) identified harsh working conditions, job uncertainties, and lack of career

prospects as the major contributors in restricting human resources personnel from joining the

industry. To the author’s knowledge, workforce sustainability could be overcome by inspiring

multi-skilling and the establishment of welfare facilities for construction workers. For developing

countries such as South Africa, government and training authorities’ interventions in reviewing

labour policies for maintaining a construction workforce could improve human resources

capacitation (Sing et al., 2018:26).

Another critical factor in sustainable performance is the availability of sufficient capital for the

contractor to carry out business (Tayeh et al., 2018:10). Funding is required for company

expenses such as; payment of subcontractors, employee salaries, and other administrative

expenses, which, if not sufficient, could result in slow progress of the work.

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2.7.8 Technology transformation

Adaptability to changing business conditions has a major influence on the performance of the

company (Elkhalifa, 2016:197). Technology transfer has transformed the industry from the

ancient labour demand that produced comparatively simple designs and buildings into complex

systems and designs (Gunduz & Yahya, 2015:77). Due to the high cost of labour productivity in

developed countries, construction projects are more technology-driven than in emerging

countries such as South Africa (Chaturvedi et al., 2018:335).

In a study conducted by Sing et al. (2018:6) in analysing Japan’s construction industry, almost

50% of the construction workforce was replaced by automation. This implies that as technology

improvements develop, there is a significant impact on the traditional workforce required to

execute works (Sing et al., 2018:6). According to Chaturvedi et al. (2018:352), these changes

requires knowledge management mechanisms to transfer and exchange skills and knowledge

obtained from various projects. This will assist in advancing good project implementation

practices which could result in sustainable performance in the construction industry.

2.7.9 Regulatory factors

Construction performance has been denoted by Hove and Banjo (2018) as having a significant

contribution to the country’ economic development. However, Rahman and Ali (2018) argued

that its performance has been closely monitored by many researchers due to the pollution it

generates from construction activities. To the authors’ knowledge, a blanket approach is not

used when determining construction regulations as they are often country-specific. Therefore,

their degree and nature may differ, depending on the type of construction work to be executed.

Hence, in analysing project success factors in the Middle East Region, factors such as sudden

changes of law and regulations were ranked the least important factors influencing construction

performance (Gunduz & Yahya, 2015:76).

The issue of regulation such as lack of statutory requirements is a concerning factor in

sustainable performance in the developing countries (Durdyev et al., 2018c:14).

In a study conducted in Iraq, a model (Bayesian Decision Tree Approach) developed to

evaluate the impact of modification to the regulations proved that modification has an impact in

23

the quality of work performed (Naji et al., 2018). The authors found that adding more regulations

to the construction project processes resulted in a high level of quality, however, more time was

needed to execute projects. Having an apparent statutory process that incorporates sustainable

construction practices with economic incentives could result in effective implementation of

sustainable construction initiatives (Durdyev et al., 2018a:570).

2.7.10 Job satisfaction

Another factor that impacts the construction industry performance is job satisfaction. Enshassi

et al. (2016:1) classified several factors that are considered to attribute to job satisfaction at

work. In the authors’ findings; emotional exhaustion, depersonalizstion, and reduced personal

achievement have a great influence on the performance of the workers. These factors were

attributed to unfair rewards and recognition, job securities, and workload which contributed to

exhaustion and fatigue of the workers. These factors are perceived to result from the dynamics

and complex nature of the industry’s activities, different backgrounds and attitudes of the

workers towards the quick changes developing within the construction industry (Enshassi et al.,

2016:43). Enshassi et al. (2016:43) examined that “staff members under stress interact less

frequently with clients and engage in fewer positive interactions with clients”.

2.7.11 Business conditions (Market competition)

Investigating the impact of competitive conditions on supplier evaluations, Seth et al.

(2018:230); and Ye et al. (2015:9) found that market competition has a major influence in the

performance of the construction industry. More than just determining the company’s ability to

survive in the market, market competition also encourages companies to have innovative ideas

to maintain their sustainability (Widuri & Sutanto, 2019:172). However, the authors identified

that higher market competition also increases a company’s probabilities of insolvency as it

reduces the company’s profitability. Seth et al. (2018:230) determined three ways in which

market competition influences construction industry sustainability, and they are briefly discussed

below;

“Positive effects in the economic dimensions – the stronger the market competition, the

better the industrial performance in the areas”

24

Negative effects on the environmental dimension – the higher the market competition,

the larger the consumption of solid waste, and steel consumption”

Positive effects on the social dimension – the higher the market competition, the better

the competition of construction sustainability performance”

The above impact is predominantly observed in the construction industry due to the market’s

perfect competitiveness. Dating back to 2017 where the construction industry annual growth

rate contracted by 0.3%, the industry continues to weaken due to intensifying competition (SACI

Report, 2018:31).

2.7.12 Leadership

To achieve success in construction projects, one needs to demonstrate the ability to co-ordinate

activities performed by various individuals with a high degree of complexity and risks (Larsson

et al., 2015:12), and this is referred to by the authors as leadership. Opoku et al. (2015a:185)

defined leadership as a process in which certain or a particular individual influences a group of

people to acquire common objectives. This is mainly an executive position within the company,

although the authors believe that leadership can emanate from all different levels of positions

within the company. Companies require individuals who are fully committed to what they do to

ensure the company’s survival and competitiveness in the market. Ametepey et al. (2015:1688)

and Ranawat et al. (2018:10114) identified several leadership factors that have a major impact

on the company’s sustainability.

2.7.13 Budget constraints/financial capacity

The impact of financial constraints on the implementation of sustainable performance in the

construction industry has been acknowledged by many studies, such as Ametepey et al. (2015);

Ranawat et al. (2018); and Windapo and Cattell (2013). The authors identified additional

financial costs as one of the major barriers in providing sustainable performance in the

construction industry, and this results in lack of the realisation of the sustainable concept.

25

2.7.14 Corruption

Most recently, the issue of corruption has been observed by many authors, such as (Chan &

Owusu, 2017; Niazi & Painting, 2017; Owusu et al., 2019), as a global concern in the

construction industry. The studies showed an increase in construction industry corruption over

the past decade which is a worrying factor for the industry. According to Chan and Owusu

(2017:9), the forms of corruption that were mostly reported in the construction industry are

bribery, fraud, collusion, embezzlement, nepotism, and extortion, and these corrupt acts

resulted in project cost overruns (Niazi & Painting, 2017:517). Paying certain bribes to

government officials or public entities in order to obtain leverage of getting contracts has been a

regular practice in many countries around the world (Thompson et al., 2017).

According to Thompson et al. (2017), there are primary drivers of unethical business

behaviours, and these are:

“Faulty oversight, enabling the unscrupulous pursuit of personal gain and self-interest,

Heavy pressures on company managers to meet or beat short-term performance targets,

and

A company culture that puts profitability and business performance ahead of ethical

behaviour”

2.7.15 Socio-cultural factors

Fellows and Liu (2016:247) refer to socio-cultural factors as the sense-making explanations

which “satisfy individuals’ needs for achieving coherence, consistency and legitimacy in

thoughts and actions”. Many authors have alluded to the importance of good construction

performance to the economic development, providing infrastructure to developing countries

such as South Africa becomes the backbone of the society for socio-economic development

(Das, 2018:15). As technology continues to develop, cross-cultural problems and complexities

of the project also continue increasing (Ranawat et al., 2018:10108). This results in diverse

perceptions and understanding of meaning of the range of signals such as drawings and

messages (Fellows & Liu, 2016:246). Per the authors’ findings, an appreciation of people’s

26

different sentiments and how they perceive their worlds allows participants to appreciate the

opinions of others and thus mitigates potential problems.

Generally, it is believed that culture has an influence on how individuals perform. Therefore, a

constructive engagement of all affected stakeholders prior to the execution of the works is vital.

According to Das (2018:15), the success of the project also lies in the stakeholder’s effective

participation. By involving stakeholders, the likelihood of reduced performance and community

conflictual situations could be minimised.

2.8 The construction industry in South Africa

2.8.1 Structure of the construction industry

The civil construction sector is a project-based industry which encompasses various firms in

temporary multidisciplinary organisations, to produce infrastructures, such as roads, building,

and factories (Kamara et al., 2002:55). The SA construction industry is diverse and fragmented,

with the construction of civil engineering structures and the construction of buildings being the

most dominant (Windapo & Cattell, 2013:65). As in any other nation around the world, the

industry contributes significantly to the country’s socio-economic development (Windapo &

Cattell, 2013:65). The Construction Industry Development Board (CIDB) is the statutory body

established in 2000 with the aim of providing an integrated construction industry development

strategy (CIDB, 2014:2). This statutory body is also responsible for the overall measure of the

industry’s performance.

2.8.2 Construction industry grading

In South Africa, contracting companies that are registered with the CIDB are graded according

to their ability to perform works within classes of works in the construction sector. For the

purpose of this study, only construction companies that are classified as grade 3 to grade 9 are

considered. Table 1-1 in chapter 1 provides a breakdown of the various designations with

minimum and maximum tender values that are applicable in terms of the South African CIDB.

27

2.8.3 Doing business in the SA construction industry

Legislation and policies have been developed by the local government which encourages open

markets, innovation and a more competitive market (CIDB, 2018:1). However, the report

indicated that some of the legislation could discourage investors from doing business within the

country. In a study conducted by the World Bank Group in 2018, South Africa was rated

number 82 in 2018 amongst the top 190 countries in the world (figure 2-2) in terms of difficulties

in doing business in SA (Doing Business in SA [DBSA], 2018:29).

From figure 2-1 below, the ease of doing business in SA has continued to be challenging since

2016 and is becoming more difficult. South Africa was ranked 73 in 2016, with a rank of 1 being

the easiest to do business with. The ranking continued to decline with 2017 and 2018 being 73

and 74 respectively. Moreover, prior to any execution of construction works, 18 procedures are

required to take place as a construction permitting process (DBSA, 2018:28). To the report’s

findings, it takes an average of 125 days to get all the approvals, and these contribute to 2.2%

of the total construction works.

Figure 2-1: Ease of doing business in SA

Source: Reducing red tape in construction industry CIDB, (2018)

Figure 2-2 provides an indication of time and costs needed by small and medium-sized

business to acquire approvals for basic construction projects. The details provided, measures

the ease of dealing with construction permits in SA. This includes inspections and all necessary

documents required, such as permits required prior, during and post completion of the works.

28

Figure 2-2: Measuring quality and efficiency of the construction industry

Source: Doing business in SA, (2018)

2.8.4 Contribution to gross domestic product (GDP)

Compared to other industries, the construction industry plays a big role in South Africa’s

economy and contributes significantly to employment and economic growth (CIDB, 2018:25).

With 4% of contribution to the country’s GDP, construction remains the biggest contributor to

Gross Fixed Capital Formation (GFCF), see figure 2-3 below.

Figure 2-3: Gross fixed capital formation (GFCF) in construction; 2010 Rand (million)

Source: Construction Monitor Report (CIDB), (2019)

29

Two main determinants of construction industry performance are the public sector and the

private sector, with state-owned entities (SOEs) being the largest contributor to public sector

spending (PricewaterhouseCoopers, 2016:7). Figure 2-4 shows the public sector forecast and

actual spending from 2010 to 2016. The actual capital expenditure relates to new construction,

property development, and other restored projects incurred by the public sector, whereas the

forecast capital expenditure represents projections made in preceding year. Looking at 2015

from the figure, the actual capital expenditure is slightly less than the forecast capital

expenditure and this indicates challenges faced in the construction industry. The majority of this

decrease is attributed to the decrease in capital expenditure by the government enterprises.

Figure 2-4: Capital expenditure for public sector (R billions) – 2010/2016

Source: SA construction report, 2016

The other significant contributor to capital expenditure in the construction industry is the private

sector, with mining being the largest player (PricewaterhouseCoopers, 2016:9). Figure 2-5

shows the private sector expenditure. From the figure, the mining sector also experiences

challenges in which mining companies are seen to be reducing their capital expenditure,

particularly from 2014 to 2016. Both figure 2-5 and 2-6 shows the decline in performance

experienced by these sectors, hence this study aimed to determine factors influencing the

construction industry performance.

30

Figure 2-5: Capital expenditure for private sector (R billions) – 2010/2016


The industry is responsible for 9.4% of construction Gross Fixed Capital Formation (GFCF) of

the country’s Gross Domestic Product (GDP) (CIDB, 2018:99). It also employs over 609 000

people, with civil engineering and building construction contributing to 369 000 and 209 271

respectively (Construction Monitor [CIDB], 2018:2). This makes the industry the second-largest

employer following the government (Doing Business in SA, 2018:29). The construction industry

contributes the biggest share of the total construction fraternity, which amounted to 63% of total

GFCF (Construction Monitoring Report, 2018:4). Figure 2-6 below shows the contribution of civil

construction towards total income in the construction industry (Construction Monitor [CIDB],

2018:3).

Figure 2-6: Capital expenditure for the private sector (R billions) – 2010/2016


31

The effect of a downturn and lack of demand in the construction industry is also illustrated in the

above figure by the decline in employment between 2016 and 2017.

32

2.9 Empirical studies

Table 2-1: Summary of factors influencing sustainable performance in CI

Summary of factors influencing sustainable performance in construction industry

Gunduz and Yahya (2018). Middle East Region Construction projects Technical skills, scope and work definition, control systems,

effective site management, project management capabilities and

commitment, political conflicts, corruption, harsh climate

conditions, and environment

Damoah and Kumi (2018) Ghana Construction projects Political interference, delays in payment, partisan politics,

bureaucracy, corruption, poor supervision, lack of commitment

by project leaders, poor planning, starting more projects than the

government can fund, and change in government

Sing, Tam, Fung and Liu

(2018)

Hong Kong Construction projects Sustainable workforce.

33


Ranawat,

Bhadoria and Trivedi

(2018)

India Construction projects Project management related factors

Decision-making effort, client satis-faction, planning effort,

construction method, site management, project monitoring, and

bad communication.

Cost related factors

Waste rate of material, escalation of material prices, cost of

rework, cost of variation orders, liquidity of organisation, cash

flow of projects, project design costs.

Client related factors

Client emphasis on low construction costs, client emphasis on

high quality of construction, client emphasis of quick

construction, ability to make decisions, confidence in

construction team, client experience, a dispute between client

and construction team.

Time-related factors

Unavailability of resources, site preparation time, average delay

because closure leading to material shortage, the average delay

in payment from the owner to client, time need to implement

variation orders, planned time for construction, average delays in

claim approval.

34


Environmental related factors

Availability of water, air quality, climate conditions, noise level,

nature of the soil, earth quake possibility, waste around the site

35


Mavi and

Standing (2018)

Australia

Construction projects

Project-related

Clear realistic objectives, project size and level of complexity,

agile project processes, minimal scope change, project

alignment with corporate strategy, urgency, cost-effectiveness of

work, met planned quality standard.

Project management related

Competent project manager, project risk and liability

management, motivated and well-integrated team, global

commitment of project team, effective consultation with key

stakeholders and beneficiaries (trust), project lifecycle

management process.

Organisational related

Project organisational structure, adequate resource availability

(finance, labour, plant, material), full top management and

sponsor support, continuous performance measurement,

maintenance of skills over time (staff retention), good

relationship with stakeholders, thorough technical

understanding/capability of project, lessons learned from

previous projects and applied to future projects, organisational

maturity level, accurate time control and feedback system, end-

36


user imposed restrictions.

External environment-related

Political stability, awareness of environmental issues and related

legislation, achieving national profile, stakeholder expectations,

number and financial conditions in sub-contracts, market

availability.

Sustainability

Energy consumption, water conservation, recycling and waste

management, recycled/reused material, cost of construction,

public comfort and health and safety

Tayeh, Al Hallaq,

Alaloul and

Kuhail

(2018)

Gaza Strip Construction projects Clear scope of the project, the experience of the design team,

the experience of the contractor, closure of crossing points, the

highly qualified technical staff, the availability of funding, the

mechanism of payments, reputation of the contractor, delay in

obtaining funds, sufficient time for design.

Durdyev,

Ismail, Ihtiyar, Bakra and

Darko (2018a)

Malaysia Construction projects Legislative process.

37


Durdyev,

Omarov,

Ismail, Lim and Shukla

(2018b)

Cambodia Construction projects Lack of awareness and knowledge, reluctance to adopt new

sustainable technologies, selection of more durable material,

material consumption, environmental impact, healthy indoor

environment. The higher cost of sustainable building options,

long pay-back period from sustainable practices, lack of

government incentives, lack of statutory requirements that cover

sustainable procurement, lack of professional/designer

capabilities, lack of client demand, lack of training and

education.

Naji et al. (2018) Iraq Construction projects Legislation adequacy

Zidane and

Andersen (2018)

Norway Construction projects Poor planning and scheduling, slow/poor decision-making

process, international administrative procedures and

bureaucracy within project organisations, resources shortage

(human resources, machinery, equipment), poor communication

and co-ordination between parties, slow quality inspection

process of the completed work, design changes during

construction/change orders, lack of commitment and clear

demand from client/sponsor/owner, office issues,

late/slow/incomplete/improper design, user issues,

38


Seth, Nemani, Pokharel

and Al Sayed (2018)

Qatar Construction industry Market competition

Arditi, Nayak, and Damci

(2017)

US Construction industry Organisational culture.

Lindhard and Larsen (2016)

Denmark

Construction projects

Communication inconsistencies and conflicts, errors or defects

in the project documents, accumulation of experience, adequate

sharing of knowledge with the other parties after construction

projects project completion, experience, using procedures,

conflict and disputes between project parties.

Enshassi, Al

Swaity and

Arain (2016)

Gaza Strip Construction projects (Emotional exhaustion, depersonalisation, and reduced personal

achievement)

Job dissatisfaction, unfair reward and treatment, ambiguity,

insecure job, workload and work-family conflict.

Enshassi,

Alkilani, and

Sundermeieri

(2016)

Gaza Strip Construction projects Knowledge management.

Elkhalifa (2016) Sudan Construction industry Socio-economic, political factors

39


Alkahtani

(2016)

Leadership, culture, structure, and processes.

Larsen, Shen, Lindhard

and Brunoe

(2015)

Denmark Construction projects Time delay

Delay or long process times caused by other authorities,

unsettled or lack of project planning, errors or omissions in

construction work, lack of identification of needs.

Affecting budget

Errors or omissions in the consultant material, errors or

inconsistencies in project documents, late user changes

affecting the project or function, lack of preliminary examination

before design or tendering, inexperienced or newly qualified

consultants, unsettled or lack of project funding.

Affecting quality

Errors or omissions in construction work, inexperienced or newly

qualified consultants, political focus on reduced project costs or

time, unsettled or lack of project planning, error or

inconsistencies in project documents.

40


Mat Isa,

Saman and

Preece (2015)

Malaysia Construction projects Firm factors

Financial resources, technical competencies, technology,

knowledge and experience, strategic orientation capabilities,

materials, human capabilities, human relationship stability.

Ye, Zhu,

Shan and Li

(2015)

China Construction Industry Market competition

Negative effect on the environmental dimension.

Positive effect on the social dimension.

Positive effect on the economic dimension.

Senaratne and

Gunawardane (2015)

Sri Lanka Construction project Selection of design team, team roles of the team members, team

role balance and team performance, collaboration among parties

Ametepey,

Aigbavboa,

and Ansah (2015)

Ghana Construction Industry Financial barriers

Fear of higher investment costs

Fear of long-payback period

Client worries in profitability

Ignorance of life cycle costs

Lack of financial resources

Political barriers

Lack of government policies and support

Lack of building codes on sustainability

Lack of government commitment

41


Lack of legislation

Management/leadership barriers

Lack of leadership

Lack of market segmentation

Lack of motivation and aspiration values of managers

Delay in decision making

Technical barriers

Lack of environmentally sustainable materials

Lack of sustainability measurement tools

Lack of exemplar ‘demonstration project’

Lack of easily accessible guidance

Lack of technical ability

Chronic skills and labour shortages

Socio-cultural barriers

Lack of demand of sustainable products by client and

stakeholders.

Cultural change resistance.

Knowledge/awareness barriers

Lack of awareness of professionals

Lack of professional knowledge

42


Lack of awareness of clients

Lack of awareness of benefits

Ignorance or misunderstanding about sustainability

Lack of education and knowledge about in sustainable design

Opoku, Cruickshank, and

Ahmed (2015)

UK Construction project Formulate policy, implement procedures, and disseminate best

practices throughout the organisation.

Windapo and

Cattell (2013)

South Africa Construction industry Cost of building material, access to mortgage/credits, high

interest rates, and high rate of failure of contracting enterprises.

43

2.10 Chapter conclusion

The study of factors influencing sustainable construction performance indicated a large diversity

of performance measures and factors influencing sustainable performance perceived by

different construction professionals and representatives. After identifying various factors from

different countries and environments as seen in table 2-1, grouping or classifying of these

factors was done depending on their nature or factors they address. The groupings are provided

in section 2.7 of this chapter and they signify the most important factors that influenced the

sustainability performance of the construction industry. From the literature reviewed, it is evident

that companies are obliged to make a profit and grow their businesses in order to be

sustainable, and this has also been indicated by Thompson et al. (2017:337) in a book called

“Crafting and Executing Strategy”.

The conclusion drawn from the above study is that having a clear scope definition has a positive

impact on the execution of the projects, and thus ensures sustainable performance. However, to

achieve a well-defined scope, companies must invest in improving their employee capabilities.

Although improving employee capabilities is perceived as expensive and not sustainable, it

could minimise or eliminate errors and omissions experienced on tender documents and

construction works during execution.

44

CHAPTER 3: RESEARCH METHODOLOGY

3.1 Introduction

In the previous chapter, the researcher delineated the literature review of the study. Therefore,

this chapter provides an insight into the methodology used for conducting the exploratory

research. A research design indicating the purpose of the study and the extent of the

researcher’s involvement with the study is outlined and the type of strategy applied for data

collection is discussed. Considering that this study aimed at determining the experiences of the

majority and concludes its findings based on what the majority of the respondents deemed

acceptable, the research approach that was followed is post-positivism.

A quantitative research method was chosen in order to achieve the desired findings, and the

details of this method are given in section 3.3 of this chapter. Measuring instruments and

boundaries (population and sample) to which the instrument was appplied are outlined. In

addition to the above, this chapter addresses how the results were analysed, and any issues or

uncertainties regarding the validity and reliability of the information provided by the respondents

are tested and addressed.

Various factors influencing sustainable performance were identified from prior studies. A survey

was conducted within the chosen population to identify applicable factors within the South

African (SA) context. The results of the survey are presented and analysed using applicable

statistical analysis tools. The implication of the findings and how to achieve performance

sustainability is discussed. The study also provides a framework for addressing the factors

identified which affect sustainable performance in the construction industry of South Africa.

3.2 Research design

Sekaran and Bougie (2013:95) described research design as the “blueprint for the collection,

measurement, and analysis of data, based on the research questions of the study”. Figure 3.1

shows the process that was followed for the successful implementation of this study.

Figure 3-1: The research design

45

Source: Sekaran and Bougie (2013)

3.2.1 Purpose of the study

Studies may either be exploratory, descriptive, or causal in nature depending on the phase or

level to which the knowledge about the particular topic being researched has progressed

(Sekaran & Bougie, 2013:96). Following the projected decline in the construction industry as

detailed in chapter 1, and some facts provided in chapter 2 (literature review) that related to

factors influencing sustainable performance, the need for addressing this performance decline

was enormous and a more comprehensive investigation and information was required to

develop a viable theoretical framework.

As a result of continuous decline in the performance of the construction industry in South

Africa, extensive preliminary work was conducted to ascertain the factors influencing

sustainable performance. The study was conducted through both primary and secondary

research in which literature was reviewed and questionnaires were sent to participants for data

gathering. This enabled the researcher to understand the magnitude or degree of the problem

identified.

PR

OB

LE

M S

TA

TE

ME

NT

DETAILS OF THE STUDY DATA ANALYSIS

Purpose of the study

Exploration

Extent of the researcher

interference

Minimal: Studying events as they nromally occur

Study setting

Noncontrived

Research

strategies

Survey research

Unit of analysis (population to

be studied)

Industry

Individuals

Sampling design

Nonprobability

Sample size =

100

Time horizon

Cross-sectional

study

Data collection

Questionnaires

Data analysis

Descriptive analysis

Correlation analysis

Relative improtance index (RII)

Kruskal-Wallis Test

46

3.2.2 Extent of the researcher interference with the study

The extent of the researcher interference with the study was “minimal interference”. According

to Sekaran and Bougie (2013:99), minimal interference allows the researcher to collect data

from participants using the research instrument to indicate their perception of the phenomenon

being researched. This is done with no interference with the regular activities and without

manipulating certain variables in order to observe the behavioural response of the depended

variables concerned.

3.2.3 Study setting

Because this study aimed at establishing the cause-and-effect relationship of the dependent

variables (factors) and independent variables (sustainable performance) the study setting that

was used is a non-contrived setting. A non-contrived setting is used where events proceed

normally without the researcher’s interference. This setting best fits a correlation study (Sekaran

& Bougie, 2013:100).

3.2.4 Research design

The survey research design was used to gather information from the intended population, and

the results were compared and explained in order to describe the behaviour and perception of

the respondents. This design was preferred for its ability to collect both qualitative and

quantitative data using several research questions and could be used with exploratory study

(Sekaran & Bougie, 2013:102).

3.3 Research approach

The research approach for this study was post-positivist. Post-positivism provides research

issues in the context of determining the experiences of the majority and concludes its findings

based on what the majority of the respondents deem acceptable (Panhwar et al., 2017:253).

Although Ryan (2015) argued that post-positivism contains contradictions between various

elements which results in a lack of confidence to embrace the approach as a whole, Creswell

(2014:7) posited that post-positivist "holds a deterministic philosophy in which causes

47

determined effects or outcomes”. Post-positivism approach is also used when the following

holds (Panhwar et al., 2017:8):

The researcher aims to inspire and stimulate examination of specific phenomena from

diverse perspectives, using different tools and methods, and in different contexts.

When the researcher aims to provide less biased and more objective results.

3.3.1 Quantitative and qualitative research

According to Bryman (2014:29), different types of research designs are conducted through

qualitative, quantitative, and/or mixed methods, with qualitative and quantitative being the most

commonly used paradigms (Weare et al., 2004:1.6). From these approaches the researcher’s

decision on the type to be undertaken is critical (Creswell, 2014:11). A study can be conducted

using either qualitative or quantitative approaches, meaning, both approaches possess

elements of flexibility which then result in a study being either more qualitative or quantitative

(Trochim et al., 2016:142).

The difference between the two approaches is framed in terms of open ended-questions

(qualitative) which allows data to be analysed concurrently with data collection (Bickman & Rog,

2008:236), whereas quantitative research provides data analysis obtained from close-ended

questions using numbers (Creswell, 2014:4). Prior to deciding the research method, the

researcher found it necessary to provide a comprehensive comparison of qualitative and

quantitative methods in order to select one that was most suitable and best addressed the

problem statement and objectives of this study.

3.3.1.1 Qualitative research

Qualitative research has been used by researchers for a very long time; developed in the 1960s

and 1970s as an alternative to a quantitative research method (Flick, 2018:11), and becoming

more noticeable during the 1990s and into the 21st century (Creswell, 2014:13). Qualitative

research is mostly applied where a researcher needs to establish a deeper understanding of a

phenomenon from the research sample (Creswell, 2014:17) and identify patterns between the

words to develop an expressive representation of the phenomenon without compromising its

richness (Leung, 2015:326). Although a qualitative approach is criticised for focusing on a

48

smaller sample size than quantitative which may, in certain instances, results in unreliable and

ungeneralisable findings (Rahman, 2017:102), below are some of the key advantages which

substantiate the use of qualitative research (Creswell, 2014:17; Rahman, 2017:108):

Qualitative research helps in achieving a deeper insight into the phenomenon from the

views of the participants by using a narrative approach to collect data,

It helps in understanding the behaviour of the candidate, interviewer, as well as cross-

cultural influences on behaviour during the interview.

3.3.1.2 Quantitative research

The argument regarding quantitative and qualitative research is attributed to differences in

assumptions about what reality is and whether or not the reality is quantifiable (Newman et al.,

1998:2). Quantitative research has been the dominating method globally in the assessment

research due to its frequency of use by researchers (Rahman, 2017:108). According to Watson

(2015:44), quantitative research is mostly used when systematic examination of a social

phenomenon or occurrence is performed using statistical or numerical data, and it also helps in

determining and understanding the “what” factors influencing company performance (Anon:6).

Therefore this method encompasses measurement and assumes that the occurrences

performed will be quantifiable (Norris et al., 2015:470). Quantitative research is best used when

the following holds (Rahman, 2017:102):

A larger sample size to allow the researcher to draw sufficient conclusions using

statistical methods,

A relatively shorter time or period for data collection, and

When a researcher intends to take a snapshot of a phenomenon without an in-depth or

deeper understanding of the participant’s experiences regarding the phenomenon.

When determining social behaviours which can be quantified and patterned as opposed

to examining and interpreting their denotations that participants bring to their own

actions.

49

3.3.2 Selecting an applicable research

Different researchers have successfully used both qualitative and quantitative methodologies in

different studies and results show that no methodology is better or worse than the other (Antwi

& Hamza, 2015:223). The researcher's capabilities contribute to a major part in ensuring that

the selected research method realises the success of the anticipated objectives. Therefore his

or her skill set and preferences need to be acknowledged when selecting an appropriate

method for the study (Patten, 2015:48). Because the researcher used a post-positivist approach

which is mostly applied in quantitative methods of data collection rather than qualitative

research (Creswell, 2014:7), a quantitative approach was considered most suitable. Also, the

researcher’s intention is to provide professionals and companies with quantifiable and

measurable hypotheses, hence a quantitative method was deemed to be suitable.

3.4 Research method

The purpose of the research methodology is to provide the rationale behind the overall research

design that has governed the study and also to provide the development of the theories, data

collection, and design of the questionnaire used (Anon:18). For the researcher to perform and

fulfil all requirements of the study, attaining knowledge and information is essential (Shahraki et

al., 2018:66). For the accomplishment of this study, the researcher conducted a quantitative

research approach in order to identify the factors affecting construction industry performance.

As the first step of the research methodology, the researcher reviewed the most recent and past

literature available, like textbooks, and journals (peer-reviewed). Although most of the studies

originated from various countries worldwide, the expert consultation was conducted through a

survey link containing the questionnaire in order to match the South African context. These

were participants who consistently dealt with construction industry challenges in their

environment.

3.5 Measuring instrument (s)

As previously mentioned by Watson (2015:44), quantitative research assumes that the

occurrences being investigated can be measured and data analysed for trends and

relationships. For this study, the measuring instrument was gathered by developing new and

adopting instruments that have been used in similar but not identical research topics. Appendix

50

B attached shows three instruments used for data collection. From Annexure A, the required

demographic information was only used for statistical analysis and this statement is also

indicated in the research questionnaire which was sent to the intended participants.

To measure the reliability and validity of the measuring instrument, an Alpha Coefficient of 0.7

was used. Alpha’s coefficient ranges from 0.0 to 1.00, and there is no one set scale considered

to be mandatory as different scales ranging from 0.5 to 0.90 have been used by various

researchers (Helms et al., 2016:656). Cho and Kim (2015:227) explained that for accuracy and

more reliability, Alpha’s coefficient should not be less than 0.7. Also, a 7-point Likert scale was

used in a way that critical occurrences would be collected. Respondents were requested to

identify and rate occurrences that resulted in high or low performance.

3.6 Research procedure

Generally, data is collected through observations performed at either one point or several points

in time using survey tools such as; longitudinal surveys and cross-sectional surveys.

Longitudinal research comprises recurrent measures on the same variables at different times

(Mertens et al., 2017:73), whereas cross-sectional research allows data to be collected at one

point in time (Creswell, 2014:157). Although the longitudinal survey provides a more robust

analysis, due to time constraints, a cross-sectional survey was selected for this study. This

survey enabled the researcher to provide an insight into the factors influencing sustainable

performance in the construction industry.

Data was collected from the targeted population sample through the administration of the

research questionnaire. Due to the large size of the sample, contact with respondents was

made via e-mails. The questionnaire was also made available to all participants through a link to

esurveycreator software in which the survey resided. This software provided convenient access

as it could be accessed through smartphones, tablets, laptops and desktop computers without

having to print the questionnaire.

3.7 Research population

The research population is defined as the overall or total entity in which the researcher's interest

is invested (Wilson, 2016:45). Sekaran and Bougie (2013:240) further define the population as

51

"the group of people, events, or things of interest that the researcher wishes to investigate”. For

this study, the research population was limited to the construction companies’ registered with

CIDB as grade 3 to grade 9 in South Africa, these companies also form part of the CIDB Small

Medium Enterprise (SME) business conditions survey which is conducted quarterly.

However, because reaching out to the whole fraternity or population in which the research was

examined was most likely impossible, a sample was drawn, which Sekaran and Bougie

(2013:241) defined as a “subset of the population” due to its element of containing some

participants selected from the population. Also, Diamantopoulos and Schlegelmilch (2000:12)

defined a sample as part of the population on which the researcher intends doing the study.

When selecting a sample, Alvi (2016:11); Diamantopoulos and Schlegelmilch (2000:12) found

that identification of a group of people (smaller in number than the population), collection of

individuals, objects or events about which the researcher intends to make inferences is critical

as the researcher will be drawing a conclusion about the whole population. Therefore, for this

study and on the bases of accessibility and time constraints to reach out to the entire

population, only construction professionals from companies that have executed or currently

execute works within government enterprises were sampled. The selection of these enterprises

was based on the amount of construction work recorded by the public sector during the financial

year 2015 to 2018 (CIDB, 2013), and also on its having a high number of construction works in

South African state-owned entities.

Within this sample, the sampling units which Sekaran and Bougie (2013:242) defined as "the

element or set of elements that are available for selection in some stage of the sampling

process" were public sector and contractor representatives, project managers, contract

managers, architects, engineers, quantity surveyors, procurement managers and officers. The

selection of the sampling units was based on the amount of exposure and involvement in

construction projects. These identified respondents possessed experience in projects containing

infrastructure, mechanical and electrical engineering, and power generation new build projects.

Participants were required to indicate their experience related to the construction background

and knowledge to participate in and provide reliable opinions for the survey. Moreover,

participants were requested to indicate what their company performance measures were and

the factors influencing sustainable performance in the construction industry.

52

Diamantopoulos and Schlegelmilch (2000:18) mention that the sampling process was used for

data collection where a sequential sample was first preferred. This process is as shown below:

Obtain results from the intended sample, if the results are not conclusive after an initial

sample is taken,

More observations will be made, should the increased sample size still not provide

conclusive results,

More population elements will be added to the sample until conclusive results are

obtained.

3.8 Sampling procedure

On the basis of impracticalities or lack of access for the researcher to compile a list of all

participants comprising the intended population, the sampling procedure that was used was a

multistage or clustering procedure. Multistage permits the researcher to identify groups or

organisations prior to obtaining names of the individuals within the cluster (Creswell, 2014:158).

A nonprobability sampling was used for this study. Although Sekaran and Bougie (2013:252)

state that the findings from the sample of the nonprobability cannot be confidently generalised

to the population, Damoah and Kumi (2018:15) argued that using convenience and purposive

sampling techniques helps to provide the researcher with rich information about the subject

under research.

The selection of the convenience sampling technique was based on the researcher’s

convenience of collecting information from the population that was conveniently available to

provide it. For the purposive sampling technique, the researcher aimed at the respondents who

could provide the anticipated information as they would be conforming to the criteria provided

for this study (Sekaran & Bougie, 2013:252). Where the researcher could not get to a sufficient

number of the intended respondents, snowballing or a quota sampling technique was used.

Although the results of these techniques minimised generalisation of the findings, they provide

an advantage of ensuring that certain groups were well represented in the research (Sekaran &

Bougie, 2013:253).

53

For reliability, stratification of the population was done prior to selecting the sample. This aimed

to ensure that perceptions of participants with significant experience in the construction industry

were obtained. Stratification also helps in ensuring that specific groups are represented and

provides attractive properties for certain classes of problems (Shields et al., 2015:310).

3.9 Data analysis

For statistical analysis, the Statistical Package for the Social Sciences (SPSS) was used. Below

are the statistical steps that were followed during analysis:

Step 1: Descriptive analysis

The first analysis to perform was a descriptive analysis. This provides information on the

distribution of variables. It also enabled measures of central trends such as mean, median,

mode and any information regarding the stability or sampling errors of certain measures

(George & Mallery, 2016:9).

Step 2: Correlation analysis

To carry out the study under review, correlation analysis was used to determine the strength

and direction of the relationship between two variables such as; company performance

measures and company performance. The following table shows interpretation guidelines that

have been used in this research (Nangolo & Musingwini, 2011:467)

Table 3-1: Interpretation guideline

.01 to .10 or -.01 to -.10 No or very weak positive or negative (-) relationship

.11 to .30 or -.11 to -.30 Weak positive or negative (-) relationship

.31 to .50 or -.31 to -.50 Moderate positive or (-) negative relationship

.51 to .80 or -.51 to -.80 Strong positive or (-) negative relationship

.81 to 1.0 or -.81 to -1.0 Very strong positive or negative (-) relationship

54

Step 3: Relative importance index (RII)

RII was used to determine the respondent's perception of the relative importance of the factors

influencing sustainable development in the South African construction industry. RII also helped

in identifying the most critical factors as perceived by the respondents. RII “is often desired

when the explanatory aspects of regression analysis are of interest” (Johnson & LeBreton,

2004:238).

3.10 Reliability and validity

For reliability, stratification of the population was done prior to selecting the sample. This aimed

at ensuring that perceptions of participants with experience in the construction industry were

obtained. Stratification also helped in ensuring that specific groups were represented and

provided attractive properties for certain classes of problems (Shields et al., 2015:310).

To stimulate confidence in the cause-and-effect relationship of the dependent variables

(sustainable performance) and independent variables (factors) of this study, internal validity was

applied. With internal validity, the researcher aimed at ensuring that measures taken included

adequate and representative respondents that inform the concept with a relatively good

background and knowledge in the construction industry. This helped in providing strong

certainties with regard to the quality of the findings. According to Sekaran and Bougie

(2013:174), internal validity also addresses the question of “To what extent does the research

design permit us to say that the independent variable A causes a change in the dependent

variable B?”


The study was conducted using a quantitative research approach which allowed the researcher

to gather information on a larger sample size in a relatively shorter time.

55

CHAPTER 4: PRESENTATION OF RESULTS AND FINDINGS

This chapter puts forward the data analysis and the results obtained thereafter.

4.1 Introduction

The aim of this study has been to determine critical factors influencing sustainable performance

in the construction industry in South Africa. In order to accomplish this objective, the data

gathered for this study were analysed and the results were presented under this chapter. The

results were presented through the following sections: Section 4.1 is the introduction of the

chapter, followed by section 4.2 which provides the descriptive statistics of the variables. This

includes analysis of demographics and simple descriptive statistics like means and standard

deviations as well as reliability of the data performed through Cronbach alpha analysis,

thereafter section 4.3 provides the correlation analyses between the variables and analyses the

demographics of the respondents, after that section 4.4 presents the relative importance index

to rank factors that influence sustainable performance in the construction industry in South

Africa.

The research data was collected through an online survey, esurveycreator. In total, 250

questionnaires were circulated and 133 were returned which is about a 53.2% response rate.

From 133 returned, 122 questionnaires could be used to achieve the objective and aim of the

study. The questionnaires circulated were designed as per the following sections, as described

in the literature study in chapter 2:

Demographics (D);

Financial performance measures (FM)

Non-financial performance measures (NFM)

Scope definition (project level) (SD)

Technical capacity (organisational level) (TC)

Knowledge management (organisational level) (KM)

Resource availability (organisational level) (RA)

Political (external level) (P)

Material management (external level) (MM)

56

Technology transformation (external level) (TT)

Regulatory (external level) (RF)

Business conditions/market competition (external level) (BC)

Leadership (organisational) (L)

Budget constraints (organisational level) (BCC)

Corruption (external/organisational level) (C)

Volatile commodity and exchange rates (external level) (VC)

Socio-cultural (organisational level) (SC)

Time-related (project level) (TR)

Performance correlation (PC)

4.2 Descriptive statistics of variables

4.2.1 Demographics

The demographic data included the type of business, turnover of the business, CIDB Grade of

the company, experience of the participants in the business, the level of management the

participants hold in the organisation as well as whether the organisation from which the

participants come has a parent organisation or not. The analyses of the demographics are

presented through figure 4.1 – figure 4.5 below.

Participants were asked to indicate the type of construction business they are operating. The

results are as follows as per figure 4.1 below: 14.75% of the participants indicated that they

work under building engineering, 19.67% of the participants indicated that they work under civil

engineering, 25.41% of the participants indicated that they work under consulting in engineering

and construction while 40.16% of the participants indicated “other”. In this study, “Other”

includes industries such as Eskom.

57

Figure 4-1: Types of business

Source: SPSS

The next demographic was for the participants to indicate the annual turnover of the business or

the grading of the business. CIDB grading and turnover of the business are linked to each other.

The “2-4” grading corresponds to R600 000 to R4 mil, the “5-6” grading corresponds to R6.5 mil

to R13 mil, the “7-8” grading corresponds to R40 mil to R130 mil, the “9” grading does not have

a limit. Noted from Figure 4.2 below, 24.59% of the participants indicated that their business has

level “2-4” CIBD grading, 10.66% of the participants indicated that their business has level “5-6”

CIBD grading, 22.13% of the participants indicated that their business has level “7-8” CIBD

grading, 17.21% of the participants indicated that their business has level “9” CIBD grading

while 25.41% of the participants indicated “other” for their CIDB grading.

The survey was sent to both CIDB and Eskom “Other” employees, hence the results contain

‘Other” which is also the majority of the survey, and these employees do not have CIDB

grading; however, they possess relevant experience and knowledge of the construction

industry.

58

Figure 4-2: Turnover of the business

Source: SPSS

The next demographic was for the participants to indicate how long they have been working in

or operating their businesses. Based on Figure 4.3 below, 25.41% of the participants have been

working in or operating their businesses for less than three years, 38.52% of the participants

have been working in or operating their businesses between 4 – 10 years, 22.13% of the

participants have been working in or operating their businesses between 11 – 15 years, 7.38%

of the participants have been working in or operating their businesses between 16 – 20 years

while 6.56% of the participants have been working in or operating their businesses for more

than 20 years.

The majority of the participants are fairly new in the construction industry. This can be seen

through the fact that 93.44% of the participants have indicated that they have been working in or

operating their businesses for less than 20 years and this is within the 25 years of South African

democracy. This means that most of the businesses were started after the end of apartheid in

1994, as before 1994, South Africa was dominated by centuries of racial and financial

discrimination and persecution which saw the white minority of South Africa being the only

people operating and benefitting within the construction industry. After 1994, the South African

government introduced racially selective programmes to right the wrongs of Apartheid by

providing the black (African, Coloureds, Indians and Chinese) South African nation economic

59

benefits that are not accessible to White South African. These selective programmes included

procedures such as “employment preference, skills development, ownership, management,

socio-economic development, and preferential procurement”.

Figure 4-3: Experience in the business

Source: SPSS

The next demographic was for the participants to indicate the level of management or position

they hold in the business. As per Figure 4.4 below, 28.69% of the participants have indicated

they hold management positions, 21.31% of the participants have indicated they hold middle

management positions, 26.23% of the participants have indicated they hold senior management

positions, 12.30% of the participants have indicated they hold supervision positions and 11.48%

of the participants have indicated they hold “other” positions.

https://en.wikipedia.org/wiki/Affirmative_action#South_Africa

60

Figure 4-4: Level of management

Source: SPSS

The next demographic was for the participants to indicate whether the business they are

working in or operating has a parent organisation or not. From Figure 4.5 below, 79.51% have

indicated that their businesses have a parent organisation, 14.75% have indicated that their

businesses do not have a parent organisation and 5.74% have indicated that they do not know

if their businesses have a parent organisation.

Figure 4-5: Parent of the organisation

Source: SPSS

61

4.2.2 Descriptive statistics

Table 4-1: Descriptive statistics

# Item mean v ar std,dev CV skewness kurtosis SE of Mean

1 FM1: Profitability 4,169 1,166 1,08 26% -1,45 1,552 0,097

2 FM2: Sales growth 3,758 1,274 1,129 30% -0,765 -0,316 0,101

3 FM3: Financial stability 4,169 1,052 1,026 25% -1,37 1,419 0,092

4 FM4: Cash flow 3,976 1,276 1,13 28% -1,162 0,648 0,101

5 FM5: Market share 3,556 1,582 1,258 35% -0,589 -0,71 0,113

6 NFM4: Business efficiency or productivity 4,202 1,057 1,028 24% -1,564 2,093 0,092

7 NFM6: Competitive strength and market standing 3,944 0,981 0,99 25% -1,284 1,528 0,089

8 NFM5: Effectiveness of planning 4,048 0,973 0,987 24% -1,204 1,104 0,089

9 NFM2: External customer (client) satisfaction 4,226 0,94 0,97 23% -1,307 1,192 0,087

10 NFM1: Quality of service and work 4,395 0,908 0,953 22% -1,968 3,783 0,086

11 NFM3: Safety 4,387 0,678 0,824 19% -1,676 3,521 0,074

12 SD1: Lack of clear project goal and vision 4,073 1,287 1,135 28% -1,3 0,826 0,102

13 SD2: Insufficient time given for the designer to design and prepare project drawings 3,927 1,499 1,224 31% -0,971 -0,174 0,11

14 SD3: Teamwork and involvement of all parties or key stakeholders in the design phase 4,274 0,965 0,982 23% -1,533 1,95 0,088

15 SD4: Failure to apply latest design codes and software during scoping 3,726 1,323 1,15 31% -0,73 -0,368 0,103

16 SD5: The ability to accurately estimate the required time to complete the project 4,323 0,887 0,942 22% -1,658 2,639 0,085

17 SD6: Lack of communication between the designer and the execution team during design phase

4,169 1,02 1,01 24% -1,279 1,109 0,091

18 SD7: Conducting project constructability workshops prior each project tendering 3,976 1,357 1,165 29% -0,902 -0,297 0,105

19 TC1: Insufficient of skil led and professional technical staff such as designer/engineer/project

manager, with relevant experience and knowledge 4,548 0,754 0,868 19% -2,403 5,96 0,078

20 TC2: Company's awareness of technical specifications required for business and their high -

quality implementation 4,177 1,058 1,028 25% -1,467 1,828 0,092

21 TC3: Lack of environmentally sustainable materials 3,419 1,725 1,313 38% -0,477 -0,975 0,118

62


22 TC4: Lack of sustainability measurement tools 3,508 1,553 1,246 36% -0,518 -0,901 0,112

23 TC5: Lack of exemplar ‘demonstration project’ 3,411 1,708 1,307 38% -0,653 -0,744 0,117

24 TC6: Lack of easily accessible guidance 3,444 1,712 1,309 38% -0,418 -1,098 0,118

25 KM1: Application of knowledge management systems to facil itate knowledge retention and

learning 4,29 1,021 1,01 24% -1,63 2,214 0,091

26 KM2: Effective mechanisms and processes to inspire sharing of information, lessons learned and open communication

4,008 1,293 1,137 28% -1,333 1,048 0,102

27 KM3: Knowledge sharing activities, where employees/parties are given sufficient time for engagements

3,782 1,277 1,13 30% -0,911 -0,153 0,101

28 KM4: Allocation of workers from one project to another in order to gain more knowledge and

share post-project lessons learned from other projects 4,121 1,18 1,086 26% -1,295 0,854 0,098

29 KM5: Awareness of professionals and understanding about sustainability 4,032 1,121 1,059 26% -1,287 1,118 0,095

30 KM6: Lack of education and professional knowledge about in sustainable design 3,863 1,55 1,245 32% -0,92 -0,293 0,112

31 RA1: Availability of site project management team 4,5 0,821 0,906 20% -2,178 4,64 0,081

32 RA2: Appropriate allocation of personnel on project sites 4,548 0,461 0,679 15% -1,642 2,929 0,061

33 RA3: Presence of local government and training authorities for review of labour policies for

maintaining construction workforce sustainability 3,581 1,4 1,183 33% -0,381 -1,036 0,106

34 P1: Lack of government legislations and policies 3,863 1,485 1,219 32% -0,836 -0,45 0,109

35 P2: Lack of building codes on sustainability 3,629 1,731 1,316 36% -0,597 -0,854 0,118

36 P3: Lack of government commitment and support 4,242 1,014 1,007 24% -1,297 0,911 0,09

37 P4: Political instability and uncertainties 4,452 0,965 0,982 22% -1,754 2,144 0,088

38 P5: Policy uncertainty 4,097 1,356 1,165 28% -1,105 0,061 0,105

39 MM1: Material availability 4,081 1,311 1,145 28% -1,092 0,011 0,103

40 MM2: Material shortage 4,008 1,374 1,172 29% -0,887 -0,562 0,105

41 MM3: Material durability (quality) 4,234 1,173 1,083 26% -1,459 1,26 0,097

42 MM4: Effective coordination of material replenishment 3,976 1,292 1,137 29% -0,81 -0,565 0,102

43 MM5: Cost of building material 4,258 1,055 1,027 24% -1,374 1,002 0,092

44 TT1: Adaptability to changing business conditions (4th industrial revolution) 4,177 1,058 1,028 25% -1,467 1,828 0,092

45 TT2: Automation which replaces construction workforce (4th industrial revolution) 3,798 1,609 1,269 33% -0,759 -0,685 0,114

46 RF1: Sudden changes of law and regulations 4,177 1,269 1,127 27% -1,195 0,148 0,101

63


47 RF2: Adding more regulations to the construction project processes 4,266 1,01 1,005 24% -1,212 0,241 0,09

48 RF3: Having apparent statutory process that incorporate sustainable construction practices

with economics incentives 3,927 1,043 1,022 26% -0,764 -0,159 0,092

49 BC1: Perceptions of the confidence in business conditions 4,032 1,088 1,043 26% -1,086 0,492 0,094

50 BC2: Investor confidence 4,282 1,212 1,101 26% -1,656 1,94 0,099

51 BC3: Insufficient demand for work 4,242 1,193 1,092 26% -1,598 1,85 0,098

52 BC4: Higher market competition between construction companies (Tendering completion) 4,298 1,073 1,036 24% -1,832 2,975 0,093

53 L1: Lack of leadership in construction industry 4,121 1,359 1,166 28% -1,333 0,846 0,105

54 L2: Lack of market segmentation (geographic and demographic) 3,565 1,5 1,225 34% -0,556 -0,717 0,11

55 L3: Lack of motivation and aspiration values of managers 4,073 0,865 0,93 23% -1,346 1,904 0,084

56 L4: Delay in decision making 4,371 0,674 0,821 19% -1,641 3,462 0,074

57 L5: Client ability to make decision 4,258 0,665 0,815 19% -1,48 3,198 0,073

58 L6: Dispute between client and construction team 4,282 1,017 1,009 24% -1,523 1,716 0,091

59 BCC1: Fear of higher investment costs 3,831 1,638 1,28 33% -0,839 -0,551 0,115

60 BCC2: Fear of long-payback period 3,879 1,457 1,207 31% -0,898 -0,315 0,108

61 BCC3: Client worries in profitability 3,976 0,983 0,992 25% -1,243 1,489 0,089

62 BCC4: Ignorance of l ife cycle costs (ability to select alternatives that impact both pending

and future costs) 3,919 1,115 1,056 27% -0,95 0,448 0,095

63 BCC5: Lack of financial resources 4,403 0,73 0,855 19% -1,561 2,229 0,077

64 BCC6: Access to mortgage/credits 4,008 1,455 1,206 30% -1,256 0,685 0,108

65 BCC7: High interest rates 4,129 1,203 1,097 27% -1,061 -0,008 0,098

66 BCC8: Reduced government spend on infrastructure related projects 4,468 0,901 0,949 21% -1,944 3,317 0,085

67 BCC9: Client emphasis on low construction costs 4,306 0,686 0,828 19% -1,379 2,117 0,074

68 C1: Lack of company's code of ethics that addresses ethical conduct (principles of right or

wrong conduct) 4,29 0,972 0,986 23% -1,558 1,983 0,089

69 C2: Lack of knowledge on business ethics 4,379 0,644 0,802 18% -1,714 4,017 0,072

70 C3: Adherence to company's code of ethics that maintain a culture that promotes the

principles of the company 4,161 1,323 1,15 28% -1,299 0,641 0,103

71 C4: A company culture that puts profitability and business performance ahead of ethical behaviour

4,105 1,249 1,118 27% -1,314 0,971 0,1

64


72 VC1: Volatility in labour markets (strikes) which resulted in l imited foreign investments within

the country 4,137 1,079 1,039 25% -1,18 0,867 0,093

73 VC2: Volatility in commodity prices and exchange rates (weakening exchange rate) 3,911 1,317 1,148 29% -0,724 -0,717 0,103

74 VC3: Declining rand value 4,121 1,034 1,017 25% -1,253 1,2 0,091

75 VC4: Downgraded economic ratings from global agencies 3,839 1,518 1,232 32% -0,677 -0,862 0,111

76 SC1: Lack of demand of sustainable products by client and stakeholders 3,637 1,42 1,192 33% -0,481 -0,791 0,107

77 SC2: Cultural change resistance 3,96 1,372 1,171 30% -0,976 -0,003 0,105

78 SC3: Stakeholder's engagement and effective participation 3,903 1,113 1,055 27% -0,798 -0,255 0,095

79 TR1: Planned time for construction 4,323 0,724 0,851 20% -1,522 2,767 0,076

80 TR2: Time taken to implement variation orders (Affecting completion) 4,315 0,738 0,859 20% -1,406 1,904 0,077

81 TR3: Average delay in claim approval (Affecting completion and cash flow) 4,25 0,839 0,916 22% -1,388 1,809 0,082

82 TR4: Average delay in payment from owner to contractor (Affecting cash flow 4,266 1,075 1,037 24% -1,454 1,341 0,093

83 PF1: The company have a clear vision and mission statements relevant to the organization's

activities and mandate 4,323 0,676 0,822 19% -1,606 3,457 0,074

84 PF2: The company have adequate business plan that is documented and measurable in

terms of performance standards in the industry 4,218 0,806 0,898 21% -1,438 2,177 0,081

85 PF3: In the last three years the company's turnover and profitabil ity has increased recommendable

2,992 1,959 1,4 47% 0,014 -1,435 0,126

86 PF4: The company performance measures are compatible with the activities being carried

out by the organization 3,79 1,013 1,006 27% -0,954 0,459 0,09

87 PF5: My understanding of the organization's performance measures is adequate 4,081 0,693 0,832 20% -1,408 3,172 0,075

88 PF6: My understanding of issues that influences the performance of the company is adequate

4,194 0,857 0,925 22% -1,486 2,356 0,083

89 PF7: The teams of employees in my company are made accountable for business

performance 3,774 1,363 1,168 31% -0,958 -0,085 0,105

90 PF8: My company have sufficient cash flows to satisfy its business objectives in a controlled

manner 3,371 2,008 1,417 42% -0,36 -1,306 0,127

91 PF9: My company strive for business excellence and overall customer satisfaction at all times

4,315 0,705 0,84 19% -1,78 4,31 0,075

92 PF10: The effectiveness of the team of employees working in my company is commendable 4,016 0,894 0,946 24% -1,176 1,336 0,085

93 PF11: My company's day-to-day operations are documented in the form of an operational manual

3,581 1,481 1,217 34% -0,482 -1,032 0,109

Source: SPSS

65

For all the variables, the number of participants in the data set was 124. The range of the

variables is based on a minimum of one and a maximum of five. The average for each variable

is contained under mean. “Profitability” (FM1) and “financial stability” (FM3) have the highest

average (4.169), as indicated in table 4-1, amongst all financial measures (FM), meaning the

participants scored “profit” and “financial stability” as the highest financial measures contributing

to the performance of the business. “Market share” (FM5) has been ranked as the lowest

financial measure with an average score of 3.556 by the participants, as indicated in table 4-1.

The coefficient of variation of all the financial measures is sitting between 25% and 35%, as

indicated in table 4-1. It can be noted that there is a much greater variability amongst the

answers provided by the participants for financial measures, as indicated in table 4-1. The

skewness of FM1 and FM3 is -1.45 and -1.37 respectively (see table 4-1), therefore the

distribution of FM1 and FM3 are skewed to the left, which means most of their values are

concentrated to the right of the mean, while extreme values are concentrated to the left. In

addition, their kurtosis values are 1.552 and 1.419, respectively as per table 4-1. Their kurtosis

values are below three and their distributions are therefore flatter than normal, which indicates a

wider range of values around the mean.

“Quality of service and work” (NFM1) has the highest average (4.395) amongst all non-financial

measures (NFM), as indicated in table 4-1, meaning the participants scored “quality of service

and work” as the highest non-financial measure contributing to the performance of the business.

The coefficient of variation of all non-financial measures is sitting between 19% and 25%, as


answers provided by the participants for non-financial measures.

The above can also be noted with standard deviations that are not close to the mean values of

the non-financial measures. The skewness of NFM1 is -1.968 (see table 4-1), therefore the

distribution of NFM1 is skewed to the left, which means most of the values are concentrated to

the right of the mean, while extreme values are concentrated to the left. In addition, NFM1

kurtosis value is 3.783 as per table 4-1. The kurtosis is larger than three and therefore the tail is

sharper than the normal distribution and shows higher concentrations around the mean.

"Lack of clear project goal and vision" (SD1) has the highest average (4.323) amongst all scope

of definition (SD) variables at a project level, as indicated in table 4-1, meaning the participants

66

scored "lack of clear project goal and vision" as the highest scope of definition (SD) variable

contributing to the performance of the business. The coefficient of variation of all scope of

definition (SD) variables at a project level is sitting between 22% and 31%, as indicated in table

4-1. It can be noted that there is a much greater variability amongst the answers provided by the

participants for the scope of definition (SD) variables. This can also be noted with standard

deviations that are not close to the mean values of the scope of definition (SD) variables.

The skewness of SD1 is -1.300 (see table 4-1), therefore the distribution of SD1 is skewed to

the left, which means most of the values are concentrated to the right of the mean, while

extreme values are concentrated to the left. In addition, SD1 kurtosis value is 0.826, as per

table 4-1. The kurtosis value is positive which means that the tail is lighter than the normal

distribution because the values are near zero. The kurtosis value is below three and its

distribution is, therefore, flatter than normal, which indicates a wider range of values around the

mean.

"Insufficient skilled and professional technical staff such as designer/engineer/project manager,

with relevant experience and knowledge" (TC1) has the highest average (4.548) amongst all

technical capacity (TC) variables at an organisational level, as indicated in table 4-1, meaning

the participants scored “Insufficient skilled and professional technical staff such as

designer/engineer/project manager, with relevant experience and knowledge" as the highest

technical capacity (TC) variable contributing to the performance of the business.

The coefficient of variation of all technical capacity (TC) is sitting between 19% and 38%, as


answers provided by the participants for technical capacity (TC) variables. This can also be

noted with standard deviations that are not close to the mean values of the technical capacity

(TC) variables. The skewness of TC1 is -2.403 (see table 4-1), therefore the distribution of TC is

skewed to the left, which means most of the values are concentrated to the right of the mean,

while extreme values are concentrated to the left. In addition, TC1 kurtosis value is 5.960 as per

table 4-1. The kurtosis is larger than three and therefore the tail is sharper than the normal

distribution and shows higher concentrations around the mean.

"Failure to apply knowledge management systems to facilitate knowledge retention and

learning” (KM1) has the highest average (4.290) amongst all knowledge management (KM)

67

variables at an organisational level, as indicated in Table 4-1, meaning the participants scored

“Failure to apply knowledge management systems to facilitate knowledge retention and learning

“as the highest knowledge management (KM) variable contributing to the performance of the

business.

The coefficient of variation of all knowledge management (KM) is sitting between 24% and 32%,

as indicated in Table 4-1. It can be noted that there is a much greater variability amongst the

answers provided by the participants for knowledge management (KM) variables. This can also

be noted with standard deviations that are not close to the mean values of the knowledge

management (KM) variables. The skewness of KM1 is -1.630 (see table 4-1), therefore the

distribution of KM1 is skewed to the left, which means most of the values are concentrated to

the right of the mean, while extreme values are concentrated to the left. In addition, KM1

kurtosis value is 2.214 as per table 4-1. The kurtosis value is below three and its distribution is,

therefore, flatter than normal, which indicates a wider range of values around the mean.

"Inappropriate allocation of personnel on project sites” (RA2) has the highest average (4,548)

amongst all resource availability (RA) variables at an organisational level, as indicated in table

4-1, meaning the participants scored “Inappropriate allocation of personnel on project sites" as

the highest resource availability (RA) variable contributing to the performance of the business.

The coefficient of variation of all resource availability (RA) is sitting between 15% and 33%, as


answers provided by the participants for resource availability (RA) variables. This can also be

noted with standard deviations that are not close to the mean values of the resource availability

(RA) variables. The skewness of RA2 is -1.642; see table 4-1, therefore the distribution of RA2

is skewed to the left, which means most of the values are concentrated to the right of the mean,

while extreme values are concentrated to the left. In addition, RA2 kurtosis value is 2.929 as per

table 4-1. The kurtosis value is below three and its distribution is, therefore, flatter than normal,

which indicates a wider range of values around the mean.

"Political instability and uncertainties" (P4) has the highest average (4.452) amongst all political

(P) variables outside the business, as indicated in table 4-1, meaning the participants scored

"Political instability and uncertainties" as the highest political (P) variable contributing to the

performance of the business. The coefficient of variation of all political (P) variables is sitting

between 22% and 36%, as indicated in table 4-1. It can be noted that there is a much greater

68

variability amongst the answers provided by the participants for political (P) variables. This can

also be noted with standard deviations that are not close to the mean values of the political (P)

variables.

The skewness of P4 is -1.754 (see table 4-1), therefore the distributions of P4 is skewed to the

left, which means most of the values are concentrated to the right of the mean, while extreme

values are concentrated to the left. In addition, P4 kurtosis value is 2.144 as per Table 4-1. The

kurtosis value is below three and its distribution is, therefore, flatter than normal, which indicates

a wider range of values around the mean.

"Cost of building material" (MM5) has the highest average (4.258) amongst all material

management (MM) variables as an external variable, as indicated in table 4-1, meaning the

participants scored "Cost of building material" as the highest political (P) variable contributing to

the performance of the business. The coefficient of variation of all material management (MM)

variables is sitting between 24% and 39%, as indicated in table 4-1. It can be noted that there is

a much greater variability amongst the answers provided by the participants for material

management (MM) variables. This can also be noted with standard deviations that are not close

to the mean values of the material management (MM) variables.

The skewness of MM5 is -1.374 (see table 4-1), therefore the distribution of MM5 is skewed to


extreme values are concentrated to the left. In addition, MM5 kurtosis value is 1,002 as per


which indicate a wider range of values around the mean.

"Inadaptability to changing business conditions (4th industrial revolution)" (TT1) has the highest

average (4.177) amongst all technology transformation (TT) variables which form part of

external variables, as indicated in table 4-1, meaning the participants scored "Inadaptability to

changing business conditions (4th industrial revolution)" as the highest technology

transformation (TT) variable contributing to the performance of the business. The coefficient of

variation of all technology transformation (TT) variables is sitting between 25% and 33%, as


answers provided by the participants for technology transformation (TT) variables. This can also

69

be noted with standard deviations that are not close to the mean values of the technology

transformation (TT) variables.

The skewness of TT1 is -1.467 (see table 4-1), therefore the distribution of TT1 is skewed to the


values are concentrated to the left. In addition, TT1 kurtosis value is 1.828, as per table 4-1.

The kurtosis value is below three and its distribution is, therefore, flatter than normal, which

indicates a wider range of values around the mean.

"Adding more regulations to the construction project processes" (RF2) has the highest average

(4.266) amongst all regulatory (RF) variables which form part of external variables, as indicated

in table 4-1, meaning the participants scored "Adding more regulations to the construction

project processes" as the highest regulatory (RF) variable contributing to the performance of the

business. The coefficient of variation of all regulatory (RF) variables is sitting between 24% and

27%, as indicated in table 4-1. It can be noted that there is a much greater variability amongst

the answers provided by the participants for regulatory (RF) variables. This can also be noted

with standard deviations that are not close to the mean values of the regulatory (RF) variables.

The skewness of RF2 is -1.212 (see table 4-1), therefore the distribution of RF2 is skewed to


extreme values are concentrated to the left. In addition, RF2 kurtosis value is 0.241 as per table

4-1. The kurtosis value is below three and its distribution is, therefore, flatter than normal, which


"Higher market competition between construction companies (Tendering completion)" (BC4)

has the highest average (4.298) amongst all business conditions/market competition (BC)

variables which form part of external variables, as indicated in table 4-1, meaning the

participants scored "Higher market competition between construction companies (Tendering

completion)" as the highest business conditions/market competition (BC) variable contributing to

the performance of the business. The coefficient of variation of all business conditions/market

competition (BC) variables is sitting between 24% and 26%, as indicated in table 4-1. It can be

noted that there is a much greater variability amongst the answers provided by the participants

for business conditions/market competition (BC) variables. This can also be noted with standard

70

deviations that are not close to the mean values of the business conditions/market competition

(BC) variables.

The skewness of BC4 is -1.832 (see table 4-1), therefore the distribution of BC4 is skewed to


extreme values are concentrated to the left. In addition, BC4 kurtosis value is 2.975 as per table



"Delay in decision making" (L4) has the highest average (4.298) amongst all leadership (L)

variables at an organisational level, as indicated in Table 4-1, meaning the participants scored

"Delay in decision making" as the highest leadership (L) variable contributing to the

performance of the business. The coefficient of variation of all leadership (L) variables is sitting

between 19% and 34%, as indicated in Table 4-1. It can be noted that there is a much greater

variability amongst the answers provided by the participants for leadership (L) variables. This

can also be noted with standard deviations that are not close to the mean values of the

leadership (L) variables.

The skewness of L4 is -1.641 (see table 4-1), therefore the distribution of L4 is skewed to the


values are concentrated to the left. In addition, L4 kurtosis value is 3.462 as per table 4-1. The

kurtosis is larger than three and therefore the tail is sharper than the normal distribution and

shows higher concentrations around the mean.

"Client emphasis on low construction costs" (BCC8) has the highest average (4.468) amongst

all budget constraints (BCC) variables at an organisational level, as indicated in table 4-1,

meaning the participants scored "Client emphasis on low construction costs" as the highest

budget constraints (BCC) variable contributing to the performance of the business. The

coefficient of variation of all budget constraints (BCC) variables is sitting between 19% and

33%, as indicated in table 4-1. It can be noted that there is a much greater variability amongst

the answers provided by the participants for budget constraints (BCC) variables. This can also

be noted with standard deviations that are not close to the mean values of the budget

constraints (BCC) variables.

71

The skewness of BCC8 is -1.944 (see table 4-1), therefore the distributions of BCC8 is skewed

to the left, which means most of the values are concentrated to the right of the mean, while

extreme values are concentrated to the left. In addition, BCC8 kurtosis value is 3.317 as per

table 4-1. The kurtosis is larger than three and therefore the tail is sharper than the normal

distribution and shows higher concentrations around the mean.

"Lack of knowledge on business ethics" (C2) has the highest average (4.379) amongst all

corruption (C) variables at an organisational level, as indicated in table 4-1, meaning the

participants scored "Lack of knowledge on business ethics" as the highest corruption (C)

variable contributing to the performance of the business. The coefficient of variation of all

corruption (C) variables is sitting between 25% and 32%, as indicated in table 4-1. It can be

noted that there is a much greater variability amongst the answers provided by the participants

for corruption (C) variables. This can also be noted with standard deviations that are not close

to the mean values of the corruption (C) variables.

The skewness of C2 is -1.714 (see table 4-1), therefore the distributions of C2 is skewed to the


values are concentrated to the left. In addition, C2 kurtosis value is 4.017, as per table 4-1. The



"Volatility in labour markets (strikes) which resulted in limited foreign investments within the

country" (VC1) has the highest average (4.137) amongst all volatile commodity and exchange

rates (VC) variables which form part of external variables, as indicated in table 4-1, meaning the

participants scored "volatility in labour markets (strikes) which resulted in limited foreign

investments within the country" as the highest volatile commodity and exchange rates (VC)

variable contributing to the performance of the business. The coefficient of variation of all

volatile commodity and exchange rates (VC) variables is sitting between 25% and 32%, as


answers provided by the participants for volatile commodity and exchange rates (VC) variables.

This can also be noted with standard deviations that are not close to the mean values of the

volatile commodity and exchange rates (VC) variables.

72

The skewness of VC1 is -1.180 (see table 4-1), therefore the distribution of VC1 is skewed to


extreme values are concentrated to the left. In addition, VC1 kurtosis value is 0,867 as per table



"Cultural change resistance" (SC1) has the highest average (3.960) amongst all socio-cultural

(SC) variables at an organisational level, as indicated in table 4-1, meaning the participants

scored "Cultural change resistance" as the highest socio-cultural (SC) variable contributing to

the performance of the business. The coefficient of variation of all socio-cultural (SC) variables

is sitting between 27% and 33%, as indicated in Table 4-1. It can be noted that there is a much

greater variability amongst the answers provided by the participants for socio-cultural (SC)

variables. This can also be noted with standard deviations that are not close to the mean values

of the socio-cultural (SC).

The skewness of SC1 is -0.481 (see table 4-1), therefore the distributions of SC1 is skewed to


extreme values are concentrated to the left. In addition, SC1 kurtosis value is -0,791 as per


which indicates a wider range of values around the mean.

“Insufficient time for construction planning” (TR1) has the highest average (4.323) amongst all

time related (TR) variables at a project level, as indicated in table 4-1, meaning the participants

scored “Insufficient time for construction planning" as the highest time related (TR) variable

contributing to the performance of the business. The coefficient of variation of all time related

(TR) variables is sitting between 20% and 24%, as indicated in Table 4-1. It can be noted that

there is a much greater variability amongst the answers provided by the participants for time-

related (TR) variables. This can also be noted with standard deviations that are not close to the

mean values of the time related (TR).

The skewness of TR1 is -1.522 (see table 4-1), therefore the distribution of TR1 is skewed to


extreme values are concentrated to the left. In addition, TR1 kurtosis value is 2.767 as per table

73



"The company has a clear vision and mission statements relevant to the organization's activities

and mandate" (PF1) has the highest average (4.323) amongst all performance correlation (PC)

variables, as indicated in table 4-1, meaning the participants scored "The Company has a clear

vision and mission statements relevant to the organization's activities and mandate" as the

highest performance correlation (PC) variable contributing to the performance of the business.

The coefficient of variation of all performance correlation (PC) variables is sitting between 19%

and 47%, as indicated in table 4-1. It can be noted that there is a much greater variability

amongst the answers provided by the participants for performance correlation (PC) variables.

This can also be noted with standard deviations that are not close to the mean values of the

performance correlation (PC).

The skewness of PF1 is -1.606 (see table 4-1), therefore the distribution of PF1 is skewed to the


values are concentrated to the left. In addition, PF1 kurtosis value is 3.457 as per table 4-1. The



4.2.3 Cronbach’s alpha

The reliability of the measuring instrument was established using Cronbach’s Alpha coefficient,

where values above 0.7 are usually regarded as most reliable (George & Mallery, 2016:129).

These values have an acceptable consistency with the specific sample.

74

Table 4-2: Cronbach’s alpha

Item Mean SD Cronbach alpha

FM1: Profitability 4,169 1,080 0,830

FM2: Sales grow th 3,758 1,129 0,860

FM3: Financial stability 4,169 1,026 0,850

FM4: Cash f low 3,976 1,130 0,820

FM5: Market share 3,556 1,258 0,870

Total FM 3,926 0,915 0,870

NFM4: Business eff iciency or productivity 4,202 1,028 0,860

NFM6: Competitive strength and market standing 3,944 0,990 0,860

NFM5: Effectiveness of planning 4,048 0,987 0,840

NFM2: External customer (client) satisfaction 4,226 0,970 0,830

NFM1: Quality of service and w ork 4,395 0,953 0,840

NFM3: Safety 4,387 0,824 0,860

Total NFM 4,200 0,748 0,870

SD1: Lack of clear project goal and vision 4,073 1,135 0,860

SD2: Insuff icient time given for the designer to design and prepare project draw ings 3,927 1,224 0,840

SD3: Teamw ork and involvement of all parties or key stakeholders in the design phase 4,274 0,982 0,820

SD4: Failure to apply latest design codes and softw are during scoping 3,726 1,150 0,820

SD5: The ability to accurately estimate the required time to complete the project 4,323 0,942 0,830

SD6: Lack of communication betw een the designer and the execution team during design phase 4,169 1,010 0,820

SD7: Conducting project constructability w orkshops prior to each project tendering 3,976 1,165 0,820

Total SD 4,199 0,705 0,850

TC1: Presence of skilled and professional technical staff such as designer/engineer/project manager, w ith

relevant experience and know ledge 4,548 0,868 0,890

TC2: Company's aw areness of technical specif ications required for business and their high-quality

implementation 4,177 1,028 0,850

75


TC3: Lack of environmentally sustainable materials 3,419 1,313 0,840

TC4: Lack of sustainability measurement tools 3,508 1,246 0,850

TC5: Lack of exemplar ‘demonstration project’ 3,411 1,307 0,860

TC6: Lack of easily accessible guidance 3,444 1,309 0,850

Total TC 3,751 0,944 0,880

KM1: Application of know ledge management systems to facilitate know ledge retention and learning 4,290 1,010 0,880

KM2: Effective mechanisms and processes to inspire sharing of information, lessons learned and open

communication 4,008 1,137 0,880

KM3: Know ledge sharing activities, w here employees/parties are given suff icient time for engagements 3,782 1,130 0,880

KM4: Allocation of w orkers from one project to another in order to gain more know ledge and share post-project

lessons learned from other projects 4,121 1,086 0,870

KM5: Aw areness of professionals and understanding about sustainability 4,032 1,059 0,880

KM6: Lack of education and professional know ledge about sustainable design 3,863 1,245 0,900

Total KM 4,016 0,904 0,900

RA1: Availability of site project management team 4,500 0,906 0,360

RA2: Appropriate allocation of personnel on project sites 4,548 0,679 0,200

RA3: Presence of local government and training authorities for review of labour policies for maintaining

construction w orkforce sustainability 3,581 1,183 0,640

Total RA 4,210 0,655 0,460

P1: Lack of government legislations and policies 3,863 1,219 0,790

P2: Lack of building codes on sustainability 3,629 1,316 0,830

P3: Lack of government commitment and support 4,242 1,007 0,780

P4: Political instability and uncertainties 4,452 0,982 0,790

P5: Policy uncertainty 4,097 1,165 0,790

Total P 4,056 0,877 0,820

MM1: Material availability 4,081 1,145 0,880

MM2: Material shortage 4,008 1,172 0,850

76


MM3: Material durability (quality) 4,234 1,083 0,880

MM4: Effective co-ordination of material replenishment 3,976 1,137 0,870

MM5: Cost of building material 4,258 1,027 0,880

Total MM 4,111 0,936 0,900

TT1: Adaptability to changing business conditions (4th industrial revolution) 4,177 1,028 0,480

TT2: Automation w hich replaces construction w orkforce (4th industrial revolution) 3,798 1,269 0,480

Total TT 3,988 0,989 0,660

RF1: Sudden changes in law and regulations 4,177 1,127 0,700

RF2: Adding more regulations to the construction project processes 4,266 1,005 0,650

RF3: Having apparent statutory process that incorporate sustainable construction practices w ith economic

incentives 3,927 1,022 0,840

Total RF 4,124 0,893 0,810

BC1: Perceptions of the confidence in business conditions 4,032 1,043 0,620

BC2: Investor confidence 4,282 1,101 0,610

BC3: Insuff icient demand for w ork 4,242 1,092 0,520

BC4: Higher market competition betw een construction companies (Tendering completion) 4,298 1,036 0,590

Total BC 4,214 0,748 0,650

L1: Lack of leadership in the construction industry 4,121 1,166 0,770

L2: Lack of market segmentation (geographic and demographic) 3,565 1,225 0,790

L3: Lack of motivation and aspiration values of managers 4,073 0,930 0,760

L4: Delay in decision making 4,371 0,821 0,750

L5: Client ability to make decision 4,258 0,815 0,760

L6: Dispute betw een client and construction team 4,282 1,009 0,800

Total L 4,112 0,703 0,790

BCC1: Fear of higher investment costs 3,831 1,280 0,860

BCC2: Fear of long-payback period 3,879 1,207 0,860

BCC3: Client w orries about profitability 3,976 0,992 0,860

77


BCC4: Ignorance of life cycle costs (ability to select alternatives that impact both pending and future costs) 3,919 1,056 0,870

BCC5: Lack of f inancial resources 4,403 0,855 0,860

BCC6: Access to mortgage/credits 4,008 1,206 0,860

BCC7: High interest rates 4,129 1,097 0,860

BCC8: Reduced government spend on infrastructure related projects 4,468 0,949 0,880

BCC9: Client emphasis on low construction costs 4,306 0,828 0,870

Total BCC 4,102 0,755 0,880

C1: Lack of company's code of ethics that addresses ethical conduct (principles of right or w rong conduct) 4,290 0,986 0,700

C2: Lack of know ledge of business ethics 4,379 0,802 0,860

C3: Adherence to company's code of ethics that maintains a culture that promotes the principles of the company 4,161 1,150 0,690

C4: A company culture that puts profitability and business performance ahead of ethical behaviour 4,105 1,118 0,700

Total C 4,234 0,813 0,810

VC1: Volatility in labour markets (strikes) w hich resulted in limited foreign investments w ithin the country 4,137 1,039 0,850

VC2: Volatility in commodity prices and exchange rates (w eakening exchange rate) 3,911 1,148 0,770

VC3: Declining rand value 4,121 1,017 0,860

VC4: Dow ngraded economic ratings from global agencies 3,839 1,232 0,760

Total VC 4,002 0,931 0,860

SC1: Lack of demand of sustainable products by client and stakeholders 3,637 1,192 0,790

SC2: Cultural change resistance 3,960 1,171 0,650

SC3: Stakeholder's engagement and effective participation 3,903 1,055 0,590

Total SC 3,833 0,936 0,760

TR1: Planned time for construction 4,323 0,851 0,820

TR2: Time taken to implement variation orders (Affecting completion) 4,315 0,859 0,860

TR3: Average delay in claim approval (Affecting completion and cash f low ) 4,250 0,916 0,790

TR4: Average delay in payment from ow ner to contractor (Affecting cash f low 4,266 1,037 0,850

Total TR 4,288 0,776 0,870

PF1: The company has a clear vision and mission statements relevant to the organisation's activities and 4,323 0,822 0,890

78


mandate

PF2: The company has an adequate business plan that is documented and measurable in terms of performance

standards in the industry 4,218 0,898 0,890

PF3: In the last three years, the company's turnover and profitability has increased commendably 2,992 1,400 0,890

PF4: The company performance measures are compatible w ith the activities being carried out by the organisation 3,790 1,006 0,880

PF5: My understanding of the organisation's performance measures is adequate 4,081 0,832 0,890

PF6: My understanding of issues that influences the performance of the company is adequate 4,194 0,925 0,880

PF7: The teams of employees in my company are made accountable for business performance 3,774 1,168 0,870

PF8: My company has suff icient cash f low s to satisfy its business objectives in a controlled manner 3,371 1,417 0,880

PF9: My company strives for business excellence and overall customer satisfaction at all times 4,315 0,840 0,880

PF10: The effectiveness of the team of employees w orking in my company is commendable 4,016 0,946 0,880

PF11: My company's day-to-day operations are documented in the form of an operational manual 3,581 1,217 0,880

Total PF 3,878 0,729 0,890

Source: SPSS

79

From table 4-2 above, only themes that have Cronbach’s Alpha value greater than 0.7 are

considererd in this study. Therefore, themes such as “resource availability (RA)”, technology

transformation”, and “business conditions” has an alpha of 0.46, 0.66, and 0.65 respectively

which is below 0.7. Although Field (2009:259) indicated that values above 0.57 could in certain

cases, be regarded as sufficient, especially for psychological constructs, for the purpose of this

study, those values are not considered.The low alpha can be attributable to few statements

under one theme or weak interrelations between the test statements. Low alpha can be

improved through the addition of relevant items.

80

4.3 Correlation analysis

Table 4-3: Correlation analysis

Correlations FM NFM SD TC KM RA P MM TT RF BC L BCC C VC SC TR PF

FM 1,00

NFM 0,86 1,00

SD 0,51 0,53 1,00

TC 0,43 0,51 0,88 1,00

KM 0,59 0,61 0,91 0,98 1,00

RA 0,49 0,51 0,70 0,69 0,72 1,00

P 0,37 0,43 0,71 0,58 0,64 0,72 1,00

MM 0,53 0,58 0,57 0,64 0,67 0,72 0,63 1,00

TT 0,43 0,53 0,77 0,66 0,70 0,66 0,73 0,59 1,00

RF 0,42 0,53 0,64 0,61 0,61 0,74 0,85 0,58 0,76 1,00

BC 0,45 0,52 0,54 0,52 0,65 0,56 0,65 0,42 0,78 0,75 1,00

L 0,45 0,46 0,69 0,67 0,76 0,59 0,79 0,48 0,67 0,60 0,75 1,00

BCC 0,52 0,45 0,56 0,65 0,59 0,64 0,61 0,76 0,53 0,57 0,55 0,60 1,00

C - 0,74 - 0,61 - 0,58 - 0,73 - 0,71 - 0,54 - 0,62 - 0,48 - 0,55 - 0,50 - 0,57 - 0,83 - 0,59 1,00

VC 0,49 0,44 0,39 0,43 0,40 0,55 0,60 0,76 0,57 0,53 0,56 0,67 0,76 - 0,53 1,00

SC 0,37 0,40 0,53 0,54 0,63 0,57 0,82 0,65 0,55 0,53 0,63 0,88 0,64 - 0,71 0,77 1,00

TR 0,62 0,57 0,57 0,44 0,53 0,52 0,73 0,60 0,43 0,42 0,41 0,74 0,75 - 0,72 0,64 0,80 1,00

PF 0,80 0,74 0,58 0,61 0,57 0,55 0,47 0,49 0,69 0,53 0,60 0,54 0,43 - 0,51 0,48 0,52 0,31 1,00

Source: SPSS

81

A Pearson product-moment correlation was run to determine the relationship between

performance measures of the construction industry and the company's performance. For each

parameter, positive and negative comparisons of correlation numbers are discussed, based on

the correlation guideline in Section 3.9. There exists a positive correlation when one parameter

decreases with the other or one increases with the other. The relationship becomes negative

when another parameter increases with the other one decreasing, and vice versa.

From Table 4.3 above, there was a strong positive relationship between FM and company's

performance (PF) (r = .80, n = 124, p < .05) as well as NFM and company’s performance (PF) (r

= .74, n = 124, p < .05), which are statistically significant. This means that the company’s

performance is directly proportional to its financial and non-financial behaviour.

The relationships between factors influencing sustainable performance (SD, TC, KM, RA, P,

MM, TT, RF, BC, L, BCC, C, VC, SC, TR) and the performance of the company (PF) in the

construction industry were as follows: There is a moderate positive relationship between PF and

P (r = 0.47, n = 124, p < 0.01), PF and MM (r = 0.49, n = 124, p < 0.01), PF and BCC (r = 0.43,

n = 124, p < 0.01) as well as PF and TR (r = 0.31, n = 124, p < 0.01) as indicated by Table 4-3.

The relationships are statistically significant at p < .01. This means that the quality of the

company could be significantly affected by political, inventory control, budget constraints and

time related factors.

There is a moderate positive relationship between PF and P (r = 0.47, n = 124, p < 0.01), PF

and MM (r = 0.49, n = 124, p < 0.01), PF and BCC (r = 0.43, n = 124, p < 0.01), PF and VC (r =

0.48, n = 124, p < 0.01) as well as PF and TR (r = 0.31, n = 124, p < 0.01) as indicated by Table

4-3. The relationships are statistically significant at p < .01. This means that the quality of the

company could be significantly affected by political, inventory control, volatile commodities and

currencies, budget constraints and time related factors.

There is a strong positive relationship between PF and SD (r = 0.58, n = 124, p < 0.01), PF and

TC (r = 0.61, n = 124, p < 0.01), PF and KM (r = 0.57, n = 124, p < 0.01), PF and RA (r = 0,55, n

= 124, p < 0.01), PF and TT (r = 0,69, n = 124, p < 0.01), PF and RF (r = 0.53, n = 124, p <

0.01), PF and BC (r = 0,60, n = 124, p < 0.01), PF and L (r = 0.54, n = 124, p < 0.01) as well as

SC (r = 0.52, n = 124, p < 0.01), as indicated in Table 4-3. The relationships are statistically

significant at p < 0.01. This means that statistically speaking, there is a link between

82

organisational performance and scope of definition, technical capacity, and knowledge

management, availability of resources, technology transformation, regulatory policy, business

conditions / market competition, leadership and socio-cultural development, while a strong

negative relationship exists between PF and C, as shown in Table 4-3, (r = 0.51, n = 124, p <

0.01) as indicated by Table 4-3, which means the relationship between the organisation's

success and bribery is inversely proportional.

83

4.4 Relative importance index

Table 4-4: Relative importance index

Variables RII Rank t value p value

FM - Average = 3,92

FM1: Profitability

83

1

0,0000

17,827

FM3: Financial stability

83

2

0,0000

17,854

FM4: Cash flow

80

3

0,0000

17,865

FM2: Sales growth

75

4

0,0000

17,984

FM5: Market share

71

5

0,0000

17,995

NFM - Average = 4,20

NFM1: Quality of service and work

88

1

0,0000

17,783

NFM3: Safety

88

2

0,0000

17,741

NFM2: External customer (client) satisfaction

84

3

0,0000

17,858

NFM4: Business efficiency or productivity

84

4

0,0000

17,857

NFM5: Effectiveness of planning

81

5

0,0000

17,944

NFM6: Competitive strength and market standing

79

6

0,0000

17,959

SD - Average = 4,20

SD1: Lack of clear project goal and vision

100

1

0,0000

17,648

SD5: The ability to accurately estimate the required time to complete the project

86

2

0,0000

17,788

84


SD3: Teamwork and involvement of all parties or key stakeholders in the design phase

85

3

0,0000

17,827

SD6: Lack of communication between the designer and the execution team during design phase

83

4

0,0000

17,807

SD7: Conducting project constructability workshops prior each project tendering

79

5

0,0000

17,887

SD2: Insufficient time given for the designer to design and prepare project drawings

79

6

0,0000

17,874

SD4: Failure to apply latest design codes and software during scoping

74

7

0,0000

17,980

TC - Average = 3,75

TC1: Presence of skilled and professional technical staff such as designer/engineer/project manager, with relevant experience and knowledge

91

1

0,0000

17,722

TC2: Company's awareness of technical specifications required for business and their high-quality implementation

83

2

0,0000

17,885

TC4: Lack of sustainability measurement tools

70

3

0,0000

18,081

TC6: Lack of easily accessible guidance

69

4

0,0000

18,079

TC5: Lack of exemplar ‘demonstration project’

68

5

0,0000

18,137

TC3: Lack of environmentally sustainable materials

68

6

0,0000

18,061

KM - Average = 4,02

KM1: Application of knowledge management systems to facilitate knowledge retention and learning

86

1

0,0000

17,756

KM4: Allocation of workers from one project to another in order to gain more knowledge and share post-project lessons learned from other projects

82

2

0,0000

17,813

KM5: Awareness of professionals and understanding about sustainability

81

3

0,0000

17,822

KM2: Effective mechanisms and processes to inspire sharing of information, lessons learned and open communication

80

4

0,0000

17,802

KM6: Lack of education and professional knowledge about sustainable design

77

5

0,0000

17,924

KM3: Knowledge sharing activities, where employees/parties are given sufficient time for engagements

85


75 6 0,0000 17,968

RA - Average = 4,21

RA1: Availability of site project management team

90

1

0,0000

17,721

RA2: Appropriate allocation of personnel on project sites

91

2

0,0000

17,729

RA3: Presence of local government and training authorities for review of labour policies for maintaining construction workforce sustainability

71

3

0,0000

18,091

P - Average = 4,06

P4: Political instability and uncertainties

89

1

0,0000

17,800

P3: Lack of government commitment and support

85

2

0,0000

17,828

P5: Policy uncertainty

82

3

0,0000

17,897

P1: Lack of government legislations and policies

77

4

0,0000

17,933

P2: Lack of building codes on sustainability

72

5

0,0000

17,990

MM - Average = 4,11

MM5: Cost of building material

85

1

0,0000

17,845

MM3: Material durability (quality)

84

2

0,0000

17,819

MM1: Material availability

81

3

0,0000

17,903

MM2: Material shortage

80

4

0,0000

17,904

MM4: Effective co-ordination of material replenishment

79

5

0,0000

17,934

TT - Average = 3,99

TT1: Adaptability to changing business conditions (4th industrial revolution)

84

1

0,0000

17,816

TT2: Automation which replaces construction workforce (4th industrial revolution)

76

2

0,0000

18,056

86


RF - Average = 4,12

RF2: Adding more regulations to the construction project processes

85

1

0,0000

17,893

RF1: Sudden changes of law and regulations

83

2

0,0000

17,994

RF3: Having apparent statutory process that incorporate sustainable construction practices with economics incentives

78

3

0,0000

17,963

BC - Average = 4,21

BC4: Higher market competition between construction companies (Tendering completion)

86

1

0,0000

17,814

BC2: Investor confidence

85

2

0,0000

17,826

BC3: Insufficient demand for work

85

3

0,0000

17,804

BC1: Perceptions of the confidence in business conditions

81

4

0,0000

17,917

L - Average = 4,11

L4: Delay in decision making

87

1

0,0000

17,714

L6: Dispute between client and construction team

85

2

0,0000

17,818

L5: Client ability to make decision

85

3

0,0000

17,828

L1: Lack of leadership in the construction industry

82

4

0,0000

17,751

L3: Lack of motivation and aspiration values of managers

81

5

0,0000

17,865

L2: Lack of market segmentation (geographic and demographic)

71

6

0,0000

18,041

BCC - Average = 4,10

BCC8: Reduced government spend on infrastructure related projects

89

1

0,0000

17,808

BCC5: Lack of financial resources

88

2

0,0000

17,840

BCC9: Client emphasis on low construction costs

86

3

0,0000

17,808

87


BCC7: High interest rates

82

4

0,0000

17,770

BCC6: Access to mortgage/credits

80

5

0,0000

17,883

BCC3: Client worries in profitability

80

6

0,0000

17,906

BCC4: Ignorance of life cycle costs (ability to select alternatives that impact both pending and future costs)

78

7

0,0000

17,859

BCC2: Fear of long-payback period

78

8

0,0000

17,952

BCC1: Fear of higher investment costs

76

9

0,0000

17,972

C - Average = 4,23

C2: Lack of knowledge of business ethics

88

1

0,0000

17,800

C1: Lack of company's code of ethics that addresses ethical conduct (principles of right or wrong conduct)

86

2

0,0000

17,833

C3: Adherence to company's code of ethics that maintain a culture that promotes the principles of the company

83

3

0,0000

17,766

C4: A company culture that puts profitability and business performance ahead of ethical behaviour

82

4

0,0000

17,858

VC - Average = 4,00

VC1: Volatility in labour markets (strikes) which resulted in limited foreign investments within the country

83

1

0,0000

17,866

VC3: Declining rand value

82

2

0,0000

17,947

VC2: Volatility in commodity prices and exchange rates (weakening exchange rate)

78

3

0,0000

17,970

VC4: Downgraded economic ratings from global agencies

76

4

0,0000

18,040

SC - Average = 3,83

SC2: Cultural change resistance

79

1

0,0000

17,847

SC3: Stakeholder's engagement and effective participation

78

2

0,0000

17,943

SC1: Lack of demand of sustainable products by client and stakeholders

88


73 3 0,0000 17,899

TR - Average = 4,29

TR1: Planned time for construction

86

1

0,0000

17,899

TR2: Time taken to implement variation orders (Affecting completion)

86

2

0,0000

17,899

TR3: Average delay in claim approval (Affecting completion and cash flow)

85

3

0,0000

17,886

TR4: Average delay in payment from owner to contractor (Affecting cash flow

85

4

0,0000

17,893

PF - Average = 3,88 RII Rank

PF9: My company strives for business excellence and overall customer satisfaction at all times

87

1

0,0000

17,809

PF1: The company has a clear vision and mission statements relevant to the organisation's activities and mandate

87

2

0,0000

17,835

PF6: My understanding of issues that influences the performance of the company is adequate

85

3

0,0000

17,833

PF2: The company has an adequate business plan that is documented and measurable in terms of performance standards in the industry

84

4

0,0000

17,806

PF5: My understanding of the organisation's performance measures is adequate

83

5

0,0000

17,893

PF10: The effectiveness of the team of employees working in my company is commendable

81

6

0,0000

17,932

PF4: The company performance measures are compatible with the activities being carried out by the organisation

77

7

0,0000

18,049

PF7: The teams of employees in my company are made accountable for business performance

76

8

0,0000

18,046

PF11: My company's day-to-day operations are documented in the form of an operational manual

72

9

0,0000

18,172

PF8: My company have sufficient cash flows to satisfy its business objectives in a controlled manner

68

10

0,0000

18,182

PF3: In the last three years, the company's turnover and profitability has increased commendably

60

11

0,0000

18,369

Source: SPSS

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Table 4-4 reflects statements and their relative importance that are influencing sustainable

performance in the construction industry of South Africa. The statements were divided into 19

themes, which are financial performance measures (FM); non-financial performance measures

(NFM); scope definition (project level) (SD); technical capacity (organisational level) (TC);

knowledge management (organisational level) (KM); resource availability (organisational level)

(RA); political (external level) (P); material management (external level) (MM); technology

transformation (external level) (TT); regulatory (external level) (RF); business conditions/market

competition (external level) (BC); leadership (organisational) (L); budget constraints

(organisational level) (BCC); corruption (external/organisational level) (C); volatile commodity

and exchange rates (external level) (VC); socio-cultural (organisational level) (SC); time related

(project level) (TR); performance correlation (PC). Participants ranked material or tools,

services and safety high, choosing them as factors affecting labour productivity negatively in the

construction industry.

Table 4.4 above confirms the statement that has been ranked highly by the participants. This

statement includes profitability (FM1) and financial stability (FM3) from financial measures

section, quality of service and work (NFM1) from non-financial measures; lack of clear project

goal and vision (SD1) from scope of definition (SD) section; Insufficient skilled and professional

technical staff such as designer/engineer/project manager, with relevant experience and

knowledge (TC1) from technical capacity section; failure to apply knowledge management

systems to facilitate knowledge retention and learning (KM1) from knowledge management

(KM) section; inappropriate allocation of personnel on project sites (RA2) from resource

availability (RA) section; political instability and uncertainties (P4) from political (P) section; cost

of building material (MM5) from material management (MM) section; inadaptability to changing

business conditions (4th industrial revolution) (TT1) from technology transformation (TT)

section; adding more regulations to the construction project processes (RF2) from regulatory

(RF) section; higher market competition between construction companies (Tendering

completion) (BC4) from business conditions/market competition (BC) section; delay in decision

making (L4) from leadership (L) section; client emphasis on low construction costs (BCC8) from

budget constraints (BCC) section; lack of knowledge on business ethics (C2) from corruption

(C) section; volatility in labour markets (strikes) which resulted in limited foreign investments

within the country (VC1) from volatile commodity and exchange rates (VC) section; cultural

change resistance (SC1) from socio-cultural (SC) section; Insufficient time for construction

planning (TR1) from time related (TR) section and the company has a clear vision and mission

90

statements relevant to the organisation's activities and mandate (PF1) form performance

correlation (PC) section.


Chapter 4 detailed all the results obtained when the research data was analysed in order to

achieve the objectives of the study.

91

CHAPTER 5: DISCUIONS, CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

5.2 Discussion

5.2.1 Discussion pertaining to objective 1

To determine critical performance measures of the construction industry and to what extent they

correlate with the company's performance.

Table 5.1 presents the top 10 critical performance measures of the construction industry in

South Africa.

Table 5-1: Critical performance measures

Measures RII Rank Category

Quality of service and work 88 1 Non-financial measure

Safety 88 2 Non-financial measure

External customer (client) satisfaction 84 3 Non-financial measure

Business efficiency or productivity 84 4 Non-financial measure

Profitability 83 5 Financial Measure

Financial stability 83 6 Financial Measure

Effectiveness of planning 81 7 Non-financial measure

Cash flow 80 8 Financial Measure

Competitive strength and market standing 79 9 Non-financial measure

Sales growth 75 10 Financial Measure

Source: SPSS

The top four critical performance measures are non-financial measures. They are more related

to customer service, the quality of work and the overall performance provided by the business to

their clients. This includes characteristics like reliability, responsiveness, empathy and tailoring,

competence and diligence, consistency, safety and security, the quality of work completed and

whether the work is fit for purpose, conformance to requirements, completeness, correctness,

accuracy, diligence, professional, communication, compliance, controls, best practices,

manageable risk, outstanding integration with other business elements, the work delivered is

clear and useable, customers are happy with the work delivered and the work is relevant and

92

adds value to customers. Without these characteristics, the business will not be sustainable.

The following are the general discussions on these top ten critical performance measures:

The results show that the "Quality of service and work" characteristic is rated first with a

mean value = 4.395 and RII = 88%. The survey has illustrated that this non-financial

measure, is the key differentiator between construction companies and gives a

competitive edge. Maintaining good and consistent quality of service and function is

essential for the organisation's continued success.

The results show that factor "Safety" is rated as the second-highest critical performance

measure with a mean value of = 4.387 and RII = 88%. This is due to the fact that there

have been profound changes to the construction processes. The types of projects, the

way these projects are carried out, and the tools used for design and communication

have all significantly changed. New technologies have also made projects more complex.

It is, therefore, important that contractors have an integrated, robust safety programme

that can meet the ever-changing needs of the industry and help them to remain

competitive.

The results show that the "External customer (client) satisfaction" attribute is rated as the

third critical performance measure with a mean value = 4.226 and RII = 84%. Customer

satisfaction remains an essential variable during the design of a construction process

and client relationship. As construction companies face rising competition, customer

relations and satisfied customers continue to receive more attention. The satisfaction of

customers makes it possible for construction companies to distinguish themselves from

their competitors and to provide sustainable benefits.

The results show that the “Business efficiency or productivity” characteristic was rated

the fourth critical measure with a mean value = 4.202 and RII = 84%. This is due to the

fact that the construction industry and the wider built environment plays a major role in

the competitiveness of nations. Furthermore, “Business efficiency or productivity” as a

critical performance measure, is contributing to these countries’ economies.

The results show that financial measures such as "profitability, financial stability, cash

flow and sales growth" are rated as the fifth, sixth, eighth and tenth critical performance

93

measures with mean value = 4.169, 4.169, 3.976, 3.758 and RII = 83%, 83%, 80%, 75%

respectively. This can be seen in figure 4-3. These measures are indications of

improvements or regressions achieved from the leading indicators, such as quality and

productivity of workers. They also assist managers of organisations in identifying special

gaps and measures within the company, assigning and assessing employees on the

performance of these actions.

Defining design context requires co-operation at the beginning of the project preparation

among specific or related parties involved. Hence effective planning is rated the seventh

critical measure with a mean value = 4.048 and RII = 81.

Growing a business without understanding business competitors is an ingredient of

failure therefore preparing market research can prevent the business being left behind by

the competition. Hence “competitive strength and market standing” is rated the ninth

critical measure with a mean value = 3.944 and RII = 79.

5.2.2 Discussion pertaining to objective 2

To investigate a comprehensive set of critical factors influencing sustainable performance in the

construction industry and to what extent these factors influenced the performance of the

company.

Table 5-2: Critical factors influencing sustainable performance in the construction

industry

# Measures RII Rank Category

1 Lack of clear project goal and vision 100 1 Project

2

Insufficient skilled and professional technical staff such as

designer/engineer/project manager, with relevant experience and

knowledge

91 2

Organisational

3 Inappropriate allocation of personnel on project sites 91 3 Organisational

4 Non-availability of site project management team 90 4 Organisational

5 Political instability and uncertainties 89 5 External

6 Reduced government spending on infrastructure related projects 89 6 Organisational

7 Lack of financial resources 88 7 Organisational

8 Lack of knowledge of business ethics 88 8 External

94

# Measures RII Rank Category

9 Delay in decision making 87 9 Organisational

10

Lack of the ability to accurately estimate the required time to

complete the project 86 10

Project

Source: SPSS

Lack of clear project goal and vision

Results from table 5-2 indicated that “lack of clear project goal and vision” was the highest

critical factors influencing sustainable performance in the construction industry with a mean

equal to “4.76” and relative importance index “100%” and P-value equals 0.000 which is

smaller than the level of significance α= 0.05. It can be concluded that the respondents

agreed to this factor as a very important factor. This is due to the fact that building a project

without knowing its goal and what it will be used for and view of perspectives is difficult. That

is related to a variety of elements including technical, financial, educational and social issues

and there must be a thorough understanding of the scope of the project to avoid disputes

and conflicts, which can possibly occur if the scope is not clearly defined. The success of the

project is the degree to which project goals and expectations are met.

Insufficient skilled and professional technical staff such as

designer/engineer/project manager, with relevant experience and knowledge

Results from table 5-2 indicated that “insufficient skilled and professional technical staff such

as designer/engineer/project manager, with relevant experience and knowledge” was the

second-highest critical factors influencing sustainable performance in the construction

industry with a mean equal “4.76” and relative importance index “91%” and P-value equals

0.000 which is smaller than the level of significance α= 0.05. It can be concluded that the

respondents agreed to this factor is one of the important factors. This is due to the fact that if

the project personnel such as designer, engineer or project manager do not possess

relevant experience, knowledge, and the technical skill required may result in poor execution

of contracts/projects, which creates margin erosion and losses. A lack of experience and

expertise might lead to a rework component for the project or delay which increases the cost

of implementing a project. Additionally, the risk of poor quality control on sites is also

increased, ultimately leading to the overall poor performance of the project.

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Inappropriate allocation of personnel on project sites

Results from table 5-2 indicated that “inappropriate allocation of personnel on project sites”

was the third highest critical factors influencing sustainable performance in the construction



respondents agreed to this factor as one of the important factors. This is due to the fact that

inappropriate allocation of personnel on project sites might cause scheduling inefficiency,

create conflicts at projects sites, and increase the risk of multiple allocations of resources at

the same time.

Non-availability of site project management team

Results from table 5-2 indicated that “non-availability of site project management team” was

the fourth-highest critical factors influencing sustainable performance in the construction



respondents agreed to this factor as one of the important factors. This is due to the fact that

non-availability of necessary materials or services could have a negative impact on the

overall performance of the project.

Political instability and uncertainties

Results from table 5-2 indicated that “political instability and uncertainties” was the fifth

highest critical factor influencing sustainable performance in the construction industry with a

mean equal to “4.76” and relative importance index “89%” and P-value equals 0.000 which is


agreed to this factor as one of the important factors. This is due to the fact that political

instability is regarded as a serious malaise, harmful to economic performance. Political

instability is likely to shorten policymakers’ horizons, leading to suboptimal short term

macroeconomic policies. It may also lead to a more frequent switch of policies, creating

volatility and thus, negatively affecting macroeconomic performance.

https://project-management-knowledge.com/definitions/s/service/

96

Reduced government spending on infrastructure related projects

Results from table 5-2 indicated that “reduced government spending on infrastructure related

projects” was the sixth highest critical factor influencing sustainable performance in the

construction industry with a mean equal “4.76” and relative importance index “89%” and P-

value equals 0.000 which is smaller than the level of significance α= 0.05. It can be

concluded that the respondents agreed to this factor as one of the important factors. This is

due to the fact that if government spending is reduced on infrastructure related projects then

infrastructure of the economy will deteriorate which in turn, will impair economic growth and

competitiveness.

Lack of financial resources

Results from table 5-2 indicated that “lack of financial resources” was the seventhhighest

critical factors influencing sustainable performance in the construction industry with a mean

equal “4.76” and relative importance index “88%” and P-value equals 0.000 which is smaller

than the level of significance α= 0.05. It can be concluded that the respondents agreed to

this factor as one of the important factors. This is due to the fact that any financial problem in

the project expenditures and payments will cause delay and cost overrun, accordingly.

Lack of knowledge of business ethics

Results from table 5-2 indicated that “lack of knowledge on business ethics” was the eighth

highest critical factor influencing sustainable performance in the construction industry with a

mean equal “4.76” and relative importance index “88%” and P-value equals 0.000 which is


agreed to this factor as one of the important factors. This is due to the fact that if the project

personnel do not have knowledge of business ethics then they pose a threat to their work,

clients and communities. A lack of business ethics endangers the future of the company,

jeopardises the public good and can have many other negative effects in a business

environment.

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Delay in decision making

Results from table 5-2 indicated that “delay in decision making” was the ninth highest critical

factors influencing sustainable performance in the construction industry with a mean equal

“4.76” and relative importance index “88%” and P-value equals 0.000 which is smaller than

the level of significance α= 0.05. It can be concluded that the respondents agreed to this

factor as one of the important factors. This is due to the fact that delay occurs when the

progress of a contract falls behind its scheduled programme, and this ultimately will lead to

the overall poor performance of the project.

Lack of the ability to accurately estimate the required time to complete the project

Results from table 5-2 indicated that “lack of the ability to accurately estimate the required

time to complete the project” was the tenth highest critical factor influencing sustainable

performance in the construction industry with a mean equal “4.76” and relative importance

index “87%” and P-value equals 0.000 which is smaller than the level of significance α=

0.05. It can be concluded that the respondents agreed to this factor as one of the important

factors. This is due to the fact that accurate time estimation is a crucial skill in project

management. Without it, the project personnel will not know how long the project will take

which might increase the costs or budget of the project.

5.3 Conclusion

The research investigated factors influencing sustainable performance in the South African

construction industry. A quantitative questionnaire was developed and distributed among

construction professionals with experience and knowledge of the industry. From the list of 15

factors identified from the literature review, ten (10) critical factors were determined and these

are presented in table 5-2. These factors are ranked according to their influence as perceived

by the respondents and differ in terms of importance and magnitude. The most important

conclusion drawn from this study is that non-financial performance measures are the most

critical measures in the construction industry in South Africa. This concurs with Willis and

Rankin's (2011:20) findings, in which the authors explained that these measures provide early

warnings which enable companies to provide or seek solutions that will bring advantage to the

affected lagging (financial measures) results.

98

The researcher believes that the results of this study can be of assistance to construction

companies and especially, to academics. Also, construction professionals can better

understand the importance and dynamics of managing projects efficiently in order to contribute

towards the industry’s sustainability. Therefore, it is the researcher’s hope that these new

findings will provide construction industry professionals with the reason to invest more effort into

knowledge sharing and management within their organisations. Furthermore, the findings

provide a deeper insight into the factors which organisations could proactively address to

ensure a sustainable performance of the industry.

5.3.1 SWOT analysis

Under this section, a strategic planning technique is presented to identify organisation

strengths, weaknesses, opportunities, and threats, based on the survey that was conducted and

the findings presented in figure 5-1 thereafter.

Figure 5-1: SWOT Analysis

https://www.bing.com/search?q=Strategic%20planning%20wikipedia

99

5.4 Recommendations

Generally, consensus exists that most of the factors reviewed in this study exist in developing

countries. However, developing strategies for the construction industry is a country-specific

issue rather than a “one-size-fits-all” for all the countries. Moreover, the challenges do not exist

within a specific scope, and nor do the solutions. Hence, having a deeper insight into the

causes of these challenges and their correlation is important to the formulation of solution-

oriented policies. Based on the discussions and findings, the following are the

recommendations:

5.4.1 Corruption

The construction industry should address corruption urgently – widespread corruption means

that the industry systems slow down as they are managing for corruption instead of the industry

systems. This could be achieved through the reduction of incentives for corruption and ensure

better enforcement of laws and penalties against corruption. Enforcement of laws could include:

Introducing business ethics policies that provide awareness to all employees,

•Cash flow •Weakening rand • Industrial unrest

•Power outages and supply

•Lack of skilled workers

•Slow growth

•Overseas opportunities

•Skills development •Rising work opportunities

•Outlook deterioration •Lack of work •Skill shortage

•High risk

•Small business growth

•Alternative construction options

Strength Weaknesses

Threats Opportunities

100

Penalties such as serious jail time where misconduct is identified, and

Introduction of a system to prosecute quickly and effectively.

5.4.2 Budget constraints

Construction companies find it difficult to obtain funding; particularly those in lower grades such

as grade 2 – 5. These are companies that, in most cases, do not have the required track record

nor have they been able to acquire the required securities demanded by the financial institutions

as collateral. Therefore, to improve this situation the government must establish new funding

models which will allow small and medium enterprises to obtain seed capital without the

stringent requirements required by commercial banks with respect to collateral. To ensure

accountability, the new funding model should be run as a private or public initiative and training,

and technical support should be provided to small businesses.

5.4.3 Policy development

The study recommends that government officials lay the ground for the development of the

construction industry through strategic planning and policy development. In this regard,

moulding policies towards effective intervention to influence the performance of the industry

should be the government’s priority. This could be achieved through influencing the general and

industry environment such as establishing and developing institutions, as well as effectively

implementing rules and regulations without making it difficult for the industry. Because

construction industry challenges are not all the responsibility of the government, all stakeholders

should get involved as they have a corresponding role to contribute.

5.5 Implementation

An implementation plan was recommended by Elkhalifa (2016:198), in which the author advised

the construction industry professionals to consider developing performance measurement tools

as an integral part of the implementation of strategic plans and policies for the development and

improvement of the industry performance. To the author’s knowledge, effectiveness and

efficiency of the key performance indicators in achieving the company’s objectives depends on

the availability of relevant, accurate, and up-to-date data.

Generally, the development and formulation of the industry information pool will facilitate the

creation of the database necessary for measuring the performance of a sustainable construction

101

industry. Therefore, this requires stakeholders to be educated and made aware of the benefits

of regularly revealing information about the performance of their companies.

5.6 Future research

Besides different methodologies that could be used to determine the most critical factors that

influence the construction industry, this study used a quantitative method in which results were

analysed using SPSS. Therefore, an examination of other methods such as qualitative, in the

determination of the factors, is the potential area for further study. This method will help in

establishing a deeper understanding of these factors as it studies the phenomena in their

entirety rather than concentrating on narrow aspects of the phenomena. The selection of

participants should cover different stakeholders and preferably from senior management of

companies since they possess a better insight into what is happening in their organisations.

102

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ANNEXURES

Appendix 1-5-1: Variables

Factor

ID Financial Performance Measures (FM)

FM1 Profitabil ity

FM2 Sales growth

FM3 Financial stability

FM4 Cash flow

FM5 Market share

FM6 Other (please specify):

Factor

ID Non-financial Performance Measures (NFM)

NFM1 Quality of service and work

NFM2 External customer (client) satisfaction

NFM3 Safety

NFM4 Business efficiency or productivity

NFM5 Effectiveness of planning

NFM6 Competitive strength and market standing

NFM6 Other (please specify):

Factor

ID Scope Definition (Project lev el) (SD)

SD1 Lack of clear project goal and vision

SD2 Insufficient time given for the designer to design and prepare project drawings

SD3 Lack of teamwork and involvement of all parties or key stakeholders in the design phase

SD4 Failure to apply latest design codes and software during scoping

SD5 Lack of the ability to accurately estimate the required time to complete the project

SD6 Lack of communication between the designer and the execution team during design phase

SD7 Failure to conducting project constructability workshops prior each project tendering

Factor

ID Technical Capacity (Organisational lev el) (TC)

TC1 Insufficient skil led and professional technical staff such as designer/engineer/project manager, with relevant experience and

knowledge

TC2 Lack of company's awareness of technical specifications required for business and their high -quality implementation

TC3 Lack of environmentally sustainable materials

TC4 Lack of sustainability measurement tools

TC5 Lack of exemplar ‘demonstration project’

TC6 Lack of easily accessible guidance

111

Factor

ID Knowledge Management (Organisational level) (KM)

KM1 Failure to apply knowledge management systems to facil itate knowledge retention and learning

KM2 Ineffective mechanisms and processes to inspire sharing of information, lessons learned and open communication

KM3 Lack of knowledge sharing activities, where employees/parties are given sufficient time for engagements

KM4 Non-allocation of workers from one project to another in order to gain more knowledge and share post -project lessons learned from

other projects

KM5 lack of awareness of professionals and understanding about sustainability

KM6 Lack of education and professional knowledge about in sustainable design

Factor

ID Resource Av ailability (Organisational level) (RA)

RA1 Non-availabil ity of site project management team

RA2 Inappropriate allocation of personnel on project sites

RA3 Absence of local government and training authorities for review of labour policies for maintaining construction workforce

sustainability

Factor

ID Political (External lev el) (P)

P1 Lack of government legislations and policies

P2 Lack of building codes on sustainability

P3 Lack of government commitment and support

P4 Political instability and uncertainties

P5 Policy uncertainty

Factor

ID Material Management (External level) (MM)

MM1 Material unavailabil ity

MM2 Material shortage

MM3 Poor quality of material

MM4 Ineffective co-ordination of material replenishment

MM5 Cost of building material

Factor

ID Technology Transformation (External lev el) (TT)

TT1 Inadaptability to changing business conditions (4th industrial revolution)

TT2 Lack of automation which replaces construction workforce (4th industrial revolution)

Factor

ID Regulatory (External lev el) (RF)

RF1 Sudden changes of law and regulations

RF2 Adding more regulations to the construction project processes

RF3 Not having apparent statutory process that incorporates sustainable construction practices with economic incentives

112

Factor

ID Business Conditions/Market Competition (External lev el) (BC)

BC1 Negative perceptions of the confidence in business conditions

BC2 Lack of investor confidence

BC3 Insufficient demand for work

BC4 Higher market competition between construction companies (Tendering completion)

Factor

ID Leadership (Organisational) (L)

L1 Lack of leadership in construction industry

L2 Lack of market segmentation (geographic and demographic)

L3 Lack of motivation and aspiration values of managers

L4 Delay in decision making

L5 Client inability to make decisions

L6 Dispute between client and construction team

Factor

ID Budget Constraints (Organisational lev el) (BCC)

BCC1 Fear of higher investment costs

BCC2 Fear of long-payback period

BCC3 Client worries about profitabil ity

BCC4 Ignorance of l ife cycle costs (ability to select alternatives that impact both pending and future costs)

BCC5 Lack of financial resources

BCC6 Lack of access to mortgage/credits

BCC7 High interest rates

BCC8 Reduced government spending on infrastructure related projects

BCC9 Client emphasis on low construction costs

Factor

ID Corruption (External/Organisational level) (C)

C1 Lack of company's code of ethics that addresses ethical conduct (principles of right or wrong conduct)

C2 Lack of knowledge on business ethics

C3 Failure to adhere to company's code of ethics that maintains a culture that promotes the principles of the company

C4 A company culture that puts profitabil ity and business performance ahead of ethical behaviour

Factor

ID Volatile Commodity and Exchange Rates (External level) (VC)

VC1 Volatil ity in labour markets (strikes) which results in l imited foreign investments within the country

VC2 Volatil ity in commodity prices and exchange rates (weakening exchange rate)

VC3 Declining rand value

VC4 Downgraded economic ratings from global agencies

Factor

ID Socio-Cultural (Organisational lev el) (SC)

SC1 Lack of demand of sustainable products by client and stakeholders

SC2 Cultural change resistance

SC3 Lack of stakeholder's engagement and effective participation

113

Factor

ID Time Related (Project lev el) (TR)

TR1 Insufficient time for construction planning

TR2 Time taken to implement variation orders (Affecting completion)

TR3 Average delay in claim approval (Affecting completion and cash flow)

TR4 Average delay in payment from owner to contractor (Affecting cash flow

Factor

ID Performance Correlation (PC)

PF1 The company has a clear vision and mission statement relevant to the organi sation's activities and mandate

PF2 The company has an adequate business plan that is documented and measurable in terms of performance standards in the industry

PF3 In the last three years, the company's turnover and profitabil ity has increased commendabl y

PF4 The company performance measures are compatible with the activities being carried out by the organi sation

PF5 My understanding of the organisation's performance measures is adequate

PF6 My understanding of issues that influences the performance of the company is adequate

PF7 The teams of employees in my company are made accountable for business performance

PF8 My company has sufficient cash flows to satisfy its business objectives in a controlled manner

PF9 My company strives for business excellence and overall customer satisfaction at all times

PF10 The effectiveness of the team of employees working in my company is commendable

PF11 My company's day-to-day operations are documented in the form of an operational manual

114

Appendix 1-5-2: VariableS analysis

Building Engineering

Variables RII Rank

FM - Average = 3,9

FM1: Profitability

91

1


84

2

FM4: Cash flow

82

3

FM5: Market share

73

4

FM2: Sales growth

72

5



90

1

NFM3: Safety

88

2


83

3


78

4


115

Variables RII Rank

78 5


78

6

SD - Average = 4,20


100

1


84

2


80

3


80

4


78

5


71

6


62

7

TC - Average = 3,75

TC1: Presence of skilled and professional technical staff such as designer/engineer/project manager, with relevant experience and

knowledge

90

1


78

2


116

Variables RII Rank

67 3


64

4


62

5


60

6

KM - Average = 4,02


78

1


77

2


74

3

KM4: Allocation of workers from one project to another in order to gain more knowledge and share post-project lessons learned from other

projects

73

4

KM6: Lack of education and professional knowledge about in sustainable design

72

5


63

6

RA - Average = 4,21


89

1


117

Variables RII Rank

86 2

RA3: Presence of local government and training authorities for review of labour policies for maintaining construction workforce

sustainability

66

3

P - Average = 4,06


80

1


77

2


69

3


69

4


63

5

MM - Average = 4,11


78

1


77

2


74

3


72

4


118

Variables RII Rank

71 5

TT - Average = 3,99


78

1


61

2

RF - Average = 4,12


79

1


78

2

RF3: Having apparent statutory processes that incorporate sustainable construction practices with economic incentives

70

3

BC - Average = 4,21


83

1


83

2


74

3


74

4

L - Average = 4,11


119

Variables RII Rank

78 1


77

2


77

3

L1: Lack of leadership in construction industry

72

4


69

5


59

6



84

1


83

2


81

3


81

4


79

5


78

6

120

Variables RII Rank


78

7


74

8


68

9

C - Average = 4,23


81

1

C2: Lack of knowledge on business ethics

79

2


71

3

C3: Adherence to company's code of ethics that maintains a culture that promotes the principles of the company

70

4

VC - Average = 4,00

VC1: Volatility in labour markets (strikes) which results in limited foreign investments within the country

81

1


71

2


71

3


67

4

121

Variables RII Rank

Variables


70

1


64

2


61

3

TR - Average = 4,29

TR4: Average delay in payment from owner to contractor (Affecting cash flow)

80

1


78

2


78

3


74

4

PF - Average = 3,88


84

1


82

2


81

3


122

Variables RII Rank

80 4


77

5

PF2: The company has adequate business plan that is documented and measurable in terms of performance standards in the industry

76

6


68

7


67

8


63

9

PF8: My company has sufficient cash flows to satisfy its business objectives in a controlled manner

57

10


52

11

Civil Engineering

Variables RII Rank

FM - Average = 3,9

FM1: Profitability

92

1


91

2

123

Variables RII Rank

FM4: Cash flow

86

3

FM5: Market share

84

4

FM2: Sales growth

80

5



95

1

NFM3: Safety

93

2


90

3


88

4


87

5


85

6

SD - Average = 4,20


100

1


89

2

124

Variables RII Rank


89

3


88

4


87

5


84

6


73

7

TC - Average = 3,75


knowledge

97

1


88

2


70

3


68

4


67

5


67

6

KM - Average = 4,02

125

Variables RII Rank


92

1


projects

90

2


88

3


82

4


81

5


79

6

RA - Average = 4,21


93

1


93

2


sustainability

78

3

P - Average = 4,06


93

1


92

2

126

Variables RII Rank


85

3


82

4


71

5

MM - Average = 4,11


87

1


83

2


78

3


76

4


74

5

TT - Average = 3,99


90

1


83

2

RF - Average = 4,12


127

Variables RII Rank

93 1


89

2


82

3

BC - Average = 4,21


98

1


97

2


90

3


89

4

L - Average = 4,11


91

1


91

2


90

3


88

4


128

Variables RII Rank

88 5


79

6



94

1


88

2


84

3

BCC3: Client worries about profitability

83

4


81

5


78

6


73

7


69

8


66

9

C - Average = 4,23


129

Variables RII Rank

93 1

C3: Adherence to company's code of ethics that maintain a culture that promotes the principles of the company

88

2


84

3


83

4

VC - Average = 4,00


84

1


83

2


75

3


73

4

SC - Average = 3,83


89

1


83

2


73

3

TR - Average = 4,29

130

Variables RII Rank


89

1


88

2


88

3


88

4

TR - Average = 4,29


96

1


93

2


93

3


93

4


89

5


89

6


88

7


131

Variables RII Rank

85 8


79

9


75

10


61

11

Consulting (Engineering and Construction)

Variables RII Rank

FM - Average = 3,9

FM1: Profitability

86

1


86

2

FM4: Cash flow

86

3

FM2: Sales growth

80

4

FM5: Market share

72

5



132

Variables RII Rank

90 1


87

2


86

3

NFM3: Safety

86

4


81

5


81

6

SD - Average = 4,20


100

1


86

2


85

3


83

4


75

5


74

6

133

Variables RII Rank


74

7

TC - Average = 3,75


knowledge

90

1


83

2


72

3


68

4


67

5


65

6

KM - Average = 4,02


86

1


projects

82

2


81

3


78

4

134

Variables RII Rank


78

5


74

6

RA - Average = 4,21


92

1


88

2


sustainability

65

3

P - Average = 4,06


91

1


84

2


81

3


72

4


68

5

MM - Average = 4,11


135

Variables RII Rank

86 1


86

2


83

3


81

4


81

5

TT - Average = 3,99


80

1


72

2

RF - Average = 4,12


82

1


79

2


79

3

BC - Average = 4,21


86

1

136

Variables RII Rank


83

2


82

3


77

4

L - Average = 4,11


89

1


86

2


84

3


81

4


79

5


70

6



91

1


90

2

137

Variables RII Rank


86

3


84

4


80

5


79

6


78

7


78

8


77

9

C - Average = 4,23


91

1


87

2


85

3


84

4

VC - Average = 4,00

138

Variables RII Rank


81

1


80

2


77

3


74

4

SC - Average = 3,83


75

1


74

2


73

3

TR - Average = 4,29


91

1


88

2


87

3


87

4

139

Variables RII Rank

PF - Average = 3,88


90

1


85

2


85

3


84

4


84

5


83

6


79

7


74

8


71

9


70

10


65

11

140

Variables RII Rank

Other

# RII Rank

FM - Average = 3,9


77

1

FM1: Profitability

75

2

FM4: Cash flow

71

3

FM2: Sales growth

70

4

FM5: Market share

63

5


NFM3: Safety

85

1


83

2


82

3


81

4


141

Variables RII Rank

80 5


73

6

SD - Average = 4,20


100

1


87

2


87

3


82

4


79

5


79

6


78

7

TC - Average = 3,75


knowledge

89

1


83

2


142

Variables RII Rank

73 3


72

4


72

5


68

6

KM - Average = 4,02


85

1


projects

82

2


81

3


79

4


78

5


78

6

RA - Average = 4,21


91

1


143

Variables RII Rank

89 2


sustainability

75

3

P - Average = 4,06


89

1


85

2


84

3


81

4


79

5

TT - Average = 3,99


88

1


86

2


84

3


83

4


144

Variables RII Rank

82 5

TT - Average = 4,14


85

1


81

2

RF - Average = 4,12


86

1


85

2


79

3

BC - Average = 4,21


86

1


84

2


82

3


81

4

L - Average = 4,11


145

Variables RII Rank

88 1


87

2


84

3


84

4


84

5


73

6



90

1


88

2


87

3


85

4


84

5


81

6

146

Variables RII Rank


81

7


80

8


78

9

VC - Average = 4,00


89

1


85

2


85

3


84

4

VC - Average = 4,21


86

1


85

2


83

3


83

4

147

Variables RII Rank

SC - Average = 3,83


82

1


81

2


76

3

TR - Average = 4,29


88

1


88

2


85

3


83

4

PF - Average = 3,88 RII Rank


87

1


84

2

PF9: My company strive for business excellence and overall customer satisfaction at all times

84

3


148

Variables RII Rank

82 4


80

5


77

6

PF4: The company performance measures are compatible with the activities being carried out by the organization

75

7


75

8


73

9


68

9


60

10

149

Appendix 1-5-3: Relative importance index

Grade 2 to 4 (R 3750 000 - R 4000 000)

Variables RII Rank

FM - Average = 3,9

FM1: Profitability

85

1


83

2

FM4: Cash flow

79

3

FM2: Sales growth

73

4

FM5: Market share

71

5


NFM3: Safety

91

1


89

2


87

3


84

4


83

5


80

6

SD - Average = 4,20


100

1


90

2


90

3

150

Variables RII Rank


88

4


84

5


81

6


74

7

TC - Average = 3,75

TC1: Presence of skilled and professional technical staff such as designer/engineer/project manager, with relevant experience and knowledge

95

1


85

2


75

3


74

4


74

5


73

6

KM - Average = 4,02


87

1


84

2


83

3

KM4: Allocation of workers from one project to another in order to gain more knowledge and share post-project lessons learned from other projects

83

4


80

5


79

6

151

Variables RII Rank

RA - Average = 4,21


93

1


91

2

RA3: Presence of local government and training authorities for review of labour policies for maintaining construction workforce sustainability

75

3

P - Average = 4,06


91

1


87

2


84

3


83

4


77

5

MM - Average = 4,11


88

1


85

2


82

3


80

4


75

5

TT - Average = 3,99


89

1


77

2

RF - Average = 4,12

152

Variables RII Rank


92

1


90

2


81

3

BC - Average = 4,21


90

1


88

2


86

3


83

4

L - Average = 4,11


86

1


85

2


84

3


83

4


83

5


69

6



91

1


90

2


89

3

153

Variables RII Rank


85

4


83

5


81

6


80

7


77

8


77

9

C - Average = 4,23


90

1


87

2


85

3


79

4

VC - Average = 4,00


84

1


81

2


73

3


71

4

SC - Average = 3,83


80

1


76

2


154

Variables RII Rank

73 3

TR - Average = 4,29


87

1


87

2


85

3


83

4

PF - Average = 3,88


93

1


90

2


87

3


86

4


85

5


83

6


83

7


80

8


77

9


75

10


67

11

Documents

Factors influencing sustainable performance in the