FACTORS AFFECTING EMPLOYEE PRODUCTIVITY IN...

FACTORS AFFECTING EMPLOYEE

PRODUCTIVITY IN THE UAE

CONSTRUCTION INDUSTRY

NABIL AILABOUNI

A thesis submitted in partial fulfilment of the requirements of the University of Brighton for the

degree of Doctor of Philosophy

September 2010

School of Environment and Technology University of Brighton

Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010

ii

ABSTRACT

Reliable productivity rates for construction trades are essential for contractors to accurately estimate the time and cost of construction projects. These rates vary considerably based on the complexity of the structure, project site constraints, and other technical, managerial, social and cultural factors. Predicting the effect of these factors will enhance the ability of the contractor to optimally utilize resources. This research therefore aims to evaluate the most significant factors that affect productivity of key construction activities namely: excavation, formwork, reinforcement, concreting, blockwork, plastering and tiling. The research focuses on the construction industry in the UAE (United Arab Emirates).

Literature review of the classical management theories and contemporary works on construction productivity led to the identification of four generic factors affecting productivity: Environmental, Organizational, Group Dynamics and Individual Factors. Three questionnaire surveys were undertaken to identify the most significant factors and the magnitude of their effect on productivity. The first survey identified the most significant factors after ranking them according to a severity index. The other two surveys identified the magnitude of the effect of these factors on productivity.

The research used Chi Square Test for Significance, which identified - Work Timings, Control by Supervision, Group Dynamics, Control by Procedures, Climatic Conditions and Material Availability as the most significant factors affecting productivity. Six sites were selected for data collection for productivity rates of the key construction activities. The significant factors were varied at three ordinal levels that afforded practical variations at site. The increase or decrease in productivity obtained was compared to the actual site average productivity and then subjected to regression analysis using MINITAB 15 Statistical Software. This resulted in the development of a regression model for each of the seven key construction activities.

Four other construction sites were selected and used for validation of the developed models. The developed models have been used to evaluate the variability in productivity of construction activities and to predict the percentage change in productivity of the selected activities when the underlying variables are varied. Review of the coefficients of the factors in the individual regression models afforded insight into those that most affect productivity of the selected construction activities. This intelligent information can help site management to create favourable conditions on site aimed at enhancing productivity rates and therefore optimal utilization of resources.

***


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TABLE OF CONTENTS

No Description Page No.

ABSTRACT ii

TABLE OF CONTENTS. iii

1 RESEARCH INTRODUCTION... 1

1.0 Chapter 1 Introduction. 2

1.1 Need for Research .. 3

1.1.1 Gap in Knowledge........ 4

1.2 Research Aim.. 9

1.2.1 Research Objectives 9

1.3 Scope of Research . 9

1.3.1 Definition of Productivity 10

1.4 Brief Outline of Research . 12

1.4.1 Review of Existing Literature and Publications. 15

1.4.2 Data Collection: Survey for Significant Factors 16

1.4.3 Data Collection: Surveys for Effect of Significant Factors. 17

1.4.4 Data Collection: Statistical Tests for Significance using Chi Square 18

1.4.5 Field Data Collection. 18

1.4.6 Homogenization of Data 20

1.4.7 Regression Analysis using MINITAB 15 software 21

1.4.8 Validation of the Models... 22

1.4.9 Model Application.. 23

1.5 Chapters Summaries 24

1.6 Conclusion... 28

2 LITERATURE REVIEW 30

2.0 Chapter 2 Introduction.. 31

2.1 Management Theories.. 35

2.1.1 The Classical Approach. 35

2.1.2 The Human Relations Approach.. 37

2.1.3 The Systems Approach 38


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2.1.4 The Contingency Approach... 39

2.2 Common Factors in Organizations... 39

2.3 Theories of Motivation. 41

2.3.1 Early Theories.. 43

2.3.2 Modern Theories: Content Theories of Motivation.. 44

2.3.3 Modern Theories: Process Theories of Motivation......... 49

2.4 Construction Industry Related Productivity Studies. 52

2.4.1 Factors Affecting Productivity (A review of contemporary

publications). 53

2.4.2 Factors Affecting Motivation of Construction Operatives

(A review of contemporary publications).. 61

2.5 Background to the UAE Construction Industry ... 71

2.5.1 Economic Characteristics of the UAE Construction Industry 71

2.5.2 UAE Labour Market 77

2.5.3 Demographic Influence and Cultural Backgrounds 78

2.5.4 Environmental Conditions.. 79

2.5.5 UAE Statutory Laws 79

2.5.6 No Trade Unions.. 80

2.5.7 UAE National Workplace Employment Relations Survey.............. 81

2.6 Factors Affecting Productivity in the UAE Construction Industry. 84

2.6.1 Environmental Factors... 87

2.6.2 Organizational Factors.. 89

2.6.3 Group / Team Factors.... 94

2.6.4 Personal Factors. 95

2.7 Conclusion .. 97

3 RESEARCH METHODOLOGY. 99

3.0 Chapter 3 Introduction....................... 100

3.1 Outline of Research Methodology 100

3.2 Development of Methodology. 102

3.3 Survey Questionnaire Design... 104

3.4 Identification of the Significant Factors Affecting Productivity in the

UAE...................................................................................................... 104


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3.5 Perception Surveys for the effect on productivity

(Survey Nos 2 & 3). 112

3.6 Analysis of the Results of the Perception Surveys (2) and (3).. 121

3.7 Chi-Square Test for Significance 122

3.7.1 Chi-Square Test for Significance (Internal Perception Survey).. 122

3.7.2 Chi-Square Test for Significance (External Perception Survey .. 123

3.8 Field Data Collection 127

3.8.1 Case Study Company . 127

3.8.2 Field Data Collection .... 127

3.9 Technical Factors Affecting Productivity 133

3.9.1 Type of Projects... 134

3.9.2 Technical Nature of the Trades 134

3.10 Conclusion 137

4 DATA COLLECTION, DATA ANALYSIS AND THE

DEVELOPMENT OF PRODUCTIVITY EVALUATION MODEL... 139


4.1 Productivity Model: Overall Versus Individual Construction Trades 141

4.2 Data Collection and Analysis... 143

4.3 Definition of Statistical Parameters Used 147

4.3.1 R2 Coefficient of Determination ...... 147

4.3.2 d Durbin Watson Statistic ..... 147

4.3.3 Alpha () Level of Significance........ 148

4.3.4 p-Value ... 149

4.4 Model Formulation .. 149

4.4.1 Homogenization of Field Data.. 150

4.4.2 Statistical Modelling Using MINITAB 15 Software. 153

4.5 Regression Models For Productivity of Construction Trades.. 157

4.5.1 Regression Model for the Excavation Trade. 158

4.6 Conclusion 180


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5 MODEL VALIDATION AND EVALUATION OF VARIABILITY

OF PRODUCTIVITY ............................ 182


5.1 Background Considerations. 183

5.2 Sites Used for Model Validation. 185

5.3 The Validation of the Model 186

5.4 Validation Excavation Productivity Model ARS Site .. 191

5.5 Validation Results .... 198

5.6 Evaluation of Factors Affecting Productivity.. 201

5.7 Conclusion ... 205

6 CONCLUSION................. 207


6.1 Work Accomplished & Challenges Faced .. 208

6.1.1 Data Collection Techniques and Accuracy ... 210

6.1.2 Broad Topic of Construction Productivity and Several

Factors in Combination .. 211

6.1.3 Understanding Technical Issues with Productivity Numbers . 212

6.1.4 Short Term Nature of Construction Projects 212

6.2 Fulfilment of Research Aim and Objectives ... 213

6.3 Practical Utilization of Model . 214

6.4 Possible Improvements in the Model .. 216

6.5 Conclusion... 220

6.6 Areas for Future Research .. 227

REFERENCES 230

BIBLIOGRAPHY.. 237

LIST OF APPENDICES.................. 245

Appendix 1 Master Field Data for Model Formulation ....................... 246 - 263

Appendix 2 Master Field Data for Model Validation ......................... 264 - 285

Appendix 3 Questionnaire Formats Used ............................................ 286 - 294


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Appendix 4 Collection of Productivity Modelling Data & Graphs.. 295 - 323

For Excavation, Formwork, Reinforcement, Concreting,

Blockwork, Plastering and Tiling Works

Appendix 5 Collection of Validation Data & Graphs ................................. 324 - 348

For Excavation, Formwork, Reinforcement, Concreting,

Blockwork, Plastering and Tiling Works

Appendix 6 Predicted Productivity for all Possible Combinations

of Factor Levels ................................................................. 349 - 383

Appendix 7 Statistical Tables and Definitions ..................................... 384 - 396

Appendix 8 Project Profiles ................................................................. 397 - 407

***


viii

LIST OF TABLES CHAPTER 1

Table 1.1 Construction Trades and Productivity Factors

for Field Data Collection. 19

CHAPTER 2

Table 2.1 Factors Affecting Job Satisfaction... 48

Table 2.2 Literature Review Matrix: Collection Of Factors Affecting

Construction Productivity (General) .. 59

Table 2.3 Motivating Factors in Construction Industry... 63

Table 2.4 Motivators and De-Motivators Ranked By Workers in Thailand. 64

Table 2.5 Differences in Project Characteristics with High/Low Productivity 66

Table 2.6 Literature Review Matrix: Motivating Factors in the Construction

Industry.. 68

Table 2.7 Literature Review Matrix: Factors Affecting Construction

Productivity across Countries 70

Table 2.8 Construction Industry Characteristics... 76

Table 2.9 2001 UAE National Workplace Employment Relations Survey

Results 83

Table 2.10 Comprehensive List of Factors Affecting Productivity: UAE

Construction Industry.... 86

CHAPTER 3

Table 3.1 Survey(1) Response Reckoner.. 105

Table 3.2 Significant Factors Affecting Productivity (First 8 within Groups). 110

Table 3.3 Significant Factors Affecting Productivity in Matrix Form.. 111

Table 3.4 Significant Factors Affecting Productivity (Fourteen Factors

with Highest Ranks)... 112

Table 3.5 Perception Survey (2) (Internal): Summary Results 114

Table 3.6 Perception Survey (2) (Internal) : Summary Percentages.... 115

Table 3.7 Perception Survey (2) (Internal): Weighted Averages 116

Table 3.8 Perception Survey (3) (External): Summary Results 117

Table 3.9 Perception Survey (3) (External): Summary Percentages 118

Table 3.10 Perception Survey (3) (External): Weighted Average Result.. 119


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Table 3.11 Combined Analysis % Wise Perception Surveys (2) & (3) ... 120

Table 3.12 Chi-square Computations: Survey Data Productivity Factors

And Their Effects Internal Survey... 124

Table 3.13 Chi-square Computations: Survey Data Productivity Factors and

their Effects External Survey.............. 125

Table 3.14 Field Variables Using Weighted Averages from Survey 2 & 3 126

Table 3.15 Sample Sites and Activities under Study for Productivity.. 129

Table 3.16 Range Of Productivity Values Trade Wise / Site Wise.. 132

Table 3.17 Factor Levels Used For Data Collection. 133

CHAPTER 4

Table 4.1 Brief Profile of Construction Projects Used For Field Data

Collection. 144

Table 4.2 Summary of Data Collected and Used For Formulating Model 146

Table 4.3a Excel Sheet Used For Model Formulation Excavation 159

Table 4.3b Regression Models Iteration Summary for Excavation 170

Table 4.4 Final Regression Models... 173

Table 4.5 Major Productivity Contributing Factors. 174

Table 4.6 Extracts from Appendix 5-8a: Excavation

Trade productivity at Various Runs /Levels of Factors.. 179

CHAPTER 5

Table 5.1 Construction Sites Used For Model Formulation and Validation 185

Table 5.2 Validation Data for Excavation - ARS Site... 192

Table 5.3 Grand Summary of Validation Data and Results.. 200

Table 5.4 Summary of Productivity Models.. 201

Table 5.5 Major Productivity Contributing Factors per Construction Trade 202

***


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LIST OF FIGURES CHAPTER 1

Fig. 1.1 Overview of the Research 14

CHAPTER 2

Fig. 2.1 Organizational Sub- Systems 40

Fig. 2.2 Maslows Hierarchy of Needs Pyramid 45

Fig. 2.3 Factor Model of Construction Labour Productivity. 57

Fig. 2.4 General Categories of Factors Affecting Productivity. 84

CHAPTER 3

Fig. 3.1 Overview of the Research.. .. 101

Fig. 3.2 Snapshot of Survey (1) Questionnaire. 104

Fig. 3.3 Extract of Survey (1) Results For Sample Computation.. 107

Fig. 3.4 Questionnaire Design for Survey 2 & 3... 113

CHAPTER 4

Fig. 4.1 Snapshot of Excel Sheet for Model Formulation - Excavation

Trade. 151

Fig. 4.2 Flow Chart : Homogenization of Field Data 153

Fig. 4.3 Grab of Minitab 15 Menu- Stat-Regression- Graphs .. 155

Fig. 4.4 Flow Chart : Statistical Modelling Using MINITAB 15 Software.. 156

Fig. 4.5a Iteration 1- Excavation Modelling Graphs 163

Fig. 4.5b Iteration 2- Excavation Modelling Graphs 165

Fig. 4.5c Iteration 3- Excavation Modelling Graphs 167

Fig. 4.5d Iteration 4- Excavation Modelling Graphs 169

Fig. 4.6a Graphical Representation of Factors affecting Excavation . 175

Fig. 4.6b Graphical Representation of Factors affecting Formwork 175

Fig. 4.6c Graphical Representation of Factors affecting Reinforcement 176

Fig. 4.6d Graphical Representation of Factors affecting Concreting .. 177

Fig. 4.6e Graphical Representation of Factors affecting Blockwork .. 177

Fig. 4.6f Graphical Representation of Factors affecting Plastering. 178

Fig. 4.6g Graphical Representation of Factors affecting Tiling... 178


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CHAPTER 5

Fig. 5.1 Flow Chart Showing Computation of Errors for Validation 186

Fig. 5.2 Flow Chart Showing Computation of Errors for Validation 189

Fig. 5.3a Error Chart For Excavation For The 2 Sigma Limits

(For Minitab 15)......................................... 195

Fig. 5.3b Error Chart for Excavation For The 15% Band

(For Minitab 15)..... 196

Fig. 5.3c Histogram of Errors (For Minitab15) 197

Fig. 5.3d Scatter plot (For Minitab 15)................................. 197

Fig. 5.3e Four in One Excavation Validation Graph ... 198

***


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ACKNOWLEDGEMENT

I would like to express my gratitude and appreciation to my supervisors - Dr.

Kassim Gidado, Mr. Noel Painting and Dr. Phil Ashton from the University of

Brighton for their patience and support, evaluation and direction and mostly their

encouragement right from the time I set out the research outline to the final

submission.

Then I would thank my partners and employees in Target Engineering

Construction Co. for their help and participation in the surveys, data collection

and positive encouragement throughout the study period. I would also like to

thank the members of my family for their help and support during my study.

***


xiii

AUTHORS DECLARATION

I declare that the research contained in this thesis, unless otherwise formally

indicated within the text, is the original work of the author. The thesis has not

been previously submitted to this or any other university for a degree and does

not incorporate any material already submitted for a degree.

Signed _______________________

Nabil Ailabouni

Dated September, 2010

***


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ABBREVIATIONS

ARCOM - Association of Researchers in Construction Management

ERG - Existence, Relatedness and Growth

GCC - Gulf Cooperation Council

GDP - Gross Domestic Product

ILO - International Labour Organization

MOLSA - Ministry of Labour and Social Affairs, UAE

OECD - Organization for Economic Cooperation and Development

PASW - Predictive Analytics Software

PPCM - Percentage Productivity as Measured

PPCP - Percentage Productivity as Predicted

SAS - Statistical Analysis Software

SPSS - Statistical Package for Social Sciences

UAE - United Arab Emirates

***


1

CHAPTER 1 RESEARCH INTRODUCTION


2

CHAPTER 1 RESEARCH INTRODUCTION

1.0 CHAPTER INTRODUCTION

Productivity rate of construction trades is one of the elements required to

accurately estimate time and costs required for the construction processes.

Projects can be better controlled if the variability in productivity of construction

trades is known, and actions taken to enhance productivity. This research is,

therefore, aimed at identifying and evaluating the factors affecting productivity

of construction trades; developing a construction productivity change model for

predicting changes in productivity as the underlying factors are varied and using

this information to create favourable conditions for enhanced productivity.

This chapter introduces the research title, aim, objectives, and definitions of

productivity while establishing the need for research against the background of

productivity of construction trades and its importance in the construction

industry, with a primary focus on the United Arab Emirates (UAE).

In fulfilment of the research objectives, a literature review was conducted to

determine the factors that affect productivity. Having identified the broad factors

affecting productivity, the most significant factors and quantification of the

magnitude of their effect in productivity rate were identified utilizing a specially

designed questionnaire that was distributed to key players in construction

industry. Field data was collected from six sites of the case study company and

was used to develop the regression models for key construction activities. The

models were validated using data from four other sites of the case study

company.

After a brief discussion on applicability of the models, the chapter concludes with

summaries of each chapter to give the reader a general outline of the research.


3

1.1 NEED FOR RESEARCH

Knowing productivity rates of various trades in the construction industry is

critical for an accurate estimation of the time and cost of a job. An improved

productivity rate helps contractors not only to be more efficient and profitable

during project execution, but also helps them to be more competitive during the

bidding stage of the projects.

The construction industry in the UAE is a multibillion dollar industry,

contributing approximately 8% to the UAE Gross Domestic Product (GDP).

(Reference UAE Yearbook 2009). Until the global economic slowdown affected

the region, the industry was buoyed by high liquidity owing to the high oil prices,

government spending and stable political environment. There is a continued

supply of comparatively cheap labour from the Asian countries. The workforce is

subject to several influences such as - different management styles, language

barriers, customs, separation from families, level of supervision, quality of

accommodation and climate. Such influences have direct impact on productivity

rates.

All contractors within the UAE face similar amount of constraints; same

specifications apply and therefore the bottom line performance of contractors is

influenced by how effective & well planned, the construction methods are, and

whether the construction operatives work at optimal productivity levels or not.

Achieving quantity and quality of results while controlling the inputs is therefore

a key challenge for all contractors. This research therefore is important as the

knowledge of factors affecting productivity will aid supervision staff to ensure

optimal conditions on site; namely ensure favourable factors for achieving

maximum productivity of operatives on their sites. This would help keeping costs

within budget, keep employee morale high and help projects to be completed on

time; help companies run their businesses profitably.


4

1.1.1 Gap in Knowledge

Review of contemporary works on the subject of productivity revealed that the

studies undertaken generally involved one construction trade (for example

masonry by some and bricklaying by others) and or one variable at a time (for

example some studied the effects of only motivation on productivity), some

studied the variables in isolation without interaction between variables and most

were generally qualitative rather than quantitative suggesting trends or

relationship but stood short of quantifying this relation.

The works of contemporary writers is briefly mentioned in this section leading to

the identification of the gap in knowledge.

Herbsman and Ellis (1990) studied the effects of project conditions termed by

them as Construction Influence Factors on the variation of productivity rates for

construction items and described the development of a statistical model that

illustrated quantitative relationships between influence factors and the

productivity rates. However the study was conducted on past records from site

and not freshly collected data. They concentrated on the construction influence

factors classified into technological and administrative factors. These were

project based conditions. Effects of the company wide environment were not

considered.

Further, the influence factors were quantified using three methods: direct, indirect

using alternate indicators (such as labour turnover for measuring motivation) and

quantification using non parametric ranking. The non parametric ranking

involved ranking the elements to a scale of 1 to 10 based on an individuals

experience, knowledge and judgment. Also the construction industry influence

factors were based on interviews with various participants in the construction

industry, determined by a group of experts and not through questionnaires.

Finally, a stepwise effect of the influence factors was adopted where each of the

factors was introduced in the model one at a time and the resultant R2 the


5

coefficient of determination - was reviewed for model adequacy. The SAS

(Statistical Analysis Software) software was used for model formulation. The

productivity model they presented contained a regression equation that utilized

the identified influence factors and gave the productivity of the particular

activity. The study however did not contain validation of the models, and it was

only suggested that the models could predict the productivity for that activity in

future projects.

Sanders and Thomas (1991) in their study of the factors affecting masonry labour

productivity identified inadequacies in previous similar studies to accurately

identify the factors. Their methodology involved the data collected from 11

masonry projects between 1986-1988 in central Pennsylvania. Data collection

was standardized in a procedures manual for consistency. Data sets were

converted to equivalent units to take care of different sizes of bricks being laid

and regression analysis was performed to develop models to relate the

productivity to the physical characteristics of the masonry units. Potential factors

identified and used in the models were based on experience, observations and

data reconciliation procedure. The project related factors identified were work

type, building elements, construction methods, and design requirements. Further

analysis of variance was done on each of these factors. The conclusions included

that 30% improvement is expected if the design is repetitive and 40%

improvement could be realized if design is improved. Expected percentage

improvement resulting from each parameter in isolation was suggested; the

combination effect of all the parameters was not studied.

Ogunlana and Chang (1998) studied worker motivation on selected construction

sites in Bangkok, Thailand. Here the data was collected from seven accessible

high rise building construction sites out of the twenty five selected. A two stage

questionnaire was used - the first being a list of needs, while the second one

contained a list of motivators and de-motivators. Further, the needs, motivators

and de-motivators were ranked by workmen as against those by supervisors and

a further cross analysis of the combined needs, motivators and de-motivators was

carried out. The final results showed that the needs of the Thai workers higher


6

pay, better accommodation, good welfare and safety compared lower to the felt

needs of fringe benefits, good relations and safe sites. It was also proved that the

needs, motivators and de-motivators of the Thai workers and those of Nigeria

were similar. The study suggested that motivation methods need to be adjusted

to the situational effects and the personal traits of the national people. The study

was limited to few factors.

Proverbs et al (1998) did a comparative evaluation of reinforcement fixing

productivity rates amongst the French, German and UK construction contractors.

The productivity rates given by the respective planning engineers formed the

basis of the research. The planning engineers from each of the 31 contractors

from UK, 13 contractors from France, and 10 contractors from Germany were

given a set of project drawings and a questionnaire. Productivity rates of the

erection of formwork, reinforcement fixing and concrete placing were asked for

but the paper focused only on one operation - reinforcement fixing for beams,

columns, floor slabs and for the entire project. The study concluded that

significant differences existed between the productivity rates used by French,

German and the UK contractors using coefficients of variation and ranks. No

models were developed. The study presented a comparison of the rate of

productivity for specific construction trades but did not suggest how to change or

improve productivity.

Mohamed and Srinavin (2002) in their study of thermal environment effects on

the construction workers productivity argued that further to air temperature,

relative humidity and wind velocity, additional thermal environment parameters

should be accounted for to enhance the predictive power of forecasting the

construction workers productivity. These factors included the mean radiant

temperature, clothing insulation, and metabolic rate. Amongst the various

techniques used to determine the effect of climatic conditions on workers

productivity, the multiple regression technique was commonly used. The study

resulted into a regression equation developed from data gathered from literature;

the equations were further validated using correlation analysis. The study was

devoted only to thermal effects on productivity.


7

Chan P (2002) in his study of the factors affecting labour productivity in the UK

construction industry discussed the various aspects affecting productivity through

a series of focus group interviews engaged in the Personal Construct Theory

(PCT). Personal Construct Theory offers the prospect of unlocking the vital

experience of people and breaks down the barrier between researchers and

research subjects. This study comprised a series of semi structured hour long

focus group interviews with construction operatives. The focus group interviews

had three main stages - construct explication, construct review and construct

validation. By engaging in the personal constructs of site management staff, four

key areas were identified as aspects leading to productivity improvements. These

are planning, teamwork, welfare, and job security. The study did bring in human

factors affecting productivity levels but did not magnify their contribution and the

measures to improve them were not discussed.

Kazaz and Ulubeyli (2006) studied the organizational factors influencing

construction manpower productivity in Turkish Construction Industry. Data was

collected using a survey questionnaire with a combination of face to face

interviews, email responses, and telephone interviews. Statistical methods were

used to analyze the data using the Relative Importance Index. A rating scale of 1

to 5 was adopted with 1 being the lowest and 5 being the highest level of effect.

Using significance intervals, the survey results showed the site management,

material management, and systematic flow of work were ranked by participants

as the three most effective organizational factors affecting the productivity. The

study ends with a detailed discussion on all the factors affecting the productivity.

No model to measure the effect of these factors on productivity was introduced.

Aiyetan and Olotouah (2006) studied the impact of motivation on workers

productivity in the Nigerian Construction Industry. Questionnaires were used in

the research which addressed the relationship between motivation and

performance. No other factors were considered. The study was limited to the

perception survey of the management staff and the operatives. The overall

recommendations included adjusted salary structure, increased welfare, increase

in salary; promotion, overtime and holiday with pay financial incentives that


8

increased motivation and therefore the productivity. There was no model

formulated and only subjective recommendations were given.

Alinaitwe et al (2007) studied the factors affecting the productivity of building

craftsmen in Uganda. The survey consisted of a questionnaire to Project

Managers from selected buildings sites, where they were asked to rank the 36

factors affecting productivity taking into account effects of time, cost and quality.

The research resulted in identifying the five highest ranked factors as

incompetent supervisors, lack of skills, rework, lack of tools / equipment and

poor construction methods. No model was proposed to measure the effect of

variability in these factors on the productivity rate. Only a subjective conclusion

was arrived at suggesting that these factors have an important effect on

productivity.

Following a review of the above referred works, a need was therefore felt for a

study involving multiplicity of factors affecting construction trades and

establishing a regression model for accurately predicting changes in productivity

with the aim of increasing the productivity so that time and cost factors are better

controlled in the project; in other words resources are optimally utilized in the

project.

After identifying the broad categories of factors affecting productivity as

Environmental, Organizational, Group Dynamics and Individual Factors, this

research for the first time closed in on six factors affecting productivity over

seven construction trades of excavation, formwork, reinforcement, concreting,

blockwork, plaster and tiling and attempts to quantify the predicted change in

productivity vis--vis the change in the factors themselves. These six factors

selected for modeling including - Timings, Supervision, Group Dynamics,

Materials, Procedures and Climate; these being selected based on three surveys

leading to the identification of the most significant factors affecting construction

productivity in the UAE.


9

Similar works involving statistical models giving predicted construction

productivity for changes in factors such as Work Timings, Level of Supervision,

Group Dynamics, Control by Procedures, Availability of Materials, Climatic

Conditions, were not come across during the literature review. In this context,

this research is thought to be a first of its kind at least in the UAE

1.2 RESEARCH AIM

The aim of this research is to evaluate factors that would affect productivity of

construction trades in order to optimize output.

1.2.1 RESEARCH OBJECTIVES

The objectives to fulfill the research aim are:

a) Identify the factors affecting productivity of the construction industry in

the United Arab Emirates.

b) Establish the significance of a selection of construction trades upon the

productivity of the construction industry in UAE

c) Develop a methodology for measuring the factors that affect productivity

of the construction industry in the UAE.

d) Develop a model for predicting changes in productivity.

e) Measure the changes in productivity.

1.3 SCOPE OF RESEARCH

This research has been conducted at construction sites in the UAE as sources of

field data, though the research aim and the findings thereof are of a general

nature.

The construction industry is characterized by multiple site conditions having

significant varying effects on the productivity rates of standard construction

trades. (Herbsman and Ellis, 1990).


10

The literature review provided the basis for classifying the four main categories

of factors affecting productivity; these being - the Environmental Factors, the

Organizational Factors, the Group Factors and the Individual Factors.

Construction trades are subject to a variety of factors that would determine the

productivity of that trade on the day; to study all of these at the same time, would

be complex; therefore the research undertakes three surveys to determine the

significant factors which are more manageable and amenable to study. The most

significant factors taken for further study include the Work Timings,

Supervision, Group Dynamics, Procedures, Material, and Climate. The reduced

number of factors means the construction industry can concentrate on controlling

them in order to improve the productivity.

These factors are very much relevant to the UAE economy as construction is the

predominant activity driving the economy after oil. The construction operatives

come from diverse background, and the caliber of supervision differs and is based

on nationality, education and experience. Procedures play an important role in

controlling the safe execution of the project and therefore affect productivity as

compliance with procedures would mean safety protection systems in place and

waiting for clearance or approval. The climate factor is obviously relevant, the

UAE having a hot humid climate; and the fact that the construction trades of

excavation, formwork, steel and concreting are out in the open.

1.3.1 Definition of Productivity

Different versions of the definition of productivity exist; some are listed in this

section with discussion leading us to the definition accepted for this research.

The Organization for Economic Cooperation and Development (OECD, 2001)

defines productivity as the ratio of a volume measure of output to a volume

measure of input used.

As per the OECD, the objectives of productivity measurement include:

a) Technology - to trace technological changes improvements


11

b) Efficiency to check if maximum output that is physically achievable with

current technology , give a fixed amount of inputs

c) Real cost savings a quest to identify real cost savings in production

d) Benchmarking production processes comparison of productivity measures

in specific production processes can help to identify inefficiencies.

e) Living Standards a simple example is per capita income of a country.

According to Sibson (1994), productivity means doing high quality work with

great efficiency. In essence it is some output per man hour. Output must be

saleable and usable and of good quality. Other simple definitions include the

amount of output per unit of input (labour, equipment, and capital).

There are many different ways of measuring productivity. For example, in a

factory, productivity might be measured based on the number of hours it takes to

produce a good, while in the service sector, productivity might be measured

based on the revenue generated by an employee divided by his/her salary.

Productivity measures could be single factor or multi-factor; the choice between

them depends on the purpose of the productivity measurement and in most

instances, on the availability of data. Productivity traditionally, refers to the

quantifiable ratio between outputs and inputs in physical terms. In the

construction industry, the quantitative measure widely used in the UAE

Construction Industry relates to the amount of construction activity for the man

hours that have been put in.

For the purpose of this research, productivity is defined as the ratio of output of

required quality to the inputs for a specific production situation. In the

construction industry; it is generally accepted as work output per man-hours

worked. This unit is generally used by most contractors in the UAE. It reflects

the measure of manual production which is being studied and also gives an

established factor for comparison over construction trades over time and over

project sites.


12

For example, excavation is measured in cubic meters of soil excavated per man

hour and plastering is measured in square meters of plaster per man hour.

Excavation and Plastering are manual operations involving a high degree of

manual labour and zero or limited mechanized assistance. This is due to the fact

that cheap labour is available in the country and in some of the projects; hand

excavation is a must because of the presence of live utilities underground.

The above definition takes into account quality and efficiency; however,

effectiveness is not covered by this definition; namely the cost benefit analysis of

the resources employed versus the output achieved is considered outside the

scope of this research.

Every management initiative strives to ensure optimal utilization of resources;

one way to do this is to increase the productivity that is to seek ways and means

to increase output.

Government, politicians, academics and economists all stress the importance of

productivity because it is an indicator of the general economic health of a

country. On the other hand, corporate management is concerned with

productivity because productivity is regarded as a main indicator of efficiency

when comparisons are made with competitors in local and global markets.

1.4 BRIEF OUTLINE OF RESEARCH

The following section briefly explains how the research data was generated,

analyzed and conclusions drawn from this data; the rationale behind the methods

chosen, the anticipated problems and how these were tackled during the research.

According to Fellows and Liu (2003), the critical consideration for selecting a

most appropriate research method is the logic that links the data collection and

analysis to yield results and thus the conclusions. Research designs therefore

must take into account the research questions; determine what data are needed

and how the data will be organized to maximize the chance of the research


13

realizing its objectives. They highlighted Survey, Experiment, Archival Analysis,

History and Case Study as the five research styles.

Research studies in the construction industry are generally conducted through

experiments, surveys or case studies. Experiments for productivity factors in

construction would mean long waiting time for results. Surveys through

questionnaires afforded relative ease of obtaining data for analysis. Random

sampling allows a small number of people to give opinions and characteristics

that can be representative of the general population. Alinaitwe et al (2007).

In section 1.1.1, pg. 3, contemporary works on productivity were outlined briefly

leading to the identification of gap in knowledge and establishing the need for

research. The research methods used by the contemporary authors include non

parametric ranking using face to face interviews, email responses, telephonic

interviews, focus group interviews, expert opinions, use of questionnaires,

followed by statistical analysis including significance testing or subjecting to

multiple regression analysis. Specialist statistical softwares such as SAS, SPSS

were utilized. Obviously, all these were preceded by a detailed literature review

of the relevant topics on productivity using classical management theories as well

as the contemporary writers. This research also undertakes a similar methodology

which is a combination of the literature review, surveys followed by significance

testing and finally case study for data collection for both model formulation and

model validation. The MINITAB 15 software was used for regression analysis

and modelling. The following subsections give an outline of the methodology

undertaken for this research.


14

Fig. 1.1: Overview of the Research


15

1.4.1 Review of Existing Literature & Publications

In order to identify the factors affecting productivity in the UAE construction

industry, it was important to first do a literature review of credible publications

of works already done in the field and acceptable to the academics. Accordingly

key authors and notable journals in the field of management and especially

construction engineering and management were reviewed and the ideology was

captured in three matrices. The reason for selecting the key authors and journals

include amongst others, credibility and wide acceptance in the field of

construction research.

Key words such as productivity, construction industry, productivity rates, UAE,

factors affecting productivity were used in search of the documents. The

literature review was confined to factors that would affect productivity in the

construction industry only as this was the subject of the research. Further

productivity in a purely manufacturing set up is different than in productivity in

construction industry. Manufacturing generally includes mass or continuous

production, and factories are stationary unlike construction projects with different

locations and constraints. Productivity rate for other sectors such as financial,

educational were not considered as part of this research. Accordingly, the

boundaries of this study are those factors that influence productivity rates in

construction industry.

The contemporary works on productivity were reviewed and three matrices were

established 1) indicating the factors affecting construction productivity, 2)

indicating the motivational factors affecting construction industry and 3)

indicating the factors affecting productivity across countries.

The classical theories, together with the information from these matrices and

experience of the researcher in the UAE construction industry led to the broad

categorization of factors affecting productivity of the construction industry in the

UAE.


16

1.4.2 Data Collection: Survey for Significant Factors

A number of research methods were considered ranging from interviews, survey

questionnaires, case studies, opinion polls and so on. Survey questionnaires

though difficult to design, can have wider coverage, are relatively cheap, avoid

embarrassment for the respondent and carries no interview bias. Interviews are

time consuming, costly, limited in coverage but can get in-depth probing during

interviews (Kothari, 2004).

For this research, the questionnaire and case study were selected as this offered

the possibility of having wider prospective respondents, elimination of any

personal bias that might develop during the interview and giving equal chances

for answering the questions under similar conditions.

The case study on the other hand was mandatory to provide the huge quantity of

data that was required for the study especially the model formulation and

validation later. Also the case study company had 30 years of productivity record

that could be used for comparison. The company also had undertaken projects

that were running concurrently, which could be used for data collection.

Accordingly three research surveys were undertaken. This was followed up by a

case study on site field data collection for measuring changes in productivity

leading to the formulation of a model and once again a case study validation of

data.

Survey 1 for Significance- The list of factors derived from literature review

were transformed into a survey questionnaire that was circulated to the key

industry players engineers, foremen and the operatives themselves. This served

as the first set of primary data which was analyzed using the Severity Index

(= Importance Index x Frequency Index). A list of significant factors affecting

productivity in the UAE construction industry was then established.


17

The results of this survey were reported at the PROBE (Post Graduate

Researchers in the Built & Natural Environment) conference, Glasgow,

Caledonian University, Scotland, November 20-22, 2007.

The survey questionnaire had a total of 61 questions and was sent to 500

participants out of which 238 responded. The questions were formulated from the

list of significant factors established by literature review. The respondents had to

answer the questions to a LIKERT scale (Kothari, 2004), as further explained in

Chapter 3.

The results were then ranked using the severity index and factors within each of

the four main categories of Environmental, Organizational, Group and Personal

Factors were presented.

Finally the highest ranked 14 factors were presented sorted in descending order.

Those were ultimately subjected to two more perception surveys Survey 2 and

Survey 3; results of which led to seven major factors of Timings, Competence of

supervisors, Salaries, Materials, Systems and procedures, Group dynamics and

Climatic conditions. The Salaries and Timings factor were merged into one factor

of Timings making the total factors that will be studied as six factors.

1.4.3 Data Collection: Surveys for Effect of Significant Factors

Two more sets of primary data were generated by conducting a perception survey

of the effect on productivity of the six factor groups; one using participants from

within the case study contracting company (Survey 2) and second using

participants external to the company (Survey 3). These were kept separate as the

results were expected to be different and reviewed at a later stage if required.

The perception survey was needed to establish the magnitude of the effect of each

of the significant factors following survey 1 and to help establish the field

variables for data collection. The effect was set at 25% as from practical

experience in the construction field, changes to the productivity by design is

seldom.


18

The survey results were presented in counts, percentages and weighted averages.

A combined analysis was also presented, which gave a percentage of the

respondents establishing the magnitude of the effect of the factors timings,

supervisor competence, salaries, materials, systems and procedures, group

dynamics, and climatic conditions for both the internal and the external survey.

Survey 2 and 3 analysis helped establish the magnitude of the effect of the

significant factors of productivity; which combined with the results of

significance testing described in 1.4.4 below, led to the establishing actual field

controllable factors affecting productivity.

1.4.4 Data Collection: Statistical Tests of Significance Using Chi Square

The results of the two perception surveys discussed above were then summarized

into a Chi Square matrix and tests of significance was conducted for both cases

separately. The factors were considered statistically significant in both cases.

The Pearson Chi Square test can be used to check goodness of fit and tests for

independence. Here the test was used to check for significance or independence.

These tests whether paired observations on variables expressed in a contingency

table are independent of each other; in this case the factors affecting productivity.

1.4.5. Field Data Collection

Three levels of variations were chosen for each of the seven factors described in

1.4.2 and 1.4.3 above using the calculated weighted averages. The three levels

have been chosen to afford a practical mechanism for variation and recording of

productivity changes. For example,

Work Timings (T) was varied at : Level 1 - Normal 8 hours work

Level 2 - 8 hours + 4 hours overtime

Level 3 - Contract Work, Fixed volumes of

work done for agreed compensation


19

Thus these three levels helped to establish a mechanism to vary conditions on site

and record productivity changes. Table 1.1 summarizes the construction trades

and the seven factor variables chosen for field data collection. These construction

trades were chosen as they are mostly done manually and offer tremendous scope

for improvement in productivity; besides being the significant activities at the

start of the project other than the mechanical, electrical and plumbing services

coming up later in the project.

Activitiesunderstudy

Factorsvariedduringdatacollection

1 Excavation Timings

Salaries

Supervision

GroupDynamics

Procedures

MaterialAvailability

Climate

2 Formwork

3 Reinforcement

4 Concreting

5 Blockwork

6 Plastering

7 Tiling Table 1.1 Construction Trades and Productivity Factors for Field Data Collection

The productivity was measured for the seven trades of Excavation (cubic metres /

man-hour), Formwork (square metres / man-hour) Reinforcement (tonnes / man

hour), Concreting (cubic metres / man-hour), Blockwork (square metres / man-

hour), Plastering (square metres / man-hour) and Tiling Works (square metres /

man-hour).

As the trades have different units of measurement, the output variable to be

measured and used in further statistical analysis was the difference in actual

productivity measured to the average productivity specific to the site, expressed

as a percentage productivity change. Expressing this as a percentage, achieved

getting a unit free figure and as such removed the problem of different units of

measurement.


20

The factors taken for data collection from the survey results were reduced to six

because it was not logical to change the salary during data collection, as this

would first make the operatives aware of the study, they could then be biased,

while other operatives not treated equally might not perform their best. Hence it

was merged with the Timing factor.

1.4.6 Homogenization of Data

Data as received from sites was reviewed and outliers were removed to ensure the

sample readings received represented a normal population. Further the site data

could include possible errors of recording, possible manipulation, computation

errors productivity outputs may have been subjected to unaccounted for factors

such as isolated activities of stoppage, waiting for inspection, unusually confined

spaces to work, varying complexities of the construction trade itself. The

technical constraints together with the size and complexity of the structure being

constructed made it difficult to fix a productivity level and therefore varying

levels of productivity were seen in the data.

A total of 1090 data sets were collected from sites. As expected; a wide variation

of measured productivity was observed. Some of the results seemed abnormal

and out of bounds, which in statistics are termed as outliers (those data that were

below the 25th percentile and above the 75% percentile). In this research, a band

of 40% was applied to retain data for further analysis. So the percentage

productivity change as measured (PPCM) values were reviewed and any values

out of 40% of the Site Average were discarded.

A band of 40% was considered an appropriate band to retain the data to first

ensure significant number of data sets remain, whilst on the other hand, to ensure

practical variation expected on site ascribed to the factors described in the last

paragraph. The band of 40% was selected based on the variations seen in actual

productivity on site and the known presence of several factors (not subject of this

study) other than the six ones under study.


21

This consideration is also in line with removing of outliers using the first and

third percentiles, ensuring at least 50% of the data sets more representative of the

population are used. In this research, total data sets of 1090 was collected as

against 812 (74 %) data sets used after the discarding those out of the 40%

band.

1.4.7 Regression Analysis using MINITAB 15 software

The MINITAB 15 software was utilized in this research as the researcher had

previous experience in using it and moreover the MINITAB 15 and the other

softwares available the Statistical Package for Social Sciences (SPSS) software

(now rebranded as Predictive Analytics Software (PASW), March 2009) offer

similar outputs. MINITAB 15 was found simpler to use. Distinctive beneficial

features of MINITAB include comprehensive and powerful statistical methods,

effective and editable graphs, and user-friendly interface. MINITAB has been

used in several textbooks spanning a broad range of categories including

archaeology, behavioural / social sciences, biological sciences, business, earth

sciences, engineering, environmental science, general statistics, health science,

mathematics, quality control, and six sigma topics. (Reference

www.minitab.com).

Data sets homogenized as above were then fed into the MINITAB 15 software

and regression analysis was performed. The output variable was the percentage

productivity change as measured (PPCM), while the input variables were the

group factors of 1) Timings, 2) Supervision, 3) Group, 4) Procedures, 5)

Availability of material and 6) Climate.

The first attempt was to find an overall model for productivity change. However

the coefficient of determination (R2) returned was very low for accepting the

model. The low value of R2 was understandable as there are several factors in

combination affecting the productivity; and one model may not fit all the trades.

This problem was overcome by opting for individual models for productivity of


22

six trades considered during data collection, namely Excavation, Formwork,

Reinforcement, Concreting, Blockwork, Plastering and Tiling.

The models formulated with a preliminary validation of models were reported at

the 25th Annual ARCOM Conference, September 7-9, 2009, Nottingham.

1.4.8 Validation of the Models

The validation of the models was done using eleven data sets from sites for the

different activities. Chapter 5 deals with validation. The process of validation

includes reviewing the data, computing the percentage productivity change as

measured (PPCM), using the appropriate site averages, using the model for

computing the percent productivity change as predicted (PPCP) and finally

computing the error which is the difference between PPCP and PPCM.

The data retained for final validation against the acceptance band of 15% was

determined from removing first the outliers within the 2 sigma limits for the

errors. Sigma is the standard deviation of the readings and from the statistical

normal curve / distribution study, it is expected that 95% of the values lie within

2 sigma bands. For the convenience of the reader, 68% lie within the 1sigma

band, while 99.7 % of the readings are expected to lie within the 3 sigma bands.

(Mendenhall et al, 2001, pg. 33)

The reasons for the 15% band are that the regression models chosen were a

straight line linear regression as against possible curvature or logarithmic

relationships. Interactions between factors were not considered. After the

removal of outliers, the regression line fitted was the optimal chosen to give as

high a value of R2 of over 70%. It was therefore expected that the predicted

increase or decrease in productivity will also follow a similar trend that is the

data points are expected to lie within an upper and lower band limits of error.

Other considerations include the broad range of complex relationships between

the model and the data, the numerous technical constraints on site with regards to


23

the expected productivity in each of the activities, the subjectivity of the factors

themselves & therefore the allocation of factor levels chosen for research.

The threshold of 15% also makes sense as actual data from time sheets, cost

control charts and productivity figures from the case study company sites over

the last 34 years indicate that the maximum increase or decrease in productivity

would be in the broader range of 40%.

Further taking into consideration the possible inaccuracies of reporting data itself

from the sites, and considering the wide variation in productivity measurement on

sites, the presence of additional technical factors not covered in the models and

review of the wide variation that was possible in productivity values for the

construction trades in actual practice; the acceptance criteria for accepting the

model to be accurate for practical use on site was set at 15%.

1.4.9 Model Application

The models can be used by construction personnel Project Managers, Engineers

and Supervisors to understand the dynamics involved in productivity of the

construction trades and investigate what best they can do to improve the

conditions that affect productivity on site.

The models provide reasonable quantification of the predicted productivity, when

the underlying factors are varied. The models are to be used judiciously,

complimented with a thorough understanding of the ground realities on the

construction site, the demography, age, training and skills of the people

themselves, the mental situation of the workers, their motivation levels; the

nature, detail and complexity of the work activities themselves.

The research and the models underlined therein therefore require the supervisors

and the site construction management in general understand that their

responsibility lies in providing favourable conditions of timings, supervision,

group dynamic, materials, procedures and of course amiable weather bringing out


24

the best in people; thus effectively increasing the output and therefore the

efficiency and productivity of the works. This will ensure construction operatives

perform at higher levels of motivation; work produced will be of acceptable

quality and at a good productivity rate; helping the activities to complete faster

and therefore the project.

Limitations if any arise because of simplifying assumptions used in the research,

the subjectivity of factor levels, the accuracy of the data itself, the existence of a

combination of several factors besides the significant factors; the possible errors

of recording and analyzing data, and the presence of human motivation. Thus the

models need to be used judiciously with caution, understanding the contribution

of each of the factor variables; but at the same time understanding the ground

realities of site execution.

1.5 CHAPTER SUMMARIES

The outline of the chapters 1-6 is included here to give a summary indication of

the contents of each chapter.

Chapter 1 Research Introduction

Chapter 1 is the introductory chapter to this research work. This chapter

introduces the research title, aim, objectives, and definitions of productivity

while identifying the gap in knowledge and establishing the need for research,

its importance in the construction industry and especially in the United Arab

Emirates (UAE). This is followed by a brief research methodology applied to

determine the factors affecting productivity; identify the most significant

factors using three surveys and then use actual productivity data to establish

and validate a regression model which can predict productivity changes in the

construction industry. After a brief discussion on applicability of the models,

the chapter concludes with summaries of each chapter to give the reader a

general outline of the research.


25

Chapter 2 Literature Review

Chapter 2 establishes the ground for this research based on the findings of the

literature review of classical management including theories on motivation,

together with published literature on subjects related to productivity.

Motivation theories such as the content theories of Maslow, McGregor,

McClelland and Herzberg are discussed followed by the process theories

namely Adams Equity Theory, Victor Vrooms Expectancy Theory and the

Porter Lawler Model on Motivation vis--vis their application in the

construction industry.

This chapter also gives background information and typical characteristics of

the UAE construction industry, the UAE labour market and survey results of

workplace employment relations in the UAE, the demography of the

workforce, the diversity of cultural backgrounds, absence of labour unions,

and the general environmental conditions of work and statutory laws prevalent

in the UAE. Further a discussion indicating the similarities and uniqueness of

conditions elsewhere in the world; and the need for improvement in

productivity. This discussion helps the reader to understand the context of the

project construction sites, at which the productivity data will be measured to

develop the models.

This is followed by a review of existing publications on productivity by

contemporary authors; this discussion culminates into matrices of factors

affecting productivity and motivating factors affecting productivity and finally

a matrix of factors affecting productivity over several countries.

These matrices form the basis for a comprehensive listing of the factors

affecting productivity. The factors affecting productivity are grouped into four

major categories of Environmental, Organizational, Group Dynamics and

Personal Factors.


26

Chapter 3 Research Methodology

Chapter 3 details the research methods applied together with a justification of

their utilization. It outlines the research process starting with the aim, the

literature review, the three surveys conducted, and the field data collected for

establishing the models, the MINITAB 15 software used for statistical

modeling and finally the validation process.

Further it also discusses the technical aspects of the construction trades

involved in the field data collection, for which, the productivity models or the

regression equations will be established. The construction trades of

Excavation, Formwork, Reinforcement, Concreting, Blockwork, Plastering

and Tiling Works have been discussed, together with other technical factors

affecting productivity of these trades. These technical factors are related to the

complexity of work, location of the site, soil strata, materials used, climatic

conditions, project specific requirements, and client involvement in the

project.

And finally, this is followed up by a brief introduction of the case study on

one contracting company, whose sites have been used for field data collection

for establishing the models and their validation.

Chapter 4 Data Collection, Data Analysis and Development of

Productivity Evaluation Model

Chapter 4 deals with the analysis of field data on productivity collected from

case study projects, removing outliers and subjecting the homogenized data to

regression analysis using the MINITAB 15 software. Regression models have

been established with an acceptable threshold of R2, the coefficient of

determination at 70% and above. Straight Line Regression has been

considered for practical application of models on the site.

The six factors affecting productivity which were used for modelling are the

Work Timings (T), Level of Supervision (S), Group Dynamics (G),


27

Availability of Materials (M), Control by Procedures (P), and the Climatic

Condition (C). The chapter summarizes the models established for each of the

seven trades of excavation, formwork, reinforcement, concreting, blockwork,

plastering and tiling for which the above mentioned factors were purposely

varied. Validation of the models is discussed in Chapter 5.

Chapter 5 Model Validation and Evaluation of Variability of

Productivity

This chapter deals with the testing and validation of the trade wise

productivity models established in Chapter 4 using the MINITAB 15

software. The validation process helps us to determine if the models can be

practically used to predict productivity changes on construction sites, when

underlying factors affecting productivity are changed. Eleven data sets were

subjected to validation by finding out the error in estimating the predicted

productivity change as against that measured on site. Outliers were removed

using the two sigma band of control limits on the individual error readings.

The balance data readings were then checked against the 15% acceptance

band. Models were accepted if the balance data readings were within this

band. Validation has been performed on eleven data sets collected from

ongoing construction sites of the case study contracting company.

For 2 sigma limits, it is seen that errors obtained between the predicted and

the actual productivity increase / decrease are within a band of 17.14% to

38.2% further justifying the initial homogenization range of 40%. Outliers

were removed using the upper control limits and lower control limits for the 2

sigma band. Out of the total 11 data sets (1963 Nos); eight data sets passed

validation as per set procedure. One data set for reinforcement at OAG site

was accepted on revalidation as only one out of the 42 was out of the

acceptable band of 15%. The other two sets which were accepted on

revalidation included data set for concreting and blockwork from the BCC

site. The revalidation used truncated data band within 20 % of the site

average. Overall the model validations were accepted indicating that

productivity models can be used to predict productivity changes within 15%


28

accuracy. The chapter further discusses the evaluation of the factors affecting

productivity. This is done by considering the coefficient terms of each of the

factors in the regression equation.

Chapter 6 Conclusion

Chapter 6 concludes the research by verifying whether research aim and

objectives were achieved, the significant problems faced and how these were

tackled and lists the lessons learnt. The limitations of the research itself are

discussed and probable areas for future research for improving the accuracy of

the models are recommended. It is also recommended that site personnel shall

understand the contribution of the factors and provide favourable conditions

which lead to enhancement of the productivity of the people, the construction

trades and therefore of the project.

1.6 CONCLUSION

This first chapter introduced the thesis title, established the aim, objectives, and

definitions of productivity while reiterating the need for research against the

background of productivity of construction trades and its importance in the

construction industry. The research is aimed at evaluating factors affecting

productivity of construction trades in order to optimize output. One of the

objectives of the research is to develop a model that can be used in the

construction industry to evaluate the percentage change in productivity once the

underlying parameters (factors) are varied. Hence the model does not measure or

predict productivity directly; it rather predicts the percentage change of the

productivity of the studied trades in relation to variation in the variables

involved. The research methodology was depicted graphically with cross

reference to chapter numbers. The research undertakes three surveys one to

establish the significance factors affecting productivity and the other two to

establish the magnitude of the effect of these factors on productivity. The field

data was collected using three levels of variation, and regression models for


29

productivity of construction trades were developed. The chapter concludes with

summaries of each chapter to give the reader a general outline of the thesis.

***


30

CHAPTER 2 LITERATURE REVIEW


31

CHAPTER 2 LITERATURE REVIEW

2.0 CHAPTER INTRODUCTION

This chapter reviews classical and contemporary theories related to management

together with published literature on productivity. The content theories of

Maslow, McGregor, McClelland and Herzberg are discussed followed by the

process theories namely Adams Equity Theory, Victor Vrooms Expectancy

Theory and the Porter Lawler Model on Motivation vis--vis their application in

the construction industry. Those have provided the study with the three matrices

containing most relevant factors that are believed to affect productivity.

The previous studies on productivity studied one trade and / or one variable at a

time, or studied the variables in isolation without interaction between variables.

Herbsman and Ellis (1990) described the development of a statistical model that

illustrates the quantitative relationships between the construction productivity

influence factors (CPIF) and the productivity rates. Most of the other studies were

qualitative rather than being quantitative.

The research by contemporary authors has been reviewed which culminates into

matrices of 1) Factors affecting productivity, 2) Motivating factors affecting

productivity and finally 3) Matrix of factors affecting productivity over several

countries.

These matrices form the basis for a comprehensive listing of the factors affecting

productivity. The factors affecting productivity are grouped into four major

categories of Environmental, Organizational, Group Dynamics and Personal

Factors.

Organizations and Individuals

Any organization is composed of individuals (people) who are organized in some

way or form in order to achieve certain objectives. In a construction company, the

general organization consists of people in the head office and people at the

construction site. The objective of a construction company are to first secure a


32

project and then execute it to clients satisfaction, giving value to the client and at

the same time, making a profit for the company.

People on site include staff such as the Project Managers, Site Engineers,

Foremen and Workmen. This research focuses on people on site - the lower tier

workmen that group of people including skilled, semi-skilled tradesmen and

labourers.

Given a construction site, these workmen are either grouped trade wise such as

carpentry group, masonry group, block or brick layer group etc. or they are

grouped into multi-skill / multi-trade groups such as a maintenance group, which

would include a mix of all relevant multi-discipline, multi-trades workmen.

Most of the construction operatives come from a varied background. They

normally have an agreed wage rate; but their performance on site, regardless of

the individuals work or cultural background, is through the process of

management, whereby the efforts of workmen are coordinated, directed and

guided towards the project objectives and hence the overall organization goals.

Mullins (2007) says effective management is the cornerstone of organizational

effectiveness. He further says that organizations aim can be achieved only

through the coordinated efforts of human resources.

The interrelated influences affecting the behaviour of people can be grouped into

those related to -

The environment technical and scientific, economic, social and

cultural, government

The organization objectives and policy, technology and methods of

work, formal structure, styles of leadership

The group structure and functions, role, relationships, group

influences and pressure

The individual personality, skills, values and attributes needs and

expectations.


33

Mullins further states that there are a series of mutual expectations of rights and

privileges, duties and obligations, which although, not formally documented, but

have an important influence on peoples behaviour. He calls it the psychological

contract.

The individual and organizational expectations forming the psychological

contract listed are significant as most of them affect the performance of the

individual; they are relevant to all industries, especially the labour intensive

construction industry. That is the reason - some of these are later grouped into the

factors affecting productivity.

The Individuals Expectations of the Organization are:

Provide safe and hygienic work conditions

Make every reasonable effort to provide job security

Attempt to provide challenging and satisfying jobs and reduce

alienating aspects of work

Adopt equitable human resource management policies and procedures

Respect role of trade union officials and staff representatives

Consult fully with staff and allow genuine participation in decisions

which affect them.

Implement best practice in equal opportunity policies and procedures

Reward staff fairly according to their contribution and performance

Provide reasonable opportunities for personal development and career

progression

Treat members of staff with respect

Demonstrate an understanding and considerate attitude towards

personal problems of staff

On the other hand, the Organizational Expectations of the Individual

Work to the best of abilities

Uphold the ideology of the organization and the corporate image

Work diligently in pursuit of organizational objectives


34

Adhere to the rules, policies and procedures of the organization,

especially the safety regulations at site

Respect authority and other colleagues

Not take advantage of goodwill by management

Be responsive to leadership influence

Demonstrate loyalty, not betray trust

Maintain harmonious relationships with work colleagues

Not abuse organizational facilities and equipment

Observe reasonable and acceptable standards of dress and appearance

Show respect and consolidation to customers and suppliers

Although it is unlikely that all the expectations of the individual and the

organizations will be fully met at all times, there is a continual process of

balancing, explicit and implicit bargaining, until both parties settle out at a

perceived fair treatment. Several authors have correctly hinted at the dynamic

nature of the psychological contract, the underlying factors are no guarantee of

lifetime employment, promotion from within, part time contracts, subcontract or

outsourcing, retrenchment in light of economic crisis and so on.

The comprehensive list of factors affecting the productivity on site was arrived at

by:-

Review of the classical management theories

Review of the published literature related to productivity on construction

sites

Establishing a Literature Review Matrix listing the factors affecting

motivation and consequently - productivity

Experience of the researcher. The researcher has had 38 years of

construction experience and has moved through the rank over a

multiplicity of projects, especially in the United Arab Emirates.


35

2.1 MANAGEMENT THEORIES

The developments in management theories and organizational behaviour can be

categorized through four approaches:

The classical approach including scientific management and bureaucracy

The human relations approach

The systems approach

The contingency approach

2.1.1 The Classical Approach

The classical approach placed emphasis on planning of work, the technical

requirements of the organization, principle of management and the assumption of

rational and logical behaviour. Focus was on clear purpose and an effective

structure, division of work, clear definition of duties and responsibilities and

maintaining specialization and coordination; emphasis was on hierarchy of

management and formal organizational relationships.

The scientific management emphasized increasing productivity from

individual workers through the technical structuring of the work

organization and the provision of monetary incentives as the motivator for

higher levels of output. The scientific approach is based on the twin goals

of productivity and efficiency as advocated by Fredrick Taylor. His

principles of scientific management comprised of three central elements -

a systematic collection of knowledge about work processes by managers;

the removal of worker discretion and control over their activities and the

creation of standard procedures and times for performing certain tasks. He

saw his methods as benefiting both worker and manager, since the worker

was encouraged to attain his peak performance and receive payment in

relation to this, but on the other hand, management obtained increased

output. (Taylor 1947)


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Henri Fayol, a mining engineer based his writing on his experience as a

French coal mining manager. He was concerned with developing a

universal approach to management and set this out in his Fourteen

Principles of Management (Fayol 1949). These are:

Division of Work - more and better work from the same effort

through the benefits of specialization

Authority and Responsibility authority brings in responsibility

and so generates

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