FACTORS AFFECTING EMPLOYEE PRODUCTIVITY IN...
-
Upload
truongkhanh -
Category
Documents
-
view
224 -
download
0
Transcript of 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.
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
iii
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
iv
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
v
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
4.0 Chapter 4 Introduction.. 140
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
vi
5 MODEL VALIDATION AND EVALUATION OF VARIABILITY
OF PRODUCTIVITY ............................ 182
5.0 Chapter 5 Introduction.. 183
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
6.0 Chapter 6 Introduction.. 208
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
vii
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
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
ix
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
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
x
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
xi
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
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
xii
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.
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
xiv
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
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
1
CHAPTER 1 RESEARCH INTRODUCTION
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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).
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
14
Fig. 1.1: Overview of the Research
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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),
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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%
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
***
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
30
CHAPTER 2 LITERATURE REVIEW
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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.
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
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)
-
Factors Affecting Productivity in the UAE Construction Industry, Nabil Ailabouni, 2010
36
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