AN ANALYSIS INTO THE EFFECTIVENESS OF CONSTRUCTION PRODUCTIVITY MEASUREMENT ON ZIMBABWEAN PROJECTS

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DECLARATION
The candidate herewith adjudges that the work presented in this Dissertation on “AN ANALYSIS INTO
THE EFFECTIVENESS OF CONSTRUCTION PRODUCTIVITY MEASUREMENT ON
ZIMBABWEAN PROJECTS” for the Bachelor of Quantity Surveying (Honors) degree presented to the
Department of Quantity Surveying in the Faculty of the Built Environment at the National University of
Science and Technology, is that ofthe candidate alone andhas not previouslybeensubmitted,inwholeorpart,
in respect of any other academic award and has not been published in any form by any person except where
duereferenceis given.
Candidate ………………………. …………………….
(GeorgeMwamloweM.) Signature Date
Supervisorormarker ……………………… ……………………
(MrT. Moyo) Signature Date

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DEDICATION
I would like to dedicate this dissertation to my Lord and Saviour Jesus Christ, without whom I
would not have made it through this program. A special dedication goes to my parents,
especially my father who has sacrificed his own desires that I may make it through my degree.
To my fiancé who has patiently walked with me through my degree program, offering support at
much needed times. To my extended family who have stood with me financially and
emotionally. Last but not least the academic staff at National University of Science and
Technology for supporting me as well during research for my dissertation.

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ACKNOWLEDGEMENTS
The author would like to thank the following persons who provided guidance, information and
valuable time in the preparation of this dissertation:
 Mr. T. Moyo my academic supervisor for the immense patience, depth and wisdom that
he offered without charge to help me finish this dissertation
 Mr. I. Mambemba, my industrial supervisor, who infused the passion for project
productivity measurement.
 Mr. L. Ncube, who helped me make the transition from Architecture to Quantity
Surveying.
 Mr. B. Gaule, who stood with me as a research lecturer and went beyond the call of duty
to help me through the most trying academic year of my life.
 My fellow classmates who walked with me in the journey to obtaining my degree, with a
special mention to; Munyaradzi Mapfumo, Arthur Chisango, Jonathan Hlabangana,
Aaron Mandizvidza, Devillious Shumba and Meluleki Dlodlo.

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ABSTRACT
The issue of Construction projects time and cost overruns has been a major concern worldwide.
Construction productivity measurement has been one of the proposed solutions which most
developed countries have embraced. This productivity measurement has taken three distinct
measurement forms, that is, task level, project level, and at industry level. This research has
focussed mainly on project level productivity because it represents the productivity of the end
products of the industry. The research has been carried out using a mixed method approach as
has been seen from literature studies to be most effective when dealing with this field. The study
area has been Bulawayo and Harare, the main cities of Zimbabwe, random and judgemental
sampling were implemented to collect the data. The findings of this study indicate that labor
productivity measurement is the most widely practiced form of construction productivity
measurement, albeit the industry is still utilizing these methods in a very basic form. The major
hindrance to measurement has been seen to be a lack of management involvement and interest in
the subject. There is industry consensus on the cost and time reducing benefits of construction
productivity measurement however the cost obligations seem to be a deterrent to use of the
system.

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LIST OF FIGURES
Figure 2.3: Multi factor Productivity measurement methods................................................ 19
Figure 4.1: Response rate per class of contractor.................................................................. 31
Figure 4.2: Experience of respondents in industry................................................................ 32
Figure 4.3: Company experience in industry......................................................................... 32
Figure 4.4: Construction productivity measures utilized on projects.................................... 34
Figure 4.5: Use of construction productivity measurement per class of contractor.............. 35
Figure 4.6: Relative frequency of use of methods................................................................. 36
Figure 4.7: Impact of productivity measurement on KPIs..................................................... 39

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LIST OF TABLES
Table 2.1: Relationship between performance, profitability and productivity…................ 10
Table 2.2: Single factor Productivity measurement methods.............................................. 13
Table 2.3: Single factor Productivity measurement methods.............................................. 18
Table 4.1: Severity index analysis of hindrances to productivity measurement.................. 36
Table 4.2: Other benefits of productivity measurement....................................................... 39

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ABBREVIATIONS AND ACRONYMS
KPIs.........................................Key Performance Indicators
CIFOZ.....................................Construction Industry Federation of Zimbabwe
MFP..........................................Multifactor productivity

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Table of Contents
DECLARATION...........................................................................................................................................i
DEDICATION..............................................................................................................................................ii
ACKNOWLEDGEMENTS.........................................................................................................................iii
ABSTRACT.................................................................................................................................................iv
LIST OF FIGURES ......................................................................................................................................v
LIST OF TABLES.......................................................................................................................................vi
ABBREVIATIONS AND ACRONYMS ...................................................................................................vii
CHAPTER 1: ................................................................................................................................................1
1.0 Introduction.........................................................................................................................................1
1.1 Background.........................................................................................................................................2
1.2 Problem statement...............................................................................................................................4
1.3 Aim .....................................................................................................................................................5
1.4 Research Questions.............................................................................................................................5
1.5 Objectives of the research...................................................................................................................5
1.6 Significance of study...........................................................................................................................5
1.7 Research Outline.................................................................................................................................6
CHAPTER 2: Literature Review ..................................................................................................................8
2.0 Introduction.........................................................................................................................................8
2.1 Construction Productivity Measurement and Performance ................................................................8
2.2 Construction Productivity Measurement Methods............................................................................11
2.2.1 Single Factor Methods ...............................................................................................................12
2.2.2 Multi Factor Methods.................................................................................................................17
2.3 Hindrances to Productivity measurement .........................................................................................18
2.4 Impact of Productivity measurement on Performance......................................................................21
2.5 Summary...........................................................................................................................................22
CHAPTER 3: Methodology........................................................................................................................23
3.0 Introduction.......................................................................................................................................23
3.1 Research Design................................................................................................................................23
3.1.1 Nature and Setting of Study.......................................................................................................23
3.1.2 Area of study..............................................................................................................................24
3.1.3 Target Population.......................................................................................................................24
3.1.4 Sampling frame..........................................................................................................................25

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3.1.5 Sample size ................................................................................................................................25
3.2 Sampling Methods ............................................................................................................................25
3.2.1 Random Sampling......................................................................................................................25
3.2.2 Judgmental Sampling.................................................................................................................26
3.3 Research Instruments........................................................................................................................26
3.3.1 Desk research.............................................................................................................................26
3.3.2 Questionnaires............................................................................................................................27
3.3.3 Case studies................................................................................................................................27
3.4 Data Presentation and Analysis Plan ................................................................................................28
3.6 Summary...........................................................................................................................................29
CHAPTER 4: Data Presentation and Analysis ...........................................................................................30
4.0 Introduction.......................................................................................................................................30
4.1 General Data Report .........................................................................................................................30
4.2 Productivity measurement in the Zimbabwean industry...................................................................32
4.2.1 Degree of Utilization..................................................................................................................32
4.2.2 Methods Utilized........................................................................................................................35
4.2.3 Hindrances to Productivity Measurement..................................................................................36
4.3 Impact of Productivity Measurement on Performance .....................................................................38
4.4 Case studies.......................................................................................................................................40
4.4.1 Case Study A: Renovation of Flats............................................................................................40
4.4.2 Case Study B: New Residence for University Students.............................................................41
4.5 Summary.....................................................................................................................................43
CHAPTER 5: Recommendations and Conclusions....................................................................................45
5.0 Introduction.......................................................................................................................................45
5.1 Project level construction productivity measurement methods ........................................................45
5.2 Hindrances to productivity measurement..........................................................................................45
5.3 Impact of Construction productivity measurement on project performance.....................................46
5.4 Recommendations to Industry ..........................................................................................................47
5.5 Areas of future study.........................................................................................................................48
5.6 Summary...........................................................................................................................................48
REFERENCES: ..........................................................................................................................................49
APPENDICES ............................................................................................................................................53
QUESTIONNAIRE ................................................................................................................................55

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CHAPTER 1:
1.0 Introduction
The term productivity in the Construction Industry has over the past decades taken many
definitions. According to Bernolak (1997) productivity means “how much and how good we produce from
the resources used.Simplyput productivityis the ratio ofinput to output. Zakieh (2010) says that productivityis
theratio oftotal output tototal timeinput.Thereforeaccordingtothesedefinitions werealisethattheproducts in
construction are the projects themselves. These are the tangible products of all the task level efforts and the
constituents of the national productivity measures. The importance of productivity growth to an individual
enterprise, an industry or an economy is something on which most economists would agree (Lowe,
1987).Productivity measurement is therefore those systems put in place to monitor and quantify the amount of
productivitygeneratedwithinapredeterminedunit oftime.
Bowen, (1984), states that productivity performance is the best indicator of economic vitality. We know for a
start the three major inputs that we find in the construction industry, i.e. labour, plant and material so then a
priori (before the fact) we can say that these are what we expect to be encountered as one side of the
measurement that we expect to be seeing in the industry. These inputs are labour, plant and material. It is firmly
believed that going back to the basics of measuring productivity at the project level would be necessary in
facilitating improvements. This is because the construction industry is largely project-based (Chan and Kaka,
2007).Whilstthemeasuringofproductivityis usuallyclassifiedintothreebroadstratawhichare; Nationallevel,
project level and task level, for this research we will lookat that one level of productivitymeasurement which is
attheprojectlevel(Huanget.al,2009)
Oftentimes the concept of productivity is mistaken with profitability or performance (Chan and Kaka, 2007)
howeverperformanceinconstructionreferstoavarietyofvariables associatedwithsuccessusuallybytheclient

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and these may include cost, time and quality. These will then form the basis for valuation of a project’s
performance.Inthisstudyperformancewillmainlyrefertoprojectcostandproject completiontime.
1.1 Background
The centrality of productivity to world competitiveness and prosperity has been a matter of interest since the
beginning of industrialization (Pekuri et. al, 2011). Although there is widespread concern that
productivity is on the decline, there has however been a marked interest in productivity studies in
construction because of the recent prioritization of innovation projects and their expected
impacts on national construction productivity (Goodrum et al., 2011)
The general accepted idiom in this vein is that we cannot improve what we cannot measure efficiently and
accurately. It is expedient for us to have accurate systems of productivitymeasurement for the evaluation of the
state of productivity and hence vitality of the said projects for the improvement of productivity and of project
viability. However, the systems used to measure productivity have been a major dispute area. This
has been largely due to the multiple and case specific definitions for the term productivity itself.
Following Zakieh (2010) definition as previously referred to, Chye (1996) also agrees that the
measurement of productivity on projects should be done holistically instead of the traditional
way of only looking at it from the labour point of view.It follows that, in order to increase
productivity, the system must either produce more or better goods from the same resources, or
the same goods from fewer resources.
Ameh and Osegbo (2011) observed that the problem in Construction however has been the
persistent cost and time overruns (implying greater resource usage to achieve either equal or less
product value). Yang et.al (2010) categorically list lost productivity or loss of productivity, as

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one essential delay cause, and that it is a resultant of a contractor accomplishing works at less
than planned rate ofproduction.It means therefore that project performance is directly related to
project productivity and these two are dependant variables. Addressing project productivity
therefore should improve on the much decried project performance.
Al-Momani (2000) conducted a survey on 130 public projects in Jordan and found delays
occurred in 106 (82%). Frimpong et al. (2003) observed that 33(70%) out of 47 projects in
Ghana were delayed. Ogunlana et al.’s (1996) study in Thailand and Kaming et al.’s (1997)
study in Indonesia found that the blame for most project delays were laid on the contractor.
Abd.Majid and McCaffer (1998) found that 50% of the delays to construction projects can be
categorized as non-excusable delays, for which the contractors were responsible. It is this
suboptimum output (delivery of projects), that we want to investigate. Time overrun affects the
project owners, contractors and other project participants. Project owners may be affected
through lost benefits that could have accrued from the completed facility, while contractors may
have to spend more on labour and plant, pay penalties as per the contract or even lose other
profitable contracts because resources for the next job are tied up on delayed projects.
Melton (2008) argues that any resource needs to be robustly managed in order to ensure that the
required level of performance is achieved (Cited by Chigara and Mangore 2012) We can also
deduce then that mismanagement of resources is a rampant source of the lack of productivity in
the construction industry and for it to be curbed there is need to assess the levels of productivity
measurement for the different resources going into the projects.

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1.2 Problem statement
In the Auditor General’s report to parliament on the management of construction projects in
Zimbabwe (2009), it was stated that the delays in project completion were an attributing factor to
perpetual cost overruns on construction projects in Zimbabwe. Among the reasons cited for the
poor project delivery rate, project monitoring was cited as a major factor. This then implies that
inadequate monitoring is somehow linked to decreased productivity which then leads to delayed
project delivery or failure. Generally in the construction industry productivity loss is seen as one
of the greatest and most severe problems (Gundecha, 2012). Kasimu(2012) states that cost
overruns have become a chronic problem in developing countries like Zimbabwe. Chigara and
Mangore, (2012) also highlight the scarcity of production resources in the construction industry
which in itself warrants a better management of and value reconciliation for their usage on
projects.
It would follow then that in the absence of proper monitoring of resource utilisation during
project delivery, resources are subject to sub-optimum use. The major underlying assertion is
that input productiveness is the major determinant of product cost and delivery time. Although
most contractors benchmark productivity on labour, Pekuri et al., (2011) say that the main
challenge with using labour productivity only is that it will not reveal the effects of productivity
in plant, material and energy input, which are a significant part of project level input variables. In
line with continuous process improvement in the industry it would be of great disadvantage to
not be able to assess the products of construction enterprise which are the projects themselves
and be able to determine whether we are meeting acceptable levels of productivity. As Drucker
(1980) states there is nothing as dangerous to an economy as a decrease in productivities because
it creates inflationary pressure, social conflict and mutual suspicion.

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1.3 Aim
To analyse Construction Productivity Measurement and performance at project level in the Zimbabwean
Construction Industrytoimprovesuccessful project delivery.
1.4 Research Questions
• What are the current methods being employed to measure construction productivityat project level in
theZimbabweanConstruction Industry?
• What is hinderingthemeasuringofproductivityatproject level inZimbabwe’s constructionindustry?
• Howhas constructionproductivitymeasurement impactedperformanceofprojects in Zimbabwe?
1.5 Objectives of the research
• To investigate the different methods of Construction Productivity Measurement at project level being
used in Zimbabwe
• To determine the major hindrances to measuring construction productivity at project level in the
Zimbabweanindustry
• To assess howproductivitymeasurement has impacted onperformance at project level
1.6 Significance of study
Studies done in the U.S. show that construction productivity in the U.S. has been on a steady decline over the
past four decades, running at about half the rate of U.S. non-farm industry (Sawyer, 2005). Studies done by
Warner, (2004) in the “Construction Industry Productivity Survey” show that 53% of construction companies

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surveyed reported that their productivityhas been flat ordecreasing over the past five years. Moreover, 81% of
those surveyed said that they could save over 5% of their annual field labour costs through better management
ofproductivity.This sets precedenceforus toanalyseour veryown Zimbabweanscenario.
A study that was conducted in 1994, which looked at 8,000 projects, showed that only 16% of the projects
could satisfy the following three famous performance criteria: completing projects on time, within the
budgeted cost and maintaining a high standard of quality (Frame, 1997).Productivity study is an economics
fundamental as it addresses the question of why we are engaging in all forms of trade, that is, for obtaining
gain. It deals with the question of how much is resulting from what we input into economic endeavour.
Motwani et al, (1995) state that a company should strive to improve productivity to the point of diminishing
returns. Simply put productivity improvement is an exercise that should be monitored and improved to its
zenith. This studywill find its place in the offices of Contractors who are interested in makingthe most of their
input in projects.
It is also crucial to note that the current industry practices brought to light through this study will bring
awareness of the best methods or the methods producing greatest utility to their proponents and in what way.
Jonsson, (2010) brings to light the shortcomings that come from trying to contain productivity analysis to an
end system tool like profit hence the need to engage in the process analysis if there is to be improvement in the
industry’s productivity. This becomes very crucial then in an industry that is viewed as being one of the most
growth stuntedindustries.
1.7 Research Outline
• Chapter 1- Introductory Chapter: An introduction to the topic, the background of study,
aim and research objectives, and the significance of the research

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• Chapter 2- Literature review: An appraisal of current literature on the subject matter of
the research, with critical analysis on methods suggested vis-a-vis industry validity and
applicability
• Chapter 3- Research Methodology: Data collection methods employed, sample space and
size and research type
• Chapter 4- Data analysis and Presentation: Detailed analysis of collected data, comments
on industry response and presentation in suitable formats
• Chapter 5- Research Findings, Conclusions and Recommendations: Reconciling
objectives to findings and stating the resultant conclusions, also stating the researcher’s
recommendations on the subject matter.
• Appendices

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CHAPTER 2: Literature Review
2.0 Introduction
This chapter aims to carry out a review on the existent literature on construction productivity
measurement at project level. It will detail the methods, hindrances and the impact of
productivity measurement on performance of projects.
2.1 Construction Productivity Measurement and Performance
Construction productivity measurement is an analysis into the ratio of total output versus the
total input of the construction process (Mawdesley and Qambar, 2000). Thomas and Mathews in
Park, (2006) stated that no standardized productivity definition has been established in the
construction industry. From the 1960s, developed countries have grappled with the problem of
productivity in the construction industry with a view to understanding the basic problem of how
to measure it (Sezer and Brochner, 2013). However the definition of productivity itself has had
its fair share in the confusion prevalent in the computation of productivity data. For example
many companies measure profitability and report it as productivity. The difference between these
two is that whereas profitability is the monetary process, productivity is a physical process
(Pekuri et al, 2012). However, productivity is eventually measured in monetary units of output
per level of input (Stainer, 1997). Profitability has been known to change for reasons unrelated to
productivity hence the base measures of productivity remain crucial.
Productivity has also been mistaken with performance. Performance is a composite of all
measures of an organisation’s competitiveness. Tangen (2005) attempted to distinguish
productivity from profitability and performance in a simplified diagram relating the three.

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Figure 2.1 Relationship between performance, profitability and productivity Source: Tangen (2005)
The measurement problem is exacerbated by the fact that the construction industry is composed
of four sectors that differ significantly in; the outputs produced, firm size, and use of technology.
The four sectors, which taken together define the construction industry, are residential,
commercial/institutional, industrial, and infrastructure (Huang et al., 2009) This means that for
each of these unique sectors, which in most cases construction companies are engaged in
simultaneously, the contractor has to develop systems of measuring the different forms of output
and the varied conditions under which all these projects are carried out.
The major drive however with productivity has been the global push towards continuous process
improvement (CPI).According to Koskela (1992), the concept of process improvement needs to
be embraced by the construction industry as a necessity. He went further on to place productivity
measurement efforts as being at the core of improving project processes. In the Business Round

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Table (BRT) in Park (2006) it was stated that in the morden business environment, construction
companies needed comprehensive measurement efforts in order to remain competitive and
business. The modern approach to productivity has thus been more holistic with more and more
industry players realising the need to be deliberate and thorough in their measurement efforts.
Huang et al. (2009) proposed what became the major delimitation criteria for this study when
they identified that the construction industry has three distinct levels as far as productivity is
concerned; task level, project level and industry level. Task refers to a specific activity like
pouring concrete or structural steel erection, projects refers to the collection of activities which
result in the renovation or the construction of a facility. Industry refers to the total portfolio of
projects within an economy.
Jonsson, (1996) highlighted that the construction company was an entity that had various
projects where the actual productivity was taking place. This then places the projects in a key
position with regards to productivity measurement as the ultimate products of construction. With
this in mind this study will focus on project level productivity because according to Park (2006),
Construction productivity rates differ between projects because of the varying environments,
characteristics, and project management efforts for each project. Therefore, when analysing
construction productivity, one should consider the drivers that cause construction productivity
differences between projects.
Chan and Kaka (2007) took the case for project based measurement of productivity further by
highlighting that although strategic levels of management were crucial in improving construction
productivity, there was a need to relate it to the projects themselves. They quote Groak (1994:
288) who reinforces this by saying that industry had gone amiss by failing to recognise the

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project site as the “defining locus of production organisation”. This meant that the industry
needed to reorient its production improving efforts to the projects, and to do this measurement
becomes critical.
2.2 Construction Productivity Measurement Methods
There is Information needed to calculate a meaningful project level productivity metric. For
instance, information yielding the task weight (share that it represents to the overall project) is
required, as is an understanding of the task flows. Because some tasks are completed in parallel,
while others in series, the composition of the task flows affects overall project productivity.
Therefore, each component of the project productivity metric contains: (1) the task weight; (2)
the raw task productivity baseline value in the denominator; (3) the raw task productivity value
for that project in the numerator; and (4) a measure of the task mix (in parallel versus in series
task flows). The project productivity index value is a function of the individual components
(Huang et al, 2009)
Huang et al (2009) also proposed that an alternative project level productivity index can be
produced as follows. We can create an index which is the quotient of two ratios, in each ratio the
numerator is the value of construction put in place and the denominator is the number of field
work hours. As noted earlier, a reference data set can be used to fix a baseline value for the ratio
of value put in place to field work hours. The baseline value for the ratio is then used as the
denominator in the index calculation. How an individual project compares to the baseline is
determined by inserting its ratio of value put in place to field work hours in the numerator of the
index.

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2.2.1 Single Factor Methods
There are a number of factors that are identified as being critical to the production process and
these are classed into two broad groups, that is; labour related factors and capital related factors
(Motwani et al, 1995). According to Dean and Harper (1998), capital is defined as “structures,
land, natural resources, machinery and other durable equipment”. The measurement of
productivity using only one of these factors is termed single factor productivity measurement
(Schreyer, 2001).
Table 2.1 Single factor productivity measurement Methods (Adapted from Schreyer, 2001)
Types of Output
Measures
Types of Input Measures
Labour Capital
Gross Output Labour productivity
(based on gross output)
Capital Productivity (Based on
gross output)
Value Added Labour productivity
(Based on value added)
Capital Productivity (Based on
value added)
Single Factor productivity measures
2.2.1.1 Labour Productivity: The norm in productivity studies in construction has been to use
labour productivity as a measure of overall productivity (Ameh and Osegbo, 2011; Goodrumet
al, 1999). The reasons cited for this standard have been the labour intensive nature of the
industry. Freeman (2008) in the Organisation for economic co-operation and development
(OECD) also highlighted the macro-economic importance of labour productivity measurement

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saying that it was a critical indicator of competitiveness in an industry. The major advantage of
labour based productivity measurement is its ease of measurement and readability.
The researcher discovered that there has been great research and development in the field of
labour productivity measurement as compared to capital productivity measurement mainly
reflected by the number of methods in use to compute labour productivity against those used to
assess capital productivity. According to Noor (1998), labour productivity measurement
techniques fall within a spectrum between two broad categories of observational methods,
namely continuous observation (e.g. direct observation and work study) and intermittent
observation (e.g. audio-visual methods, delay surveys and activity sampling) (Cited in Chan and
Kaka, 2007)
2.2.1.1.1. Manpower Surveys- These are a system which utilizes foremen or work supervisors
in order to monitor the time input of laborers, the number per task and the delays if any, against
the quantity of production. Tucker (1988) proposed that through such surveys it was seen that
skilled craftsmen spent just as much time on productive tasks as non-productive tasks due to
management inefficiencies and antiquated methods.
Basically only the major trades are surveyed which means that a number of supporting tasks,
which may contribute heavily to the project outcome, might not be measured or accounted for.
However because of the levels of interdependence of job packages on a project, the progress on
one job package will usually be a good estimate of other levels of productivity. Berg (1999)
commented that when used as performance measurement tools, manpower surveys take little
time to implement. The primary disadvantage however is that they can become routine, and if
action isn’t taken to correct problems, jobsite personnel might lose interest in the effort

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2.2.1.1.2 Time cards- Cards are made for different tradesmen with common tasks listed, common delays
can also be listed on the cards and noted when theyoccur. Time cards can be made for labour as well as plant.
Time Cards were originally proposed by Stokes (1984) to be an effective way of tracking labor productivity.
He posited that instead of using time cards to record hours worked only, that they should be coded with task
codes which wouldthenindicatethetimespent oneachtask.
Berg (1999) also deals with the technique noting the weaknesses that are encountered whilst trying to
implement this method on project sites. He noted at the top of cons, that the time card method takes a
conscientious effort and is time consuming. Further, because it is continuous, the paperwork related to it is also
perpetual and must be maintained. He also highlighted the fact that it was prone to “lying” as Foremen might
interpret it as a way of checking on their leadership and present skewed information. However he still
maintained that this method was the most accurate way of actually tracking the workers productive and non-
productivetime.
2.2.1.1.3 Activity analysis- This is a continuous productivity improvement process which efficiently
measures the time expenditure of workers onsite and identifies productivity inhibitors that management must
reduce or eliminate to provide workers with more time for direct-work activities (Gouett, 2010). Unlike Work
sampling, activity analysis is a full time project assessment tool. It can be used on plant as well which makes it
broaderin its applications.
2.2.1.1.4 Time Analysis- Noor (1998) identifies that there are some industry players who do not have a
formal productivitymethod ofmeasurement but theyrefertotheirWork Breakdown Structure(WBS) ontheir
programs so as to analyse their progress. It has been seen that such companies will periodically compute

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estimates of how far they have progressed on the project against the planned projections, and this they use to
make decisions on whether to accelerate or increase manpower for the purposes of improving their
productivity. It is also these analyses that are used to determine the effectiveness of their work strategies in
terms of producing the desired output rates. The limitations noted by Noor are that the method neglects the
complex combinations of tasks and how they contribute to the overall outcome. Tangen (2005) also notes that
project programs have to be “extremely detailed” from the onset in order to be of any use in assessing
productivityoftheproject.
2.2.1.1.5 Work Sampling- Random observations of certain tasks are made by trained observers and non-
productivityascertained.Useofvideo cameras and skilledmanpower is madeuseoftorecordcertain activities
on site. From these observations, conclusions are drawn as to the sources of non-productive time. The method
can beregardedbylabourers as toointrusiveandtheworkermoraleis reduced.
There is also the issue of inadequate capture of the project environment and hence the misrepresentation of
actual project productivity due to largesse extrapolation. Radosavljevic and Horner (2002) in studying the
United States construction productivity in masonry, and formwork found that productivity in most cases was
not normally distributed as previously espoused in the industry and hence the need to adopt other statistical
tools in order to extrapolate and produce expected values for areas that needed to be worked on. According to
Berg (1999), this method is very intrusive but it is ideal if an end to productivity losses on particular work
sections is tobeinvestigated andadequatelydealt with.
The direct observation methods have been found to be very comprehensive in nature albeit their
implementationis just as equallytaxingon contractor’s funds (Noor, 1988). Theyare alsotimeconsumingand
can be very tedious for the labourers engaged in the task of measuring. However the methods are very
reflective of the actual work being done when carried out diligently. It should be noted that productivity

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measurement will itself be viewed as a non-productive activity in the industry because it does not add to the
physical progress ofthestructuredirectly.
2.2.1.2 Capital Productivity: This is a monetary value based measure of productivity. In most
cases Materials cannot be directly traced per unit input and hence monetary input value is
compared to monetary output value (Schreyer, 2001). Plant productivity is more easily computed
because of technological advances which have brought on board timing devices which can keep
track of the period of use of a particular piece of equipment (Motwani et al, 1995).
It carries the same advantage as labour productivity which is an ease of readability because basic
prices are used to compute the productivity data. The disadvantage however, is that, like labour
productivity measurement, it is a partial measure based on a jointly influenced factor, that is, it
assumes mutual exclusivity on dependant factors. Where materials are countable, or where the
activity being carried out by a particular type of plant is easily quantifiable, it was seen that the
Unit count method can be employed.
2.2.1.2.1 Unit count- Mostly used for countable work portions or tasks for example, brick counts, paving,
installed units, number of items lifted by a crane or loads transported by a tipper. It is however not useful if the
work is monolithic or if it’s a multiple number of heterogeneous elements. The final productivity value is
obtained usingthe followingformula;
Quantityindex ofvalueadded
Quantityindex ofinput

17 |
2.2.2 Multi Factor Methods
These are methods that take into consideration the inter-dependence of factors of production to
assess construction productivity (www.dbrownmanagement.com, 2010). The two most documented
are the capital-labour method and the Capital-Labour and intermediate inputs (energy, materials
and services) also called the KLEMS method (Schreyer, 2001).
Table 2.3 Multi factor productivity measurement Methods (Adapted from Schreyer, 2001)
Types of Output
Measures
Types of Input Measures
Capital & Labour Capital, Labour Intermediate
inputs (energy, materials,
services)
Gross Output Capital-Labour MFP (based
on gross output)
KLEMS multi factor productivity
Value Added Capital-Labour productivity
(Based on value added)
-
Multifactor productivity(MFP) measures
2.2.2.1 Capital-Labour Method: This is a method which uses a combination of capital and
labour to compute the productivity of a project. According to Schreyer (2001) the value added by
each input is computed and weighted as a function of the bill rates and compared with the total
quantity weighted averages of all inputs, that is, productivity of project is obtained using the
formula below:

18 |
Quantity index of value added
Quantity index of combined labour and capital input
The labour productivity and capital productivity values to be used in computing the indices are
obtained using any of the above detailed methods.
2.2.2.2 KLEMS Method: This is a very detailed and comprehensive productivity measure
which is mainly used to aggregate industry wide or sectorial performance. It is therefore not
popularly used to assess project level productivity (Shreyer, 2001 and Huang et al, 2009). The
following formula defines how the KLEMS productivity metric is obtained:
Quantity index of gross output
Quantity index of combined inputs
Its main advantage is that it acknowledges the contribution made by intermediate inputs and
therefore represents the best measure of technical improvements in an industry. A drawback
however is cited as being the requirements for large amounts of data to be compiled which
means that more resources have to be spent in the production of the KLEMS productivity metric
(www.oecd.com).
2.3 Hindrances to Productivity measurement
It is difficult to define a standard productivity measure because companies use their internal
systems which are not standardized (Park, 2006). In studying the literature it seems the problem
of productivity measurement has gone so far as being called “impossible” (James, 1980). Chan

19 |
and Kaka (2007) described the reluctance of the industry to measure productivity as a type of
inertia. It is their thinking that there was first an inherent industry reluctance to engage
themselves in productivity measurement practices, before there was a real technical difficulty to
engage in the practice. This is attributed to the fact that the construction industry has been on the
outskirts of real productivity improvement and measurement for such a large part of the
industry’s existence.
Sezer and Brochner (2013) highlighted that one of the primary hindrances to measuring
productivity is the difference in definition of productivity itself in the industry. The industry is
split into so many trades and with each project and each contractor who engages in a particular
trade, there are an array of techniques and tools and combinations of labourers that are available
to accomplish the job. This means that for every unique project type we would be developing a
new measure for productivity. Unfortunately this is not practical and neither is it useful as a base
measure of growth studies in productivity.
2.3.1 Complexity of methods: The methods used in productivity studies have been a major
source of industry reluctance to indulge itself in it. From the huge disparity in project types to the
intricate differences between projects of similar nature, the changes and tweaking required to
retain validity of measures and comparisons seems to be a major industry turn-off (Chan and
kaka, 2007) It was also noted by Goodrum et al (2002), that the relationship between aggregate
and activity level productivity was complex to compound. This was realized to be a major reason
why industry professionals were reluctant to engage in the exercise.

20 |
2.3.2 Lack of Management Interest in Productivity Assessments: Arditi and Mochtar (2000)
in reviewing factors that were driving productivity measurement in construction companies
noted that Management involvement was a major driver. It meant that without management
interest in productivity of projects, there was no fuel for employing the system on sites.
2.3.3 Rate of Task allocation changeover: In an article by the National Research Council of
Canada (NRCC, 1993), cited as one of the major hindrances to productivity measurement was
the rate of task changeover on a construction site. It has been seen that especially with more
general labourers, the rate at which their assigned tasks and areas of work changes, makes it hard
if not impossible to gather the productivity data.
2.3.4 Cost to company: Burton F, (1991) states that the major goal of any construction company
is to maximize their profitability by reducing the cost of production. He further goes on to note
that productivity measurement has costs associated with it in terms of the labour employed to do
it, the recording material, the logging devices and the increased non-productive time spent on the
measurement. When the perceived benefit of carrying out the measurement is not seen to
outweigh the costs, most companies will not carry out the measurement.
2.3.5 Commensurability Problem: Broman (2004) identified the difficulty in productivity
measurement as being that each variable in the process is not measurable against the same
standard. This means for each variable a different standard of measurement has to be employed,
therefore considering the large number of variables that can be on a project this becomes an
arduous task to carry out.

21 |
2.4 Impact of Productivity measurement on Performance
For a contractor, field productivity represents the single biggest risk and differentiating factor
when it comes to project execution (www.dbrownmanagement.com, 2010). This implies that
assessing the productivity on a project can greatly increase contractors’ awareness of this risk
and help them mitigate it. Winch and Carr (2001) gave a shocking industry study when they
realized that of the companies that they studied, the very fact that they were doing a productivity
study led the workforce to increase their productivity rates. In other words, the competitiveness
and drive to please increased when they perceived that their work was being held to some kind of
standard.
Stokes (1984) was the first to note that worker morale was boosted by noting their daily
accomplishments when he was making his case for task recording time cards. Chang (1991) also
highlighted that worker morale was one key resultant from productivity improvement. He found
that contactors who measured their productivity were better placed to improve it and hence they
experienced a greater morale on their projects. Conversely he discovered that poor worker
morale led to poorer productivity.
Santosh and Apte (2014) in studying productivity measurement also noted that the labor force
was motivated by getting feedback on their performance. They found a greater performance was
achieved because the workers were aiming at set targets. They went on to list more benefits that
they found were accrued to contractors from performing productivity measurement, namely;
 Decreased total cost of production
 Decreased total duration of production

22 |
 Improved Quality of work
 Higher profitability
 A tool for management to use in continuous improvement
2.5 Summary
This chapter has given us an in-depth literature review highlighting what construction
productivity measurement is all about, what are the current methods in use to carry it out, what
are the hindrances that have been cited and what have been seen to be the performance related
benefits of construction productivity measurement.

23 |
CHAPTER 3: Methodology
3.0 Introduction
This chapter describes clearly and concisely how the study was carried out. It outlines the
methods used to conduct this research. These concerns are the research design, research
instruments, data collection procedures, data presentation, and analysis plans.
3.1 Research Design
3.1.1 Nature and Setting of Study
There are three main research designs to choose from when looking at methodology to use in
research. Firstly there is the Qualitative (Interpretive approach), the Quantitative research
(Positivist research paradigm) and the mixed method (A combination of the first two), (Panas,
and Pantouvakis, 2010). The two aforementioned researchers also state that the complexity of
construction productivity measurement has led to the adoption of multiple method approaches in
researching it.
The Qualitative method of research has helped us to get general principles that govern or hinder
construction productivity. It has been useful in arriving at specific factors that affect on-site
performance (Park, 2006). The quantitative approach has been useful in that it gave us an
empirical, measureable picture of construction productivity. It is based in Mathematics, Statistics
and Probability and has assisted us with drawing accurate simulations of prevailing Industry
trends (Martinez and Ioannou, 1999).
The third and last type which is the Mixed Method approach is also called Triangulation. As
there are various types of triangulation, it is important to distinguish the selected approach from

24 |
the other available options. Webb et al. (1996) proposed that once a proposition is reinforced by
two or more processes of measurement its likelihood for error is greatly reduced. Denzin (1970)
extended the idea of triangulation beyond its conventional association with research methods and
designs.
The researcher used Methodological triangulation, which refers to the use of more than one
method for gathering data. In essence there was use of both questionnaires and case studies to
collect data in an approach further distinguished by Denzin, (1970) as between method
triangulation, meaning the use of two methods to triangulate data in lieu of one method but
having within it variations. It is this type of triangulation that caters for the use of qualitative and
quantitative methods of data collection as was employed by this researcher.
3.1.2 Area of study
This research was carried out in Bulawayo and Harare. According to Charizeni, (2009) these are
the two industrial hubs of the nation from which I expected to obtain the most construction
projects. These were also the places which were most likely to have industry trendsetters.
3.1.3 Target Population
According to Nanayakkara, (1999), stratified populations offer a more fair representation when
dealing with the construction industry. In this research, our strata are defined by the Construction
Industry Federation of Zimbabwe (CIFOZ) registration system. Our population was those
companies that are duly registered members of the above institution as building construction

25 |
companies rated according to capacity and capitalisation and in total they are 95. The strata were
6 in number and named alphabetically from A-F, with the most capitalised falling into Group A
and the least into group F.
3.1.4 Sampling frame
A sampling frame is a list of elements that compose a defined target population. In carrying out
a pilot book and literature study it was evident that, the major stakeholders in the industry when
it comes to construction productivity are; Quantity Surveyors, Engineers, Project Managers/
Contract Managers, and Site Agents working for contractors. These then became my focussed
sample frame.
3.1.5 Sample size
According to Hanke & Reitsch, (1992) it is not necessary to sample the entire population or even
a major portion of it to achieve results, the researcher can use a relatively small part of the
population to represent the whole. The sample though small in size if carried out accurately, will
be adequate enough to enable one to make accurate and valid generalisation. According to
Oppenhein, (1992) Sample accuracy is more important than sample size. Therefore the
researcher used a sample of 40 Contractors which represent 44% of the population.
3.2 Sampling Methods
3.2.1 Random Sampling

26 |
In this method, each item in the population has the same probability of being selected as part of
the sample as any other item. To achieve this, the researcher numbered each member of the
population and then used a random number generator to select the required sample. This
approach was used to select the respondents for questionnaires.
3.2.2 Judgmental Sampling
In judgmental sampling, the person doing the sample uses his/her knowledge or experience to
select the items to be sampled. Patton (1990), states that it can be more useful to identify
respondents who are more likely to have the required information. The researcher used the
method to select the case study sites.
3.3 Research Instruments
Research instruments are tools used for collecting information data needed to find solutions to
the problem under investigation.
3.3.1 Desk research
Basically this entails a data gathering approach of using information from past publications and
statistics. This is the research that was carried out from, past publications, books, other
researches and online publications so that we compile the already available data and information
on other similar studies. These studies were then used to do comparative studies and to draw
inferences on similarities and differences arising from actual findings.

27 |
3.3.2 Questionnaires
Questionnaires are research tools which consist of questions which seek to get an opinion of the
respondent who cannot be directly interviewed due to limited time and or availability. They were
used for those who are busy and cannot be interviewed. The thrust was to obtain from
respondents the kind of information that would help us assess the level of Construction
Productivity measurement on projects in Zimbabwe. They were also aimed at getting
information which relates to performance issues in project delivery and thus allowing us to
assess the performance trends on Zimbabwean projects. A questionnaire was designed and
distributed to each of the above recipients. The questionnaire comprised of more close ended
questions so that the data obtained is uniform and comparable and fewer open ended questions
which needed further clarifications. Close ended questions were either dichotomous offering two
alternatives which are mutually exclusive for example, YES/NO, or multiple choice offering
three or more alternatives. They were less time consuming and thus more prone to getting
responses from recipients and they are easy to administer if need be. Due to the sensitivity of the
information to be obtained, the questionnaire was designed in such a way that the respondents’
identity and the identity of the company they work for are kept anonymous.
3.3.3 Case studies
According to Yin (2003) a case study design should be considered when: (a) the focus of the
study is to answer “how” and “why” questions; (b) you cannot manipulate the behaviour of those
involved in the study; (c) you want to cover contextual conditions because you believe they are
relevant to the phenomenon under study; or (d) the boundaries are not clear between the

28 |
phenomenon and context. Specific questions should be asked in order to determine what the case
is that is to be investigated (Baxter et al, 2008). According to Stake (1995), the case has to then
be bound in order to remain within the scope. Yin (2003) categorizes case studies as either:
exploratory, explanatory or descriptive. The researcher conducted a multiple case study of two
cases in order to examine trends that are consistent or some conflicting phenomena with regards
to productivity of projects in Zimbabwe.
The Case studies will looked at the following areas:
1. Company Profile i.e. Name, Duration of Operation, CIFOZ Category, Staff complement
2. Project Profile i.e. Value, Type, Worker complement,
3. Methods used on productivity measurement i.e. Detail methods, note frequency, note post
measurement response.
4. Performance Appraisal i.e. Look at the issues affecting performance on the site, note the
methods used to assess project performance
3.4 Data Presentation and Analysis Plan
The data was presented in Tables i.e. presenting information in row by column format. Figures-
presenting data in pictorial form through use of graphs, pie charts and time series plots Data
Analysis is the process of systematically applying statistical and/or logical techniques to describe and
illustrate, condense and recap, and evaluate data. Descriptive statistical data analysis was used to analyse
data. Data obtained was analysed using the aid of tables, bar graphs and pie charts. There was also the use
of the Relative Importance and Severity Indices. The data was collected as both quantitative and
qualitative therefore there was need to first discriminate the two sets of data. Some of the qualitative data
was used to make statistical inferences that were assessed by compiling in the form of the tables and pie

29 |
charts and bar charts to reveal the trends in the various projects. The quantitative data was also compiled
in the same format and presented in such a way that it can be visible to the consumers of the information.
For example the pie charts will assist with best illustrating information to do with industry segmentation
with regards to certain factors or trends.
3.6 Summary
This chapter has focused on the core task of how the researcher carried out the study. The data
choosing, presentation and analysis have been set out so as to provide a guideline on the way the
research was carried out to achieve the objectives set out in the beginning. The following chapter
will then continue to analyse the collected data in the way specified above.

30 |
CHAPTER 4: Data Presentation and Analysis
4.0 Introduction
This Chapter focuses on presenting and analyzing data obtained from fieldwork. The industry
respondents’ profiles, the degree to which productivity measurement is utilized in Zimbabwe, the
methods so employed, the hindrances to productivity measurement and the perceived benefits
with regards to performance are assessed.
4.1 General Data Report
Of the 95 contractors who are based in Bulawayo and Harare, Questionnaires were sent to 40
contractors by post and by hand delivery. 25 responses were received which represent 63% of the
total sample. Of those the spread in terms of class of contractor according to the CIFOZ system
was as follows;
Fig 4.1 Response rate per class of contractor
This shows firstly, that there is a higher interest in productivity issues as the size of contractor
increases.
56%
8%
16%
12%
0%
8%
0%
10%
20%
30%
40%
50%
60%
A B C D E F
Response
rate
Class of Contractor
Response rate per Class of Contractor

31 |
The respondents’ were composed of 76% Quantity Surveyors, 20% Engineers and 4% other
professions showing us that the Quantity Surveyor is the main reference point when it comes to
monitoring productivity issues on construction projects. The experience of the respondents in the
industry was seen as follows;
Fig 4.2: Experience of respondents in the construction industry
The skew on the graph indicates that the industry in Zimbabwe is largely young. The reason for
this may be due largely to an intense brain drain syndrome that has resulted from the economic
crisis which has crippled the industry (www.standardnews.co.zw).
Fig 4.3: Company experience in Zimbabwean Industry
24%
44%
20%
8% 4%
Experience of Respondents in the
Construction Industry
0 to 2years
3 to 5years
6 to 10years
11 to 15years
15years and above
8%
28%
64%
Company Experience in Zimbabwean
Industry
0 to 4 years
5 to 15 years
16 years and above

32 |
The company experience in the industry has been seen to be more inclined towards the more
established contractors, that is, those that have more than 15 years of experience in the industry
contributing more than 60% of the whole respondents. This may be attributed to the fact that
smaller contractors have found it hard to keep operating in the face of the economic challenges.
The Capital markets have not been functioning and hence it has negatively impacted the
operations of the smaller industry players who rely heavily on borrowed capital (Saungweme,
2011; cited in Chigara, 2012).
4.2 Productivity measurement in the Zimbabwean industry
4.2.1 Degree of Utilization
Of the sampled building contractors in Bulawayo and Harare, it was realized firstly that 64% of
the respondents were employing productivity measurement in one form or another. Of those,
36% of the respondents were not using productivity measurement as part of their construction
project management. This was very similar to literature trends where 31% of respondents said
they had no formal measures of productivity (Motwani et al, 1995) This indicates that although
economic hardships are prevailing in Zimbabwe, this has not caused a significant divergence of
the industry from other industries studied world-wide.
The distribution of which factors are being measured at project level was as follows;

33 |
Fig 4.4: Productivity measures utilized on projects
The above table shows that by far labour productivity is viewed as the dominant productivity
measure on Zimbabwean projects. This is consistent with studies in literature (Dean and Harper,
1998; O’Grady, 2008; Stiedl, 1998). There is an industry consensus that labour monitoring
equates to project productivity monitoring. However 20% of respondents are using both labour
and plant (Capital) as a benchmark for assessing productivity. This can be seen as an addition on
labour productivity measurement as well as validation of the existence of capital-labour
productivity measurement in Zimbabwe. (Schreyer, 2001) Only one respondent utilizes a
measure of labor, plant and material to assess project productivity. The use of construction
productivity measurement by contractor class was found to be as follows;
8
1
0
5
0
1
0
2
4
6
8
10
Labor Only(L) Plant Only(P) Material
Only(M)
L&P L&M L, M &P
Number
of
users
Construction Productivity measures Utilized on Projects
[ Single Factor measures ] [ Multi-Factor productivity ]

34 |
Fig 4.5: Use of construction productivity measurement per class of contractor
This graph shows us that the majority of industry players who carry out productivity
measurement are the Group A Contractors. This is probably due to their resource base and due to
the greater proportion of risk posed by not measuring productivity for the size of projects that
they embark on (www.dbrownmanagement.com). The graph also shows the respondents per class
who engage in productivity measurement compared to the total who responded. 79% of Group A
respondents carry out productivity measurement compared to 50% for both Group B and C. The
sharp rise to 100% in group D might be attributable to sampling bias as results are very
inconsistent with the observed trends.
Firstly the larger contractors have more resources available to them therefore they can better
absorb the costs of productivity measurement activities. We can also cite the increased risk borne
by the larger contractor due to handling bigger projects as an incentive for them to carry out
0
2
4
6
8
10
12
14
16
Group A Group B Group C Group D Group E Group F
Number
of
respondents
Class of contractor
Total Number of
respondents
Respondents
measuring
productivity
Use of Construction productivity measurement per class of
contractor

35 |
productivity analysis, whereas it can also be stated that the smaller contractors have very small
projects that do not entail much complexity and hence no formal measure of productivity is done
as supervisors verbally deal with issues arising on site. It was also seen that 7 out of the 9
respondents who were not using productivity measurement did not have any knowledge about
the management system.
4.2.2 Methods Utilized
The following is a relative frequency table depicting Productivity measurement methods and the
degree to which they are being utilized on Zimbabwean projects.
Fig 4.6: Relative frequency of use of methods
The combined relative frequencies of Manpower Surveys, Time Cards and Work Sampling is
comparatively larger than that of Unit Count and Time Analysis. This is consistent with findings
in literature that indicate that usage of labour productivity parameters is most widely practiced
(Park, 2006). Work Sampling is almost non-existent in the industry. This is probably due to the
relatively cost intensive nature of the method (Berg, 1999). It should also be highlighted that the
0.26
0.25
0.02
0.25
0.23
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Manpower
Surveys
Time Cards Work Sampling Unit Count Time Analysis
Relative
frequency
Method Utilized
Relative Frequency of use of methods

36 |
form in which these methods are being used is largely basic in comparison to some of the forms
which have been arrived at in other more developed countries where index based metrics have
been adopted which can be used industry wide to do analyses (Schreyer, 2001). We must infer
that the Zimbabwean construction industry is lagging behind world productivity measurement
efforts.
It was also found that over 60% of Contractors said that their laborers did not respond positively
to the measurement of productivity. It was found to be a source of offense for many. This could
also be cited as a hindrance to measurement by some contractors. It also tells us that most
probably the way they are implementing these methods is flawed at the psychological thrust
being portrayed by management.
4.2.3 Hindrances to Productivity Measurement
The respondents were asked to mention any hindrances that they encountered as they endeavored
to measure productivity. They were also given a list of common hindrances gleaned from
literature to assess and indicate the level to which they were hindering their own efforts. The
results according to a severity index (SI) analysis came out as follows;
Table 4.1 Severity index analysis of hindrances to productivity measurement
Hindrance Score SI Rank
Lackofmanagementinterestinproductivity
reports
67 0.136 1
Lackofpersonnel 62 0.126 4
Inadequateprojectbudget 64 0.130 3
Complexityoftasks 64 0.130 3
Rateoftaskallocationchangeover 66 0.134 2

37 |
Lackofmaterialtrackingschedules 50 0.102 6
Lackofplanttimingdevices 57 0.116 5
Plantmulti-tasking 62 0.126 4
Using the Severity Index it is clear from Industry’s perspective that the lack of management
interest in productivity measurement ranks as the most severe hindrance. From literature
however, it was the rate of task allocation changeover, and the complexity of tasks that were the
highest ranking and most frequently appearing hindrances (Goodrum et al, 2002; Broman, 2004;
Motwani et al). It is most likely that the recent change in most company structures due to
national policy like the indigenization policy, has led to the coming in of a relatively
inexperienced management group in construction. It is also likely that management is ignorant of
the benefits that this management system can add to their project performance as highlighted by
Zakhieh (2010).
It should be noted that the relative differences in the indices for the top four factors is minimal,
hence we can conclude that project budget constraints, rates of task allocation changeover and
complexity of tasks are also significant hindrances to productivity measurement. This is a
worldwide cry amongst construction industry professionals which as a developing nation, the
Zimbabwean industry players might have to find innovative ways of dealing with (Broman,
2004).
The least ranking hindrances were found to be lack of plant timing devices and lack of material
tracking schedules. According to Schreyker (2001), these hindrances relate to the measurement

38 |
of capital productivity. We can therefore conclude that the industry is better equipped to measure
capital productivity than it is to measure labour productivity.
4.3 Impact of Productivity Measurement on Performance
Of all the respondents, 100% agreed that productivity measurement does help to increase project
performance. Of those companies that do carry out productivity measurement, 90% of them
carry it out at least once a week of which 50% of them carry it out daily. This shows how vested
they are in the system, and also it highlights why other players might not want to carry it out
because it is very involving in terms of time and cost implications for the project.
Using the Severity Index it was found that of the Triple constraints, which are Key Performance
Indicators (KPI), the most impacted indicator due to carrying out productivity measurement was
Project Completion Time. This was followed by the project cost reduction which is an apparent
down-flow from reduced project times. It was however noted that most respondents concur that
project quality was not impacted much by the exercise of productivity measurement. This can be
reasoned through easily because the major thrust of productivity measurement has been towards
cost and time reduction and not really as a Quality control system albeit it does impact quality of
work for the better (Zakhieh, 2010).

39 |
Fig 4.7: Impact of construction productivity measurement on KPIs
A further assessment of other benefits that have been cited in literature as being attributable to
productivity measurement produced the following table computed using the RII;
Table 4.2: Other benefits of construction productivity measurement
Area Score RII Score
Workermorale 42 0.077 9
Projectprofitability 61 0.112 3
Costcontrolofmaterials 57 0.105 6
Projecttimedelivery 59 0.108 4
Qualityofwork 49 0.090 8
Workerpunctuality 71 0.130 1
Plantoptimization 59 0.108 4
Competitivebidcompilations 54 0.099 7
Projectlabourcosts 66 0.121 2
Safeworkprocedures 27 0.050 10
0.41
0.34
0.25
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
Time
Cost
Quality
Severity Index
KPI
Severity Index Bar Graph of impact on KPIs

40 |
From the above table it is apparent that contractors who utilize productivity measurement have
received the greatest benefit from the increased punctuality by workers on site. This is probably
due to the fact that workers are more conscious of the impact of delay on their end output. Apart
from the already stated cost reduction benefits, contractors also cited profitability of projects as
another major benefit of productivity measurement. This follows on from the cost reduction on
major inputs and reduction of non-productive labor time.
4.4 Case studies
4.4.1 Case Study A: Renovation of Flats.
The Project is a $6 million renovation of flats into commercial offices being carried out by a
reputable class “A” contractor with over 25 years of experience in the construction industry.
There are 3 staff members and at its peak the project had 66 workers on site.
Major Works include:
Excavations, concreting, demolitions of concrete and masonry structures, brickwork, painting,
plastering, electrical and plumbing.
Measurement procedures:
Labor Productivity data is captured daily using manpower surveys. A foreman allocates tasks
and is responsible for capturing daily productivity and reporting it to the site agent in a weekly
meeting. An assessment is made as to whether the laborers are being productive or not, and what
the reasons are. It was noted that the contractor had strict productivity targets and was making

41 |
use of stringent punitive measures including dismissal for unjustifiable failure to meet
productivity targets.
The Site agent noted that there were many complex activities taking place because the works
were being done on a very old building (commissioned 1924). This was a hindrance to
measuring the productivity of workers who were constantly trans-locating to other tasks and
other areas to attend to emergency tasks. He also noted that material shortages were also a
hindrance to labor productivity.
The contractor was also carrying out plant productivity measurement. There was intensive use of
a compressor for the demolitions and the unavailability of the clock was seen by the researcher
as a point of dispute with the clients, and worse still as a productivity indicator. Without a fully
functioning clock on a piece of plant, it becomes the contractors word unless if there is a clerk of
works who is constantly monitoring the plant. The major hindrance cited by the contractor on
plant productivity measurement was the fact that due to the site restrictions and the novelty of
some of the tasks carried out by the plant, it was not reasonable to use conventional plant outputs
as a benchmark. In such instances, productivity was halted altogether.
This contractor cited amongst the performance related benefits of employing productivity
measurement:
 Cost savings on labour
 Enhanced target meeting due to increased worker productivity
 Ease of contract management
4.4.2 Case Study B: New Residence for University Students

42 |
The project is a $12Million proposed student’s hostels, Kitchen and Dining area with Sub-
warden houses. The Contractor is a Class A contractor with over 50 years’ experience in building
and civil works. There are 12 staff members and 360 laborers on the site.
Major Works include;
Excavations, concreting, brickworks, plastering, painting, plumbing and electricals.
Measurement procedures:
A very deliberate attempt at measuring productivity is in force on the site with the task of
recording outputs of labour and plant being carried out by a trained “Checker” and recorded
daily. For labour productivity the system in place is such that; twice a day the “checker” goes out
into the site with a printed plan of the works which has works recorded on it for example
“concrete to strip footings: Male Hostel”. He will then mark out on the plan to the level at which
works have been carried out. This is done in consultation with the supervisor for the said works
so as to note any areas pertinent to either the above normal or below expected productivity by the
workers. The same “Checker” produces a labor allocation sheet every day to note how long a
worker has been engaged in a particular task.
At the end of the week, the “Checker” submits the weeks work plans and labor allocation sheets
to the Quantity Surveyor on site. The Quantity Surveyor (QS) then calculates the quantity of
work carried on every allocated task during the week using conventional measurement standards
and units. The labor hours are collated in such a way that the basic rates of the laborers at tender
and the current rates of the individuals are multiplied by the total hours worked by each laborer.
This results in a quantification of the labor increased costs. The quantity of work done is also
multiplied by the tender rates for the items and a total tender allowable cost is found for labor.

43 |
Then the tender rates for the individuals who were actually employed to do the task are collated
and compared to the tender allowables. This shows whether there was a loss or gain on labor
productivity for a particular task. Then all the Gains and losses for the project are summed up to
give a net project labor gain or loss on labour. These results are reported to the head office of the
contractor weekly.
Plant hours are recorded daily as well against assigned tasks. These will be taken to the QS who
in turn calculates the productivity. (see appendix for typical plant return sheet) Material issues to
both main contractor and subcontractors is recorded and reconciled by the Store man. The QS is
then tasked with evaluating the material used against the projected in what they called a
profitability report. It was seen that not all materials were monitored. The major materials
monitored were, Cement, Stone, Bricks, Sand and Rebar.
The contractor cited that their use of productivity measurement was driven by the derived
benefits from the use of the tool. Among the benefits they noted:
 That workers were more punctual due to the monitoring
 Ease of tracking sources of project losses
 Maximization of plant capacity and consequently profitability
 Ease of bid preparation for tendering on other similar projects
 Cost savings on labour
4.5 Summary
This Chapter has looked at the data that has been collected from industry with an attempt to
summarize the data, present it in a way that will make it appreciable and to analyze it. In this
regard, various tables have been made use of in order to summarize the data in a way that can be

44 |
absorbed with relative ease. It was also expedient to use indices that would bring out the most
severe and the most relevant parts of the data collected. The next chapter will deal with
conclusions and recommendations that the student has made.

45 |
CHAPTER 5: Recommendations and Conclusions
5.0 Introduction
This Chapter will look at the conclusions that the researcher has reached, the recommendations
that researcher wishes to make to industry players and the recommendations for areas of further
study based on findings from this study.
5.1 Project level construction productivity measurement methods
 There is a significant use of labour productivity measurement as the main construction
productivity measurement method on Zimbabwean projects, that is, 53% of the
respondents.
 There is also a significant acceptance of capital measurement as a productivity
measurement tool, however there are more contractors using multifactor productivity
than those using single factor capital productivity measurement.
 The methods being used in productivity measurement are in a rudimentary form. There
was no contractor using any metrics to compute their productivity values hence only raw
and unadjusted measures and data is available.
5.2 Hindrances to productivity measurement
 The most significant hindrance to the productivity measurement agenda on projects is the
lack of management interest in productivity issues.

46 |
 Project budgets that do not cater for productivity measurement are also a major hindrance
to measurement of productivity. It is likely that the productivity measurement efforts are
also being affected by the economic downturns.
 The complexity of the measurement methods also ranks high on factors hindering
productivity measurement. It has been noted that those who are using the methods lament
the complexity of the methods even in their most basic state.
 The rate of task allocation changeover is another major ranking factor of concern.
Unfortunately this factor is an industry characteristic that cannot be eradicated but needs
more innovative ways or rather more complex tracking systems that can effectively keep
track of labourers.
5.3 Impact of Construction productivity measurement on project performance
 Project completion time is the most impacted performance indicator amongst the KPIs.
Therefore construction productivity measurement is likely to lead to more timeous
completion times of projects.
 According to industry players, it lowers project cost as well. We can expect final project
values that are within tolerable deviations from estimated completion cost values.
 The quality of project although affected by measuring construction productivity is the
least affected of the KPIs. This means that quality control has to be implemented jointly
with construction productivity measurement in order to deliver projects within required
time, cost and at the optimum quality.

47 |
 In general, employees are more punctual when they know that their productivity is being
measured. This indicates that they will respond to productivity measurement by
increasing productivity.
 More profitable projects can be expected when there is a culture of productivity
measurement.
 Construction productivity measurement and construction site safety have very low
correlation according to industry players.
5.4 Recommendations to Industry
 Firstly the researcher recommends that there be an equipping of students studying
construction management related courses, for example, Quantity Surveying and
Engineering, with productivity measurement expertise so that they get into the industry
with an appreciation for the management tool.
 More earnest involvement by management in tracking and monitoring productivity at a
project level. This should see a productivity measurement culture emerging in
Zimbabwe’s construction industry.
 In the same way that safety has been seen to be of the essence in managing a project
successfully, the researcher would encourage industry to view productivity measurement
as a cost saving and not as a cost incurring process. This should see it being placed in
project budgets.

48 |
5.5 Areas of future study
 The researcher recommends a further study to be carried out on productivity measurement at
project level on a nationwide scale. This should allow us to have a more comprehensive report on
the industry in Zimbabwe.
 The researcher also recommends a study into how industry construction productivity indices and
metrics are computed so that this can provide industry with much needed know-how about
productivity metrics.
 The researcher also recommends a study into industry level construction productivity
measurement methods as the next level of interest that we should investigate. This should allow
us to investigate and recommend updated methods for the local industry.
5.6 Summary
This chapter has focussed on the researcher’s conclusions based on the findings made in this
study. It has also explored the recommendations made by the researcher to industry with regards
to productivity measurement and also some recommendations of future study stemming from this
research

49 |
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54 |
APPENDIX 1: QUESTIONNAIRE

55 |
QUESTIONNAIRE
Dear Sir/Madam
I am studying for a Quantity Surveying degree at the National University of Science and Technology. As
part of my course, I am writing a dissertation titled “An analysis into Construction Productivity
measurement and performance in Zimbabwe”
My dissertation may be made available to other students and the general public in the university library. I
will ensure your anonymity by excluding identifiable personal data from the dissertation. However, please
be aware that one of your colleagues or any other person who knows that you have taken part in the study
may be able to recognise your input from what is said. Your participation in this study is on a voluntary
basis and you are free to withdraw from the study if you inform me by the 4th
of April 2014.
If you have any questions about my study, I will be glad to answer them. You can reach me on my mobile
phone on 0773218645 or by email mwamlowegeorge@gmail.com You can also contact my supervisor
Mr. T. Moyo for further information by e-mail
Please sign and date the statement below if you are willing to participate. Many thanks for your interest in
my research,
Yours sincerely,
George Mwamlowe
Consent agreement
I have read the above statement and understand its contents. I have been given the opportunity to ask
questions and discuss any concerns. I agree to participate in the study as it has been explained. I
understand that extracts of the interview may be used, in anonymous form, in the student’s dissertation.
However I understand also that my identity will not be disclosed by the researcher or the University.
Name .Date .
PLEASE RETURN SIGNED COPY TO THE STUDENT, AND RETAIN A COPY FOR YOUR OWN
RECORDS

56 |
SECTIONA: RESPONDENTPROFILE
1. Companyname:……………………………………………...
2. Classofcontractor:…………………………………………...
3. Typeofjob/Position:…………………………………………
4. Profession:…………………………………………………..
5. Respondent’sexperienceinconstructionindustry:
Less than 2 years
3 - 5 years
6 - 10 years
11 - 15 years
16 years and above
6. CompanyexperienceinconstructionIndustry:
Less than 5 years
5 - 10 years
15 years and above
SECTIONB:PRODUCTIVITYMEASUREMENT
MethodsUtilized
a) Doesyourcompanycarryoutproductivitymeasurementforitsprojects?
Yes
No

57 |
b) If NOto(a),pleasebrieflyoutlinewhy…………………………………………………….
………………………………………………………………………………………………………
………………………………………………………...…………………….
c) If YESto(a)whichofthefollowingismonitoredontheprojectintermsofproductivity?
Labour
Plant
Material
d) Brieflydescribethemethod(s)thatareemployedtodotheaboveonyour
project?………………………………………………………………………………………………
………………………………………………………………………………………………………
………………………………………………………………………
e) Ofthebelowmentionedpleasetickthemethod(s)thathavebeenusedonyourprojectstomeasure
constructionproductivity
METHOD TICK
ManpowerSurveys(Foremancapturestheproductionpertaskdoneandnotesthe
numberofpeopleworkingonit,andanydelays)
Timecards(Cardsaremadefordifferenttradesmenwithcommontaskslisted,common
delayscanalsobelistedonthecardsandnotedwhentheyoccur)
WorkSampling(Randomobservationsofcertaintasksaremadebytrainedobservers
andnon-productivityascertained)
Unitcount(Mostlyusedforcountableworkportionsortaskse.gbrickcounts,column
counts,installedunits)
TimeAnalysis(Scheduledagainstactualcompletiontimeiscollatedandanalysedfor
specificworkportions)

58 |
f) Howdoworkersrespondtoproductivitymeasurement?
Positively/theywelcomeit
Negatively/areoffendedbyit
HindrancestomeasuringproductivityatprojectlevelinZimbabwe
g) WhathindrancescanyoucitetoLabourproductivitymeasurement?
………………………………………………………………
………………………………………………………………
………………………………………………………………
h) WhathindrancescanyoucitetoMaterialproductivitymeasurement?
………………………………………………………………
………………………………………………………………
………………………………………………………………
i) WhathindrancescanyoucitetoPlantproductivitymeasurement
………………………………………………………………
………………………………………………………………
………………………………………………………………
j) Usingaratingsystemof 0–5,where0isnoeffectand5isthemostsevereimpact,indicatetheimpactof
the followingfactorsinhinderingproductivitymeasurement.

59 |
AREA SCORE
Lackofmanagementinterestinproductivityreports
Lackofpersonnel
Inadequateprojectbudget
Complexityoftasks
Rateoftaskallocationchangeover
Lackofmaterialtrackingschedules
Lackofplanttimingdevices
Plantmulti-tasking
SECTIONC:PRODUCTIVITYMEASUREMENTANDPERFORMANCE
ImpactofproductivitymeasurementonProjectperformance
a) Howoftendoyoucarryoutproductivitymeasurementonyourprojects?
>=daily >=onceaweek Onceafortnight Onceamonth Uponrequest
1 2 3 4 5
b) Doesthishelptoincreaseperformanceoftheprojectinanyway?
Yes
No
c) If YESto(a),pleaseusethescaleof1–5,where5isgreatestimpactand1isleastimpacttoassessthe
followingperformanceindicators

60 |
PerformanceIndicators Impact
ProjectCompletionTimes
ProjectCost
ProjectQuality
d) IfNOto(a)pleasestatewhy
………………………………………………………………………………………………………
……………………………………………………………………………
e) Usingascaleof1–5,where5isthe“greatestbenefit”and1isthe“lowestbenefit”,assessthebenefitsof
productivitymeasurementonthefollowing:
AREA SCORE
Workermorale
Projectprofitability
Costcontrolofmaterials
Projecttimedelivery
Qualityofwork
Workerpunctuality
Plantoptimization
Competitivebidcompilations
Projectlabourcosts
Safeworkprocedures

AN ANALYSIS INTO THE EFFECTIVENESS OF CONSTRUCTION PRODUCTIVITY MEASUREMENT ON ZIMBABWEAN PROJECTS

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