oein

o,

Faculty of Management Studies, University of Delhi, Delhi 110 007, India

2013 Elsevier Ltd. APM and IPMA. All rights reserved.

Development projects play a key role in the growth of

create economic wealth, deliver social welfare services and at the

economic as well as social and environmental dimensions.n evaluated on then literature (Ahadzie002; Salleh, 2009).as the iron trianglenomic aspects and

ignores other relevant aspects of development projects. In view of

Available online at www.sciencedirect.com

ScienceDireInternational Journal of Project Managemethis shortcoming of traditional criteria, Organisation for Eco-nomic Co-operation and Development's (OECD) DevelopmentAssistance Committee (DAC) has introduced a performanceeconomies in developing countries in terms of their contributiontowards Gross Domestic Product (GDP), employment generationand provision of an important market for materials and productsproduced by other sectors of the economy (ILO, 2001). Theunique features of a development project in terms of its ability to

However, these projects have mostly beecriteria of cost, time and quality, as revealed iet al., 2008; Atkinson, 1999; Chan et al., 2This traditional approach, popularly known(Atkinson, 1999) merely captures the ecoKeywords: Constituency Development Fund; Development projects; Performance evaluation framework; Key performance indicators; Kenya

1. Introduction same time its possibility to create negative environmental impactsuggest that this kind of a project needs to be evaluated onReceived 25 October 2012; received in revised form 11 July 2013; accepted 23 July 2013

Abstract

The present work attempts to develop a multidimensional performance evaluation framework of development projects by considering allrelevant measures of performance. In order to demonstrate the applicability of this performance evaluation framework, it has considered the case ofConstituency Development Fund (CDF) projects constructed between 2003 and 2011 in Kenya and collected the viewpoints of 175 respondentscomprising clients, consultants and contractors involved in the implementation of CDF projects with regard to their perception on 35 performancerelated variables. A ve-point Likert scale was used as a response format for different variables with the assigned values ranging from 1 = StronglyDisagree to 5 = Strongly Agree. Exploratory factor analysis was applied to the collected data which gave rise to an instrument consisting of 27items representing six factors. The ndings further reveal that the items constituting these six factors essentially represent six key performanceindicators (KPIs) namely time, cost, quality, safety, site disputes and environmental impact. The relative inuence of each KPI towards overallperformance of construction projects shows that time is the most important measure followed by cost while safety comes last in order of importancein the performance evaluation of CDF construction projects. The ndings of this study have signicant bearing on other similar kind ofdevelopment projects undertaken in developing countries.A performance evaluation frameworkAn empirical study of Constituency D

construction projects

Christopher Ngach Corresponding author. Tel.: +91 11 27666382 88; fax; +91 11 27667183.E-mail addresses: cngacho@yahoo.com (C. Ngacho), ddas@fms.edu

(D. Das).

0263-7863/$36.00 2013 Elsevier Ltd. APM and IPMA. All rights reserved.http://dx.doi.org/10.1016/j.ijproman.2013.07.005f development projects:velopment Fund (CDF)Kenya

Debadyuti Das

www.elsevier.com/locate/ijproman

ctnt 32 (2014) 492507evaluation criterion of development projects based on relevance,efficiency, effectiveness, impact and sustainability (Beck, 2006;Chianca, 2008; Ika et al., 2012). This criterion, popularly known

l ofas the five pillars of development projects (Beck, 2006; Ika et al.,2012), though seems to capture both economic and social aspects,do not adequately address the environmental elements that areconsidered quite important in these kinds of projects. Further theresearchers have suggested hardly any objective measure uponwhich the five pillars can be operationalised.

Academic researchers with a view to overcoming the limitationsof the traditional performance evaluation criteria of time, cost andquality have suggested the inclusion of additional measures ofperformance. These include safety of the project site (Billy et al.,2006; Haslam et al., 2005; Ortega, 2000), site disputes (Tabish andJha, 2011), environmental impact (Eriksson andWesterberg, 2011)and community/client/customer satisfaction (Ali and Rahmat,2010; Chan and Chan, 2004; CURT, 2005). These contributions,although widen the scope of performance evaluation amongstdevelopment projects, are skewed towards either societal orenvironmental aspects. None of the above has provided a balancedand holistic performance evaluation framework which wouldcapture all essential and unique features of a development project.

The current study is an attempt to develop a multidimensionalperformance evaluation framework of construction projectsincorporating all essential elements of project performance. Theapplicability of this framework has been illustrated with thehelp of a case study of Constituency Development Fund (CDF)projects constructed during the period between 2003 and 2011 inthe Western Province, Kenya. This is a quantitative survey thatsought the perceptions of clients, consultants and contractorsinvolved in the construction of these projects. The broad findingsprovided in the Findings and Discussion section elucidate theneed for inclusion of additional measures namely safety, sitedisputes and environmental impact into the traditional measuresof time, cost and quality.

The following section provides an account of literaturereview relating to the performance evaluation of developmentprojects. Section 3 presents the details relating to the purposeand nature of CDF projects undertaken in Kenya. Section 4discusses research methodology in detail. Section 5 deals withthe findings pertaining to the type of CDF projects and relevantdescriptive statistics. This section describes the development ofproject performance measurement scale through exploratoryfactor analysis and validation of the same. Section 6 presentsthe managerial implications of the findings and highlights itscontribution to the project management literature. It also showsthe limitations of the present work and future research directions.

2. Literature review

Majority of the researchers associated with constructionproject management have talked about the importance of time,cost and quality (Ahadzie et al., 2008; Kaliba et al., 2009;Kamrul and Indra, 2010; Zuo et al., 2007) while evaluating theperformance of public or private construction projects. The useof these three metrics can be traced back to the inception ofproject management concept in 1950s. As early as in 1989,

C. Ngacho, D. Das / International JournaKerzner reported that project management has been tradition-ally described as managing or controlling company resourceson a given activity within time, cost and performance (Kerzner,2006). This conventional criterion has been hailed for havingprovided a basis in evaluating the extent of success acrossprojects (Cao and Hoffman, 2011), being simple (Toor andOgunlana, 2006), being easy and timely to measure (Willard,2005) and its ability to capture the tangible benefits of theprojects (Litsikakis, 2009). The use of these three dimensions isstill considered a good practice for some projects, while for othersit could undermine some important project outcomes. Critics ofthese criteria have indicated that they do not adequately cover allaspects of performance measurement (Gardiner, 2000), arerelated to each other (Shenhar et al., 2002) and are rigid in itsperformance evaluation of projects (Bassioni et al., 2004). Inorder to improve a project evaluation system, one has to consideruniqueness in project characteristics, appropriateness of projectgoals and changes taking place in project environment. A projectperformance measurement criterion should consider diversity andboth technical and societal needs of the project (Barclay andOsei-Bryson, 2010).

In pursuit of a new criterion, major development agenciesinternationally have adopted the evaluation criteria defined by theOrganisation for Economic Co-operation and Development's(OECD) Development Assistance Committee (DAC) (Beck,2006; Chianca, 2008; Ika et al., 2012) in evaluating developmentprojects. The definition contains five evaluation criteria namelyrelevance, efficiency, effectiveness, impact and sustainability thatshould be used in assessing development projects. The relevanceof a project describes the extent to which the aid activity is suited tothe priorities and policies of the target group, i.e. recipient, donorand beneficiaries of the project. Whereas the above definitionsdescribe relevance as an ex-ante element of evaluation, it canalso be applied ex-post, once the project is completed and handedover to the beneficiaries. The ex-ante relevance of a project ispresumably taken care of during project identification andselection phase. Community development projects are identifiedby the respective communities based on their needs. Such projectsundergo rigorous approval procedure before their ultimateconstruction. This ensures that they are relevant to the commu-nities where they are constructed. At ex-post evaluation, oneshould ask whether the originally intended relevance has beenrealised. Such a question can only be answered by the actualbeneficiaries of the project when they start reaping its benefitsbecause economic growth and the general wellbeing of acommunity occur during a project's operational phase as it takestime to realise those benefits.

Efficiency is a measure of how economic resources/inputs(funds, expertise, time, etc.) are converted to the desired results(Beck, 2006; Chianca, 2008). It includes the cost of resources aswell as the construction cost of the development. Effectiveness isthe extent to which the project's objectives were achieved, or areexpected to be achieved and seeks to determine the factors thatinfluence achievement or non-achievement of the objectives(Beck, 2006; Chianca, 2008). Further, projects' Impacts refer tothe direct or indirect positive and negative, primary and secondarylong-term effects produced by a project. This pillar assesses

493Project Management 32 (2014) 492507whether the effects achieved correspond with the needs, problemsand issues that are to be addressed by the construction projects(Beck, 2006; Chianca, 2008). Sustainability, according to Chianca

l of(2008) refers to the continuation of benefits from a project aftermajor development assistance has been completed. This is inreference to the post implementation phase and assesses whetherthe project will continue to operate as intended. This OECDdefinition reveals that it hardly covers ecological sustainabilitywhich is also important in a development project.

The proposed five pillars, though considered an improve-ment over the iron triangle, suffer from several weaknesses.According to Chianca (2008), these pillars focus more on theneeds of funding agency than on those of targeted communities.Secondly, efficiency and sustainability merely focus on monetaryaspects and ignores the intangible elements that could bring aboutincreases in cost. Thirdly, the definitions of pillars are ambiguousand overlapping, particularly those of relevance, effectivenessand impact. This lack of operationalisation makes it difficult forthe project implementing agency to objectively measure all thepillars and poses a major challenge in capturing the economic,social and environmental dimensions of development projects.Finally, attainment of the five pillars becomes further complicat-ed due to the uniqueness in the environment that surrounds theseprojects.

In the quest for widening the project performance evaluationcriteria due to the inherent shortcomings of the iron trianglecriterion, some researchers (Billy et al., 2006; Haslam et al.,2005; Ortega, 2000; Zuo, 2011) have argued that it is importantto incorporate safety aspects of the project in performanceevaluation because the construction industry is the most unsafeindustry because of its high rate of fatalities. In most developingcountries, the construction industry is mainly labour intensive andthe majority of the workforce working on construction sites isunskilled. The workers are, therefore, exposed to risk and healthhazards inherent in construction projects that require adequatesafety provisions (Zuo, 2011). Project safety, being a humaneissue, needs to be considered separately from time, cost andquality dimensions.

Few other researchers (David, 2009; Tabish and Jha, 2011)have given emphasis on dispute resolutionwhichmight otherwiselead to disagreements amongst project participants and derailthe project objectives. There could also be disputes arising out ofcommunity protests against the development project which couldaffect project construction. Dispute resolution is part of stake-holder management and hence should be part of project per-formance evaluation criteria (David, 2009).

According to Eriksson and Westerberg (2011), constructionprojects have irreversible impact on the local environmentbecause construction processes not only consume huge energybut also create the most waste, use large quantity of non-energyrelated resources and are responsible for the most pollution.Environmental impact, being indirect and long term in nature,can hardly be captured under any of the three traditionalmeasures, but has implications on sustainability of the project.It is, therefore, necessary to include environmental impact intothe performance metric of construction project performance(Chen et al., 2010; Medineckiene et al., 2010; Tan et al., 2011;

494 C. Ngacho, D. Das / International JournaTsoulfas and Pappis, 2008). Currently, a number of regulatoryincentives are pushing organisations to adopt environmentallyfriendly construction methods to ensure that they develop thecapability of delivering sustainable projects within acceptablecost constraints. Shao andMller (2011) reported that communitysatisfaction is the ultimate goal of every construction project andhence it must be considered while evaluating its performance.This view is supported by Chan and Chan (2004), CURT (2005),Ali and Rahmat (2010), who found that client/user/customersatisfaction is an important goal of every construction project andhence should be considered when evaluating project perfor-mance. However, Miles et al. (2008) observed that communitysatisfaction is a consequence of overall project performanceand hence the same cannot be used as one of the metrics ofconstruction project performance but as an outcome of theoverall construction project performance. It is manifestedthrough the community's wellbeing in terms of improvedhealthcare, education development, provision of employmentopportunities and enhanced business activities.

The foregoing discussion reveals that the researchers haveattempted to overcome the weaknesses of traditional measures ofproject performance by introducing several intangible measures.However, the additional measures suggested by the researchershave been used mostly in isolation as pointed out by Ngacho andDas (2011, 2013). Hardly anyone has provided a comprehensiveand operationalisable performance evaluation framework ofdevelopment projects which would encompass economic, socialand environmental aspects. Although five pillars were considereda great attempt to include all relevant dimensions of projectperformance, it does not adequately address the issues of ecologicalsustainability. Further it suffers from the lack of objectivity andoperationalisability. We have come across hardly any study whichhas considered all relevant performance measures of developmentprojects and also made them operationalisable. The current study isan attempt to bridge the gap towards that direction.

3. The nature of CDF projects in Kenya

Constituency Development Funds (CDFs) are decentralisedfunding schemes that disburse funds from central governmentdirectly to electoral constituencies for setting up of local in-frastructure projects. Decentralization as a system of governmentinvolvement in grassroots' projects and community developmenthas evolved over time. National governments in several countries,including India, Pakistan, Bhutan, Jamaica, Papua New Guinea,Uganda, Tanzania and Kenya continue to disburse developmentexpenditure through policies such as CDF in order to realiseequitable development in all political regions of the countries. InKenya, CDF was initiated by the Kenyan government in 2003 inorder to spur development in rural areas so as to ensure equitabledevelopment in the country. It is aimed towards constructionprojects at the grassroots level in order to achieve the developmentnecessary for poverty reduction, employment generation, improve-ment of rural economy and overall national development bybringing facilities and services closer to people. According to theCDF Act (2003), the Government should allocate at least 2.5% ofthe ordinary revenue to CDF (Wanjiru, 2008). However, this

Project Management 32 (2014) 492507amount has been increasing consistently and currently stands ataround 4% of the revenue. Under CDF Act (2003), funding isusually done for a completely new project or for renovation of an

population. Sample size of each stratum was found out by

l ofexisting facility. Further the project must be development orientedand not recurrent. Broadly CDF projects fall under four differentcategories, viz. (1) educational facilities (32%), (2) health facilities(26%), (3) water & physical infrastructure including light industries(27%) and (4) agriculture, security, social services & wildlife(15%) (Baskin, 2010). Further, according to the CDF Act (2003),the stakeholders included in the management of the CDF includethe public and community groups, CDF management agencies,existing government institutions, and Member of Parliament ofeach constituency.

The CDF projects involve the participation of all stakeholdersat different stages starting from project identification to eventualimplementation and monitoring (Wanjiru, 2008). In the very firststage, the community, through formal meetings, identify theirdevelopmental needs and the kind of projects they require. Theseneeds are then forwarded to the Constituency Development FundCommittee (CDFC), the District Projects Committee (DPC) andeventually, CDF National Management Board (NMB). TheBoard scrutinises and recommends projects for funding. Theseprojects are implemented by the various Project ManagementCommittees (PMCs) on behalf of the CDFC and for the benefit ofthe community. Once project construction is completed, they aretransferred to the respective line ministries which manage theprojects on behalf of the government. The line ministries providenecessary staff and ensure maintenance of the facilities through acost sharing policy in which majority of the costs are borne bygovernment and a very minor part is shared by the beneficiaries.Further the beneficiaries do not contribute anything towardsproject cost. Monitoring and evaluation of completed projects isundertaken by the CDF monitoring Unit, the National manage-ment Board, District Development Officer (DDO), relevantgovernment line ministries, and other national agencies like theNational Taxpayers Association (NTA). Wanjiru (2008) reportsthat at present, the monitoring systems instituted under the CDFAct (2003) are not thorough enough to evaluate performance ofthe projects as it only entails visits to the project site and a verbalreport on the project, which gives a very superficial picture.

4. Research methodology

The key issue in the present study is to develop a multidimensional performance evaluation framework of developmentprojects. This involved, first of all, identification of the targetprojects in the study site and the stakeholders associated withthose projects which enabled us to define the target population.This was followed by the design of a survey instrument and testingthe reliability and validity of the same. Finally with the help oftrained field investigators, the survey instrument was administeredamongst the respondents.

4.1. Study site and the identification of the target projects

The present study has been carried out in 24 constituencieslocated in the western province of Kenya. Different types of

C. Ngacho, D. Das / International JournaCDF projects are undertaken in these constituencies. Inspectionof the list of projects in each constituency revealed that therewere over 4000 projects undertaken between 2003 and 2011.utilizing the following formula;

Sample size of individual stratum ni n Ni= N1 N 2 NK ; for i 1; 2k;

where Ni is the population size of i-th stratum and n is the totalsample size required (Kothari, 2003).

Applying the above formula, the sample size of clients,consultants and contractors came out to be 164, 35 and 59respectively. Once stratified sample containing respondentsThis period was considered because prior to 2003, constructionprojects used to be undertaken separately by line ministries andthus was very difficult to access data during that time. With thecreation of CDF in 2003, information regarding all construc-tion projects became readily available. Out of 4000 projectsundertaken between 2003 and 2011, those projects have beenconsidered as target projects which are involved in theconstruction of educational facilities, health facilities, lightindustries and agricultural markets. It was found that only586 projects were involved in the construction of the abovefacilities. Thus these 586 projects qualified as target projectsin the present study.

4.2. Target population, sample size and sampling

The 586 projects identified above allowed us to define thetarget population (i.e. target respondents) of the current study.We identified three categories of target respondents, namelyclients, consultants and contractors. There were 586 clients for586 projects. In addition, there were 124 consultants and 212contractors registered by Architectural Association of Kenya(AAK) and Kenya contractors' associations (KCA) countyoffices in Western province respectively. Therefore, the size ofthe target population became 922 consisting of 586 clients, 124consultants and 212 contractors.

For the purpose of determining the sample size, first of all,20% of the size of the target population, 95% confidence leveland 5% precision level were considered and the populationstandard deviation was worked out by making use of theestablished formula (Malhotra and Dash, 2011), which cameout to be 34.64. Subsequently, with this value of populationstandard deviation, the sample size was recalculated, whichcame out to be 184.4. Further, anticipating invalid responsesand reluctance of the respondents to participate in theinterview, an extra 40% of the sample size was incorporatedin the final sample size. Thus we finally ended up with asample size of 258, which may be considered as 28% of thetarget population.

For determining the sampling scheme, a stratified samplewas used to reflect the diverse stakeholders of the target

495Project Management 32 (2014) 492507from three different strata was determined, we employedsimple judgmental sampling for the purpose of selecting 258respondents.

and proper medical facilities were provided (0.765), wouldslightly improve the value of Cronbach's alpha as indicated in the

l of4.3. Design of survey instrument

It is assumed in this study that the ex-ante relevance of CDFconstruction projects has been considered at the initial phaseof project selection from the perspective of the beneficiaries.The selected projects are already under implementation phase.Therefore, it is not appropriate to assess the relevance of theseprojects further at this stage whose ex-ante relevance hasalready been established. Moreover, the respondents of thestudy include clients, consultants and contractors involvedin project construction, who are solely concerned with thedesign, construction and delivery of the projects. Therefore,this particular aspect is deemed redundant while designing thesurvey instrument of CDF projects.

Keeping this in mind, a list of performance related variablesrelevant to CDF projects was derived from literature and shownto 5 experts comprising 2 professors in the area of projectplanning, 2 practitioners and 1 consultant in order to securetheir viewpoints regarding the suitability of the same in per-formance measurement of development projects in Kenya.Both the professors possess more than 10 years of teaching andconsulting experience for many government projects. Due totheir rich experience, they were thought to be familiar witheconomic, socio-cultural and political environment surroundingvarious projects earmarked for this study. The two practitionerswere chairmen of Kenya association of contractors, Busiaand Kakamega counties, Western Kenya. The choice of thesepracticing managers was based on their rich experience withconstruction projects in their respective regions. The fifthexpert, a regional public works officer in charge of the BusiaCounty since 2003, is responsible for all CDF constructionprojects funded by the Government in Busia County. Theexperts confirmed that the measurement items identified wererelevant to CDF construction projects in Kenya.

The literature review coupled with the feedback received fromthe experts on the performance measurement items enabled us todesign a structured questionnaire for the purpose of assessing theperformance of CDF projects.Questionnaire was divided into twosections. The first section of the questionnaire contains questionsrelating to the background information of the respondents, the typeof CDF construction projects they are currently involved in, theprocurement approach followed in the current project and theirexperience associated with construction projects in general andCDF construction projects in particular etc. The second sectioncontains questions pertaining to the perception of the respondentson their level of agreement on various statements representingdifferent aspects of project performance. A total of 35 performanceindicator variables were identified. A five-point Likert scale wasused as a response format for different variables with the assignedvalues ranging from 1 = Strongly Disagree to 5 = Strongly Agree.The purpose of this section of the questionnaire is to secureopinions of the respondents on the above variables with referenceto a specific project they had been involved in. The questionnairewas presented to the same experts once again with a view to

496 C. Ngacho, D. Das / International Journaseeking their expert opinion on the adequate and appropriatecoverage of all the items affecting the performance of constructionprojects and also the user-friendliness and overall workability ofbrackets of the above variables. However, the above variableswere not discarded at this stage due to some plausible reasons.Firstly, the improvement achieved through the deletion of theabove variables as mentioned in the brackets was marginal.Secondly, the inclusion of the above three variables doesmaintain the Cronbach's alpha coefficient at 0.751, which iswell above 0.7 threshold value. Finally, this being a pilot studythe questionnaire. They stated that some of the questions need tobe rephrased for ease of understanding, given the varying level ofeducation of the prospective respondents. The entire exerciseultimately helped us in achieving the content validity of thequestionnaire.With the help of language experts, the questionnairewas also translated into local (Swahili) language for thoserespondents, who cannot properly understand English language.

4.4. Reliability of the survey instrument

In order to find out the reliability of the survey instrument, apilot survey was carried out amongst 5 contractors (including 2sub-contractors), 4 consultants and 21 clients, who wereworking on ongoing construction projects. The sample used inthis survey was drawn primarily from a database of contrac-tors, consultants and clients in the Western province main-tained by the CDF regional office in Western Province. Theserespondents were found to have over 7 years of experiencein the construction industry and had been involved in theconstruction of CDF projects for at least 3 years. Further, theyhandled over 4 CDF projects per year in various constituencies.This bears testimony to the fact that these respondents werequite experienced in providing relevant information requestedin the questionnaire.

In the first stage, the variability of responses on each mea-surement item amongst 30 respondents was checked throughthe scores of standard deviations. The standard deviations wereall found to be greater than 1 except in two variables namelyAll stakeholders supervised project quality and Propermedical facilities provided. Despite their low standarddeviations, these variables were not deleted because of theirtheoretical significance in the measurement of constructionproject performance.

In the next stage, scale reliability (internal consistency) wasinspected using Cronbach's coefficient alpha. A scale is said tobe reliable, if Cronbach's coefficient alpha of the scale is wellabove the threshold value of 0.700 and the acceptable minimumof 0.600 (Hair et al., 2006). In this study, the Cronbach'scoefficient alpha for the entire scale consisting of 35 measure-ment variables was 0.751 with relatively high corrected item-to-total correlations indicating the presence of high internalconsistency in the measurement scale. Investigation of individualvariables shows that deletion of three variables, namely Disputesdue to frequent changes (0.761), increased solid waste (0.757)

Project Management 32 (2014) 492507and the variables being important in theoretical sense, the samemight improve the measurement of construction project perfor-mance at a later stage.

4.5. Data collection

24 field investigators were selected, one each for each of the24 constituencies those constitute the Western province. Theywere trained on the contents of the survey instrument and thedata collection exercise in general and asked to ensure that theycarried a copy of the questionnaire in both versions, English and

dominance of the Educational and Health projects amongstCDF construction projects can be attributed to the fact that themajority of the people residing in rural areas in Kenya aremainly women and children as men usually migrate to urbancentres in search of employment. Owing to this, the projectsthat focus on children and women, namely Educational andHealth projects have multiplier effect on the quality of life in

sific

497C. Ngacho, D. Das / International Journal of Project Management 32 (2014) 492507Swahili while visiting a particular constituency for fieldwork.Simultaneously, all three categories of respondents includingclients, consultants and contractors were identified. The contactnumber, e-mail address, postal addresses of all 922 respondentscomprising clients, consultants and contractors spread over 24constituencies were collected from the database maintained byCDF regional office, Western Kenya and were then contactedthrough telephone calls and e-mails for seeking an appointmentalong with the date, time and venue from them. Based on theappointment, the field investigators visited the designated placein person with a hard copy of the questionnaire with a view toeliciting responses from them through face-to-face interview. Theresponse rate turned out to be moderate which is normallyexpected in survey research.

5. Research findings and discussion

At the end of eight weeks, the field investigators were able tosubmit 196 completed questionnaires, out of which 21 question-naires were found either incomplete or improperly filled. Thus thetotal number of effective response came out to be 175, indicating avalid response rate of 67.8%. These responses were found to befrom different categories of stakeholders who worked on differenttypes of projects. Once the data was received, it was checked formissing values, inconsistency and negatively framed responses.The scores on negatively framed questions were suitably reversedand all the scores were fed into SPSS software (version 20). Wepresent an overview of the characteristics of the projects andrespondents' demographic profile and the related descriptivestatistics. Finally the results of exploratory factor analysis (EFA)carried out through SPSS (version 20.0) are presented.

5.1. Description of CDF construction projects andtheir procurement approaches

Table 1 presents relevant data pertaining to the type of theprojects surveyed and their procurement approaches.

The projects surveyed in this study consist of 65 Educationalprojects (37.1%), 44 Health facilities (25.1%), 33 IndustrialEstates (18.9%) and 33 Agricultural Markets (18.9%). The

Table 1Types of CDF projects & their procurement approaches.

Total Project clas

Educational

Procurement approach usedDesign/build 8 (4.6%) 2 (25.0%)

Competitive bid 70 (40.0%) 32 (45.7%)Negotiated general contract 97 (55.4%) 31 (31.9%)Total 175 (100%) 65 (37.1%)rural areas.Table 1 show that three procurement approaches are used

in securing construction of CDF projects in Western province,Kenya. These are (i) Design/build, (ii) Competitive bid and (iii)Negotiated general contract. Majority of these projects wereprocured through Negotiated general contracts (55.4%), followedby Competitive bidding (40%) and Design/Build (4.6%). Allthese procurement approaches differ from each other in terms ofallocation of responsibilities, sequencing of activities, processand procedure and organisational approach in project delivery(Abdul et al., 2006). The results indicate that the negotiatedgeneral contract is the most popular procurement method usedamongst CDF construction projects in Kenya. Under this ap-proach, a single prime contractor is entrusted with the re-sponsibility of undertaking the entire work. The contractoris accountable for the activities taking place on the project.This approach creates common project goals and objectives andacts as a single point of responsibility that enhances projectcommunications.

5.2. Status of CDF construction projects

Three major problems facing CDF construction projectsare said to be time overrun, cost overrun and quality defects.Together, they provide a picture of the existing status of CDFconstruction projects surveyed. The occurrence of time over-run,cost over-run and quality defects occurred across different typesof projects is presented in Table 2.

The findings in Table 2 indicate that 153 amongst 175projects surveyed (87%) in this study experienced time overrunranging from less than six months to more than 12 months.However, much of the delay was for less than 6 months (54%).The table shows the magnitude of time overrun across all fourkinds of projects and reveals that the proportion of time overrunwas maximum amongst Industrial Estates (approx. 97%) andminimum amongst Agricultural Markets (81.8%). Koushki etal. (2005) reported delays of construction projects in the rangeof 1 month to 9 months and above on almost 100% of theprojects undertaken. In contrast, Bordat et al. (2004) observedthat only 12% of all publicly funded transportation projects in

ation

Health care Industrial estate Agricultural market

4 (50.0%) 1 (12.5%) 1 (12.5%)

5 (7.1%) 17 (24.3%) 16 (22.9%)

35 (36.1%) 15 (15.5%) 16 (16.5%)44 (25.1%) 33 (18.9%) 33 (18.9%)

Indiana, United States experienced average time overrun of3 months. The differences in the findings in respect of theoccurrences of time overrun in these studies with the currentone could be attributed to different socio-economic environ-ment under which the projects were implemented. Further theimportance of different projects in different regions varieswhich leads to varying degree of time over-run. The currentfindings show that the projects that were widely implemented(Educational, Health and Agriculture) in the Western Province,Kenya experienced relatively low time overrun due to theirhigh importance to the needs of the community.

Table 2 also demonstrates that 84 out of 175 projectssurveyed (48%) incurred cost overrun during their implemen-tation in the range of less than Ksh. 100,000 to Ksh.500, 000.While considering cost over-run across the types of projects,the table further indicates that the same was maximum (60.6%)in case of Agricultural projects and minimum (39.4%) in caseof Industrial Estates. In addition, 52.3% of Health Care projects

Estates (8). Researchers (Jha and Iyer, 2006; Love et al., 2010;Ogano and Pretrius, 2010; Palaneeswaran et al., 2007; Yung andYip, 2010) found that quality defects is a common phenomenonamongst public construction projects.

5.3. Respondents' profile

Table 3 attempts to capture the respondents' profile in termsof their position on the project, their experience in the con-struction industry and how long they have been involved inCDF construction projects.

The respondents comprised 92 Clients (52.6%), 29 Consul-tants (16.6%) and 54 Contractors (30.9%). Almost half ofthe respondents (49.7%) have been in the project constructionindustry for 36 years followed by those with over six yearsexperience (32.0%) and only 32 respondents (18.3%) hadexperience of less than 3 years. Further, most of the respondents

icat

21 (23.1%) 20 (22.0%) 13 (14.3%)

498 C. Ngacho, D. Das / International Journal of Project Management 32 (2014) 492507and 43% of Educational projects experienced cost over-run.While exploring the causes and effects of cost overrun onpublic construction projects in Ethiopia, Nega (2008) foundthat the highest proportion of cost over-run took place in HealthCare projects and the lowest proportion in Educational projects.Bordat et al. (2004) mentioned that about 55% of all projectsundertaken by Indiana Department of Transportation experi-enced cost overruns of varying magnitude across different typesof projects. The current study shows that the proportions ofCDF projects experiencing cost overrun are relatively low.

Quality defects of a construction project are measured byhow the constructed quality deviates from the prescribedtechnical specifications of the project. In the current study, 116projects (66.3%) were found to have been free from apparentdefects with only 4 projects (2.3%) recording quality defects of20% or more. Of those projects that suffered from qualitydefects, majority were Educational (19) followed by Health Careprojects (18), Agriculture Markets (14) and finally Industrial

Table 2Status of CDF construction projects.

Total Project classif

Educational

Time overrunNone 22 (12.6%) 10 (45.5%)Less than 6 months 95 (54.3%) 36 (37.9%)612 months 41 (23.4%) 12 (29.3%)over 12 months 17 (9.7%) 7 (41.2%)

Cost overrunNone 91 (52.0%) 37 (40.6%)Less than Ksh. 100,000 54 (30.8%) 18 (33.3%)Ksh. 100,001Ksh. 300,000 25 (14.3%) 9 (36.0%)Ksh. 300,001Ksh. 500,000 5 (2.9%) 1 (20.0%)

Quality defects (variations)None 116 (66.3%) 46 (39.7%)

Less than 20% 55 (31.4%) 17 (30.9%)20% and more 4 (2.3%) 2 (50.0%)Total 175 (100%) 65 (37.1%)18 (33.3%) 6 (11.2%) 12 (22.2%)5 (20.0%) 5 (20.0%) 6 (24.0%)0 (0.0%) 2 (40.0%) 2 (40.0%)

26 (22.4%) 25 (21.6%) 19 (16.3%)(70.3%) have specifically been involved in the construction ofCDF projects for a period exceeding 3 years. This adds credenceto the study in the sense that the views expressed by therespondents are based on their actual experience associated withthe construction industry in general and CDF construction projectsin particular. Jin et al. (2007) reported a similar comparativeanalysis of respondents amongst the stakeholders working onbuilding projects in China.

An analysis of the average value of projects respondents hadhandled in the past revealed that majority had handled relativelysmall projects. Of the projects surveyed, 84 projects (48.0%) had avalue of less than Ksh. 10,000,000 (1 USD = Ksh. 85), 56 projects(32.0%) had a value of Ksh. 10,000,000 to Ksh. 15,000,000whereas 35 projects (20.0%) were large with values above Ksh.15,000,000. This information indicates that apart from havingadequate experience in terms of years the respondents have beeninvolved in construction projects, they had also handled projects ofdifferent sizes.

ion

Health care Industrial estate Agricultural market

5 (22.7%) 1 (4.5%) 6 (27.3%)21 (22.1%) 17 (17.9%) 21 (22.1%)14 (34.1%) 11 (26.8%) 4 (9.8%)4 (23.5%) 4 (23.5%) 2 (11.8%)17 (30.9%) 7 (12.7%) 14 (25.5%)1 (25.0%) 1 (25.0%) 0 (0%)44 (25.1%) 33 (18.9%) 33 (18.9%)

l of5.4. Descriptive statistics of performance measures

The responses on 35 variables relating to project perfor-mance provided by the respondents were included in thepresent study. The mean and standard deviations of the scoreson responses are presented in Table 4 as shown below.

The minimum and maximum values were 1 and 5 respectivelyfor 34 out of 35 variables, indicating that, in general, respondentsused the entire 5 point survey scale. The mean score rangedbetween 2.25 (this construction project has adversely affected thequality of groundwater level-PV12) and 3.86 (near missesoccurred quite often during construction-PV21). Standarddeviations were found to be above 1 except in three variables;no changes were introduced in the design of the current projectduring project execution - PV15 (0.896), near misses occurredquite often during construction PV21 (0.889) and propermedical facilities were available for people working on the

Table 3Respondents' profile.

Total Respondent's position on the project

Client Consultant Contractor

Experience in construction of projectsb3 years 32 14 (43.8%) 11 (34.4%) 7 (21.9%)36 years 87 60 (69.0%) 4 (4.6%) 23 (26.4%)N6 years 56 18 (32.1%) 14 (25.0%) 24 (42.9%)

Respondent involvement in CDF projectsb3 years 52 14 (26.9%) 11 (21.2%) 27 (51.9%)36 years 97 70 (72.2%) 13 (13.4%) 14 (14.4%)N6 years 26 8 (30.8%) 5 (19.2%) 13 (50.0%)

Value of CDF projects worked on in the last 3 yearsOver ksh. 15,000,000 35 15 (42.9%) 7 (20.0%) 13 (37.1%)Ksh. 10,000,00015,000,000 56 32 (57.1%) 2 (3.6%) 22 (39.3%)Up to ksh. 10,000,000 84 45 (53.6%) 20 (23.8%) 19 (22.6%)Total 175 92 (52.6%) 29 (16.6%) 54 (30.0%)

C. Ngacho, D. Das / International Journaproject PV35 (0.961). However, the standard deviationvalues of these three variables being close to 1 indicate that theresponses to these three questions varied considerably amongstthe respondents.

5.5. Assessing the factorability of performancemeasurement items

For assessing the factorability of 35 performance measure-ment variables, we found out the correlation for each pair of 35variables which is demonstrated with the help of a correlationmatrix. This shows the strength of relationship between everypair of items and is summarised below (Table 5).

The correlation matrix in the above table suggests that thesample is characterised by high degree of related variables whichcould be grouped together. With the exception of a few variables, alarge number of significant correlations are found amongstdifferent pair of variables in this matrix: 193 correlations significantat 5% level and 160 correlations significant at 1% level. This givesus an indication that exploratory factor analysis (EFA) could becarried out in the whole dataset.However, before carrying out EFA, the overall significanceof the correlation matrix and its factorability needed to be testedwith the help of Bartlett's test of sphericity and KaiserMeyerOlkin (KMO) measure of sampling adequacy respectively.Bartlett's test statistics was found significant at 0.000 levels,which indicates the presence of non-zero correlations in thecorrelation matrix. Further the KMO measure of the samplingadequacy turns out to be 0.656. Although both the tests met theminimum criteria for carrying out factor analysis in the dataset,observation of the correlations along the diagonal of the anti-imagecorrelation matrix in SPSS output revealed that seven variables hadtheir KMO values less than 0.5, which indicates that the dataset, inits current form, is still not suitable for factor analysis (Hair et al.,2006). These variables were iteratively removed one after anotherstarting with the one whose correlation along the diagonal of theanti image matrix was the lowest (Jahmane et al., 2011). However,after the removal of five variables, it is found that all variables hadindividual KMO values greater than 0.5. This resulted in theimprovement of overall KMO measure of sampling adequacy to0.687. Further Bartlett's test statistics was found significant at0.000 levels. These measures indicate that the reduced set ofvariables is appropriate for factor analysis.

5.6. Factor analysis following varimax rotation

Principal components analysis (PCA) was used with varimaxrotation given that the primary purpose was to identify theunderlying factors. Initially all 30 variables were allowed to loadfreely on various factors so long as they had eigenvalue greaterthan one. Using the scree plot generated, we were able to extractthe appropriate number of factors which turned out to be six.Therefore, while identifying the final factors underlying the KeyPerformance Indicators (KPIs), the process was subjected to fourconditions: (i) the number of factors fixed at six, (ii) deletion ofitems with loadings of less than 0.5 or cross loadings of greaterthan 0.5, (iii) retention of only those factors with at least twoitems and (iv) the number of factors extracted should account forat least 60% of the variance (Field, 2005; Hair et al., 2006;Malhotra and Dash, 2011).

Factor analysis was iteratively repeated and items deletedsequentially resulting in a final instrument of 27 items. The 27-item 6 factor instrument accounted for 73.023% of the variancein the dataset. From the analysis, it is evident that seven variablesloaded under factor 1 seem to be associated with project timeperformance. The second factor comprises six variables whichreflect the cost dimension of project performance. The fourvariables under factor 3 represent construction project perfor-mance relating to site disputes whereas the three variables underfactor 4 attempt to capture environmental impact dimension ofproject performance. The three variables under factor 5 areassociated with project quality and the remaining three variablesthat load on factor 6 reflect safety of a project.

The above table reveals that Time performance factor isthe most important measure of construction project perfor-

499Project Management 32 (2014) 492507mance, having the highest eigenvalue of 4.128 and accountingfor 15.289% of the variance in the dataset. This is followed bythe measure Cost performance factor with an eigenvalue of

uctiof tnal

l ofTable 4Descriptive statistics of performance variables.

Performance variable

PV1: There has not been any increase in the cost of raw materials during constrPV2: Labour costs more or less remained stable over the period of constructionPV3: The project experienced minimum variations and hence hardly any additiovariations was incurred.

PV4: The required equipments were available at pre budgeted rates.

500 C. Ngacho, D. Das / International Journa3.854 which explains 14.275% of the total variance. The thirdmost important performance measure was found to be sitedispute factor with an eigenvalue of 3.705 and explaining13.721% of the variance while the fourth important measure turnsout to be Environmental impact factor with an eigenvalue of3.045 and contributing to 11.279% of the total variance. The lasttwo performance measures in order of importance were Qualityperformance factor and Safety performance factor with aneigenvalue of 3.031 and 1.953 respectively. The varianceexplained by these two factors is 11.225% and 7.234%respectively. These six constructs of performance constitute

PV5: The amount/quantity of different type of resources required during the implemmatched with those estimated during planning stage.

PV6: There were no incidences of fraudulent practices and kickbacks during projecPV7: There were no incidences of agitation by the trade unions in the current projePV8: There were no serious dispute between the client and contractor due to non adspecifications.

PV9: Disputes were observed due to the frequent changes in the design of the currePV10: Dispute resolution meetings were often held during project execution.PV11: At time of project completion, there were no financial claims that remainedproject.

PV12: This construction project has adversely affected the quality of groundwater lPV13: All required resources for the project were delivered on time during executioPV14: A clear plan was formulated and an efficient planning and control system wathe current project up-to-date.

PV15: No changes were introduced in the designs of the current during project exePV16: Harmonious relationship between labour and management existed in the prono work disruptions were reported during project execution.

PV17: This project has led to air pollution in the adjoining areas.PV18: This project has led to depletion of the precious natural and mineral resources inPV19: There has been an increase in solid waste due to the construction of the currPV20: Accidents were often reported during project construction.PV21: Near misses occurred quite often during construction.PV22: Fatalities did occur on this project during construction.PV23: The construction work utilised environmentally friendly technology.PV24: This project has led to the increased release of toxic material.PV25: No delays were experienced in securing funds during project implementationPV26: At the time of handover, the current project was free from apparent defectsPV27: The project contractors were often called back during the Defects Liability PeriPV28: Weather and climatic conditions did not have much impact on delaying the pPV29: The current project has utilised reusable and recyclable materials in construcPV30: The right material was used for the construction work.PV31: Employees working in the current project possessed requisite skills and most ofon similar kinds of projects in the past.

PV32: A sound quality management system was strictly adhered to during project ethe current project.

PV33: Training was imparted to the workers in order to develop a positive attitudethem to apply the right method of work.

PV34: All stakeholders associated with the current project supervised the quality ofits phases.

PV35: Proper medical facilities were available for people working on the project.

Note: Responses collected on a five point Likert scale (1 Strongly Disagree; 5 Scores on negatively framed statements were reversed.Minimumscore

Maximumscore

Meanscore

Std.dev

on of this project. 1 5 3.24 1.422he current project. 1 5 3.18 1.145cost attributable to 1 5 3.27 1.252

1 5 3.21 1.371

Project Management 32 (2014) 492507the key performance indicators (KPIs) of CDF constructionprojects.

5.7. Validation of the KPIs

5.7.1. ReliabilityReliability of the scale comprising KPIs was established

through Cronbach's alpha coefficient which tested internalconsistency of the items. The 27-item scale had a reliability of0.817 (standardised value of 0.808) which is well above 0.70recommended for similar studies (Hair et al., 2006; Malhotra

entation phase 1 5 3.22 1.269

t execution. 1 5 2.92 1.362ct. 1 5 2.86 1.275herence to the 1 5 2.81 1.285

nt project. 1 5 2.84 1.1831 5 2.70 1.234

unsettled from this 1 5 2.54 1.363

evel. 1 5 2.25 1.052n of this project. 1 5 2.90 1.298s designed to keep 1 5 3.12 1.146

cution. 1 5 3.09 .896ject site and hence 1 5 3.18 1.204

1 5 2.59 1.100the surrounding areas. 1 5 2.96 1.126ent project. 1 5 2.93 1.145

1 5 3.23 1.3162 5 3.86 .8891 5 3.38 1.0431 5 2.54 1.0101 5 3.10 1.316

. 1 5 3.31 1.2631 5 3.32 1.291

od to repair defects. 1 5 2.70 1.247roject. 1 5 3.30 1.110tion work. 1 5 2.70 1.370

1 5 2.46 1.138them had worked 1 5 2.58 1.261

xecution phase of 1 5 2.49 1.066

and also to enable 1 5 2.54 1.123

the project in all 1 5 3.03 1.397

2 5 3.79 .961

Strongly Agree).

Table 5Original correlation matrix.

PV1 PV2 PV3 PV4 PV5 PV6 PV7 PV8 PV9 PV10 PV11 PV12 PV13 PV14 PV15 PV16 PV17 PV18 PV19 PV20 PV21 PV22 PV23 PV24 PV25 PV26 PV27 PV28 PV29 PV30 PV31 PV32 PV33 PV34 PV35

PV1 1PV2 .74 1PV3 .70 .68 1PV4 .81 .75 .87 1PV5 .01 .06 .07 .04 1PV6 .07 .01 .07 .09 .52 1PV7 .25 .05 .11 .13 .03 .01 1PV8 .05 .27 .15 .15 .01 .04 .75 1PV9 .01 .25 .08 .08 .03 .04 .72 .92 1PV10 .16 .06 .09 .07 .04 .02 .57 .77 .69 1PV11 .45 .26 .33 .42 .00 .07 .65 .34 .31 .19 1PV12 .30 .40 .28 .35 .05 .05 .25 .24 .17 .25 .52 1PV13 .24 .18 .14 .23 .01 .00 .11 .19 .17 .07 .10 .23 1PV14 .09 .04 .07 .13 .06 .00 .00 .00 .04 .02 .15 .16 .73 1PV15 .04 .07 .11 .03 .04 .04 .03 .08 .04 .13 .13 .08 .41 .43 1PV16 .20 .01 .01 .19 .05 .01 .03 .06 .01 .15 .22 .18 .62 .66 .52 1PV17 .04 .03 .11 .07 .04 .06 .21 .20 .15 .11 .09 .05 .14 .02 .17 .12 1PV18 .02 .18 .02 .04 .07 .02 .24 .23 .18 .12 .13 .16 .17 .06 .30 .10 .57 1PV19 .05 .13 .03 .05 .04 .06 .20 .21 .16 .12 .04 .13 .25 .00 .24 .12 .62 .79 1PV20 .00 .09 .01 .02 .50 .34 .04 .01 .07 .09 .09 .03 .08 .08 .12 .12 .01 .06 .07 1PV21 .01 .03 .03 .01 .01 .12 .02 .05 .08 .01 .04 .08 .05 .01 .01 .04 .03 .03 .05 .45 1PV22 .05 .08 .07 .07 .03 .07 .02 .00 .01 .09 .08 .00 .01 .02 .06 .02 .06 .09 .04 .52 .34 1PV23 .00 .00 .11 .06 .07 .02 .20 .18 .12 .11 .05 .01 .07 .04 .04 .03 .86 .44 .54 .04 .07 .05 1PV24 .01 .05 .02 .00 .09 .02 .08 .04 .02 .08 .03 .00 .06 .02 .12 .01 .03 .03 .00 .19 .41 .02 .03 1PV25 .34 .21 .16 .21 .04 .08 .10 .02 .03 .03 .18 .09 .77 .45 .44 .57 .00 .05 .12 .10 .05 .02 .07 .10 1PV26 .04 .16 .05 .01 .02 .02 .28 .32 .36 .18 .12 .11 .40 .43 .10 .32 .05 .16 .12 .17 .21 .08 .10 .01 .30 1PV27 .21 .27 .30 .32 .09 .03 .03 .14 .08 .01 .07 .13 .34 .29 .07 .17 .19 .04 .10 .09 .15 .11 .18 .06 .23 .12 1PV28 .29 .04 .17 .28 .10 .07 .22 .13 .21 .01 .21 .26 .56 .53 .19 .57 .05 .06 .15 .15 .07 .02 .03 .05 .47 .64 .29 1PV29 .06 .02 .10 .06 .04 .03 .06 .08 .07 .13 .00 .02 .18 .18 .22 .13 .03 .08 .10 .04 .02 .05 .02 .07 .15 .03 .02 .03 1PV30 .33 .37 .44 .46 .15 .11 .11 .08 .04 .02 .27 .08 .19 .26 .11 .04 .05 .07 .04 .00 .09 .06 .02 .03 .12 .05 .40 .27 .02 1PV31 .18 .20 .22 .18 .06 .06 .01 .09 .12 .07 .08 .04 .09 .11 .18 .04 .03 .14 .08 .00 .02 .04 .07 .00 .02 .03 .19 .02 .03 .28 1PV32 .30 .33 .39 .40 .16 .11 .22 .02 .04 .11 .36 .10 .18 .29 .12 .05 .07 .12 .02 .03 .11 .06 .02 .06 .08 .01 .37 .23 .01 .92 .30 1PV33 .34 .39 .46 .49 .12 .09 .15 .06 .02 .06 .29 .07 .23 .28 .06 .06 .02 .00 .12 .04 .11 .05 .08 .04 .16 .03 .37 .29 .03 .92 .28 .88 1PV34 .10 .06 .06 .10 .28 .22 .07 .06 .07 .02 .04 .12 .07 .04 .01 .02 .01 .07 .01 .00 .01 .35 .03 .22 .11 .07 .09 .02 .01 .00 .04 .02 .01 1PV35 .07 .01 .07 .06 .24 .21 .05 .05 .10 .02 .02 .02 .03 .07 .03 .04 .03 .07 .02 .18 .10 .12 .03 .24 .07 .11 .05 .01 .03 .03 .03 .07 .04 .15 1

Correlation coefficient and p values are as follows: N 0.15 denotes p-value b 0.05 or significant at 5% level; N 0.20 denotes p-value b 0.01; or significant at 1% level.

501C.Ngacho,D

.Das

/International

Journalof

Project

Managem

ent32

(2014)492507

and Dash, 2011). The Cronbach's alpha coefficient for eachfactor was as follows: Time performance measure: 0.867; costperformance measure: 0.869; site disputes measure: 0.917;quality performance measure: 0.916; safety performance mea-sure: 0.699; and environmental impact measure: 0.875. Thisdemonstrates that the factors extracted from the analysis areconsidered adequate in the performance measurement of CDFconstruction projects.

5.7.2. Content validityThe content validity of the instrument measuring KPIs was

achieved while designing the survey instrument. This wascarried out through extensive literature review followed bysecuring opinions from the experts comprising academics andpractitioners through in-depth interviews. This is discussed indetail in Section 4.3.

5.7.3. Convergent and discriminant validityIn order to assess the convergent and discriminant validity of

the 27-item scale, we made use of the correlation matrix providedin Table 7. The inter-item correlation of the scale had a mean of0.100, while the smallest inter-item correlation within eachperformance measure are as follows: time performance factor:

To assess discriminant validity, the correlates under eachfactor in the correlation matrix were counted to find out thenumber of times they had higher correlations with items of otherfactors than items of its own factor in the correlation matrix. Fordiscriminant validity to be present, the count should be less thanone half of the potential comparisons (Wang et al., 2007).Examination of the correlation matrix in Table 7 reveals that thereare 88 violations of discriminant validity out of 378 possiblecomparisons. Further, promax rotation of factors resulted in lowcorrelations amongst the factors, an indication of the presenceof discriminant validity. The statistical results show that thevariables that loaded under each of the factors adequatelyexplained the same, i.e. had high convergence. Similarly, eachfactor was distinct as evidenced by the correlations.

5.8. Theoretical framework

KPIs determined through factor analysis of performancevariables and validated through appropriate tests enabled us todevelop a theoretical framework of construction project perfor-mance linking the performance metrics and overall projectperformance. This is shown in Fig. 1.

This framework (Fig. 1) demonstrates that the projectperformance can be described in terms of Time variables

502 C. Ngacho, D. Das / International Journal of Project Management 32 (2014) 4925070.100, cost performance factor: 0.280, site disputes factor: 0.573,environmental impact factor: 0.438, quality performance fac-tor:0.875 and safety performance factor: 0.338. These correla-tions are significantly greater than zero (p b 0.000), providingevidence for convergent validity.Fig. 1. Performance evaluation fram(TY1 TY6), Cost variables (CY1CY6), Quality variables(QY1.QY5), Safety variables (SY1SY3), variables relating toSite disputes (DY1DY6) and Environmental impact (EY1EY4).A brief description of the factors constituting the above variablesis given below.ework of development projects.

5.8.1. Factor 1 represents time performance measureTime performancemeasure, as shown in Table 6, is considered

to be the most important factor amongst all six constructs. In thisconstruct, the highest loading is observed in timely delivery ofproject resources (0.862) while the lowest one is found in athandover there were no apparent defects (0.572). Theoretically,the variable at handover there were no apparent defects shouldhave been loaded under Quality performance measure but resultsof factor analysis reveal that it loaded under Time performancemeasure. A closer look at the survey instrument indicates that therespondents perceived defects in project to be a quality-relatedattribute but this ultimately leads to delay in project handover andconsequently its use. This might be the possible reason whythe above variable loaded under Time performance measure.Similarly, the item Harmonious relationship on site is widelythought to be associated with disputes during construction. In thecurrent study, this item loaded on time. The respondents perceivedisharmony at workplace giving rise to disruptions of work thateventually lead to delay in certain activities of the project. Limand Mohamed (1999) considered project completion time to be

the first criterion for project success. Other researchers (Kamruland Indra, 2010; Khosravi and Afshari, 2011) have termed time tobe the most important factor in the performance measurementof construction projects. CDF projects, being community based,utmost importance is given to time dimension because thefunding of these projects is always done annually based on itsprogressive performance.

5.8.2. Factor 2 represents cost performance measureAsmentioned earlier, this factor is considered the secondmost

important performance measure of CDF projects. The highestloading is observed in Equipment at budgeted rates (0.875) asrevealed in Table 6. The table, further, shows that the variableadversely affected the quality of groundwater level, loaded onthis factor with a loading of 0.586 despite it being reflective ofenvironmental impact measure. This implies that the projectstakeholders would require more resources to minimise thenegative impact of the project on groundwater level therebyleading to an escalation in project cost. Another variable nofinancial claims at completion loaded on cost performance

Table 6Results of the factor analysis.

Components

1 2 3 4 5 6

Cronbach's alpha () 0.867 0.869 0.918 0.875 0.966 0.699TV1: Timely delivery of resources .862TV2: Harmonious relationship on site. .832TV3: A clear plan was formulated. .800TV4: No delays in securing funds. .772TV5: No effect of weather and climatic conditions. .734TV6: No design changes. .586TV7: At handover there were no apparent defects .572CV1: Equipments at pre budgeted rates. .875CV2: Stable labour costs .863

503C. Ngacho, D. Das / International Journal of Project Management 32 (2014) 492507CV3: No increase materials costCV4: Minimum variations cost.CV5: Adverse effect on quality of groundwater level.CV6: No financial claims at completion.DV1: No serious dispute due to specifications.DV2: Disputes due to the frequent changesDV3: No incidences of trade union agitationDV4: Dispute resolution meetingsEV1: Project has led to air pollution.EV2: Increased solid waste.EV3: Utilised environmentally friendly technology.EV4: Project has led to depletion natural resources.QV1: Right material was used for the construction work.QV2: A sound QMS adhered to.QV3: Workers were trained on positive attitudesSV1: Accidents were reported.SV2: Fatalities did occur.SV3: Near misses occurred.Eigen value 4.128Percentage of variance explained 15.289Cumulative percentage 15.289

Extraction method: principal component analysis.Rotation method: varimax with Kaiser normalization.Rotation converged in 6 iterations.

KaiserMeyerOlkin measure of sampling adequacy = 0.692.Bartlett's test of sphericity = 4137.533.Significance = 0.000..854

.806

.586

.508.951.922.845.788

.884

.855

.826

.810.933.919.911

.835

.783

.7263.854 3.705 3.045 3.031 1.95314.275 13.721 11.279 11.225 7.23429.564 43.285 54.564 65.789 73.023

141599453984

l ofTable 7Correlation matrix of variables after grouping according to factor analysis.

TV1

TV2

TV3

TV4

TV5

TV6

TV7

CV1

CV2

CV3

CV4

CV5

CV6

TV1 1TV2 .62 1TV3 .73 .66 1TV4 .77 .57 .45 1TV5 .56 .57 .53 .47 1TV6 .41 .52 .43 .44 .19 1TV7 .40 .32 .43 .30 .64 .10 1CV1 .23 .19 .13 .30 .28 .03 .01 1CV2 .18 .01 .04 .21 .04 .07 .16 .75 1CV3 .24 .20 .09 .34 .29 .04 .04 .81 .74 1CV4 .14 .01 .07 .16 .17 .11 .05 .87 .68 .70 1CV5 .23 .18 .16 .09 .26 .08 .11 .35 .40 .30 .28 1CV6 .10 .22 .15 .18 .21 .13 .12 .42 .26 .45 .33 .52DV1 .19 .06 .00 .02 .13 .08 .32 .15 .27 .05 .15 .24 .3DV2 .17 .01 .04 .03 .21 .04 .36 .08 .25 .01 .08 .17 .3DV3 .11 .03 .00 .10 .22 .03 .28 .13 .05 .25 .11 .25 .6DV4 .07 .15 .02 .03 .01 .13 .18 .07 .06 .16 .09 .25 .1EV1 .14 .12 .02 .00 .05 .17 .05 .07 .03 .04 .11 .05 .0EV2 .25 .12 .00 .12 .15 .24 .12 .05 .13 .05 .03 .13 .0EV3 .07 .03 .04 .07 .03 .04 .10 .06 .00 .00 .11 .01 .0EV4 .17 .10 .06 .05 .06 .30 .16 .04 .18 .02 .02 .16 .1QV1 .08 .12 .08 .10 .15 .12 .17 .02 .09 .00 .01 .03 .0QV2 .01 .02 .02 .02 .02 .06 .08 .07 .08 .05 .07 .00 .0QV3 .05 .04 .01 .05 .07 .01 .21 .01 .03 .01 .03 .08 .0

504 C. Ngacho, D. Das / International Journameasure with a loading of 0.508, which theoretically should haveloaded under Site Disputes. However, this variable seems tohave financial implications on the project in terms of penaltiesand interests that would accrue to the project implementing body.Construction cost has been identified as an important measure ofperformance in almost all studies relating to the performance ofconstruction projects (Chan and Chan, 2004; Zou et al., 2007;Kaliba et al., 2009). In their study, Khosravi and Afshari (2011)ranked cost as the second most important measure of projectperformance. The current study puts much emphasis on regularmonitoring of CDF projects with a view to finding out how thefunds allocated to a construction project are spent.

5.8.3. Factor 3 represents site disputes measureThis factor comes third in order of explaining the variance in

the dataset. The variable no serious disputes on the projecthad the highest loading of 0.951 on this factor while thevariable dispute resolution meetings was found to have theleast loading (0.788). Project stakeholders have the responsi-bility to minimise disputes amongst themselves and formulatestrategies to govern their relationships during project construc-tion. David (2009) and Tabish and Jha (2011) have observedthat project performance measurement should also consider thelevel of work related disputes encountered during constructionand after. According to Abidin (2007), construction disputesare common and could have serious implications on

SV1 .19 .04 .26 .12 .27 .11 .05 .46 .37 .33 .44 .08 .27*SV2 .18 .05 .29 .08 .23 .12 .01 .40 .33 .30 .39 .10 .36SV3 .24 .06 .28 .16 .29 .06 .03 .49 .39 .34 .46 .07 .29

Correlation coefficient and p values are as follows: N 0.15 denotes p-value b 0.05level.Note: Shaded areas represent variables grouped together by factor analysis.DV1

DV2

DV3

DV4

EV1

EV2

EV23

EV4

QV1

QV2

QV3

SV1

SV2

SV3

1.92 1.75 .72 1.77 .69 .57 1.20 .15 .21 .11 1.21 .16 .20 .12 .62 1.18 .12 .20 .11 .86 .54 1.23 .18 .24 .12 .57 .79 .44 1.01 .07 .03 .09 .01 .07 .04 .06 1.00 .01 .02 .09 .06 .04 .05 .09 .52 1.05 .08 .02 .01 .03 .05 .07 .03 .45 .34 1

Project Management 32 (2014) 492507performance of construction projects. CDF constructionprojects are characterised by many stakeholders whoserelationships are likely to create disputes. These disputes, ifnot checked, could derail project construction and hinderattainment of project objectives.

5.8.4. Factor 4 represents environmental impact measureThis factor comes fourth in order of the total amount of

variance explained by the same. The variable the project hasled to air pollution in the adjoining areas had the highestloading (0.884) on this factor while the variable the project hasled to the depletion of natural resources had the least loading(0.810). Chan and Chan (2004) reported that environmentalissues in construction have become a global concern andtherefore, should be considered an integral part of construction.A few other researchers (Chen et al., 2010; Gangolells et al.,2009; Medineckiene et al., 2010; Shen et al., 2010; Tan et al.,2011) have also acknowledged the importance of environmen-tal impact in the performance of construction projects. Allconstruction projects should address environmental issues forthe purpose of achieving sustainable development.

5.8.5. Factor 5 represents quality performance measureThough this factor was ranked the second least amongst six

performance measures, it is indispensable in the performanceevaluation of CDF projects. The highest loading for this factor

.08 .04 .11 .02 .05 .04 .02 .07 .01 .06 .09 1.02 .04 .22 .11 .07 .02 .02 .12 .03 .06 .11 .92 1

.06 .02 .15 .06 .02 .12 .08 .00 .04 .05 .11 .92 .88 1

or significant at 5% level; N 0.20 denotes p-value b 0.01; or significant at 1%

l ofwas found on right material was used for the construction work(0.933) while the item workers were trained on positive attitudeand methods had the lowest loading (0.911). The inclusion ofquality in the performance measurement of construction projectshas been reported by several researchers including Jha and Iyer(2006), Palaneeswaran et al. (2007), Ogano and Pretrius (2010),Love et al. (2010) and Yung and Yip (2010). This measure ofproject performance was, however, ranked third by Khosravi andAfshari (2011) in their study of success measurement amongstpower plant, utility and cogeneration construction projects. Chanand Chan (2004) observed that quality is an important measure ofproject performance because it constitutes the guarantee thatthe project will serve its purpose. Poor quality in projects resultsin numerous reworks which unnecessarily undermine otherproject performance indicators. Supervision of project qualityis, therefore, the most important activity which needs to beundertaken by all project stakeholders.

5.8.6. Factor 6 represents safety performance measureThis factor comes last in order of its explanatory power of

explaining the total variance. The highest loading of this factoris observed on accidents were often reported (0.835) and theleast one is associated with near misses occurred (0.726).Chan and Chan (2004) acknowledged the role of safety inconstruction and stated that it is important for all projectstakeholders to ensure that there are no accidents during theentire construction period. Haslam et al. (2005), Billy et al.(2006) and Zuo (2011) indicated that project safety shouldalways be considered due to the risky nature of constructionactivities when evaluating the performance of constructionfacilities. It is important for every project organisation to focuson safety during construction because if accidents occur, bothcontractors and clients may be subject to legal claims, financialloss and delay in the overall completion of construction project.

6. Conclusion and managerial implications

The empirical findings of the study and the subsequentanalyses suggest that the performance of development projectsdoes not merely depend on the traditional internal criteria oftime, cost and quality. It also depends on another internalmeasure, safety and two external measures namely site disputesand environmental impact. This has enabled us to develop amulti-dimensional performance evaluation framework of de-velopment projects. Out of the six performance measures, time,cost and quality continue to capture economic dimension whilesafety and site disputes account for societal aspects and finallyenvironmental impact takes care of environmental aspects.

The above six factors constitute the six KPIs in the presentstudy. Project time turns out to be the most important KPI followedby project cost while project safety had the lowest level ofimportance amongst six KPIs. Accordingly the project stake-holders should allocate considerable amount of resources intoaddressing the issues relating to time and cost of development

C. Ngacho, D. Das / International Journaprojects. Another important finding of the study indicates that inorder of importance, quality comes out to be the fifth out of sixKPIs. This partially contradicts the generic findings of projectmanagement literature which considers quality as one of the mostimportant KPIs. An examination into the nature of developmentprojects reveals that the surrounding community who are the actualbeneficiaries of these projects is required to bear a meagerpercentage of operational costs while availing of the servicesand they hardly contribute anything towards the initial cost ofconstruction. Given that these are poverty alleviation interventions,the entire construction cost is borne by the government or thefunding agencies. Therefore, those involved in the construction ofdevelopment projects endeavour to achieve good performance inthe tangible performance dimension of time and cost at the expenseof quality, a relatively less tangible dimension.

The managers involved in the construction of developmentprojects, while evaluating its performance should

Understand the needs of the community through properinvolvement of the representatives of the community andother stakeholders and accordingly select suitable projectswhich would cater to their needs.

Understand the urgency of evaluating development projectson multi-dimensional performance measures incorporatingeconomic, social and environmental aspects.

Develop appropriate operational metrics to reflect the threebroad dimensions of performance of development projects.

Develop a holistic performance evaluation framework ofdevelopment projects consisting of the six KPIs with thehelp of 27 observable performance related variables.

Identify the relative importance of each KPI and accordinglyallocate appropriate material and managerial resources to thesame with a view to achieving satisfactory and balancedperformance on all KPIs.

The intended objectives of development projects can beproperly realised in a corruption free environment. It is a wellknown fact that these kinds of projects are severely affected by thescams prevalent in many countries. Further these projects arecharacterised by the involvement of many stakeholders withvarying interests, numerous bureaucratic hassles and of course,varying political interests, which facilitates corruption. Accordingto Sohail and Cavil (2008), corruption which includes bribery,embezzlement, kickbacks and fraud in construction projectsundermines the delivery of infrastructure services. These practicescan lead to increases in cost, extension of time and poor quality ofconstructed facilities. The element of corruption has not beenincluded in the present study which could be taken up as anextension of this work.

The study has several limitations. First, both ex-ante andex-post relevance of development projects should be assessed bythe actual beneficiaries of the project. The current study assumesthat the ex-ante relevance of the projects has been considered bythe representatives of the community at the initial stage. It has notcaptured the viewpoints of the community directly for appraisingex-ante relevance of projects. Further ex-post relevance of theseprojects has not been covered in the present work. This could be

505Project Management 32 (2014) 492507taken up as a potential area of research. Secondly, the data for thedevelopment of the survey instrument was gathered in oneprovince in Kenya and hence there is an important need to

l ofundergo cross-cultural validation of the instrument using datagathered from other provinces of Kenya and other developingcountries as well in order to enhance the generalization of items.Further there is a need for carrying out confirmatory factoranalysis (CFA) to establish the soundness of measurement scale.These could be taken up as another future study. Thirdly, thepresent study did not investigate the causal relationships amongstthe six KPIs of project performance. CFA can analyze the causalrelationships amongst these six KPIs of project performance.This could also be taken as another future area of research.

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A performance evaluation framework of development projects:An empirical study of Constituency Development Fund (CDF)construction projects in Kenya1. Introduction2. Literature review3. The nature of CDF projects in Kenya4. Research methodology4.1. Study site and the identification of the target projects4.2. Target population, sample size and sampling4.3. Design of survey instrument4.4. Reliability of the survey instrument4.5. Data collection

5. Research findings and discussion5.1. Description of CDF construction projects and their procurement approaches5.2. Status of CDF construction projects5.3. Respondents' profile5.4. Descriptive statistics of performance measures5.5. Assessing the factorability of performance measurement items5.6. Factor analysis following varimax rotation5.7. Validation of the KPIs5.7.1. Reliability5.7.2. Content validity5.7.3. Convergent and discriminant validity

5.8. Theoretical framework5.8.1. Factor 1 represents time performance measure5.8.2. Factor 2 represents cost performance measure5.8.3. Factor 3 represents site disputes measure5.8.4. Factor 4 represents environmental impact measure5.8.5. Factor 5 represents quality performance measure5.8.6. Factor 6 represents safety performance measure

6. Conclusion and managerial implicationsReferences