INNOVATIVE METHOD IN DETERMINING FACTORS THAT INFLUENCE PROJECT SUCCESS

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1 INNOVATIVE METHOD IN DETERMINING FACTORS THAT INFLUENCE PROJECT SUCCESS Siti Rashidah Mohd Nasir 1, Muhd Zaimi Abd.Majid 2 and Ismail Mohamad 3 12 Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor, Malaysia 3 Dept of Mathematics, Faculty of Science, University Technology Malaysia, Johor, Malaysia A project is considered successful when the project is complete on time, within budget and accordance to specification. The way project success was measured changed over the life of the project, with time and cost typically being the most important priorities during the project definition to execution phases. The findings from the questionnaire survey of 141 respondents have identified fifteen (15) major factors that influence the successful completion of a public school project. A principal component analysis with varimax orthogonal rotation method revealed six (6) latent factors representing underlying structure for the success of project completion. Factor 1 represents contractors management problem that accounts for % of variance compared to Factor 2, represents labour problem accounts for % of variance; Factor 3, contractors financial problem accounts for % of variance; Factor 4, subcontractor delay, accounts for % of variance; Factor 5, material and machineries problem accounts for % of variance; and Factor 6, weather condition accounts for 7.389% of variance. Analysis of the data indicated that within the six factors, six variables contributed a high factor loading, suggesting the successful completion of a project may be influenced by these six measures. Keywords: critical project success, factor analysis. INTRODUCTION Project success is defined as the overall achievement of project goals and expectations. Many previous researches have conducted studies on this basis, however, in determining the suitable factors or parameters for project success is hardly agreed upon. Although time and cost are typically the most important parameters measured for project success, however, client satisfaction became the most important priority and success criterion at the termination phase. This was consistent across industries and over time. There were differences in importance among major stakeholders regarding what is critical to project success, what is measured during project phases and what the project priorities were. Apparently, with these discrepancies, shows that project success was often wrongly measured. This emphasises the need to define project success factors and criteria as well as priorities at the outset of the project, and aligning stakeholders on these critical elements. Therefore, this paper aims to determine the suitable key factors that influence project success from client and contractors perspectives. Under this aim, perspectives from both client and contractor are sought through questionnaire survey to identify factors that influence the completion of project. Also, to be able to use that as the parameters that can represents the project performance during the construction phase. This study is important because project team members will know the important factors that they must pay closer attention to, in order that their projects can be completed within budget and schedule, to acceptable level of quality, and to owners satisfaction. Those significant factors that are controllable could then be properly managed to increase the chances of project success

2 In the next section, key factors are determined. Literature relating to project performance and the critical success factors is reviewed. The research method is presented, followed by the results. Analysis technique using factor analysis is then described, followed by the results. A discussion follows, whereby it is shown that 6 of the 15 key factors can be used as key parameters during construction and these key factors are discussed at later section. LITERATURE REVIEW There have been many efforts of research in determining factors that influence project success where, Ashley et al (1987) and Pinto and Slevin (1988) are some of the major contributors in identification and examination of critical success factor empirically in the 1980s. Sandivo et al. (1992) examined the contribution of factors such as project team experiences, contracts, resources, and information available to project success. Mohsini and Davidson (1992) tested the influence of a number of conflict-inducing organizational variables on performance of project using traditional procurement method. Tiong (1996) identified six critical success factors for build-operate-transfer projects. Pocock et al. (1997) examined the impact of improved project interaction on performance. Konchar and Sanvido (1998) conducted an empirical study that examined nearly 100 explanatory and interacting variables to explain project cost, schedule, and quality performance of three procurement systems (construction management risks, design and build, and design/bid/build). Maloney (1990) conducted a study on evaluation of project performance in terms of time, cost and quality in determining whether project objectives are met. However, achieving success in completing a project should be something much more important than simply meeting cost, schedule and performance specifications. Parfitt and Sanvido (1993) in their research suggested that the goals and expectations relate to a variety of elements include of technical, financial, educational, social, and professional issues. Jaselskis and Ashley (1991), Sanvido et al (1992) and Chua and Loh (1997) have conducted research to identify critical success factors for project success using quantities measures of various factors. In their studies it reveals that the impact of experience possessed by project key personnel is important towards project success. In different research by Freeman and Beale (1992) and Riggs et al. (1992) suggested that project success criteria should be recognised from respective viewpoints of different project participants. Xiao and Proverbs (2003) suggested that to improve contractor performance, contractor is advised to focus on construction time, reduce delays, maintain a stable workforce and establish partnership with subcontractors. Another research conducted by Xiao and Ling (2006) have identified factors such as insufficient communication; over interference from partner; adhere to mutual goals; empower staff with authority; excessive demands from partners; and involve contractor in the project early shall influence the project performance in China

3 METHODOLOGY In this study, questionnaire survey was conducted throughout Peninsular Malaysia, Sabah and Sarawak where the questionnaires were sent through postal to PWD states and district comprises of 9 states and 78 districts and to 150 contractor organizations. The questionnaires were addressed to the Director and Assistant Director of the PWD states; the District Engineer of the PWD districts; and the director of the contractor organization. The list of factors that led to poor performance of project was identified from literature review that conducted through various management journals. These factors were developed and adopted as a framework in this study. In designing the questionnaires, the work of Belout and Gauvreau (2003); Ling and Min (2004); and Ling (2004) were drawn, in terms of developing appropriate survey instruments for measuring factors that influencing project success. This framework is adapted in this study to enable to measure the degree of opinion focusing on client, consultant and contractor. The first section require the respondents to indicate their background e.g. role in current job, years of experience and organization they are working with i.e. PWD or Contractor and the Contractor s grade. The remaining ten (10) sections of the questionnaires, respondent were asked to indicate their perception of the factors that comprises of project related factors, PWD related factors, design related factors by Education Work Branch (EWB), contractor related factors, material, labour, plant & equipment, external factors, contractual and project participant commitment factors. The level of focus associated with each item was measured using a 5-point Likert scale. (No Influence, Low Influence, Average Influence, High Influence, Very High Influence). The validity of the data was examined using Cronbach s alpha test. This measure of internal consistency is recommended for the analysis of an appreciation scale such as Likert (Kaplan and Saccuzzo, 1993). In this study, the alpha coefficients were 0.98, suggesting that the questionnaire is a reliable measure. The item reliability analysis also suggested none of the factors needs to be omitted. Pilot study of the questionnaires is conducted in the ten (10) different organizations consist of 2 numbers of PWD as client, 4 numbers of EWB as consultant and 4 numbers of contractors. Several comments made by the experts on the questionnaires during the pilot study have been taken into consideration. In the main survey a total of 354 nos. of questionnaires were distributed to PWD states (18 nos.), PWD Districts (156 nos.), EWB (30 nos.) and contractors (150 nos.) The contractors were selected randomly from the list of PWD who has completed the school projects. RESULTS AND DISCUSSION In total, one hundred forty one (141) respondents returned the completed questionnaires. This represents a reasonable response rate of 40%. Figure 1 shows the distribution of respondents based on type of organization where respondents from PWD, EWB and contractors organization are 44%, 19% and 37% respectively. The data collected was analysed using factor analysis of Statistical Package for Social Sciences (SPSS) version

4 organisation PWD EWB PWD district&state Contractor Contractor 36.88% PWD EWB 19.15% 43.97% PWD district&state Figure 1. Distribution of respondent by organisation Selection Of Factors Initially, there were 104 factors that influencing the completion of school project. Mean rank was performed to produce the top 30 most influential factors from the survey conducted. In order to validate the results, interview with 10 different contractors were conducted where the final 15 variables were then selected and shown in Table

5 Table 1: Mean Rank Descriptive Statistics Contractor's financial difficulties Contractor's bad cash flow during construction Shortage of labour Contractor's poor site management and supervision Contractor's deficiencies in planning and scheduling at pre-construction stage Late delivery of materials and equipment Shortage of skill labour Delays in subcontractor's work Inadequate contractor experience Low labour productivity Lack of control over site resources allocation by contractor Inadequate of contractor's managerial and supervisory personnel Main Contractor's lack of control of subcontractors works Frequent breakdowns of construction plant and equipment Unexpected bad weather condition Mean Factor Analysis Exploratory principal component factor analysis, with varimax rotation, was conducted to condense the information contained in the original 15 variables into a smaller set of factors with a minimum loss of information (Hair et al., 1998). Factor analysis were performed on the 15 selected variables using principal components analysis and from the correlation matrix table, determinant of R matrix is which is> This indicates that the data has no problem with multicollinearity. In summary, all questions in questionnaires are correlated fairly with all others. Therefore there is no need to consider eliminating any variables. Table 2. KMO and Bartlett s Test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy..830 Bartlett's Test of Sphericity Approx. Chi-Square df 105 Sig

6 Table 2 shows that Kaiser-Meyer-Olkin (KMO) values are which is more than 0.5. Kaiser (1970) stated that KMO static varies between 0 and 1. A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors. He also recommends accepting values between are considered in the great range. The diagonal elements values of the anti-image correlation matrix are all above 0.5. Therefore there is no need to exclude any variables from the analysis. The off diagonal elements for these data the value are considered small. Table 2 also shows that Bartlett s test significant value is 0.000(p<0.001). This indicates that for these data Bartlett s test is highly significant, and therefore factor analysis is appropriate. It also suggesting that R matrix is not an identity matrix; therefore it can be expected that there are some relationship between the variables. Table 3 shows that the analysis has produced 6 latent factors where Factor 1 accounted for 38.58%, which considerably more variance than the remaining five. Factor 2 accounted for 13.53%, Factor 3 accounted for 8.71%, Factor 4 accounted for 7.77%, Factor 5 accounted for 4.98% and Factor 6 accounted for 4.38% of variance. However after the extraction process, Factor 1 accounts for 16.83% of variance as compared to 16.09% (Factor 2), 13.73% (Factor 3), 13.57% (Factor 4), 10.35%(Factor 5) and 7.39% (Factor 6). Table 3 shows that only 4 latent factors follows the Kaiser s criterion (eigen value >1) while another 2 factors has eigen value <1. After several attempt in running factor analysis, the extraction was then fixed to 6 numbers of factors due to the fact that the 15 factors can be best explained by common themes. The reliability of factor analysis is also depends on sample size and MacCallum et al (1999) suggested that with communalities above 0.6 as in Table 4, relatively small samples (less than 100) maybe perfectly adequate. Therefore for this research, 141 numbers of samples was deemed adequate for factor analysis

7 Table 3. Total Variance Explained Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis. Rotated using orthogonal rotation (varimax) is performed because factors are expected to be independent. Table 5 shows the factors and its predictor variables after rotation. By having this results enable the researcher to identify common themes. For this study, the suppression of factor loading less than 0.4 and ordering variables by loading size makes interpretation considerably easier. This was based on Steven s (1992) suggestion that this cut-off point was appropriate for interpretative purpose (i.e. loading greater than 0.4 represent substantive values). Table 4. Communalities Communalities Contractor's deficiencies in planning and scheduling at pre-construction stage Contractor's bad cash flow during construction Contractor's financial difficulties Lack of control over site resources allocation by contractor Contractor's poor site management and supervision Inadequate of contractor's managerial and supervisory personnel Inadequate contractor experience Delays in subcontractor's work Late delivery of materials and equipment Frequent breakdowns of construction plant and equipment Unexpected bad weather condition Shortage of labour Main Contractor's lack of control of subcontractors works Low labour productivity Shortage of skill labour Initial Extraction Extraction Method: Principal Component Analysis

8 In Table 5 the questions that load highly on Factor 1 seem to all relate to contractor. There are contractor s deficiencies in planning and scheduling at pre-construction stage; contractor s poor site management and supervision; lack of control over site resources allocation by contractor; and inadequate of contractor s managerial and supervisory personnel. Therefore it is appropriate to categorise this factor as contractor s management problem. The questions that load highly on Factor 2 all seem to relate to labour. Factor 2 consists of shortage of skill labour, shortage of labour and low labour productivity. Therefore this factor can be categorised as labour problem. Factor 3 consists of contractor s financial difficulties and contractor s bad cash flow during construction; it is therefore categorised as contractor s financial problem. Factor 4 consists of main contractor s lack of control of subcontractor s works; inadequate contractor experience and delays in subcontractor s work. Therefore, Factor 4 is categorised as subcontractor s problem & contractor s experience. Factor 5 consists of frequent breakdowns of construction plant and equipment and late delivery of material and equipment. Therefore, Factor 5 is categorised as material and machineries problem. Finally, the question that loads highly on Factor 6 is unexpected bad weather condition; therefore it is categorised as weather condition. This analysis seems to reveal that in reality, is composed of six sub-scales: contractor s management problem; labour problem; subcontractor s problem and experience; contractor s financial problem; machineries and material problem; and weather condition. Table 6 shows the six constructs and their respective sub-components that influence the successful completion of a public school project. These six constructs can be illustrated in Figure 2, component plot in rotated space, plotted in three-dimensional axes to show the grouping of common themes. Table 5. Rotated Component Matrix Rotated Component Matrix a Component Contractor's deficiencies in planning and scheduling pre-construction stage Contractor's poor site management and supervision Lack of control over site resources allocation by con Inadequate of contractor's managerial and supervis personnel Shortage of skill labour Shortage of labour Low labour productivity Contractor's financial difficulties Contractor's bad cash flow during construction Main Contractor's lack of control of subcontractors w Delays in subcontractor's work Inadequate contractor experience Frequent breakdowns of construction plant and equ Late delivery of materials and equipment Unexpected bad weather condition Extraction Method : Principal Component Analysis Rotation Method : Varimax with Kaiser Normalisation a. Rotation converged in 11 iterations

9 Figure 2. Component Plot in Rotated Space Table 6. Summary of factor with common themes Factor Factors Number 1 contractor s deficiencies in planning and scheduling at pre-construction stage contractor s poor site management and supervision lack of control over site resources allocation by contractor inadequate of contractor s managerial and supervisory personnel 2 shortage of skill labour shortage of labour low labour productivity 3 contractor s financial difficulties contractor s bad cash flow during construction 4 main contractor s lack of control of subcontractor s works delays in subcontractor s work inadequate contractor experience 5 frequent breakdowns of construction plant and equipment late delivery of material and equipment Common Themes contractor s management problem labour problem contractor s financial problem subcontractor s problem & contractor s experience material and machineries problem 6 unexpected bad weather condition weather condition Analysis of the data indicated that within the six factors, six variables contributed a high factor loading (Table 5). These six variables with highest factor loading include: contractor s deficiencies in planning and scheduling at preconstruction stage 31 31

10 (factor 1), shortage of skill labour (factor 2), contractor s financial difficulties (factor 3), main contractor s lack of control of subcontractor s works (factor 4), frequent breakdowns of construction plant and equipment (factor 5) and unexpected bad weather condition (factor 6) with factor loading of 0.811, 0.821, 0.877, 0.805, and respectively. These six variables with highest factor loading suggesting the successful completion of a project may be influenced by these six measures. CONCLUSION The finding contributes to the understanding of the factors that influence the successful completion of a project shall depend on these 6 major factors. These major factors include: contractor s management problem; labour problem; contractor s financial problem; subcontractor s problem & contractor s experience; material and machineries problem; and weather condition. The usefulness of the findings is that in order to ensure project success, more attention should be paid to contractor s management in which client should carefully selected contractor with good management skill and also can provide an experienced site technical personnel that can manage the project efficiently. The practical application of this research finding for contractor is that in order to ensure that their project are successful, they should concentrate on the six measures identified in this study. REFERENCES Ashley, D B, Laurie,C S, and Jaselkis, E J,(1987) Determinants of construction project success. Project Management Journal, 18(2), Belout, A, and Gauvreau, C, (2003) Factors Influencing Project Success: The Impact Of Human Resource Management International Journal of Project Management, Vol. 22, Chua, D K H and Loh, P K (1997) Neural Network for Construction Project Success Expert Systems with Applications, 13(4), Field, A (2003). Discovering Statistics Using SPSS for Windows. Sage Publications, London. Freeman, M, and Beale, P (1992) Measuring Project Success Journal of Project Management, 23(1), Hair, J F, Anderson, R E, Tatham, R L and Black, W C (1998), Multivariate Data Analysis, Prentice-Hall, Englewood Cliffs, NJ. Jaselskis, E J, and Ashley, D B (1991) Optimal allocation of project management resources for achieving success Journal of Construction Engineering and Management, ASCE, 117(2), Kaiser, H F (1970) A second generation little jiffy. Psychometrika, 35,

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