Structural equation model for assessing impacts of contractor's performance on project success

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Abstract

Contactor's expertise and performance play a significant role in successful delivery of a project. While clients strive hard to make the best decisions in selecting the right contractor for the right job, a clear understanding of the underlying attributes associated with contractors' selection in the context of achieving successful project outcomes is critical. In an attempt to understand these pre-emptive qualification criteria and their links to contractors' performance on a project, a hierarchical structural model is established. By employing the structural equation modelling technique, the model adapts a total of 29 technical attributes across five confirmatory factors namely, soundness of business and workforce (SBW), planning and control (PC), quality performance (QP), past performance (PP) and overall project success (OPS). Based on the survey data collected across medium size construction projects in Australia, the results of the model confirmed that technical planning and controlling expertise of contractor is key in achieving success on projects. With a clear understanding of the significance of these factors in the context of contractors' performance, these findings could potentially contribute to the development of a company's procedures or to enhance existing knowledge of contractor prequalification practices.

Introduction

Contractors' prequalification is one of the widely published topics in construction industry research. However, the relationship between prequalification criteria and contractors' ability to meeting clients' requirements and achieve project success remains largely unanswered and is an important topic for investigation (Holt, 1998, Mbachu, 2008). Every construction project is unique and comprises of unique complexities and risks across many issues throughout the construction process. Increasing complexity in design and involvement of multitude of stakeholders in modern construction projects, add further challenges for both clients and contractors in matching the required skills and capabilities to deliver the project successfully. Incorrect selection of a contractor for a given project may potentially lead to a bad legacy of failure coupled with its consequences such as poor business practices or bankruptcy (Uher & Runeson, 1984, Holt et al., 1995). Contractors' ability to succeed on a project depends on diverse inherent attributes ranging from project complexity, technical expertise to organisational capability and risk management practices. Thus, a robust prequalification process for selecting the right contractor is an important first step for ensuring success in candidate projects (Arslan et al., 2008). The intent of this research is to provide a clear understanding of such factors incorporating client's, design team's and contractors' viewpoints. Identification of these underlying factors from the successful project delivery perspective and their relationships to the project functions should help both clients and contractors in achieving successful project outcomes.

This study aims to examine the effectiveness of prequalification criteria in contractor selection from a successful project delivery perspective. Based on previous studies (Doloi, 2009, Kumaraswamy & Matthews, 2000, Hatush & Skitmore, 1997), four critical factors associated with the contractors' selection have been identified for confirmatory analysis. A Structural Equation Model (SEM) has been developed highlighting the relational links between the factors associated with the contractors' prequalification criteria and the overall project success. By analysing the standardised coefficients among the factors in the SEM model, the research attempts to create a better understanding of the effects of prequalification criteria in contractor selection in both project development and delivery contexts. The outcome of the model is particularly important for all the stakeholders (namely main contractors, client and consultants) to prioritise the factors and underlying attributes in terms of their criticality for developing contractual arrangements and assuming responsibilities in order to obtain the desired project outcomes.

Section snippets

Contractors' prequalification in construction

Over the past few decades, numerous studies have reported various approaches for dealing with contractors' prequalification problem across different project types (Holt, 1998). This section summarises the review of pertinent research with an emphasis on types of problems and the adopted approaches related to the current study. In the US, Russell et al. (1992) investigated the industry evaluation of the perceived impacts of 20 decision factors and 67 underlying attributes for contractors'

Theoretical framework

The aforementioned studies provide the theoretical basis to construct the model for this study. It is assumed that contractors' soundness of business and workforce (SBW), planning and control (PC) ability, quality performance (QP) record and past performance (PP) collectively determine the overall project success (OPS). Soundness of business and workforce (SBW) is defined as a factor involving seven key attributes namely yearly turnover, working capital, technical expertise, defect liability

Research methodology

The survey method was adopted to test the hypotheses proposed in this study. A questionnaire survey was designed for respondents to assess the performance of projects they had participated in and to evaluate the influence of soundness of business and workforce, planning and control, quality performance and past performance on those projects. The questions were phrased to ask the respondents an affirmative response on the relevant indicators impacting the overall success on a project. A sample

Model specification and refinements

The initial structural model hypothesised in Fig. 1 was analysed using AMOS 16.0 (Byrne, 2010). As discussed by Molenaar and Washington (2000), the initial SEM that was based on theoretical expectations and past empirical findings, was found to be premature without meeting the standard indices of model fit (such as t-statistics and R-Squares for model equations). A feasible model should be selected based on the recommended Goodness-Of-Fit (GOF) measures and the model that satisfies both

Reliability of constructs

In order to evaluate the appropriateness of the measurement model used for final SEM, the strength of the measurement model was established by performing the Cronbach's reliability test (Jin et al., 2007). For Cronbach's alpha, a cut-off value of 0.7 is used to indicate the acceptable level of initial consistency. As seen from Table 5, the attributes measuring all four latent variables in the final SEM resulted in a high degree of reliability above the cut-off value.

Results of SEM analysis and discussion

Fig. 2 depicts the final model after deleting the non-significant paths. As seen, all of the path coefficients are positive (except one) and significant at p < 0.05 and thus the significance of the measured attributes to the model is augmented.

The final SEM results suggested that planning and control has the highest correlation (with a standardised coefficient = 0.87) with the overall project success. The influence of the factor ‘soundness of business and workforce’ was found to be the second

Conclusions

Previous studies identified past performance and financial stability of contractors as highly critical factors for achieving success on projects. However, in contrast, the findings of this research suggest that these factors do not directly affect project success. While lack of contractors' soundness of business and workforce may have been a major reason behind project inefficiencies, these inefficiencies found to have greater influence on contractors' planning and controlling ability in

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