Examining Influence of Construction Projects’ Quality Factors on Client Satisfaction Using Partial Least Squares Structural Equation Modeling
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 5
Abstract
Quality and client satisfaction is a long-established focus of attention in the construction industry due to its significant role in the success or failure of a project, yet few studies have examined the impact of a lack of quality on client satisfaction in the context of public construction projects. This study, therefore, proposes that lack of quality in construction projects has a significant impact on client satisfaction using a partial least squares structural equation modeling (PLS-SEM) technique. A conceptual model was developed for assessment of client satisfaction, and the model consisted of two main latent variables. Through the questionnaire survey, an empirical analysis was carried out to test the conceptual model. Data were collected from 484 construction experts employed in the Pakistani public construction industry. The result of the analysis found that the value of the model was 0.454, which revealed that lack of quality in projects has a significant impact on client satisfaction. Based on the final form of the model, the construction-related factor () has a greater influence on client satisfaction. The goodness of fit index of the conceptual model was measured to be 0.54, which revealed that the conceptual model has appropriate reliability and validity and fits the data correctly. This study adds to the body of knowledge by offering new insights and contributes toward greater understanding of lack of quality in public projects and its impact on client satisfaction, and use of applied advance statistical method, PLS-SEM, which is previously missing in the literature. The lessons from this study would be helpful for policymakers and decision makers to focus on highlighted issues and problems faced by the public construction industry worldwide.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research work was supported by the National Natural Science Foundation of China under Grant No. 71372085.
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Journal of Construction Engineering and Management
Volume 145 • Issue 5 • May 2019
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©2019 American Society of Civil Engineers.
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Received: Jan 22, 2018
Accepted: Nov 7, 2018
Published online: Mar 12, 2019
Published in print: May 1, 2019
Discussion open until: Aug 12, 2019
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