Using Advanced Statistical Methods to Identify the Drivers of Knowledge Sharing Intention among Construction Workers
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 2
Abstract
Front-line construction workers possess the hands-on knowledge required to execute projects. However, insufficient research has been conducted on the drivers of workers’ behavior with respect to knowledge sharing. This study aims to develop an integrative understanding of the factors affecting knowledge sharing intention among construction workers. Starting with factors from the literature, a survey was prepared to record the responses of construction workers to questions about their view of the work environment and the knowledge sharing process, and 137 usable responses were collected from 16 construction building sites. Advanced statistical methods—exploratory factor analysis (EFA) and structural equation modeling (SEM)—were used to identify the factors, among which are social and professional contributions, and study the links between them. The results indicate strong links among organizational climate factors, professional contributions, social contributions, and attitude. These results were used to propose methods to improve the working environment, enhance the knowledge sharing process, and, therefore, improve productivity and overall project performance. This study contributes to the body of knowledge on construction labor and, generally, knowledge management in construction by offering a data-driven framework to understand the drivers of workers’ behavior with respect to knowledge sharing and propose techniques to enhance the knowledge sharing process. The results lay the foundation for sustaining knowledge on construction sites and develops the groundwork for future research on the link between construction workers’ behavior and project performance.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. These include the survey responses, EFA results, and SEM results.
Acknowledgments
The authors would like to thank the American University of Beirut’s University Research Board for funding this study (Project 24709, Award 103604). Thanks go to Daoud Kiomjian for his help with data collection.
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Journal of Construction Engineering and Management
Volume 148 • Issue 2 • February 2022
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© 2021 American Society of Civil Engineers.
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Received: May 9, 2021
Accepted: Oct 26, 2021
Published online: Dec 7, 2021
Published in print: Feb 1, 2022
Discussion open until: May 7, 2022
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