Predictive Modeling of Workplace Stress among Construction Professionals
Publication: Journal of Construction Engineering and Management
Volume 140, Issue 3
Abstract
As a high-stress working environment, the construction industry affects the health and well-being of people who work in it. Knowing what factors contribute to or ameliorate workplace stress, and their interrelationships, are important. Data from 350 cases that reflect self-perceived stress experiences were collected via an online questionnaire survey of architects, civil engineers, quantity surveyors, and project and construction managers in South Africa. These data were used to conduct logistic regression modeling to explore the relationship between perceived stress at work, workplace demands, job control, and workplace support. The strength of the relationship of 13 factors with perceived stress was modeled. Although all the cataloged factors contribute to a predictive likelihood of high workplace stress, the strongest predictive factor is the perceived presence of work-life imbalance. The perceived need to work long hours is overwhelmingly mediated through imbalance experienced between work and life/family commitments. A perceived need to prove oneself at work also strongly mediates this relationship, whereas the extent of support received from colleagues in difficult situations may serve as a strong moderator. An amended model of occupational stress is proposed that distinguishes between mediating and moderating factors, providing possible target areas for organizations wishing to address employee stress issues. The contribution of this work lies in its examination of the work stress experienced by construction professionals in a developing country that is characterized by economic hardship and social problems.
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Acknowledgments
This work is based on the research supported in part by the National Research Foundation of South Africa [Grant specific unique reference number (UID) 85376]. The Grantholder acknowledges that opinions, results, and conclusions or recommendations expressed in any publication generated by the NRF supported research are those of the author(s) and that the NRF accepts no liability whatsoever in this regard.
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Published In
Journal of Construction Engineering and Management
Volume 140 • Issue 3 • March 2014
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jul 27, 2013
Accepted: Oct 10, 2013
Published online: Dec 2, 2013
Published in print: Mar 1, 2014
Discussion open until: May 2, 2014
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