Van Tam, N. (2026) Evaluating risk distribution: A stakeholder-driven causal analysis of genai adoption risks in construction project management. Journal of Construction Engineering and Management, 152(7): 04026098, ISSN 0733-9364
Abstract
This study investigates the primary risks associated with the adoption of Generative Artificial Intelligence (GenAI) in construction project management (CPM) through a comparative analysis of stakeholder perceptions. Employing a rigorous mixed-method design, this research integrates quantitative survey data from industry professionals, expert-based causal modeling using DEMATEL, and subsequent validation interviews to reveal the interdependencies among critical risk factors. The results of the one-way ANOVA and Tukey's post hoc tests demonstrated statistically significant differences in perceived risk severity among four major stakeholder groups. In addition, the DEMATEL model provided profound insights by categorizing the identified risks into "cause"and "effect"groups, revealing three significant characteristics of the GenAI adoption risk landscape in CPM. The key findings include (1) the high-impact yet symptomatic nature of incorrect decision making; (2) the identification of human-centric risks as foundational causal drivers; and (3) the revelation of GenAI's contextual limitations as an underestimated foundational risk. To translate these findings into practice, a stakeholder-oriented heatmap matrix was developed to assess the effectiveness of risk mitigation strategies across different stakeholder groups. The novelty of this research lies in its empirical, stakeholder-based causal modeling of GenAI risks in CPM, representing one of the earliest attempts to integrate perception analysis with expert-driven DEMATEL modeling to uncover causal risk structures. Practically, it provides a stakeholder-specific mitigation framework that offers realistic and transferable guidance for safer and more responsible GenAI adoption, particularly in developing and transitional economies in which digital transformation is accelerating but institutional capacities remain uneven.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | causal model; stakeholder; generative artificial intelligence; mitigation strategy; project management; risk management |
| Index terms: | transformation, risk management, construction project management, effectiveness, risk factor, modelling, project management, artificial intelligence, interview, strategy, survey, comparative analysis, decision-making, validation, mitigation, risk mitigation |
| Subjects: | environmental hazards, financial risk, analytical methods, decision analysis, performance management, management, project management theory and practice, professional development, data analysis and analytics, artificial intelligence, business, risk assessment, data collection methods |
| Topics: | Quality Management, Risk Management, Sustainability, Engineering Principles, Project Management, Digital Applications, Business Strategy, Cost Management, Information Management, Research Practice |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here