Cheng, M.; Chong, H. Y.; Xu, Y. and Wu, H. (2026) Exploring the determinants of generative artificial intelligence use intention: A mixed methods study. Journal of Construction Engineering and Management, 152(7): 04026091, ISSN 0733-9364
Abstract
Generative artificial intelligence (GAI) is driving digital transformation in the architecture, engineering, and construction (AEC) sector and has become an influential tool for enhancing design processes, project management, and operational efficiency. However, the determinants of GAI use intention remain unclear. Therefore, based on stimulus-organism-response (SOR) theory, this study adds empirical insights into the significant influence of reasons (for and against) and intentions to use GAI in the AEC sector. Semistructured interviews were conducted with 17 construction professionals to identify the most relevant factors for and against the use of GAI. Survey data from AEC stakeholders were analyzed using a mixed-methods approach that incorporates partial least squares-structural equation modeling and fuzzy set qualitative comparative analysis. The findings suggest that behavioral enablers/inhibitors underlie the rationale for forming the intention to use GAI, and dual pathways explain the reasons for and against its use. Specifically, behavioral enablers include perceived interactivity, perceived usefulness, and perceived ease of use; behavioral inhibitors include AI anxiety, perceived distrust, and perceived risk. Moreover, the institutional environment moderates the impact of attitude on intention, thereby enhancing the intention to use GAI. Three configurations of rationalism, success, and utilitarianism patterns that trigger GAI use intention are identified. This study not only contributes to knowledge extension by clarifying the determinants of GAI use intention in the AEC sector but also provides valuable insights for policy formulation and a roadmap for implementing GAI in the future.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | architecture, engineering, and construction sector; generative artificial intelligence; use intention |
| Index terms: | fuzzy set, transformation, mixed method, efficiency, project management, artificial intelligence, determinant, construction professional, survey, structural equation modelling, perceived usefulness, qualitative comparative analysis, design process, interview, construction sector, partial least square, configuration, perceived ease of use, anxiety |
| Subjects: | research methods, decision-making and optimization, behavioral psychology, data collection methods, risk assessment, business, mental health and wellbeing, design methods, artificial intelligence, performance management, professional development, project management theory and practice, industry analysis, statistical analysis, systems engineering |
| Topics: | Risk Management, Engineering Principles, Project Management, Health and Safety, Quality Management, Information Management, Research Practice, Business Strategy, Digital Applications, Design Practice |
| Descriptive scope: | 5 PCTEA |
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