A CONTEXTUAL FRAMEWORK FOR UNDERSTANDING BEHAVIORAL INTENTION TO ADOPT ARTIFICIAL INTELLIGENCE IN CONSTRUCTION PROJECTS
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Abstract
This study examines the factors that influence the behavioral intention to adopt Artificial Intelligence (AI) in the Thai construction industry using an extended adoption framework. Given the industry’s high-risk and complex environment, the framework was broadened to incorporate partner support, competence, human resource (HR) readiness, top management support, performance expectation, compatibility, and safety awareness. A quantitative, cross-sectional survey was conducted with 418 respondents, including managers, engineers, and staff from construction firms. Statistical analysis confirmed that partner support, competency, HR readiness, top management support, performance expectation, and compatibility positively influenced AI adoption intention, although compatibility unexpectedly showed a negative effect. Among all factors, occupational health and safety awareness emerged as the most influential predictor, emphasizing the central role of safety in shaping adoption behavior. Conversely, barriers such as unclear communication about project compatibility, misaligned team collaboration, and inconsistent employee attitudes hindered adoption. The proposed model explained approximately 50% of the variance in behavioral intention, demonstrating strong explanatory power. Theoretically, this research enriches existing knowledge by extending adoption frameworks with safety- and organization-oriented constructs, offering a more holistic perspective on AI adoption in construction. Practically, the findings underscore the importance of leadership commitment, employee readiness, and transparent communication. Construction managers are encouraged to strengthen workforce competency, promote a strong safety culture, and ensure effective communication to facilitate the integration of AI. Overall, this study provides valuable implications for both scholars and practitioners seeking to advance AI adoption in high-risk industries.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้ ไม่ใช่ความคิดเห็นและความรับผิดชอบของผู้จัดทำ บรรณาธิการ กองบรรณาธิการ และคณะวิทยาการจัดการ มหาวิทยาลัยราชภัฏอุดรธานี ความรับผิดชอบด้านเนื้อหาและการตรวจร่างบทความแต่ละเรื่องเป็นความคิดเห็นของผู้เขียนบทความแต่ละท่าน
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