随着建设行业的数字化转型,融合传统工程建设与人工智能技术已成为迫切需求。本研究以技术接受模型和技术-组织-环境框架为理论基础,运用结构方程模型分析了建设行业中人工智能技术采纳行为的影响因素。结果表明,在技术层面,人工智能技术的特性显著影响用户的感知易用性和有用性,进而直接影响采纳行为;而技术复杂性虽对感知有用性影响不显著,但明显增加了感知易用性的难度。在环境方面,外部环境对感知有用性有显著正向影响,但对感知易用性影响较弱,同时也直接影响采纳行为。组织层面上,针对人工智能技术的充分准备能显著提升员工的感知易用性、感知有用性并促进采纳行为,而领导层的支持则主要增强员工的感知易用性,进而推动其采纳行为。这表明,工程建设企业在推动人工智能技术应用时,应综合考虑技术、组织和环境三方面的关键因素,以制定更有效的战略。

The advancement and implementation of artificial intelligence (AI) technologies have engendered numerous novel opportunities and challenges for the construction sector. In an era where digital transformation is incrementally taking root in the construction industry, the imperative integration of AI with traditional construction enterprises is a fundamental demand for current development. However, the majority of existing research within the realm of construction is chiefly concentrated on the application of AI technologies, leaving a discernible gap in the exploration of AI adoption. Thus, investigating how to efficaciously propel the construction industry to adopt AI more proficiently bears significant practical relevance. The present study, grounded in information technology adoption theories, amalgamates the Technology Acceptance Model—attuned to the individual level of IT adoption—with the Technology-Organization-Environment Framework, oriented toward organizational-level IT adoption. By synthesizing variables at both individual and organizational levels, a conceptual model was erected for this research, alongside the development and design of a corresponding metric scale. Subsequently, leveraging surveys and structural equation modeling executed through SPSS and AMOS software, this study empirically dissects the determinants influencing the adoption behaviors of AI within the construction industry, delving into the intrinsic mechanisms that shape employees' willingness to embrace AI technologies. The findings uncover significant insights: Concerning the internal components of the Technology Acceptance Model, the intent to adopt AI markedly sways adoption behaviors, with perceived ease of use and perceived usefulness exerting substantial impacts on the actual adoption of AI. Perceived ease of use also positively influences perceived usefulness, consistent with expectations of the Technology Acceptance Model. Pertaining to technical attributes, AI's intrinsic capabilities such as autonomous decision-making, real-time monitoring, collaborative processing, and optimization analysis all profoundly sway perceived ease and usefulness, directly affecting adoption behaviors. However, the complexity of the technology itself does not significantly affect perceived usefulness but has an evident negative impact on perceived ease of use. Within the organizational domain, resource allocations for AI technology substantially influence employees' perceptions of ease and usefulness, as well as their adoption behaviors. Furthermore, executive leadership's decision-making support in projects primarily shapes employees' perceived ease of use and directly affects adoption behaviors. In the environmental domain, while government incentives and industry trends tend to have a weaker influence on perceived ease of use, they significantly affect perceived usefulness and directly catalyze the adoption of AI technologies. By emphasizing the technological, organizational, and environmental perspectives, this study elucidates critical factors in AI technology adoption within the construction industry and informs the formulation of strategies for AI adoption. It accelerates digital transformation within the construction sector and enhances firms' capabilities to navigate new technologies, offering a framework that could bolster ongoing industry development. This research also furnishes a valuable model for scholars exploring the current state of AI adoption in the construction industry and provides robust support for the sector's sustained progression.