A systematic review of technology acceptance models and theories in construction research

解释力 技术接受与使用的统一理论 技术接受模型 知识管理 期望理论 过程(计算) 预测能力 管理科学 计算机科学 风险分析(工程) 可用性 工程类 心理学 业务 哲学 操作系统 认识论 人机交互 社会心理学
作者
Chukwuma Nnaji,Ifeanyi Okpala,Ibukun Awolusi,John Gambatese
出处
期刊:Journal of Information Technology in Construction [International Council for Research and Innovation in Building and Construction]
卷期号:28: 39-69 被引量:2
标识
DOI:10.36680/j.itcon.2023.003
摘要

Technology use in the construction industry fosters improvements in schedule, safety, cost, productivity, and quality. In this domain, the construction technologies adoption highly depends on stakeholders, who may exhibit some resistance to operational use. This underscores the importance of determining technology integration success using effective methods such as predictive and explanatory modelling. Although existing literature has provided some critical insight into the use of these models and theories, there is no domain-based synthesis on the utility of these models and theories as tools to facilitate the integration of emerging construction technologies. Therefore, this paper provides a systematic review and content analysis showcasing different methods and theories for investigating technology acceptance and generates insights expected to guide future technology acceptance studies. Using a three-phase systematic review process, 35 relevant articles were identified and analysed. This review identified perceived ease of use, perceived usefulness, social norm, attitude, perceived behavioural control, and facilitating conditions as key constructs impacting workers’ intention to accept a construction technology. TAM, TPB, and UTAUT were identified as popular choices for developing hybrid models, while UTAUT provided a relatively higher predictive power. Finally, seven areas for further exploration were discussed. This study contributes to construction knowledge by providing a better understanding of technology acceptance research and generating fundamental insights needed to develop robust and effective predictive and explanatory models for advancing technology acceptance research which would support successful technology integration.
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