A systematic review of technology acceptance models and theories in construction research
Autor(en): |
Chukwuma Nnaji
Ifeanyi Okpala Ibukun Awolusi John Gambatese |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Journal of Information Technology in Construction, Februar 2023, v. 28 |
Seite(n): | 39-69 |
DOI: | 10.36680/j.itcon.2023.003 |
Abstrakt: |
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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Datenseite - Reference-ID
10715725 - Veröffentlicht am:
21.03.2023 - Geändert am:
21.03.2023