Investigating Adoption of High Prefabrication Level Technologies for Enterprises: an Integrated Model

ABSTRACTABSTRACTPrefabricated construction (PC) contributes to the sustainability of the construction sector, with higher levels of prefabrication providing better performance in theory. However, enterprises have great expectations for the performance of high prefabrication level technologies (HPLTs) but poor adoption behaviours in practice. To address this issue, this study explored the mechanisms of HPLTs adoption from the enterprise perspective, by complementing an integrated model that combines expectations and the fit between tasks and technologies, based on the unified theory of acceptance and use of technology and the task-technology fit theory. The significance of paths affecting the adoption behaviour toward HPLTs was identified by the partial least squares structural equation modelling. The results show that adoption intention and task-technology fit are excellent predictors of adoption behaviour toward HPLTs. Social influence, facilitating conditions, task-technology fit, effort expectancy and task characteristics positively affect adoption intention, while performance expectancy is not found to influence adoption intention. The mediating effect analysis indicates that social influence currently has the largest indirect effect on adoption behaviour, followed by facilitating conditions and task-technology fit. The findings contribute to building a bridge between the expectations and adoption behaviours of HPLTs, and provide guidance for the effective promotion of HPLTs.KEYWORDS: Prefabricated constructionhigh prefabrication level technologiesconstruction technology adoptionthe unified theory of acceptance and use of technologytask-technology fit theory AcknowledgementsThe authors wish to thank all contributors to this work. This includes survey and interview participants, and those involved in the distribution of the survey.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grant 72071027 and National Natural Science Foundation of China under Grant 72101044.