Willingness to accept metaverse safety training for construction workers based on extended UTAUT.

Safety training (ST) is essential in avoiding unsafe behavior of construction workers. With the rise of metaverse technology, metaverse safety training (MST) has gradually become a new model to guide construction workers in safety production. An in-depth study of construction workers' willingness to accept the metaverse safety training (WAMST) helps improve its effectiveness, but studies need to pay more attention to it. This study constructs a conceptual model of WAMST for construction workers, and the influencing factors of WAMST are explained based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT). It established a Structural equation modeling to verify the relationship between influencing factors. An example verifies the feasibility of the model. The results show that the framework significantly contributes to the willingness of construction workers to participate and improves safety awareness. Specifically, performance expectancy, effort expectancy, social influence, and convenient conditions significantly affect the construction workers' willingness to accept. Convenient conditions have a direct effect on actual behavior. Willingness to accept plays a mediating role between performance expectancy and actual behavior. Perceived trust moderates the effect between willingness to accept and actual behavior, and the force of positive interpretation increases proportionally. It confirms how to improve the safety capacity of construction workers and provides references for governments, enterprises, and projects to formulate ST strategies.

Identifying the implementation effect of technology transfer policy using system dynamics: a case study in Liaoning, China.

Given that technology transfer provides an important boost for promoting national economic development, technology transfer policy (TTP) has attracted more and more attention from academia and industry. The government issued many policies. However, the implementation effect of TTP still needs to be clarified. This study is carried out from the progressive level of "text content-influence path-implementation effect.'' It aims to adopt a systematic analysis method to analyze policy tools and policy implementation stages, then builds a conceptual framework of the influence path of TTP. Then the relationship between variables in the qualitative model was clarified, and the system dynamics (SD) model was used to build a quantitative model with four feedback loops. Finally, taking Liaoning, China as an example, the system simulation and sensitivity analysis of the main parameters are implemented in Vensim PLE. Different policy tools have different roles in the TTP impact stages of research, transfer, and industrialization. Based on the data of 2013-1019, the SD model constructed in this paper can be used to predict the implementation effect of TTP during 2020-2015. Simulation and sensitivity analysis results provide practical enlightenment for government departments to improve the implementation effect of the existing TTP. This study also provides other researchers with a systematic understanding for improving the implementation effect of TTP with a "text content-influence path-implementation effect" conduction chain and provides new insights for further research on TTP.