OBJECTIVE: The aim of this study is to test the unified theory of acceptance and use of technology (UTAUT) model for explaining the intention to use exoskeletons among industrial workers. BACKGROUND: Exoskeletons could help reduce physical workload and risk for injuries among industrial workers. Therefore, it is crucial to understand which factors play a role in workers' intention to use such exoskeletons. METHOD: Industrial workers (N = 124) completed a survey on their attitudes regarding the use of exoskeletons at their workplace. Using partial least squares (PLS) path modeling, the UTAUT model and a revised version of the UTAUT model were fitted to these data. RESULTS: The adapted UTAUT model of Dwivedi et al. (2017) was able to explain up to 75.6% of the variance in intention to use exoskeletons, suggesting a reasonable model fit. CONCLUSION: The model fit suggests that effort expectancy (how easy it seems to use an exoskeleton) plays an important role in predicting the intention to use exoskeletons. Social influence (whether others think workers should use exoskeletons) and performance expectancy (how useful exoskeletons seem to be for work) play a smaller role in predicting the intention to use. APPLICATIONS: This research informs companies about the optimal implementation of exoskeletons by improving the determinants of acceptance among their workers.
OBJECTIVE: The aim of this study is to test the unified theory of acceptance and use of technology (UTAUT) model for explaining the intention to use exoskeletons among industrial workers. BACKGROUND: Exoskeletons could help reduce physical workload and risk for injuries among industrial workers. Therefore, it is crucial to understand which factors play a role in workers' intention to use such exoskeletons. METHOD: Industrial workers (N = 124) completed a survey on their attitudes regarding the use of exoskeletons at their workplace. Using partial least squares (PLS) path modeling, the UTAUT model and a revised version of the UTAUT model were fitted to these data. RESULTS: The adapted UTAUT model of Dwivedi et al. (2017) was able to explain up to 75.6% of the variance in intention to use exoskeletons, suggesting a reasonable model fit. CONCLUSION: The model fit suggests that effort expectancy (how easy it seems to use an exoskeleton) plays an important role in predicting the intention to use exoskeletons. Social influence (whether others think workers should use exoskeletons) and performance expectancy (how useful exoskeletons seem to be for work) play a smaller role in predicting the intention to use. APPLICATIONS: This research informs companies about the optimal implementation of exoskeletons by improving the determinants of acceptance among their workers.
Keywords:
experience; statistics and data analysis; structural equation modeling; technology acceptance; wearable devices