An Application of UTAUT2 on Social Recommender Systems: Incorporating Social Information for Performance Expectancy

The recently proposed extended Unified Theory of Acceptance and Use of Technology (UTAUT2) offers new opportunities for exploring the acceptance of consumer technologies. This study utilizes UTAUT2 to explore the user acceptance of social recommender systems that have become more attractive owing to improved content personalization and adaptation to user preferences. Scholars have shown that these systems could improve a recommendation's accuracy. However, the UTAUT2's applicability and the explanation of performance expectancy for social recommender systems are still unclear. We developed a UTAUT2-based framework and tested it in a quantitative study with 266 participants. The structural equation model results show that UTAUT2 is applicable in the context of social recommender systems. Furthermore, the user's social network information, profile information, and reading behavior positively influence performance expectancy and the intention to adopt a social recommender system. Therefore, incorporating social information might overcome the shortcomings of other classic recommender systems.