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Presentation
The Determinants of Acceptance of Recommender Systems: Applying the UTAUT Model
AMCIS 2012 Proceedings
  • Yen-Yao Wang, Accounting and Information Systems , Michigan State University , East Lansing, MI, United States.
  • Anthony Townsend, Management Information Systems, Iowa State University, Ames, IA, United States.
  • Andy Luse, Management Information Systems, Iowa State University, Ames, IA, United States.
  • Brian Mennecke, Management Information Systems, Iowa State University, Ames, IA, United States.
Abstract
This study investigates how consumers assess the quality of two types of recommender systems, collaborative filtering and content-based, in the content of e-commerce by using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model. Specifically, the under-investigated concept of trust in technological artifacts is adapted to a modified UTAUT model. Additionally, this study considers hedonic and utilitarian product characteristics, attempting to present a comprehensive range of recommender system acceptance. A total of 51 participants completed an online 2 (recommender systems) x 2 (products) survey. The results suggested that type of recommender systems and products did have different impacts on the behavioral intention to use recommender systems. This study may be of importance in explaining factors contributing to use recommender systems, as well as in providing designers of recommender systems with a better understanding of how to provide a more effective recommender system.
Citation Information
Yen-Yao Wang, Anthony Townsend, Andy Luse and Brian Mennecke. "The Determinants of Acceptance of Recommender Systems: Applying the UTAUT Model"
Available at: http://works.bepress.com/brian_mennecke/21/