The Adaptation of a Post-Acceptance Model for Information System Continuance in Recommender Systems

Abstract

Recommender systems (RS) are extensively deployed to provide online users with advisory services, and the design of RS functional features has received substantial attention in academic studies. The social aspects of human-RS interactions, however, have been less explored. Furthermore, measuring user experience, though natural in a business environment, is often challenging for RS research. Therefore, this study provides the first empirical test of the adaptation of a post-acceptance model for information system continuance in the context of recommender systems. An experimental design is used and a questionnaire is developed to analysis. The results demonstrate that the proposed model is supported and the visual recommender system can indeed significantly enhance user satisfaction and continuance intention.