Abstract
Context-Aware Recommender Systems (CARSs) are becoming commonplace. Yet, there is a paucity of studies that investigates how such systems could affect usage behavior from a user-system interaction perspective. Building on the Social Interdependence Theory (SIT), we construct a research model that posits cooperative learning as a trait of users’ interactions with CARSs and outline a proposed empirical study for validating the hypothesized relationships in this model. Specifically, we draw on interdependencies in human-machine relationships to postulate positive interdependence as an antecedent of users’ promotive interaction with CARSs, which in turn, dictates the performance of such recommender systems. Furthermore, we introduce scrutability features as design interventions that can be harnessed by developers to mitigate the impact of users’ promotive interaction on the performance of CARSs.
Original language | English |
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Title of host publication | ICIS 2017 Proceedings |
Place of Publication | Atlanta, GA |
Publisher | Association for Information Systems. AIS Electronic Library (AISeL) |
Publication date | 2017 |
Pages | 11 |
Publication status | Published - 2017 |
Event | 38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017: Transforming Society with Digital Innovation - Coex Convention Center , Seoul, Korea, Republic of Duration: 10 Dec 2017 → 13 Dec 2017 Conference number: 38 https://icis2017.aisnet.org/ |
Conference
Conference | 38th International Conference on Information Systems: Transforming Society with Digital Innovation, ICIS 2017 |
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Number | 38 |
Location | Coex Convention Center |
Country/Territory | Korea, Republic of |
City | Seoul |
Period | 10/12/2017 → 13/12/2017 |
Internet address |
Series | Proceedings of the International Conference on Information Systems |
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ISSN | 0000-0033 |
Keywords
- Context-aware
- Recommender systems
- Cooperative learning
- User-system interaction
- Human-machine relationships