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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 |
| Editors | Kiyota Hashimoto, Naoki Fukuta, Tokuro Matsuo, Sachio Hirokawa, Masao Mori, Masao Mori |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 369-372 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538606216 |
| DOIs | |
| Publication status | Published - 2017 Nov 15 |
| Event | 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan Duration: 2017 Jul 9 → … |
Publication series
| Name | Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 |
|---|
Other
| Other | 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 |
|---|---|
| Country | Japan |
| City | Hamamatsu, Shizuoka |
| Period | 17-07-09 → … |
Fingerprint
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Information Systems and Management
Cite this
}
The Adaptation of a Post-Acceptance Model for Information System Continuance in Recommender Systems. / Liang, Wen Yau.
Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017. ed. / Kiyota Hashimoto; Naoki Fukuta; Tokuro Matsuo; Sachio Hirokawa; Masao Mori; Masao Mori. Institute of Electrical and Electronics Engineers Inc., 2017. p. 369-372 8113272 (Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - The Adaptation of a Post-Acceptance Model for Information System Continuance in Recommender Systems
AU - Liang, Wen Yau
PY - 2017/11/15
Y1 - 2017/11/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85040620967&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040620967&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI.2017.147
DO - 10.1109/IIAI-AAI.2017.147
M3 - Conference contribution
AN - SCOPUS:85040620967
T3 - Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
SP - 369
EP - 372
BT - Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
A2 - Hashimoto, Kiyota
A2 - Fukuta, Naoki
A2 - Matsuo, Tokuro
A2 - Hirokawa, Sachio
A2 - Mori, Masao
A2 - Mori, Masao
PB - Institute of Electrical and Electronics Engineers Inc.
ER -