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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
EditorsKiyota Hashimoto, Naoki Fukuta, Tokuro Matsuo, Sachio Hirokawa, Masao Mori, Masao Mori
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages369-372
Number of pages4
ISBN (Electronic)9781538606216
DOIs
Publication statusPublished - 2017 Nov 15
Event6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 - Hamamatsu, Shizuoka, Japan
Duration: 2017 Jul 9 → …

Publication series

NameProceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017

Other

Other6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017
CountryJapan
CityHamamatsu, Shizuoka
Period17-07-09 → …

Fingerprint

Recommender systems
Information systems
Social aspects
Design of experiments
Acceptance
Industry

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

Cite this

Liang, W. Y. (2017). The Adaptation of a Post-Acceptance Model for Information System Continuance in Recommender Systems. In K. Hashimoto, N. Fukuta, T. Matsuo, S. Hirokawa, M. Mori, & M. Mori (Eds.), Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 (pp. 369-372). [8113272] (Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2017.147
Liang, Wen Yau. / The Adaptation of a Post-Acceptance Model for Information System Continuance in Recommender Systems. Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017. editor / Kiyota Hashimoto ; Naoki Fukuta ; Tokuro Matsuo ; Sachio Hirokawa ; Masao Mori ; Masao Mori. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 369-372 (Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017).
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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.",
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Liang, WY 2017, The Adaptation of a Post-Acceptance Model for Information System Continuance in Recommender Systems. in K Hashimoto, N Fukuta, T Matsuo, S Hirokawa, M Mori & M Mori (eds), Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017., 8113272, Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 369-372, 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, Hamamatsu, Shizuoka, Japan, 17-07-09. https://doi.org/10.1109/IIAI-AAI.2017.147

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 proceedingConference contribution

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Liang WY. The Adaptation of a Post-Acceptance Model for Information System Continuance in Recommender Systems. In Hashimoto K, Fukuta N, Matsuo T, Hirokawa S, Mori M, Mori M, editors, Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017. 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). https://doi.org/10.1109/IIAI-AAI.2017.147