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Why are Some Recommendation Systems Preferred?

Author

Listed:
  • Gaofeng Yi

    (Supratman University of Surabaya (Indonesia))

Abstract

There has been wide interest in exploring ways to provide more efficient personalized recommendation systems (RSs) in order to attract customers and increase product sales. The majority of the existing researches are concerned with improving the accuracy and effectiveness of the recommendation algorithms, or focusing on how to limit perceived risks, with the aim of increasing consumer satisfaction. Unlike these mentioned studies, this research begins from the perspective of customer-RS interaction, and ends in revealing the mechanisms involved in consumers’ acceptance of recommendations by using the technology acceptance model. The empirical study results show that perceived interpersonal interaction is an important factor that directly affects university students’ intentions to use RS, while perceived ease- of- use influences them in an indirect way through mediation of perceived usefulness. On this basis, the study thus provides suggestions on how to supply an improved interaction with easy and useful personalized RS.

Suggested Citation

  • Gaofeng Yi, 2020. "Why are Some Recommendation Systems Preferred?," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 14(2), pages 76-86.
    • Handle: RePEc:hig:fsight:v:14:y:2020:i:2:p:76-86
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    References listed on IDEAS

    as
    1. Yi He & Qimei Chen & Sakawrat Kitkuakul, 2018. "Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy," Cogent Business & Management, Taylor & Francis Journals, vol. 5(1), pages 1459006-145, January.
    2. Carlson, Jamie & O’Cass, Aron & Ahrholdt, Dennis, 2015. "Assessing customers’ perceived value of the online channel of multichannel retailers: A two country examination," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 90-102.
    3. Virginia Fernández-Pérez & Ana Montes-Merino & Lázaro Rodríguez-Ariza & Patricia Esther Alonso Galicia, 2019. "Emotional competencies and cognitive antecedents in shaping student’s entrepreneurial intention: the moderating role of entrepreneurship education," International Entrepreneurship and Management Journal, Springer, vol. 15(1), pages 281-305, March.
    4. Kolar, Tomaz & Zabkar, Vesna, 2010. "A consumer-based model of authenticity: An oxymoron or the foundation of cultural heritage marketing?," Tourism Management, Elsevier, vol. 31(5), pages 652-664.
    5. Rex E. Pereira, 2001. "Influence of Query-Based Decision Aids on Consumer Decision Making in Electronic Commerce," Information Resources Management Journal (IRMJ), IGI Global, vol. 14(1), pages 31-48, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    online personalized recommendation system (RS); technology innovation; customer choice; customer-RS interaction; adoption intention;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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