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Analyzing continuance intention of recommendation algorithms

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  • Kim, Jiwhan
  • Nam, Changi

Abstract

As recommendation algorithms have been increasingly applied to content personalization services, scholars are voicing concern about the negative impacts of these algorithms, for instance filter bubbles and ideological polarization. This research attempts to analyze the various factors influencing users' continuance intention of recommendation algorithms through structural equation modeling. Based on the Expectation-Confirmation Model, this study proposes an extended framework to empirically examine the impact of confirmation, perceived usefulness, perceived enjoyment, perceived ease of use, perceived risk, and subjective norm on satisfaction and continuance intention. Results indicate that confirmation positively impacts satisfaction, perceived usefulness, and perceived enjoyment. Furthermore, all constructs had a significant effect on satisfaction as well as continuance intention. A group comparison analysis of consumers primarily using news recommendation algorithms and multimedia recommendation algorithms uncovered differences between the two groups. Managerial implications on how to retain recommendation algorithm users are suggested based on the results.

Suggested Citation

  • Kim, Jiwhan & Nam, Changi, 2019. "Analyzing continuance intention of recommendation algorithms," 30th European Regional ITS Conference, Helsinki 2019 205190, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itse19:205190
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    References listed on IDEAS

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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. David W. Gerbing & James C. Anderson, 1992. "Monte Carlo Evaluations of Goodness of Fit Indices for Structural Equation Models," Sociological Methods & Research, , vol. 21(2), pages 132-160, November.
    3. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    4. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    5. Zuiderveen Borgesius, Frederik J. & Trilling, Damian & Möller, Judith & Bodó, Balázs & de Vreese, Claes H. & Helberger, Natali, 2016. "Should we worry about filter bubbles?," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 5(1), pages 1-16.
    6. Forsythe, Sandra M. & Shi, Bo, 2003. "Consumer patronage and risk perceptions in Internet shopping," Journal of Business Research, Elsevier, vol. 56(11), pages 867-875, November.
    7. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    8. Gediminas Adomavicius & Jesse C. Bockstedt & Shawn P. Curley & Jingjing Zhang, 2013. "Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects," Information Systems Research, INFORMS, vol. 24(4), pages 956-975, December.
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