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Beyond user experience: What constitutes algorithmic experiences?

Author

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  • Shin, Donghee
  • Zhong, Bu
  • Biocca, Frank A.

Abstract

Algorithms are progressively transforming human experience, especially, the interaction with businesses, governments, education, and entertainment. As a result, people are growingly seeing the outside world, in a sense, through the lens of algorithms. Despite the importance of algorithmic experience (AX), few studies had been devoted to investigating the nature and processes through which users perceive and actualize the potential for algorithm affordance. This study proposes the Algorithm Acceptance Model to conceptualize the notion of AX as part of the analytic framework for human-algorithm interaction. It then tests how AX shapes the satisfaction with and acceptance of algorithm services. The results show that AX is inherently related to human understanding of fairness, transparency, and other conventional components of user-experience, indicating the heuristic roles of transparency and fairness regarding their underlying relations of user experience and trust. AX can influence the user perception of algorithmic systems in the context of algorithm ecology, offering useful insights into the design of human-centered algorithm systems. The findings provide initial and robust support for the proposed Algorithm Acceptance Model.

Suggested Citation

  • Shin, Donghee & Zhong, Bu & Biocca, Frank A., 2020. "Beyond user experience: What constitutes algorithmic experiences?," International Journal of Information Management, Elsevier, vol. 52(C).
    • Handle: RePEc:eee:ininma:v:52:y:2020:i:c:s0268401219314161
    DOI: 10.1016/j.ijinfomgt.2019.102061
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    Citations

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    Cited by:

    1. Lukas-Valentin Herm & Theresa Steinbach & Jonas Wanner & Christian Janiesch, 2022. "A nascent design theory for explainable intelligent systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2185-2205, December.
    2. Jonas Wanner & Lukas-Valentin Herm & Kai Heinrich & Christian Janiesch, 2022. "The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2079-2102, December.
    3. Deriu, Valerio & Pozharliev, Rumen & De Angelis, Matteo, 2024. "How trust and attachment styles jointly shape job candidates’ AI receptivity," Journal of Business Research, Elsevier, vol. 179(C).

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