Beyond user experience: What constitutes algorithmic experiences?

Donghee Shin*, Bu Zhong, Frank A. Biocca

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

93 Citations (Scopus)


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.

Original languageEnglish
Article number102061
JournalInternational Journal of Information Management
Publication statusPublished - Jun 2020

Scopus Subject Areas

  • Information Systems
  • Computer Networks and Communications
  • Library and Information Sciences

User-Defined Keywords

  • Affordance
  • Algorithm
  • Algorithmic experience
  • Algorithmic trust
  • Fairness
  • Transparency
  • User experience


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