Beyond user experience: What constitutes algorithmic experiences?. (June 2020)

• Record Type: Journal Article
• Authors: Shin, Donghee; Zhong, Bu; Biocca, Frank A.
• Is Part Of: International journal of information management. Volume 52(2020)
• Publication Date: 2020-06
• Subjects: Algorithm -- Algorithmic experience -- Algorithmic trust -- Transparency -- Fairness -- User experience -- Affordance; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2019.102061
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: Algorithmic experience (AX) as an analytic framework for the experience of and interaction with algorithm systems. Conceptualizes AX and tests how it affects the satisfaction with and acceptance of algorithm services. The heuristic roles of transparency and fairness regarding their underlying relations of user experience and trust. The embedded algorithm can influence the user perception of the algorithm system. 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.