Paper Number
ICIS2025-1106
Paper Type
Short
Abstract
In recent years, public pushback against algorithmic recommendations on social media has intensified, driven by growing awareness of their influence on user perceptions and decisions, along with rising ethical and legal concerns. This study focuses on resistance to dynamically learning recommendation algorithms — an area that goes beyond traditional IT resistance or algorithm aversion in decision-making contexts. Although interest in this topic is growing, its conceptual complexity remains insufficiently examined. We investigate the core components of user resistance to algorithms by identifying distinct resistance objects, behavioral responses, and their underlying motivations. Grounding the concept in the IS literature, we clarify its relationship to adjacent constructs and employ semantic analysis to structure our exploration. Drawing on interviews with 45 social media users, we offer preliminary insights toward building a theoretical framework. The paper concludes with proposed next steps and a discussion of the study’s expected contributions.
Recommended Citation
Zhou, Ya; Hu, Meng; and Luo, Cheng, "Decomposing User Resistance to Algorithms on Social Media" (2025). ICIS 2025 Proceedings. 1.
https://aisel.aisnet.org/icis2025/impl_adopt/impl_adopt/1
Decomposing User Resistance to Algorithms on Social Media
In recent years, public pushback against algorithmic recommendations on social media has intensified, driven by growing awareness of their influence on user perceptions and decisions, along with rising ethical and legal concerns. This study focuses on resistance to dynamically learning recommendation algorithms — an area that goes beyond traditional IT resistance or algorithm aversion in decision-making contexts. Although interest in this topic is growing, its conceptual complexity remains insufficiently examined. We investigate the core components of user resistance to algorithms by identifying distinct resistance objects, behavioral responses, and their underlying motivations. Grounding the concept in the IS literature, we clarify its relationship to adjacent constructs and employ semantic analysis to structure our exploration. Drawing on interviews with 45 social media users, we offer preliminary insights toward building a theoretical framework. The paper concludes with proposed next steps and a discussion of the study’s expected contributions.
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14-Implementation