Work

The Process Model of Long-Term Users’ Subjective Cognition on Algorithmic Recommendation Platforms: An Exploratory Analysis

Year: 2026

Type: article

Abstract: The interaction between algorithmic recommendation platforms (ARPs) and users shapes users’ subjective cognition. However, the process through which subjective cognition changes during long-term user–algorithm interactions has not been sufficiently elucidated. Drawing on users’ experiences, this study employs grounded theory to construct a process model of subjective cognition among long-term users of ARPs. The findings indicate that sustained interactions with these platforms are associated with changes in users’ perceived subjective cognition. Initially, long-term users described excessive reliance on platform functionalities as being associated with a perceived weakening of autonomous judgment and a diminished subjective cognition. Once this perceived erosion reaches a critical threshold, users adopt diverse forms of cognitive resistance through which they may reconstruct their subjective cognition and renegotiate their relationships with ARPs. The resulting process model describes how users’ subjective cognition may move from passive acceptance to reflexive resistance and ultimately to negotiated coexistence, offering a process-oriented perspective on long-term user–algorithm interactions. It also provides practical implications for developing more transparent, controllable, and user-centered algorithmic recommendation systems and for enhancing users’ algorithmic literacy.

Source: Data

DOI: 10.3390/data11090238

Authors: Ying Hu, Tianfeng Li, Yichun Chen

Institutions: Central South University, Fudan University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: Ministry of Education of the People's Republic of China