From limitation to possibility: affordances and tactical evolution in user–GenAI interaction

Purpose This study explores how users negotiate the limitations of generative artificial intelligence (GenAI) systems through tactical practices and how these practices contribute to the dynamic shaping of affordances within constrained system environments. The research aims to reveal the co-evolutionary mechanisms between user agency and technological affordances in everyday information practices. Design/methodology/approach Drawing on in-depth interviews with 20 active GenAI users in China, the study collected 193 Affordance Enactment Episodes. A six-stage thematic analysis was conducted to trace users’ interaction trajectories, integrating de Certeau’s theory of everyday practice and Leonardi’s relational affordance theory to construct a spiral model of user–GenAI interaction. Findings The study identifies five core information practices in which users perceive, encounter constraints, and actualize GenAI affordances through various tactics such as iterative prompting, role-setting, and cross-tool integration. It further reveals a three-stage evolution of user tactics—from initial exploration to proficient integration—accompanied by a deepening understanding of GenAI’s systemic boundaries. Affordances are shown to be relational and emergent, produced through user agency rather than inherent to the technology. Originality/value By bridging everyday practice theory and relational affordance theory, this study contributes a novel analytical framework for understanding user–GenAI interaction. It highlights affordance actualization as a dynamic, user-driven process shaped through tactical engagement with system constraints, offering implications for AI literacy, human–AI collaboration, and user-centered system design.

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Publication Details

Journal
Journal of Documentation
Published
2025-10-15
DOI
https://doi.org/10.1108/jd-07-2025-0183
Primary Topic
Innovative Human-Technology Interaction
Type
article
Field-Weighted Citation Impact
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article

From limitation to possibility: affordances and tactical evolution in user–GenAI interaction

Zhen Zeng, Nan Yang, Zhengbiao Han, Haoyu Zhao
Journal of Documentation
Innovative Human-Technology Interaction
article

From limitation to possibility: affordances and tactical evolution in user–GenAI interaction

Zhen Zeng, Nan Yang, Zhengbiao Han, Haoyu Zhao
article en

Abstract

Purpose This study explores how users negotiate the limitations of generative artificial intelligence (GenAI) systems through tactical practices and how these practices contribute to the dynamic shaping of affordances within constrained system environments. The research aims to reveal the co-evolutionary mechanisms between user agency and technological affordances in everyday information practices. Design/methodology/approach Drawing on in-depth interviews with 20 active GenAI users in China, the study collected 193 Affordance Enactment Episodes. A six-stage thematic analysis was conducted to trace users’ interaction trajectories, integrating de Certeau’s theory of everyday practice and Leonardi’s relational affordance theory to construct a spiral model of user–GenAI interaction. Findings The study identifies five core information practices in which users perceive, encounter constraints, and actualize GenAI affordances through various tactics such as iterative prompting, role-setting, and cross-tool integration. It further reveals a three-stage evolution of user tactics—from initial exploration to proficient integration—accompanied by a deepening understanding of GenAI’s systemic boundaries. Affordances are shown to be relational and emergent, produced through user agency rather than inherent to the technology. Originality/value By bridging everyday practice theory and relational affordance theory, this study contributes a novel analytical framework for understanding user–GenAI interaction. It highlights affordance actualization as a dynamic, user-driven process shaped through tactical engagement with system constraints, offering implications for AI literacy, human–AI collaboration, and user-centered system design.

Journal of DocumentationVol. 81(5-6)
Nanjing Agricultural University (CN), Guizhou University of Finance and Economics (CN), Beijing Institute of Big Data Research (CN)
Openalex Percentile: Top 70%
Innovative Human-Technology Interaction
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