Affordances of, attachment to and satisfaction with generative artificial intelligence: an affordance actualization perspective

Purpose Generative Artificial Intelligence (GAI) offers innovative services to users. For GAI companies, it is crucial to ensure user satisfaction in the face of fierce competition. Unlike traditional AI in automation, GAI services are augmentation and designed for value co-creation with users, which transform users into empowered stakeholders. This study investigates the interrelationship and boundaries of GAI satisfaction by distinguishing between satisfaction with process and outcome. It adopts an affordance actualization perspective, integrated with attachment theory, to examine how affordances influence GAI satisfaction through both affective and cognitive dimensions. Design/methodology/approach An online survey with ChatGPT-augmented contexts was conducted with 529 respondents, and the collected data are analyzed via partial least squares-based structural equation modeling. Findings The co-creation fosters a positive correlation between the process and the outcome, with individual competence (i.e. tech-savviness) as a boundary condition. Perceived creativity and enjoyment are identified as affective affordances and are predictors of GAI identity, while perceived credibility and serendipity are identified as cognitive affordances and are predictors of GAI dependency. GAI identity and dependency further enhance users’ satisfaction with process and outcome. Originality/value This study extends knowledge on GAI satisfaction by distinguishing satisfaction with process and outcome and captures the element of user heterogeneity by individual competence. This study also develops an affective-cognitive framework of GAI affordances and conceptualizes the actualization process, which contributes to empirical experience and practical applications.

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Publication Details

Journal
Information Technology and People
Published
2025-05-17
DOI
https://doi.org/10.1108/itp-05-2024-0667
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
2.37
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article

Affordances of, attachment to and satisfaction with generative artificial intelligence: an affordance actualization perspective

Yongqiang Sun, Qinwei Li, Bo Yang
1 citations
Information Technology and People
Ethics and Social Impacts of AI
2.37
article

Affordances of, attachment to and satisfaction with generative artificial intelligence: an affordance actualization perspective

Yongqiang Sun, Qinwei Li, Bo Yang
article en
1 citations

Abstract

Purpose Generative Artificial Intelligence (GAI) offers innovative services to users. For GAI companies, it is crucial to ensure user satisfaction in the face of fierce competition. Unlike traditional AI in automation, GAI services are augmentation and designed for value co-creation with users, which transform users into empowered stakeholders. This study investigates the interrelationship and boundaries of GAI satisfaction by distinguishing between satisfaction with process and outcome. It adopts an affordance actualization perspective, integrated with attachment theory, to examine how affordances influence GAI satisfaction through both affective and cognitive dimensions. Design/methodology/approach An online survey with ChatGPT-augmented contexts was conducted with 529 respondents, and the collected data are analyzed via partial least squares-based structural equation modeling. Findings The co-creation fosters a positive correlation between the process and the outcome, with individual competence (i.e. tech-savviness) as a boundary condition. Perceived creativity and enjoyment are identified as affective affordances and are predictors of GAI identity, while perceived credibility and serendipity are identified as cognitive affordances and are predictors of GAI dependency. GAI identity and dependency further enhance users’ satisfaction with process and outcome. Originality/value This study extends knowledge on GAI satisfaction by distinguishing satisfaction with process and outcome and captures the element of user heterogeneity by individual competence. This study also develops an affective-cognitive framework of GAI affordances and conceptualizes the actualization process, which contributes to empirical experience and practical applications.

Information Technology and PeopleVol. 39(2)
Wuhan University (CN)
Openalex Percentile: Top 12%
Ethics and Social Impacts of AI
2.37
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