Logo image
Back
Theorizing the Consumption Values of Generative Artificial Intelligence: Insights from a Systematic Literature Review
Preprint   Open access

Theorizing the Consumption Values of Generative Artificial Intelligence: Insights from a Systematic Literature Review

Amanda Balasooriya, Darshana Sedera and Varun Grover
Pacific Asia journal of the Association for Information Systems, Vol.66, pp.1-37
AIS
26/06/2026
url
Theorizing the Consumption Values of Generative Artificial IntelligenceView
Preprint (Author's original) Open Free to Read

Metrics

1 Record Views

Abstract

Generative Artificial Intelligence Theory of Consumption Values Formative Measurement Model Consumption Values Adoption
Background: In recent times, generative artificial intelligence (GenAI) has gained substantial prominence. Anecdotal commentary and research evidence have shown that GenAI can be beneficial across various contexts, industry sectors, and geographical areas, and purport to provide new and unique consumption values to its users. Despite its benefits, the value propositions of GenAI are challenged in a range of ethical, technical, social, and economic perspectives. Considering the growing value propositions and opposing concerns, this study explores and develops a multi-dimensional model that gauges the value propositions of GenAI and its attributes at the individual level. Method: This study, using the theory of consumption values (TCV) as a conceptual lens, systematically synthesizes the consumption values and their attributes within the GenAI context reviewing 119 GenAI adoption studies published in top-tier journals. From these studies, we extracted the value attributes individuals perceive when adopting GenAI, guided by the reported hypotheses and significance levels. By assessing the predictive power of each identified value attribute, we determined which consumption values were well-established for inclusion in the proposed model. Results: By mapping the identified value attributes to the constructs of TCV, we found 7 attributes of functional value, 5 attributes of emotional value, 5 attributes of epistemic value, 8 attributes of conditional value and 5 attributes of social value. Based on these findings, we developed a formative measurement model, which captures the multifaceted nature of GenAI. Conclusion: By clarifying and structuring these value attributes, this study contributes a coherent framework that advances theoretical understanding and offers practical insights for designing more user-aligned GenAI solutions.

Details

Logo image