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Why do users continue to use GCAI? A mixed-methods study

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Listed:
  • Xiong, Li
  • Zhou, Yuepeng
  • Hua, Yipu

Abstract

Generative Conversational Artificial Intelligence (GCAI) is reshaping human-computer interaction, yet the functional attributes associated with continuance intention remain underexplored. This study employs a mixed-methods approach. Study 1 integrates BERTopic modeling with Uses and Gratifications (U&G) theory to operationalize unstructured user reviews into standardized stimulus inputs. Study 2 tests the proposed cognitive pathways using survey data. The results reveal that response accuracy exhibits the strongest association, correlating with higher trust through its positive link to performance expectancy and negative link to effort expectancy. In contrast, human-like empathy is associated with higher performance expectancy and effort expectancy, reflecting a complex tension between perceived benefits and costs. Trust serves as a vital mechanism bridging between cognitive evaluation and continuance intention. This study contributes a robust attribute framework and clarifies the relationships linking cognitive evaluation to continuance intention, offering strategic implications for system design and user retention.

Suggested Citation

  • Xiong, Li & Zhou, Yuepeng & Hua, Yipu, 2026. "Why do users continue to use GCAI? A mixed-methods study," Technology in Society, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:teinso:v:86:y:2026:i:c:s0160791x26000989
    DOI: 10.1016/j.techsoc.2026.103309
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