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What Drives User Intention to Continue Using Conversational AI? How Functional and Emotional Values Influence Continuance Intention

Md Rasel Al Mamun et al.

AIS Transactions on Human-Computer Interaction2025https://doi.org/10.17705/1thci.00216article
AJG 2ABDC A
Weight
0.37

Abstract

The growing popularity of conversational AI has created numerous opportunities for individuals and businesses. However, traditional IS use theories fail to capture the empirical reality of conversational AI artifacts. Thus, we identify a need to evolve new theoretical perspectives that provide meaningful insights for scholars and practitioners. Thus far, researchers generally agree that conversational AI generates two forms of consumer values—functional and emotional—but they have primarily focused on transient emotional values. In this study, we investigate the impact of functional values (i.e., perceived usefulness) and enduring or sustainable emotional values (i.e., emotional attachment) on users’ intention to continue using conversational AI. We evaluated our research question by drawing on the lens of attachment theory, theory of consumption values, and agentic IS literature. We conducted an online survey with 288 participants. The results show that both functional and emotional values are important for continuance intention. However, functional values predicted continuance intentions better than emotional values. Additionally, we found a positive association between emotional and functional values. These findings contribute to the conversational AI use literature by merging previously disparate literature streams to concomitantly examine the impact of functional and emotional values on user continuance intentions.

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https://doi.org/https://doi.org/10.17705/1thci.00216

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@article{md2025,
  title        = {{What Drives User Intention to Continue Using Conversational AI? How Functional and Emotional Values Influence Continuance Intention}},
  author       = {Md Rasel Al Mamun et al.},
  journal      = {AIS Transactions on Human-Computer Interaction},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.17705/1thci.00216},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.