A Study on the Factors That Influence Consumers’ Continuance Intention to Use Artificial Intelligence Chatbots in a Pharmaceutical e-Commerce Context
Abstract
Purpose This study aims to explore the antecedent factors affecting consumers’ intention to continue using artificial intelligence (AI) chatbots in pharmaceutical e-commerce. This study aims to provide a comprehensive understanding of how technical characteristics and user satisfaction influence continuance intention, offering insights for enhancing AI chatbot systems and customer service strategies. Design/methodology/approach This study adopts both online and offline data collection methods and uses structural equation modelling with AMOS 26.0 to investigate the factors influencing consumers’ continuance intention to use AI chatbots in the pharmaceutical e-commerce context. The research model integrates technical characteristics of AI chatbots, service expectation confirmation, emotional satisfaction, cognitive satisfaction and e-health literacy. Findings The results of this study indicate that perceived flexibility, accuracy, reliability and convenience of AI chatbots positively influence service expectation confirmation, while anthropomorphism does not have a significant impact. Both emotional and cognitive satisfaction positively influence consumers’ continuance intention. This study highlights the importance of optimizing AI chatbot systems to improve service efficiency and consumer satisfaction. Originality/value This study uniquely integrates the pharmaceutical e-commerce context into AI chatbots research, addressing an existing gap in context-specific studies. This s…
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Studies focusing on intention usually follow extended TAM/ECT/TPB logic, in which quality/usefulness and confirmation lead to higher satisfaction and/or attitudes, which then predict continued use, purchase intention, or loyalty.
“Studies that focus on intention usually follow the extended TAM/ECT/TPB logic, which says that quality/usefulness and confirmation lead to higher satisfaction and/or attitudes, which then predict outcomes like continued use, purchase intention, or loyalty”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Beliefs about one's ability (such as perceived task-solving competence) strengthen the usefulness and trust routes; delegation is more likely when the agent is seen as competent for the task.
“First, beliefs about one's ability (such as perceived task-solving competence) strengthen the usefulness and trust routes stronger.Delegation is more likely when the agent is seen as competent for the task [51,52,69].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Systems designed to minimize friction augment perceived utility and user satisfaction, thereby reinforcing trust and subsequent intention outcomes.
“Systems designed to minimize friction (e.g., expedited information access, simplified input, structured guidance, enhanced navigation) augment perceived utility and user satisfaction, thereby reinforcing trust and subsequent intention outcomes (e.g., retention, purchase intention, reliance-oriented metrics) [6,44,45,48,50,52,67,69].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
The net-positive pattern is evident in dominant satisfaction/continuance pathways according to numerous studies.
“According to numerous studies [44,46,48,50,69], this pattern is evident in dominant satisfaction/continuance pathways.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
In the main empirical cluster (Autonomy-1, recommendation/support scope), positive outcomes usually follow a utility/assurance chain: performance cues and interaction quality make people feel more useful and satisfied, which builds trust and leads to downstream adoption/continuance or purchase-intent outcomes.
“In the main empirical cluster (Autonomy-1, recommendation/support scope), positive outcomes usually follow a utility/assurance chain: performance cues and interaction quality make people feel more useful and satisfied, which builds trust and leads to downstream adoption/continuance or purchase-intent outcomes (e.g., [44,46,48,50,69]).”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
The utility/assurance lane predominates in cross-sectional SEM studies within the corpus.
“This lane predominates in crosssectional SEM studies within the corpus [44,46,48,50,69].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Experiments confirm autonomy to powerlessness/reactance mechanisms and interface-driven reactance shifts, whereas surveys focus on usefulness/satisfaction/trust chains.
“This disparity is evident in both experimental and survey methodologies: experiments confirm autonomy → powerlessness/reactance mechanisms [23,40] and interface-driven reactance shifts [24], whereas surveys focus on usefulness/satisfaction/trust chains [42,44,69].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Perceived usefulness/satisfaction mediating design cues to trust/continuance and trust mediating performance cues to intention are mediation structures frequently reported in the mapped studies.
“Perceived usefulness/satisfaction mediating design cues → trust/continuance and trust mediating performance cues → intention are two examples of mediation structures frequently reported in the mapped studies [44,46,48,50,69].”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Satisfaction declines when designs trigger reactance or cognitive/affective overload, which depress downstream evaluations.
“It declines when designs trigger reactance or cognitive/affective overload, which depress downstream evaluations.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Spend/GMV/WTP is positive in field and some survey settings.
“Positive in field and tings some survey set-”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Satisfaction/CX/perceived service quality is mostly positive, but design can introduce negative affect (reactance, overload).
“Mostly positive, but design can introduce negative affect (reactance, overload)”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Satisfaction rises via problem-solving, confirmation, usefulness, and interaction/service quality.
“Satisfaction rises via problem-solving, confirmation, usefulness, and interaction/service quality.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [69]
Cited by
Sorted by citations
Balaskas, S. (2026) · Information · doi:10.3390/info17030222 · Cited by 8 · see citing works
- Authors
- Jia, J., Chen, L., Zhang, L., Xiao, M., & Wu, C.
- Affiliations
- Changzhou University (CN)
- Changzhou University School of Business, , Changzhou,
- Tsinghua University (CN)
- Konkuk University (KR)
- Hanyang University (KR)
- Venue
- The Electronic Library
- Publisher
- Emerald
- Published
- 2025-02-26
- Volume
- 43
- Issue
- 3
- Pages
- 303-321
- ISSN
- 0264-0473 (ISSN-L)
- 1758-616X
- OpenAlex
- W4407983528
- Cited by
- 20
- References
- 0
- Citations by year
- 2026: 15 · 2025: 5
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Electronic Library (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Keywords
- AI chatbots, continuance intention, pharmaceutical e-commerce, user satisfaction, emotional satisfaction, cognitive satisfaction, digital health literacy, anthropomorphism, perceived flexibility, perceived accuracy, perceived reliability, perceived convenience, customer relationship management
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