The DOI matches its Crossref record; 127 of the 128 references it cites were found in a registry or an index.
Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty
Abstract
Purpose: This study investigates whether and how the service quality of artificial intelligence (AI) chatbots affects customer loyalty to an organization. Design/methodology/approach: Based on the sequential chain model of service quality loyalty, this study first classifies AI chatbot service quality into nine attributes and then develops a research model to explore the internal mechanism of how AI chatbot service quality affects customer loyalty. The analysis of survey data from 459 respondents provided insights into the interrelationships among AI chatbot service quality attributes, perceived value, cognitive and affective trust, satisfaction and customer loyalty. Findings: The results show that AI chatbot service quality positively affects customer loyalty through perceived value, cognitive trust, affective trust and satisfaction. Originality/value: This study captures the attributes of the service quality of AI chatbots and reveals the significant influence of service quality on customer loyalty. This study develops research on service quality in the information system (IS) field and extends the sequential chain model of quality loyalty to the context of AI services. The findings not only help an organization find a way to improve customers' perceived value, trust, satisfaction and loyalty but also provide guidance in the development, adoption, and post-adoption stages of AI chatbots.
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“Aiming at this, AI fairness is used in chatbot design to avoid biased responses or inappropriate behavior towards users[300,301].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [300]
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Metadata
- Authors
- Chen, Q., Lu, Y., Gong, Y., & Xiong, J.
- Affiliations
- Huazhong Agricultural University (CN)
- Huazhong University of Science and Technology (CN)
- École de management de Lyon (FR)
- ESSCA School of Management (FR)
- Venue
- Internet Research
- Publisher
- Emerald
- Published
- 2023-01-17
- Volume
- 33
- Issue
- 6
- Pages
- 2205-2243
- ISSN
- 1066-2243 (ISSN-L)
- OpenAlex
- W4316468117
- Semantic Scholar
- 94eded573134ce189a6362aed40b62d3f26b60b8
- Cited by
- 260
- References
- 128
- Citations by year
- 2026: 60 · 2025: 109 · 2024: 70 · 2023: 21
- FWCI
- 24.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Internet Research (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- AI chatbots, service quality, customer loyalty, perceived value, cognitive trust, affective trust, customer satisfaction, AI services, customer retention
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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