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Cited by 18AI in Service InteractionsCustomer Service Quality and LoyaltyTechnology Adoption and User Behaviour

AI Chatbots in E-Commerce: Enhancing Customer Engagement, Satisfaction and Loyalty

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Vebrianti, Riri, Aras, Muhammad, Putri, Mega Silviana Sukarni, Swandewi, Ida Ayu·2025·PaperASIA
DOI 10.59953/paperasia.v41i2b.445
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Abstract

The rapid advancement of Artificial Intelligence (AI) has led to the widespread adoption of chatbots in customer service, particularly in e-commerce. As part of digital marketing strategies, chatbots serve as an automated yet interactive tool to enhance customer satisfaction and foster long-term customer loyalty. This study aims to analyze the combined impact of response time, information quality, perceived usefulness (PU), and perceived ease of use (PEOU) on customer satisfaction and loyalty in the e-commerce sector. Although research on chatbot effectiveness continues to grow, most previous studies have examined these factors separately. However, no study has explored how these four variables work together not only to improve customer service efficiency but also to build long-term customer loyalty and strengthen the digital relationship between customers and brands. By employing a quantitative approach with an explanatory research design, this study surveyed 400 respondents with experience using chatbots as customer service in e-commerce companies. Structural Equation Modeling SEM was utilized to analyze the relationships between chatbot service attributes, customer satisfaction, and customer loyalty. The findings reveal that all four chatbot service attributes significantly enhance customer satisfaction, strengthening customer loyalty. These results highlight the importance of integrating chatbot effectiveness into customer relationship management CRM and digital engagement strategies. By contributing to the development of CRM concepts in AI-driven customer service, this study provides insights for businesses seeking to optimize chatbot technology to enhance customer experience and build long-term relationships, as well as an addition to the literature for further research.

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  • The empirical research on the effects of agentic commerce is still dispersed but suggests an interrelated set of commerce and psychological effects.

    “The empirical research on the effects of agentic commerce is still dispersed but suggests an interrelated set of commerce and psychological effects”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [44]

    The Introduction (page unavailable) says previous e-commerce chatbot studies examined service attributes separately, and the Discussion (page unavailable) links perceived usefulness and ease of use with satisfaction and loyalty. That supports the claim's general idea of dispersed research and related customer and commercial outcomes, but the paper studies customer-service chatbots, not the broader effects of agentic commerce. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • 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 [44]

    Section 2.5 (page unavailable) links easy navigation and low-effort chatbot use to satisfaction, acceptance, and trust. Section 4.2.1 reports that perceived ease of use and usefulness positively influence satisfaction, which in turn positively influences loyalty. The paper does not establish the full proposed chain from friction reduction through increased perceived usefulness and trust to purchase intention or reliance-oriented outcomes. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Properly designed assistants may increase loyalty when they balance effectively between entertaining, providing information, and usability, as well as overcoming concerns about privacy risks.

    “properly designed assistants may increase loyalty when they balance effectively between entertaining, providing information, and usability, as well as overcoming concerns about privacy risks”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as Vebrianti et al. [44]

    The Technology Acceptance Model section (page not provided) discusses information, enjoyment, and ease of use as factors in satisfaction or continued use. The Discussion (page not provided) reports that customer satisfaction affects loyalty. The paper does not establish that balancing those features increases loyalty, and our copy does not address overcoming privacy risks as a condition for doing so. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • 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 [44]

    The Technology Acceptance Model (TAM) section links perceived usefulness to satisfaction, and §4.2.1 reports that information quality and perceived usefulness significantly influence satisfaction, which in turn positively affects loyalty. The cited paper does not establish the claimed extended TAM/ECT/TPB framework or a pathway through confirmation and attitudes to purchase intention; its tested outcome is loyalty. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • As systems move toward truly agentic, transaction-executing commerce, the main limitation is measurement: a significant portion of the evidence deduces delegation from adoption or continuation intentions, rather than from direct observation of explicit delegation choices or verified execution.

    “As systems move toward truly agentic, transaction-executing commerce [24,40,44,45,50,52,67], the main limitation is measurement: a significant portion of the evidence deduces delegation from adoption or continuation intentions, rather than from direct observation of explicit delegation choices or verified execution.”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [44]

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Cited by

Sorted by citations

Authors
Vebrianti, R., Aras, M., Putri, M. S. S., & Swandewi, I. A.
Affiliations
Binus University (ID)
ORCID
Venue
PaperASIA
Publisher
Simo Excel PLT
Published
2025-05-02
Volume
41
Issue
2b
Pages
248-260
ISSN
0218-4540 (ISSN-L)
DOI
10.59953/paperasia.v41i2b.445
OpenAlex
W4410045460
Cited by
18
References
8
Citations by year
2026: 13 · 2025: 5
FWCI
7.22
Type
journal article
Language
en
Open access
Licence
Locations
PaperAsia (publishedVersion, cc-by-nc)
Funders
Binus University
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
chatbots, e-commerce, customer satisfaction, customer loyalty, customer engagement, response time, information quality, perceived usefulness, perceived ease of use, customer relationship management, digital marketing

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