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Cited by 2Technology Adoption and User BehaviourAI in Service InteractionsConsumer Behavior and Marketing Influence

The Impact of Digital Innovation on E-Commerce Young Customer Satisfaction In Vietnam

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Le, Minh-Hoa, Duong, Que-Nhu, Nguyen, Hieu-Nghia, Au, Quynh-Nhu, Pham, Nhat-Nam·2025·Journal of Central Banking Law and Institutions
DOI 10.21098/jcli.v4i1.264
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Abstract

This study looks at the influence of digital innovation, notably AI-driven chatbots, on e-commerce consumer satisfaction among young customers in Vietnam. It investigates key factors influencing user satisfaction employing frameworks such as the Uses and Gratifications Theory (U&G), Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT), including utilitarian, hedonic, technological, and social gratifications, privacy risk, and social influence. This research applies a quantitative method, with data collected through an online survey utilising snowball sampling, yielding responses from 1,007 individuals aged 18 to 30. SPSS and PLS-SEM tools are used in the statistical analysis. This study finds that utilitarian, hedonic, technological, and social gratifications positively and substantially impact user satisfaction. Aside from this finding, when engaging with chatbots, consumers are often affected by suggestions and endorsements from peers and their larger social context. This highlights the significance of peer validation and social dynamics in determining user satisfaction. Additionally, Privacy Risks do not substantially impact satisfaction, indicating that customers prioritise practical and emotional advantages over data security concerns when engaging with chatbots. Practical implications include strategically using digital innovation, making reasonable assumptions about privacy risks, and adding social elements to improve consumer satisfaction in Vietnam’s thriving e-commerce industry. This study provides valuable insights for companies navigating digital innovation in Vietnam’s e-commerce ecosystem and digital banking.

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  • Privacy may function as a conditional constraint rather than a uniform inhibitor because privacy risk and surveillance concerns-including creepiness and fear of data misuse-appear in resistance and barrier pathways and exhibit heterogeneous significance across settings and user groups.

    “Third, privacy may function as a conditional constraint rather than a uniform inhibitor because privacy risk and surveillance concerns-including creepiness and fear of data misuseappear in resistance and barrier pathways and exhibit heterogeneous significance across settings and user groups [4,42,49].”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [42]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • 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 [42]

    The Data Analysis Methods section (page unavailable) describes a survey, and the Structural Model Assessment reports a positive association between utilitarian gratification and user satisfaction. The paper does not test trust as part of a usefulness–satisfaction–trust chain; its model lists satisfaction as the outcome and contains no trust variable. The experimental claims are attached to other citations, not this source. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Additional evidence suggests that risk/privacy may be insignificant compared to gratification and utility drivers in specific contexts/samples, and that effects may differ based on usage frequency/experience.

    “Nonetheless, additional evidence suggests that risk/privacy may be insignificant compared to gratification and utility drivers in specific contexts/samples [42], and that effects may differ based on usage frequency/experience [4].”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [42]

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Authors
Le, M.-H., Duong, Q.-N., Nguyen, H.-N., Au, Q.-N., & Pham, N.-N.
Affiliations
Can Tho University (VN)
Venue
Journal of Central Banking Law and Institutions
Publisher
Bank Indonesia, Central Banking Research Department
Published
2025-01-09
Volume
4
Issue
1
Pages
79-112
ISSN
  • 2809-9885 (ISSN-L)
  • 2827-7775
DOI
10.21098/jcli.v4i1.264
OpenAlex
W4406811513
Cited by
2
References
0
Citations by year
2025: 2
FWCI
2.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Central Banking Law and Institutions (publishedVersion, cc-by-nc-sa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
# (#)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Behavior and Marketing Influence
Keywords
digital innovation, e-commerce, customer satisfaction, young consumers, AI chatbots, uses and gratifications theory, technology acceptance model, UTAUT, privacy risks, social influence, peer validation, Vietnam

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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.

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