The Moderating Role of Personalized Recommendations in the Trust–Satisfaction–Loyalty Relationship: An Empirical Study of AI-Driven e-Commerce
Open accessAbstract
Purpose: This study investigates the effects of trust, satisfaction, and loyalty on AI-driven e-commerce, with a particular focus on how personalized recommendations moderate these relationships. It aims to explore how personalized AI features reshape consumer perceptions and decision-making. Design/methodology/approach: A quantitative research approach was used to collect data from a diverse group of e-commerce users who had interacted with AI-based recommendation systems. An online survey employing standardized scales for trust, satisfaction, loyalty, and personalization was administered, and data were analyzed using structural equation modeling (SEM) to test the hypotheses. Findings: The study reveals that trust has a significant positive influence on both satisfaction and loyalty. Personalization further strengthens these relationships by moderating the trust–satisfaction–loyalty dynamic. Satisfaction partially mediates the relationship between trust and loyalty, with the model’s explanatory power improving by 5% when personalization is included as a moderator. These results highlight the pivotal role of personalized recommendations in shaping consumer trust and satisfaction in AI-driven e-commerce. Practical implications: Businesses can use personalized recommendation systems to enhance trust and satisfaction, thereby fostering loyalty. For example, platforms like Amazon and Netflix have successfully employed personalized AI algorithms to boost customer retention and engagement. Transparency features, such as explaining why certain products are recommended, and cultural sensitivity in algorithm design can further enhance customer trust and acceptance. e-commerce organizations should also invest in data privacy measures and clear algorithms to maintain consumer confidence while leveraging AI to improve customer experience and achieve sustainable competitive advantages. Originality/value: This study contributes to the growing body of knowledge on AI-driven e-commerce by demonstrating how personalized recommendations influence trust, satisfaction, and loyalty. It provides actionable insights for leveraging AI tools to build stronger consumer relationships in dynamic digital marketplaces.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [30]
The Summary of the literature review (page unavailable) links fulfilled expectations to satisfaction and satisfaction to loyalty, and discusses TAM and Expectation-Confirmation Theory. The Structural equation modelling section (page unavailable) also reports positive effects of chatbot service quality and satisfaction on loyalty. The paper does not establish the broader claim that intention studies usually follow combined TAM/ECT/TPB logic, or the asserted paths to attitudes, continued-use intention, and purchase intention. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe utility chain dominates: quality/usefulness leads to attitude/satisfaction, which leads to intention.
“Utility chain dominates: quality/usefulness → at-titude/satisfaction → intention.”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [30]
The Structural equation modelling (SEM) section reports that chatbot service quality predicts loyalty and satisfaction predicts loyalty; the Questionnaire design section says the loyalty measure includes repeat-purchase intentions. But the paper identifies trust—not quality or usefulness—as the predictor of satisfaction, and does not establish that the proposed utility chain dominates. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPurchase intention/adoption intention is mostly positive via utility/trust, but can decrease under high autonomy.
“Mostly positive via utility/trust, but can decrease under high autonomy”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [30]
The Summary of the literature review (page unavailable) links useful AI chatbots and trust to customer loyalty, while Questionnaire design and scale description (page unavailable) says the loyalty measure includes intentions for repeat purchases. The paper does not establish that purchase or adoption intention decreases under high autonomy; its discussion of user control concerns improved trust and loyalty, not that proposed decrease. (LLM, conf=0.76)
Experimental — accuracy not yet measuredHigh-autonomy cues can reduce intention via powerlessness/threat-to-freedom, unless urgency/scarcity raises benefit salience and attenuates resistance.
“High-autonomy cues can reduce intention via powerless-raises benefit salience and attenuates resistance. ness/threat-to-freedom, unless urgency/scarcity”
From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [30]
The Summary of the literature review (page unavailable) discusses control of chatbot interactions, and Structural equation modelling (SEM; page unavailable) reports a possible weakening of the trust–satisfaction relationship through personalization. Neither addresses high-autonomy cues reducing intention through powerlessness or threats to freedom, or urgency and scarcity attenuating resistance. Searches for those terms and their paraphrases found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 4× · see citing works
“Such a continuous presence to fine tune recommendations helps in increasing customer retention and increase profit by ensuring that the products made available suit the users desires [30, 31, 43].”
InfluentialHair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0517-x · Cited by 2,712 · see citing works
Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102168 · Cited by 2,620 · cited 4× · see citing works
“…and loyalty, and therefore, it becomes crucial to understand how the use of AI-based recommendation systems affects these relationships [30, 31] This is the case because, being new concepts, there is not enough literature on the roles of AI and other associated technologies in e-commerce…”
InfluentialThe Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Komiak, S. Y. X., & Benbasat, I. (2006) · MIS Quarterly · doi:10.2307/25148760 · Cited by 1,374 · see citing works
Fang, Y., Qureshi, I., Sun, H., McCole, P., Ramsey, E., & Lim, K. H. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.2.04 · Cited by 770 · see citing works
de Fine Licht, K., & de Fine Licht, J. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00960-w · Cited by 334 · cited 2× · see citing works
“…information on how the particular AI-based recommendation method works, for instance, the outreach programs that show logical rationalization of each recommendation made by systems like Spotify—reduces the perceived risk and tension and increases user participation and confidence [36, 38].”
Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty
Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023) · Internet Research · doi:10.1108/intr-09-2021-0686 · Cited by 260 · see citing works
Zhang, T. (., Agarwal, R., & Lucas, H. C. (2011) · MIS Quarterly · doi:10.2307/41409964 · Cited by 233 · see citing works
Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce
Khrais, L. T. (2020) · Future Internet · doi:10.3390/fi12120226 · Cited by 228 · see citing works
Buyers' trust and mistrust in e-commerce platforms: a synthesizing literature review
Soleimani, M. (2022) · Information Systems and e-Business Management · doi:10.1007/s10257-021-00545-0 · Cited by 130 · cited 2× · see citing works
“Most especially, it explains how satisfaction is a mediator and how personalization is a moderator in the consumer relationship in trust to loyalty conversion through digital facilitation [8, 9].”
Flagged first, then by citations
Research works printed but not linked: 30
- Kamal M, Himel AS (2023) Redefining modern marketing: an analysis of AI and NLP's influence on consumer engagement, strategy, and beyond. Eigenpub Rev Sci Technol 7(1):203-223
- Vidhya V, Donthu S, Veeran L, Lakshmi YS, Yadav B (2023) The intersection of AI and consumer behavior: predictive models in modern marketing. Remitt Rev 8(4):2410-2424
- Necula SC, Păvăloaia VD (2023) AI-driven recommendations: a systematic review of the state of the art in e-commerce. Appl Sci 13(9):5531
- Qin Y, Yin S and Liu F (2023) Navigating criminal responsibility in the digital marketplace: implications of network-neutral help behaviour and beyond-5G networks in e-commerce transactions. J Knowl Econ. 1-29.
- Faraoni M, Rialti R, Zollo L, Pellicelli AC (2019) Exploring e-loyalty anteced- ents in B2C e-commerce: empirical results from italian grocery retailers. Br Food J 121(2):574-589
- Slack N, Singh G, Sharma S (2020) The effect of supermarket service quality dimensions and customer satisfaction on customer loyalty and disloyalty dimensions. Int J Qual Serv Sci 12(3):297-318
- Vijay ST, Prashar S, Sahay V (2019) The influence of online shopping values and web atmospheric cues on e-loyalty: mediating role of e-satisfaction. J Theor Appl Electron Commer Res 14(1):1-15
- Arora S, Athavale VA, Maggu H and Agarwal A (2020) Artificial intelligence and virtual assistant-working model. In: Mobile Radio Communications and 5G Networks: Proceedings of MRCN 2020 (pp. 163-171). Singapore: Springer Singapore.
- Malhotra G, Kharub M (2025) Elevating logistics performance: harnessing the power of artificial intelligence in e-commerce. Int J Logist Manag 36(1):290-321
- Chakraborty T, Sharada VS and Sai SDD (2025) Transactions to relation- ship: customers engagement in the digital age. In: Managing Customer- Centric Strategies in the Digital Landscape (pp. 25-50). IGI Global.
- Basuvaraj M and Rastogi K (2025) Illuminating the convergence of artificial intelligence (AI) and business intelligence (BI) in e-commerce and fin-tech in India. In: AI-Powered Business Intelligence for Modern Organizations (pp. 243-264). IGI Global.
- Rather RA (2019) Consequences of consumer engagement in service marketing: an empirical exploration. J Glob Mark 32(2):116-135
- Aslam W, Hussain A, Farhat K, Arif I (2020) Underlying factors influenc- ing consumers' trust and loyalty in e-commerce. Bus Perspect Res 8(2):186-204
- Khan RU, Salamzadeh Y, Iqbal Q, Yang S (2022) The impact of customer relationship management and company reputation on customer loyalty: the mediating role of customer satisfaction. J Relation Market 21(1):1-26
- Borraz-Mora J, Hernandez-Ortega B, Melguizo-Garde M (2020) The influ- ence of generic-academic competences on satisfaction and loyalty: the view of two key actors in higher education. J High Educ Policy Manag 42(5):563-578
- Cheng Y, Jiang H (2022) Customer-brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts. J Product Brand Manag 31(2):252-264
- Hu X, Chen X, Davison RM (2019) Social support, source credibility, social influence, and impulsive purchase behavior in social commerce. Int J Electron Commer 23(3):297-327
- Ifekanandu CC, Anene JN, Iloka CB, Ewuzie CO (2023) Influence of artificial intelligence (AI) on customer experience and loyalty: mediating role of personalization. J Data Acquis Process 38(3):1936
- Kathuria S, Rana S (2023) Linking Customer E-Service Quality with Artificial Intelligence-Based Business Environment. Artificial Intelligence in Customer Service: The Next Frontier for Personalized Engagement. Springer International Publishing, Cham, pp 259-279
- Deng G, Zhang J, He L and Xu Y (2023) Research on the impact of e-commerce platform's AI resources on seller opportunism: a cultiva- tional governance mechanism. Nankai Business Review International, (ahead-of-print).
- Chen JS, Le TTY, Florence D (2021) Usability and responsiveness of artifi- cial intelligence chatbot on online customer experience in e-retailing. Int J Retail Distrib Manag 49(11):1512-1531
- Jiang K, Qin M, Li S (2022) Chatbots in retail: How do they affect the continued use and purchase intentions of Chinese consumers? J Consum Behav 21(4):756-772
- Efendy,DK and Bharata W (2025) Pengukuran perilaku pengguna produk apple menggunakan Perspektif expectation confirmation theory DAN flow theory. J Econ, Bus Account (Costing). 8(1): 48-57.
- Wang X, Howard JL, Zhong L (2025) Why do perfectionists procrastinate (or not)? Exploring the interaction between perfectionism dimensions through self-determination theory. Appl Psychol 74(1):e12563
- Cochran WG (1963) Sampling Techniques. Wiley, New York
- Fang Y, Qureshi I, Sun H, McCole P, Ramsey E, Lim KH (2014) Trust, satisfac- tion, and online repurchase intention. MIS Q 38(2):407-A9
- Harris LC, Goode MM (2004) The four levels of loyalty and the pivotal role of trust: a study of online service dynamics. J Retail 80(2):139-158
- Shrestha N (2020) Detecting multicollinearity in regression analysis. Am J Appl Math Stat 8(2):39-42
- Ribeiro OMPL, Vicente CMFDB, Sousa CN, Teles PJFC, Trindade LDL, Martins MMFPDS, Cardoso MFPT (2021) Scale for the environment evalu- ation of professional nursing practice: construct validation. J Nurs Manag 29(6):1809-1818
- Afthanorhan A, Ghazali PL and Rashid N (2021) Discriminant validity: a comparison of CBSEM and consistent PLS using Fornell & Larcker and HTMT approaches. In Journal of Physics: Conference Series (Vol. 1874, No. 1, p. 012085). IOP Publishing.
Cited by
Sorted by citations
Hu, S., Liu, J., Li, H., Yin, J., & Liu, X. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20040290 · Cited by 11 · see citing works
Balaskas, S. (2026) · Information · doi:10.3390/info17030222 · Cited by 8 · see citing works
- Authors
- Hassan, N., Abdelraouf, M., & El-Shihy, D.
- Affiliations
- New Giza University (EG)
- King Salman International University (EG)
- Corresponding author
- Hassan, Noha
- Abdelraouf, Mohamed
- El-Shihy, Dina
- Venue
- Future Business Journal
- Published
- 2025-04-10
- Volume
- 11
- Issue
- 1
- ISSN
- 2314-7202 (ISSN-L)
- 2314-7210
- OpenAlex
- W4409314336
- Cited by
- 96
- References
- 10
- Citations by year
- 2026: 67 · 2025: 29
- FWCI
- 118.70
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Future Business Journal (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- personalized recommendation, consumer trust, customer satisfaction, customer loyalty, algorithmic transparency, data privacy, 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 ›
Are you an author, or do you see a problem with this page? Request a review
