Beyond Usefulness: Credibility as the Driver of Consumer Trust and Satisfaction in AI Chatbot Interactions—Evidence from Moroccan E-Commerce
Authors
School of Business, Nanjing University of Information Science and Technology, Nanjing (China)
School of Business, Nanjing University of Information Science and Technology, Nanjing (China)
Article Information
DOI: 10.51584/IJRIAS.2026.110400121
Subject Category: Management
Volume/Issue: 11/4 | Page No: 1612-1623
Publication Timeline
Submitted: 2026-04-15
Accepted: 2026-04-20
Published: 2026-05-13
Abstract
AI chatbots have become central to e-commerce customer service globally, yet the trust mechanisms driving their adoption in emerging markets remain poorly understood. Existing research, grounded largely in the Technology Acceptance Model, emphasizes perceived usefulness and personalization as primary adoption drivers — constructs whose predictive validity in low AI-exposure, high uncertainty-avoidance markets cannot be taken for granted. This study proposes and tests a credibility-first framework among Moroccan e-commerce users, arguing that in high uncertainty-avoidance cultures, credibility — defined as the perceived honesty, reliability, and transparency of the chatbot — will be the primary feature activating consumer trust, while perceived usefulness, interactivity, and personalization are expected to have weak or non-significant effects. Survey data from 150 respondents were analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) via the lavaan package in R, with mediation tested through 5,000 bootstrap resamples. Model fit was excellent (CFI = 0.978, RMSEA = 0.076). Results confirm that credibility is the primary significant predictor of trust (β = 0.486, p < .001), while usefulness, interactivity, and personalization are non-significant. Trust strongly predicts satisfaction (β = 0.532, p < .001), and full mediation is confirmed exclusively for the credibility–trust–satisfaction pathway (indirect β = 0.259, p < .001). These findings establish the credibility highway as the primary verified route to consumer satisfaction in this market, with direct implications for chatbot design priorities, personalization strategy, and AI governance policy in comparable emerging markets.
Keywords
AI chatbots; consumer trust; credibility
Downloads
References
1. Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49. https://doi.org/10.1016/j.jretai.2014.09.005 [Google Scholar] [Crossref]
2. Allioui, H., & Mourdi, Y. (2024). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors, 23(19), 8015. https://doi.org/10.3390/s23198015 [Google Scholar] [Crossref]
3. Castillo, D., & Farrugia Caruana, L. (2025). Unveiling customer expectations of chatbot interactions: A systematic literature review and research agenda. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2025.2588815 [Google Scholar] [Crossref]
4. Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the bot: Anthropomorphism and anger in customer–chatbot interactions. Journal of Marketing, 86(1), 132–148. https://doi.org/10.1177/00222429211045687 [Google Scholar] [Crossref]
5. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]
6. Gefen, D. (2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725–737. https://doi.org/10.1016/S0305-0483(00)00021-9 [Google Scholar] [Crossref]
7. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson. [Google Scholar] [Crossref]
8. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press. [Google Scholar] [Crossref]
9. Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions, and organizations across nations (2nd ed.). SAGE Publications. [Google Scholar] [Crossref]
10. Mantouzia, S., Youssef, S., El Mouatassim, A., Ourahou, Y., Jafi, H., Zouhouredine, I., & Hamliri, M. (2025). Leveraging AI for business intelligence: A pathway to improved organizational performance in Morocco's manufacturing sector. Multi Science Journal, 2025(412). https://doi.org/10.31893/multiscience.2025412 [Google Scholar] [Crossref]
11. Matosas-López, L. (2024). The influence of brand credibility and brand loyalty on customer satisfaction and continued use intention in new voice assistance services based on AI. Journal of Marketing Analytics, 13(1), 180–201. https://doi.org/10.1057/s41270-023-00278-8 [Google Scholar] [Crossref]
12. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792 [Google Scholar] [Crossref]
13. Minkov, M., & Hofstede, G. (2012). Hofstede’s fifth dimension: New evidence from the World Values Survey. Journal of Cross-Cultural Psychology, 43(1), 3–14. https://doi.org/10.1177/0022022110388567 [Google Scholar] [Crossref]
14. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 [Google Scholar] [Crossref]
15. Prakash, A. V., Joshi, A., Nim, S., & Das, S. (2023). Antecedents and consequences of customer satisfaction with AI-based chatbots. International Journal of Information Management Data Insights, 3(2), 100189. https://doi.org/10.1016/j.jjimei.2023.100189 [Google Scholar] [Crossref]
16. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02 [Google Scholar] [Crossref]
17. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2019). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. E. Vomberg (Eds.), Handbook of market research (pp. 587–632). Springer. https://doi.org/10.1007/978-3-319-57413-4_15 [Google Scholar] [Crossref]
18. Soni, V. D. (2024). Personalization versus privacy: Consumer trust in AI-driven marketing. International Journal of Innovative Research in Technology, 11(1), 183–188. [Google Scholar] [Crossref]
19. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 [Google Scholar] [Crossref]
20. Yang, Y., Tavares, J., & Oliveira, T. (2024). A new research model for artificial intelligence–based well-being chatbot engagement: Survey study. JMIR Human Factors, 11, e59908. https://doi.org/10.2196/59908 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Indirect Effect of Liquidity and Activity on Company Value with Profitability as an Intervening Variable
- Effect of Financial Skills, Knowledge, and Attitude on The Financial Behaviour of Clergy
- A Decade of Review: Trends in Budget Execution and Financial Performance of Development Projects in Tanzania (2014/15-2023/24)
- The Influence of Pre-Project Planning on the Budget Absorption Rate of Public Funded Infrastructure Projects in Kenya a Comparative Case Study of Narok, Migori, and Kisii County Government Projects
- Assessment of Factors Influencing Digital Transformation in Hotels’ Facility Management in Abuja Metropolis, Nigeria