ISSN: 2222-6990
Open access
The study examined the relationship between human-centered Marketing 5.0 practices and consumer trust among university students in Klang Valley, Malaysia. It was grounded in Marketing 5.0 as the primary framework and guided by the Technology Acceptance Model and Commitment-Trust Theory, with a particular emphasis on predictive, contextual and augmented marketing as consumer-oriented applications of AI. The data were collected through a quantitative, cross-sectional survey using convenience and purposive sampling to get 200 usable responses from the university students aged 18-29 who have experience in digital-platform or online-transaction. The data were gathered by a self-administered online questionnaire with 20 measurement items on a five-point Likert scale and analyzed using IBM SPSS Statistics. Consumer trust was significantly positively related to all three practices. The standardized contribution (?) was highest for augmented marketing (? = .371, p < .001), followed by contextual marketing (? = .352, p < .001) and predictive marketing (? = .141, p = .022). The three predictors accounted for 60.9% of the variance in consumer trust (R² = .609). The results of predictive and contextual marketing should be interpreted with caution as these measures had lower internal consistency and factor suitability. The results offer actionable insights for companies aiming to create clear, meaningful, and responsive AI-driven marketing strategies that support consumer trust.
Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: Progress, challenges, and future directions. Humanities and Social Sciences Communications, 11, 1568. https://doi.org/10.1057/s41599-024-04044-8
Alanazi, T. M. (2022). Marketing 5.0: An empirical investigation of its perceived effect on marketing performance. Marketing and Management of Innovations, 13(4), 55–64. https://doi.org/10.21272/mmi.2022.4-06
Alshaketheep, K., Mansour, A. M., Al-Ma’aitah, M. M. L., Dabaghia, M. N., & Dabaghie, Y. M. (2024). Leveraging AI predictive analytics for marketing strategy: The mediating role of management awareness. Journal of System and Management Sciences, 14(2), 71–89. https://doi.org/10.33168/jsms.2024.0205
Awad, N. F., & Krishnan, M. S. (2006). The personalization–privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly, 30(1), 13–28. https://doi.org/10.2307/25148715
Boerman, S. C., & Smit, E. G. (2023). Advertising and privacy: An overview of past research and a research agenda. International Journal of Advertising, 42(1), 60–68. https://doi.org/10.1080/02650487.2022.2122251
Chan, C. K. Y., & Lee, K. K. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10, 60. https://doi.org/10.1186/s40561-023-00269-3
Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552–568. https://doi.org/10.1016/j.jbusres.2022.02.088
Cheng, Y., & Jiang, H. (2022). Customer–brand relationship in the era of artificial intelligence: Understanding the role of chatbot marketing efforts. Journal of Product & Brand Management, 31(2), 252–264. https://doi.org/10.1108/JPBM-05-2020-2907
Chi, N. T. K., & Hoang Vu, N. (2023). Investigating customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Transactions on Intelligence Technology, 8(1), 260–273. https://doi.org/10.1049/cit2.12133
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727–1739. https://doi.org/10.1080/10447318.2022.2050543
Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98
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
Department of Statistics Malaysia. (2026). ICT use and access by individuals and households survey report, 2025. https://www.dosm.gov.my/portal-main/release-content/ict-use-and-access-by-individuals-and-households-survey-report-2025
Djafarova, E., & Bowes, T. (2021). Instagram made me buy it: Generation Z impulse purchases in the fashion industry. Journal of Retailing and Consumer Services, 59, 102345. https://doi.org/10.1016/j.jretconser.2020.102345
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505
GlobalData. (2026, April 17). Malaysia ecommerce market to register 9.4% growth in 2026, forecasts GlobalData. https://www.globaldata.com/media/banking/malaysia-ecommerce-market-to-register-9-4-growth-in-2026-forecasts-globaldata/
Hair, J. F., Page, M., & Brunsveld, N. (2022). Essentials of business research methods (4th ed.). Routledge. https://doi.org/10.4324/9780429203374
Hallikainen, H., & Laukkanen, T. (2021). Trustworthiness in e-commerce: A replication study of competing measures. Journal of Business Research, 126, 644–653. https://doi.org/10.1016/j.jbusres.2019.12.033
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Irgui, A., & Qmichchou, M. (2023). Contextual marketing and information privacy concerns in m-commerce and their impact on consumer loyalty. Arab Gulf Journal of Scientific Research, 42(3), 1150–1170. https://doi.org/10.1108/AGJSR-09-2022-0198
Janson, A. (2023). How to leverage anthropomorphism for chatbot service interfaces: The interplay of communication style and personification. Computers in Human Behavior, 149, 107954. https://doi.org/10.1016/j.chb.2023.107954
Kasemrat, R., Kraiwanit, T., & Yuenyong, N. (2025). Predictive analytics in customer behavior: Unveiling economic and governance insights through machine learning. Journal of Governance & Regulation, 14(1), 318–331. https://doi.org/10.22495/jgrv14i1siart8
Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. https://doi.org/10.5395/rde.2013.38.1.52
Koneti, C. (2025). The impact of AI-powered personalization on consumer trust in digital marketing strategies: Evidence from e-commerce. Advances in Consumer Research, 2(4), 2272–2283.
Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for humanity. Wiley.
Li, C., Hao, R., Li, N., & Zhang, C. (2025). Measuring customer experience in AI contexts: A scale development. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 31. https://doi.org/10.3390/jtaer20010031
McKee, K. M., Dahl, A. J., & Peltier, J. W. (2024). Gen Z’s personalization paradoxes: A privacy calculus examination of digital personalization and brand behaviors. Journal of Consumer Behaviour, 23(2), 405–422. https://doi.org/10.1002/cb.2199
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.1177/002224299405800302
Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847
Sarwar, M. S., & Malik, M. (2025). The impact of AI-powered personalisation on consumer trust and purchase intention: A cross-cultural comparative study of Gen Z consumers in the United Kingdom and Pakistan. Majestic Global Trends in Marketing & Supply Chain Management, 1(1), 1–14. https://doi.org/10.65080/mgtmscm.v1i1.4
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864
Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4–11. https://doi.org/10.12691/ajams-9-1-2
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial intelligence-based intelligent products. Telematics and Informatics, 47, 101324. https://doi.org/10.1016/j.tele.2019.101324
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
White, T. B., Zahay, D. L., Thorbjørnsen, H., & Shavitt, S. (2008). Getting too personal: Reactance to highly personalized email solicitations. Marketing Letters, 19(1), 39–50. https://doi.org/10.1007/s11002-007-9027-9
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3), 1–13. https://doi.org/10.33151/ajp.8.3.93
Xu, Y., & Chen, L. (2025). Personalized recommendations and consumer trust: The role of perceived control and locus of control. Acta Psychologica, 261, 105936. https://doi.org/10.1016/j.actpsy.2025.105936
Zhu, Y. Q., & Kanjanamekanant, K. (2021). No trespassing: Exploring privacy boundaries in personalized advertisement and its effects on ad attitude and purchase intentions on social media. Information & Management, 58(2), 103314. https://doi.org/10.1016/j.im.2020.103314
Louis, K. B., Din, N. N. O., Ahmad, N., Singh, J. S. K., & Pereira, C. R. (2026). Human-Centered Marketing 5.0 Practices and Consumer Trust among University Students in Klang Valley. International Journal of Academic Research in Business and Social Sciences, 16(11), 621-637.
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