AI vs. Human Influencer Branding Comparative Effectiveness of AI-Driven Virtual Influencers vs. Human Influencers in Consumer Engagement
DOI:
https://doi.org/10.5281/zenodo.17053804Keywords:
ai-driven influencers, virtual influencers, human influencers, influencer marketing, brand engagement, consumer engagement, digital influencers, social media marketingAbstract
The AI mass adoption in digital marketing has brought a paradigm shift in influencer branding, notably the introduction of AI-based virtual influencers. These algorithmically generated beings, which have hyper-realistic aesthetics, consistent brand communications, and personalization based on data are now increasingly challenging human influencers who historically control the influencer economy. This paper conducts a comparative study of how AI-enhanced virtual influencers and human influencers can be used to form consumer behavior, brand loyalty, and purchase intention. Using consumer psychology, theories of marketing communication, and human-computer interaction models, the study examines how viewers find credibility, relatability and authenticity in their encounters with virtual and human-portrayed personas.
The methodology combines both quantitative and qualitative designs incorporating a structured survey of the digital consumers (n=500) of various demographics, in-depth interview with marketing professionals and the evaluation of the engagement metrics of social media campaigns involving the human and AI influencers. The results indicate that human influencers still outperform AI-based influencers on perceived authenticity, emotional appeal, and long-term trust-building, whereas virtual influencers are more effective concerning novelty, aesthetics, cost-effectiveness and precision-driven content personalization. In addition, the research provides insights into differences in the generation of consumer reactions: Gen Z users are much more open and curious about AI influencers, whereas millennials and older generations remain as attached to human influencers.
The comparative observations point to the fact that the effectiveness of influencer type is very situational and depends upon product category, culture dimensions, and campaign goals. An example is that AI influencers work best in the field of technology, fashion, and luxury branding where aspirational visuals and innovativeness is a driving force and human influencers are more convincing in the field of lifestyle, wellness, and socially sensitive where genuineness and compassion are paramount.
The study adds to the emergent literature on employing artificial intelligence in branding tactics by providing a fine sense of the consumer attitude to new online personas. At the end of the paper, the author suggests the implementation of a hybrid-type of co-influencing, where brands have the opportunity to capitalize on the advantage of both AI-based and human influencers to leverage consumer engagement, cost management, and market flexibility. All these findings are of great importance to marketers, advertisers, and digital strategists who want to navigate the influencer ecosystem that is changing in an increasingly AI-driven environment.
Downloads
References
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95. https://doi.org/10.1007/s11747-019-00695-1
Belanche, D., Casaló, L. V., & Flavián, C. (2021). Artificial intelligence in fintech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 121(7), 1512–1534. https://doi.org/10.1108/IMDS-09-2020-0538
Belanche, D., Flavián, M., & Pérez-Rueda, A. (2022). Virtual versus human influencers in online advertising. Computers in Human Behavior, 126, 106983. https://doi.org/10.1016/j.chb.2021.106983
Casaló, L. V., Flavián, C., & Ibáñez-Sánchez, S. (2018). Influencers on Instagram: Antecedents and consequences of opinion leadership. Journal of Business Research, 117, 510–519. https://doi.org/10.1016/j.jbusres.2018.07.005
Chopra, S., & Kamal, P. (2022). Understanding consumer trust in AI-enabled influencer marketing: A conceptual framework. International Journal of Consumer Studies, 46(4), 1245–1260. https://doi.org/10.1111/ijcs.12756
Djafarova, E., & Trofimenko, O. (2019). ‘Instafamous’–Credibility and self-presentation of micro-celebrities on social media. Information, Communication & Society, 22(10), 1432–1446. https://doi.org/10.1080/1369118X.2018.1438491
Jin, S. V., Muqaddam, A., & Ryu, E. (2019). Instafamous and social media influencer marketing. Marketing Intelligence & Planning, 37(5), 567–579. https://doi.org/10.1108/MIP-09-2018-0375
Kapitan, S., & Silvera, D. H. (2016). From digital media influencers to celebrity endorsers: Attributions drive endorser effectiveness. Marketing Letters, 27(3), 553–567. https://doi.org/10.1007/s11002-015-9363-0
Lou, C., & Yuan, S. (2019). Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58–73. https://doi.org/10.1080/15252019.2018.1533501
Moustakas, E., Lappas, G., Patelis, T., & Balakrishnan, J. (2020). The moderating role of Instagram influencer type and product type on consumer trust and purchase intention. Journal of Retailing and Consumer Services, 54, 102001. https://doi.org/10.1016/j.jretconser.2019.102001
Robinson, S., & Botta, R. (2023). Virtual influencers and brand authenticity: Consumer perceptions of computer-generated endorsers. Journal of Marketing Communications, 29(2), 177–195. https://doi.org/10.1080/13527266.2021.1911673
Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. Journal of Retailing and Consumer Services, 53, 101742. https://doi.org/10.1016/j.jretconser.2019.01.011

Published
How to Cite
Issue
Section
ARK
License
Copyright (c) 2025 Shivangi Mishra

This work is licensed under a Creative Commons Attribution 4.0 International License.
Research Articles in 'Management Journal for Advanced Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.