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Academic interest in virtual influencers has exploded since 2021, but the field remains dispersed across disciplines; studies cluster around authenticity, trust and anthropomorphism, with China the largest producer of publications.

Virtual influencers in advertising and marketing communication: mapping the intellectual landscape of research on consumer psychology and human-AI interaction
Donia Khalfallah · August 04, 2026 · Frontiers in Communication
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This systematic review and bibliometric analysis of 116 articles finds virtual influencer research has accelerated since 2021, is increasingly interdisciplinary but intellectually fragmented, and concentrates on themes of authenticity, trust, anthropomorphism, and consumer engagement.

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Introduction This study examines the psychological foundations of virtual influencer research within the broader context of artificial intelligence (AI) and data-driven personalization, advertising, marketing communication, and human–AI interaction. As AI-generated social actors become increasingly embedded in digitally mediated environments, research on virtual influencers has expanded rapidly across psychology, communication, marketing, and media studies. However, the field remains conceptually fragmented, limiting understanding of its dominant psychological themes, intellectual structure, and future research directions. Methods This study employed the SPAR-4-SLR protocol to conduct a systematic literature review of virtual influencer research. Articles were retrieved from four major databases and screened using predefined inclusion and exclusion criteria, resulting in a final sample of 116 peer-reviewed studies published between 2015 and 2025. Bibliometric techniques, including performance analysis and science mapping using VOSviewer, were applied to examine publication trends, collaboration patterns, influential contributors, and thematic structures within the field. Results The findings indicate that virtual influencer research has expanded rapidly since 2021 and has evolved into an increasingly interdisciplinary field. China emerged as the leading contributor in terms of publication output, while analyses of publications, citations, and co-authorships revealed a fragmented intellectual structure, with influence distributed across multiple research teams. Keyword co-occurrence analysis identified seven thematic clusters, highlighting authenticity, trust, anthropomorphism, consumer engagement, artificial intelligence, and digital marketing as the dominant research themes. Discussion This study integrates systematic literature review and bibliometric analysis to provide a comprehensive overview of the intellectual development of virtual influencer research. The findings indicate that the field is increasingly shaped by the convergence of consumer psychology, artificial intelligence, advertising, and marketing communication, with authenticity, trust, anthropomorphism, and consumer engagement emerging as its principal research themes. By synthesizing fragmented evidence, this study provides a structured roadmap for future research on human–AI interaction, digital identity, parasocial relationships, and virtual influencer marketing. The findings also contribute to advertising and marketing communication by clarifying the dominant research directions, identifying emerging knowledge gaps, and highlighting opportunities for future theoretical and empirical development.

Summary

Main Finding

Virtual influencer research has grown rapidly (especially since 2021) and evolved into an interdisciplinary field bringing together consumer psychology, AI, advertising, and marketing communication. The literature is intellectually fragmented (influence spread across multiple teams and geographies), with core psychological themes centered on authenticity, trust, anthropomorphism, parasocial interaction, and consumer engagement. China leads in publication output; the field is dominated by cross‑sectional and experimental work, with notable gaps in longitudinal, cross‑cultural, and large‑scale field evidence.

Key Points

  • Scope and dataset
    • Systematic bibliometric review of 116 peer‑reviewed, open‑access journal articles published 2015–2025.
    • Search and selection followed the SPAR‑4‑SLR protocol across Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online.
  • Main thematic clusters (from keyword co‑occurrence)
    • Authenticity and disclosure
    • Trust and credibility
    • Anthropomorphism and social presence
    • Consumer engagement and behavioral intentions
    • Artificial intelligence / generative AI
    • Digital marketing and branding
    • Parasocial relationships and emotional attachment
  • Intellectual structure and trends
    • Rapid growth since 2021 and increasing interdisciplinarity (psychology, communication, marketing, computer science).
    • Fragmented co‑authorship and citation networks (no single dominant research school).
    • China as top contributor by output; influential works and authors dispersed.
  • Methodological profile and limitations
    • Heavy reliance on experiments and cross‑sectional designs; few longitudinal or large-scale field studies.
    • Excluded non‑English, conference papers, grey literature — may miss practitioner/industry evidence.
  • Key conceptual insights
    • Virtual influencers can affect attitudes, trust, engagement, and purchase intention, but human influencers often remain more effective.
    • Perceived credibility, attractiveness, and perceived authenticity mediate effectiveness.
    • Cultural and regulatory contexts moderate responses.

Data & Methods

  • Protocol: SPAR‑4‑SLR (systematic, transparent search and selection).
  • Databases: Scopus, Web of Science, IEEE Xplore, Taylor & Francis Online.
  • Inclusion criteria: English, peer‑reviewed, open‑access journal articles, 2015–2025; focus on virtual influencers, AI avatars, synthetic media in marketing/communication/consumer behavior.
  • Final sample: 116 articles.
  • Bibliometric techniques:
    • Performance analysis (publication trends, country/journal/author productivity).
    • Science mapping using VOSviewer (co‑authorship networks, citation analysis, keyword co‑occurrence clustering).
  • Limitations noted by the author:
    • Language and publication‑type restrictions (potential omission of relevant non‑English and grey literature).
    • Bibliometric approaches identify structure/themes but cannot by themselves establish causal effects.

Implications for AI Economics

  • Market structure and competition
    • Emergence of virtual influencers can change supply of promotional services (lower marginal costs to scale content), potentially altering price formation for influencer services and platform monetization.
    • Platform competition and network effects may strengthen around creators (virtual or human) — platforms that enable AI persona creation or superior targeting could capture more advertiser surplus.
  • Advertising effectiveness, demand, and welfare
    • Heterogeneous effectiveness (human > virtual in many contexts) implies firms must weigh cost savings from AI against lower conversion rates; economic models should incorporate authenticity/trust as demand shifters.
    • Disclosure, transparency, and perceived authenticity are policy levers that can materially affect ad effectiveness and consumer welfare; regulation (mandatory disclosure) will shift equilibria between human and virtual influencer adoption.
  • Labor markets and reallocation
    • Potential displacement or repricing of human influencer labor, especially mid‑tier creators; study needed on income distribution and transition costs.
    • New roles (AI persona designers, data curators) may arise — analyze skill‑biased demand shifts.
  • Investment and firm strategy
    • Firms face an investment/tradeoff decision: build in‑house virtual influencers vs. hire human influencers; structural models can quantify return on investment under varying trust/authenticity parameters.
    • Virtual influencers that enable personalization at scale change the value of data assets; economic models should incorporate data‑driven targeting effects and privacy/regulation costs.
  • Measurement and empirical strategy recommendations for economists
    • Use large platform/advertiser panel data, A/B tests, randomized trials, and natural experiments to quantify causal effects of virtual influencer campaigns on clicks, sales, and long‑run customer value.
    • Structural demand models should include parameters for perceived authenticity, anthropomorphism, and parasocial attachment to predict long‑run adoption and pricing.
    • Cross‑country and longitudinal analyses are crucial: cultural heterogeneity and regulatory regimes (e.g., disclosure laws) will change adoption and welfare outcomes.
    • Estimate labour market impacts using difference‑in‑differences or synthetic control approaches in markets where virtual influencer adoption is staggered.
  • Policy and welfare considerations
    • Disclosure/transparency rules affect trust and effectiveness — regulators must balance consumer protection and innovation incentives.
    • Potential externalities (misinformation, identity misuse, deceptive persuasion) justify economic assessment of social costs and targeted regulation.
  • Specific research questions for AI economics
    • How do virtual influencers affect the equilibrium prices and quantities in the influencer advertising market?
    • What is the marginal cost vs. marginal effectiveness tradeoff for virtual vs. human influencers across product categories?
    • How does mandatory disclosure of virtual origin change consumer surplus and advertiser ROI?
    • What are the distributional consequences (winners/losers) of widespread virtual influencer adoption for creator incomes and platform rents?

Short actionable takeaways for researchers in AI economics - Combine platform/transactional data with randomized/adaptive experiments to estimate causal ROI of virtual influencer campaigns. - Incorporate psychological mediators (authenticity, trust, parasocial attachment) into structural demand and pricing models. - Pursue cross‑national and longitudinal designs to capture regulatory and cultural moderation. - Analyze labor market impacts and platform market power dynamics resulting from scalable AI personas.

Reference (paper summarized) - Khalfallah D. (2026). Virtual influencers in advertising and marketing communication: mapping the intellectual landscape of research on consumer psychology and human–AI interaction. Frontiers in Communication, 11:1894984. DOI: 10.3389/fcomm.2026.1894984.

Assessment

Paper Typereview_meta Evidence Strengthn/a — This is a systematic literature review combined with bibliometric mapping rather than an empirical study testing causal relationships; it synthesizes existing evidence but does not itself implement causal identification. Methods Rigorhigh — The study uses a recognized systematic-review protocol (SPAR-4-SLR), searches four major bibliographic databases (Scopus, WoS, IEEE Xplore, Taylor & Francis Online), applies explicit inclusion/exclusion criteria, limits to peer-reviewed journal articles, and employs established bibliometric tools (VOSviewer) for science mapping — although the English-only and open-access restrictions and the exclusion of grey literature are notable limitations. SampleFinal dataset of 116 peer-reviewed, open-access English-language journal articles on virtual influencers and synthetic media published between 2015 and 2025, retrieved from Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online; conference papers, book chapters, grey literature, and non-English studies were excluded. Themeshuman_ai_collab adoption GeneralizabilityRestricted to English-language, open-access journal articles — may omit important non-English or paywalled work, Excluded conference papers, book chapters, and grey literature which may contain technical or emerging findings, Database selection (four sources) may bias coverage toward certain disciplines and publishers, Focus on advertising/marketing contexts limits applicability to other domains of AI deployment (e.g., workplace automation, public sector), Time-bounded to 2015–2025; rapidly evolving generative-AI developments after 2025 are not covered

Claims (10)

ClaimDirectionOutcomeConfidence & EvidenceDetails
The final review dataset contained 116 peer-reviewed journal articles on virtual influencers published between 2015 and 2025. Research Productivity positive Size and temporal coverage of the virtual influencer research literature
Reading fidelity high
Study strength high
n=116
0.4
Virtual influencer research expanded rapidly after 2021. Research Productivity positive Growth in annual virtual influencer research publications
Reading fidelity high
Study strength medium
n=116
0.24
China was the leading contributor to virtual influencer research in terms of publication output. Research Productivity positive Country publication output in virtual influencer research
Reading fidelity high
Study strength medium
n=116
0.24
The intellectual structure of virtual influencer research is fragmented, with influence distributed across multiple research teams. Research Productivity mixed Concentration and structure of scholarly influence and collaboration
Reading fidelity high
Study strength medium
n=116
0.24
Seven thematic clusters dominate virtual influencer research, particularly authenticity, trust, anthropomorphism, consumer engagement, artificial intelligence, and digital marketing. Research Productivity positive Thematic concentration of virtual influencer scholarship
Reading fidelity high
Study strength medium
n=116
7 thematic clusters
0.24
A prior meta-analysis found that human influencers generally outperform virtual influencers in generating consumer engagement and purchase intentions. Consumer Welfare negative Consumer engagement and purchase intentions
Reading fidelity high
Study strength high
n=135
0.4
Perceived credibility and attractiveness were identified as the principal mechanisms underlying influencer effectiveness. Consumer Welfare positive Influencer effectiveness, including consumer engagement and purchase intentions
Reading fidelity high
Study strength high
n=135
0.4
A systematic review of 51 journal articles identified consumer trust as the central mechanism linking disclosure transparency with consumer engagement in virtual influencer marketing. Consumer Welfare positive Consumer engagement as related to disclosure transparency and trust
Reading fidelity high
Study strength medium
n=51
0.24
Cultural and regulatory contexts significantly influence consumer responses to virtual influencers, implying that virtual influencer strategies should be adapted to different market environments. Consumer Welfare mixed Consumer responses to virtual influencer marketing
Reading fidelity high
Study strength medium
n=51
0.24
The reviewed literature identifies limited longitudinal research, cross-cultural evidence, and interdisciplinary collaboration as important research gaps. Research Productivity negative Coverage and methodological breadth of the virtual influencer research literature
Reading fidelity high
Study strength medium
n=116
0.24

Notes