Virtual Voices for a Sustainable Future: A Systematic Scoping Review on Virtual Influencers
Abstract
1. Introduction
2. Literature Review
2.1. Social Media Influencers in Environmental Communication
2.2. Virtual Influencers: Conceptualizations and Advantages
2.3. Research Gaps in VIs and Environmental Sustainability
- RQ1: How do cognitive, affective, relational, and inhibitory mechanisms explain VI effectiveness in sustainability communication?
- RQ2: Under what conditions (audience characteristics, behavioral cost, cultural context) do VIs outperform or underperform human influencers?
- RQ3: How does strategic anthropomorphism fit, i.e., alignment between VI design, audience values, and behavioral context, determine persuasive outcomes?
- RQ4: How do methodological choices (stimuli type, measurement approach) constrain the generalizability of findings in this emergent field?
- RQ5: Through which psychological pathways do VI campaigns translate awareness into sustainable behavioral intentions?
- RQ6: What theory-driven research pathways can advance understanding of VI-led sustainability communication?
3. Methodology
3.1. Identification
3.2. Screening and Eligibility
3.3. Risk of Bias and Certainty Assessment
3.4. Descriptive Analysis
3.5. Analytical Approach and Framework Development
4. Theory–Context–Characteristics–Methodology (TCCM)-Based Results
4.1. Theoretical Perspectives
4.2. Geographical Context
4.3. Characteristics
4.4. Methods
5. Results and Theoretical Synthesis
5.1. How VI Design Features Operate Through Strategic Anthropomorphism Fit
5.1.1. Virtual Influencer Design
5.1.2. Message and Narrative Strategy
5.1.3. Media and Cause Context
5.2. When VI Effectiveness Varies: Audience and Contextual Boundary Conditions
5.2.1. Audience-Level Moderators
5.2.2. Campaign- and Context-Level Moderators
5.3. Strategic Anthropomorphism Fit: Pathways to Environmental Outcomes
- (1)
- Cognitive mechanisms, including perceived credibility, cognitive trust, and consequential awareness, mediate the effects of VI design and message framing on brand attitudes and behavioral intentions [48,50,53]. In particular, human (vs. anime) VIs are perceived as more real and credible [16] and similarly, SMIs (vs. VIs) are perceived as more human and credible [79], which in turn enhances brand attitude and purchase intention. Perceived source credibility also enhances perceived trust, which enhances not only willingness to follow the VI but also pro-environmental behavior [51,68] and trust in CSR [48]. Similarly, cognitive trust is higher for human VIs, which enhances pro-environmental persuasion effectiveness [50]. However, found that trust positively mediates anthropomorphism on pro-environmental intentions only when there is a low level of racial homophily [16].
- (2)
- Relational mechanisms, particularly parasocial relationships, represent a second powerful pathway of influence. Strong parasocial bonds with VIs enhance relationship commitment, which in turn positively affects brand attitudes and purchase intentions [59]. Although VIs are generally perceived as more socially distant than SMIs [14], the formation of parasocial relationships can partially compensate for their lower baseline credibility. Indeed, strong parasocial engagement with VIs has been shown to offset credibility deficits and sustain persuasive effectiveness in sustainability contexts [79].
- (3)
- Affective mechanisms constitute a central yet ambivalent pathway. Emotional engagement with VIs can enhance younger consumers’ willingness to follow virtual agents and adopt sustainable habits [68]. Nevertheless, VIs remain effectively disadvantaged relative to SMIs, who are more readily attributed to altruistic motivations. These altruistic attributions strengthen perceived influencer-cause congruence, thereby improving brand attitudes and green purchase intentions [15]. Importantly, the affective limitations of VIs are not fixed. Strategic design choices, particularly high levels of anthropomorphism, can enhance affective resonance with pro-environmental advocacy and increase overall persuasion effectiveness [50]. These findings suggest that affective mechanisms amplify the effectiveness of VI only when emotional resonance and moral alignment are carefully calibrated.
- (4)
- Inhibitory mechanisms (including AI skepticism and algorithmic reactance) systematically constrain the effectiveness of VIs by foregrounding concerns about authenticity, emotional depth, and moral agency in non-human communicators [48,49,58]. While perceived human-identity threat does not significantly differ between SMIs and VIs [15], transparency-related cues introduce distinct frictions: standardized AI disclosures often generate confusion about an influencer’s ontological status, heighten sensitivity to algorithmic bias and stereotyping, and intensify doubts about the genuineness of relational bonds with artificial agents [58]. Importantly, sponsorship disclosure produces asymmetric effects, undermining credibility and increasing CSR skepticism more strongly for SMIs than for VIs, likely because audiences hold lower expectations of transparency and commercial intent for non-human entities [48].
6. Discussion and Future Research Agenda
6.1. Research Pathway 1: Strategic Anthropomorphism Fit
6.2. Research Pathway 2: Message Design and Narrative Strategy
6.3. Research Pathway 3: Audience Heterogeneity and Moderation
6.4. Research Pathway 4: Mechanisms: Cognitive, Affective, Relational, and Inhibitory
6.5. Research Pathway 5: Long-Term Impact and Societal Implications
7. Study Limitations
8. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
| SMIs | Social Media Influencers |
| SMI | Social Media Influencer |
| VIs | Virtual Influencers |
| VI | Virtual Influencer |
| AI | Artificial Intelligence |
| TCCM | Theory–Context–Characteristics–Methodology |
| SSR | Systematic Scoping Review |
| CASA | Computers Are Social Actors |
| NARS | Negative Attitude toward Robots Scale |
Appendix A. The Search Query for Scopus
Appendix B. Summary Table of Data Pool
| Citation | Key theories | Method/Design | Environmental focus | Main Findings |
|---|---|---|---|---|
| Barbosa and Real de Oliveira (2025) [58] | Transformative Advertising Research (TAR) framework; Technological Affordance Theory; Parasocial Interaction Theory | Qualitative/Multi-method approach | Sustainability impact | VIs enable hyper-personalization, cross-platform narratives, gamification; higher engagement than humans; concerns: transparency, bias, authenticity |
| Chen et al. (2025) [20] | Media richness theory; Source credibility theory | Quantitative/Experimental design | Hunger as the social cause (which is a global issue without extreme polarizations of opinion) | VR format alone insufficient for VI impact; behind-the-scenes disclosures ↓ perceived authenticity & influence |
| Diao et al. (2025) [59] | Social Identity Theory; Technology Acceptance Model; Uncertainty Reduction Theory; Para-social interaction | Quantitative/Survey | Environmental Stewardship | Expertise, similarity, attractiveness, PSI → ↑ relationship commitment → ↑ brand attitude → ↑ purchase |
| Duong and Tran (2024) [12] | Social performance theory | Qualitative/Content analysis | Promoting sustainable consumption | VIs advocate sustainability via awareness-raising & beauty-showcasing; use storytelling, visuals, interaction |
| Gerrath et al. (2024) [14] | Stereotype content model | Mixed/multi-method design | Pro-environmental causes/climate change | VIs reduce motive skepticism vs. humans; warmer VI messages ↑ engagement, especially among low expert-trust audiences |
| Hoai Lan et al. 2025 [13] | Social performance theory | Qualitative/Content analysis | Environmental messaging | VIs use informative content + visuals for sustainability; audience responds with admiration, inspiration, support |
| Huang et al. (2024) [49] | Dual-system processing theory | Quantitative/Multi-methods design | Ecological products/Eco-product purchase intention | Anime-like VIs > human-like for emotional response & eco-purchase; mixed narratives ↑ narrative presence → ↑ intention |
| Jiang et al. (2024) [25] | Construal level theory; Uncanny valley theory | Quantitative/Experimental design | Ecological products/Eco-product purchase intention | Human VIs > anime for credibility & purchase intention; credibility mediates; anime better for low-involvement products |
| Kleinlogel et al. (2023) [80] | Not identified | Quantitative/Experimental design | Energy saving | VR-immersed pro-env knowledge → ↑ energy-saving attitudes vs. traditional media; no diff. between immersive formats |
| Lim et al. (2025) [48] | "Expectation violation theory; Source credibility theory | Quantitative/Experimental design | Sustainable lifestyle influencer | HI > VI in source credibility; sponsorship disclosure ↓ message credibility for HIs only; credibility → ↑ CSR outcomes |
| Liu and Wu (2025) [50] | Elaboration Likelihood Model (ELM); Source credibility theory; Computers as Social Actors (CASA) theory; Uncanny valley theory | Quantitative/Experimental design | Source Type: Environmental expert vs. non-expert & Pro-Environmental Persuasion Effectiveness | High anthropomorphism ↑ pro-env behavior; non-expert VIs more persuasive; hope > fear appeals; cognitive trust mediates |
| Nazir and Wani (2025) [53] | Uncanny valley theory | Quantitative/Experimental design | Sustainable product purchase intention & environmental knowledge & environmental activism | Human influencers > VIs for message effectiveness; negative messages more effective; consequential awareness → ↑ activism |
| Riyat et al. (2025) [68] | VBN theory; AIDUA framework; SSRIT framework | Quantitative/Multi-methods design | Promoting sustainable consumption | Biosphere values → hedonic motivation → willingness to follow VI; trust & emotions key for youth sustainable habits |
| Tung and Lan (2024) [81] | Not identified | Qualitative/Case study & content analysis | Various environmental issues & issues related to sustainable practices | VI "Leya" uses nature visuals, storytelling, companionship tone; audience shows admiration + skepticism |
| Wan et al. (2024) [16] | Anthropomorphism theory; Social identity theory | Quantitative/Experimental design | Pro-environmental brhavior: low-cost & high-cost | Low anthropomorphism + low homophily ↓ trust (low-cost behaviors); high anthropomorphism + low homophily ↑ trust (high-cost) |
| Wan et al. (2025) [51] | CASA paradigm; Source credibility models; Source attractiveness model | Quantitative/Survey | VIs with Pro-environmental behavior & Environmental self-identity | Social cues & credibility → ↑ social presence & trust → ↑ pro-env behavior; self-identity moderates |
| Wang et al. (2025) [15] | Mind perception theory; Match-up hypothesis | Quantitative/Experimental design | Sustainable product purchase intention | Human > VI for green products; VI perceived lower altruism/congruence → ↓ brand attitude/purchase; rational language suits VIs |
| Yan et al. (2024) [79] | Not identified | Quantitative/Experimental design | Pro-environmental causes/climate change | HIs > VIs in credibility & purchase; VIs stronger parasocial ties; effects moderated by appeal type & product involvement |
| Yang et al. (2025) [21] | CASA | Quantitative/Computational content analysis | CSR | Humanlike VIs generate higher engagement than cartoonlike VIs in CSR content |
Appendix C. Forward-Looking Research Agenda
| Theory | Context | Characteristics | Method | Integrated Research Questions |
|---|---|---|---|---|
| Research Pathway 1. Strategic Anthropomorphism Fit | ||||
| Anthropomorphism Theory [66] Uncanny Valley [52,98] Social Identity Theory [62] | Low- vs. high-cost pro-environmental behavior Cultural setting (e.g., individualist vs. collectivist) | VI design: humanlike vs. anime vs. nonhuman forms Cultural signaling: local vs. foreign identity cues Expertise framing: expert vs. non-expert positioning Emotional expression: gratitude, hope, guilt, pride | Lab/field experiments (factorial designs Longitudinal tracking of behavioral intentions Cross-cultural comparisons Eye-tracking/fMRI for affective and cognitive processing | How does the fit between VI anthropomorphism level, audience identity (e.g., environmental self-identity, expert skepticism), and behavioral cost moderate trust, credibility, and pro-environmental intentions? Under what cultural or demographic conditions does non-expert positioning enhance (vs. undermine) VI credibility and behavioral impact? How do emotional expressions (e.g., gratitude in anime VIs) interact with product involvement and message framing to mitigate uncanny effects and boost persuasion? |
| Research Pathway 2. Message Design & Narrative Strategy | ||||
| Elaboration Likelihood Model [78] Persuasion Knowledge Model [99] Construal Level Theory [64] | Issue type: climate change vs. plastic pollution vs. CSR Media modality: TikTok (short-form, interactive) vs. VR (immersive) vs. Instagram (visual-static) | Message appeal: hope vs. fear; rational vs. emotional Narrative style: sharing-oriented vs. persuasive Transparency cues: backstage disclosure, sponsorship labeling Interactivity: duets, stitches, polls, AR filters | Computational content analysis in captions or comments Experimental designs with behavioral outcome measures (e.g., click-through, willingness to pay, actual donation) Mixed-methods: survey & in-depth interviews | When and why does hope-framing outperform fear appeals (e.g., across demographic segments)? Does backstage disclosure reduce authenticity only in high-involvement contexts, or is its effect moderated by VI humanness and audience epistemic motivation? How does narrative immersion (e.g., storytelling with green visuals) mediate the effect of message warmth on social-psychological distance and behavioral intentions? |
| Research Pathway 3. Audience Heterogeneity & Moderation | ||||
| Theory of Planned Behavior [100] Value–Belief–Norm Theory [101] Negative Attitude toward Robots Scale (NARS; [102]) | Platform ecology: TikTok (Gen Z) vs. LinkedIn (professionals) National culture: individualism/collectivism, power distance, long-term orientation | Follower traits: environmental knowledge, innovativeness, AI skepticism, self-construal, racial/cultural homophily VI-audience similarity: appearance, values, lifestyle, language | Moderated mediation models Cross-national representative surveys Archival data (engagement metrics) + ML-based audience segmentation (e.g., clustering by comment sentiment) | To what extent does racial homophily interact with anthropomorphism to shape trust? Does this effect reverse for high-cost symbolic actions? How do AI skepticism and NARS jointly moderate the effect of VI humanness on parasocial interaction and downstream behavior? Does consumer innovativeness amplify the impact of stylized (e.g., anime) VIs on relationship commitment, especially for low-involvement green purchases? |
| Research Pathway 4. Mechanisms: Cognitive, Affective, Relational & Inhibitory | ||||
| Computers Are Social Actors (CASA) [54] Social Cognitive Theory [103] Psychological Reactance Theory [104] | Behavioral domain: sustainable consumption vs. activism vs. donation vs. policy support | Mediators: perceived credibility, trust, parasocial interaction, self-efficacy, guilt/pride, perceived autonomy, uncanniness Inhibitors: algorithmic suspicion, moral licensing, reactance to persuasion | Serial/parallel mediation modeling Neuro-marketing tools: fMRI (for reward/empathy circuits), EEG (for cognitive load/attention), GSR (arousal) | How do relational compensation (strong parasocial interaction offsetting low credibility) and affective resonance jointly mediate VI effectiveness, and under what identity conditions (e.g., high environmental self-identity)? Does perceived VI autonomy (vs. human scripting/backstage control) buffer brand transgression effects, and is this effect contingent on disclosure transparency? When does algorithmic reactance override flattery-induced authenticity, especially among high persuasion-knowledge audiences? |
| Research Pathway 5. Long-Term Impact & Societal Implications | ||||
| Behavioral Spillover Theory [105] Social Learning Theory [106] Dark Side of Information Technology Use [107] | Societal level: greenwashing risk, Diversity, Equity, and Inclusion (DEI) representation gaps, digital literacy divide Policy/governance context: European Union (EU) AI Act, Federal Trade Commission (FTC) guidelines, platform-specific AI disclosure norms (e.g., TikTok, Meta) | Behavioral persistence: habit formation vs. novelty decay Moral licensing/rebound effects Brand-VI fit: luxury vs. mass-market, activist vs. corporate Creator–representation alignment, e.g., minority-coded VI produced by nonminority teams | Field experiments with real-world behavioral tracking (e.g., smart meters, purchase logs) Panel/longitudinal surveys Ethnographic design with marginalized communities Comparative policy analysis & regulatory foresight | Do repeated VI exposures lead to sustainable habit formation, or do they trigger moral licensing and rebound effects? How does minority VI creator-representation misalignment (e.g., white creators designing Black-coded VIs) affect perceptions of brand sincerity and DEI commitment? Under what conditions does VI promotion of pro-social behavior spill over to non-promoted domains (e.g., from recycling to transport choices)? How do platform-level policies (e.g., TikTok’s AI disclosure mandate) moderate consumer trust and engagement with pro-environmental VIs? |
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| Keywords Associated with “Virtual Influencers” | Keywords Associated with “Environmental Sustainability” |
|---|---|
|
|
| Theory | Frequency | Key Authors | Papers Identified |
|---|---|---|---|
| Source Credibility Theory | 5 | Hovland et al. [46]; Reeves & Nass [47] | [20,48,49,50,51] |
| Uncanny Valley Theory | 4 | Mori et al. [52]; Arsenyan & Mirowska [17] | [25,49,50,53] |
| Computers are Social Actors (CASA) | 4 | Nass et al. [54]; Nass & Moon [55] | [16,21,50,53] |
| Parasocial Interaction Theory | 2 | Horton & Wohl [56]; Goffman [57] | [13,58,59] |
| Social Performance Theory | 2 | Durkheim & Fields [60]; Turner [61] | [12] |
| Social Identity Theory | 2 | Tajfel & Turner [62] | [16,59] |
| Theory of Mind Perception | 2 | Liu & Lee [63] | [15,49] |
| Construal Level Theory | 2 | Trope & Liberman [64] | [25,49] |
| Anthropomorphism Theory | 1 | Duffy [65]; Epley et al. [66] | [16] |
| Value–Belief–Norm (VBN) Framework | 1 | Stern [67] | [68] |
| Artificial Intelligence Device Use Acceptance (AIDUA) Framework | 1 | Gursoy et al. [69] | [68] |
| Social Service Robot Interaction Trust (SSRIT) Framework | 1 | Chi et al. [70] | [68] |
| Transformative Advertising Research (TAR) framework | 1 | Gurrieri et al. [71] | [58] |
| Technological Affordance Theory | 1 | Gibson [72] | [58] |
| Expectation Violation Theory | 1 | Burgoon [73] | [48] |
| Source Attractiveness Model | 1 | McGuire [74] | [16] |
| Media Richness Theory | 1 | Daft & Lengel [75] | [20] |
| Stereotype Content Model | 1 | Fiske et al. [76] | [14] |
| Dual-System Processing Theory | 1 | Cacioppo et al. [77] | [49] |
| Elaboration Likelihood Model (ELM) | 1 | Petty et al. [78] | [50] |
| Variable Categories | Constructs | References |
|---|---|---|
| Independent variables (IVs) | ||
| Influencer type | Human SMI vs. VI; Anime vs. Human-like; alone VI or mixed VI with HI | [15,16,25,48,49,50,53,79] |
| Influencer attributes | Expertise, attractiveness, familiarity, similarity, parasocial interaction, authenticity, homophily | [51,58,59] |
| Media/format manipulations | 360° vs. regular; backstage disclosure; IVR vs. traditional; doppelganger vs. avatar | [20,80] |
| Message features | Warmth level | [14] |
| Hedonic motivations | Biosphere value; Awareness of consequences; Ascription of responsibility | [68] |
| Social cues | Social influence; anthropomorphism; warmth; competence | [51,68] |
| Moderator variables | ||
| Audience characteristics | Environmental knowledge; Consumer innovativeness; Trust in experts; Environmental self-identity | [14,21,51,53,58,59] |
| VI characteristics | Racial homophily (foreign vs. local-like); Environmental expert vs. non-expert | [16,50] |
| Message characteristics | Sponsorship disclosure; Advertising appeal (Compassion vs. Innovation); narrative types (persuasion vs. sharing-oriented); emotional appeal (pride vs. gratitude); language type (rational vs. emotional); Emotional Appeal (fear vs. Hope appeal) | [15,25,48,49,50,79] |
| Product characteristics | Product involvement (Low vs. High) | [25,79] |
| Mediator variables | ||
| Source-related mechanisms | Source & message credibility; perceived authenticity; perceived altruistic motivation; perceived congruence | [15,20,48] |
| Trustworthiness | Trust in CSR; perceived credibility; trust in VI; Cognitive Trust | [25,50,68,79] |
| Relational mechanisms | Parasocial relationship; relationship commitment; social-psychological distance | [14,58,59,79] |
| Persuasion mechanisms | Message effectiveness; role-model influence | [20,53] |
| Attitudinal mechanisms | Attitudes towards the pro-environmental cause; consequential awareness | [14,53] |
| Technology-/adoption-based mechanisms | Performance expectancy; effort expectancy; emotions | [68] |
| Emotions | Affective resonance with pro-environmental advocacy | [50,68] |
| Dependent variables (DVs) | ||
| Pro-environmental/prosocial outcomes | Activism; engagement with the pro-environmental cause; norms/attitudes/behaviors; Sustainable product purchase intentions; Donation intentions | [14,16,20,51,53,80] |
| Brand-related outcomes | Brand attitude; purchase intention | [25,49,58,59,79] |
| Influencer-related outcomes | Attitudes toward VI; willingness/objections to follow | [20,68] |
| CSR evaluative outcomes | CSR skepticism | [48] |
| Variable Categories | References | Percentage |
|---|---|---|
| Method | ||
| Quantitative | [15,16,20,21,25,48,49,50,51,53,59,68,79,80] | 73.7 |
| Qualitative | [12,13,58,81] | 21.1 |
| Mixed methods | [14] | 5.2 |
| Research design (primary) | ||
| Experiment (online/lab/IVR; factorial designs) | [14,15,16,20,25,48,49,50,53,79,80] | 57.9 |
| Survey | [51,59,68] | 15.8 |
| Content analysis/observational social-media data | [12,13,21,81] | 21.1 |
| Qualitative multi-method case study (e.g., case analysis, interviews, focus groups, institutional analysis) | [58] | 5.2 |
| Stimulus/empirical materials (primary) | ||
| Platform-mimicking stimuli (fictitious profiles/posts/ads; standardized message layouts; scripted video stimuli) | [14,15,16,20,25,48,49,50,53,79] | 52.6 |
| Immersive VR exposure (avatar-delivered IVR instruction) | [80] | 5.2 |
| Naturally occurring social-media content (posts/profiles/comments as data) | [12,13,21,81] | 21.2 |
| Survey-based recalled experience (no standardized stimulus; respondents reflect on prior exposure) | [51,59,68] | 15.8 |
| Case-based qualitative materials (non-standardized stimulus) | [58] | 5.2 |
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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Voutsa, M.C.; Georgiou, Y.; Charalambous, D. Virtual Voices for a Sustainable Future: A Systematic Scoping Review on Virtual Influencers. Sustainability 2026, 18, 2730. https://doi.org/10.3390/su18062730
Voutsa MC, Georgiou Y, Charalambous D. Virtual Voices for a Sustainable Future: A Systematic Scoping Review on Virtual Influencers. Sustainability. 2026; 18(6):2730. https://doi.org/10.3390/su18062730
Chicago/Turabian StyleVoutsa, Maria C., Yiannis Georgiou, and Demetris Charalambous. 2026. "Virtual Voices for a Sustainable Future: A Systematic Scoping Review on Virtual Influencers" Sustainability 18, no. 6: 2730. https://doi.org/10.3390/su18062730
APA StyleVoutsa, M. C., Georgiou, Y., & Charalambous, D. (2026). Virtual Voices for a Sustainable Future: A Systematic Scoping Review on Virtual Influencers. Sustainability, 18(6), 2730. https://doi.org/10.3390/su18062730

