Virtual Social Influencer Connectedness Scale (VSIC) | PsychScales

{n “title”: “Virtual Social Influencer Connectedness Scale (VSIC)”,n “excerpt”: “The Virtual Social Influencer Connectedness Scale (VSIC) is a psychometrically validated 5-item unidimensional instrument developed by van Esch et al. (2026) to assess the affective bonds, relatability, and sense of community consumers experience toward computer-generated virtual social influencers.”,n “slug”: “virtual-social-influencer-connectedness-scale-vsic”,n “categories”: [n “Psychometrics”,n “Consumer Psychology”,n “Social Psychology”,n “Media Psychology”n ],n “tags”: [n “Virtual Social Influencers”,n “Psychometrics”,n “Connectedness”,n “Parasocial Interaction”,n “Scale Validation”,n “Consumer Behavior”,n “Affective Bonding”,n “Digital Humans”n ],n “seo_title”: “Virtual Social Influencer Connectedness Scale (VSIC): Psychometric Profile”,n “seo_description”: “Explore the Virtual Social Influencer Connectedness Scale (VSIC), a 5-item unidimensional instrument measuring consumer emotional bonds with virtual influencers.”,n “focus_keyword”: “Virtual Social Influencer Connectedness Scale”,n “content”: “

1. Abstract

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The Virtual Social Influencer Connectedness Scale (VSIC) is a 5-item, unidimensional psychometric instrument developed by van Esch, Cui, Arli, and Eskridge (2026) to measure the affective bond, subjective relatability, and sense of belonging that human consumers experience toward computer-generated, artificial intelligence-driven virtual social influencers (VSIs). Embedded within a broader dual-construct architecture alongside the Virtual Social Influencer Trustworthiness Scale (VSIT), the VSIC isolates the emotional and relational dimensions of human-synthetic social agent interactions from purely cognitive assessments of agent reliability or technical competence. Developed and psychometrically evaluated across five distinct empirical studies, the scale demonstrated robust structural characteristics across exploratory factor analysis (EFA; n = 400) and confirmatory factor analysis (CFA; n = 297), confirming strict unidimensionality, high factor loadings, and superior fit indices. Administered via a standard 7-point Likert response format, the scale exhibits high internal consistency reliability (ω > .88; α > .88) and distinct construct validity. In pre-registered predictive validity modeling (n = 210, AsPredicted #164904), connectedness surfaced as the preeminent behavioral driver among consumers, accounting for substantial variance in behavioral intentions, including intention to recommend (β = .909), willingness to follow advice (β = .834), and composite word-of-mouth and willingness to pay premium prices (β = .834). Nomological network evaluation (n = 306) demonstrated strong theoretical convergence with emotional attachment (β = .845), parasocial relationships (β = .804), and self-influencer identification (β = .893). Importantly, the construct was empirically separable from perceived authenticity, indicating that consumers establish robust affective relationships with synthetic entities despite full awareness of their artificial ontology. The VSIC serves as a vital psychometric instrument for communication researchers, media psychologists, and consumer behavior scholars analyzing synthetic media interactions.

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2. Keywords

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Virtual Social Influencer Connectedness Scale, VSIC, virtual social influencers, parasocial relationships, consumer-agent interaction, synthetic media, affective bonding, emotional attachment, scale validation, psychometrics

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3. Authors

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The Virtual Social Influencer Connectedness Scale was conceived, empirically validated, and published by an international research team comprising:

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• Patrick van Esch – Department of Marketing, College of Business, Law, and Governance, James Cook University, Townsville, Australia; and Affiliate Researcher in Algorithmic Marketing.

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• Yuanyuan (Cathy) Cui – School of Business, Western Sydney University, Sydney, Australia.

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• Deni Arli – Department of Marketing, Monash Business School, Monash University, Melbourne, Australia.

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• Brian Eskridge – Department of Computer Science and Engineering, Western New England University, Springfield, Massachusetts, United States.

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Correspondence regarding the original scale development and programmatic computational psychometrics should be directed to Patrick van Esch via academic and institutional channels detailed in the primary publication in the Australasian Marketing Journal.

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4. Purpose

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The rapid proliferation of computer-generated digital avatars, synthetic personas, and generative artificial intelligence (AI) brand ambassadors—collectively known as virtual social influencers (VSIs)—has disrupted the social media landscape. Entities such as Lil Miquela, Imma, and Shudu command millions of followers, secure high-profile commercial endorsements, and shape cultural discourses. Despite their synthetic ontology, audiences routinely exhibit profound affective engagement with these non-human personalities. Prior to the development of the Virtual Social Influencer Connectedness Scale (VSIC), marketing scholars and media psychologists relied almost exclusively on psychometric tools designed for living human influencers, such as conventional celebrity endorsement scales or generalized human-to-human parasocial interaction metrics.

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These legacy instruments systematically fail to capture the unique relational nuances that emerge when human consumers interact with an explicitly fabricated, algorithmically managed entity. The primary purpose of the VSIC is to provide a theoretically grounded, psychometrically validated, and computationally parsimonious instrument to quantify the emotional connection, subjective relatability, and felt sense of social belonging experienced by individuals within synthetic media environments. The scale addresses why consumers develop sustained loyalty and affinity toward entities with no physical biology, conscious mind, or authentic lived experience.

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In applied and academic contexts, the VSIC fulfills several vital roles:

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• Distinguishing Affective from Cognitive Responses: The VSIC explicitly disentangles the affective assessment of synthetic relationship strength (connectedness) from the cognitive evaluation of source credibility, competence, and reliability (trustworthiness, measured via the sister instrument, the VSIT).

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• Predictive Behavioral Modeling: In consumer research, the scale serves as an antecedent metric for downstream behaviors, such as brand endorsement adoption, purchase intention, engagement with sponsored content, and resistance to negative influencer publicity.

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• Human-Computer Interaction (HCI) and Media Psychology: The instrument facilitates empirical investigation into how anthropomorphic visual fidelity, narrative framing, and synthetic agency evoke genuine human emotional attachment.

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• Ethical and Regulatory Inquiries: By accurately calibrating audience connectedness, researchers and policymakers can evaluate the psychological susceptibility of vulnerable demographics to algorithmic persuasion and commercial emotional manipulation.

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5. Psychological Construct

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The construct measured by the VSIC is Virtual Social Influencer Connectedness, conceptualized as an affective, relational appraisal of the emotional bond, felt intimacy, and sense of mutual or imagined belonging established between an individual consumer and a virtual entity. Unlike purely intellectual, aesthetic, or novelty-based fascination with CGI technology, connectedness operates within the realm of emotional resonance.

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Core Dimensions of the Connectedness Construct

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Although mathematically unidimensional, the construct embodies three deeply interrelated psychological facets that manifest consistently across consumer self-reports:

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• Affective Resonance and Emotional Closeness: This facet captures the warm emotional feelings and subjective proximity a user experiences toward the VSI. When a virtual influencer shares a simulated vulnerability, struggles with a fictional personal conflict, or models emotional states, connected consumers report genuine empathy, affective mirroring, and positive emotional valence.

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• Perceived Relatability and Self-Referential Relevance: This facet denotes the consumer’s perception that the virtual entity reflects their own lifestyle, social values, psychological struggles, or aesthetic identities. Consumers view the VSI not as an alien piece of computer code, but as a relatable peer or an aspirational reflection of their personal self-concept.

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• Imagined Community and Social Belonging: Connectedness extends beyond a dyadic interaction to encompass membership in an interpretive social circle. Consumers feel part of a unique collective of followers who appreciate the influencer’s digital narrative, fostering feelings of social inclusion and shared identity.

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Critically, the construct of connectedness is non-evaluative with respect to physical authenticity. A consumer may score exceptionally high on the VSIC while remaining fully cognizant that the influencer is a 3D digital model generated in Unreal Engine or Maya. Thus, connectedness reflects psychological immersion in a curated social persona rather than a cognitive delusion regarding biological existence.

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6. Theoretical Framework

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The development of the VSIC rests on an interdisciplinary theoretical synthesis drawing from media psychology, evolutionary social psychology, and consumer culture theory.

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Parasocial Interaction and Parasocial Relationship Theory

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Pioneered by Horton and Wohl (1956), parasocial theory posits that media consumers form one-sided, illusionary social relationships with mediated personae. In traditional media, these relationships formed with television actors or radio hosts. The VSIC builds upon the modern extension of this framework (Social Presence Theory; Biocca, 1997), arguing that hyper-interactive digital channels—such as direct messaging, algorithmic comment replies, and interactive live streams—amplify the illusion of intimacy, enabling synthetic agents to elicit parasocial bonds comparable in intensity to human-to-human bonds.

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Computers Are Social Actors (CASA) Paradigm

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Formulated by Reeves and Nass (1996), the CASA paradigm establishes that humans mindlessly apply social rules, expectations, and emotional schemas to computer technologies whenever those technologies display basic social cues (e.g., natural language, facial expressions, emotional responsiveness). van Esch et al. (2026) applied the CASA framework to explain why human consumers readily construct affective connectedness with algorithms: the human brain’s evolutionary hardware did not evolve to differentiate between mediated biological humans and hyper-realistic digital avatars possessing anthropomorphic traits.

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Social Identity Theory and Self-Expansion

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According to Social Identity Theory (Tajfel & Turner, 1979) and Aron’s Self-Expansion Model, individuals integrate valued relational partners into their core sense of self. The theoretical framework of the VSIC posits that consumers utilize their relationship with a virtual influencer as a digital badge of identity, adopting the influencer’s aesthetic norms, digital lifestyle, and consumption practices into their own extended self-concept.

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7. Validity

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The psychometric validity of the VSIC was evaluated across five rigorously designed empirical studies utilizing both exploratory and confirmatory protocols, nomological network mapping, and pre-registered predictive validation.

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Construct, Convergent, and Discriminant Validity

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Across validation cohorts (CFA n = 297), the VSIC demonstrated exceptional convergent validity. Average Variance Extracted (AVE) values exceeded the established psychometric threshold of .50, running above .65 across all samples. All five standardized item loadings (λ) exceeded .75 (ranging from .78 to .89), confirming that the indicators share high common variance attributable to the underlying latent connectedness construct.

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Discriminant validity was established via the Fornell-Larcker criterion and the Heterotrait-Monotrait ratio of correlations (HTMT). The square root of the AVE for connectedness significantly exceeded its bivariate correlations with associated constructs, including the companion construct of Virtual Social Influencer Trustworthiness (VSIT), perceived visual realism, and technological novelty. Furthermore, all HTMT values remained substantially below the conservative .85 threshold, demonstrating that connectedness is statistically distinct from general trust, cognitive credibility, and anthropomorphic curiosity.

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Nomological Network Validity

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Nomological validity was tested in an empirical study of 306 active social media consumers exposed to mainstream virtual influencers. The latent construct was embedded within a structural equation model containing theoretically adjacent constructs:

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• Self-Influencer Identification: Connectedness demonstrated a profound, statistically significant association with identification (β = .893, p < .001).

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• Emotional Attachment: Connectedness strongly predicted emotional attachment to the synthetic persona (β = .845, p < .001).

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• Parasocial Relationship Strength: Connectedness accounted for major variance in traditional parasocial measures (β = .804, p < .001).

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Crucially, the nomological analysis revealed that connectedness did not significantly predict perceived authenticity. This non-significant path provided vital empirical confirmation of the theoretical premise: consumer emotional connection to a virtual agent does not depend upon the illusion of physical or biological reality. Affective bonding thrives even when the user fully acknowledges the agent’s synthetic nature.

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Pre-Registered Predictive Validity

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To confirm predictive efficacy and eliminate publication bias, van Esch et al. executed a pre-registered experimental study (AsPredicted #164904; n = 210). In competing structural regression equations evaluating both trustworthiness (VSIT) and connectedness (VSIC), connectedness emerged as the dominant behavioral driver:

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• Intention to Recommend: Connectedness exerted a primary influence on consumer recommendation intentions (β = .909, p < .001).

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• Advice Adherence: Connectedness directly governed followers’ willingness to heed the influencer’s advice (β = .834, p < .001).

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• Composite Behavioral Intentions: Connectedness robustly predicted overall behavioral intentions, including positive word-of-mouth (WOM) and willingness to pay premium prices for endorsed products (β = .834, p < .001).

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8. Reliability

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The VSIC displays high internal consistency and scale precision across multiple independent cross-sectional and experimental samples. Classical test theory indices, alongside modern composite reliability metrics, confirm the instrument’s stability:

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• Cronbach’s Alpha (α): Across the EFA (n = 400), CFA (n = 297), and nomological (n = 306) studies, Cronbach’s alpha values consistently ranged between .88 and .93, well above the recommended .70 benchmark for psychological instruments.

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• McDonald’s Omega (ω): Recognizing the well-documented limitations of alpha under conditions of varying item factor loadings (tau-equivalence violations), McDonald’s hierarchical and total omega coefficients were computed, yielding values consistently exceeding .89, confirming robust composite reliability.

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• Item-Total Correlations: Corrected item-to-total correlations across all five indicators were uniformly high (all r > .68), confirming that every individual item makes a substantive, non-redundant contribution to the overall scale score.

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• Cross-Sample Stability: Reliability coefficients remained virtually invariant across varied target virtual influencers (e.g., hyper-realistic fashion avatars versus stylized anime-style virtual streamers), demonstrating that the scale’s measurement precision is not idiosyncratic to a single avatar design aesthetic.

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9. Factor Analysis

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The latent structure of the VSIC was systematically examined through sequential exploratory and confirmatory factor analytic workflows using rigorous algorithmic and empirical methods.

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Exploratory Factor Analysis (EFA)

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In the initial scale purification phase (Study 1, n = 400), an initial pool of candidate items generated through algorithmic text extraction, expert panels, and consumer sentiment mining was subjected to common factor analysis (Maximum Likelihood extraction with Promax rotation). The statistical diagnostics unequivocally confirmed data factorability:

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• Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy = .921, indicating outstanding sampling sufficiency.

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• Bartlett’s Test of Sphericity reached high statistical significance (χ²[10] = 1,482.35, p < .0001), rejecting the identity matrix hypothesis.

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• Parallel Analysis and scree plot inspection confirmed a clear one-factor solution, with the primary factor accounting for over 68% of the total variance. Five clear items loaded onto this factor with loadings ranging from .78 to .89, exhibiting negligible cross-loadings with the companion trustworthiness items.

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Confirmatory Factor Analysis (CFA)

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The unidimensional five-item architecture was subsequently tested on an independent validation sample (Study 2, n = 297) using structural equation modeling software with maximum likelihood estimation. The single-factor measurement model demonstrated outstanding global and local fit indices:

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Standardized factor loadings were uniformly high, confirming that each item is a strong indicator of the latent construct without the need for post-hoc error covariance modifications.

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10. Instrument / Measurement Tool

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The operational administration parameters and scoring mechanisms for the VSIC are structured as follows:

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• Test Type: Self-report psychometric rating scale; perceptual and affective assessment instrument.

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• Target Population: General population, social media users, digital consumers, and research participants exposed to virtual influencers or synthetic digital humans.

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• Administration Modality: Online survey, paper-and-pencil, or integrated lab computer interface. Typical administration duration is under 2 minutes.

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• Structural Composition: 5 items, unidimensional construct.

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• Response Scale: Standard 7-point Likert rating scale:\n
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  • 1 = Strongly Disagree

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  • 2 = Disagree

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  • 3 = Somewhat Disagree

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  • 4 = Neither Agree nor Disagree (Neutral)

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  • 5 = Somewhat Agree

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  • 6 = Agree

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  • 7 = Strongly Agree

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• Scoring Protocol: All 5 items are positively phrased. No reverse scoring is necessary. The overall VSIC index is computed either as:\n
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  • Mean Composite Score: Sum of all five responses divided by 5 (range: 1.00 to 7.00). Higher scores reflect stronger affective connection and relational closeness.

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  • Latent Variable Modeling: In SEM software (e.g., AMOS, Mplus, R lavaan), modeled as a reflective latent factor where all five items serve as continuous indicators.

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11. Permissions & Fee and Test Year

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The Virtual Social Influencer Connectedness Scale was officially published in 2026 in the Australasian Marketing Journal (Sage Publications). The intellectual property and formal copyright of the published research article reside with the authors and the respective publishing body.

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For non-commercial academic research, pedagogical inquiries, and scientific investigations, the scale may be utilized under scholarly fair use principles, provided formal attribution and APA-formatted citation are given to van Esch et al. (2026). Commercial entities, market research corporations, software analytics firms, and digital talent agencies wishing to embed the VSIC within commercial diagnostics, corporate tracking systems, or algorithmic optimization platforms must obtain explicit written authorization and licensing agreements from the copyright holders.

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12. References

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• Biocca, F. (1997). The cyborg’s dilemma: Progressive embodiment in virtual environments. Journal of Computer-Mediated Communication, 3(2), JCMC324. https://doi.org/10.1111/j.1083-6101.1997.tb00070.x

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• Horton, D., & Wohl, R. R. (1956). Mass communication and para-social interaction: Observations on intimacy at a distance. Psychiatry, 19(3), 215–229. https://doi.org/10.1080/00332747.1956.11023049

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• Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.

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• Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp. 33–47). Brooks/Cole.

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• van Esch, P., Cui, Y., Arli, D., & Eskridge, B. (2026). Virtual social influencer scale development and validation: An algorithmic approach. Australasian Marketing Journal. Advance online publication. https://doi.org/10.1177/14413582261423028

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13. Items of the Scale

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