Name: How Virtual Celebrity Characteristics Drive Purchase Intention: Testing the Stimulus-Organism-Response Framework with Structural Equation Modeling (Video) | JoVE Journal
Uploaded: 2026-03-03T14:40:00.000+00:00
Duration: 7 min 35 s
Description: Discover how virtual celebrity attributes drive consumer purchase intention using PLS-SEM and the SOR framework. This study reveals content quality as the strongest predictor of satisfaction and loyalty.

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5 minutes

This work was supported by research funding from aSSIST University.

The study qualified for exemption from full ethical review under institutional minimal-risk guidelines, as it involved an anonymous online survey with no personally identifiable information collected. An informed consent form describing the study purpose, voluntary participation, anonymity, and data usage was presented at the beginning of the survey, and only consenting participants proceeded.
1. Sample size determination
The final sample (N = 249) was determined based on statistical power requirements for PLS&#8211;SEM. The maximum number of predictors for a single endogenous construct was six (three virtual celebrity characteristics and three control variables for satisfaction; satisfaction, interaction term, and controls for loyalty). Power analysis indicated that detecting a medium effect size (f2 = 0.15) at &#945; = 0.05 and 0.80 power required approximately 97 observations. The achieved sample size exceeded this threshold, ensuring adequate power for structural, mediation, and moderation analyses.
2. Participant recruitment and screening
Participants were recruited via an online research panel using purposive nonprobability sampling to ensure prior experience with virtual celebrities. Eligibility was restricted to individuals aged 18 or older. Screening and attention-check items were included, and invalid responses were excluded. Data collection concluded once the target sample size was reached.
3. Standardized definition and survey instructions
Participants were provided with a standardized definition of virtual celebrities as digitally created audiovisual personas with stable visual identity, voice, and recurring narrative content. They were instructed to respond based on their actual experiences.
4. Survey Instrument development and administration
Validated measurement scales were adapted to the virtual celebrity context (Table 1) where all constructs were measured using 7-point Likert scales (1 = strongly disagree; 7 = strongly agree). The questionnaire included sections on appearance, voice, content quality, satisfaction, loyalty, personalization, purchase intention, and demographics. Platform controls prevented duplicate submissions.
Table 1: Measurement items and sources. Measurement items used for each construct include appearance attractiveness, voice attractiveness, content quality, satisfaction, loyalty, personalization, and purchase intention. Sources of scale adaptation are provided. <a href="https://www.jove.com/files/ftp_upload/70685/Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
5. Data preparation and cleaning
Data were exported in CSV format. Incomplete, failed-screening, and failed attention-check responses were removed. The cleaned dataset was imported into PLS&#8211;SEM software for analysis.
6. Measurement and structural model evaluation
All constructs were specified as reflective. Reliability was assessed using Cronbach&#8217;s alpha and composite reliability (&#8805; 0.70). Convergent validity was evaluated using AVE (&#8805; 0.50), and discriminant validity was assessed using the Fornell&#8211;Larcker criterion and HTMT (&#60; 0.90).
Structural paths were specified according to the conceptual model. Multicollinearity was assessed using Variance Inflation Factor (VIF) &#60; 5.0. Bootstrapping with 5,000 subsamples generated path coefficients, t-values, p-values, 95% confidence intervals, and effect sizes (f2).
<ol>
	<li>Mediation analysis 
	Sequential mediation (virtual celebrity attributes &#8594; satisfaction &#8594; loyalty &#8594; purchase intention) was tested using bootstrapped indirect effects (5,000 subsamples) with 95% confidence intervals.</li>
	<li>Moderation analysis 
	Interaction terms tested the moderating effects of personalization. Bootstrapping estimated moderation coefficients and effect sizes, and interaction plots illustrated conditional effects.</li>
	<li>Predictive relevance 
	Predictive performance was assessed using blindfolding and/or out-of-sample procedures. Q2_predict values, RMSE, and MAE were compared against a linear benchmark model. Predictive relevance was supported when Q2_predict &#62; 0 and PLS errors were lower than the benchmark.</li>
	<li>Reporting and verification 
	All reliability, validity, structural, mediation, moderation, and predictive results were compiled for reporting. Outputs were cross-checked against software results to ensure accuracy and consistency.</li>
</ol>
7. Troubleshooting and modifications
If screening failures were high, eligibility criteria and instructions were refined. If reliability (CR &#60; 0.70) or AVE (&#60; 0.50) fell below thresholds, poorly performing indicators were removed while maintaining theoretical coherence. Multicollinearity issues (VIF above limits) were addressed by reassessing construct specifications. If mediation or moderation effects were non-significant, sample size or operationalization was re-evaluated. Bootstrapping used 5,000 subsamples to ensure stable estimates.

The authors declare no conflicts of interest.

This study examines how virtual celebrity characteristics influence satisfaction, loyalty, and purchase intention within an SOR framework, with personalization as a moderator. Three key insights emerge: content quality is the strongest driver of satisfaction relative to appearance and voice; satisfaction influences purchase intention only indirectly through loyalty (indirect-only mediation); and personalization strengthens the satisfaction&#8211;loyalty relationship but does not directly affect purchase intention.
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Celebrity endorsement remains widely used, and firms increasingly adopt virtual celebrities across digital media channels1. Advances in AI and related technologies have diversified virtual celebrity forms and improved scalability and interaction2.
Virtual celebrities are hybrids of virtual characters and traditional celebrities3 and are defined here as digitally created personas that achieve public recognition and sustained attachment through recurring narrative-based interaction3,4. This study focuses on audiovisual virtual celebrities in consumer-facing digital marketing. Although firms increasingly rely on them to influence consumer behavior5, research has paid limited attention to how virtual celebrity experiences translate into satisfaction, loyalty, and purchase intention.
Prior work has examined behavioral, cognitive, and affective perspectives4, emphasizing credibility and authenticity6,7, perceived authenticity8, identity disclosure9, information quality10, and emotional responses11. Studies have also highlighted attractiveness cues such as appearance, voice, and content quality12, yet often fail to explain how these factors drive consumer outcomes13,14. As a result, favorable evaluations do not consistently translate into purchase intention, and evidence on the satisfaction–purchase relationship remains mixed.
To address this gap, this study examines how appearance, voice, and content quality shape satisfaction, loyalty, and purchase intention, while testing personalization as a moderator15,16 using the SOR framework.
This study contributes by conceptualizing virtual celebrity attractiveness as appearance, voice, and content17, clarifying loyalty as the key mechanism linking satisfaction to purchase intention18,19, and identifying personalization as an amplifier of the satisfaction–loyalty relationship20.
Stimulus-Organism-Response (SOR) framework 
The SOR framework explains how environmental stimuli influence internal states and behavioral responses21 through a sequence of stimuli (S), organismic states (O), and responses (R). It has been widely applied in contexts such as the Metaverse, gaming, retail, advertising, e-commerce, and celebrity endorsement22,23,24,25,26,27,28. However, while SOR structures relationships among virtual celebrity attributes, evaluations, and outcomes, it does not fully explain how evaluations become behavioral intentions. Therefore, this study integrates relationship marketing and loyalty theory29 to clarify the satisfaction–loyalty–purchase intention mechanism.
Consumer responses to virtual celebrities are increasingly shaped by experiential cues such as audiovisual attractiveness and content engagement4,30. Appearance and voice influence initial impressions, while content quality sustains emotional and narrative engagement14,31. Although these factors foster positive evaluations, prior research shows that favorable perceptions alone may not drive purchase intention. Relationship constructs such as loyalty and commitment appear to convert positive experiences into behavioral intentions32,33. Accordingly, appearance, voice, and content quality are modeled as stimuli; satisfaction and loyalty as organismic states; and purchase intention as the behavioral response. Loyalty is conceptualized as psychological commitment, and purchase intention as the conative outcome.
Celebrity endorsement 
Celebrity endorsement is a widely used marketing strategy34 in which recognized individuals promote products or services35. Its use has expanded globally1,36,37, particularly with the rise of social media influencers37. Computer-generated influencers have emerged as alternatives to human endorsers37, operating primarily through digital platforms38.
Extensive research has examined endorsement effectiveness39,40, proposing models such as source credibility41, meaning transfer35, extended source attractiveness42, celebrity-brand fit43, and process-transfer effects44. These frameworks emphasize credibility, attractiveness, symbolic meaning transfer, and brand congruence45, providing a foundation for understanding endorsement mechanisms in virtual contexts.
Virtual celebrity 
Recent studies examine AI-generated influencers, VTubers, and virtual brand ambassadors across digital environments46, emphasizing interaction, personalization, and authenticity. Media richness theory suggests richer media enhance communication effectiveness47, and virtual characters exemplify such richness through immersive, multi-sensory interaction48, influencing emotional and social responses49.
Although “virtual celebrity” encompasses various digital personas50, this study focuses on visually embodied audiovisual virtual celebrities with stable identities and recurring content. Prior research frames their influence through behavioral, cognitive, and affective perspectives4. Behavioral research examines engagement and purchase behavior32,51; cognitive research highlights credibility, authenticity8, identity disclosure9,52, and information quality10; affective research emphasizes emotional attitudes11.
Virtual celebrity characteristics 
Building on source credibility theory, research has examined anthropomorphism, attractiveness, trustworthiness, expertise, and parasocial interaction in virtual contexts1,32,53. Anthropomorphism positively shapes consumer responses54,55 and aligns with the Computers Are Social Actors paradigm56, extended to media agents57, chatbots58, and virtual influencers. However, virtual celebrities vary in realism4, and uncanny valley theory suggests near-human representations may evoke discomfort59.
Technological advances have shifted virtual celebrities toward dynamic, content-generating agents, increasing the importance of experiential attributes such as content quality and voice expressiveness. Research has examined synthetic voice14, facial appearance13, appearance–voice congruence60, audiovisual attractiveness30, and responses to computer-generated faces61.
This study conceptualizes virtual celebrity attractiveness through appearance, voice, and content. Physical attractiveness influences social evaluation62, consistent with the halo effect62, and is expected to enhance satisfaction. Content quality represents the narrative and experiential dimension of virtual celebrity output and is conceptually distinct from service quality63,64,65.
Satisfaction 
Satisfaction reflects consumers’ cognitive and emotional evaluation of whether experiences meet expectations66,67 and is shaped by perceived performance relative to expectations68,69. It has been widely studied in online contexts70 and is central to performance assessment71. Attractiveness influences satisfaction in service settings72,73, and voice attractiveness has shown similar effects74. Accordingly, appearance, voice, and content quality are expected to shape satisfaction in virtual celebrity contexts.
Accordingly, the following hypotheses are proposed: 
H1: The Appearance attractiveness of virtual celebrities positively influences consumer satisfaction. 
H2: Voice attractiveness of virtual celebrities positively influences consumer satisfaction. 
H3: Content quality of virtual celebrities positively influences consumer satisfaction.
Loyalty 
In this study, loyalty is conceptualized as attitudinal loyalty, reflecting a consumer’s psychological commitment and bond with the virtual celebrity rather than actual repeat behavior19,33. Loyalty represents a willingness to maintain the relationship despite barriers75 and has been widely examined as an outcome of satisfaction across online settings67,76, tourism77, and VTuber endorsement contexts33. Accordingly, the following hypothesis is proposed: 
H4: Consumer satisfaction with virtual celebrities positively influences loyalty to virtual celebrities.
Purchase Intention on the goods and services endorsed by virtual celebrities 
Purchase intention reflects a consumer’s likelihood and readiness to purchase a product or service67,78,79 and is influenced by factors such as satisfaction, trust, loyalty, and enjoyment78. Loyalty has been shown to strongly predict purchase intention80. In virtual celebrity contexts, research has examined purchase intention effects8,52 and the mediating role of satisfaction between attractiveness and purchase intention81,82. Accordingly, the following hypotheses are proposed: 
H5a: Consumer satisfaction positively influences purchase intention indirectly through loyalty. 
H5b: The direct relationship between satisfaction and purchase intention is non-significant when loyalty is included in the model. 
H6: Consumer loyalty toward virtual celebrities positively influences purchase intention.
Personalization (personalized service) 
Interactivity originates from sociology and refers to relationship-based communication83, often enabled through technology84. AI has strengthened interactive marketing through personalized services46, making personalization central to digital marketing effectiveness85. Personalization involves tailoring content or interaction to individuals to improve outcomes86 and support engagement and value co-creation87. Advances in big data and digital traces enable firms to personalize communication using behavioral histories and self-disclosed user information88,89,90.
Prior studies show that personalization moderates key relationships such as quality–satisfaction91, trust–satisfaction16, and technology adoption under privacy concerns92, as well as other relational pathways24,25,26,27. In this study, personalization is treated as a contextual moderator shaping how satisfaction and loyalty translate into purchase-related outcomes. The following hypotheses are proposed: 
H7a: Perceived personalization strengthens the relationship between satisfaction and loyalty. 
H7b: Perceived personalization strengthens the relationship between satisfaction and purchase intention. 
H7c: Perceived personalization strengthens the relationship between loyalty and purchase intention.
Conceptual research model 
Figure 1 presents the conceptual model grounded in the stimulus–organism–response framework: appearance, voice, and content (stimuli) influence satisfaction and loyalty (organism), with purchase intention (response) arising primarily through loyalty. Personalization strengthens the satisfaction–loyalty relationship and is modeled as a moderator of selected structural paths.
<img alt="Virtual celebrity influence path model diagram: stimuli-organism-response with variables CAA, CVA, CCA." class="xfigimg" src="/files/ftp_upload/70685/70685fig1.jpg" data-altupdatedat="1777953618000" data-alt="Figure 1"> 
Figure 1: Research model based on the stimulus–organism–response framework. Conceptual model illustrating the relationships among virtual celebrity attributes (appearance, voice, and content quality) as stimuli, organismic variables (satisfaction and loyalty), and behavioral outcome (purchase intention). Personalization is modeled as a moderator of the satisfaction–loyalty relationship. Indirect effects through loyalty are depicted. Note. CAA = Characteristic appearance attractiveness; CVA = Characteristic voice attractiveness; CCA = Characteristic content attractiveness; LO = loyalty; PE = Personalization; PI = Purchase Intention; SA = Satisfaction. <a href="https://www.jove.com/files/ftp_upload/70685/70685fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>Qualtrics&lt;br/&gt; Version: N/A</td><td>Qualtrics, LLC</td><td>N/A</td><td>Survey coding, https://www.qualtrics.com/</td></tr><tr><td>Prolific&lt;br/&gt; Version: N/A</td><td>Prolific</td><td>N/A</td><td>Online survey pannel provider, https://www.prolific.com/</td></tr><tr><td>SmartPLS&lt;br/&gt; Version: 4.0.1</td><td>SmartPLS GmbH</td><td>N/A</td><td>PLS-SEM analysis,https://www.smartpls.com/</td></tr><tr><td>ChatGPT&lt;br/&gt; Version: 5.2</td><td>OpenAI</td><td>N/A</td><td>Enhancing readability and grammar check, https://www.chatgpt.com/</td></tr></tbody></table>

how virtual celebrity characteristics drive purchase intention: testing the stimulus-organism-response framework with structural equation modeling

How Virtual Celebrity Characteristics Drive Purchase Intention: Testing the Stimulus-Organism-Response Framework with Structural Equation Modeling

In recent years, firms have begun introducing virtual celebrities to promote their products and brands across various media channels. This research employs the stimulus-organism-response (SOR) framework to empirically investigate how virtual celebrity attractiveness factors, such as appearance, voice, and content quality, can influence consumer satisfaction, loyalty, and purchase intention. Partial least squares structural equation modeling (PLS-SEM) was used to test the hypotheses based on a sample of 249 participants who had engaged in virtual celebrity experiences. The findings indicate that content quality is the strongest predictor of user satisfaction and that satisfaction indirectly influences purchase intention through loyalty. Furthermore, personalization moderates the satisfaction-loyalty relationship, suggesting that personalized experiences would enhance the engagement of virtual celebrities and fans. The findings indicate that firms considering virtual celebrity campaigns should focus more on emotion-eliciting, narrative-consistent content rather than visual realism. Furthermore, investments in loyalty-building programs and personalized engagement solutions are necessary to transform consumer satisfaction into sustained behaviors.

Table 2 summarizes the demographic characteristics of the final sample (N = 249), including gender, age, education level, and frequency of engagement with virtual celebrities. The sample showed balanced gender representation (52.6% female) and consisted primarily of young to middle-aged adults with relatively high educational attainment. Engagement frequency was concentrated in the weekly–monthly range, with fewer users per day. When included as a control variable, usage frequency did not significantly a...

In the context of the virtual celebrity experience, content quality most strongly drives satisfaction; satisfaction, in turn, increases purchase intention indirectly via loyalty. Personalization strengthens the satisfaction-loyalty link, so firms should prioritize emotion-evoking, narrative-consistent virtual celebrity content and personalized loyalty programs.

In recent years, firms have begun introducing virtual celebrities to promote their products and brands across various media channels. This research ...

This study analyzes how virtual celebrity attributes influence consumer satisfaction, royalty, purchase intention, and how personalization strengthens satisfaction-driven royalty. Existing studies identify alternative means and the credibility, but inadequately explain process outcomes. This study clarifies the mechanisms via SOR framework, mediation, personalization, moderation.To begin, define the structural model for Partial Least Squares Structural Equation Modeling, or PLS-SEM. Specify a maximum of six predictors for the endogenous constructs, including three virtual celebrity characteristics and three control variables. Set the statistical parameters, such as medium effect size, significance level, and statistical power for the power analysis.Now, run the power analysis to estimate the minimum required sample size. Recruit participants through an online research panel using purposive non-probability sampling. Restrict eligibility to individuals aged 18 years or older.Insert screening questions and attention check items in the survey to verify eligibility and response quality. Review survey responses and remove entries that fail screening or attention check criteria. Stop data collection once the predetermined target sample size is achieved.Start by presenting a definition describing virtual celebrities as digitally created audio-visual personas with stable visual identity, voice, and recurring narrative content. Instruct participants to answer survey questions based on their actual experiences with such virtual celebrities. Develop the questionnaire by including survey sections covering appearance, voice, content quality, satisfaction, loyalty, personalization, purchase intention, and demographics.Measure all constructs using 7-point Likert scales where one indicates strongly disagree and seven indicates strongly agree. Prevent duplicate submissions through platform control settings. Export the collected responses in comma-separated values or CSV format and import the cleaned dataset into the PLS-SEM software.For measurement and structural model evaluation, specify all constructs as reflective constructs. Evaluate reliability using Cronbach's alpha and composite reliability or CR greater than or equal to 0.7. Assess convergent validity using average variance extracted or AVE greater than or equal to 0.5.Evaluate discriminant validity using the Fornell-Larcker criterion and Heterotrait-Monotrait or HTM ratio and ensure that it is less than 0.9. Specify the structural paths according to the conceptual model and assess multi-collinearity using the variance inflation factor or VIF values below five. Run bootstrapping with 5, 000 subsamples to estimate path coefficients, T-values, confidence intervals, and effect sizes.Estimate indirect effects using bootstrapping with 5, 000 subsamples and 95%confidence intervals to test the sequential mediation from virtual celebrity attributes to satisfaction, satisfaction to loyalty, and loyalty to purchase intention. Create interaction terms to test the moderating effects of personalization. Estimate moderation coefficients and affect sizes using bootstrapping.Generate interaction plots to visualize conditional effects. After predictive performance assessment, compare Q-squared predict values, root mean square error, and mean absolute error against a linear benchmark model. Confirm predictive relevance when Q-squared predict values exceed zero and PLS errors are lower than the benchmark.Reliability and convergent validity tests showed that all constructs exceeded recommended thresholds, confirming the measures are both reliable and valid. Discriminant validity assessed using the Fornell-Larcker criterion and Heterotrait-Monotrait ratio confirms that all constructs are empirically distinct. Structural model evaluation indicates moderate explanatory power and acceptable predictive performance.Predictive relevance is confirmed with Q-square predict values ranging from 0.280 to 0.485. The predictive performance of the PLS-SEM model is compared with a linear regression benchmark. Lower root mean square error and mean absolute error values indicate better prediction accuracy.Statistical analysis identifies which hypotheses in the model are supported by the data. Appearance, voice, and content quality significantly influence customer satisfaction. Satisfaction increases loyalty, which in turn leads to higher purchase intention.Mediation analysis indicates that satisfaction affects purchase intention indirectly through loyalty. Personalization strengthens the relationship between satisfaction and loyalty, but does not affect purchase intention. The protocol can help measure how virtual celebrity attributes affect satisfaction, loyalty, and purchase intention using surveys and structural modeling.In this protocol, key challenges included recruiting participants virtual celebrity and clarifying inconsistent relationships between satisfaction and purchase intention reports in prior studies. This research facilitates future studies on emerging AI technologies across cultural differences and the long-term effect of personalization and virtual celebrity trait.

how-virtual-celebrity-characteristics-drive-purchase-intention

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Discover how virtual celebrity attributes drive consumer purchase intention using PLS-SEM and the SOR framework. This study reveals content quality as the strongest predictor of satisfaction and loyalty.

How Virtual Celebrity Characteristics Drive Purchase Intention: Testing the Stimulus-Organism-Response Framework with Structural Equation Modeling (Video) | JoVE Journal

How Virtual Celebrity Characteristics Drive Purchase Intention: Testing the Stimulus-Organism-Response Framework with Structural Equation Modeling

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