Digital humans are increasingly deployed in customer-facing service contexts, yet guidance for systematically designing and evaluating their believability remains limited. Although prior research identifies principles associated with believable agent behavior, these insights are often fragmented, under-operationalized and outcome focused in nature. As the state-of-the-art stands, little to no guidance is offered as to how designers can reason holistically about digital human experiences. Our work addresses this gap, proposing a design framework (as an artefact) that conceptualizes believability as a higher-order construct organized into dimensions, characteristics, and attributes-the aim being to support design reasoning and comparative evaluation under a realistic service scenario. Adopting a design science research approach, the framework is developed through a synthesis of prior literature, stakeholder engagement, and co-creation activities-the outcomes articulated as a nascent design theory. The framework is instantiated through two alternative digital human brand ambassadors, evaluated in the context of a higher education customer service scenario. Evaluation is explicitly structured around criterion, causal, and context validity, with conservative analytic controls applied to bound inference. Our findings show that the framework can be meaningfully applied in use, and that framework-guided design choices can produce perceptible, though selective, differentiation in user judgments of believability as stands. Differentiation is most robust for personality-related and selected behavioral characteristics, while other characteristics show weaker or context-sensitive effects. Notably, some appearance-related perceptions differ despite controlled visual features, suggesting that believability judgments are holistic and shaped by cross-dimensional interactions. Overall, the outcomes contribute to a mutable, design-oriented framework that supports structured comparison and diagnostic insight, providing a foundation for cumulative and context-aware research on believable digital humans.
Generative AI has accelerated the adoption of hyper-realistic digital humans and digital twins of public figures, while also enhancing their intelligence. However, research on the interaction between digital human identity type and intelligence level remains limited. This study investigates which identity types of digital humans-hyper-realistic digital humans, celebrity digital twins, and expert digital twins-most effectively influence consumer purchase decisions, and how these effects vary across intelligence levels. The results indicate that, relative to hyper-realistic digital humans, celebrity digital twins enhance product recommendation effectiveness primarily through emotional trust, whereas expert digital twins do so through both cognitive and emotional trust. Under low intelligence conditions, hyper-realistic digital humans elicit greater emotional trust, whereas identity type does not significantly affect cognitive trust. In contrast, under high intelligence conditions, expert digital twins generate higher cognitive trust than celebrity digital twins and hyper-realistic digital humans, while expert and celebrity digital twins elicit greater emotional trust than hyper-realistic digital humans. These findings identify optimal digital human identity types and effective identity-intelligence combinations for enhancing consumer trust and purchase decisions, offering both theoretical and practical insights for firms developing digital humans.
Social media influencers are individuals who have amassed a significant following on platforms such as Instagram, YouTube, and TikTok due to their expertise, charisma, or relatability. They create content tailored to specific niches and often collaborate with brands for sponsored content, influencing their followers' purchasing decisions. Virtual influencers (VIs), however, are computer-generated personas created by brands or agencies and are designed to mimic human social media influencers. Despite being virtual rather than authentically human, VIs have gained traction, particularly among younger demographics, challenging traditional influencer marketing paradigms. The purpose of this research is to examine if parasocial relationship theory explains the behavior of their followers-and thus, if the theory holds true in the context of VIs. A literature review of social media influencers and virtual influencers is conducted to build a base for the research. Our research model, built on parasocial relationship theory, examines the links from attractiveness, intimate self-disclosure, influencer-brand congruence, and sponsorship transparency to parasocial relationship and, in turn, the impact of parasocial relationship on purchase intention. Survey results support our hypotheses. The unique moderating influences of object-related authenticity and activity-related authenticity are also observed. Implications for VI creators and the brands that use them are presented, along with directions for future research.
While user feedback is widely recognized as a catalyst for innovation, firms creating digital cultural products (DCPs) face a critical dilemma: in a sea of contradictory user voices, who should they listen to? The existing literature offers limited guidance, presenting conflicting evidence on the role of feedback valence and often overlooking the crucial distinction between novice and expert users. To address this gap, we develop a nuanced framework to investigate how four distinct types of user feedback-positive novice (PNF), positive expert (PEF), negative novice (NNF), and negative expert (NEF)-affect the pace of innovation in the mobile gaming industry. Drawing on a dataset of 363 games, our findings reveal a counterintuitive dynamic: both PNF and PEF are associated with a slower pace of innovation, while NNF and NEF are associated with a faster pace. Furthermore, the feedback from expert users exerts a significantly stronger influence than that of novices. We also find that the constructive quality of feedback is a powerful moderator, amplifying the positive effects of negative feedback and mitigating the inhibiting effects of positive feedback. This study contributes to the literature by helping to resolve the theoretical debate on feedback valence, offering a more sophisticated model of user heterogeneity, and highlighting the critical role of feedback quality in driving innovation. Our results provide actionable insights for managers on how to strategically navigate their user communities to foster sustained innovation.
Metaverse holds significant potential to enhance retail sales through increased consumer engagement. Drawing on insights from the diffusion of innovation theory, flow theory, and extended self-theory, this study investigates: (1) the impact of personal innovativeness (PI) (low vs high) on immersion, interactivity, and flow, and its subsequent effect on engagement; (2) the effect of engagement on purchase intention; and (3) the moderating role of psychological inspiration in the relationship between immersion, interactivity, flow, and engagement in metaverse retail. The results from two experimental studies revealed that PI influences immersion and interactivity, but not flow, which, in turn, affects engagement in the metaverse retail environment. Furthermore, engagement significantly impacts purchase intention, and psychological inspiration moderates the effect of interactivity on engagement. These findings highlight the critical role of PI in driving engagement through immersion and interactivity in the metaverse. This study provides novel insights for both theory and practice in the field of metaverse retail.
