An Emerging Theory of Avatar Marketing

Abstract

Avatars are becoming increasingly popular in contemporary marketing strategies, but their effectiveness for achieving performance outcomes (e.g., purchase likelihood) varies widely in practice. Related academic literature is fragmented, lacking both definitional consistency and conceptual clarity. This article makes three main contributions to avatar theory and managerial practice. First, to address ambiguity with respect to its definition, this study identifies and critically evaluates key conceptual elements of the term avatar, offers a definition derived from this analysis, and provides a typology of avatars’ design elements. Second, the proposed 2 × 2 avatar taxonomy suggests that the alignment of an avatar’s form realism and behavioral realism, across different contingencies, provides a parsimonious explanation for avatar effectiveness. Third, the authors develop an emerging theory of avatar marketing, by triangulating insights from fundamental elements of avatars, a synthesis of extant research, and business practices. This framework integrates key theoretical insights, research propositions, and important managerial implications for this expanding area of marketing strategy. Lastly, the authors outline a research program to both test the propositions and insights as well as advance future research.

A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays

“…Equation 4presents the optimal emotions to display so as to maximize sales. These results not only make original contributions to the extant literature but also offer guidance to design technology-inspired service agents (e.g., avatars, virtual news anchors) to be more humanlike (Crivelli and Fridlund 2018;Miao et al 2022).…”

Robots should be seen and not heard…sometimes: Anthropomorphism and AI service robot interactions

“…Both of these dimensions are informed by Epley et al's (2007) theory of anthropomorphism which guides the current study—put plainly, one is about looking human (D1) and the other about being able to converse in a human‐like way (D2). These two dimensions represent both form and behavioral realism (Miao et al, 2021), and importantly, are two ways of increasing the accessibility of anthropocentric knowledge structures associated with elicited agent knowledge. Third is the addition of the “Consumer choice of interaction level” (D3) dimension (top middle).…”

“…This pairing of conversational ability and appearance may hold particular relevance for service robots, which are the focus of this paper. Wirtz et al (2018, p. 909) define service robots as “…system‐based autonomous and adaptable interfaces that interact, communicate and deliver service to an organization's customers.” Research notes that AI agents like service robots are judged not only on their behavioral realism but their form realism as well (Miao et al, 2021). In this study, behavioral realism is represented by social interaction opportunity (i.e., the ability to converse), and form realism is captured in human‐like appearance.…”

The Rise of New Technologies in Marketing: A Framework and Outlook

“…Anthropomorphized chatbots can influence consumer response in consumer-initiated service interactions (Crolic et al 2022). In addition, avatars are increasingly used in firmconsumer interactions, where the extent of an avatar's form and behavioral realism is a major determinant of its effectiveness (Miao et al 2022). Augmented reality (AR) is used in retailing to facilitate firm-consumer interactions, which, as a "try before you buy" technology, is especially effective when consumers are uncertain about products (Tan, Chandukala, and Reddy 2022).…”

“…Specifically, technologies that are further along are more likely to have produced hard data because a sufficient number of firms or consumers have adopted the technology to enable the antecedents or consequences of adoption to be empirically observed and quantitatively analyzed (e.g., augmented reality as in Tan, Chandukala, and Reddy [2022], livestream sales calls in Bharadwaj et al [2022]). Other technologies fall earlier in the adoption cycle, and their adoption antecedents or consequences can only be established experimentally (e.g., chatbots as in Crolic et al [2022], AI-based recommendations as in Longoni and Cian [2022]) or conceptually (e.g., genetics as in Daviet, Nave, and Wind [2022], platforms as in Wichmann, Wiegand, and Reinartz [2022], avatars as in Miao et al [2022]).…”