More than meets the eye? Visual storytelling and optimal distinctiveness of new ventures in online B2C markets

Abstract

We examine how new ventures in online B2C markets use product images for visual storytelling in their strife for optimal distinctiveness, that is, appearing as different as conformingly possible. Using machine learning approaches for object recognition and analysis on images of 1,312 entrepreneurial products offered on Amazon Launchpad, we analyze the effect of visual semantics—the meaning contained in the visual objects identified—on audience evaluation across product categories. We construe visual semantics in terms of their fit—the extent to which their meaning differs from competitors within their category, as well as their richness—the amount of meaning conveyed. We find that both effects are strongly contextualized by the product category they are used in. In non-distinct product categories—those that share frequent relations with others in the meaning system—a high semantic fit is beneficial, but this relative advantage diminishes with increasing product category distinctiveness. In distinct categories, semantic richness is evaluated favorably, but this effect diminishes with decreasing product category distinctiveness. High semantic fit and richness also mutually accentuate each other, especially in increasingly distinct categorical contexts. Our work shows that visual storytelling not only allows entrepreneurs to express differentiation and conformity, but that it also can serve as an effective tool to handle categorical contexts with heterogeneous audiences and evaluative complexities. For managers, our work provides clear guidelines for designing and using semantics in product images to appear more or less unique to consumer audiences in online B2C markets.