Interactive display advertising increasingly employs delayed product disclosure in multi-stage formats, yet little is known about how they affect purchase behavior when exposure to the reveal depends on users’ interaction. This article studies interactive blind lead-in (BLI) ads with a randomized field experiment: over 109,000 customers of a book retailer were assigned to a BLI ad with delayed reveal or a static ad with immediate disclosure. The experiment identifies a boundary condition on the transfer of prior interactivity findings to user-controlled delayed-reveal advertising: assignment to the BLI format does not increase average purchase conversion, as interactivity’s positive mechanisms need not produce a conversion gain when most exposures end before the reveal. Using random assignment as an instrument, completing the BLI sequence increases long-term purchase conversion among compliers (a 46.6% lift; 437 completions per incremental purchase), indicating that delayed-reveal interactivity is effective only when users meaningfully interact with the ad. Completion varies across ads consistent with a Cost-of-Progress framework: early informational cues predict higher completion, late-stage textual and visual complexity predicts lower completion, and social cues predict higher completion when placed before the final stage. A supplementary study shows that the downstream impact of BLI interaction depends on overall hedonic experience.
Product feature prioritization guides product managers to identify the most important product features or feature mixes (e.g., consumers’ simultaneous consideration of cleanliness and sleep quality with value for the money in hotel services) out of various features characterizing the product category. In e-commerce, consumers’ online product reviews are a critical source of information detailing their consumption experiences. However, e-commerce consumers usually discuss only a few features in their online reviews. This data structure creates a problem of missing information. We test multiple missing data imputation methods and empirically select the best one for the next step (called feature engineering) to identify important features and feature mixes. In identifying important features, most research has focused on individual features without considering the simultaneous dependencies involving multiple features. Our feature engineering approach considers not only individual features but also a mix of features in product evaluations. Thus, our procedure for feature prioritization allows product managers to identify important feature mixes for existing product improvements and new product development. Our empirical applications involve four product categories (hotels, restaurants, moisturizers, and tablets) examined under various conditions; moreover, they consistently underscore the crucial role of consumers’ value-for-the-money perceptions in selecting and evaluating product quality features.
Salesperson attractiveness (SA) plays a pivotal role in marketing; however, existing research has focused mainly on SA derived from physical cues, overlooking its nonphysical aspect. To fill this gap, two studies were conducted in the livestreaming context. Study 1 focused on SA prediction and interpretation. Using a dataset of 1,933 livestreaming videos, this research customized a multimodal machine learning model with a marketing metric (product category). The customized model integrates the salesperson's verbal, vocal, and visual cues, as well as their cross-modal interactions, achieving the highest prediction accuracy of 92.17% in predicting SA. An explainable AI model was subsequently applied to identify how specific verbal, vocal, and visual cues contributed to the prediction of SA. Study 2 explored the sales effect of SA and its boundary condition. The results indicate a U-shaped relationship between SA and sales, with this relationship being more pronounced for experience products than for search products. Overall, this research offers a more comprehensive understanding of SA by integrating both physical and nonphysical cues, establishing an empirical framework to predict and interpret SA and providing marketers with novel insights into its effect on livestreaming e-commerce sales.
The exponential growth of digital content has transformed how organizations interpret consumer sentiment and anticipate societal trends. Among these trends, perceived injustice, a subjective experience of unfair treatment, poses significant implications for social dynamics and economic stability. While previous research on digital content emphasizes real-time, impulsive, and moderated formats such as social media reactions, this article explores the overlooked signaling potential of deliberate, less moderated content. A linguistic time series analysis of over 58 million words across 1,620 self-published e-books published over the course of 15 years reveals that expressions of perceived injustice embedded in deliberate content are correlated with consumer confidence and protest participation up to 12 months later. These findings extend current interactive marketing scholarship by integrating linguistics with time series analysis, offering both conceptual insights and practical implications for organizations aiming to anticipate disruptive consumer attitudes and actions.
The success of most social media platforms depends on their ability to attract and retain users. Although more than half of the world’s population uses some form of social media, consumers are increasingly discontinuing their use of these platforms. This research investigates a previously unexamined factor that influences consumers’ decisions to abandon (i.e., deactivate or delete) social media, users’ perceptions of their own inauthenticity on social media. Five studies (including one in the appendix) find that greater self-perceptions of online inauthenticity increase consumers’ willingness to abandon social media. Offering insight into the mechanism, the authors reveal the underlying role of self-threat. Specifically, perceived online self-inauthenticity threatens the self-concept, and consumers can counter this threat by disposing of the inauthentic social media account. That is, social media abandonment may be a compensatory response to the self-threat evoked by one’s own inauthenticity. Altogether, this work makes theoretical contributions to the literatures on social media use, identity, and service termination, while also providing guidance to social media platforms that seek to maintain active users and curtail abandonment.
