As mobile devices have become a necessity in our daily lives, mobile advertising is also prevalent. Accordingly, it is critical for practitioners to understand how consumers visually attend to mobile advertisements. One popular way of doing so is via eye-tracking methodology. However, scant eye-tracking research exists in mobile settings due to technical challenges, e.g., cumbersome data annotation. To tackle these challenges, the authors propose an object-detection machine learning (ML) algorithm-You Only Look Once (YOLO) v3-to analyze eye-tracking videos automatically. Moreover, we extend the original YOLO v3 model by developing a novel algorithm to optimize the analysis of eye-tracking data collected from mobile devices. Through a lab experiment, we investigate how two types of ad elements (i.e., textual vs. pictorial) and shopping devices (i.e., mobile vs. PC) affect consumers' visual attention. Our findings suggest that (1) textual ad elements receive more attention than pictorial ones, and such differences are more pronounced in ads on mobile devices than those on PCs; and (2) mobile ads receive less attention than PC ads. Our findings provide managerial insights into developing effective digital advertising strategies to improve consumers' visual attention in online and mobile advertisements.

Social media listening has become an integral part of many companies marketing strategies. Using a unique dataset of social media comments to 413 movie trailers, we document the systematic differences in sentiments expressed on Facebook and YouTube. First, Facebook comments are less likely to involve sentiments. Second, when sentiments are expressed, Facebook comments tend to be more positive than those on YouTube. Third, on both platforms, comments are more likely to express sentiments after a movie's release than before it. Furthermore, the sentiment gap between Facebook and YouTube diminishes after a movie's release. We propose a behavioral explanation for our findings based on network structure and social desirability bias and test our hypothesis with an experiment. Finally, we demonstrate that cross-platform sentiment divergence is significantly associated with box office revenue.

Herding behavior refers to the behavior of individuals behaving similarly as a group without directions to coordinate. Herding can demonstrate rational characteristics. When consumers believe that others may have private information about a product, they infer unobserved information through other people’s behaviors, thereby engaging in similar actions themselves. While rational herding behavior has been found mostly in high involvement environments such as the financial markets, this paper provides evidence that such behavior may also occur in a comparatively lower involvement environment such as retailing. To demonstrate herding behavior and test shoppers’ rationality in such, the authors employ a unique dataset from a major TV shopping channel. In this setting, information about other buyers’ purchase decisions is only sometimes observed by shoppers. Evidence suggests that herding happens among shoppers and the herding behavior appears to exhibit rationality. The authors find that herding effects (1) are stronger when relative price discount is smaller, (2) are more prominent for a product category with less digitalizable attributes, and (3) appear to happen mainly in the earlier part of a sales pitch when shoppers have less information about a product and are more uncertain about their product valuation.