The internet of things (IoT) is rapidly becoming one of the most popular emerging technologies in business and society. One of the major verticals that has recently begun to effectively use IoT technologies is the retail industry. Given the unprecedented opportunities IoT generates for brands and retailers, it is important to glean timely insights regarding the business value of IoT and understand whether the introduction of an IoT technology as an alternative purchase channel for consumers affects the sales of physical products. Using empirical data from a multinational online retailer who adopted an IoT technology that largely automates the consumers’ purchases and employing a quasi-experimental framework, we study the effect of the introduction of IoT as an alternative sales channel on product sales. Our analyses reveal a statistically and economically significant increase in sales and demonstrate the business value of the IoT channel for retailers and brands. In addition, we conduct other analyses of IoT to delve into the effect of heterogeneity and empirically validate the underlying mechanisms by examining the impact of IoT for products in different price ranges, levels of substitutability, and product categories. For instance, our analyses reveal that less expensive and more differentiated products, as well as experience and utilitarian goods, can accrue higher benefits leveraging more effectively novel IoT technologies. This is the first paper to study the impact of an IoT technology on product sales, drawing important implications for devices and technologies largely automating the purchase process.

The authors investigate how the geographical distance between online users is associated with electronic word-of-mouth (eWOM) effectiveness. Their research leverages variation in the visibility of eWOM messages on the social media platform of Twitter to address the issue of correlated user behaviors and preferences. The study shows that the likelihood that followers who are exposed to users’ WOM subsequently make purchases increases with followers’ geographic proximity to the users. The authors propose social identification as a potential mechanism for why geographical distance still matters online in eWOM: because consumers may form a sense of social identity based on their physical location, information regarding the spatial proximity of users could trigger online social identification with others. The findings are robust to alternative methods and specifications, such as further controlling for latent user homophily by incorporating user characteristics and embeddings based on advanced machine-learning and deep-learning models and a corpus of 140 million messages. The authors also rule out several alternative explanations. The findings have important implications for platform design, content curation, and seeding and targeting strategies.