Online retail has become more prominent around the world in the last decade. As a result, online retailers' website performance is increasingly important. Previous literature has extensively studied customer sensitivity to service speed and wait times in offline services. In “Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail,” Gallino, Karacaoglu, and Moreno extend this literature to online retail. They study the impact of delays in online retail on customer behavior. They estimate sizable negative effects of website slowdowns on online sales and conversion rates. Moreover, they explore how customer sensitivity to online delays varies throughout customers' shopping journeys. They find that the impact of waiting times varies along the different stages of the shopping journey, with customers becoming more sensitive to slowdowns at the checkout stage. Their findings have implications for website design decisions. This research is especially relevant in the current regulatory environment with ongoing policy debates about net neutrality.

Most online sales worldwide take place in marketplaces that connect sellers and buyers. The presence of numerous third-party sellers leads to a proliferation of listings for each product, making it difficult for customers to choose between the available options. Online marketplaces adopt algorithmic tools to curate how the different listings for a product are presented to customers. This paper focuses on one such tool, the Buybox, that algorithmically chooses one option to be presented prominently to customers as a default option. We leveraged the staggered introduction of the Buybox within a prominent product category in a leading online marketplace to study how the Buybox impacts marketplace dynamics. Our findings indicate that adopting Buybox results in a substantial increase in marketplace orders and visits. Implementing Buybox reduces the frictions customers and sellers face. On the customer side, we find a reduction of search frictions, evidenced by an increase in conversion rates and a higher impact of Buybox on the mobile channel, which has significantly higher search frictions than desktop channel. On the seller side, the number of sellers offering a product increases following the implementation of Buybox. Customers benefit from lower prices and higher average quality levels when competition in Buyboxes is high. After the introduction of the Buybox, the marketplace also becomes more concentrated. Our paper contributes to the burgeoning literature on the role of algorithms in platforms by examining how algorithmic curation impacts the participants of the marketplace as well as the marketplace dynamics.

In the dynamic e-commerce environment, social commerce has emerged as a revolutionary force, transforming how consumers interact and transact online. This paper investigates the differences in customers’ search and purchase patterns between a prominent online retailer’s burgeoning social commerce channel, the WeChat mini-program, and its native mobile app. We analyze the customers’ entire journey through a sequential search model that encapsulates decisions from channel selection to product search, search termination, and the final purchase. This study contributes to the search model literature by being the first to estimate both fixed and marginal search costs in a sequential search model in an omnichannel retail environment. We calculate fixed search costs, marginal search costs, and preferences for each channel, revealing differences in customers’ behaviors across channels. Our analysis shows that customers’ fixed search costs are higher, but marginal costs are lower on WeChat channel compared to the App channel. Also, customer characteristics like historical spending levels and search timing influence their search costs. From these insights, we suggest strategies tailored to each channel capitalizing on the differences in customers’ search costs. The first strategy encourages search initiation by lowering fixed search costs through peer-to-peer link sharing in the WeChat channel. The second strategy aims to minimize marginal search costs using search-triggering coupons in the App channel. Implementing these strategies significantly boosts conversion rates and profits for the online retailer. This research is one of the first to explore the differences between traditional retail channels and emerging social commerce channels.

Many customers today shop across multiple channels. Previous literature has documented the importance of endogenous factors, such as retailers' operational strategies, new store openings, and customer demographics on customers' channel choice in an omnichannel setting. In this paper, we shed light on the impact of an exogenous factor—weather conditions—on retailers' B&M store and online sales as well as on customers' channel choice. Using online and B&M store data from a worldwide winter apparel retailer and daily weather and climate normals data at the zip code level, we find the following: (1) Negative (positive) temperature deviations, i.e., cold (hot) days, lead to a significant increase (decrease) in sales both for online and offline channels. The effects are stronger for the offline channel. (2) Cold days induce customers to migrate to the offline channel, whereas hot days and snowy days lead them to purchase through the online channel. Moreover, our findings indicate that although weather significantly affects retailers store traffic and sales, retailers' staffing practices are suboptimal; they understaff on cold days and overstaff on hot days. We also discuss the implications of our findings for retailers' omnichannel strategies.

Problem Definition: The use of real-time information in on-demand services provides agents with access to an unprecedented amount of information about their competitors. We study the impact of the increased availability of real-time information on the behavior of strategic agents and the implications of this phenomenon for service efficiency. Academic/Practical relevance: E-hailing companies provide service via independent contractors; thus, the platforms do not have direct control over the service location of their service agents. Therefore, it is important to understand how sharing real-time information impacts agent behavior and the implications of this behavioral response for the platform efficiency.

The impact of delays has been widely studied in various offline services. The focus of this study is online services and we explore the impact of in-process delays---measured by website speed---on customer behavior. We leverage novel retail and website speed data to investigate how delays impact online sales and how customer sensitivity to in-process delays varies across the different stages of a customer's shopping journey. We estimate sizable adverse effects of website slowdowns on online sales. Using threshold regression models, we show that customers exhibit diminishing sensitivity to increases in website slowdowns. Our results suggest that waiting times affect customer abandonment differently at different stages of the shopping journey. Customers are more sensitive to slowdowns at the checkout stage. Our findings have implications for website design decisions such as improving website speed at the checkout stage, selecting third-party content providers, and customizing the design of mobile and desktop channels. The paper's results are especially relevant in the current regulatory environment with ongoing policy debates about net neutrality.