Problem definition: Online marketplaces have revolutionized online sales by creating platforms that connect millions of buyers and sellers. Although the presence of numerous third-party sellers attracts customers, it also results in a proliferation of listings for each product, making it difficult for customers to choose between the available options. To address this issue, online marketplaces employ algorithmic tools to curate and present different product listings to customers. Although tools that assist customers in choosing between different products, such as recommender systems and reviews, have been studied extensively, there is limited evidence regarding tools that help customers choose between different listings of the same product. This paper focuses on the buybox algorithm, an algorithmic tool that prominently presents one option as the default choice to customers. Methodology/results: We assess the influence of the buybox on marketplace dynamics by examining its staggered introduction within a major product category in a leading online marketplace. Our results show that the implementation of buybox increases the number of orders and enhances the efficiency of the customer journey. This is evidenced by an increase in conversion rates and a more pronounced buybox effect on the mobile channel, where search frictions are higher compared with the desktop channel. The introduction of buybox simplifies the process of posting new products on the marketplace, potentially reducing friction for sellers. We find supporting evidence for this hypothesis, because the number of sellers offering a product increases after the introduction of buybox. Managerial implications: Our analysis reveals that a buybox is an effective tool for reducing search frictions and stimulating competition among sellers. Customers benefit from lower prices and higher average quality levels when competition in a buybox is intense. However, the marketplace becomes more concentrated following the introduction of the buybox, representing an unintended consequence that platforms and vendors should manage. Our study contributes to the growing literature on algorithms in platforms by examining how algorithmic curation affects marketplace participants and overall marketplace dynamics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0254.

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.

We examine how paying workers a fixed amount for a pre-specified quantity of work vis-à-vis paying workers a fixed amount for a pre-specified amount of time for a quality-focused task affects the time spent per unit of work and the quality achieved. Across two experiments, we find no evidence—contrary to our expectations—that workers paid for a set quantity of work spend less time on each unit (i.e., work faster) than workers paid for a set amount of time. In fact, in our second experiment, we find workers paid for a set quantity of work spend more time on each unit on average. These workers also achieve greater quality. These findings, together with the worker outcomes we observe when workers are told what is valued (quality, speed, or both), are consistent with the idea that the design of fixed compensation schemes can influence employee effort and performance, despite no compensation consequences, by implicitly communicating what is valued.

Millions of nanostores serve bottom-of-the-pyramid consumers in emerging markets. Their suppliers, consumer packaged goods (CPG) companies, struggle with high operational costs that largely stem from shopkeepers' liquidity constraints. We empirically investigate whether suppliers can improve operational performance by allowing nanostore shopkeepers to delay order payment by a short period of time. We term this delayed payment alternative "order-based trade credit" (OBTC) and examine the key trade-off that suppliers face when transacting with it. While OBTC can create efficiency gains when selling and delivering products to nanostores, it is risky, as shopkeepers might default on their credit lines. By leveraging data from a nanostore supplier offering OBTC, we assess the effect of this novel policy on the operational performance of the supplier through a difference-in-differences approach. We find that OBTC leads to substantial gains for nanostore suppliers across a range of important operational drivers. Therefore, the benefits of OBTC compensate the risk that suppliers take in financing shopkeepers' inventory under a wide range of scenarios.

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.

Problem definition: How should retail staffing levels be set? While cost of labor is well understood, the revenue implications of having the right staffing level are hard to estimate. Moreover, these implications vary by store; hence, staffing levels should vary as well. Academic/practical relevance: We provide a novel method for setting store associate staffing at the individual store level. We discuss a field implementation that tested this methodology. Methodology: We use historical data on revenue and planned and actual staffing levels by store to estimate how revenue varies with the staffing level at each store. We disentangle the endogeneity between revenue and staffing levels by focusing on randomly occurring deviations between planned and actual labor. Using historical analysis as a guide, we validate these results by changing the staffing levels in a few test stores. We implement the results chain-wide and measure the impact in a large specialty retailer. Results: We find that the implementation validates predictions of the historical analysis. The implementation in 168 stores over six months produces a 4.5% revenue increase and a nearly $7.4 million annual profit increase. The impact of staffing level on revenue varies greatly by store. Managerial implications: Our paper makes three contributions to academic literature and to retail practice. First, we describe a process by which retailers can improve the most common industry practice: set store labor to be proportional to forecasted store revenue. Our proposed approach systematically sets the labor level in each store. Second, we demonstrate the effectiveness of that process via a field test and then via chain-wide implementation over a six-month time period. Finally, most retailers set store labor at the same level across stores, proportionate to revenue. We show that this is not the best approach because the revenue impact of store labor varies by store. The stores in our study that could benefit from relatively more labor were those with high potential demand, closely located competition for that demand, and experienced store managers. Overall, we provide the first simple but rigorous, field-tested approach that any retailer can use to increase revenue and profitability through better labor management.

Firms compete in an increasingly omnichannel environment. Customers no longer travel a single linear path but traverse a complex map invoking many channels, firm-owned and external, seamlessly through integrated technology. The associated changes in consumer behavior and the ways that firms engage consumers have led many to reshape the way they innovate their product portfolios. This article presents a structured overview of some of the most striking changes to firms’ new product development (NPD) processes in B2C settings. Enlisting the classic NPD funnel, it describes how the omnichannel environment and its technologies affect speed and execution in each development stage. It illustrates key changes with examples from packaged goods, consumer technology, and fashion.

Problem definition : How much, if at all, does training in product features increase a sales associate’s sales productivity? Academic/practical relevance : A knowledgeable retail sales associate (SA) can explain the features of available product variants and give a customer sufficient confidence in the customer’s choice or suggest alternatives so that the customer becomes willing to purchase. Although it is plausible that increasing an SA’s product knowledge will increase sales, training is not without cost and turnover is high in retail, so most retailers provide little product-knowledge training. Methodology : We partner with two firms and collect data on more than 50,000 SAs who had access to training. We assemble a detailed data set of the training history and individual sales productivity over a two-year period. We conduct econometric analysis to quantify the causal effect of training on sales. Results : For SAs who engaged in training, the sales rate increases by 1.8% for every online module taken, which is a much higher benefit than the direct or indirect costs associated with this training. Brand-specific training has a larger effect on the focal brand; however, there is a positive effect on other brands the SA sells. We also assess how the training benefit varies depending on the SA’s tenure, sales rate prior to training, and number of modules taken. Managerial implications : We present evidence of a novel training mechanism that can be extremely attractive to retailers. Online training tools, such as the one we study, have two characteristics that should not be overlooked. First, it is the brands, not the retailers, that create, develop, and pay for the training content. Second, the incentives are such that SAs invest their own time, rather than time on the job, to train, and this makes the retailer’s investment in the training a profitable proposition.

This book examines the challenges and opportunities arising from omni-channel retail. New digital channels challenge retailers to redesign their fulfillment and execution processes. We examine operations management, the supply chain transformations associated with fulfilling an omni-channel demand.

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.

The authors study how faster delivery in the online channel affects sales within and across channels in omnichannel retailing. The authors leverage a quasi-experiment involving the opening of a new distribution center by a U.S. apparel retailer, which resulted in unannounced faster deliveries to western U.S. states through its online channel. Using a difference-in-differences approach, the authors show that online store sales increased, on average, by 1.45% per business-day reduction in delivery time, from a baseline of seven business days. The authors also find a positive spillover effect to the retailer’s offline stores. These effects increase gradually in the short-to-medium run as the result of higher order count. The authors identify two main drivers of the observed effect: (1) customer learning through service interactions with the retailer and (2) existing brand presence in terms of online store penetration rate and offline store presence. Customers with less online store experience are more responsive to faster deliveries in the short run, whereas experienced online store customers are more responsive in the long run.

What is the relationship between inventory and sales? Clearly, inventory could increase sales: expanding inventory creates more choice (options, colors, etc.) and might signal a popular/desirable product. Or, inventory might encourage a consumer to continue her search (e.g., on the theory that she can return if nothing better is found), thereby decreasing sales (a scarcity effect). We seek to identify these effects in U.S. automobile sales. Our primary research challenge is the endogenous relationship between inventory and sales—e.g., dealers influence their inventory in anticipation of demand. Hence, our estimation strategy relies on weather shocks at upstream production facilities to create exogenous variation in downstream dealership inventory. We find that the impact of adding a vehicle of a particular model to a dealer’s lot depends on which cars the dealer already has. If the added vehicle expands the available set of submodels (e.g., adding a four-door among a set that is exclusively two-door), then sales increase. But if the added vehicle is of the same submodel as an existing vehicle, then sales actually decrease. Hence, expanding variety across submodels should be the first priority when adding inventory—adding inventory within a submodel is actually detrimental. In fact, given how vehicles were allocated to dealerships in practice, we find that adding inventory actually lowered sales. However, our data indicate that there could be a substantial benefit from the implementation of a “maximize variety, minimize duplication” allocation strategy: sales increase by 4.4% without changing the total number of vehicles at each dealership. This paper was accepted by Vishal Gaur, operations management.

Problem Definition : Omnichannel retailers face hard choices when they decide how to improve the way they serve their customers. They use such levers as improving delivery and return policies, providing access to detailed product information and reviews, and offering lower prices. Because it is difficult for firms to excel on all dimensions simultaneously, it is crucial for them to have a profound understanding of the trade-offs consumers make when evaluating their offering. Academic/Practical Relevance : If managers have biased perceptions of consumer trade-offs, these biases can represent serious obstacles to designing a winning operational strategy. We investigate the degree and nature of such managerial biases and propose a remedy for them. Methodology : We use a state-of-the-art empirical approach to measure consumer prefer- ences for five dimensions of online channels, part of a larger omnichannel ecosystem (delivery policy, return policy, product information, branding, and pricing), and four product categories. Results: We reveal that managers with experience in these categories have biased perceptions of what consumers prefer. Our analyses also show that it is very hard to identify experts with more accurate knowledge based on characteristics observable to the firm (e.g., experience, tenure, gender). Averaging predictions across individual managers show benefits from the so-called “wisdom of the crowd” and help to overcome individual biases. Managerial Implications: Across the four categories, the crowd’s predictions outperform more than 96 percent of the individual managers’ predictions, resulting in more than 17 percent increased accuracy over the average manager. Our results also show that groups of as few as 5 to 10 managers already make a smart crowd, enhancing the feasibility of this strategy to overcome individual managerial biases in omnichannel retailing.