Knowledge that Transforms

ABSTRACTThe rapid growth of on‐demand meal delivery platforms has heightened competition, making customer retention a critical priority. While prior research on order dispatch algorithms has largely focused on minimizing delivery time or delay, the direct impact of delivery performance on repeat purchases remains underexplored. Using transactional data from an online meal delivery platform in China, we empirically investigate the asymmetric effects of early and late deliveries on customer repurchasing behavior. To address potential endogeneity, we introduce driver experience and local knowledge, two previously overlooked factors in platform algorithms, as novel instrumental variables. The survival analysis shows that late deliveries significantly reduce future orders, while early deliveries provide only limited benefits. Guided by these empirical insights, we develop a simulation‐based evaluation of different order dispatch algorithms, revealing that maximizing future orders, rather than minimizing delivery time or delays, yields the highest future orders. These insights offer actionable recommendations for platform managers, stressing the importance of strategic adjustments in dispatch algorithms and integrating heterogeneous treatment effects into algorithmic design. By merging operational delivery performance with consumer behavior insights through causal inference and optimization, this study provides a novel end‐to‐end framework for creating data‐driven dispatch algorithms that enhance both service efficiency and customer retention.

AbstractWe analyze channel integration between a last‐mile delivery platform and a general merchandise retailer in two distinct stages: (1) platform delivery access (PDA), where the retailer continues to offer standard delivery through its own website but directs customers to the platform's website for new same‐day delivery; and (2) integrated delivery access (IDA), where customers can continue to use same‐day delivery service at the delivery platform website but can purchase products in a single order with both same‐day and standard delivery options at the retailer's website. We perform a quasi‐experiment using consumer spending data from retailer, target, and delivery platform, Shipt. We find that PDA provides positive impacts to the delivery platform through increased sales. IDA, on the other hand, increases the retailer's online channel sales but does not impact the delivery platform's sales. Moreover, we find that the positive effects of PDA on the delivery platform's sales are stronger in markets where online grocery penetration is lower, indicating that the effects were likely driven by increased purchases for groceries. Finally, the positive effect of IDA on the retailer's online channel sales is stronger in markets where the retailer has a greater loyal customer base and online grocery penetration is lower.

ABSTRACTAlthough the spread of misinformation on centralized social media platforms has received significant attention, few studies compare centralized and decentralized platforms, and how to mitigate misinformation diffusion in newly emerging blockchain‐based decentralized social network platforms. We study misinformation diffusion between the decentralized and the centralized platforms and identify three decentralized governance mechanisms to mitigate the spread of misinformation in decentralized networks: user flagging, user article ratings, and user reputation. Our empirical experiments using agent‐based simulations leveraging real‐world data from two platforms reveal two findings. First, comparing misinformation diffusion between the decentralized Steemit and the centralized Pokec platforms suggests that in the absence of any mitigation mechanisms, misinformation in decentralized platforms affects more users, at faster rates, and reaches shorter distance from the misinformation initiating user. Second, within the decentralized platforms, misinformation affects fewer users, at slower rates, and reaches shorter distance from the misinformation initiating user in the presence of mitigating mechanisms than in their absence. We discuss the implications of these results both for the understanding of misinformation diffusion and for the governance of decentralized social network platforms.

AbstractA surge in technological advancements and innovations has spurred the rise of on‐demand meal delivery platforms. Despite their widespread appeal, these platforms face two critical challenges (i.e., order batching and demand allocation) in effectively managing the delivery process while maintaining reliability. In response, this study aims to address these two challenges by examining the effects of real‐time demands and restaurant density on delivery reliability, as well as how the type of driver (i.e., in‐house versus crowdsourced drivers) moderates these effects. We evaluated our model with a unique dataset obtained from one of the top three on‐demand meal delivery platforms in China, and our research sheds light on several key findings. Specifically, our study finds inverted U‐shaped relationships between real‐time demands and delivery reliability and a positive relationship between restaurant density and delivery reliability. In addition, it reveals that crowdsourced drivers perform better than in‐house drivers under high real‐time demands. This study contributes to the literature by clarifying how delivery reliability can be influenced by real‐time demands and restaurant density. The results offer important implications for on‐demand meal delivery platforms to improve delivery performance and allocate demands amid complicated market conditions.

Abstract Retailers face a challenging trade‐off in maintaining versus restricting long‐established lenient return policies. On the one hand, lenient return policies have become an important part of retailers' value propositions and play a significant role in stimulating consumer purchases. On the other hand, lenient return policies increase the volume of product returns, which hurts profitability. Motivated by observing an increase in restrictive changes to long‐established lenient return policies, we investigate consumer reactions to such changes and their managerial implications. Through a series of experiments with diverse consumer samples, we find that restrictive changes, such as shortening return time windows or introducing restocking fees, decrease consumer trust in retailers and lead to lowered purchase, positive word‐of‐mouth, and loyalty intentions. We also find that providing managerial transparency, in the form of communicating the rationale for restrictive changes, can attenuate the negative consumer reactions to such changes. Moreover, rationales that emphasize the cost of handling returns versus blaming opportunistic and abusive returners are similarly effective. Our findings contribute to the growing academic literature on consumer return policy design and provide actionable insights to retail managers.

ABSTRACTContributing to emerging research on corporate governance and operations management, our study examines the connection between board diversity and workplace safety. We propose that boards with a higher representation of female directors prioritize and thus enhance workplace safety due to women's distinct social‐cognitive perspectives on stakeholders, risk avoidance, and regulatory compliance. We also consider two conditions that may strengthen the relationship between female board representation and workplace safety: (1) the power of the female directors on the board, which may cause them to speak out more, and cause others to heed their perspectives; (2) accountability pressures faced by the board, which may increase board member epistemological motivation (the willingness to invest effort to fully understand the board's tasks and decisions being made). Analyzing a unique dataset that covers 1442 firm‐year observations across 266 firms, we show that female board representation improves workplace safety when women have more power and when boards face greater accountability pressures. In an empirical extension, we examine how the representation of racial/ethnic minority directors impacts workplace safety and the synergistic effect of female and minority board representation. Our findings deepen our understanding of how board diversity interacts with situational factors to influence operational outcomes.

Abstract We investigate how innovation intermediaries address shortfalls in the capabilities that buyers and suppliers must have to access each other's knowledge for innovation purposes, also referred to as indirect capabilities. Prior research on supplier‐enabled innovation has identified various capabilities that buyers need in order to collaborate with innovative suppliers. It recognizes that suppliers also require capabilities to access buyer knowledge. However, we still know little about the role of innovation intermediaries—actors who are neither buyers nor suppliers, but still influence innovation processes and outcomes in supply networks. Our case‐based research shows that intermediaries create workspaces for R&D and experimentation, help to refine definitions of requirements and de‐risk novel solutions, support contracting, and facilitate solution implementation. We contribute to research on supplier innovation by developing a model of intermediaries' activities and underlying capabilities, and their impact on innovation sourcing outcomes. We elaborate the indirect capabilities theoretical perspective by introducing additional types of indirect capabilities for collaborative innovation in supply chains, and showing how these capabilities interrelate. We furthermore extend the literature on innovation intermediaries by elucidating hitherto unexplored capabilities for intermediation and adding insights regarding the contribution of intermediaries to open innovation processes.

ABSTRACT Due to the COVID‐19 pandemic, global supply chains have experienced sustained impacts from unprecedented complex disruptions in different combinations and at different times. From an efficiency perspective, do these complex supply chain disruptions call for more complex risk management strategies? To answer this, we built an empirically grounded discrete event simulation model, the results of which were analyzed using data envelopment analysis. Results show that with unprecedented complex disruption patterns, a multi‐strategy portfolio approach is usually less efficient than a single‐strategy or a do‐nothing approach unless the strategy portfolio has certain characteristics. The most efficient strategy portfolios typically consist of a moderate number of diverse strategies. Too many strategies in a portfolio can be problematic, leading to increased costs that outpace improvement in revenue and service level. Results illustrate that even a strategy that generally performs poorly can be part of a very good strategy portfolio and vice versa. This study provides nuanced and novel findings that contribute to the resolution of the literature debate about the value of multi‐strategy portfolios in addressing complex disruption patterns. Highlighting the value of a strategy portfolio view, these insights help firms better prepare for the next complex and sustained global supply chain disruptions.