Knowledge that Transforms

ABSTRACT The implementation of servitization as a business strategy remains a significant challenge for firms seeking to offer integrated product‐service bundles. Despite the growing body of research in the servitization literature, an integrative framework for explaining the recurring failures in servitization efforts has yet to emerge. Through a qualitative meta‐analysis of empirical evidence documented in existing case studies, we develop a process theory that examines the tensions between product and service businesses during the service business development process. Our study identifies three common servitization failure modes, highlights the contributing factors across various stages of servitization, and establishes causal links between structural elements and behavioral outcomes to explain the heterogeneity in service business performance. By adopting a process perspective, we provide a more nuanced understanding of servitization dynamics, offering new insights that complement and extend established theories on firm growth and capability development. We conclude by discussing managerial implications for firms pursuing a servitization strategy and outlining future research steps to test and further refine the proposed theory.

RETRACTION: E.C. Falcone and J.W. Ridge , “,” Journal of Operations Management 70 , no. ( 2024 ): 600 – 629 , https://doi.org/10.1002/joom.1302 . The above article, published online on 27 March 2024 in Wiley Online Library ( http://onlinelibrary.wiley.com/ ), and its correction ( https://doi.org/10.1002/joom.1350 ), have been retracted by agreement between the journal Editors‐in‐Chief, Elliot Bendoly and Rogelio Oliva; the Association for Supply Chain Management; and Wiley Periodicals LLC. A third party raised concerns with the editors regarding data irregularities throughout the article's text and tables. Following an investigation, the editors noted that the identified transcription errors significantly impacted the article's conclusions, and affected their confidence in the results reported. The authors provided raw data and analysis code for review, which allowed for identification of the transcription and methodological errors. However, the editors determined that the authors’ explanation and data was not sufficient to resolve the concerns, and therefore the article must be retracted.

ABSTRACT The increasing availability of secondary data on supply chain relationships has created new opportunities for empirical research in supply chain management. Datasets from sources including Bloomberg SPLC, FactSet, and CompuStat may support empirical analyses of decision‐making, strategic behaviors, governance mechanisms, and dynamics of inter‐organizational relationships of supply chains. However, the complexity and interconnectedness of supply chains and the inconsistent quality and coverage of supply chain data sources present empirical challenges, which have limited the scope and depth of current empirical research on supply chains. To address these challenges, this study investigates and compares the three commonly used supply chain databases and introduces a data‐focused roadmap for supply chain research using secondary data sources. This roadmap presents a process featuring data source selection, unit‐of‐analysis decisions, and appropriate econometric treatments, for example, endogeneity, selection bias, and correlated errors in supply chains, which contributes to the supply chain management literature by improving consistency, generalizability, and reliability in empirical supply chain research.

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.