Knowledge that Transforms

We examine the causal effects of nearby terrorist attacks on supplier–customer relationships. We find that for supplier firms located near terrorist attacks, the probability of relationship termination with their major customers increases by 2.9 percentage points within two years following the attacks. The major customers’ intensified perceptions of supply chain risk largely drive the relationship termination. Further analyses show that major customers tend to switch to suppliers with lower terrorism risks after they end their relationships with the suppliers near attacks. This study provides new insights into the consequences of terrorism by extending the focus to the response of a key stakeholder group (i.e., trade partners) instead of the attack-afflicted firms per se.

The relationship between product variety and sales has been extensively researched, but almost exclusively from the perspective of a new goods retail firm. Closed-loop supply chains for used goods, such as automobiles, offer unique challenges in terms of using reverse flows to create product variety at the retail location. Firms in used goods industries are unable to define their product mix a priori and may not even know which goods will be available to add to their sellable inventory. In this article, we use data from a used automobile retailer to explore these issues. A better understanding of the relationship between variety and sales can help used automobile retailers improve how they define and structure product variety at retail locations. Improved management of used automobile sales has obvious financial implications, but it also has important environmental implications, given the large contribution of passenger vehicles to overall emissions. This study implements a cluster analysis using consumer-facing variables to understand how customers view product variety for used automobiles. Our results show that three distinct classes of inventory exist, primarily driven by a key characteristic—body type—which differs from the traditional definition of product variety for new vehicles in the existing literature. A two-way fixed effects regression is then used to understand the relationship between product variety at the firm's retail locations and sales. The results demonstrate a nonlinear relationship between product variety and sales and, intriguingly, suggest that increasing product variety in some classes can be used to drive sales in others. Our findings yield important contributions for managers in used automobile firms and extend the broader literature on closed-loop supply chains. Specifically, our research demonstrates important differences in how product variety should be managed with used goods relative to new goods by a firm seeking to maximize sales.

Much of the responsibility for advancing the circular economy has been directed towards firms, yet many reuse opportunities can only be achieved through environmentally compliant, household-level recycling behaviors. In response, policymakers and recycling organizations are using a range of feedback mechanisms to promote household recycling that meets local quality standards. However, the effectiveness of these tactics remains unclear, and stakeholders are divided on the appropriateness of their use. In this research, we examine the role of two popular feedback mechanisms—information-only and information-plus-penalty—in correcting households’ curbside recycling behaviors. With information-only feedback, households are provided with best practices for recycling and are not penalized for their errors. With information-plus-penalty feedback, households also receive information, but temporarily forfeit their recycling services. While previous studies have explored the use of information and penalties as feedback mechanisms to guide behavioral changes, there is mixed evidence of their effectiveness, particularly in the recycling context. We address this research gap by analyzing unique data collected from a 2019 curbside auditing effort that occurred in a large, Mid-Western city. Our analysis leverages econometric methods, and recycling feedback and performance data from 25,359 audits across 11,899 households and 15 recycling routes. We find that information-only feedback mechanisms, while preferred by some stakeholders, are not associated with improvements in recycling quality (measured using household contamination rates). By contrast, our results indicate that punitive mechanisms (i.e., information-plus-penalty) involving cart refusals are associated with significant reductions in contamination rates: that is, households that receive punitive feedback reduce their contamination rate severity by 59%, and are 75% less likely to commit a violation in the future. More importantly, we do not find evidence that punitive feedback mechanisms generally discourage households’ participation in recycling programs (measured using future set out rates). Our study informs sustainable operations management literature by investigating how curbside feedback mechanisms, with differing levels of severity, influence critical dimensions of households’ recycling performance (i.e., recycling quality and participation). We also inform policymakers on how curbside feedback mechanisms can be more effectively leveraged to enhance opportunities for material reuse.

Live streaming has significantly transformed the landscape of both offline and online retail operations. This article explores the optimal timing and circumstances under which a firm with a specific product should launch a live streaming channel, and if so, whether it should use third-party streaming, self-run streaming, or a combination of both. We demonstrate that no single channel structure is universally superior to the others: the firm's optimal channel strategy depends on the third-party streamer's popularity and bargaining power, the investment and broadcasting effort cost for the live streaming channel, consumer's channel preference and extra cost for watching live streaming, cross-channel spillover, as well as the price sensitivity of a product. In general, live streaming is most beneficial for firms selling products that aren't highly price sensitive, where traditional pricing tool is less important for attracting consumers. Start-ups should collaborate with either highly popular streamers or those with a small but dedicated following, avoiding those with intermediate popularity. In contrast, established firms should partner with streamers who have a moderate level of popularity. An established firm considering leveraging two or more channels concurrently should additionally take into consideration the cross-channel spillover, channel encroachment cost, and channel competition. Our research reveals that, contrary to expectation, price in live streaming channels may not always be lower than those in traditional channels. Furthermore, highly popular streamers do not always demand higher revenue-sharing ratios and slotting fees. This research sheds light on the key decisions for firms considering live streaming commerce: whether to adopt it, when to integrate it into their strategy, and how to effectively implement it.

Demand forecasting for seasonal products becomes especially challenging in the case of fast innovations, where the product portfolio is upgraded every season. In addition to the problem of forecasting demand without any historical data, companies also have to deal with frequent stockouts, which bias past sales and provide an unreliable anchor for making new forecasts. We show how one can use machine learning models to leverage information on comparable products from the past together with experts’ forecasts to improve forecasting accuracy. A machine learning forecast using only statistical features results in a forecast error reduction of 24%, measured by weighted mean absolute percentage error, compared to a purely judgmental prediction on data from Canyon Bicycles. Better yet, an integrated human-machine forecast leads to a further 14% reduction in forecast error, indicating that experts’ predictions remain essential for forecasting demand for rapidly innovating seasonal products. The combination of the experts’ knowledge of the future and the machine learning algorithms’ ability to leverage historical information works best in this setting.

Non-financial incentives such as badges, ranks, and status are often used to encourage user participation on online platforms. This study focuses on the effect of one such incentive, “status,” in the context of a third-party restaurant-review platform. In contrast to previous research that has mainly focused on the effects of such incentives on subsequent contributions from users who gained statuses, we explore how the intrinsic and perceived quality of content generated by users is impacted after users lose their statuses. Using natural language processing techniques to extract quality metrics from online reviews in our dataset, we exploit a quasi-experimental setting and demonstrate that even though the intrinsic quality of reviews significantly decreases after a reviewer is demoted by a platform, consumers on the platform nonetheless perceive these reviews as disproportionately useful. We draw on inequity theory and the elaboration likelihood model to theoretically support our empirical results, as well as conduct mechanism analyses to rule out alternative explanations. Furthermore, we find that temporal associations with a platform or with an elevated status do not moderate the effect of status loss on the intrinsic and perceived quality of reviews written post-demotion. The implications of our findings are significant for platform managers who manage the design of status-driven recognition systems and must determine how the change in status should be displayed on the platform.

In this article, we consider hybrid manufacturing/remanufacturing inventory systems that produce a single product to satisfy demands over a finite planning horizon. In each period, the firm receives random demand and returns of end-of-life products. A serviceable product can be manufactured from ample raw materials or remanufactured from a returned product. The two operations possess random dedicated capacities. The firm’s objective is to minimize the expected total discounted cost over the planning horizon. We partially characterize the firm’s optimal inventory policy when the two capacities are positively dependent and completely characterize it when only one capacity is random. When there is ample manufacturing capacity, we connect the model with an auxiliary dual-sourcing inventory model and derive a more detailed structure of the optimal policy. Finally, our numerical study provides actionable insights into the effects of random capacities. Among others, we find that approximating a slightly/moderately variable remanufacturing capacity as its deterministic mean capacity or ignoring the correlation between two random capacities under a multi-period setting incurs a limited cost to the firm.

This study explores the application of formal metaphorical transfer to construct theory regarding supply chain resilience, a topic of increased significance due to rising supply chain disruptions. We propose an ecological resilience perspective to illuminate the complex, dynamic nature of supply chain systems. Our research pivots around two questions: (1) Can the resilience of endotherms (warm-blooded animals) serve as a conceptually robust source phenomenon for metaphorical transfer to the study of supply chain resilience? (2) What theory-based principles can be derived from this metaphor to enhance our understanding of supply chain resilience? After rigorously establishing the conceptual equivalence between endotherm resilience and supply chain resilience, we identify a set of theory-based principles that provide insights into the evolving field of supply chain resilience. These principles help illuminate the adaptive and predictive dimensions of supply chain resilience. This paper contributes to theory building in operations management and supply chain management while suggesting new avenues for future research.

E-commerce platforms are playing an increasingly important role in influencing manufacturers’ supply chain and product decisions. An emerging supply chain innovation, known as the platform-based consumer-to-manufacturer (PC2M) model, has been initiated by several large e-commerce platforms based on established digital links between consumers and manufacturers. These links enable consumer inputs into manufacturers’ operations, indirectly by capturing consumer preferences from platform-accumulated big data and directly by enabling consumer interaction with manufacturers through the e-commerce platform. Although manufacturers are increasingly integrating PC2M into new product development (NPD), there is little research on operations innovations in connection with the PC2M model and its impact on manufacturers’ new product success. To fill this research gap, we investigate the PC2M model of JD.com, a leading e-commerce platform in China that launched the PC2M model in 2018. We first identify two uses of PC2M by manufacturers to facilitate product development—platform-enabled big data analytics (PBA) and platform-enabled simulated product trials (PST)—and explore how PC2M enables operations innovations in NPD. Next, drawing on the knowledge-based view, we develop research hypotheses and empirically examine whether PC2M adoption improves new product performance using a large-scale, transactional dataset from JD.com. Through a series of carefully executed analyses, our study consistently finds that use of either PBA or PST in manufacturers’ NPD processes improves new product performance. We also explore how these effects vary across product types and markets with varying new product introduction rates. The findings offer important managerial insights for improving new product success in today's data-rich environment.

This position paper reframes the ongoing relevance versus rigor debate in operations research (OR) as a Kuhnian epistemological crisis, in which the dominant paradigm of quantitative modeling shows signs of exhaustion. Humanitarian fleet management is presented as an empirical case of extensive operations theory, which has not been implemented by the stakeholders who paid for its production. We propose a possible way out of the crisis by combining “hard” and “soft” OR, illustrating the potential with a selected problem structuring method. Optimization solutions can become more productive by first surfacing the organizational context of decision-making. The illustration emphasizes that hard and soft OR are not binary opposites but interlocking, mutually empowering components which expand the evidence base. Shifting the current paradigm toward more engaged scholarship could counteract the ongoing theoretical drift, for more strategic impact on the pressing problems of today.