Journal of Operations Management最新文献

E. C. Falcone and J. W. Ridge, “An Investigation of Corporate Social Responsibility Conformity: The Roles of Network Prominence and Supply Chain Partners,” Journal of Operations Management 70, no. 4 (2024): 600–629.

In the second paragraph on page 616, the text “The results are consistent with H3 as the coefficient of the interaction term Network prominence ² * SC industry CSR congruence shows a positive and significant effect (β = 0.313; p < 0.01).” This is incorrect. The β = 0.313 should be β = 0.640.

We apologize for these errors.

Across fields of scholarship, ever since scholarship has existed, there have been numerous discussions opining on what theory is, why it is useful and how best to craft theoretical arguments and frameworks. Every few years, a new discussion particularly relevant to a domain of study emerges. Often the intention of such discussions is to reiterate critical points made in the past as still applicable. In other instances, the discussions attempt to recast and reshape perspectives on theory. Both reiteration and alternate perspectives can prove valuable, as new scholars enter the field and as priorities for journals, editors and review teams evolve.

These points are also of interest to contemporary discussions at the Journal of Operations Management (JOM). As an outlet long regarded for impactful empirical work in the field, we have long been interested in the appropriate use of theory and have also had a long history of intervening in our field to re-emphasize the ‘what’, ‘why’ and ‘how’ of meaningful theoretical structures and argumentation. As editors of the journal, we believe it is valuable to reiterate what is well-accepted regarding the role and nature of effective theory in research, whether we are discussing grand theories, theoretical frameworks, mid-range theory or theoretical arguments for specific mechanisms. However, we also strongly believe that it is critically valuable to outline how theoretical contributions may differ, while still offering considerable value to a research effort and the field.

What is core to the substantive nature of theoretical contributions, of course, must be driven by priorities regarding its role; just as the selection of empirical methods must be driven by the claims emerging from theoretical arguments (even nascent ones), and insights for future scholars driven by observation and analysis. By outlining contemporary priorities that define meaningful theory we are in a far better position to simultaneously expand perspectives on how theoretical contributions can be made, as well as challenge or dispel some often difficult-to-justify criticisms that scholars (authors, reviewers and editors) confront regarding what is ‘good’ theory.

According to Fried (2020), this “statistical equivalency” is one of the fundamental reasons that we cannot escape the need for well-reasoned theoretical arguments, designed to help us make sense of highly complex settings, in which a wealth of observed signals is accompanied by a wealth of unobserved signals. It is exactly when phenomena are not straightforward and mechanisms are not obvious, where sensemaking, and associated deliberate research inquiry, is critical.

In the same vein, a ‘complete theory’, akin to a physical law, doesn't present much of a motivator for research—if there is no uncertainty regarding cause and effect, there is little reason to expect that an inquiry into such phenomena would be of interest to

We examine the impact of vampire attack, a unique platform entry strategy in the blockchain ecosystem, on the operational performance of the incumbent platform. During the vampire attack period, the entrant (attacker) clones the incumbent platform and offers tokenized incentives to entice users away from the incumbent. Prior studies offer little insight into the impact of vampire attack strategy because of its uniqueness in platform cloning, tokenized incentives, and targeted attacks. We implement a quasi-experimental design by leveraging the first and most famous vampire attack launched by SushiSwap (the attacker) against Uniswap (the incumbent). We examine both the deposit-side and exchange-side impacts of the vampire attack on the operational performance of the liquidity pools on Uniswap. Surprisingly, we find that the vampire attack has no significant effect on the liquidity provision on the deposit side. Even more surprisingly, the vampire attack significantly increases the incumbent's trading volume on the exchange side. We further uncover the underlying mechanisms contributing to these intriguing results. We also demonstrate the efficacy of the novel tokenized incentives strategy. We show the generalizability of our findings by examining an alternative vampire attack event in the context of non-fungible token marketplaces. Our study offers significant contributions to the literature on the implications of blockchain on platform operations and platform competition in operations management.

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.

In hog farming, optimizing hog sales is a complex challenge due to uncertain factors, such as hog availability, market prices, and operating costs. This study uses a Markov Decision Process (MDP) to model these decisions, revealing the importance of the final weeks in profit management. The MDP's intractability due to the curse of dimensionality leads us to employ Deep Reinforcement Learning (DRL) for optimization. Using real-world and synthetic data, our DRL model outperforms existing practices. However, it lacks interpretability, hindering trust and legal compliance in the food industry. To address this, we introduce “managerial learning,” extracting actionable insights from DRL outputs using classification trees that would have been difficult to obtain otherwise. We leverage these insights to devise a smart heuristic that significantly beats the heuristic currently used in practice. This study has broader implications for operations management, where DRL can solve complex dynamic optimization problems that are often intractable due to dimensionality. By applying methods, such as classification trees and DRL, one can scrutinize solutions for actionable managerial insights that can enhance existing practices with straightforward planning guidelines.

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.

Operating flexibility supports a firm's resilience strategy during challenging times by enabling them to promptly cut down operating costs associated with unproductive resources. We employ a real options model to formalize this insight. Our empirically grounding analytics motivate a firm-level proxy for downscale operating flexibility (FLEX), which effectively captures the adjustment frictions across different contexts of firms' operations. Using U.S. data between 1961 and 2020, we show that operating flexibility mitigates the risk of stock price crashes, especially during periods of economic recession. Consistent with the loss-curtailment mechanism, the operating flexibility effect is more pronounced for firms with lower productivity/profitability or higher operating leverage and is further amplified during longer and more severe recessions. Managers may avail themselves of our well-tested empirical measure of operating flexibility to guide their efforts in building a more resilient operations structure.

The field of operations and supply chain management (OSCM) has a long history of identifying and engaging with risk and uncertainty in operational practices.¹ We provide a brief review of uncertainty in the OSCM domain, alongside an overview of our special issue (SI) call and accepted manuscripts. This serves as a starting point for the introduction of a new theoretical framework that reframes uncertainty as unresolved states of possibility. In this framework, the term possibility can refer to a broad array of OSCM actions and solutions including the novel application of existing approaches or technology as well as completely novel practices that enhance organizational outcomes. We illustrate the path-dependent evolution in these possibilities, alongside the limitations and opportunities imposed on the set of available possibilities resulting from concurrent evolution in the broader socio-technical system. We present the benefits of deploying a broader array of methodologies in the empirical study of what is, and is not, possible at discrete points in time, as well as the dual process of constraint and expansion in possibilities over time. The resulting empirical efforts to understand possibilities in turn enable novel theory development, elaboration of existing OSCM theory, and opportunities for bridging to other disciplines.

Much of the OSCM literature views uncertainty through the lens of risk (real and probabilistic, or perceived). Other areas of management examine uncertainty from the perspective of available solutions and possible alternatives, as well as opportunity by design (e.g. in entrepreneurial ventures, strategic decision making, and policy), see Alvarez and Barney (2007), Dimov (2016), and McBride and Wuebker (2022). This latter framing shifts uncertainty away from a troublesome factor to be mitigated and towards a source of potential value creation and progress. We conceptualize economic activity and operations as part of a larger, nearly decomposable, evolving structure (Simon, 1962, 2002). This offers the opportunity to redirect OSCM uncertainty research towards a deeper reflection on what actions and solutions are possible or impossible in an evolving socio-technical system—a system in which OSCM is deeply embedded (Arthur, 2009; Simon, 2002). Artificial systems evolve not just through competition and selection but also through purposeful cultivation and design (Simon, 1996). Thus, with technology in the socio-economic context of a complex system, OSCM is part of this larger autopoietic³ system—continuously regenerating itself, from itself (Holland, 1995). This centres our attention on the temporal aspect of our work. Specifically, it requires a consideration of how the world is, how it can and cannot be in the future, alongside how it could have b