Journal of Operations Management最新文献

The world is witnessing more supply-base disruptions, where multiple suppliers of a buying firm simultaneously experience disturbed operations. Compared to single-supplier disruptions, supply-base disruptions create a more uncertain situation for a purchasing manager, yet they can also reveal improvement opportunities. Hence, it is theoretically and practically valuable to understand why a purchasing manager might not be willing to explore these opportunities. Adopting a sensemaking perspective, we investigate how two dimensions of supply-base disruption severity, breadth and depth, influence managers' perception of disruption severity and post-recovery action (i.e., suggesting supply-base restructuring ideas). We conducted multiple scenario-based experiments with practitioners and triangulated the experimental results with interviews, finding that both breadth and depth have diminishing, positive effects on perceived severity. Interestingly, depth is less influential than breadth. These findings reveal the circumstances under which the severity of a complex, disruptive situation could be misestimated. Our results also show that supply-base structural complexity (a cue of the task environment) amplifies the positive effect of perceived severity on a manager's inclination to walk into the unknown to propose supply-base restructuring ideas. These findings provide an explanation for management nonresponse after a supply-base disruption.

Treville, S., Browning, T. R., & Oliva, R. (2023). Empirically grounding analytics (EGA) research in the Journal of Operations Management. Journal of Operations Management, 69(2), 337–348.

This is to correct the attribution of citations to references in Tables 2 and 3 that became misaligned during the copyediting process.

The sixth row of Table 2 should refer to de Treville, Petty, et al. (2014).

The seventh row of Table 2 should refer to de Treville, Schürhoff, et al. (2014).

The second row of Table 3 should refer to de Treville, Bicer, et al. (2014).

We apologize for this error.

Many antecedents identified as essential to supply chain resilience (SCR) are often studied independently, without considering their synergistic effects. Based on a case study and resource orchestration theory, this article focuses on configurations of different antecedents regarding supply chain integration and big data analytics capability to develop proactive and reactive SCR. Using survey data from 277 Chinese manufacturing firms, we consider three dimensions of supply chain integration, information integration, operational integration and relational integration, and three dimensions of big data analytics capability, technical skills, managerial skills and data driven-decision culture, and conduct fuzzy-set qualitative comparative analysis (fsQCA) to explore antecedent configurations generating high proactive and reactive SCR. We find that multiple antecedent configurations can achieve high SCR and configurations for high proactive and reactive SCR are not identical, which may involve alternative effects across different antecedents. We further implement propensity score matching analysis and reveal that firms following these configurations for high SCR also have better economic and operational performance. Moreover, we check the robustness of findings by using secondary data and attributes analysis with machine learning. This article complements and extends existing SCR literature from the configurational perspective and provides practical insights for managers to build SCR.

One important question in public-private (PP) projects is how to manage coopetition—simultaneous cooperation and competition among project members. Prior studies on the governance of PP projects showed the importance of governance mechanisms to deal with major events such as technical or organizational disruptions but paid limited attention to the management of coopetition. At the same time, research on the management of coopetition mostly focused on industrial coopetition, whereas PP projects also entail public-private coopetition. Seeking to better understand how governance mechanisms may help manage coopetition in PP projects, we conducted an in-depth study of Galileo—a large PP project aimed at delivering Europe's own satellite-based navigation system. The findings show how three core aspects of project governance—(i) mechanisms (joint vs. separate use of contractual and relational mechanisms), (ii) form (lead organization vs. shared governance), and (iii) goals (to promote cooperation and/or prevent competition)—jointly explained the emergence and (mis)management of knowledge- and value-related coopetitive tensions. In turn, these tensions prompted a series of adaptations in the governance of the project. Our study contributes to a co-evolutionary understanding of the governance of PP projects and offers implications for practitioners seeking to (re)design PP project governance.

Drawing on real options and resource dependence theories, this study examines how firms adjust their innovation investments to address trade policy effect uncertainty (TPEU), a type of firm-specific, perceived environmental uncertainty capturing managers' difficulty in predicting the impacts of potential policy changes on business operations. To develop a context-dependent, time-varying measure of TPEU, we apply bidirectional encoder representations from transformers, an advanced deep learning technique. We analyze the texts of mandatory management discussion and analysis sections of annual reports from 3181 publicly listed Chinese firms. Our sample comprises 22,669 firm-year observations spanning the years 2007 to 2019. The econometric analyses show that firms experiencing higher TPEU will reduce innovation investments. This effect is stronger for firms facing lower competition, involving more foreign sales, and not owned by the state. These findings provide clarity on previously inconclusive results by showcasing the significant influence of policy effect uncertainty, as opposed to policy state uncertainty, on firms' decisions regarding innovation investments. Additionally, these findings underscore the importance of resource dependence factors as crucial contextual factors in this decision-making process.

The practice of offering refurbished products has become increasingly prevalent, yet limited research has studied the potential consequences of this emerging product strategy on the market performance of the corresponding brand-new products. This study addresses this gap by examining the impact of refurbished products in markets characterized by network effects and standards competition, while considering the developmental stage of the technology standard employed by the products. Introducing refurbished products, primarily remanufactured from returned items, can pose challenges such as sales cannibalization and negative perceptions of product quality. We propose that introducing refurbished products can also generate positive effects, because the availability of refurbished products can be perceived as an indication of sizable prior sales of the brand-new counterparts, alleviating consumers' concerns regarding an insufficient user base and limited availability of complementary products. Our empirical and experimental studies consistently demonstrate that the availability of refurbished products adversely affects the market value and purchase intention of the corresponding brand-new products employing established technology standards. In contrast, the introduction of refurbished products does not have such a detrimental effect on products utilizing unestablished standards. Moreover, for a specific group of consumers, this green product strategy increases their purchase intention of brand-new products. The managerial implications of the findings are discussed.

There has been a resurgence of interest in the role of operational focus in the healthcare operations literature in the backdrop of increasing demand for efficient and effective care. However, the evidence on the benefits of focus in healthcare is mixed. Our study proposes that a key piece of this puzzle that is been largely missing is an explicit consideration of the complexity of patient care needs. Specifically, our study serves to answer the questions: How does the complexity of care requirements affect care delivery operations? How does focus across the hierarchical levels of care affect care delivery outcomes across complexity regimes? The empirical analysis in the study is based on a large generalizable dataset of 246,663 patient discharges across 26 categories at 154 hospitals. We develop a multi-factor measure of complexity of care requirements. The study results point to the deleterious impact of complexity on the costs of care. Next, our findings highlight the differential impact of focus across hierarchical levels (task-level focus, category-level focus, and selective focus in related areas) on the costs of care. Third, our study findings highlight the role of focus in mitigating the effects of complexity on costs in a healthcare setting. We discuss the implications of the study findings for theory and practice, and the directions for future research.