Journal of Operations Management最新文献

Research in healthcare suggests that repeated interaction between a provider and a patient can support better decision-making, resulting in improved efficiencies. To date, these repeated interactions enabling continuity of care have not been studied in hospital inpatient settings. During a hospital stay, decisions related to patient treatment are usually made by two key decision-makers: the attending physician (AP) and the operating physician (OP). Under the single decision-making approach (S-DMA), the AP and OP are the same; in contrast, under the dual decision-making approach (D-DMA), the AP and OP are different. In recent years, there has been an increasing trend toward the use of D-DMA over S-DMA across U.S. hospitals owing to scheduling conflicts. Although research outside healthcare operations management has argued for benefits from both approaches, their impacts on a patient's hospital stay are unclear. In this study, we address this gap by investigating the effects of S-DMA and D-DMA on patient care outcomes in terms of patient length of stay (LOS), treatment cost, and mortality. Data for our study come from the state of Florida and involve 520,554 cardiology patients treated by 9483 APs and 18,398 OPs at 241 hospitals between 2014 and 2016. We account for both patient and physician selection issues when choosing a particular decision-making strategy. Our results suggest that, on average, using S-DMA is associated with reduced patient LOS and treatment cost but has no effect on mortality. We also find that S-DMA is more beneficial for patients with low comorbidity and low process uncertainty, whereas D-DMA is more beneficial for patients with high comorbidity and high process uncertainty. Our results are robust to alternative explanations. We demonstrate that a single decision-maker offers benefits in the context of healthcare delivery, but dual decision-makers may yield benefits when caring for patients with high comorbidity and high process complexity. We discuss the implications of these findings for appropriately deploying S-DMA and D-DMA in inpatient services.

We 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.

A 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.

Does the speed of adopting environmental practices impact financial benefits? The strategy literature discusses the contingencies under which firms can gain an early-mover advantage or a late-mover advantage. This research examines the effect of adoption speed on two types of environmental practices: environmental innovation practices (EIP) and environmental management practices (EMP). The results show that early adoption of EIP increases competitive advantage when firms face intense competition. In comparison, we show that early adoption of EMP increases competitive advantage when firms face extremely low competition or have moderate to high levels of slack resources. The study contributes to the literature by revealing the nuances, contingencies, and boundary conditions of when it pays to be green. Prior research shows mixed results when studying firms' decisions to implement environmental practices, which implies that it may not pay to be green. This study shows that firms can get an early mover advantage from environmental practices, but it depends on the type of environmental practices, the firm's internal slack resources, and the firm's external competitive environment.

We develop a novel construct, diversion risk, defined as the potential for post-retail diversion that results from increased sales of hazardous goods. We examine diversion risk in the context of prescription opioid sales in the United States. We ask how supply base attributes and nonprofit ownership influence the creation of opioid diversion risk. We use performance-outcome expectancy theory to hypothesize that pharmacies organize supply bases to help them avoid negative evaluations and that nonprofit ownership alters expectancy concerning legal but questionable behavior. We develop and test multilevel hypotheses explaining how supply base complexity, chain size, and nonprofit ownership influence diversion risk. Our analysis of DEA data from 2006 to 2019 finds that after accounting for other attributes, supply base complexity is positively related to diversion risk, within and between firms. Retail chain size is negatively related to diversion risk in the within-firm model, but positively in the between firm model. Testing our nonprofit hypothesis reveals that nonprofit pharmacies also use size and supply base complexity to manage diversion risk. This research sheds light on the dynamics of diversion risk in pharmaceutical supply chains. It has practical implications for the industry, potentially informing future policy and practice addressing this critical issue.

In this editorial, we build upon the increased attention of the operations management (OM) community toward field experiments and the recent publication of the Pre-Approved Research Designs Special Issue that provided an initial test of Registered Reports as a novel review process for field experiments in OM. Addressing lingering concerns voiced by the editorial team and learning from the experiences of journals from other disciplines that implemented Registered Reports, we introduce a new initiative and outline a new review process in the Journal of Operations Management ($��\mathrm{JOM}�$) to motivate theory-focused field experiments in OM.

Empirical research in OM strives to uphold two key virtues: providing managerially relevant insights and making meaningful theoretical contributions. Field experiments¹—testing treatments in controlled digital and physical field settings—tend to possess the managerial relevance virtue because of their strong engagement with key stakeholders and the practical problems that they face. By contrast, the theoretical contributions of a field experiment depend on the extent to which its insights enrich our causal understanding of real-world phenomena. Critically, the degree to which scientific research based on field experiments in OM can be distinguished from consulting engagements and industrial experiments depends on the extent of its contributions to theory. Indeed, there are many different ways of contributing and forms of contribution to OM theory; however, they all serve a common purpose: to build a causal understanding of relevant OM phenomena. Field experiments can most decisively establish this causal understanding in the complexity of real-world operations (Ibanez & Staats, 2019). Thus, OM research that tests theoretically motivated treatments in field settings and provides sufficiently deep causal explanations for the observed effects, while also opening a door for future inquiry, possesses both virtues of empirical research. Consequently, we believe that the field of OM can benefit significantly from such research.

Despite the fact that empirical research employing field experiments as the main methodological approach is on the rise in OM, it has traditionally lagged other disciplines such as economics, marketing, and information systems (Gao et al., 2023). For example, Simester (2017) reports that 37 field experiments were published in the top five marketing journals between 2010 and 2014, a period in which OM published a total of five field experiments in its top five journals (Gao et al., 2023). Between 2005 and 2021, only 31 research articles with field experiments were published in three top-tier OM journals: JOM, Manufacturing & Service Operations Management (MSOM), and Production and