Journal of Operations Management最新文献

Inadequate nurse staffing continues to challenge healthcare delivery in the United States. In this research, we undertake a fine-grained, unit-level analysis to understand the relationships between nurse staffing, nurse turnover, and pressure ulcers, the latter of which is a key nursing-sensitive care quality indicator. We examine these relationships within two types of hospital units: intensive care units (ICUs) and medical-surgical (MedSurg) units, which have unique patient mixes and needs. Using hospital unit-level data between 2008 and 2017, we show that nurse staffing primarily affects nurse turnover in ICUs, and that the adverse effects of nurse turnover on care quality tend to be stronger in ICUs than in MedSurg units. These findings provide important theoretical insights into the varying roles of staffing, turnover, and quality across organizational units. The findings suggest that hospital administrators may prioritize staffing needs for ICUs over MedSurg units to maintain strong quality performance on measures such as pressure ulcers. Further, our study reveals that staffing requirements for ICUs may be inadequate compared with MedSurg units. Thus, there is a need to evaluate existing guidelines on ICU staffing.

Leveraging an asset-sharing operating model, sharing economy platforms have disrupted incumbent firms in many industries. This research draws on the asset orchestration perspective to examine incumbents' asset orchestration (i.e., operational and governance actions) as they respond to threats from the sharing economy and the effectiveness of these actions in curbing a sharing economy firm's performance. We also analyze how the sharing economy firm's competitive moves counteract incumbents' asset orchestration responses. Our longitudinal empirical analysis in the accommodation industry demonstrates that the growth of sharing economy increases incumbent hotels' asset-based operational actions, which reduce the sharing economy's performance. Although incumbents' asset governance actions increase as the local sharing economy grows, they do not seem to hinder sharing economy's performance. This research highlights the role of asset orchestration in the competition between incumbents and the sharing economy and contributes to the operations and sharing economy literature.

We consider a budget hotel chain's revenue management problem of deciding how to dynamically allocate capacity to multiple segments of customers. Our work solves an industrial-sized problem faced by practitioners, with the reality of implementation motivating us to develop a tailored reinforcement learning approach. Our approach proceeds in two steps. First, a recommended average discount is computed with a reinforcement learning algorithm. Then, the recommended average discount is turned into a capacity allocation through a linear program. This approach overcomes the challenges of characterizing demand and estimating cancellations, and it facilitates hotel managers' acceptance of the revenue management system. We implement this approach in the hotel chain in a pilot study and assess its effectiveness using synthetic control methods. Our approach improves the key operational performance measure—revenue per available room—by 11.80%. There is heterogeneity in how the pilot hotels improve their revenue per available room. Some mainly increase their occupancy rate, some mainly increase the average daily room rate, while others experience significant increases in both. Further analysis shows that our approach uncovers the individual sources of suboptimal performance in pilot hotels and correspondingly improves decision-making. Our work demonstrates that a reinforcement learning approach for hotel revenue management is promising.

Although prior research in operations management has explored the working capital—firm performance relationship, the results from these studies remain inconclusive, with studies finding positive, curvilinear, or even insignificant relationships. This is largely due to contingent factors that make this relationship both complex and idiosyncratic. To strengthen the beneficial effect of working capital on performance, firms must therefore make appropriate investments that would foster more objective, informed, and firm-specific working capital choices. This article examines one such investment, namely in information technology (IT), that can allow firms to optimize the working capital–firm performance relationship. This is important, as the role of IT in this relationship is yet to be explored. Using proprietary IT data from the Harte Hanks database, and based on a sample of 1,054 US-based manufacturing firms during 2011–2013, we find that IT investment positively moderates the performance effects of inventory, payables, and receivables cycles, and that these moderating effects vary by the type of IT investment, namely IT infrastructure and IT labor. Drawing on the theory of the Smart Machine, we explain how IT infrastructure and IT labor perform distinct roles that can help automate (i.e., use technology to increase the speed and accuracy of process execution) and/or informate (i.e., use technology to create new information), thereby moderating the working capital–firm performance relationship. We argue and find evidence that, due to the largely transactional nature of working capital processes, IT infrastructure has a relatively stronger moderating effect on performance than IT labor.

This study investigates the relationship between impulse buying and product shortages in the context of television home shopping. Home shopping networks adopt promotional tactics to pitch sales and even encourage consumers' impulse buying. Marketing and sales management often assume that home shoppers' impulse buying will increase sales and profits, not considering the possibility that a significant number of impulse purchases could be canceled or returned. In general, product shortages driven by consumers' impulse buying may create a phantom stockout condition whereas non-impulsive consumers are deprived of purchasing opportunities while products are in the process of being canceled or returned. This is the first large-scale empirical study that addresses the relationship between impulse buying and product shortages in a network retail context. Based on actual transaction data, a novel research plan is developed to measure the impact of consumers' impulse buying on the retailer's revenue and product shortages. The findings indicate that impulse buying may cause product shortages directly. We conduct a post hoc analysis to investigate the differences in the impact of impulse buying between newly introduced and existing products and between hedonic and utilitarian products. Based on the empirical findings, we provide managerial implications for home shopping network retailers.

The implementation of digital transformation programs requires careful allocation of software developers to a variety of digital products and services with different levels of modularity. This paper investigates how deadline orientation (an individual-level preference of developers for completing work close to deadlines) and architectural modularity (a characteristic of products) influence central outcomes in software development. We argue that architectural modularity positively interacts with deadline orientation to influence software quality and the job satisfaction of developers. Our empirical analyses, using rare and high-quality data from 131 software developers and 29 product owners working at a captive software development center in India of a leading global software firm, confirm our hypotheses. We contribute to the literature on software development by showing that the fit between the technological characteristics of the software product (i.e., architectural modularity) and people factors (i.e., the temporal work style preferences of developers) plays an important role in shaping both software quality and job satisfaction. Our study has wider implications for the literature on software development, temporal work styles, and architectural modularity. It is instructive for practitioners tasked with hiring or allocating software developers for software products with varying technological characteristics in their digital transformation efforts.

This paper investigates the capacity decisions of complementary suppliers who produce different components of a final product. The suppliers solicit private forecast information from a buyer who has more precise information regarding the market as compared to the suppliers. In this context, the lowest capacity built among suppliers—termed as effective capacity—represents the bottleneck of a supply chain, which in turn determines the throughput of the entire channel. The standard analysis based on full rationality posits that the capacity decisions of suppliers are based on their prior belief of demand, with no consideration of the buyer's information dissemination or the number of peer suppliers. We test the predictions experimentally, and our laboratory observations reject the prediction of rational model. Then, we develop a behavioral model based on suppliers' heterogeneity in the processing of demand information provided by the buyer. Our behavioral model indicates that suppliers lower their capacity levels when the number of suppliers increases, thereby exacerbating the supplier bottleneck. While the buyer may exaggerate the market demand to ensure abundant supply, interestingly, the inflation can benefit suppliers by increasing their capacity levels. In this manner, the inflation of the buyer can serve to mitigate the supplier bottleneck, thereby resulting in a win–win outcome for both the suppliers and the buyer.