Journal of Operations Management最新文献

Our research examines how to integrate human judgment and statistical algorithms for demand planning in an increasingly data-driven and automated environment. We use a laboratory experiment combined with a field study to compare existing integration methods with a novel approach: Human-Guided Learning. This new method allows the algorithm to use human judgment to train a model using an iterative linear weighting of human judgment and model predictions. Human-Guided Learning is more accurate vis-à-vis the established integration methods of Judgmental Adjustment, Quantitative Correction of Human Judgment, Forecast Combination, and Judgment as a Model Input. Human-Guided Learning performs similarly to Integrative Judgment Learning, but under certain circumstances, Human-Guided Learning can be more accurate. Our studies demonstrate that the benefit of human judgment for demand planning processes depends on the integration method.

We study the potential negative impact of imbalanced compensation schemes on firm performance. We analyze data from a radiology workflow platform that connects off-site radiologists with hospitals. These radiologists select tasks from a common pool, while service level is defined by priority-specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay-to-workload ratio receiving poorer service. We investigate this hypothesis, showing turnaround time is decreasing in pay-to-workload for lower priority tasks, whereas it is increasing in workload for high-priority tasks. Crucially, we find evidence of an externality effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing their likelihood of delay, thus partially defeating the purpose of the priority classes.

We experimentally investigate whether mass customization enhances sustainability and firm outcomes in a fast fashion context. Fast fashion delivers fashion trends to consumers quickly and cheaply but has detrimental effects on the environment (e.g., waste accumulation, water pollution). To mitigate these harmful effects, we examine how different points of customer involvement in mass customization affect the anticipated number of months to product disposal and willingness-to-pay for mass-customized products. We employ a series of experiments and find that consumer perceptions of the degree of customization increase as the point of customer involvement shifts upstream from Use to Assembly to Fabrication to Design and that the anticipated number of months to disposal and willingness-to-pay increase as the point of customer involvement shifts upstream to Design. We also find that the implementation of customer involvement in mass customization matters. Overall, these results provide evidence that mass customization via Design may not only help slow fast fashion down, which has major sustainability implications, but it may also present a win-win opportunity for both the environment and firms (in terms of the bottom line—provided, of course, that it does not have any major cost disadvantages).

Recent past has witnessed a great proliferation of sharing economy platforms with great successes, but also many failures on their journey toward institutionalization, that is, their establishment and acceptance in society. To develop insight on factors contributing to this institutionalization process, this paper relies on the literature in institutional entrepreneurship and investigates sharing economy platforms in China, highlighting institutional voids that are encountered, and suggesting ways on how they can be overcome. These insights are derived through a multiple case study approach involving 61 semi-structured interviews with managers from eight sharing economy platforms (DiDi, Uber China, Huochebang, Yunmanman, OfO, Mobike, Evcard, and Zhida) across four sharing economy industries in China (ride-sharing, logistics-sharing, bike-sharing, and car-sharing). The findings highlight the importance of exchange and collaboration with stakeholders (allies) in the network, as well as the effective response to emerging issues through the adaptation of the business models. In addition, mergers and acquisitions, and the role of government are identified as critical components in aiding platforms in their progression toward institutionalization. Further cognitive, regulative, and normative factors are found to represent valuable institutional pillars that serve as a fertile foundation throughout the institutionalization process. Overall, the findings provide an intriguing insight into the journey of Chinese sharing economy platforms toward institutionalization.

In a service exchange setting, the supply management literature generally assumes, with notable exceptions, the availability of complete information regarding supplier reliability. Highlighting the information asymmetry in supplier evaluation and using signaling theory, we argue that for a focal buyer, a supplier's downstream ego-network instability, that is, other buyers' turnover in a supplier's network from one period to the next, acts as a signal of supplier unreliability, thereby reducing the price that the buyer pays to the supplier in a service exchange. Furthermore, we suggest that focal buyer–supplier relationship strength and structural equivalence weaken the negative effect of instability because the buyer has a more direct and positive experience with the supplier. Using a dataset of 3263 unique dyads formed by 260 buyers (shipoperators) and 493 suppliers (shipowners) during the 2000–2018 period in the container shipping charter market, we find support for our hypotheses, except for the contingent effect of structural equivalence. Our study contributes to signaling literature and network research by developing a supplier's downstream ego-network instability as a salient heuristic for a focal buyer's pricing decisions. These findings equip buyer managers who may not accurately foresee supplier service quality in the charter market with a new supplier evaluation tool: a supplier's downstream ego-network instability.

We study the effectiveness of two theoretically and practically relevant interventions designed to increase familiarity with and thereby stimulate the appeal of and willingness to pay (WTP) for remanufactured (refurbished) consumer products that are often found repulsive by consumers: (1) educating consumers about the remanufacturing process, (2) providing physical exposure to remanufactured products. We find that education does not cause an increase in the appeal of and WTP for remanufactured consumer products. Providing physical exposure to remanufactured products, relative to text and text-plus picture or video modalities, significantly increases both the appeal and WTP as a result of increasing perceived quality and decreasing disgust. Sellers can benefit from marketing remanufactured consumer products through physical channels (i.e., brick-and-mortar, outlet, showroom stores) as opposed to solely through online channels, which is the common practice among many sellers.

This paper investigates a supply chain governed by a flat penalty service-level contract in which missing the target fill rate can lead to costly operational disruption. We focus on near-miss bias: (1) the preference for near-miss events, that is, risky production quantities that reach the target but narrowly avoid disruption; and (2) riskier decision-making due to such preferences. We propose a reference-dependent behavioral model that explains the near-miss bias. The findings of a laboratory experiment show that production quantities are evaluated based on realized profits and are below the optimal model prediction. Contracts associated with lower perceived severity, that is, the ratio of flat penalty to wholesale price, result in lower production quantities than those with higher perceived severity, even though the standard model does not predict any effect. A structural estimation analysis indicates that the behavioral model performs better than the standard model in terms of predictive accuracy and goodness of fit. Our analysis provides insights for managers who design supply chain contracts in settings with considerable risk of disruption due to a shortage of critical parts.

While considerable progress has been made in understanding gender mismatch and bias in the physical workplace, there is a limited understanding of how these biases manifest in online platforms where gender masking and manipulation can easily occur. In this study, we collaborate with an online product and service firm in Asia and propose a field experiment design to examine how gender bias influences people-centric operations in online platforms. By assigning different gender combinations of clients and consultants, we examine how the effect of gender bias differs depending on the gender match or mismatch of the entities in the information exchange process. Using 7 months of data, we provide preliminary evidence of gender biases of different client-consultant gender pairings. Theoretical and practical implications are discussed.

The Hospital Readmissions Reduction Program imposes financial penalties on hospitals with excess readmission rates for various conditions, including coronary artery bypass graft (CABG) surgery. While prior research has focused mainly on hospital-specific factors and their impact on patient mortality, surgeon risk factors and patient outcomes, such as readmission risk, have received less attention. We study three drivers of CABG readmission risk—hospital and surgeon case volume, variation in surgeon volume, and surgeon familiarity with hospitals. Drawing on unique patient data sets of CABG surgeries spanning 3 years, we study the relationships between hospital and surgeon case volume, readmission risk, and postoperative length of stay (PLOS). We find evidence of an inverted U-shaped relationship between case volume and readmission risk and a U-shaped relationship with PLOS. Our results also indicate that surgeon volume variation moderates the relationship between surgeon volume and readmission risk, where readmission risk increases with greater variation in surgeon volume. We observe that the impact of surgeon experience on readmission risk at the focal hospital outweighs their experience at other hospitals. Our study has significant implications for hospital resource management as it highlights the role of surgeon and hospital experience in improving patient outcomes after CABG surgery.