Journal of Operations Management最新文献

{"title":"Calling It Quits: A Behavioral Perspective on Exploration and Exploitation in R&D Project Termination","authors":"Fei Li,&nbsp;Matthew Semadeni,&nbsp;Tyson R. Browning","doi":"10.1002/joom.70034","DOIUrl":"10.1002/joom.70034","url":null,"abstract":"<div>\u0000 \u0000 <p>Not every R&amp;D project will succeed, necessitating a careful selection of which R&amp;D projects to pursue and which to terminate. Timely termination decisions free up scarce resources for more promising projects. Yet, prior research has yielded inconclusive results on how firms terminate exploratory versus exploitative R&amp;D projects. Given that early-stage R&amp;D project termination is a decision made under high uncertainty, we adopt a behavioral perspective to reconcile the inconsistent findings. We propose that a firm's preference for terminating exploratory versus exploitative projects depends on three sources of contextual feedback: parallel projects, prior collaboration experience, and firm performance. We test our hypotheses using drug development projects from pharmaceutical firms over 11 years, finding that exploratory projects are less likely to be terminated relative to exploitative ones when the number of parallel projects is limited, when they are conducted with an existing partner, or when firm performance falls below aspiration.</p>\u0000 </div>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 3","pages":"417-438"},"PeriodicalIF":10.4,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Internet of Things: Reducing the Bullwhip Effect in Hospital Supply Chains","authors":"Onyi Dillibe,&nbsp;Ravi Aron,&nbsp;Praveen Pathak,&nbsp;Krabuanrat Tanasak","doi":"10.1002/joom.70023","DOIUrl":"https://doi.org/10.1002/joom.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>We investigate how tracking the running times of durable medical equipment connected to the Internet of Things (IoT) can be used to estimate the demand for MRO products and consumable supplies (MRO &amp; CS) and lessen the Bullwhip Effect in healthcare supply chains (HSCs). By analyzing a unique data set consisting of orders for medical supplies in a multi-echelon HSC spanning 12 quarters (3 years), using two-stage econometric modeling and counterfactual analysis methods, we find that the sharing of these usage and consumption signals significantly improves forecast accuracy, lowers order size, and reduces the bullwhip effects of MRO &amp; CS. We find that the supply chain benefits of tracking equipment running time are amplified by the scope of the relational exchanges between supply chain partners. We analyze the two dominant IoT networking paradigms—Edge and cloud IoT—and find that the benefits of signal sharing are significantly higher under the Edge IoT network. We also find that the tracking of medical equipment has second-order effects that extend to products whose consumption is correlated with the tracked products. Our research makes important theoretical contributions to the HSC and technology management literatures and provides valuable insights into how emergent technologies can be used to address the distinctive challenges of healthcare supply chains.</p>\u0000 </div>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"279-296"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Analysis of Laparoscopic and Robotic Minimally Invasive Surgeries: Surgeon Experience, Learning, and Task Performance","authors":"Pradeep K. Pendem,&nbsp;Sriram Narayanan,&nbsp;Roger R. Dmochowski,&nbsp;Vikram Tiwari","doi":"10.1002/joom.70016","DOIUrl":"https://doi.org/10.1002/joom.70016","url":null,"abstract":"<p>The study examines the transfer of learning in the context of different surgical methods used to complete surgical procedures. Surgeons' total experience is characterized by the procedure (i.e., focal or different) and the surgical technology or method used (i.e., laparoscopic-assisted minimally invasive surgery [LAS] or robot-assisted minimally invasive surgery [RAS]). Using data from a large US university hospital with specialized surgical departments, and drawing on task-technology fit and fit appropriation model theories, we propose three novel hypotheses that focus on different facets of how learning transfers across methods and tasks for individuals. The findings contribute to research on individual learning by distinguishing between method learning (how the task is performed) and task learning (the knowledge of the task itself). We find the following key findings. First, the experience from the RAS (LAS) method has a significant (no) effect in reducing LAS (RAS) duration for the same procedure. Second, learning from other procedures is contingent on the method used to complete the task. Specifically, the experience from other procedures completed using the LAS (RAS) method increases (decreases) the LAS (RAS) duration. Overall, the results indicate that the transfer of learning from RAS to LAS is greater than from LAS to RAS. This suggests that learning transfer across technologies is asymmetrical and requires careful consideration regarding how accumulated surgical experience from various technologies impacts task performance. From a task-technology fit and fit appropriation model theory perspective, our study highlights the importance of technologies' capabilities in knowledge transfer across methods.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"297-315"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scaling Emerging Healthcare Technology: Managing Paradoxical Tensions in A Connected Health Platform","authors":"Yichuan Wang,&nbsp;Minhao Zhang,&nbsp;Jens K. Roehrich,&nbsp;Rui Ma,&nbsp;Yunkai Zhai","doi":"10.1002/joom.70013","DOIUrl":"https://doi.org/10.1002/joom.70013","url":null,"abstract":"<p>To address growing geographical disparities in healthcare access and quality, connected health platforms (CHPs) have emerged as promising solutions. However, rapidly scaling CHPs poses significant challenges, particularly in managing tensions among multiple entities. This study examines the digital scaling process of a CHP through the lens of paradox theory, focusing on tensions and their management in resource-constrained environments. We conducted a 13-year longitudinal case study of a CHP encompassing over 300 hospitals in China. Our findings reveal three distinct yet interconnected phases of digital scaling: digital foundation building; system integration and governance; and continuous improvement and innovation. We demonstrate that tensions evolve over time during the scaling process, with one dominant type prevailing in each phase, challenging previous assumptions that different tensions emerge simultaneously across multiple entities. We also identify phase-specific “both-and” responses employed by rural hospitals to manage tensions despite experiencing resource constraints. We provide guidance to organizations operating in resource-constrained environments on the management of paradoxical tensions across a complex digital scaling process.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"233-256"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On Telemedicine Reimbursements and Hospital Adoption: A Policy Analysis Using Causal Inference Methods","authors":"Kang Bok Lee,&nbsp;Andrew Hildebrandt,&nbsp;Nitish Patidar,&nbsp;Shashank Rao","doi":"10.1002/joom.70021","DOIUrl":"https://doi.org/10.1002/joom.70021","url":null,"abstract":"<div>\u0000 \u0000 <p>There continue to be substantial differences in the quality and access to healthcare available to communities across the United States. Researchers have called this the healthcare divide. Telemedicine, a technology-mediated intervention that promises to bridge this healthcare divide, has been discussed by some as the great leveler. Policymakers, therefore, continue creating legislation that may drive hospitals to adopt telemedicine. Yet, how (or whether) such legislation achieves the goal of telemedicine adoption (and relatedly, healthcare divide reduction) is unclear. In the current study, we investigate a phased introduction of telemedicine reimbursement policies across various states in the United States between 2014 and 2019 to explore the causal effects of such policies on hospital adoption of telemedicine. Results suggest that while reimbursement policies drove the adoption of telemedicine services, this adoption was restricted to inpatient services rather than outpatient ones. We ground our investigation on extant theoretical insights from institutional isomorphism and highlight the limitations in our understanding of the adoption of telemedicine by hospitals in the country.</p>\u0000 </div>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"257-278"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spillover Effects From Health Information Systems Integration: Evidence From Prescription Drug Monitoring Programs","authors":"Yufei Shen,&nbsp;Dorothy Lianlian Jiang,&nbsp;Dimitrios A. Andritsos,&nbsp;Xitong Li","doi":"10.1002/joom.1367","DOIUrl":"https://doi.org/10.1002/joom.1367","url":null,"abstract":"<div>\u0000 \u0000 <p>To ease prescribers' burden in using statewide Prescription Drug Monitoring Programs (PDMPs), many US states have moved to better integrate PDMP data with patients' electronic health records (EHRs). While such PDMP-EHR integration is likely to reduce local formal prescription opioid consumption, in this paper, we explore the possibility that it induces individuals to seek opioids across state borders and/or at the local black market. We find that for a focal county, its neighboring state's PDMP-EHR integration leads to an increase in the focal county's consumption of prescription opioids, an undesirable cross-border spillover effect. Moreover, we find that this spillover effect is mitigated when prescribers in the focal county have access to the neighboring state's PDMP data via interstate PDMP data sharing. We also find nuanced evidence that a state's PDMP-EHR integration might induce an increase in its black-market prices of prescription opioids. While vertical information integration in a region (i.e., a state's PDMP-EHR integration) may fulfill its intended benefit, our findings suggest it may also result in a spillover effect on neighboring areas. Notably, a complementary horizontal information integration (in this case, interstate PDMP data sharing) could mitigate such an undesirable spillover effect. We discuss implications for the design of care delivery processes, particularly prescription processes, that rely on the studied health information systems.</p>\u0000 </div>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"176-196"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operational Process and Agency Considerations in Managing Emerging Technologies in Healthcare","authors":"Elliot Bendoly,&nbsp;Meng Li,&nbsp;Jeffrey Smith,&nbsp;Paul A. Pavlou","doi":"10.1002/joom.70029","DOIUrl":"https://doi.org/10.1002/joom.70029","url":null,"abstract":"&lt;p&gt;In recent years, emerging technologies have seen explosive, and arguably unprecedented, growth in most industries. The healthcare industry has been no exception (Dai and Tayur &lt;span&gt;2020&lt;/span&gt;). We are at a crucial point where close consideration of the operational and strategic implications of emergent technologies in the healthcare industry is both observable and fully warranted. Emerging technologies, such as the internet of things (IoT), genetic technologies, 3D printing, advanced social platforms, as well as advanced analytics (e.g., artificial intelligence [AI] and other big data-driven analytics systems) are driving rapid growth and transformation in this industry (Gardner et al. &lt;span&gt;2015&lt;/span&gt;; Ganju et al. &lt;span&gt;2020&lt;/span&gt;; Xu et al. &lt;span&gt;2021&lt;/span&gt;; Adjerid et al. &lt;span&gt;2022&lt;/span&gt;; Rajpurkar et al. &lt;span&gt;2022&lt;/span&gt;). What is now needed are theoretical and practical insights to help identify new opportunities, operational challenges (and their solutions), arising from the advancement and adoption of these powerful emerging technologies in healthcare.&lt;/p&gt;&lt;p&gt;Consider some of the current opportunities in healthcare: Emerging technologies are empowering physicians and improving patient care in multiple ways (Ferrand et al. &lt;span&gt;2018&lt;/span&gt;; Mukherjee and Sinha &lt;span&gt;2020&lt;/span&gt;). Developments in AI capabilities have been perhaps most notable in public discourse. AI has been deployed to augment diagnostic, clinical, and even surgical functions, therapy selection, risk prediction, and disease stratification. Resulting benefits include reductions in medical errors and increases in case throughput (Kalis et al. &lt;span&gt;2018&lt;/span&gt;). 3D printing technology has now advanced to the point at which it can be used to produce a wide range of customized medical devices, prosthetics, and implants. It can also fill emergent needs in place of dedicated production capacity for healthcare products (Bendoly, Chandrasekaran, et al. &lt;span&gt;2024&lt;/span&gt;). Robotic surgery has also been leveraged to greatly increase the precision of complex surgical activities.&lt;/p&gt;&lt;p&gt;Emerging technologies are also changing the social reach and special opportunities for healthcare delivery (Bavafa et al. &lt;span&gt;2018&lt;/span&gt;). The interest in facilitating physically separate service engagement between patients and physicians, prompted in part by the COVID-19 pandemic, has prompted large-scale adoptions of telehealth technologies, often termed telemedicine, through which patients can access medical care remotely. Online consultations and live medical streaming are becoming normal experiences for many, permitting patients to obtain physical and mental health support virtually. Advanced wearables bring forth additional implications for such engagement. Ambulatory facilities, often located close to where patients live in suburban or rural areas, are becoming more valuable for patients when integrated with the aid of Electronic Health Record (EHR) systems. Robotic surgical options","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"164-175"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Generative AI for Value Co-Creation: An Intervention-Based Randomized Field Experiment in a Healthcare Context","authors":"Yanwu Song,&nbsp;Tingting Yan,&nbsp;Fu Jia,&nbsp;Lujie Chen,&nbsp;Hao Li","doi":"10.1002/joom.70012","DOIUrl":"https://doi.org/10.1002/joom.70012","url":null,"abstract":"<div>\u0000 \u0000 <p>Owing to limited healthcare resources, there has been increased demand for artificial intelligence (AI) interventions to treat mental health problems of chronic disease patients in developing countries. However, it is challenging to overcome the AI trust crisis in the healthcare context and develop AI that improves the patient's personalized experience and the quality of care. Elaborating on the value co-creation theory using an actor–network theory (ANT) approach, this study examines how generative artificial intelligence (GenAI) can improve post-discharge care for patients with cardiovascular disease in resource-limited settings. Using an intervention-based research approach in collaboration with a major hospital in China, researchers co-designed a GenAI intervention with potential users and various stakeholders. Through a randomized controlled trial, we further evaluated the impact of a co-created GenAI intervention on the post-discharge self-confidence and quality of care of 114 patients. Compared with the standard post-discharge care process, chronically ill patients who received the GenAI intervention experienced a 6.049-point (out of a total of 80 points) decrease in state anxiety and an 87.8% decrease in the 30-day readmission risk. The insights gained from the intervention process, as interpreted using the ANT approach, expand the generic framework of value co-creation to include a more GenAI-mediated network of human and non-human objects. Results reveal GenAI's boundary-spanning and integrating roles as a critical node in the emerging, dynamic, value-creating actor network. The inclusion of “a nonparticipant observe” allows us to offer cognitive explanations for why GenAI works. Overall, this study contributes to healthcare operations management by designing a process for developing and implementing GenAI to improve healthcare operations.</p>\u0000 </div>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"197-232"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking Privacy in Healthcare: The Impact of Explanations on Privacy Concerns and Self-Disclosure to Conversational Technologies","authors":"Hashai Papneja,&nbsp;Sarv Devaraj","doi":"10.1002/joom.70026","DOIUrl":"https://doi.org/10.1002/joom.70026","url":null,"abstract":"<p>While artificial intelligence (AI)-based conversational technologies offer exciting prospects in healthcare, the lack of transparency and elevated privacy concerns in using such technologies remain a challenge and make much-needed information difficult to obtain while administering patient care. Approaches that emphasize transparency and interpretability of AI systems provide a promising avenue to address these concerns. In this study, we explore the role of transparency-enhancing explanations as a way for caregivers to elicit truthful disclosure of otherwise private information from patients. Specifically, we explore how automated explanations provisioned by conversational technologies can help reduce the user's privacy concerns and bring about self-disclosure, thus helping to improve key outcomes such as accurate diagnosis and effective treatment. Through an online experiment with 556 participants in a healthcare context, we uncover the mediating effects of two critical factors, informational justice and perceived relevance, on privacy concerns. We find that explanations foster perceptions of informational justice and perceived relevance in the user, which help reduce privacy concerns and bring about self-disclosure. The study's findings have implications for researchers as well as practitioners who leverage conversational technologies in healthcare and other service contexts.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 2","pages":"316-351"},"PeriodicalIF":10.4,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Discrete-Time Hazard Regression: Bayesian Regularization for Dynamic Churn Modeling","authors":"Ping Chou,&nbsp;Yen-Chun Chou,&nbsp;Howard Hao-Chun Chuang","doi":"10.1002/joom.70027","DOIUrl":"https://doi.org/10.1002/joom.70027","url":null,"abstract":"<div>\u0000 \u0000 <p>Discrete-time hazard regression is widely applied to firm-level time-to-event analyses and increasingly to individual churn behaviors. While the discrete logit hazard model for panel data remains the workhorse in OM, both fixed-effects and random-effects specifications have key limitations. The Grassia(II)-Geometric (G2G) model—allowing for dynamic churn and asymmetric heterogeneity—offers a compelling alternative for hazard regression and churn prediction. To address high-dimensional data, we propose Bayesian sparsity modeling via spike-and-slab LASSO (SSL) that enables simultaneous regularization and inference—both crucial for explanatory hypothesis testing. Simulation studies show that even when data are generated from logit functions with additive heterogeneity, asymmetric heterogeneity undermines logit regression, whereas G2G-SSL achieves stronger out-of-sample fit with lower false discovery rates. We further validate its predictive power in an empirical study on voluntary employee turnover at a Fortune 500 manufacturer. The proposed approach outperforms a range of advanced methods, improving both churn time prediction and cost efficiency for prescriptive workforce planning. Finally, we outline methodological extensions and provide a modeling procedure for discrete hazard regression analysis. By importing insights from marketing and Bayesian statistics, our work enables OM scholars to move beyond traditional hazard models and better capture real-world heterogeneity across a broad range of contractual churn scenarios.</p>\u0000 </div>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"72 1","pages":"131-158"},"PeriodicalIF":10.4,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145887883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}