Journal of Operations Management最新文献

{"title":"Objectives and Guidelines for Advancing Research on Inter-Organizational Operations in the Journal of Operations Management","authors":"Xiaosong Peng, David, Sriram Narayanan","doi":"10.1002/joom.70004","DOIUrl":"https://doi.org/10.1002/joom.70004","url":null,"abstract":"<p>In today's business environment, firms must manage the intricate interdependencies between their internal operations and a broad network of external entities. Establishing and maintaining robust connections with a diverse array of stakeholders—including suppliers, customers, third-party service providers, regulatory bodies, external research and development (R&D) organizations, and academic institutions such as universities, has become essential. An interorganizational view of operations is fundamental to an accurate understanding of the context in which process development and improvement occurs, and therefore, the potential for operational actions to generate tangible outcomes. By working collaboratively with these external entities, firms can not only optimize operational performance but also foster innovation, adaptability, and sustained competitive advantage. Thus, researching the drivers, processes, and outcomes of interorganizational operations at different levels of organizations is central to the mission of the Journal of Operations Management.</p><p>While there is no formal definition of inter-organizational operations (IOO) in the operations and supply chain management literature, several related definitions exist. At JOM, we adopt the perspective provided by Oliver (<span>1990</span>), and akin to that of Dyer and Singh (<span>1998</span>), defining inter-organizational relationships as the transactions, flows, and linkages that underlie the relationships between operations in different organizations that collaborate in networks to achieve shared goals.</p><p>The landscape of interorganizational operations thus includes suppliers, customers, ecosystem partners (e.g., third-party service providers), academic entities, and policy stakeholders that often share key human, physical, and knowledge assets with firms. Such organizations exist both in local and global environments. Furthermore, inter-organizational operations (IOO) encompass not only physical, informational, and financial flows but also the movement of talent (people), ideas and knowledge, and legal rights (e.g., franchises), among other things. These flows often occur outside the conventional supplier-customer relationships, such as those involving universities, consulting firms, and other professional service or knowledge providers. In many instances, relationships among these various entities can be little more than arms-length and transactional, if they formally exist at all (i.e., in some cases, firms merely exist in a shared ecosystem). In contrast, in other instances, highly embedded operational co-dependencies are more emblematic. Indeed, operational dynamics are often shaped by both competitive and coopetitive capabilities. A prominent example of coopetition is the relationship between Microsoft and OpenAI—Microsoft is a major investor in OpenAI, yet the two also compete (e.g., Microsoft Copilot vs. ChatGPT).</p><p>As a result, key domains of operational activi","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 5","pages":"725-728"},"PeriodicalIF":6.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524613","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 the Yield: Enhancing Agricultural Sustainability Through Operations Management","authors":"Christian F. Durach, Dayna Simpson, Frank Wiengarten, Zhaohui Wu","doi":"10.1002/joom.1375","DOIUrl":"https://doi.org/10.1002/joom.1375","url":null,"abstract":"<p>Historical records indicate that the collapse of many ancient civilizations, such as those of the Sumer, the Mayans, the Indus Valley, and Rome, was partly driven by the failure of agricultural systems (Diamond <span>2011</span>; Raman <span>2024</span>). Many modern farming systems around the globe are potentially approaching similar failures as they struggle with critical challenges such as changing climate or soil biodiversity loss, pressures to reduce costs, new technologies, and infectious diseases or pest outbreaks such as avian bird-flu (Caserta et al. <span>2024</span>; Chen and Chen <span>2021</span>; Cinner et al. <span>2022</span>; Guo et al. <span>2022</span>; Shi (Junmin) et al. <span>2019</span>).</p><p>Agriculture remains essential to the continuity and stability of human civilization. Recognizing its central role, the United Nations has designated ending hunger and achieving food security as a core Sustainable Development Goal (SDG 2). Yet competing pressures to increase agricultural output and lower costs are more often than not in conflict with constraints on natural resources, which have led to major challenges for agricultural supply chains and the environment, its labor force (e.g., increased exploitation, migration), and animals in the system (Howard and Forin <span>2019</span>; Rossi and Garner <span>2014</span>; Wiengarten and Durach <span>2021</span>; Yang et al. <span>2024</span>). Furthermore, these conflicts have been exacerbated by power imbalances between large markets and small suppliers, and because the degradation of agricultural regions has taken a proportionally greater toll on less well-developed economies (Gómez and Lee <span>2023</span>).</p><p>Scholarly attention to production systems within the agricultural economics domain has a long history (Le Gal et al. <span>2011</span>). In recent years, researchers in this field have focused increasingly on identifying more sustainable methods of agricultural production (e.g., Campi et al. <span>2021</span>; Christiaensen et al. <span>2021</span>; Giller et al. <span>2021</span>; Jayne and Sanchez <span>2021</span>; Rehman et al. <span>2022</span>; Touch et al. <span>2024</span>). This has sought, for example, more efficient, less impactful, or technology-driven methods of production that improve yield and reduce harm. These topics are very much in the wheelhouse of the operations management (OM) discipline, yet contributions from OM researchers that explore within, or offer solutions to agricultural systems, have been limited. Given OM's foundational focus on production processes and systems, however, our field is uniquely positioned to meaningfully address the challenges currently facing global agricultural production systems.</p><p>In the present paper, we recognize and reflect on this gap in OM in order to provide a foundation for the special issue (SI) on sustainable agriculture. The goal of the SI was to achieve two key objectives: (i) raise awareness within","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 4","pages":"516-528"},"PeriodicalIF":6.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.1375","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206712","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":"Switching the Telescope Lens: A Sociomaterial Perspective of Sustainable Agricultural (Proto)Practices Transfer in an Agrifood Supply Chain","authors":"Minelle E. Silva,&nbsp;Karina A. Santos,&nbsp;Susana C. F. Pereira,&nbsp;Linda C. Hendry","doi":"10.1002/joom.1369","DOIUrl":"https://doi.org/10.1002/joom.1369","url":null,"abstract":"<p>This study investigates the implementation and transfer of sustainable agricultural practices (SUSAPs) across a multitier agrifood supply chain (SC) using Brazilian poultry farming as the empirical context. We conduct an interpretive case study of buyer–supplier–subsupplier triads, including those certified under Global Good Agricultural Practices (GAP) and noncertified counterparts, using interviews, observations, and secondary data. Adopting a sociomaterial perspective, we investigate how SUSAPs' components—meanings, materials, and competencies—are embedded within specific SC tiers and transferred across the triad. A zoom-in analysis reveals that only animal welfare is a fully adopted practice, whereas waste management, working conditions, and biosecurity remain in development as protopractices. A zoom-out analysis of SUSAPs' components shows limited buyer influence across the triad, while first-tier suppliers facilitate SUSAP transfer. We advance theory by demonstrating how a sociomaterial perspective explains the degree of SUSAPs' implementation and transfer, and introducing the <i>boomerang effect</i>, illustrating how first-tier suppliers enable SUSAP implementation among certified and noncertified subsuppliers to ensure safer and more sustainable products. These insights help managers transfer SUSAPs into their SCs by leveraging first-tier suppliers as boundary spanners.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 4","pages":"529-549"},"PeriodicalIF":6.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.1369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206705","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":"From Power to Sustainability? Unpacking the Role of Justice in Agricultural Commodity Supply Networks","authors":"Felipe Alexandre de Lima,&nbsp;Evelyne Vanpoucke,&nbsp;Stefan Gold,&nbsp;Stefan Seuring","doi":"10.1002/joom.1372","DOIUrl":"https://doi.org/10.1002/joom.1372","url":null,"abstract":"<p>Agricultural commodity supply networks in the Global South are essential for securing the global supply of crops and livestock. However, they are challenged by power asymmetries, which cause injustice and jeopardize social equity, environmental stewardship, and economic viability for disadvantaged actors. To address this challenge, it is imperative to understand how power impacts justice and sustainability. To this end, we examined a supply network in Mato Grosso, Brazil, that faced power asymmetries through 49 semi-structured interviews, field observations, and archival data. The analysis unveiled three forms of power use—excessive, strategic, and balanced—and associated tactics, impacting justice and sustainability outcomes in various ways. We illustrate, for example, how excessive power manifested in traders' abusive tactics, who compelled farmers to accept quality discounts due to external factors, such as heavy rain or poor road conditions. In response to these injustices, farmers cascaded the pressure through the supply network, disproportionately affecting disadvantaged actors, for instance, by withholding rural workers' wages for low productivity or eradicating wildlife deemed detrimental to profitability. Based on these findings, we provide a set of six propositions and a theoretical model that elucidate how power can be leveraged to foster fairer and more sustainable agricultural commodity supply networks.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 4","pages":"550-574"},"PeriodicalIF":6.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.1372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206706","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":"Empirically Grounding Analytics (EGA) Research: Approaches, Contributions, and Examples","authors":"Arnd Huchzermeier,&nbsp;Panos Kouvelis","doi":"10.1002/joom.1373","DOIUrl":"https://doi.org/10.1002/joom.1373","url":null,"abstract":"&lt;p&gt;Empirically Grounding Analytics (EGA) in operations and supply chain management is a research area at the intersection of empirical and analytical studies. Spearman and Hopp (&lt;span&gt;2021&lt;/span&gt;) identified it as an underserved research area with great opportunity for input. To clarify what EGA is, we use a quote provided in the JOM editorial on the subject (see de Treville et al. (&lt;span&gt;2023&lt;/span&gt;)) as a definition: “an EGA paper combines mathematical, stochastic, and/or economic modeling with empirical data…. Empirically grounding an analytic model creates knowledge by linking analytical insights to what has been observed using empirical methods (such as case studies, action research, field experiments, interviews, or analysis of secondary data) to establish a theoretically and empirically relevant question.”&lt;/p&gt;&lt;p&gt;De Treville et al. (&lt;span&gt;2023&lt;/span&gt;) propose a framework for discussing EGA research approaches and assessing contributions, summarized in Figure 1 of their editorial. We will refer to this framework rather extensively in our discussion of work in this Special Issue. We provide a “deconstructed” version of this figure, with some added details, in Figure 1.&lt;/p&gt;&lt;p&gt;Research in “empirical grounding” of analytical models can be conceptually viewed as offering two different ways to drive research and lead to impactful contributions. The “left side” approach has as its end goal to establish analytical models verifiably linked to data and observations reflecting the real operational setting. This approach contributes a “calibrated fit” of the model to the operational decision reality. It requires careful empirical justification of modeling assumptions and parameters. The calibration of model parameters involves collecting representative data from the realistic setting, with any remaining model assumptions and approximations well justified for the real situation. The expectation of these grounded models is a high quality of solutions for the approximated real decision problem.&lt;/p&gt;&lt;p&gt;The “right side” approach pursues empirical assessment of model results, solution quality, and applicability of insights in addressing issues encountered in real practice. It carefully verifies that (a) an effective implementation of the model reasonably and accurately depicts the operational setting and decision situation; (b) the obtained solutions lead to improved performance; and (c) incorporating analytical insights and tools leads to improved managerial practice for this setting.&lt;/p&gt;&lt;p&gt;In most cases, “left side” research leads to well-calibrated models with strong hints for improved solution quality and useful insights to be further tested in the real setting and actual practice. “Right side” research carefully tests and confirms the wisdom of new insights and tools, leading to improved practice in the operational setting. However, such testing and analytical insights may reveal irregularities and complexities not effectively depicted in the models, thus","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 4","pages":"418-425"},"PeriodicalIF":6.5,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.1373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206518","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":"Generative AI and Empirical Research Methods in Operations Management","authors":"Timofey Shalpegin,&nbsp;Tyson R. Browning,&nbsp;Ajay Kumar,&nbsp;Guangzhi Shang,&nbsp;Jason Thatcher,&nbsp;Jan C. Fransoo,&nbsp;Matthias Holweg,&nbsp;Benn Lawson","doi":"10.1002/joom.1371","DOIUrl":"https://doi.org/10.1002/joom.1371","url":null,"abstract":"","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 5","pages":"578-587"},"PeriodicalIF":6.5,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524522","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":"Nudging Tactics for Enhanced Compliance With Condition-Based Maintenance Guidelines","authors":"Mateus do Rego Ferreira Lima,&nbsp;Elliot Bendoly,&nbsp;Nathan Craig,&nbsp;Kenneth K. Boyer","doi":"10.1002/joom.1366","DOIUrl":"https://doi.org/10.1002/joom.1366","url":null,"abstract":"<p>Condition-based preventive maintenance (CB-PM), dependent on robust and precise indicators of equipment quality, stands to gain advantages from the integration of sensor technologies. Yet, the effectiveness of such systems relies on an important factor: people. Even in possession of ideal CB-PM policies, imperfect adherence can lead to higher equipment downtime, maintenance costs, and increased safety hazards. Here, we describe a normative model of optimal CB-PM policy determination; specifically, a generalized means by which to determine a valuemaximizing quality threshold as a guideline for triggering PM (preventative maintenance). Motivated by field data, we consider the opportunity cost of not adhering to such optimal policies; that is, premature or delayed responses. We design and execute a controlled laboratory study, exposing participants to two critical manipulations that we theorize might influence adherence: (1) The presence of a supplemental secondary signal, of a type common to time-based preventive maintenance (TB-PM), (2) a pre-task priming intended to emphasize the value of discretized task completion. Results showed that the combination of CB-PM and TB-PM signals, along with completion priming, significantly increases adherence to CB-PM guidelines. We demonstrate that individuals exposed to this combination of treatments forfeit far less value than those receiving CB-PM signals alone.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 5","pages":"700-724"},"PeriodicalIF":6.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.1366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144525077","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":"Deadline Effect in Stroke Patient Care: A Temporal Motivation Theory Perspective of Process Management","authors":"Brandon Lee,&nbsp;Seokjun Youn,&nbsp;Lawrence Fredendall","doi":"10.1002/joom.1360","DOIUrl":"https://doi.org/10.1002/joom.1360","url":null,"abstract":"<p>Stroke is a highly time-sensitive medical emergency, and earlier treatment is crucial. Drawing on Temporal Motivation Theory, we investigate a “deadline effect” in stroke care and analyze how two deadlines, that is, a <i>medically oriented</i> one (administering Tissue Plasminogen Activator, TPA, within 4.5 h of symptom onset) and a <i>goal-oriented</i> one (the 60-min in-hospital target from <i>Target</i>:<i>Stroke</i>), shape care consistency. We define a deadline effect as a variable task processing rate under time pressure from a pending task completion deadline, which can cause inconsistent care. Clinicians may work more slowly when patients arrive soon after symptom onset, given ample time remains before the 4.5-h TPA window. Using an accelerated-failure-time model and addressing patient selection bias, we find that shorter onset-to-door times correlate with longer door-to-needle times, and vice versa, confirming the medically oriented deadline effect. As a result, care time may vary considerably based on how much of the TPA window remains. Under <i>Target</i>:<i>Stroke</i>, a goal-driven national initiative in the United States to improve stroke care quality, stroke teams face an additional 60-min in-hospital deadline. Our findings show that the initiative prompts stroke teams to prioritize the tighter goal and maintain a more consistent care pace, regardless of patients' arrival times. Our mechanism analyses reveal two boundary conditions for the main findings: (i) when the downstream time segment ends with a mid-point patient care milestone rather than the strict TPA administration deadline or (ii) when the system congestion level is high, the main findings do not hold, advancing the deadline effect literature from an operational standpoint. Furthermore, our major findings are robust to other confounding factors and model assumptions, ruling out alternative explanations. Notably, post hoc analyses confirm that <i>Target</i>:<i>Stroke</i> fosters consistent time performance without adversely affecting other health outcomes, advocating its efficacy. In sum, we highlight the operational implications of multiple deadlines in stroke care, extending the broader deadline effect literature. For hospital clinicians, properly set goals can stabilize care processes and strengthen overall performance, emphasizing the strategic value of well-designed deadlines in time-critical healthcare settings.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 5","pages":"670-699"},"PeriodicalIF":6.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.1360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524935","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":"Operations Management in the Pharmaceutical Industry","authors":"Gopesh Anand,&nbsp;George P. Ball,&nbsp;John V. Gray,&nbsp;Ujjal Kumar Mukherjee","doi":"10.1002/joom.1365","DOIUrl":"https://doi.org/10.1002/joom.1365","url":null,"abstract":"&lt;p&gt;The pharmaceutical manufacturing industry has an annual revenue of $1.2 trillion and employs approximately two million people worldwide (Brocker &lt;span&gt;2024&lt;/span&gt;). The drugs produced by the operations of this industry, which include all activities from scientific innovation to supply chain management, play an important role in the health and well-being of millions of people around the world (OECD &lt;span&gt;2025&lt;/span&gt;). Recent disruptions, especially the COVID-19 pandemic, exposed critical limitations in global pharmaceutical operations, spurring widespread concern (Shih &lt;span&gt;2020&lt;/span&gt;). In the U.S., the Biden Administration deemed pharmaceuticals one of four critical national supply chains, the others being semiconductors, large capacity batteries, and minerals (White House &lt;span&gt;2021&lt;/span&gt;). Further, Congress mandated a report from the National Academies of Sciences, Engineering, and Medicine (NASEM) focused on securing the nation's medical product supply chains against quality and supply disruptions (NASEM &lt;span&gt;2022&lt;/span&gt;). Despite the recognition of its importance, operational challenges in this industry remain prevalent. Drug shortages reached a record high in 2024 (ASHP &lt;span&gt;2024&lt;/span&gt;), and their duration has been increasing (USP &lt;span&gt;2024&lt;/span&gt;). Further, quality issues remain common (e.g., Callahan et al. &lt;span&gt;2024&lt;/span&gt;), and the drug recall trend continues to climb (Ghijs et al. &lt;span&gt;2024&lt;/span&gt;).&lt;/p&gt;&lt;p&gt;The opacity and complexity of pharmaceutical operations are two factors driving the continued quality and resilience issues. As Figure 1 depicts, much of the complexity in the U.S. pharmaceuticals industry stems from intermediaries and payors, who are often vertically integrated and powerful, and who can create and benefit from opacity. Additional complexity comes from the roles of powerful regulators, who oversee, among other things, approvals to produce drugs and ongoing drug quality and safety. We discuss many of these forms of opacity and complexity in detail in the next section.&lt;/p&gt;&lt;p&gt;Operations such as these call for rigorous academic explorations that highlight the unique context of the industry (Joglekar et al. &lt;span&gt;2016&lt;/span&gt;). Operations scholars, for example, can address questions related to balancing cost and quality (Lapré and Scudder &lt;span&gt;2004&lt;/span&gt;; Parmigiani et al. &lt;span&gt;2011&lt;/span&gt;), enhancing the resilience of operations and supply chains (Kim et al. &lt;span&gt;2015&lt;/span&gt;; Shen and Sun &lt;span&gt;2023&lt;/span&gt;), implementing new technologies (Angelopoulos et al. &lt;span&gt;2023&lt;/span&gt;), and demonstrating benefits to, and ways to establish, greater transparency (Buell et al. &lt;span&gt;2017&lt;/span&gt;; Lee et al. &lt;span&gt;2021&lt;/span&gt;). Further, operations researchers can identify the role that powerful regulators, such as the Food and Drug Administration (FDA), play with regards to operational performance dimensions such as innovation, resilience, cost, and quality (Wang et al. &lt;span&gt;2025&lt;/span&gt;). Despite all that operations scho","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 3","pages":"302-313"},"PeriodicalIF":6.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joom.1365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818612","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":"Encounter Decisions for Patients With Diverse Sociodemographic Characteristics: Predictive Analytics of EMR Data From a Large Chain of Clinics","authors":"Ujjal Kumar Mukherjee,&nbsp;Han Ye,&nbsp;Dilip Chhajed","doi":"10.1002/joom.1363","DOIUrl":"https://doi.org/10.1002/joom.1363","url":null,"abstract":"<div>\u0000 \u0000 <p>Managing chronic diabetes care is a major challenge faced by healthcare organizations because it requires resource commitment over a long duration, high levels of patient engagement in the care process, and the socioeconomic and racial diversity of the patient population significantly affect care outcomes. Therefore, it is important to personalize chronic care treatment to improve chronic care outcomes. We propose a decision framework for the predictive management of diabetes that can help reduce the population-level risk of diabetes. We use machine learning on clinical measures, demographics, and socioeconomic status of a large patient population from a chain of clinics in the Midwestern United States to predict the future health conditions of individual diabetes patients. Furthermore, we use the predictive analytic model outcome to build a decision analytic framework to optimally allocate encounters to individual patients. Also, we propose a heuristic solution to the optimal resource allocation model for implementation purposes. We make theoretical and methodological contributions by identifying and combining clinical, demographic, and socioeconomic factors to predict future diabetes risk for patients and demonstrate the use of the predicted risks for optimal resource utilization. Another significant contribution is demonstrating that a data-driven predictive encounter allocation, considering the socioeconomic and demographic factors influencing health risks across patient populations, can promote more equitable healthcare delivery. Finally, we discuss implementation issues and actions.</p>\u0000 </div>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"71 4","pages":"447-482"},"PeriodicalIF":6.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206998","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}