【期刊追踪】JOM 2026年第72卷精选论文（一）

本期从Journal of Operations Management（JOM）2026年第72卷相关文章中，遴选具有代表性的研究成果，主要聚焦人工智能与需求计划、技术开发外包与竞合合作、数字技术与安全管理能力、众包配送、供应链信息共享与运营效率，以及运营管理定性研究方法等议题。研究综合采用实验研究、企业调查、大规模平台数据、供应链关系数据及系统性文献回顾等方法，探讨AI标签与算法绩效如何影响人的预测调整行为、竞争对手何时能够成为技术合作伙伴、数字技术资源如何转化为企业安全管理能力、任务运营特征如何影响众包司机接单行为，以及共同分析师如何促进供应链信息传递并提升供应商运营效率，同时进一步反思运营管理定性研究的严谨性与理论推理过程。

1、AI还是普通算法？需求预测中人们会如何回应AI

2、技术开发外包：什么时候应该与竞争对手合作？

3、企业如何形成安全管理能力？制度力量与数字技术资源的共同作用

4、众包配送不只是钱的问题：运营特征如何影响司机接单行为？

5、供应链中的共同分析师能否提高供应商的运营效率？

6、提升运营管理定性研究的严谨性：从程序主义走向反思性推理

AI还是普通算法？需求预测中人们会如何回应AI

标题：Is AI an Algorithm by Any Other Name? Behavioral Reactions to AI- and Model-Based Demand Planning Algorithms

作者：Finnegan McKinley; Rebekah Brau; John Aloysius; Adriana Rossiter Hofer

DOI： 10.1002/joom.70045

摘要：

With the ongoing deployment of AI algorithms, managers do not know whether existing demand planning processes account for possible differences in human behavior when using AI-based systems in comparison to legacy model-based systems. This study examines how human behavior may differ when performing demand forecasting tasks due to the disclosure of algorithm type (AI or model) along with associated algorithm performance (low and improving). Using signaling theory, we hypothesize that algorithm type and performance influence user forecast adjustment behavior. We find support for these predictions across two laboratory experiments and a large quasi-natural field experiment with approximately 575,000 observations from a multinational retailer. We find no significant direct effect of algorithm type independent of performance in the lab. In contrast, in the field, users implement significantly greater adjustments for AI-based algorithms compared to model-based algorithms. Across both contexts, algorithm performance, whether low or improving, has a significant direct effect on user adjustments, with users adapting their behavior to the algorithm's performance. Finally, we find that in the lab and the field, users' responses to low performance are amplified when the forecasts originate from AI-based algorithms. Our findings underscore the nuance and complexity in which users interact with AI-based algorithms compared to model-based algorithms and demonstrate the value of signaling theory for understanding human–AI collaboration.

中文要点：

本文探讨在需求预测中，当员工知道预测结果来自AI而不是传统模型时，其人工调整行为是否会发生变化。研究基于信号理论，通过两项实验室实验以及一家跨国零售商约57.5万条真实业务数据进行检验。结果发现，在实验环境下，仅仅将算法标记为AI并不会显著改变用户行为；但在真实企业环境中，员工会对AI预测进行更大幅度的人工调整。同时，算法过去的表现也会显著影响员工反应，尤其当AI表现较差时，人们的调整幅度更大。研究表明，AI进入运营决策后，最终结果不仅取决于算法性能，也取决于人如何理解和回应AI，为理解需求计划中的人机协作提供了新的视角。

技术开发外包：什么时候应该与竞争对手合作？

标题：Technology Development Outsourcing: When to Join Forces With a Rival?

作者：Tingting Yan; Hubert Pun; Dina Ribbink

DOI：10.1002/joom.70042

摘要：

Knowing the challenges of collaborating with a competitor in developing new technologies, firms sometimes still choose a competitor instead of a noncompeting technology provider. To explore why, this study adopts an inter-organizational trust view to explain the formation of a technology development outsourcing relationship. Using a vignette-based experiment with procurement managers, results show how three product- and competitor-related factors: product newness, competitor market size, and product substitutability, affect a purchasing manager's intention to choose a competitor. The post hoc analysis confirms the two sources of inter-organizational trust, competence and integrity, in explaining the influences of the three factors. Using results from two waves of interviews, a behavioral experiment and a rational agent math model, this study explains competitor selection behaviors in a triadic context with a noncompeting provider as the default option. Contributing to the interface of technology outsourcing, co-opetition in innovation, and supplier selection literature, these findings can help managers assess product and competitor attributes in deciding whether to collaborate with a competitor in a technology development outsourcing context.

企业如何形成安全管理能力？制度力量与数字技术资源的共同作用

标题：How Do Firms Develop Safety Management Capabilities? The Impact of Institutional Forces and Digital Technology Resources

作者：Lin Zhang; Zhen Shao; Jose Benitez

DOI：10.1002/joom.70048

摘要：

In the digital age, industrial firms are facing significant challenges in achieving digitally-enabled safety management (DSM, i.e., leveraging digital technologies to mitigate industrial accidents) due to rigid institutional structures and underutilized digital technology resources. To address this, we develop a research model integrating institutional theory and resource-based view theory, and position safety management capabilities (i.e., the abilities of a firm to sense, seize, and reconfigure its safety operational routines and deploy new ones for DSM) as a pivotal process mechanism translating institutional forces (i.e., institutional isomorphism and top management support for DSM) and digital technology resources (i.e., digital technologies that are available and utilized for DSM) into improved safety performance. Using time-lagged survey data in combination with secondary data from 216 industrial firms in China, our findings reveal that institutional isomorphism and top management support exert cascading influences on safety management capabilities. The alignment of digital technology resources with institutional forces significantly facilitates the development of safety management capabilities, emphasizing their synergistic role in fostering adaptive and strategic safety practices. Furthermore, safety management capabilities serve as the critical intermediary enabling institutional and resource factors to drive superior safety outcomes. Our results advance the theory of safety management and provide actionable insights for industrial firms to effectively operationalize DSM.

中文要点：

本文关注企业拥有数字技术以后，为什么并不一定能够直接改善安全绩效。研究整合制度理论与资源基础观，将安全管理能力定义为企业识别安全风险、抓住数字化安全机会以及重新配置安全运营流程的能力，并利用中国216家工业企业的时间滞后问卷和二手数据进行检验。研究发现，制度同形压力和高层管理支持会促进企业形成安全管理能力，而数字技术资源只有与这些制度和组织因素形成匹配时，才能更有效地转化为安全管理能力。进一步来看，安全管理能力是制度力量和数字资源最终改善安全绩效的重要中介。研究说明，拥有数字技术并不等于拥有数字化能力，真正重要的是企业能否把技术嵌入具体的运营流程。

众包配送不只是钱的问题：运营特征如何影响司机接单行为？

标题：There Is More to Crowdshipping Than Money: Understanding How Operational Characteristics Influence Driver Behaviors

作者：Nicolò Masorgo; David D. Dobrzykowski; Christopher S. Tang; Brian S. Fugate

DOI：10.1002/joom.70046

摘要：

The increasing cost of last-mile delivery has motivated omnichannel grocery retailers to implement crowdshipping. Crowdshipping is a platform-based last-mile delivery model that relies on engaging drivers who are independent contractors to quickly accept delivery tasks. While studies often focus on the impact of monetary incentives, we draw on perspectives from the service operations literature to explore the moderating effect of three theoretical mechanisms (effort efficiency, uncertainty, and utility), on the curvilinear relationship between monetary incentives and driver engagement. Specifically, we test how delivery density, delivery type (attended vs. unattended), and time of the day interact with monetary incentives to influence driver's task acceptance response time. Econometric analyses of a dataset comprising about two million observations from a US Fortune 100 grocery retailer's crowdshipping platform confirm the “diminishing negative” effect of remuneration on task acceptance time. More importantly, we reveal that task operational characteristics affect how monetary incentives influence task acceptance time. While task density and unattended deliveries amplify the curvilinear relationship, evening tasks flatten it and are accepted slower than those scheduled in the daytime. Our findings suggest that drivers evaluate tasks based on perceived effort, uncertainty, and utility, offering valuable insights for platforms and future research.

中文要点：

本文研究众包配送平台中司机是否接单以及多快接单的问题，并进一步考察除了金钱奖励之外，配送任务本身的运营特征是否也会影响司机行为。研究利用一家美国财富100强食品零售企业众包配送平台约200万条真实订单数据进行分析。结果表明，提高报酬确实可以缩短司机的接单时间，但这种作用存在明显的边际递减。与此同时，配送密度较高和无需消费者现场接收的配送任务会强化金钱激励的效果，而晚间任务不仅更难被快速接受，金钱激励的效果也会减弱。研究说明，平台并不能简单依赖“加钱”解决接单问题，还应通过优化配送密度、任务方式和时间安排改善司机参与。

供应链中的共同分析师能否提高供应商的运营效率？

标题：Do Common Analysts Along Supply Chains Facilitate Suppliers' Operational Efficiencies?

作者：Jie Han

DOI：10.1002/joom.70055

摘要：

This study investigates the implications of common analysts covering both sides of a buyer–supplier dyad for suppliers' operational efficiencies. Drawing on the knowledge sharing perspective, we consider a positive association between common analysts and supplier operational efficiency. Using 20,359 buyer–supplier dyads collected from Compustat during the period 1994 to 2017, we empirically find that the existence of common analysts significantly enhances suppliers' operational efficiencies. Additionally, the positive association is amplified when the supplier's bargaining power is lower or the buyer's demand uncertainty is higher. We further examine the underlying mechanisms through which common analysts improve supplier operational efficiency by showing that common analysts transmit buyer-related information to suppliers via both private and public channels and help suppliers reduce bullwhip effects. Finally, compared with other relational ties, common analysts uniquely mitigate suppliers' bullwhip effects and, in turn, have a stronger positive impact on their operational efficiencies. Collectively, our evidence highlights the distinctive and important role of common analysts in transferring operational information along supply chains and improving supply base outcomes.

中文要点：
本文研究一个分析师同时跟踪供应商和其客户时，是否能够成为供应链中的信息桥梁，并进一步改善供应商运营效率。基于知识共享视角，研究利用Compustat数据库中1994—2017年的20359个买方—供应商关系样本进行实证分析。结果发现，共同分析师的存在能够显著提高供应商运营效率，而且当供应商议价能力较弱或买方面临较高需求不确定性时，这一作用更加明显。机制分析进一步表明，共同分析师能够通过公开和私人渠道将买方信息传递给供应商，帮助供应商降低牛鞭效应，并最终改善运营效率。与其他关系纽带相比，共同分析师在减少信息扭曲方面具有独特作用。研究将传统上属于资本市场的信息中介引入供应链研究，说明供应链信息不仅可以通过上下游企业直接共享，也可以借助第三方主体进行传递。

提升运营管理定性研究的严谨性：从程序主义走向反思性推理

标题：Advancing Rigor in Operations Management Qualitative Research: From Proceduralism to Reflexive Reasoning

作者：Miriam Wilhelm; Katja Wölfl; Eugenia Rosca; Kirstin Scholten

DOI：10.1002/joom.70051

摘要：

Qualitative research has evolved into a valued scientific approach to generate novel theoretical contributions in operations management (OM). However, a limited understanding of what constitutes methodological rigor in qualitative research has resulted in a proceduralized application of research practices and templates, hindering further progress. After tracing the origins of discipline-specific rigor in OM—closely linked to the inductive (positivistic) case study method—we present an alternative perspective: rigor as researchers' demonstration of reflexivity in deliberate reasoning processes that infer theoretical insights from data. Based on a review of 87 qualitative research articles in three leading empirical OM journals (Journal of Operations Management, Production and Operations Management, and Journal of Supply Chain Management), published between 2015 and 2024, we trace the (limited) evolution of rigor in qualitative OM research across three time brackets. We then compare the current state of rigor in qualitative OM research with that of three leading management journals (Academy of Management Journal, Organization Science, and Strategic Management Journal) by examining the most recent time bracket (2023–2024). Through the analysis of discipline-specific rigor developments and the cross-field comparison, we identify areas of progress alongside persistent rigor gaps. Based on these gaps, we develop a series of probing questions to enhance researchers' reflexive reasoning, illustrated with examples from both fields. Our contribution provides a reflexivity-based framework that enables researchers to move beyond procedural compliance toward rigorous qualitative research, ultimately yielding OM theories with greater explanatory power.

中文要点：

本文讨论运营管理领域的定性研究究竟应该如何理解“方法严谨性”。作者指出，目前不少定性研究倾向于把遵循标准步骤和模板等同于严谨，例如强调案例数量、三角验证、编码流程和数据饱和，却没有充分解释为什么采用这些方法以及这些方法如何支持理论推理。研究系统回顾了2015—2024年发表于JOM、POM和JSCM的87篇定性研究，并与AMJ、Organization Science和SMJ在2023—2024年的相关研究进行比较。结果发现，运营管理定性研究仍存在较明显的程序主义倾向。为此，作者提出以反思性推理补充传统的程序透明性，强调研究者应清楚说明从数据到理论结论的推理过程。文章进一步提出一套基于反思性的研究框架和问题清单，为案例研究、访谈研究等定性研究提升理论解释力提供方法指导。