Operations Management Scholars Research Articles

Carbon neutrality: Operations management research opportunities

AbstractClimate change, primarily driven by greenhouse gas emissions (GHGs), is a pressing environmental and societal concern. Carbon neutrality, or net zero, involves reducing carbon dioxide emissions, the most common GHG, and then balancing residual emissions through removing or offsetting. Particularly difficult challenges have emerged for firms seeking to reduce emissions from Scope 1 (internal operations) and Scope 3 (supply chain). Incremental changes are very unlikely to meet the objective of carbon neutrality. Synthesizing a framework that draws together both the means of achieving carbon neutrality and the scope of change helps to clarify opportunities for research by operations management scholars. Companies must assess and apply promising technologies, form new strategic relationships, and adopt novel practices while taking into account costs, risks, implications for stakeholders, and, most importantly, business sustainability. Research on carbon neutrality is encouraged to move beyond isolated discussions focused on specific tactics and embrace a more, though not fully, holistic examination. Research opportunities abound in both theoretical and empirical domains, such as exploring tradeoffs between different tactics, balancing portfolios, and investigating the strategic deployment of initiatives over time. As a research community, we are critically positioned to develop integrative insights at multiple levels, from individual processes to horizontal and vertical partnerships and ultimately to large‐scale systemic realignment and change.

Gender mismatch and bias in people‐centric operations: Evidence from a randomized field experiment

AbstractResearch at the interface of operations management (OM) and gender bias has mostly focused on operational outcomes such as hiring decisions on behalf of the employer (or firm). Largely overlooked is how the design of operational processes exacerbates (or diminishes) the amount of gender bias exhibited on behalf of the customer in a people‐centric operations environment. In this study, we conduct a randomized field experiment with a partner firm to assess gender mismatch and bias in client‐consultant exchanges. The experimental design enables us to examine gender bias within dyadic exchanges when there are gender matches (female client‐female consultant or male client‐male consultant) or gender mismatches (female client‐male consultant or male client‐female consultant). We find that reporting the consultant's gender significantly increases the client's likelihood of leaving more and higher reviews, increases the clickthrough rate on recommended products, and that the effect is stronger for females than for male consultants. We also provide support for the heterogenous effects of client experience depending on the gender (mis)match in client‐consultant exchanges, including whether the prior effects hold when there is gender masking or manipulation (e.g., reported female consultant when actually male). Our findings offer important theoretical contributions and practical implications for OM scholars and managers.

Determinants of Operations Management Faculty Salary: Is Publishing in “A” Journals the “Bottom Line”?

This study investigates the determinants of salary for operations management scholars. Is it as some opine that the only thing that matters is the publication count in “A” journals? How do the full range of activities: research, teaching, service, and administration, affect salary? The present research seeks to shed light on these questions and is predicated on a data set that consists of the research, teaching, and service outcomes, along with salary, for a full census of operations management faculty at 22 public universities (227 faculty) for base, 9-month salary and a subset of 15 universities (150 faculty) for total annual compensation. The results demonstrate partial support for the hypotheses that only “A” publications are financially rewarded, with publications in Production and Operations Management having the highest correlation with salary. The salary reward for publishing in “A” journals is unaffected by number of coauthors. Publications in other academic journals, top-tier practitioner journals, and top journals in sister-fields, and measures of impact, such as citations, H-index, and research awards provide no additional explanatory value of salary. Likewise, service and teaching awards do not add explanatory value. Female Full Professors are paid roughly $23,000 less in base salary than their male counterparts. Non-publishing factors that significantly contribute to salary include changing university affiliations (only for Associate and Full Professors), taking on administrative duties, as well as certain qualitative measures such as achieving Fellow status at the Production and Operations Management Society.

Behavioral multi‐lever decision‐making: A study of consumer return policy, price, and inventory decisions

AbstractConsumer return policies have been long recognized and studied by operations management scholars as an important managerial lever in a retail environment. Yet, the behavioral aspects of return policy decision‐making and interaction of return policy decisions with other common operational decisions have not been investigated to date. We present a behavioral analysis of return policy decision‐making in a retail environment with aggregate demand and individual product valuation uncertainties. Leveraging a generalized newsvendor context, we conduct a randomized behavioral experiment to understand how individuals make each of the three key retail decisions—refund amount, price, and order quantity—and the causal effect of salvage value on these decisions. We find that decision‐makers exhibit behavioral regularities in making decisions across the three levers and they react to changes in the operating conditions in a boundedly rational manner, suggesting the use of heuristics. Based on behavioral regularities that we observe in our data—that is, responses, time‐dependent effects, and decision dependencies—we develop a process theory based on behavioral decision‐making tenets that offers a new direction with testable hypotheses for future research. The process theory describes a conditional decision‐making heuristic that leads to a propagation of decision errors across different levers as well as lever‐specific decision biases.

Opportunities in farming research from an operations management perspective

AbstractWe review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers’ welfare and the interests of other stakeholders have drawn OM researchers’ attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field‐based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming.

Optimal joint decisions of production and emission reduction considering firms’ risk aversion and carbon tax rate

Carbon tax represents governments’ approach to steering the economy towards a greener future. Our research question focuses on the impact of carbon tax policy on a firm’s production decision and revenue and on total social welfare. In particular, we assume that a firm’s decision is subject to its behavioural considerations or, in other words, its risk attitude. We model a government plan to charge a carbon tax, and a risk-averse (or risk-neutral) firm needs to plan its production and estimate its profit if the carbon tax policy is implemented. We show that it is possible for a risk-averse firm’s optimal profit, in some cases, to be higher than that of a risk-neutral firm when facing a carbon tax. This implies that the risk-averse attitude is not necessarily harmful to the firm’s profit. For operations management scholars, our model highlights the importance of integrating firms’ behavioural responses into the carbon tax price. Our model suggests that for governments to implement carbon tax policy effectively, they should make carbon prices vary under certain conditions and not worry about price variation antagonising firms at large.

Artificial intelligence in service industries: customers’ assessment of service production and resilient service operations

Artificial intelligence (AI) is increasingly embedded into service firms’ operations. However, production systems and operations management scholars have not yet examined if AI-empowered service operations are positively judged by service customers. To bridge this gap, this study draws on the three-factor theory of customer satisfaction applied to online review data, to capture the effect of AI-empowered service operations on overall customer satisfaction, operationalised by means of online review ratings. Based on text analytics techniques applied to a sample of more than 50,000 TripAdvisor ORs covering 35 international hotels in Asia and America, we develop a penalty–reward contrast analysis. The findings reveal that the effects of customer interaction with mechanical AI on customer satisfaction with service operations are asymmetric: positive customer interaction with mechanical AI positively and significantly influences overall customer satisfaction with AI-empowered service operations, whereas negative customer interaction with mechanical AI does not significantly alter customer satisfaction. Taken together, these findings suggest that mechanical AI constitutes a key element of resilient AI-empowered service operations.

Introduction to the special issue on mobility, climate change, and economic inequality

Operations Management (OM) is at the heart of two global sustainability objectives in the 21st century: to reduce the negative impact of operations and supply chains on the climate, and to improve access to good jobs, in order to address growing income inequality. Nowhere is this more evident than in the mobility sector. Mobility and transportation systems are key producers of greenhouse gas emissions and pollution. They are also vital providers of employment and access to work and have been fundamental to global economic growth. Addressing climate change is one of the most urgent tasks facing society. Research by the World Economic Forum (WEF, 2019) identified extreme weather events and the failure of climate change mitigation and adaptation as potentially the two most severe impacts on the world. Failure to address climate change may result in insurmountable costs to our social and economic structures. Meanwhile, advances in technology, such as automation, have drastically increased efficiency while reducing the cost of transportation. As a result, motorized movement continues to increase globally (IEA, 2021). Transport accounts for 37% of carbon emissions from end-use sectors (IEA, 2021). And while lockdowns during the COVID-19 pandemic reduced the demand for transportation in 2020, by 2021 emissions were back on their pre-pandemic growth track. To reverse the alarming growth in carbon intensity of mobility systems, shifts in transportation modes as well as operational and technical energy efficiency improvements are required (IEA, 2021). Transportation and vehicle production systems are woven into the fabric of global and local economies, and in many places have been a significant source of middle-class jobs such as trucking, auto assembly, and bus driving. Moreover, the mobility provided by our transportation systems has an important effect on patterns of land use, resource consumption, and access to jobs. To ensure broad participation in achieving climate goals, good jobs must be retained, and the trend of widening income inequality reversed. Without concerted efforts of practitioners, policymakers, and researchers, it is unlikely that climate and social goals will be achieved. Mobility and transportation systems have long been a core setting of OM research. Most studies in this area have focused on traditional outcomes such as quality, cost, and delivery (QCD)—in line with the historical focus of most firms. Supply chain participants used their market power to extract concessions from other participants. Inevitably linked with intense competition are uncoordinated management, bottlenecks, and information deficits and asymmetries, all of which characterize global transportation systems. Added to this are a complex mix of public and private actors, diverse and variable customer needs, and overlapping and at times conflicting regulatory regimes. The result is a system that is vulnerable to disruptions and damaging to social and environmental structures. Whether Lean—based on the Toyota production system and just-in-time principles—and its many interpretations contributed to this state of mobility and vehicle production systems is discussed elsewhere (e.g., Browning & de Treville, 2021; de Treville & Antonakis, 2006; MacDuffie, 1995; Schonberger, 2007). Important here is that the precedence of short-term calculations over long-term thinking about climate and social externalities resulted in an unsustainable system. Addressing this is in line with the call to broaden the focus of OM research to include value for all stakeholders affected by operational processes (Browning & de Treville, 2021; Gray et al., 2020). OM scholars are well-positioned to support this move (Pagell & Shevchenko, 2014). One way is to reconsider the traditional idea that sustainability and sound business practices are separate phenomena that need to be balanced (e.g., Wu & Pagell, 2011). Current global challenges raise the question of whether business practices can ever be considered “sound” if their social and environmental impacts are negative. Mobility and transportation systems provide a useful setting to demonstrate the need for expanding the objectives, stakeholders, and research methodologies included in OM research. They are mature industries and among the first to be affected by four global phenomena that require a widening of scope: regulatory attention, supply chain globalization, disruptive technological innovation, and a shift from ownership to usership. Regulatory attention dates back to the beginning of the industry, which is unsurprising considering the impact of mobility and transportation systems on public space and economic life. Due to its negative environmental impact, it has also been a key subject of climate regulations since the 1970s. This long-term pattern of regulatory involvement has only been reinforced by recent trends. Increasing attention to climate change resulted in more stringent environmental regulations in most developed economies. Moreover, to address growing inequality, the European Commission in February 2022 set the first steps in addressing global working conditions by presenting a directive for “corporate sustainability due diligence” (EU, 2022). Its goal is to make all companies based or operating in the European Union, except SMEs, responsible for the “effective protection of human rights included in international conventions” of those who work in their global supply chains. Public policymakers still grapple to address some recent trends, such as autonomous driving and the prominent presence of shared mobility solutions in public space. OM scholars have been encouraged to actively contribute to public policy (Helper et al., 2021), and mobility and transportation systems are settings that would benefit from this attention. Supply chain globalization is another phenomenon that has historically been tied to mobility and transportation systems (Cohen & Mallik, 1997). Efficiency gains in transportation and shipping enabled global economic growth (Levinson, 2016). The surge in outsourcing concomitant with the rise of China in the 1990s deeply impacted the automotive industry. The relocation of production replaced the stable, blue-collar jobs that used to be common in vehicle production systems with insecure, low-paying jobs in lower labor cost countries (Weil, 2014), where child labor, forced labor, hazardous conditions, and lax environmental standards may be common practice. A reverse trend is reshoring, which can be driven by geopolitical and economic considerations, but also by social and environmental ones. Here also, OM scholars are well-equipped to provide insights (Gray et al., 2013). Technological change is hardly a new phenomenon but again one where mobility and transportation systems can provide key insights as an early setting. Both process and product innovation are permanent features of mobility and transportation systems. Process automation has increased efficiency and reduced the number of physically strenuous jobs throughout the history of the industry. While this has led to the displacement of blue-collar workers, it also increased the need for some good jobs such as machine programmer and operator. Recent changes in propulsion technologies, motivated by environmental concerns, reinforce the trend of ever-changing skills requirements throughout the mobility and transportation sectors and their supporting industries. The surge in digital tasks further accelerates the need for adaptation from bus, train, and truck drivers, production line-workers, vehicle part suppliers and purchasers, engineers, city planners, policymakers, gas station employees, and many more. With their knowledge of process change as well as engagement with frontline workers, OM scholars are encouraged to study and support changing job designs and processes. A more recent trend, in which mobility and transportation were early movers, is changed patterns of consumption that replaced end-of-line sales with subscription schemes for usership. A steep increase in mobility options caused even public transit monopolies to face new competitors such as ride-hailing providers. This shift might spell the end of the steep postwar growth in volumes and types of vehicles produced and thereby reduce the number of jobs in the vast automotive supply chain. OM scholars are well-positioned to support the design of shared usership systems that require fewer physical assets and thus lead to more responsible consumption. A short-term focus on local optimization requires limited information-sharing. It is a well-known operational principle that investments in transparency between supply chain parties can reduce the exposure to risk and improve efficiency (Lee et al., 1997). However, in a competitive setting, information asymmetry between supply chain actors is often exploited for individual gains. This exacerbates overall supply chain weaknesses and hides climate and social damage. It is not surprising, therefore, that supply chain sustainability and transparency are often discussed in tandem (Busse et al., 2017; Gualandris et al., 2021; Jira & Toffel, 2013). Without making actual practices transparent to all stakeholders, addressing environmental and social problems is impossible. To achieve this, a systems view is needed that includes diverse objectives, stakeholders, and research methodologies (Lee & Tang, 2017). Advances in supply chain management in general, and technology-enabled monitoring of suppliers specifically, have allowed leading companies to safeguard output QCD while reducing their own risks. While leading global firms strictly enforce production output standards in their global supply chains, labor and environmental conditions at their suppliers fall outside their responsibility. The EU directive on corporate sustainability due diligence reverses this trend. The directive recognizes that all stakeholders are needed to ensure a more sustainable future, and thereby forces firms to take more of a systems view. Answering these questions can explicate the link between operational decision making and environmental and socioeconomic outcomes for all stakeholders. By making their research accessible to practitioners, policymakers, and consumers, OM scholars can support the formulation of actionable improvements. Wang et al. (2022) focus on last-mile transportation systems, which have important implications for congestion and pedestrian space, the carbon footprint of online retailers, and employment conditions for drivers. Last-mile transportation systems are typified by interactions among multiple organizations with significant market-power asymmetries. Policy makers in the US have traditionally focused on costs, but Wang et al. (2022) encourage them to oversee the misuse of market power by large retailers. This may support smaller companies that are currently losing out in the increased consolidation of this market. To reduce the climate impact of last-mile delivery, Wang et al. (2022) suggest that policymakers incentivize firms to increase off-peak or drop-off point deliveries and replace their existing vehicles with less-polluting ones. This paper is a good example of how OM scholars, with their extensive research experience in transportation systems, can support policy initiatives aimed at increased sustainability and economic equality. Naumov et al. (2022) study “cash-for-clunkers” (C4C) policies designed to accelerate the adoption of electric vehicles in the US. They suggest that C4C policies can considerably reduce vehicle fleet emissions at a reasonable cost per ton of CO2. C4C policies bring advantages of cost reductions through scale economies, charging infrastructure deployment, model variety, and consumer awareness. These advantages are amplified when deployed together with policies promoting renewable electricity production and a gas tax or carbon price, underscoring the usefulness of a systems view on improving the climate impact of mobility systems. A systems view also exposes the potential weakness of the C4C policy: Gas taxes and carbon prices often disproportionately harm lower-income individuals. But by bringing these unintended effects to the surface they are part of the discussion rather than ignored. This paper shows that positive effects in one dimension, such as reduced emissions due to a propulsion technology switch, may lead to negative effects in another, such as increased social inequality. Adopting a systems view can preempt this. Samson and Swink (2022) discuss the closure process of Toyota Australia's plant in Melbourne and its subsequent results. They adopt an engaged research approach and apply psychological contract theory to provide an in-depth examination of this closure process. Toyota's investment in a respectful plant closure included upskilling and reskilling of workers, which minimized the negative impact on workers and local communities. This paper shows how building up a stock of employee goodwill supports a transition period—even if the ultimate goal is the closure of facilities. Operational decisions such as the relocation of production greatly impact the social and economic wellbeing of workers and their communities, and it is important that these outcomes are included in OM research. This paper illustrates how OM scholars, due to their proximity to production processes and frontline workers, can study the impact on local employees and communities of global relocation decisions. Mobility and transportation are key to social life, public space, and economic well-being. They are also major polluters. To ensure the support that is needed to make an urgent shift in how we consume mobility and transportation, a new approach is needed. This Special Issue includes examples how OM scholars can contribute to this task. This Special Issue would not have been possible without the careful attention and generous time given by the Associate Editors and reviewers. Merieke Stevens, Susan Helper, and David Keith collaborated on the Call for Papers. Merieke Stevens, Susan Helper, David Keith, and José Holguín-Veras acted as guest editors for the papers submitted to this Special Issue. Merieke Stevens wrote the editorial, with Susan Helper and José Holguín-Veras providing suggestions. Special thanks go out to John Paul MacDuffie who provided comments on a final draft of the editorial. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Susan Helper was involved in this Special Issue until March 31st, 2021. David Cantor, Iowa State University. Cuneyt Eroglu, Northeastern University. Mark Frohlich, Indiana University. Jason Miller, Michigan State University. Rogelio Oliva, Texas A&M University. Mark Pagell, University College Dublin. Guangzhi Shang, Florida State University. Zhaohui Wu, Oregon State University. Serkan Akturk, Clemson University. Hadi Amini, Florida International University. Katsuki Aoki, Meiji University. Yash Babar, University of Wisconsin-Madison. Timothy Baker, Washington State University. Emek Basker, US Census Bureau. Tobias Brandt, University of Münster. Victor Cantillo, Universidad del Norte. Sangho Chae, Tilburg University. Alastair Fraser, University of Sydney. Vincent de Gooyert, Radboud University. John Paul MacDuffie, University of Pennsylvania. Tarikere Niranjan, ETH Zürich. Anton Ovchinnikov, Queen's University. Alexandru Roman, California State University. Jeroen Struben, Emlyon Business School. Sriram Venkataraman, University of South Carolina. Miriam Wilhelm, WU Vienna. Qinghua Zhu, Shanghai Jiao Tong University.

On the causality and plausibility of treatment effects in operations management research

Empirical research in operations management (OM) has made rapid strides in the last 30 years, and increasingly, OM researchers are leveraging methods used in the econometrics and statistics literature to assess the causal effects of interventions. We discuss the two key challenges in assessing causality with observational data (i.e., baseline bias, differential treatment effect bias) and how dominant identification approaches such as matching, instrumental variables, regression discontinuity, difference‐in‐differences, and fixed effects deal with such challenges. We surface the key underlying assumptions of different causal estimation methods and discuss how OM scholars have used these methods in the last few years. We hope that reflecting on the plausibility and substantive meaning of underlying assumptions regarding different identification strategies in a particular context will lead to a better conceptualization, execution, evaluation, dissemination, and consumption of OM research. We conclude with a few thoughts that authors and reviewers may find helpful in their research as they engage in discourse related to causality.

Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance

The role of artificial intelligence (AI) in augmenting healthcare is expected to grow substantially in future decades. Current research in medical AI focuses on developing, validating, and implementing point‐level AI applications in an ad hoc manner. To harness the full power of AI to improve the patient experience and outcomes at a societal scale, however, requires a gestalt shift—with a systematic understanding of AI in the context of healthcare—and so results in its widespread adoption. This translates to four pillars of incorporating AI into healthcare workflow, including physician buy‐in, patient acceptance, provider investment, and payer support (the “4Ps”). To achieve these 4Ps, it is imperative to design AI‐augmented healthcare delivery systems in view of (1) how physicians integrate AI into their clinical practice and (2) how patients perceive the role of AI in healthcare delivery. This will in turn boost provider investment and payer support. In this paper, we draw from the literature to discuss a series of research questions, including barriers to physician buy‐in and patient acceptance, transparency and disclosure, service design, and strategies for increasing AI uptake. We shed light on the principles of purposeful design for AI‐augmented healthcare delivery systems and propose a research agenda for operations management scholars to consider as they continue to strengthen their engagement with both healthcare professionals and AI developers.

Toward the understanding of the constituents of organizational culture: The embedded topic modeling analysis of publicly available employee‐generated reviews of two major U.S.‐based retailers

Organizational culture is considered an important factor affecting several operations‐oriented functions in an organization. However, the research surrounding organizational culture and what constitutes organizational culture remains understudied in the operations management (OM) discipline. This study utilizes 24,965 employee‐generated (EG) text reviews of two major U.S.‐based retailers and applies an advanced embedded topic modeling technique to identify prevalent cultural elements that retail operational workers in these organizations deemed most important. Overall, the analysis yielded 19 distinct cultural elements across the two retailers with unique elements belonging to each organization. The study provides a novel way to study organizational culture in the OM discipline and describes the workings of different text analytics methods in detail. In addition to making a methodological contribution to the field, this study provides actionable insights to practitioners and lists several avenues of future research for OM scholars interested in utilizing text analytics to study organizational culture and other operations‐oriented research problems using text data.

Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view

This study attempts to understand the role of artificial intelligence-driven big data analytics capability in humanitarian relief operations. These disasters play an important role in mobilizing several organizations to counteract them, but the organizations often find it hard to strike a fine balance between agility and resilience. Operations Management Scholars’ opinion remains divided between responsiveness and efficiency. However, to manage unexpected events like disasters, organizations need to be agile and resilient. In previous studies, scholars have adopted the resource-based view or dynamic capability view to explain the combination of resources and capabilities (i.e., technology, agility, and resilience) to explain their performance. However, following some recent scholarly debates, we argue that organizational theories like the resource-based view or dynamic capability view are not suitable enough to explain humanitarian supply chain performance. As the underlying assumptions of the commercial supply chain do not hold true in the case of the humanitarian supply chain. We note this as a potential research gap in the existing literature. Moreover, humanitarian organizations remain sceptical regarding the adoption of artificial intelligence-driven big data analytics capability (AI-BDAC) in the decision-making process. To address these potential gaps, we grounded our theoretical model in the practice-based view which is proposed as an appropriate lens to examine the role of practices that are not rare and are easy to imitate in performance. We used Partial Least Squares (PLS) to test our theoretical model and research hypotheses, using 171 useable responses gathered through a web survey of international non-governmental organizations (NGOs). The findings of our study suggest that AI-BDAC is a significant determinant of agility, resilience, and performance of the humanitarian supply chain. Furthermore, the reduction of the level of information complexity (IC) on the paths joining agility, resilience, and performance in the humanitarian supply chain. These results offer some useful theoretical contributions to the contingent view of the practice-based view. In a way, we have tried to establish empirically that the humanitarian supply chain designs are quite different from their commercial counterparts. Hence, the use of a resource-based view or dynamic capability view as theoretical lenses may not help capture true perspectives. Thus, the use of a practice-based view as an alternative theoretical lens provides a better understanding of humanitarian supply chains. We have further outlined the limitations and the future research directions of the study.

Pragmatism and Abductive Reasoning in Operations Management: Insights from Design Science

To address managerially relevant problems, scholars in operations management are increasingly adopting a design science based approach, that is, a pragmatic problem solving approach undergirded by abductive and inductive reasoning. Despite its increased adoption, the full potential of the design science approach and range of its application remains underappreciated by operations management scholars. To elaborate on how the design science approach can usefully be employed by operations management scholars, the current study examines five studies that adopted it. Treating these studies as exemplars in the Kuhnian sense, this study identifies six analytic moves that can enhance the rigor and relevance of scholarship in operations management. The study concludes with some reflections on the implications of adopting the design science approach in OM scholarship.

OM Forum—Reducing Food Waste: An Operations Management Research Agenda

Problem definition: It is estimated that one third of the food produced worldwide is wasted. This has been recognized as a critical problem by the United Nations, the U.S. Environmental Protection Agency, the European Union, and industry groups because of its implications for the environment, conservation of resources, and global hunger. Academic/practical relevance: The problem of food waste represents a significant research opportunity for the Operations Management (OM) community because it is closely connected with OM topics and methodologies in supply chain technology and management, incentives and coordination, business model innovation, and behavioral operations. Research in food waste can lead to novel academic contributions and have meaningful impact on practice. Methodology: Using the academic literature, industry literature, and interviews with managers, this article describes the problem of food waste, identifies its root causes and implications, argues for the importance of research in this area in OM, and develops a research agenda for OM scholars to contribute to the theory and practice of food waste reduction. Results: The agenda is organized around five themes: 1) supply chain technology, 2) supply chain logistics, 3) incentives and coordination in the supply chain, 4) business model innovation, and 5) behavioral operations. Managerial implications: This article aims to stimulate research on food waste in our field.

Examining Strategy Improvements in Hospitals: Linking Trade-Offs to Performance Frontiers

The notion of trade-offs has long been recognized by operations management scholars as a necessary constraint to operations strategy. The increasing importance of this concept is reflected in numerous scholarly works, all seeking an understanding on how firms compete with their limited set of resources. A large majority of the past studies have primarily focused on the empirical validation of trade-offs mainly in manufacturing, with few that have visualized trade-offs as a result of performance frontiers in service sectors. In this article, using longitudinal data, we test and validate trade-offs in a public healthcare service-based setting through a performance frontier lens. Our analyses show that better performing hospitals on the basis of quality and cost-efficiency are those that are closer to their performance frontiers and exhibit a cost trade-off. Those that are situated further away demonstrate a trend for quality and cost improvements. However, despite the positions on the frontier, quality always seemed to be the prerequisite dimension for all hospitals. We believe this reaffirms the logic surrounding the sand cone model, albeit in old industry not known for quality.

Financial Cross-Ownership and Information Dissemination in a Supply Chain

Problem definition: We examine the effect of financial cross-ownership—a situation in which a retailer holds stocks in a competitor—on two crucial operational decisions in a supply chain with competing retailers sourcing from a single supplier: information acquisition and production output. Academic/practical relevance: Financial interconnectedness between competing retailers raises fundamental questions regarding the way information is managed in such markets and the way it affects consumer welfare. Thus, in addition to the relevance to operations management scholars, this subject is of potential interest to policy makers and regulators. Although financial cross-ownership has mainly been unchallenged by regulators, the European Commission has recently called for a deeper understanding of the competitive aspects of this investment tool. Methodology: We develop a game-theoretic model, in which we analyze a supply chain comprised of an incumbent retailer holding stocks in an entrant and both retailers source from a mutual supplier. The incumbent can obtain costless demand information, and the supplier decides whether to leak this information if it is available to him or her. Results: We demonstrate that holding stocks in a rival better aligns the incentives of the rival retailers and results in a lower competition level during the production stage. However, financial cross-ownership can also result in an increased incentive for information acquisition, even when the information is later leaked to the entrant. The acquisition of information benefits not only the retailers but can also make the consumers better off. Managerial implications: Our work contributes to the heated policy debate regarding the competitive effects of financial cross-ownership. In addition, we are the first, to the best of our knowledge, to study the way financial cross-ownership affects operational decisions. Specifically, we show that financial cross-ownership provides incentives to acquire demand information even under the threat of information leakage.

Editorial: The Innovation and Project Management Department in the Journal of Operations Management

The department structure at the Journal of Operations Management (JOM) has evolved since its creation in 2016 (Browning & de Treville, 2018; Guide Jr & Ketokivi, 2015b). Part of this evolution was the creation of a department for Innovation and Project Management (IPM) in May of 2019. Although the department is a recent addition, JOM has published papers on the topics of innovation and project management (PM) since its founding in 1980. To help prospective authors and reviewers understand the expectations of the IPM department, it is useful to look back at what JOM has published (e.g., the IPM topics on which the papers have focused, and the research methods they have used) and how this trajectory has changed over the years. These patterns can inform current IPM researchers and suggest opportunities for future work. As this department enters its second year of operation, we are taking the opportunity to bring the JOM community up to date on what is happening with the department and share some of our thoughts and aspirations for its growth. From 1980 through early December, 2019, 36 papers have appeared in JOM with an innovation focus (they mentioned the word “innovation” in their title or abstract and addressed an innovation-related topic; this includes only those with firm-level focus and excludes five papers with a project management [PM] focus that are included in the next section). Broadly, these 36 papers can be divided into two categories: 20 (56%) focusing on internal, firm-level factors (e.g., internal team autonomy, manufacturing-marketing coordination) and 16 (44%) focusing on factors external to the firm (e.g., supply networks, buyer–supplier relationships). The first JOM paper related to innovation was published in 1982. (See the stylized timeline in Figure 1.) Using a case-study methodology, this study focused on how the introduction of computer-integrated manufacturing (CIM) systems influenced the product-innovation process within firms. A second paper, related to process innovation, was published in JOM after a long gap in 1989. Using qualitative research techniques, this study focused on innovation in the manufacturing context. The long gap between the first two innovation-focused papers in the 1980s might have two underlying explanations. First, innovation, both product- and process-related, did not appear to be a topic within the core focus of operations management (OM) researchers during those times. This may reflect the fact that neither graduate coursework nor dissertations were focused on those topics during those years. Although the 80s was a period of a major challenge to American manufacturing by Japanese companies that required significant investment in product and process innovation, there appears to have been a significant lag between what was happening in industry during the time and when research on these topics began to appear. Given that many did not see innovation as a core topic within OM during the 80s, a number of academic studies by business school researchers relating to innovation were published in specialized outlets such as Journal of Product Innovation Management, IEEE Transactions on Engineering Management, R&D Management, and Industrial Marketing Management. In the 1990s, JOM published five papers relating to innovation. Two papers used data, two were conceptual and one used analytical/numerical techniques. Thus, through its first two decades, JOM published only seven papers related directly to innovation, all focusing on internal, firm-level factors. The first decade of the 2000s saw a sharp increase in this number, with about 13 papers related to innovation. The decade also marked a beginning of a stream of papers that focused on understanding how factors external to a firm affect product and process innovation, with the first paper in this stream published in 2001, followed by six papers between the 2006 and 2009 period (one each in 2006 and 2009 and two each in 2007 and 2008). The primary research design during this decade was survey-based (9), followed by conceptual (2), secondary data (1), and qualitative (1). In the 2010s, this rate increased with 16 papers related to innovation (with nine papers focusing on factors external to the firm). An explanation for the increase may be that the meaning and focus of innovation have expanded, with research going beyond product and process innovation to business model innovation, sharing economy platforms (e.g., crowdsourcing), and service innovation. For the papers published from 2010 onwards, nine used survey-based technology and six used secondary data (with one paper involving a meta-analysis). What is interesting to note is that all the survey-based papers were published in 2010–2015, whereas the papers using secondary data were all published in 2014–2019. Innovation processes within firms are often seen as representing their intellectual property and proprietary knowledge. Often, in the absence of a firm's willingness to share proprietary data from its innovation process, the survey research design serves as the research design of choice for researchers to frame questions and obtain responses. However, in recent years, the increasing availability of public and subscription-based datasets, as well as the increasing breadth and depth of empirical training across OM departments, have gradually led to more research on innovation using secondary data. JOM continues to welcome survey-based research that meets appropriate standards for rigor (Guide Jr & Ketokivi, 2015a; Ketokivi, 2019). Overall, over the past two decades, the role of inter-organizational relationships (in supply chains) has been the major focus of the innovation studies published in JOM, with the majority of these studies focusing on the supplier side and how engagement with the supplier affects innovation processes and outcomes. This is understandable because supply management and sourcing are considered core topics in OM; however, the reduced focus on customers, users, or business-to-customer relationships is concerning, especially in this age of new business models and sharing economy platforms, which rely on high engagement with consumers/users to develop products and service offerings. We believe that increasing focus on these emerging innovation topics can help shift the focus toward user engagement in innovation. To date, we have not seen any published paper relating to innovation in JOM that uses experimental methods (e.g., laboratory, field, or natural experiments). Given the growth of the behavioral OM domain and the increased use of experiments as a mode of research design for OM studies in the last decade, the use of experimental methods for innovation research in JOM would seem to be an open opportunity. Although large-scale, empirical studies using archival datasets or survey data remain an important means for testing hypotheses that have strong external validity, a deeper understanding of the causal mechanism(s) involved requires a researcher to rule out of alternative explanations, which can be achieved through carefully designed laboratory and/or field experiments or by exploiting a natural experiment setting. Indeed, it would serve both the department and the broader community of innovation researchers within the OM community to engage in greater efforts to use experimental techniques to supplement empirical studies using secondary or survey datasets, or on its own, to identify clearer causal explanations of managerial actions observed in innovation settings. Finally, compared with other areas at JOM, innovation has not been studied much with qualitative research techniques such as case studies.1 We see this as another large area of opportunity. Over the past 40 years, through early December 2019, JOM has published 66 papers that we deemed PM-related (i.e., they mentioned the word “project” in their title or abstract and addressed a PM-related topic). The types of projects addressed have varied, including all kinds of research and development (R&D), new product development (NPD), and improvement (e.g., Lean and Six Sigma) projects, sometimes within a particular industry such as information technology, software development, professional services/consulting, or construction. Of the 66 papers, three were published in 1980 (firmly establishing PM within the scope of the new OM journal), and by 1987, the total rose to eight, with all but one of these (on R&D projects) having to do with project scheduling. (See the stylized timeline in Figure 2.) The next PM paper did not appear until 6 years later, in 1993; but, this began a resurgence as 17 more PM papers emerged through the rest of the 1990s. Ten of these were about project scheduling (nine from 1993–1996, one in 1998). With the exception of one further paper in 2001 about critical chain scheduling, the topic of project scheduling disappeared from JOM (with JOM's turn towards empirical, theory-based work in the late 1990s [Meredith, Krajewski, Hill, & Handfield, 2002]). In the late 1990s, PM research in JOM began to diversify, with five papers appearing on NPD projects and two on improvement projects. Despite the term “continuous improvement,” much process improvement (Lean, Six Sigma, etc.) occurs in discrete projects. (We excluded papers that focused on process improvement rather than on management of the improvement project itself.) Six papers (1997, 1998, 2010, 2011, 2012, 2015) studied improvement projects and two (2011, 2019) dealt with applying Lean to NPD projects. R&D and NPD also tend to be packaged into discrete projects, and these have been a frequent topic in JOM since the late 1990s. Since 1997, 20 papers have dealt with NPD projects (five in the late 1990s, nine in the 2000s, and six in the 2010s), and five more with R&D projects (two in the 2000s and three in the 2010s). Other PM topics include project uncertainty and risk management (three papers), project-manager behavior (two papers), project characterization, project resource management (two papers), the phenomenon of rework, PM tools, project selection, project team management, and project termination (one paper each). A cross-cutting theme in several of the above papers deals with inter-project relationships, including multi-project scheduling (under contention for a common pool of resources), inter-project relationship management, and project portfolio management. Researchers have employed a variety of methods in the PM research published in JOM. The majority (16 of 18) of the early papers on project scheduling used analytical models and optimization. Two tutorials were also published before 2000. These early publications would no longer fit well with the journal. From 1997 to 2015, surveys have been the most prominent methodology, used by 25 of the 66 papers. Seven papers used case studies (1998, 1999, 2002, 2006, 2010, 2011, 2019). A recent trend has been to use secondary and archival datasets (five papers in 2002, 2012, 2015, 2016, 2019). Other methods used include experiments (three papers), simulation (two papers), action research, meta-analysis, and mixed methods (one paper each). JOM has also published three conceptual papers pertaining to PM (1980, 2000, 2014). The historical precedence of IPM research in the OM field since its early days (e.g., Clark & Fujimoto, 1991; Wheelwright & Clark, 1992) and the above summaries of the IPM papers published in JOM over the past 40 years highlights two key points. First, innovation and PM represent important building blocks for a firm's supply-chain processes as well as its internal processes, serving as a source of continuous growth, rejuvenation, and sustained competitive advantage for firms. Second, the importance of these two domains in regard to a firm's supply chain and its internal operations highlight the continued importance of these domains to OM researchers and practitioners (Table 1). The IPM department handles manuscripts pertaining to the management of innovation—including the design and development of products, processes, and services—and various kinds of projects (temporary organizations with one-time deliverables). Operations and supply chain management involves organizing work all along the continuum from novel to repetitive. Projects exist towards the novel side of the continuum, and managing projects requires different methods and perspectives from managing repetitive work. Innovation, product development, service development, and process improvement are typically managed as projects, as are many operational efforts in a variety of industries such as construction, aerospace, technology, software, consulting, and accounting—across private and public sectors. A marketing study, a consulting job, an audit, a merger, an information technology implementation, an improvement or change initiative, a product or service development effort, a crowdsourcing contest, and a building construction or remodeling are all examples of projects. Projects come in varied sizes, including programs and megaprojects composed of interdependent projects. Multiple projects may also be managed as a portfolio. Manuscripts submitted to the IPM department may address topics related to innovation in an OM context, project management in an OM context, or both—but “both” is an option, not a requirement. As with all submissions to JOM, articles must represent original, empirical research that is grounded in theory and relevant literature (including operations management, innovation, project management, and other relevant fields) and makes a substantial contribution. We are open to all empirical research approaches based on qualitative and quantitative data from cases, experiments, secondary sources, or surveys, and we expect submissions to adhere to high standards of rigor and practical relevance. Notwithstanding these points, it is hard to ignore the fact that 102 IPM papers out of over 1,300 published in JOM since its inception—about 8% of published papers—underrepresents the impact that these two domains carry within the OM field and practice. To this end, we hope that the creation of a new department at JOM with an IPM focus will redress this imbalance, serving both as a forum and an opportunity for more OM researchers to conduct empirical research in these domains and consider JOM as a top-tier outlet for publishing their work. The IPM department aspires to address this imbalance in three ways. First, it seeks to be an attractive venue for authors to submit empirical papers relating to IPM topics. Second, it seeks to publish innovative and original papers on all topics relating to the intersection of OM and IPM. Third, it seeks to stimulate IPM research in new, interesting, and relatively unexplored domains (we provide a few example areas in the next section) that are of direct consequence to practice and society. From its inception in May 2019 through June 2020, the department has received 37 submissions, of which 30 manuscripts have been decided and seven are in progress. Out of the 30, 12 (40%) were desk rejected without going out for review, nine (30%) were rejected after receiving reports from at least two referees and an associate editor, five (17%) were rejected with an option to resubmit (i.e., the review team collectively felt that addressing their comments would require the development of a new manuscript that is worth further consideration), and four (13%) received a first-round major revision decision. In terms of IPM manuscript throughput time, the median times are 89 days for those going through the review process and 23 days for desk rejections. Consistent with JOM's editorial philosophy (Browning & de Treville, 2018), the IPM department strives to provide constructive and developmental reviews irrespective of the decision. Beyond the focus on theoretical contribution and practical relevance, the connection to prior, published OM research is a central area of focus for the IPM department, given JOM's readership. Reviewers and readers should be able to answer the question, “Why is this relevant to OM scholars or JOM?” Not making this clear is a key reason for unfavorable reviews. The creation of the IPM department provides both a platform and an opportunity for OM scholars to conduct empirical research across these overlapping domains, revisit and reframe existing knowledge in each domain, and challenge our current understanding. Although all IPM topics are welcome for submissions, we would like to draw attention to some topics or interest areas that represent critical but understudied IPM challenges faced by businesses and society today. In contrast to projects in the private sector, where the objective is to maximize profit and create wealth for shareholders, the objective of projects in the public sector is to maximize public welfare. Although the two objectives may not always be orthogonal to each other, a focus on welfare maximization (over profit maximization) often means that public sector projects are not subjected to the same competitive pressures and scrutiny given to private-sector projects. Furthermore, public sector projects often involve multiple government agencies and third-party contractors and are typically executed within a labyrinth of government regulations, which increases coordination challenges. In light of the above points, prior research that has primarily examined IPM challenges in the private sector does not appropriately capture the breadth or the depth of such challenges in the public sector. A program is a collection of interrelated projects carried out to achieve a broader strategic goal or implement a major organizational change. Programs are also referred to colloquially as “large projects,” and in practice, the line between a program and a large project is often blurred. Program management requires the coordinated execution of multiple projects (with potentially different stakeholders) that typically compete with one another for scarce organizational resources. Notwithstanding the importance of programs for firm growth and survival, the dynamics of program execution and the challenges associated with program management have received limited attention in the OM and broader management literature. Many projects do not plan enough; many plan poorly. Furthermore, some projects invest too much in a particular plan and efforts to adhere to it, even after its rationale has been overcome by events and the project's requirements have changed considerably. How much planning is enough for different types of projects, and what project characteristics make a difference? What methods, tools, and practices increase planning effectiveness, and under what conditions? And how should planning occur in agile projects? Answering these questions will likely require empirical studies of a variety of projects (e.g., engineering, information technology, infrastructure development) across industries with different clockspeeds, and the development and the operationalization of measures relating to the methodologies and outcomes of project planning. An innovation contest is a specific kind of project that is typically carried out in a digital, internet-based platform. A central advantage of innovation contests is their ability to produce a large number of diverse solutions to problems within a short period of time by allowing many individuals and organizations to participate simultaneously in idea generation and problem solving. Such contests have become increasingly popular among firms across many industries as an alternative mechanism to reduce the search costs associated with finding solutions to complex business and technical problems. As the use of innovation contests continues to grow in industry, academic scholars are increasingly focusing their research efforts on: (1) understanding the dynamics of problem solving among participating contestants and (2) identifying contest mechanisms or characteristics that incentivize contestants to develop effective solutions—two areas which are of much interest to the IPM department. Developing economies present a large and thriving consumer base for both multinational and domestic firms, which are increasingly investing in innovation and R&D projects to cater specifically to these consumers. Yet the infrastructural challenges (e.g., inadequate physical infrastructure, lower average skill level of workforce) and the bureaucratic hurdles (e.g., weak intellectual property laws, corruption, limited government support for start-up firms) prevalent in developing economies make the conditions less favorable for planning and executing such projects. Investigating these infrastructural and bureaucratic hurdles could advance IPM knowledge. The above list of topics is merely indicative rather than comprehensive, highlighting relevant and under-researched areas that merit greater attention by IPM researches in OM. Several other PM-specific topics are also noted elsewhere (Browning, 2017). The IPM department is open to papers employing almost any kind of empirical method as well as empirically grounded analytical papers, conceptual papers, and literature reviews (as described in Browning & de Treville, 2018)—although, currently, IPM papers using intervention-based research (IBR) methods will usually be handled by the IBR department (q.v., Chandrasekaran, de Treville, & Browning, 2020). The formation of the IPM department represents an opportunity for building upon the productive and impactful trajectory of past IPM research published in JOM. We are grateful to the authors, reviewers, and associate editors over the past year who have contributed to this department, and we look forward to this department's continued development as a leading avenue for empirical IPM research.

Editorial: Intervention‐based research (IBR)—What, where, and how to use it in operations management

It has been 4.5 years since the establishment of the Design Science (DS) department in the Journal of Operations Management (JOM). Even with the recent name change to the department of Intervention-based Research (IBR), its mission remains as discussed in the initial essay (Van Aken, Chandrasekaran, & Halman, 2016, p. 1): “to publish high quality research articles that derive new theoretical and managerial insights by engaging with practice and solving complex field problems.” JOM has received over 65 DS/IBR submissions and accepted 8 of these papers on topics that include sourcing, healthcare delivery, product development, and humanitarian operations. Table 1 lists these published articles. The objective of this editorial is to share with prospective authors and reviewers the insights gained from this experience. Problem: Sharing point of sale (PoS) data is potentially useful only where conventional demand planning is inadequate such as product introductions, promotions, and seasonal peaks. The determinants of successful information sharing are difficult to assess agnostic to context. Intervention: Researchers and key stakeholders codesigned and observed a collaborative product introduction process that used PoS data. Contributions: Identify multiple contextual factors and three mechanisms that allow organizations to develop and benefit from collaborative sales and operational planning. Improving home care: Knowledge creations through engagement and design (Groop, Ketokivi, Gupta, & Holmström, 2017) Problem: Increased need for homecare nurses to serve an ageing population in Finland. Theoretical objectives are often assumed and lead to impractical solutions. Intervention: Initial design for solving this as a problem to minimize transportation (travelling salesperson problem) was not viable. Iteratively codesigned a solution by resolving conflicting objectives between stakeholders. Contributions: Providing insights on how field problems may have different objectives that are invisible for the researcher or the practitioner. It is when these two agents come together that these problems are solved and sustained. Problem: Fluctuations in weather due to climate change creates financial losses for manufacturers when they incur lower-than-expected sales due to adverse weather conditions. These indirect effects of weather impact manufacturers differently. Intervention: Develop a mechanism to determine weather-related supply risk specific to product characteristics and then design varying forms of financial compensation based on the weather index-based instrument to improve coordination among supply chain partners. Contributions: Insights into how traditional financial instruments such as futures, swaps, and options can help supply chain managers make better weather-related decisions by providing actionable weather-sensitive models to help understand the disruptive effects of weather variability. Problem: Performance-based contracts are challenging to design and implement in the context of outsourced services due to the coproduction problem. Intervention: Traditional output or process controls did not work in service-outsourcing contexts. Intervention: A new contracting approach involving collaborative KPIs wherein supplier's performance, and hence, the rewards and penalties is a function of buyer behaviors. Tested and refined this solution across three cases. Contributions: Newer insights on contract design that is different from the traditional approaches discussed in the literature. Identified four mechanisms that explain the outcomes of the design intervention. Problem: What are the mechanisms and outcomes of remixing 3D models in the context of additive manufacturing performance? Each remixing opportunity requires contextual analysis. Intervention: Worked with designers from Thingiverse—a social community sharing digital designs—to understand different purposes for remixing and its effect compared to not-remixing these designs. Contributions: Insights on how organizations can create appropriate structures for their designers to effectively use remixing strategies that can help improve productivity and creativity during the design process. Problem: Relief organizations do not always have the appropriate fleets for supporting their missions. Intervention: Traditional approaches to making centralized decisions fail, while decentralized decision-making leads to inefficiencies or excess fleets when not needed. Developed a step-wise linear regression model to determine the fleet sizes based on number of locations, small partners, and large partners to accurately predict fleet sizes and worked with several agencies from the United Nations High Commissioner for Refugees (UNHCR) to implement the model with decentralized decision-makers. Contributions: Developed design propositions on how to determine fleet sizes for different humanitarian organizations based on the operating context of the humanitarian organization. Problem: The nature of 3D printing or additive manufacturing allows for different levels of outsourcing that require nontraditional coordination and supply management practices. Intervention: Emergence of a bidirectional partial outsourcing model through the practice of order-book smoothing wherein the 3D printers alternate between the roles of an outsourcer and a subcontractor. Contributions: Additive manufacturing operations can benefit from different level of outsourcing depending on the demand level and commonality of the technology. The benefits of bidirectional partial outsourcing are dependent on collaboration that may reduce traditional risk in supply chain relationships. Problem: How to match the wide-range service inquiries to the right service agents in a call center to minimize service times and improve service quality? Intervention: Developed a new mechanism to triage customer service calls that was better than existing models used in practice based on existing theories. The newer triage model was developed using machine learning technique and was developed in conjunction with the experts from an organization. Contributions: Theoretical understanding on how machine learning triage technique can improve accessibility—effectiveness interaction in service settings. We first observed that the articles making it through the peer-review process to acceptance in JOM tended to deviate from common DS practices as applied in neighboring disciplines such as information systems (for an in-depth review of DS in some other disciplines, see March & Smith, 1995; Hevner, March, Park, & Ram, 2004; Wieringa, 2014; Baskerville, Baiyere, Gregor, Hevner, & Rossie, 2018; Rai, 2017). Solutions to problems of information systems, for example, often involve a design artifact such as an algorithm or process model—a man-made solution that can then be made available more widely. Designed artifacts usually make a practical rather than a theoretical contribution. Given JOM's emphasis on theoretical contributions, we discovered that most DS articles were not a good fit with the journal. As we reflected on these published articles, we realized that the element of their contributions that benefited from being incubated in the review process was that which stemmed from the active involvement of the researchers in deploying operations management (OM) theories and tools, along the lines described by Simon (1996) in distinguishing between natural sciences and the sciences of the artificial. We thus made the decision to change the department's name to IBR to reflect this evolution in thinking at the editorial level. The change was announced during the 2019 Academy of Management meetings. Oliva (2019) drew on Checkland's (1985) approach to provide direction in the use of intervention as an OM research method. Interventions are defined as the use of a method (M) to apply a basket of theories (T) to bring a problem situation (S) to S*. If the intervention succeeds in moving the problem situation from S to S*, then the theory has been confirmed. However, a successful move from S to S* often requires adjustments to either T or M. If the adjustments are to M—as applies to a traditional DS contribution—what is learned tends to be practical in nature and is likely to be atheoretical. If, however, the required adjustment concerns T, the resulting insights about T may produce a theoretical contribution. We have also witnessed occasions where what emerges is that S*—where everyone had agreed to head—turns out to be the wrong place to go. Answering the question “Why did we end up in the wrong place?” also offers interesting theoretical insights. Adding guidance to calls that researchers step outside their ivory tower (e.g., Van de Ven & Johnson, 2006; Van Mieghem, 2013), IBR guides how to formalize the kind of surprise that such mixing with practice is able to provide and how to form that surprise into a theoretical contribution. To illustrate our points, we reflect on a few of the DS papers published in JOM and explain what was interesting from these works when observed through the lens of IBR. Consider the study by Groop et al. (2017) that set out with the objective to improve the delivery of home healthcare to the aging population in Finland, with S* being an expected improvement in service for given resources. The problem was framed along the lines of the well-known traveling salesperson optimization that seeks to minimize the travel time of caregivers (e.g., home health nurses). The authors were able to convince the review team of the reasonableness of this formulation. The relevant theory was translated into methods that were implemented, forming the intervention. Rather than bringing the situation to the expected S*, the intervention instead reduced system performance. This unexpected result triggered abductive logic as researchers and decision makers worked together to figure out what had gone wrong. They realized that improving the efficiency of caregiver travel only improved system performance if the freed-up capacity was available when capacity was actually needed. However, the traveling salesperson formulation increased resource availability at a time when it was not needed. The research team working with the decision makers from the home health agency reformulated the problem as an inventory problem and developed a second intervention that then improved system performance and was rolled out to other cities in Finland. Because of the plausibility of the traveling salesperson formulation, a failed intervention was needed to unlock the theory-reframing exercise. Interventions can also result in the refinement of an initial basket of theories, as exemplified by Akkermans et al. (2019). These authors studied the change in service firm performance when output controls were added to outsourcing contracts. The theory that formed the basis for the intervention predicted that supplier performance would improve (see Ouchi 1979), but—contrary to expectations—it declined. The abductive logic triggered by the unexpected results led to further exploration, which revealed that supplier performance depends on buyer as well as supplier performance in a service-outsourcing relationship. Adding conventional output controls made it more difficult for firms to develop proper mechanisms to manage these relationships. The authors then explored whether this unexpected finding also held in other service-outsourcing relationships and discovered that it did. The researchers then collaborated with a group of service firms to develop a contracting mechanism that added key performance indicators for the buyer to those specified for the supplier. The updated method, referred to as “collaborative service contracting,” was then tested and refined by being applied to a second service provider. This intervention thus led both to a refinement of the theory basket and of the method, and so, it meets the criteria for IBR and DS research, with the impact being the expected S to S* improvement. Kuntz and Van Wassenhove (2019) investigated how changes in fleet sizes affected the ability of relief organizations to support their missions. The authors used data from the fleet management unit of the Office of the United Nations High Commissioner for Refugees to show that both centralizing fleet sizing at the fleet management level and decentralizing it at the country office level resulted in inefficiencies (too many or too few vehicles across county locations). A proposed intervention based on traditional vehicle management approaches was seen to be inappropriate for this context, triggering a reevaluation of the applicable theory basket. The authors worked together with the Fleet Management Unit to develop simple prediction models, discovering that three independent variables sufficed to provide a highly accurate estimate of fleet sizes. The unsuccessful application of traditional theory—that control would be either centralized or decentralized—also led the research team to recognize the need to facilitate communication between country offices and the Fleet Management Unit. The realization that the common assumption—that an efficient fleet size will stem from either centralized or decentralized decision-making—is incorrect, and the resulting model—that combines prediction with facilitated communication—are now being rolled out to hundreds of country offices. In contrast to the above examples, Brusset and Bertrand (2018) demonstrated a traditional DS approach in their development of weather-based financial instruments to improve supply chain coordination. Along the lines described in the 2016 essay by Van Aken et al., these authors extended a tool used in finance for the supply chain context to solve a problem in one chain. After the initial implementation was successful, they applied the tool in two other kinds of supply chains. In addition to describing the designed tool, the manuscript also deepens understanding of the role of weather hedging in supply chain management. Although the first three papers (Groop et al., 2007; Akkermans et al., 2019; Kunz & Van Wassenhove, 2019) were accepted by the DS department editors, they diverged from the traditional DS view of creating and testing an artifact as exemplified by Brusset and Bertrand (2018). Rather, all three papers described research that began from a failed attempt to apply theory to a problem, resulting in a new understanding of the problem or an expansion of the set of theories used to solve the problem. The department remains receptive to the traditional DS approach (e.g., Ilk et al., 2020). The feedback process that results from confronting a theory with reality has now been shown to produce knowledge that may well meet the requirements for publication in JOM, so we expect to see more papers being published in JOM that use the IBR approach than DS. The contribution of IBR is both distinguished and generated by the shift to abductive reasoning that interventions may put into motion when they do not go as planned. Much inductive and deductive logic builds on heuristics that define the world and make a given problem tractable. IBR comes into its own when that initial set of assumptions—often unquestioned—does not lead to an appropriate problem formulation. IBR projects may not start as such but may rather take on their IBR identity when the problem being studied goes from seemingly well understood to ill defined (as described by Simon (1996)). Well-defined problems can be addressed without the need for an active framing exercise. As unexpected events reveal the ill-defined nature of the problem, the shift to abduction is accompanied by reframing activity. The switch to abductive logic facilitates a recalibration of the theory basket, ideally bringing the problem back to being well understood, albeit in a new way. In Table 2, we position IBR in the ecosystem of common research methods organized according to the dominant mode of reasoning used and the typical ordering of the research cycle. Modes of reasoning include deductive, abductive, and inductive (Van de Ven, 2007). Deductive reasoning typically starts off with a logical theory or hypothesis that is then examined to reach a specific conclusion. Inductive reasoning, instead, starts off broadly from a specific observation, which is then analyzed for patterns resulting in propositions and theoretical generalizations. Abductive reasoning starts with an observation similar to inductive reasoning but then uses preexisting explanations similar to deductive reasoning to group observations for the best possible explanation. While all three modes of reasoning may exist in all forms of OM research, one is more likely to dominate the others depending on the research cycle. In Table 2, we also list three common research cycles seen in OM. All research approaches begin from the problem but differ in the next steps of forming a theory, finding evidence, and developing insights. The most common approach for OM (the first column in Table 2) concerns the case where the problem leads to theory, which then guides the collection of evidence, which then leads to insights. This approach—often referred to as hypothetical–deductive research—includes traditional survey, lab experimental, and secondary data research. Table 3 gives some examples of research using this approach, along with the types of knowledge generated, whether it is descriptive (know-what), procedural (know-how), or explanatory (know-why). Fisher and Raman (1996) (normative model) Problem ➔ theory➔ evidence ➔ insights. After observing inventory and stock behaviors in the apparel industry, researchers combined statistical, information system, and optimization tools to create an approach to estimate demand probability and support decision-making for quick response manufacturing. This was tested using data from a retailer. The implementation of the theory produced the expected results. Bode and Wagner (2015) (traditional empirical) Problem ➔ theory➔ evidence ➔ insights. Researchers developed hypotheses from theory and used supply chain disruptions from 462 firms to explore how supply chain structure affected disruptions. They did not influence the design of these structures. Staats, Dai, Hofmann, and Milkman (2016) (traditional empirical) Problem ➔ theory➔ evidence ➔ insights. Researchers derived hypotheses about sustaining process compliance that they then tested using data from an RFID/electronic monitoring implementation in 42 hospitals. Evidence on factors that affected compliance was identified. Groop et al. (2017) (IBR) Researchers engaged with the stakeholders from the homecare systems to rethink the care design and develop a different approach to delivering care after their initial application of theories to yield surprising results. Chandrasekaran et al. (Chandrasekaran, Linderman, Sting, & Benner, 2016) (grounded theory) Know-what (continuous planning, universal risk tracking, and cross-level communication help identify and manage shifts) Problem ➔ evidence ➔ theory➔ insights. Researchers observed that projects in high-tech organizations toggled between incremental and radical innovation. This research began from observation of a potentially interesting regularity, and the objective of the research was more to explore this regularity than to solve a problem. Bell, Gallino, and Moreno (2017) (natural experiment) Know-why (online retailers increase their operational efficiencies and influence customer buying behaviors by having a physical presence) Problem ➔ evidence ➔ theory➔ insights. Researchers used natural experiments on store opening from Warby Parker and studied customer buying behaviors combined with other forms of data (e.g., U.S. census data) to derive insights on why physical presence helps online retail channels. Wu and Choi (Wu & Choi, 2005) (traditional qualitative) Problem ➔ evidence ➔ theory ➔ insights. Researchers collected interview and other data on buyers and their strategic suppliers that they used to develop insights about the supplier collaboration practices. The research cycle in the third column of Table 2 starts by combining an identified problem or phenomenon with evidence from the field, which then leads to theory development and insights. We find that this approach is used in qualitative research studies, especially in grounded theory work. Table 3 also gives some examples of this approach from the OM literature. The middle part of Table 2 covers a research cycle that is less common in OM research. Here, the problem leads to an iteration between theory and evidence. IBR comes into its own when attempts to apply existing theory to a problem are interrupted by reality. Decisions are made based on the understanding that has emerged from observation: T is applied, using M, expecting to move S to S*. This is normal implementation. However, the unexpected arrival at S′ instead of S* triggers abductive reasoning. This abductive logic serves to reopen M and/or T: The unexpected arrival at S′ provides warrant for the step backward on the knowledge continuum. Traditionally, this unexpected arrival and the need for iteration that it implies have all too often led to the research project being abandoned as it does not fit the hypothetical–deductive research paradigm. One of our objectives in incubating IBR research at JOM is to encourage OM scholars not to be daunted by the unexpected arrival to S′ but rather to consider whether toggling to abductive logic would create an opportunity to generate new knowledge. The stylized representation of different research methods in Table 2 is not intended to be exhaustive but rather to offer a snapshot to communicate our general understanding of how these research methods fit together. There is undoubtedly some overlap between the methods and the reasoning that is not depicted in Table 2. As an example, traditional empirical research predominantly uses deductive reasoning, supported by abductive reasoning when the hypotheses are not supported by evidence; learning comes in part from the insights emerging from the post hoc analysis. It may occasionally happen that a consulting project beginning from the application of generally accepted theory, following the usual process of T applied via M to change S, would generate sufficiently surprising results to warrant a rethinking of T. Such results have historically been difficult to publish in an academic journal because the research questions and data collection were not spelled out at the beginning of the project. The incubation of IBR at JOM explicitly opens the door to such projects as long as the theoretical contribution is sufficient. Davis (1971) called for interesting research, and that call has spread throughout the domain of management research. As we consider what emerges from IBR, it has come to our attention that one of the advantages of this approach is to create an environment that allows the emergence of research questions that are interesting in Davis’ sense. Davis (1971: 313) wrote, “All of the interesting propositions I examined were found to involve the radical distinction between seeming and being, between the subject of phenomenology and the subject of ontology…an interesting proposition was always the negation of an accepted one.” More broadly, interesting findings may also appear as qualifications or augmentations of accepted propositions. The intervention in IBR begins from an accepted proposition, providing the opportunity for its negation, augmentation, or qualification through implementation. “division between the ‘Laymen’ who have ‘Conventional Wisdom’ about the topic on the one side and the ‘Experts’ who have ‘Esoteric Knowledge’ about the topic on the other…an intellectual speciality, which began in a dialectical relationship with an assumption of the common-sense world, evolved and progresses by means of an internal dialectic with its own basic assumption. Anyone who wishes to assert a proposition that will be found interesting by laymen as well as experts must deal with the dilemma of this double dialectic. On the one hand, his proposition will interest experts only if it denies the ground-assumption of their discipline. On the other hand, his proposition will interest laymen only if it denies a ground-assumption of the common-sense world. But since the ground-assumption of experts is already a denial of a ground-assumption of laymen, he will find that any proposition which interests experts (because it denies their ground-assumption) will not interest laymen (because it affirms their ground-assumption), and vice versa. What will be interesting to the one will be obvious to the other.” (Davis, 1971: 330–331) We close with the hope that incubating IBR research will lead to interesting theories. We invite researchers to go into organizations and boldly translate plausible theories into interventions, thereby either confirming them or generating the surprises that trigger abductive logic and rich, novel insights. Descriptions of the confirmation of theories from implementation or replication works are likely to fit well in practitioner-oriented journals such as Interfaces. Implementations that upset our understanding and provide fresh insight may provide material for a publication in a theory journal, and we hope that this editorial will aid authors in exploring this route. The authors acknowledge valuable feedback from Professors Mikko Ketokivi, Rogelio Oliva, and Kevin Linderman on earlier versions of this manuscript.

Advancing Empirical Science in Operations Management Research: A Clarion Call to Action

In this paper, we examine the rise of empirical operations management research in Manufacturing & Service Operations Management (M&SOM). We review 91 empirically focused papers published in M&SOM since its inception in 1999, classifying them based on data type and data collection methodology. The results show that although empirical operations management research has made great strides thus far, empirical research still lags analytic modeling, and much work remains to be done. We call on operations management scholars to broaden and increase their use of empirical tools as a vital source of new knowledge creation; and we highlight the benefits of filling methodological gaps, especially in case and survey research. Drawing on the dynamics associated with paradigm shifts, we offer a new research paradigm that we term combinative research value, in which these two empirical techniques, coupled with other approaches (e.g., web scraping, analyses of big data, and/or analytic modeling), can help accelerate our understanding of changing operating strategies and practices. We advocate for a tighter integration of analytical and empirical operations management knowledge in order to address the challenges and opportunities of the fast-paced, enormous forces of change in the 21st century.

Smart City Operations: Modeling Challenges and Opportunities

We discuss some recent developments in smart city initiatives across the world to motivate the opportunities and challenges that such initiatives pose, and we categorize them into three themes: data access and collection, end-user utility, and economic viability of different solutions. We recognize that the academic literature that can help in addressing some of these challenges is at its nascent state and provide guidelines on how manufacturing and service operations management scholars can contribute to the global smart city movement.