About

Dr. Elliot Bendoly is the Richard M. Ross Chair in Management, Editor-in-Chief of the Journal of Operations Management, and Professor of Operations and Business Analytics at the Fisher College of Business, the Ohio State University. His research focuses on technology operations strategy, collaboration and group dynamics, and task complexity and uncertainty. In 2024, his research was listed in the top-10 of most influential articles published in the 30-year history of the POM Journal.

Research topics

• Computer Science
• Sociology
• Economics
• Business
• Marketing
• Social Science
• Microeconomics
• Artificial Intelligence
• Political Science
• Operations management
• Public relations
• Knowledge management
• Process management
• Management
• World Wide Web
• Industrial organization
• Social psychology
• Management science
• Psychology

Selected publications

• <div> Coordinating Condition-Based Maintenance and <span>User-Facing Technology Investments</span></div>

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• <scp> <i>JOM</i> </scp> Forum: Theory Testing Is Theory Generation

  Journal of Operations Management · 2026-03-09 · 1 citations

  articleOpen accessCorresponding

  In this paper, we propose that theory-testing research offers just as much potential for generating theory as theory-building and theory-elaborating research, the two variants typically associated with theory generation (Ketokivi and Choi 2014; Lee et al. 1999). Responding to Bendoly and Oliva's (2025) call for searching meaningful theoretical pathways for research contributions, we suggest that theory-testing research has always constituted a meaningful pathway to theoretical contributions when it extends beyond merely applying theory to challenging, expanding, and elaborating it. These extensions can lead to significant adjustments in bodies of knowledge over time as research programs progress. To understand the generative aspect of theory testing, we must distinguish it from theory application. When we apply theory, the objective is usually to address a practical problem, without the interest of contributing to an ongoing theoretical conversation. In empirical operations management (OM) research, the application of factory physics offers an illustrative example: Researchers apply concepts such as Little's Law and laws of variability to improve factory productivity (Schmenner and Swink 1998). In this context, theory consists of the relevant applicable laws that are treated as given, which makes theory effectively axiomatic from an epistemological point of view (Popper 1935/2005, 51).1 In stark contrast to theory application, the fundamental idea in theory-testing research is to place the theory itself under empirical scrutiny. Accordingly, theory is no longer treated as self-evident and certain but propositional and conjectural, subject to revisions (Lakatos 1970; Popper 1963). As an example of theory-testing research, consider Williamson's (1971) question “Why do firms integrate vertically?” This question gave birth to transaction cost economics (TCE), one of the most influential and established research programs on organizational boundaries (Santos and Eisenhardt 2005). The theoretical essence of TCE is succinctly captured by the discriminating alignment hypothesis: “Transactions, which differ in their attributes, are aligned with governance structures, which differ in their costs and competencies, in a discriminating (mainly transaction cost economizing) way” (Williamson 1996, 46–47). Importantly, this statement is not meant as axiomatic but conjectural, as the word ‘hypothesis’ implies: Whether actual governance decisions align transactions and governance structures in a “mainly transaction cost economizing way” is to be settled empirically. Consider Walker and Weber's (1984) seminal TCE-based study that examined the make-or-buy decision in the final assembly of automobiles. TCE-as-conjecture becomes salient in the discussion section where several TCE's central propositions are called into question based on the empirical analysis. For example, the finding that “the effect of transaction costs on make-or-buy decisions was substantially overshadowed by comparative production costs” (Walker and Weber 1984, 387) is inconsistent with TCE's original central proposition that transactions will be aligned with governance structures in “mainly transaction cost economizing” (Williamson 1996, 47, emphasis added) way. When the qualifier “mainly” is interpreted as conjectural and malleable, empirical research not only tests but also informs theory. Walker and Weber's (1984) findings suggest that while transaction costs are relevant, they constitute only a portion of total costs, which are decisive in make-or-buy decisions. Such findings, and many others, have expanded TCE's focus over time from transaction costs to total costs. Another more recent development is that instead of focusing on costs, researchers have incorporated the revenue side into the comparative analysis as well (Ketokivi and Mahoney 2020). More generally, reviews of empirical TCE literature (e.g., Macher and Richman 2008) demonstrate how TCE as a theory has developed significantly over time, mainly through the broadening of its scope. TCE illustrates a general and essential characteristic of theory-testing research: When theory is taken as conjectural, testing theory also generates theory through marginal adjustments. Such adjustments link individual theory-testing research efforts to a broader theoretical conversation and, consequently, enable the accumulation of theoretical knowledge and theory progress. We do not witness similar accumulation in knowledge communities where theories are merely applied.2 Theory-testing research is often described as hypothetico-deductive (Mantere and Ketokivi 2013). We submit that the label “deductive” is accurate for theory application but inaccurate for theory testing; for the latter, the descriptively accurate term is hypothetico-abductive. In this section, we seek to establish this by comparing reasoning in theory testing versus theory application. To understand the role of abduction, we need to distinguish between two central reasoning tasks in theory-testing research: connecting theoretical and observational statements (the theorist's concern) and connecting observational statements with data (the statistician's concern) (Meehl 1990, 116). The statistician's concern is comparatively straightforward, and there is no difference between theory application and theory testing: The statistician's concern is addressed using the established tools of statistical inference, that is, a combination of deductive and inductive reasoning. Differences are found in how the researcher addresses the theorist's concern (Figure 1). In theory application, the theorist's concern is methodologically comparatively simpler. When theory is merely applied, there is no feedback arrow from observational predictions to theory. Furthermore, if theory consists of empirically salient concepts, observational predictions can be deduced from the theoretical foundation (Schmenner and Swink 1998)—hence the term hypothetico-deductive. The case of theory testing is comparatively more complex, as adjustments to theoretical conjectures do not follow a deductive, computational logic (Mantere and Ketokivi 2013). Rather, adjustments are iterative steps of abductive inferences which adjust conjectures based on often surprising findings (Peirce 1877). As an example, let us revisit TCE's discriminating alignment hypothesis. Its central terms (e.g., transaction, governance structure, competence) are theoretical and must be translated from the language of theory into the language of empirical observation. Given that translation involves several possible, non-obvious interpretations (Quine 1951), the reasoning process cannot possibly be deductive. Similarly, since translation does not involve generalization of any kind, it cannot be inductive either. The only remaining form of reasoning is abduction, which is indeed the reasoning tool by which theory-testing researchers bridge the theoretical to the empirical. The abductive translation process is generative because it creates new meaning for theoretical concepts (Gadamer 1975). In their make-or-buy study, Walker and Weber (1984) translated TCE's general concept of uncertainty into volume uncertainty and further into unpredictable fluctuations in demand for components in automobile final assembly. This translation created specific and contextualized—in a word, new—meaning for the concept of uncertainty. The other complicating factor has to do with the feedback arrow to theory (Figure 1). Specifically, testing hypotheses is ultimately a means to the end of testing theoretical conjectures. Empirical evidence that is consistent with the hypothesis constitutes an instance of positive corroboration, whereas inconsistency means negative corroboration (Popper 1935/2005, 264–266). Both kinds not only inform theory but may also lead to adjustments and elaborations. The feedback arrow to theory makes the reasoning process in theory-testing significantly more complex than in theory-application research because it involves the use of modus tollens.3 The use of modus tollens becomes particularly complex in the case of negative corroboration: What conclusions do we draw about theory if the evidence is inconsistent with a theoretical prediction? In his seminal contribution to the literature on theory testing, Lakatos (1970, 133) maintained that in the case of negative corroboration, we are not permitted to direct the modus tollens to the “hard core” of the theory but to its “protective belt” (i.e., measurement issues, data quality, contextual issues, and other problems or oversights that might have given rise to the failed prediction). This is particularly relevant when the theory under scrutiny has amassed a high degree of positive corroboration from past research, or, as Meehl (1990, 108) put it, has “money in the bank.” To suggest that all this money would be forfeited based on just one instance of negative corroboration is both unreasonable and methodologically dubious: There are no defensible methodological principles that permit us to immediately direct the modus tollens to the hard core of the theory. Reasoning about corroboration is an abductive process. The specific form of abduction used in back-translating the empirical to the theoretical differs from the abduction used in translating the theoretical to the empirical; consistent with Bendoly and Oliva's (2025, 7) terminology, we label these “abduction a posteriori” and “abduction a priori,” respectively.4 Understanding how theory testing is theory generation hinges specifically on understanding these two variants of abduction. The connection from abduction to theory generation stems from the fact that abduction is the only form of reasoning that allows the introduction of new ideas in the conclusion of a reasoning process (Locke et al. 2008). Bendoly and Oliva's (2025, 7) observation that abduction is a form of sensemaking offers a useful starting point for establishing that theory testing generates theory. Because both the practices and the objectives of our sensemaking are diverse (Weick 1995), so are the forms of abduction: some forms are selective, others creative; some are theoretical, others empirical; some are explanatory, others non-explanatory; some incorporate only observables while others include unobservables; and so on. Given that there are literally dozens of variants of abduction (Hoffmann 2011; one must be about the specific form In the we the use of abduction in the two of theory-testing of the theoretical conjectures in Walker and Weber (1984) addressed the role of a has in a the of a (Walker and Weber 1984, where the hypothesis to the that we hypotheses from theory, Walker and Weber that the hypothesis was and not from does not include in a as a factor in production the hypothesis incorporated not only TCE's theoretical logic but also contextual Ketokivi and is well that in the automobile the final in a informs the make-or-buy To be there are other in which the final has no relevant in a which effectively the make-or-buy decision into a while the hypothesis about was consistent with it was by contextual as The reasoning used was not but abduction, described by emphasis added) as a given theory core to new application Walker and Weber TCE to the case of associated with the assembly of theory testing is the a and of the theoretical conjectures. the a the a involves a because one from one language to Both in the case of positive and negative corroboration, this involves an where the conclusion is theoretical, the reasoning an to an This is no longer abduction but to the of abduction 2008). The word a and must be examined for positive and negative we we to In case the empirical are consistent with the theory the word the researcher not to the a theory without as this can lead to For example, and point that many of the empirical as for TCE can be to other theories as we not we to When empirical are inconsistent with theory the word the researcher to a of the we to the application of modus Given that the empirical evidence the has failed are we to are the theoretical of the the theory and if in does it have to be Understanding the use of abduction both a and a the generative potential of theory-testing We the of a theory with the of its empirical application. For example, TCE is often described as a theory of the make-or-buy the make-or-buy decision is merely one of TCE's empirical Macher and TCE's empirical to have significantly over In contrast with that on (Williamson TCE-based ultimately to any that can be as a structure, organizational structure, organizational and and Richman More generally, theory-testing research can be generative when it a a for The other aspect of to the a TCE is merely one example of empirical testing not only to but also to significant theoretical adjustments and elaborations. The to total costs and to not only costs but also the and revenue side of transactions has significantly by empirical these have based on empirical we submit that the essence of the contributions is indeed As theoretical contributions are not about generating new theory from about This is the by which theory-testing research is Empirical analysis application and theory testing both an role in empirical In this paper, we have to establish that the has always generative potential to our theoretical To this we must our abductive reasoning practices both in the a and a of in reasoning this involves that theories to new empirical on the one and to theory based on negative corroboration on the This the process by which abductive sensemaking the of theoretical Bendoly and an pathway to theory. The As in the original discussion theory testing is generative because it involves abduction. Researchers must theoretical ideas into a that is and often ideas have to from the evidence to theory, which for they have be because the evidence can often be in more than one and a core it has More such point to problems with or scope. time, these kinds of adjustments a theory can and its this in theory from new theories and more from through theory testing (e.g., and This is a that for the of the and, for in a that a theoretical contribution as a for a question by general principles about how to contributions to theory how do put into What can to these principles in To this of methodological research to a theory contributions and Importantly, demonstrate the practical and of this do this by with the illustrative case of TCE theory as Specifically, how et al. which the of of the meaningful theory contributions by of the steps some of Researchers must by where a theory is or et al. by that TCE has but the of the suggest there is much to their study the of TCE's central the need to evidence on TCE's core and to and this in the addresses the need to use evidence to theoretical on evidence than empirical et al. a of to TCE's This findings into than The is that this addresses the that theory testing must be and because often are Researchers must a theory from where it The et al. study core TCE predictions discriminating from that uncertainty from TCE theoretical This researchers where a theory or with emphasis on theoretical development incorporate instead of as et al. theory to firms under volume uncertainty and theory to merely lead to more These theories not they used to effect Importantly, this addresses theory generation by how theories et al. Researchers use effect to the of theoretical et al. that TCE predictions are often their are (e.g., to These a of TCE's and the conclusion that transaction costs only of This theory from an logic the and of et al. is essential for theory testing and et al. that are a of specific and that are when are both specific and than when they are merely interpreted this as evidence that empirical often and theoretical boundaries between TCE and This is a example of theory through and both TCE and or findings be treated as for theory et al. that the TCE's that transaction costs, and governance such as governance may are than the these as for theory development than of empirical this with the that are the of theory not to theory section theoretical et al. by specific when versus how TCE with other theories under and how governance transaction costs. this in the addresses from theory testing to theory In the to the general epistemological principles into a it is essential to into this and the a study will and theory in and other The When was by his physics from the of the when with a In the a with propose a theory, a study to the theory, the study to and the to or the theory, to the to the language that a without terms or abduction. In their Ketokivi and focus mainly on the and in that is, a study to a theory, which they label as “abduction a priori,” and the to the theory, which they label as “abduction a their new are when terms and are but their to these two since both and their are more and than they these Ketokivi and a finding are the if the is is, how be and how their be may be useful to with some positive analysis by and and researchers in their of and their it is usually to a to it. Such may is, of the and problems we do not well as which of are well or This by for between theory and study as the does with Walker and Weber and for between and theoretical interpretations of practical from such may researchers to understand the their and The of analysis of are no only In a of and there is no study for or to observation can only be by more (e.g., in which case the may be by and of it. under or to may or study may be by (e.g., or or by on data or to for their the and are in the theory to the example of “mainly transaction cost to fundamental concepts time and the of and demand some about is in study and some from and in under may to in the Walker and Weber (1984) when and TCE theory for a specific in a specific but is an is a more concern than about with his question for to TCE to not the concept it to the time of the study, given to and it is to that study is a that will be in some the we can for is a practical of study that more on and tools than a of The is also of where also the modus tollens example in the an in which the or of empirical can be interpreted of for the of where is the of the theory is the of the is the of the is the of the empirical and the remaining are the of the this would not only to for the of but also knowledge defensible about the or other so that for or for its This is more than but is further by two of to and the of to some of the may to their their or with in which case many of the from study example, to between and (Weick researchers may be of some or of in of these is often of may always more of an than an As are or they are and they are Swink researchers their as or by of the (i.e., hypotheses research much of is theory testing research in in other is not testing is In researchers often to a study and for well any theory is research in of in literature is by than theory. Researchers theories to to research and This a form of than that described by Ketokivi and theory application as using a laws to an in researchers often to a for theoretical do researchers with a theory and an to or its core In practical is a and many would that this is the of its to such as and the and the has most through such as the and than researchers have theories from other (e.g., organizational theory, some view this on theories as a it is a of of the theories we have as a the in most and was as production or management in these was by theories (e.g., theory, to in the past has more into and empirical research where is is that and by and researchers to about In the has the of its the time, in an to we have theory development as a central research More than on theories from other theoretical in is the of research through a of a but in direct to There have The over versus in one The rise of operations in the of through most theories in with meaningful theoretical the of theory described by Ketokivi and and the of our can under we researchers to new Given the in our Theory-testing extensions and are to than theory-building The broadening of established as transaction cost the or the theory of Such have these theories by new and it may be much to theory testing to new theory Importantly, negative corroboration is often the most for theory When methodological can be surprising or findings the of a abductive by Ketokivi and as Ketokivi and also point theory development also a abduction. may be particularly researchers be hypothesis and than on the logic of a theory This would and more in the that empirical than merely or to a theoretical is to This would in our to that can a contribution as a to theory and would need to for in our or through data analysis to hypothesis This logic the on which to for research by observation than by to an established theoretical such as these may the development of new theories in the time, the to our empirical and to the of both and research so will us the of much theory, not (Schmenner et al. Bendoly The Ketokivi and a case that research with the of testing theory has always generative We central abductive reasoning in both the a and a of theory testing the of new theoretical consistent with our view that theories are but a of from to of In our terms and their to research that on we by theoretical conjectures and to than to that in or empirical on how their analysis and our understanding of while about the role of in theory We are particularly to how their of abduction a and a the connection between abductive sensemaking and the of theoretical that we as a meaningful pathway for research analysis of how Walker and Weber (1984) translated TCE's concept of uncertainty into meaning illustrates the of generative reasoning we in we to and the in that for Ketokivi and draw a between theory application theory is axiomatic and there is no feedback to and theory testing theory is conjectural and feedback is this is for the of a that is central to our In research, researchers use theory to in The theory is it is as not As in the more general case described when from the theory as they the researcher is into the of abductive sensemaking that Ketokivi and The creates a where application and testing are not but The in our and theory the process but the data by the into theory development research does not into side of Ketokivi and and its because it one of the for theory generation in the of to theoretical abductive Ketokivi and establish that abduction is the reasoning form through which theory testing becomes theory not all of abduction are created that abduction is generative is a The more question for our a abductive to to and from a We have that the of a can be the by it be in and empirically These apply with to the abductive inferences theory When Walker and Weber (1984) as a factor not from that abduction all is, it was with TCE's in contextual knowledge of automobile and with The example because the abduction was not because it was merely Furthermore, and a practical for the abductive Ketokivi and When a surprising finding from theory testing, the researcher must a new for the makes need not a is the is as we have in the of and The point is that abduction structure, not just We submit that the case for generative testing when in the of researchers is the study of how and to feedback structures, variability and the of and and This for the case for generative testing because particularly for abduction. When an researcher a general theoretical concept into Walker and Weber translated into in automobile final are not merely are theory. The contextual translation that Ketokivi and as an a abductive is, in the where is where concepts concepts, and where our contributions the of variability feedback or empirical for a abduction. When us in an context, the process itself often the is not organizational boundaries or but in the of This is theory testing in has generative because the but because the are we an that Ketokivi and makes but does not In our and we from Ketokivi and analysis that generative theory testing is where these two study as an through a abduction. when a to the the researcher into new theoretical to for was The a is, in the bridge between our two a This has a practical researchers in theory testing be permitted by and from deductive to abductive sensemaking the The of which we have is particularly in theory-testing research because it the a that Ketokivi and so effectively

  PublisherDOI

• Enabling Empowerment over Exploitative Specialization: The Role of Total Productive Maintenance in Precarious Work Contexts

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Environmental Performance, Strategic Partner Support, and Performance Backsliding in Supply Chains

  Journal of Business Logistics · 2025-09-04

  articleOpen accessSenior author

  ABSTRACT Supply chains are clearly instrumental to firm‐level environmental performance. Yet in research examining these effects, distinctions between arms‐length relationships (largely transactional buyers and suppliers) and other influencers, such as strategic research partners (i.e., entities who jointly maintain legal commitments to shared knowledge and resources, with common service or product development interests) are often ambiguous. In our work, we aim to investigate this distinction. Combining arguments that reflect institutional theory, stakeholder theory, and expectancy disconfirmation theory, we anticipate positive associations between the environmental performance of strategic partners and the future performance of related focal firms. We posit these associations to be more easily observable than those between a firm and its arms‐length relations. We further suggest that, due to the level of integration and codependency with strategic research partners, losses in environmental performance (backsliding) will be associated with dampened links between strategic partner performance and subsequent firm performance. By weaving together evidence from thousands of firm‐year observations, merging representative fields from FactSet, CSRHub, and Compustat sources, we find support for these associations. Implications for future research and practice are discussed.

  PublisherOA PDFDOI

• Meaningful theoretical pathways for research contributions

  SSRN Electronic Journal · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Fuzzy Signals and Postponement: Teeing-up Risk in Maintenance Operations

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Operational Process and Agency Considerations in Managing Emerging Technologies in Healthcare

  Journal of Operations Management · 2025-10-27 · 1 citations

  articleOpen access1st authorCorresponding

  In recent years, emerging technologies have seen explosive, and arguably unprecedented, growth in most industries. The healthcare industry has been no exception (Dai and Tayur 2020). We are at a crucial point where close consideration of the operational and strategic implications of emergent technologies in the healthcare industry is both observable and fully warranted. Emerging technologies, such as the internet of things (IoT), genetic technologies, 3D printing, advanced social platforms, as well as advanced analytics (e.g., artificial intelligence [AI] and other big data-driven analytics systems) are driving rapid growth and transformation in this industry (Gardner et al. 2015; Ganju et al. 2020; Xu et al. 2021; Adjerid et al. 2022; Rajpurkar et al. 2022). What is now needed are theoretical and practical insights to help identify new opportunities, operational challenges (and their solutions), arising from the advancement and adoption of these powerful emerging technologies in healthcare. Consider some of the current opportunities in healthcare: Emerging technologies are empowering physicians and improving patient care in multiple ways (Ferrand et al. 2018; Mukherjee and Sinha 2020). Developments in AI capabilities have been perhaps most notable in public discourse. AI has been deployed to augment diagnostic, clinical, and even surgical functions, therapy selection, risk prediction, and disease stratification. Resulting benefits include reductions in medical errors and increases in case throughput (Kalis et al. 2018). 3D printing technology has now advanced to the point at which it can be used to produce a wide range of customized medical devices, prosthetics, and implants. It can also fill emergent needs in place of dedicated production capacity for healthcare products (Bendoly, Chandrasekaran, et al. 2024). Robotic surgery has also been leveraged to greatly increase the precision of complex surgical activities. Emerging technologies are also changing the social reach and special opportunities for healthcare delivery (Bavafa et al. 2018). The interest in facilitating physically separate service engagement between patients and physicians, prompted in part by the COVID-19 pandemic, has prompted large-scale adoptions of telehealth technologies, often termed telemedicine, through which patients can access medical care remotely. Online consultations and live medical streaming are becoming normal experiences for many, permitting patients to obtain physical and mental health support virtually. Advanced wearables bring forth additional implications for such engagement. Ambulatory facilities, often located close to where patients live in suburban or rural areas, are becoming more valuable for patients when integrated with the aid of Electronic Health Record (EHR) systems. Robotic surgical options also present a similar degree of availability, enabling physicians to perform surgery without the need for travel. In short, emerging technologies not only have the potential to enhance the performance of healthcare systems and create innovative healthcare models, they also revolutionize the way healthcare operates in terms of care delivery, patient routing, allocation of resources, and the organizational design of healthcare systems (Dai and Tayur 2020). Such advancements, powered by these emerging technologies, collectively signal a paradigm shift toward more decentralized, accessible, and patient-centric models of healthcare delivery. Opportunities for process enhancement and innovation aside, numerous operational challenges also exist. These stem from complexities in the implementation of emerging healthcare technologies at scale, advancement of adoption and use, and design of maintenance plans and implementation policies (Heim et al. 2021; Stevens and van Schaik 2020). These issues are also highly intertwined. The acceptance (thus, adoption and use) of these emerging technologies by physicians and patients is directly related to the measurable effectiveness of their implementation and success, as well as to the manner in which further development and maintenance activities occur (Jussupow et al. 2021). Physicians may resist the large-scale implementation of emerging new healthcare technologies due to the fear of being diminished or even replaced, while patients may feel uncomfortable receiving advice provided remotely by virtual physicians through telemedicine, and especially by non-human AI agents. Further complicating things, emerging technologies also often raise legal and ethical issues, particularly in healthcare, such as liability issues for the application of new technologies in clinical settings and ethical issues for compliance with healthcare regulatory frameworks (Rajpurkar et al. 2022). Entangled in these are privacy concerns that can emerge relating to the processes by which patient data is collected in support of emerging technology use. Taken together, these practical challenges stress the need for integration strategies that balance the promise of emerging technologies with human trust, ethical safeguards, and systemic readiness across the healthcare ecosystem to achieve that promise. From an Operations Management perspective, these challenges raise the most fundamental question of how best to design processes that capitalize on the value that emerging technologies may provide in healthcare, while sufficiently accounting for the constraints and complex system relationships inherent to healthcare. In order to effectively chart a course of practical and theoretically informed consideration, and given the critical role of engaged interaction between physicians and patients, physicians and technology, and patients and technology, it is critical to adopt a socio-technical perspective. That is, it is not sufficient to merely consider the attributes of a technology when considering its impact on operational processes; rather, one must consider the manner in which those technological attributes are capitalized on in actual social application, or conversely, are either hindered or involved in misapplication. To that end, an understanding of the agency and role that patients, physicians, and specific emerging technologies play across a given service process is similarly critical. Ultimately, a socio-technical perspective, as a theoretical lens, is useful for designing healthcare operations that harness the potential of emerging technologies while accounting for human interactions, ethical issues, and systemic complexities that reflect real-world care delivery. In this Special Issue and associated discussion, we endeavor to draw attention to such issues and provide guidance for future work in this domain. A testament to the value of these considerations has been the Special Issue itself, which saw significant interest since its call for papers (CFP) in late 2023. A total of 70 formal submissions were made to the SI, 19 of which were considered for full review. While the full SI team was responsible for managing the SI call and intake of articles, and are thus listed on each article, only those without conflict of interest were involved in the review of each submission. Of those 19, seven were ultimately accepted (10% acceptance rate), based on their holistic contribution, and fit to the extant editorial mission, the SI CFP, and the body of knowledge represented in the literature on healthcare operations and technology and operations management, more broadly. When it comes to technology management and the associated operational outcomes that such management derives, a number of attributes distinguish healthcare from other contexts. These stem largely from the nature of the services rendered to patients, who are typically inherently involved in the conduct of the process (as principals, agents, and co-producers, as we will discuss further in the next section), while also embodying inputs and outputs of the service process. As a result, technology management efforts must not only be conducted with keen attention to the well-being of these individuals, but also an eye on such measures as operational efficiency. Further, and somewhat uniquely, technological developments in healthcare must not only attend to the benefit that such technology can yield for the health outcomes of patients, it must also attend to the care in which patient information is collected, stored, and used in order to deliver those outcomes over time. Failing to take care of the human-informational infrastructure can prove as harmful to patients in the long term as can failures in care delivery. In sum, effective technology management in healthcare demands a dual focus on operational performance alongside the ethical management of both patient well-being and data. This underscores the healthcare sector's uniquely human-centered technological complexity standards. For these reasons, regulations play a significant role in affirming standards across processes in healthcare. Such standards can touch everything from hiring patterns to contracting, capacity management to triage. Most salient to our present discussion, regulation can influence technology management decisions from the very onset of new tool development, through implementation, use, and maintenance of technology in healthcare settings. Technology management must be closely aligned with compliance standards such as HIPAA (Health Insurance Portability and Accountability Act) in the United States, in for data and regulations across the that at These can technological since such efforts the of data across and In as have such efforts must closely attend to measures to patient data and other also the of new technologies that may the nature of healthcare operational often additional which healthcare and the further of and in settings where is a and emergent case needs be often to more toward new best the the of innovation in healthcare is with new medical devices, telehealth and health not The of innovation has been in part by in data and in the and the of healthcare as a for This is particularly the case in that may not such as healthcare and between is often as from a technological and is as in the best interest of an operational even when both are on the most outcomes for patients and These issues are of one advancements, such as AI or such as the of advanced or that we may such as most of the can opportunities for changing how work is can also additional who play new in operational that we to be or and a given such as AI can complexity in process engagement. As et al. point such technologies can in agency operational regulations can the of these advanced technologies to fully the in which patients and physicians other healthcare are the are more a concerns that with the of even the most advanced technologies In sum, emerging technologies not only the of healthcare delivery but also the and regulatory that operational medical a and their patient will the of AI in The AI will at aid the in or medical to the the AI will at aid the patient in medical and to the in the be that AI in a role to both the and the patient and medical that while such engagement can yield highly healthcare process it also some that are to the potential for this to the that have long between physicians and patients, with associated on and the of both patient and to attend to information provided by the in the the potential for or that can and as well as the challenges inherent to a in the From these practical concerns emerge process performance and operational system as well as potential legal and The challenges and opportunities by are not the of Operations Management numerous of the consideration of and their impact on processes and outcomes et al. and et al. et al. the specific operational implications of healthcare physicians, patients, and technology have been to particularly for powerful emerging technologies, such as For our to and practical future the new of healthcare technology management in the of AI and other powerful emerging technologies, we must consider theoretical for that can we to on the operational implications by new of process engagement. Ultimately, our understanding of healthcare demands not only theoretical but a of how and value across systems. this the of Operations Management can to the future of patient healthcare, where human and technological innovation to deliver more and healthcare What theoretical help as in the Operations Management with the healthcare technology management While may be numerous for this discussion, we will focus on that most to the of complex and technology management, both our of Operations Management and process and in of the of to is that of and its from constraints to the the of to the which and can be and and which critical options can be et al. 2024). In short, it to is may have given to the current and The in the highly complex and highly that healthcare process systems is in the of that in That is, it be an to the to the of the that patients and healthcare take over the of an not to the of patient and from process or due to the The is by the that is not the of physicians or other healthcare (e.g., care While the arguably can in in systems where multiple with patients and with a wide range of patients, and are to that a of in the this is the that patients may not feel fully their or or fear of can to or 2022; et al. which not only the of information but also both patients and healthcare to From the patient perspective, can while from the organizational it potential that may legal or compliance implications and 2015; and of data process can help these by on patient and and 2018). Such both a and a that benefits patients and In short, in which data is often most to prove critical in or where healthcare are in the of are often the very that are by of for both patients and which that critical et al. 2024). It is in this role that and future healthcare technology can play a and for a more and of the healthcare of That is, healthcare technology in the has the potential to healthcare with the of that can the in both and care and and of patients, and critical in this an effective to identify and when are used other the processes in have very of on the more and healthcare efforts that they to further augment The of technologies that support such critical include everything from used in the of as well as of and in the to more and in both is Further, given the of in healthcare it is fundamental that such be sufficiently to that This is in since it an information technology infrastructure that can a wide of data and The system data must be and those data from these be they or more advanced must have to an of information to the needs of a as well as for more in or Ultimately, we are to in the healthcare industry is toward more as well as more and data and with an of ultimately a to process et al. et al. et al. 2024). The value of being to the of patient both in the to the effectiveness of the provided to a but also in more large-scale It to enhance the of healthcare through the need to patients but also has the potential to patients by the Such to process can greatly enhance and may also help these and their outcomes The and between a specific information and a they to The is typically to have a of information to and is some of in of the a of is that will not and in of the by the The for this are often to the of and et al. from a by the that they not need to the as by the from the that the fundamental to the may have been to the at the of the engagement These can yield from the that the have for value While in most of relationships the patient is the and the is the numerous have also that agency can be for processes or et al. et al. and 2024). Physicians have a keen interest in the well-being of patients, and that they patients will with benefit both to the patient and the and Physicians can also be as of other thus their in patients their such to patients as of can be by more also to related of patients are not the of but are the of In this for some of the such as the of or the of 2024). Such a the of the role to be by the with the patient in an with the that the patient the care by the of the patient as the can have on a of agency by physicians and patients, on the nature of the healthcare are provided in other point that healthcare processes are complex and as of with In the course of a healthcare service can be and the of the and the can from one to this potential for in agency across of service in as Ultimately, due to the nature of healthcare processes and the potential for in across it is not to both patient and by as of healthcare outcomes and and et al. et al. et al. 2021). a of this between the and the (e.g., from to an engagement between these such as patients and In this the the service process with a design of to be by the When the some of they may to the design with an design This A in the to (as a on of (as a either the new or being to further Such is not in of medical of or This underscores the of healthcare service where and may based on This that operational models must this that effective care often from a systems that support such engagement is for trust, improving and technology with human of technology developments of healthcare settings has the of in agency and the the to for information and care has health (as well as in that the has to as health some to this shift as the et al. patients, as well as those an advanced of a healthcare delivery may such It may influence when and how they the how they on by physicians, or they to The of health information of the has patients in healthcare complicating more care health can information and can be highly empowering for In some it can augment relationships through more and highly engaged it can also be a of and when the between patients through from physicians and Physicians can be to care options for by these patients (as to and play in of The of associated and of healthcare can be but it can also be and in processes that have been It can even over more to other processes and other What we have is the potential impact that a technology can have on the of the term one that to a and a which not in These technologies, such as can be from an agency as That is, patients and physicians can technology as needed to that they not technology can include and surgical in and greatly that of a can also include the of printing resources, for highly customized In such these technologies by design for the of as highly and to the patient or These technologies, while not inherently as that the capabilities of both patients and physicians, the need to agency in healthcare as across human and non-human AI with In consider the nature of technologies that not only to for but also provide in for the of engagement with other We to these as AI by models, for is of not only to but also by of their to those engaged with the provided by 2024). design in critical to customized (e.g., or have been as of in design with associated (Bendoly, Chandrasekaran, et al. 2024). In such those with these technologies are typically in a to or fully provided by the The to which that in current is What is the that processes may present opportunities for between and advanced technology in even that have not in the and for which extant healthcare processes have not been of how technology can play the role of in to that technology technologies AI are engagement by enabling that in healthcare. as in the case of technology, highly technologies made through in AI can patients and physicians to and that they not have These technologies can and between patients and physicians toward of as in the they are also in their to consider the of that patients and physicians are can things and more their errors may not be due to the through which they these concerns in the of of of of it to be to the and AI a of from an operations management perspective. between physicians and patients, to a significant on the of and and The of the by one the processes that both in these and agency It also mental models of the clinical case that both the and patient and as these and The of technologies, such as as an additional that can draw from the of the and A draw that the of et al. and and to be Ultimately, the of demands by the of and the to is considering While patient and mental models, specific to the case at can be highly as in and (e.g., such may not the mental That is, while we can technology from engagement in an in the of technologies comes from the of data that they are The of an case can be in the that and by A of its of can have benefits with to but that can also the manner in which in and this of technology impact may in work to the benefits that the of process as is the that agency can have on of in the healthcare system and the of a (as in are well in management and have been to errors in numerous in the of Operations Management et al. and 2021). the of a the for errors by physicians and patients due to agency in healthcare operations management a and where technologies, and human to both patient outcomes and organizational performance et al. and 2020). The seven papers accepted to the Special Issue insights the healthcare operations in terms of how it is by emerging These with the theoretical in the and to agency patient operational and we the papers that with the frameworks the are and and and as each is in the by an that opportunities for future A of transformation in healthcare operations is the integration of advanced technologies, such as and organizational to operational and patient This integration technology adoption to include and the it opportunities to identify and in healthcare from or even For et al. how policies with and to create while et al. how complexity can in these et al. how systems strategic to to for designing more such the need for models that design and operational strategies of and across settings. This on the influence of and in technology and The papers in this understanding of how systemic integration and operational occur in et al. the between and the impact of health on a they that but the increases they a potential The the on and the of the of for et al. a with a data data from and to how policies and impact the adoption of they that policies have a on adoption but not for that the decisions are largely by and operational benefits as to patient et al. the implementation of systems across in and that are with and these how and operational strategies healthcare that effective technology not only and patient outcomes but also and that and the process operations management on how health technologies, such as telemedicine, health and can be effectively integrated clinical This designing processes that are and to patient while and interaction standards. For et al. how multiple in care influence process the of operational while et al. how and strategies that without such work that process innovation must not only for the technology but also for the and that its et al. present a case in the of a health The identify a of across and how through a that et al. over at a to in an has a but and while the is not These papers that the effective adoption of health technologies not only on their capabilities but also on the of management, complex and that clinical and system In healthcare, as in other service patients as of in the design and delivery of their such as telemedicine, and patients to take a more from and data to with Mukherjee et al. how allocation for care can while across and and how in care and the operational implications of interaction These that not only on patient engagement but also on the organizational and process that that engagement. et al. a with patients to the role of AI as a in The that can enhance performance and patient when with and use, but it fully human and privacy concerns in healthcare through a and of that benefit privacy concerns and increase of practical strategies for a to health these that patients, as co-producers, can enhance care and but this potential on organizational technology use, and attention to the of patient and in effective and healthcare these seven papers a healthcare ecosystem where technologies, and in Operations Management by in insights that and patient engagement. These reflect a for operations to Operations Management in of the and of healthcare delivery, and they opportunities to create and healthcare systems where technology, human and to care delivery and may be in healthcare.

  PublisherOA PDFDOI

• Aligning TPM and Contingent Worker Policies: Approaches for Empowerment and Skill Enhancement

  Academy of Management Proceedings · 2025-07-01

  article

  The growing reliance on contingent workers in modern organizations raises important questions about the effectiveness of this staffing approach. Through a socio-technical theory lens, we investigate how operational practices, particularly total productive maintenance (TPM), can address these concerns. In this work, we examine the value of these practices in interactions with, and potentially in place of, common human resource emphasis on high-performance work practices (HPWP) for contingent workers. Our study draws on data from 76 Portuguese manufacturing firms, combining matched-sample surveys of operations managers and human resource managers, supplemented by financial data from Informa D\&B and Informação Empresarial Simplificada. This multi-source approach allows us to analyze how different combinations of TPM and HPWP affect firm financial performance. We further conduct 14 post-hoc semi-structured interviews with operations and human resource managers from the firms in our dataset to contextualize and clarify the nuances in our findings. The results reveal that when autonomous TPM investment is low, there are observable financial benefits from investments in broad HPWP skill development for contingent workers. In such low TPM settings, efforts to enhance worker empowerment demonstrate negative financial returns. However, under high autonomous TPM conditions, contingent worker empowerment exhibits a significant positive relationship with financial performance. In such high TPM settings, supplemental investments in broader HPWP skill development fail to yield additional value. These findings suggest that the effectiveness of skill enhancement and empowerment initiatives for contingent workers depends critically on the TPM context. The implications for coordinated operations and human resource planning in the use of contingent workers are discussed.

  PublisherDOI

• Meaningful Theoretical Pathways for Research Contributions

  Journal of Operations Management · 2025-01-01 · 17 citations

  articleOpen access1st authorCorresponding

  Across fields of scholarship, ever since scholarship has existed, there have been numerous discussions opining on what theory is, why it is useful and how best to craft theoretical arguments and frameworks. Every few years, a new discussion particularly relevant to a domain of study emerges. Often the intention of such discussions is to reiterate critical points made in the past as still applicable. In other instances, the discussions attempt to recast and reshape perspectives on theory. Both reiteration and alternate perspectives can prove valuable, as new scholars enter the field and as priorities for journals, editors and review teams evolve. These points are also of interest to contemporary discussions at the Journal of Operations Management (JOM). As an outlet long regarded for impactful empirical work in the field, we have long been interested in the appropriate use of theory and have also had a long history of intervening in our field to re-emphasize the ‘what’, ‘why’ and ‘how’ of meaningful theoretical structures and argumentation. As editors of the journal, we believe it is valuable to reiterate what is well-accepted regarding the role and nature of effective theory in research, whether we are discussing grand theories, theoretical frameworks, mid-range theory or theoretical arguments for specific mechanisms. However, we also strongly believe that it is critically valuable to outline how theoretical contributions may differ, while still offering considerable value to a research effort and the field. What is core to the substantive nature of theoretical contributions, of course, must be driven by priorities regarding its role; just as the selection of empirical methods must be driven by the claims emerging from theoretical arguments (even nascent ones), and insights for future scholars driven by observation and analysis. By outlining contemporary priorities that define meaningful theory we are in a far better position to simultaneously expand perspectives on how theoretical contributions can be made, as well as challenge or dispel some often difficult-to-justify criticisms that scholars (authors, reviewers and editors) confront regarding what is ‘good’ theory. Models with excellent fit tell us little about the data-generating mechanisms… A well-fitting factor model cannot be taken as evidence that a psychological construct… exists…. Similarly, a network model with good fit to the data cannot tell us where we need to intervene in a causal system. (Fried (2020), 275) According to Fried (2020), this “statistical equivalency” is one of the fundamental reasons that we cannot escape the need for well-reasoned theoretical arguments, designed to help us make sense of highly complex settings, in which a wealth of observed signals is accompanied by a wealth of unobserved signals. It is exactly when phenomena are not straightforward and mechanisms are not obvious, where sensemaking, and associated deliberate research inquiry, is critical. In the same vein, a ‘complete theory’, akin to a physical law, doesn't present much of a motivator for research—if there is no uncertainty regarding cause and effect, there is little reason to expect that an inquiry into such phenomena would be of interest to a research community. Fortunately, in the domains that are studied in management, we seldom come close to complete theories. Occasionally we find enough evidence to corroborate what we might refer to as grand theories and associated frameworks. More often, we observe, or perceive, phenomena that exhibit patterns (either across a body of literature or direct observations in the field) that inspire us to question whether such patterns are repeatable. Indeed, theories are never finished products but rather exist along a continuum of sensemaking from vague hunches to detailed accounts of causal mechanism (Mohr 1982; Weick 1989), where the initial phases of theorizing often include the creation or definition of constructs and narratives to account for the observed phenomenon. With the rise of replication discussions so prominent today, it would be a mistake to forget that methods are merely a means to an end, that they are bound to be imperfectly replicable in observations and analyses they yield. The most critical aspect of replication comes down to whether we can reinforce existing understanding, or whether such attempts at sensemaking require modification, qualification or replacement. That should be the primacy of replication interest for research communities; with a possible exception for communities focused on methodological contributions. Similarly, researchers certainly must be permitted to demonstrate thought that aligns with (replicates) existing theoretical arguments, based on the identification of repeated insights from whatever source, just as they must be permitted to deviate from such arguments if the patterns they encounter do not align. In the complex contexts that characterize management research domains, it is not helpful to expect scholars to identify universal laws, nor is it appropriate to bind them to recognizing or aligning with claims that others have made to that end. Furthermore, it should be noted that not all theoretical arguments (hypotheses or propositions) are created equal. There are potential explanations that are clearly better than others. How do we assess the quality of a potential explanation? Bunge (1967), articulates the desired attributes of well-formulated scientific hypotheses as (1) logically sound, (2) grounded in previous knowledge, and (3) empirically testable. We believe that the quality of a conjecture can be judged by the extent to which it fulfills these criteria.1 Thus, while two alternative explanations might be equally capable of explaining the data, we can easily assess which has more scientific credibility based on those criteria, for example, ‘a hard object hit and broke the glass’ versus ‘a soft object hit and broke the glass.’ If we accept the three points listed above as fundamental to the value and role of theory and the desirable attributes of claims, it is also clear, based on our experience with the editorial process, that certain misconceptions regarding what makes “good theory” continue to exist. We outline a few of these fallacies here, along with why they must be deemed to be fundamentally flawed. False claim: “Theoretical arguments must explicitly cite existing ‘named’ theories” Why is it False? While reference to extant work is important as a means of emphasizing contribution, and while research is expected to build off of the ideas and observations of existing work, thus positioning a research effort and serving as an argument for fit with a journal, its departments or special issue efforts, the logic of theoretical arguments should stand on their own. Reference to extant ‘named’ theories can be useful as reinforcement examples and analogy but are secondary (Ketokivi, Mantere, and Cornelissen 2017). The best theoretical arguments, regardless of what serves as their grounding, demonstrate meaningful backings (named or otherwise) and thoughtful considerations of how associated warrants might be qualified as claims are developed, regardless of whether analogous claims exist elsewhere (Toulmin 1958/2003; Ketokivi and Mantere 2021). A good test of a strong theory is to strip away reference to ‘named’ theories; if the arguments are strong, they should remain strong in that absence. Along similar lines, there is no expectation that all tenets of cited theory must be drawn upon for effective theoretical argument development. As even ‘named’ theories remain speculative, not all aspects of such theories will always prove relevant. The job of a researcher is deliberately select what backings can be most useful in developing their warrants. That implies that ‘kitchen-sink’ approaches to drawing on theory should never exist as de facto expectations. False claim: “Theoretical arguments cannot draw on more than X ‘named’ theories” Why is it False? Related to the prior point, if extant ‘named’ theories serve predominantly as vehicles for supporting arguments, as examples or analogy, and remain secondary to actual theoretical argumentation, there is no reasonable rationale to enforce a limit on the diversity of such support. Certainly, all authors are encouraged to make each of their points in a parsimonious manner, however that is a matter of exposition rather than theoretical construction. False claim: “Theoretical arguments must comprehensively capture all possible mechanisms that could provide alternative arguments for what has or might be observed” Why is it False? If all mechanisms could be accounted for, arguments would cease to be theoretical and associated research (at least empirical research) would not be warranted; much in the same way that the re-examination of a specific theoretical argument might be the purview of replication research, rather than novel contribution. Theoretical arguments regarding mechanisms are important, and the recognition of alternative explanations and counterarguments can demonstrate ample consideration by researchers, however, even alternative explanations are only ‘possible’ pathways. They are seldom ‘laws’, and if those pathways have not themselves been sufficiently investigated there is no reason to place them on a pedestal higher than the arguments posed by a research team (provided those are sound). Valuing theoretical arguments as motivators for work should allow a range of research efforts to be motivated, seek out evidence and contribute to discussions. False claim: “Theoretical arguments must focus on the same unit of analysis as the one used in empirical analysis” Why is it False? Clearly domains of management rely, at least in part, on the participation of individuals as decision makers, responding to signals and scenarios. Occasionally this fact is brushed over, and higher-level dynamics focused on, but unless we are studying a fully automated system, free of human involvement, we are in fact working with theory rather than laws. As long as this is the case, nearly all management theory must have human activity implicitly embedded within it. When developing theory regarding the relational dynamics within dyads of firms, while corporate level considerations are useful, drawing on individual level cognitive arguments must also be acceptable. A close read of Rousseau's (1985) classic discussion of units of analysis outlines the value of hierarchical perspectives, with lower-level units (e.g., human decision processes and action) subsumed within large units (e.g., organizational action and outcomes). While this perspective argues that variance at lower levels occur at a more frequent unit of time than those at higher levels, extrapolating upwards in unit scale is acceptable; empirically the risk of losing detail through aggregation exists, but no fundamental barrier to potential for theoretical increase in level exists. Similarly, when making arguments regarding the interactions of two firms, an empirical focus on the dynamics associated with one member of that dyad should also be deemed acceptable. Theoretical arguments must permit out-of-the-box extensions as well as more focused inquiry. False claim: “Theoretical arguments must be far more comprehensive in advance of a research plan than they are following observation and analysis” Why is it False? Some minimal level of upfront theoretical argumentation should be sufficient to motivate research, or tell the backstory of why research was done, but posteriori theoretical argumentation should not be ignored and in fact may be far more critical. To be clear, some degree of grounded theoretical argumentation should exist both at the front and back end of manuscripts. The scope of grounding and argumentation may be very different on each end. For example, the recognition of observed anomalies at the front end of the work may not only be a critical aspect motivating the deeper research inquiry, but it can represent research questions that inspire a considerable theory development effort following that deeper inquiry. However, no research effort begins in a vacuum of theory. Even if anomalies do not align with existing explanations, it is important to recognize these inadequate theoretical arguments in advance of an abductive process. In such instances, the ‘theoretical contribution’ of the work will be far more reliant on the back end rather than the front end, but the front end should set the stage. Having said that, even work for which theoretical arguments are front-loaded should afford time to posteriori theoretical considerations. Confronting these fallacies, understanding and rationalizing their flaws, is important for authors, but is equally important for reviewers and editors to consider as they work to promote the expansion of knowledge in their disciplines. We must remain focused on adding value recognizing what is and what is not of critical value in the research design, rather than getting tied up in false and unjustified expectations. Accordingly, every submission to JOM is first assessed, as per the aims and scope of the journal (wwwJOM-hub.com), on whether it is empirical and it makes a substantial contribution to theoretical and practical understanding in Operations Management. As we have outlined in an earlier editorial (Bendoly and Oliva 2024), doing empirical work in an operational context is not enough, in and of itself, to qualify as contribution. For a manuscript to fit the mission of JOM it must either test OM theoretical claims or derive insights that inform OM theory or practice (i.e., new claims that require independent testing or that provide normative guidance for practitioners). Thus, while upfront theoretical arguments that are based on non-OM theories (e.g., extra-disciplinary ‘named’ theories), or elemental theoretical backings, can be used to develop arguments and testable hypotheses, the full arguments themselves should be grounded in OM perspectives. Further, the empirical results should be leveraged deliberately to inform OM theoretical and practical understanding. We have found that hypotheses based on convoluted theoretical arguments aimed mainly at justifying the use of available data, are not as effective as simple but logical, arguments in supporting the development of insights from the results. Once more, hypotheses to be tested need to be assessed by their ability to further derive operational insights. In recognizing what is truly important when it comes to theory, and pushing aside concerns that are not ‘real’ concerns, we can now focus on the fruitful pathways available to authors as their embark on theoretical considerations in their work, and as reviewers and editors approach efforts to further develop such work. Figure 1 presents a generalization of two paths available to authors as they leverage observations and theory to build meaningful contributions to the field. The common path (Path A) that flows from left to right in Figure 1, often beginning with a more academic-literature inspired motivation, tends to have many recognizable attributes a front end theoretical positioning and a approach to from at least some posteriori theoretical discussion which we will research is by the identification of research made by of extant of knowledge, through grounded argumentation to While this is, by the most common of submission to this is clearly not the only approach scholars can and have taken in developing contributions. alternate path (Path and predominantly from empirical from right to left in the of Figure The observation of empirical which have not been fully by extant research, or the observation of phenomena that existing theories, the effort down the path of can we what we are rather than do we expect to our The of this not need to be fully theoretical it can be of constructs and to the observed by its very also an into abductive sensemaking, where we are theoretical arguments to how observations fit into a in that have not been In doing we are implicitly future observations in specific rather than existing observations to theoretical That is, the claims of such sensemaking arguments often the of with the that they are up by empirical efforts, alternate of evidence in of inquiry as can come in the of or a the of constructs and narratives to phenomena and the abductive of theoretical arguments that the outlined in 1 are as much as a contribution as the empirical testing of those How are these paths to research approaches that we across our of research at from for to to with the in developing of these could a theory back end with theory motivating approaches at of and certainly motivation, to some minimal at the front end as Figure presents the processes through which we theory inspired and the a range of empirical that make use of data from domain of JOM develop or theories about those processes and how they should be way of empirical efforts to and the processes and on the potential for the observed of Figure If the observed are not by existing theory or they anomalies from what is expected from the theory, we need to potential and this is in Figure 1 and is by the abductive if these even if not inspired by theoretical do existing theories and explanations, we can on the existing theory from the of specific A way of empirical is to test this place through attempt to the and the in of while the and of the the risk and of field efforts to are clearly of to all the in place at JOM and Oliva are not always possible and and or the is to some units but to are to either the claims if not increase their alternative way to test claims is to on explicitly for the study or from other data efforts causal claims through and These approaches a A and to the in Figure through one through the the for work, and the other of the fact that all observation and data is by the theoretical claims that are A way of empirical is to intervene theory to in that is, use the theory to provide While JOM has editorial not to focus on as contributions there is ample potential to about the and of a theory when to use it to or a The creation of the in JOM has the path to use to test and develop theory within the context of a where the researcher with as an of in the The fact that the might require to the and that are not often what was by the theory, the to new data from the processes that could to to the theory used to the As research A to the from an existing but the data from the to derive insights for theory (Path the created through in Figure of the empirical and the role of theoretical argumentation, both a and with different of on the is fundamental regardless of what we It specific but also clearly from others. role and are on what is but we much of it. the end of the in a scientific the to assess the contribution of an empirical study is its contribution to theory. If the is and we are only making sense of or clearly the of a new theory that can be tested is enough of a contribution. However, if the of the study is to test existing theory with secondary data or through and the from the study in the example, how theories need to be What are new research questions that are be these a for the contribution to be What all this for reviewers and As we have in the JOM editorial team all reviewers and their associated are to be is not it is a It is also not merely have very specific They identify of but make deliberate efforts to help authors up those The role of at is not that of role is not to provide an up or down role is that of substantial and should never merely for where that Furthermore, with specific to theory, a review should never merely for a theoretical should also not to the fallacies posed in such as a to sufficiently reference extant theory, or comprehensively mechanisms. If a relevant theory for use as analogy or and a is with that theoretical it is the job of the to be in the authors the consideration of that work. If a mechanism that the the authors should it is on the review to be regarding what that mechanism might If as a is but include that in review as such a clearly doesn't serve to help develop a There are on the of guidance and editors should regarding theory. For example, reviewers and editors should not for That is, it is for reviewers to an team to develop theoretical arguments to be a if the for such is based on results emerging from the existing analysis in the While some authors may recognize such as some may not and still others may it is the only way to through the review To be clear, such action on the of reviewers or editors is should help authors arguments that they have used to motivate their methods and analysis. It is also fully to position arguments posteriori in the interest of future In both instances, reviewers are to be in this offering specific rather than for However, that by the analysis be accounted for by the of new front end theoretical arguments if they a is not an path for reviewers to Furthermore, reviewers and editors need to be fully of the very that strong contributions can on a that not from an identification of a but rather from direct If we are to researchers at JOM and other to with we must that some of that is to to the recognition of and anomalies that have not been and that such observations are at least as important not more than drawn predominantly from extant work. We must be to these highly abductive paths taken by authors, while still authors to what is in the of thoughtful sensemaking that all for impactful theoretical contributions.

  PublisherOA PDFDOI

• Nudging Tactics for Enhanced Compliance With Condition‐Based Maintenance Guidelines

  Journal of Operations Management · 2025-04-10 · 5 citations

  articleOpen access

  ABSTRACT Condition‐based preventive maintenance (CB‐PM), dependent on robust and precise indicators of equipment quality, stands to gain advantages from the integration of sensor technologies. Yet, the effectiveness of such systems relies on an important factor: people. Even in possession of ideal CB‐PM policies, imperfect adherence can lead to higher equipment downtime, maintenance costs, and increased safety hazards. Here, we describe a normative model of optimal CB‐PM policy determination; specifically, a generalized means by which to determine a valuemaximizing quality threshold as a guideline for triggering PM (preventative maintenance). Motivated by field data, we consider the opportunity cost of not adhering to such optimal policies; that is, premature or delayed responses. We design and execute a controlled laboratory study, exposing participants to two critical manipulations that we theorize might influence adherence: (1) The presence of a supplemental secondary signal, of a type common to time‐based preventive maintenance (TB‐PM), (2) a pre‐task priming intended to emphasize the value of discretized task completion. Results showed that the combination of CB‐PM and TB‐PM signals, along with completion priming, significantly increases adherence to CB‐PM guidelines. We demonstrate that individuals exposed to this combination of treatments forfeit far less value than those receiving CB‐PM signals alone.

  PublisherDOI

Frequent coauthors

• Daniel G. Bachrach

  32 shared

• Kevin Linderman

  12 shared

• Jeff K. Stratman

  Texas Christian University

  11 shared

• Christopher S. Tang

  11 shared

• Eve D. Rosenzweig

  Emory University

  9 shared

• Christian C. Blanco

  The Ohio State University

  8 shared

• Kathy Koontz

  8 shared

• Stephanie Eckerd

  University of Tennessee at Knoxville

  7 shared

Education

• PhD in Operations and Decision Technology, Kelley School of Business

  Indiana University Bloomington

  2001

• MS in Operations and Decision Technology, Kelley School of Business

  Indiana University Bloomington

  1999

• BS in Materials Engineering, Materials Science and Engineering

  Case Western Reserve University

  1996

• BA in Economics (Industrial/Developmental), Economics

  Case Western Reserve University

  1996

Awards & honors

• OSU Pace Setters Service award (2025)
• OM Distinguished Scholar (Academy of Management) (2015)
• Emory University's 2014 Crystal Apple Award for undergraduat…