Empirical Research In Operations Management Research Articles

Pioneering role of the Production and Operations Management in promoting empirical research in operations management

We describe the pioneering role of the Production and Operations Management ( POM) in promoting empirical research in innovation, operations, and supply chain management. We also review and discuss the most influential empirical papers published in POM based on each paper's Google Scholar citation counts. We selected the top 200 cited papers from the 2085 papers published or forthcoming in POM to identify the most influential empirical papers. We classify these 200 papers into three categories: (a) review and conceptual development, (b) analytical, and (c) empirical papers, and then compare these papers across the categories in terms of citation counts. Next we analyze the 75 empirical papers from the top 200 cited papers published in POM by covered topics, data sources, and data analysis methods. Focusing the analysis on empirical papers among the top 200 cited papers can underrepresent emerging themes among the more recently published empirical papers. Therefore, we also analyze 19 more recent empirical papers from 2016 to 2021 that are not covered in the top 200 cited papers but have been impactful given the brief time since their publication. We conclude by offering our thoughts on how editors, reviewers, and authors can work together to further enhance and ensure the quality and influence of future operations management (OM) empirical science research.

Why Empirical Research Is Good for Operations Management, and What Is Good Empirical Operations Management?

Empirical research in operations management has increased steadily over the last 20 years. In this paper, we discuss why this is good for our field and offer some comments on the qualities we admire in an empirical operations management paper.

OM Forum—Empirical Research in Operations Management: From Field Studies to Analyzing Digital Exhaust

The amount of empirical research in operations management that has been published in this journal and in Management Science has recently witnessed a significant increase. Beyond this increase in numbers, research questions and empirical methodologies have also changed substantially. Whereas empirical research in the 1990s was primarily carried out at the firm, plant, or project level, recent research focuses much more on microlevel phenomena. This shift in the unit of analysis has been made possible by advances in data generation and collection technologies. This trend is likely to continue as the digitization of modern work creates massive data trails, which archive the state of an operation at a very fine level of granularity. I will refer to such data, which result as a by-product of the digital planning and control of operations, as digital exhaust. As such microlevel data become available abundantly, new opportunities for research arise. However, such data also pose new challenges, including the need for careful theory development and a deep contextual understanding of the underlying data generation process.

Cluster analysis in empirical OM research: survey and recommendations

PurposeThe purpose of this paper is twofold. First, the authors provide a survey of operations management (OM) research applications of traditional hierarchical and nonhierarchical clustering methods with respect to key decisions that are central to a valid analysis. Second, the authors offer recommendations for practice with respect to these decisions.Design/methodology/approachA coding study was conducted for 97 cluster analyses reported in six OM journals during the period spanning 1994-2015. Data were collected with respect to: variable selection, variable standardization, method, selection of the number of clusters, consistency/stability of the clustering solution, and profiling of the clusters based on exogenous variables. Recommended practices for validation of clustering solutions are provided within the context of this framework.FindingsThere is considerable variability across clustering applications with respect to the components of validation, as well as a mix of productive and undesirable practices. This justifies the importance of the authors’ provision of a schema for conducting a cluster analysis.Research limitations/implicationsCertain aspects of the coding study required some degree of subjectivity with respect to interpretation or classification. However, in light of the sheer magnitude of the coding study (97 articles), the authors are confident that an accurate picture of empirical OM clustering applications has been presented.Practical implicationsThe paper provides a critique and synthesis of the practice of cluster analysis in OM research. The coding study provides a thorough foundation for how the key decisions of a cluster analysis have been previously handled in the literature. Both researchers and practitioners are provided with guidelines for performing a valid cluster analysis.Originality/valueTo the best of the authors’ knowledge, no study of this type has been reported in the OM literature. The authors’ recommendations for cluster validation draw from recent studies in other disciplines that are apt to be unfamiliar to many OM researchers.

Theory usage in empirical operations management research: a review and discussion

Purpose The purpose of this paper is to analyze more than three decades of theory testing published in leading operations management (OM) journals. Design/methodology/approach This piece examines the amount of theory testing, the extent to which theories are tested multiple times, and the disciplinary origins of the theories that are tested. Findings The analysis revealed that empirical OM researchers have increasingly responded to demands for more theory-driven knowledge over time. OM researchers are developing and using a wide array of domestic theories to understand empirical data. The examination also revealed a substantial focus on theory borrowed from other scientific fields. Originality/value The findings here suggest that OM is clearly a maturing discipline. As the discipline matures, it is important to consider to what extent borrowed theories and frameworks can offer value to OM. A preliminary vetting model is advanced in order to critically assess foreign theory. It is hoped that future screening promotes only the most useful non-domestic theory, thereby ensuring sufficient journal space for domestic theory and resulting in effective solutions to the pressing, practical problems of the OM field.

The Use of Latent Semantic Analysis in Operations Management Research

ABSTRACTIn this article, we introduce the use of Latent Semantic Analysis (LSA) as a technique for uncovering the intellectual structure of a discipline. LSA is an emerging quantitative method for content analysis that combines rigorous statistical techniques and scholarly judgment as it proceeds to extract and decipher key latent factors. We provide a stepwise explanation and illustration for implementing LSA. To demonstrate LSA's ability to uncover the intellectual structure of a discipline, we present a study of the field of Operations Management. We also discuss a number of potential applications of LSA to show how it can be used in empirical Operations Management research, specifically in areas that can benefit from analyzing large volumes of unstructured textual data.

The effects of supplier-to-buyer identification on operational performance—An empirical investigation of inter-organizational identification in automotive relationships

Over the past decade conceptual and empirical research in operations management has embraced the idea that collaborative supplier–buyer relationships are a source of competitive advantage for manufacturing firms. Anecdotal evidence from the Japanese and U.S. automotive industry and emerging research suggests that inter-organizational identification of suppliers with their buyers, termed supplier-to-buyer identification, is an unexplored factor of relational advantage. This study presents a model and empirical test that supplier-to-buyer identification fosters superior operational performance by enhancing trust, supplier relation-specific investments, and information exchange. Through a survey of 346 automotive supplier–buyer relationships, the findings show that supplier-to-buyer identification directly impacts supplier relationship-specific investments and information exchange, although most of the latter effect is mediated by trust. The findings also indicate that supplier relation-specific investments and information exchange play different but complementary roles in influencing operational performance. The results suggest new directions for supplier–buyer relationship research in operations management and important managerial implications.

Advancing operations management theory using exploratory structural equation modelling techniques

The structural equation modelling (SEM) technique has been touted as a useful tool for tightening links between theoretical and empirical operations management (OM) research. Despite SEM's increasing prominence in the field, leading scholars continue to call for a deeper infusion of theory into empirical OM research. To strengthen ties between theory and analysis in OM research, this study evaluates previous OM applications of SEM and identifies specific ways we can use SEM to advance operations management theory. Through judicious use of SEM techniques, we believe that OM researchers have the opportunity to confirm and extend existing theoretical frameworks. Further, we offer guidance on how to operationalise measurement models such that researchers accurately depict the causality of a construct. To demonstrate how to advance theory, we use an illustrative example of SEM in an OM context based upon data gathered from a survey of over 200 respondents.

Strengthening the Empirical Base of Operations Management

Isuggest that the prospering fields of physics, medicine, and finance illustrate the value of a strong empirical dimension to research that is well integrated with theoretical research. I use empirical research in these fields to formulate a framework for classifying empirical research and illustrate that framework with a few selected examples in operations management. I offer some advice on data sources and approaches to conducting empirical research and suggest ways strengthening empirical research in operations management. This is obviously a partial treatment of a large subject and represents my personal point of view. This paper should encourage comments by others to further develop the topic and to offer alternative points of view.

Empirical Research Published in Production and Operations Management (1992–2005): Trends and Future Research Directions

We review and evaluate empirical research in more than 150 papers published in Production and Operations Management ( POM) during 1992 to 2005 to assess how far the papers' authors have met the journaľs stated objective of promoting empirical research. We also assess the diversity of articles in terms of the purposes of research, data collection approaches, and data analysis techniques. We classify the empirical research articles based on their primary purpose (theory building, theory verifying, application, and providing evidence), data collection approach (case study, qualitative research, archival research, survey‐based research, laboratory research, and field research), data analysis technique (descriptive statistics, various multivariate statistical techniques, and mathematical modeling), and operations topics (strategy, quality, and supply chain management). We also discuss directions for future empirical research in operations management. During 1992 to 2005, the articles based on empirical data have increased substantially from 30 to 50 percent of all articles published in POM. During 1992 to 1998, about three‐fourths of the empirical‐research‐based articles published in POM focused on the manufacturing industry, but recently the gap between the numbers of manufacturing‐ and service‐focused articles published in POM has almost disappeared. While a previous assessment of articles published in a range of operations management journals showed that almost all of the empirical articles were based on either surveys or case studies, our results indicate that POM has published articles that were based on a much wider and more diverse range of data collection approaches. Production and Operations Management has clearly established itself as a leading outlet for publishing empirical research in operations management.

Relationships between theory‐driven empirical research in operations management and other disciplines

Ž . Recently, operations management OM has observed a surge in field-based, empirical research, with concurrent emphasis on theory as a research driver. In this paper, theory is defined, and its crucial role in OM explained. This paper goes beyond simply stating that theory is required for ‘good’ research, and explains at a fundamental, intuitive level the critical importance of theory. The metaphor of theory as a ‘lens’ will be utilized to explain the role of theory in research and in the knowledge base. Because formal theorizing in other fields is generally more mature than in OM, because other fields have previously dealt with the role of theory, and because OM is small relative to other fields, it is prudent to examine the history of other disciplines for lessons they have learned. While OM has constructed ‘home-grown’ theories, such as the cumulaŽ . tive model Ferdows and De Meyer, 1990 , the Ž . customer contact model Chase and Tansik, 1983 , Ž and the process–product matrix Hayes and Wheel. wright, 1979 , OM lags disciplines such as sociology and economics in the creation of formal, researchoriented theoretical perspectives. Additionally, many of OM’s areas of study are interdisciplinary, requir-

Meta-analysis applied to operations management: Summarizing the results of empirical research

The writing of every new management study should include an enquiry into previous, related investigations. This approach helps to build up a comprehensive picture of the phenomena being considered. Researchers working in isolation tend to repeat past mistakes and rarely achieve much progress. Progress in management sciences comes from building on the efforts of those who have already worked on the subject. Research synthesis is therefore an important task in the theory building/theory testing process. In recent years, quantitative research synthesis (meta-analysis) has gained increasing acceptance. Meta-analysis offers a powerful set of tools for extracting quantitative information from a body of related research. It has already been applied in the fields of archaeology, astronomy, chemistry, education, geophysical science, psychology and public policy. However, it seems suitable even for management researchers since quantitative research is becoming more accepted in management disciplines. The aim of this study is to identify the conditions of applicability of quantitative methods in summarizing past research by drawing overall conclusions from many separate studies. Meta-analysis will be applied to a specific topic of operations management (OM): the link between JIT levers and manufacturing performance with particular reference to time performance. More specifically, an application of meta-analysis will be made with reference to two of the main steps towards the development of an empirically-tested theory: testing the adequacy of the measurement of variables and testing the linkages between the variables. The preliminary application of meta-analysis to OM has revealed distinct potential. However, in order for this potential to be fully exploited, a set of conditions should be established when publishing empirical research in operations management, in order to simplify the comparison of different studies which are related to the same issue. This recommendation is presented together with the final results of the present study.

A review and classification of empirical research in operations management

AbstractThe progress in the use of empirical research methods has been a topic of interest in the Operations Management (OM) area for the last 15 years. It is considered desirable to accompany traditional OM research, which develops and tests theories using mathematics, modelling, and simulation, with research that makes use of empirical data. The use of empirical research helps support the understanding of OM practices within industry. This paper expands and updates previous literature review studies dealing with empirical research in OM. The journal publications reviewed for this paper were published in the years 1986 to 1995. This is a review of the articles published in thirteen journals that are traditional outlets for OM research. These articles are classified and the results of these classifications presented. There has been an increase in the amount of published articles that make use of empirical research methods in the OM area, both in number and as a percentage of the total number of articles published in these journals.

A clarification on conceptual and methodological issues related to the Job Characteristics Model

AbstractWe provide a comment on ‘Comparing the effects of cellular and functional manufacturing on employees' perceptions and attitudes’ by Shafer, Tepper, Meredith, and Marsh (Journal of Operations Management, 1995, 12, 63–74). We identify and explicate three conceptual concerns related to their description of the Job Characteristics Model, as well as two methodological concerns with using the Job Diagnostic Survey to measure the five job characteristics and the Motivating Potential Score as an aggregate index of a job's ‘motivating potential’. We intend this explication to serve as a clarification and guidance for further conceptual and empirical research in operations management involving the Job Characteristics Model, the Job Diagnostic Survey, and the Motivating Potential Score. Our remarks are intended to be constructive and are offered in the spirit of scholarly advancement.