Impact factor as a metric to assess journals where OM research is published

This paper investigates impact factor as a metric for ranking the quality of journal outlets for operations management (OM) research. We review all prior studies that assessed journal outlets for OM research and compare all previous OM journal quality rankings to rankings based on impact factors. We find that rankings based on impact factors that use data from different time periods are highly correlated and provide similar rankings of journals using either two-year or five-year assessment periods, either with or without self-citations. However, some individual journals have large rank changes using different impact factor specifications. We also find that OM journal rankings based on impact factors are only moderately correlated with journal quality rankings previously determined using other methods, and the agreement among these other methods in ranking the quality of OM journals is relatively modest. Thus, impact factor rankings alone are not a replacement for the assessment methods used in previous studies, but rather they evaluate OM journals from another perspective.     

A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations management research

Mediation as a theory testing approach has witnessed considerable adoption among Operations Management (OM) researchers. Although mediation-testing methods have evolved tremendously in the past decade, their dissemination in the OM field has not seen parallel growth. These advanced techniques facilitate the testing of existing and complex hypotheses in a more precise manner. With the intent of critically evaluating existing and alternative methods for conducting mediation analysis needed to support sophisticated empirical research, this paper first reviews OM studies that tested for mediation in the past eleven years (2002–2012) from top-tier OM journals. Four commonly used mediation approaches were identified. Based on principles of good theory building, type of mediation model, and properties of empirical data, we evaluate the existing methodologies and make recommendations on how to improve the rigor of OM mediation testing. Using published OM studies in top journals as examples, we then illustrate the relevance and advantages of these recommendations, as well as their ease of use. Furthermore, we empirically show that more robust and insightful results can be achieved by adopting these techniques, which in turn have the promise of leading to better theory building and testing in the field of operations management.     

Emergent clustering methods for empirical OM research

To date, the vast majority of cluster analysis applications in OM research have relied on traditional hierarchical (e.g., Ward's algorithm) and nonhierarchical (e.g., K-means algorithms) methods. Although these venerable methods should continue to be employed effectively in the OM literature, we also believe there is a significant opportunity to expand the scope of clustering methods to emergent techniques. We provide an overview of some alternative clustering procedures (including advantages and disadvantages), identify software programs for implementing them, and discuss the circumstances where they might be employed gainfully in OM research. The implementation of emergent clustering methods in the OM literature should enable researchers to offer implications for practice that might not have been uncovered with traditional methods.     

Renaissance of case research as a scientific method

Since the seminal article by Eisenhardt (1989), scholarly interest in case research has mushroomed in operations management and organization sciences. Volumes of methodological texts are matched with a massive amount of empirical research that seeks to apply and further develop case research as a scientific method. What is missing from this literature is a treatment of the methodological diversity of case research. In this paper, we seek to unveil this heterogeneity by describing three distinct methodological accounts of case study: theory generation, theory testing, and theory elaboration. Each approach has its own idiosyncrasies, in particular when it comes to the interplay between theory and empirics. A typical case research incorporates both existing theories and empirical data to varying degrees. In light of this heterogeneity, we re-interpret key aspects of extant contributions and discuss guidelines for future case research. We propose that ultimately, case research rigor is determined by attention to idiosyncrasy and transparency of reasoning. We conclude by arguing that we have witnessed in the past 25 years in organization research what amounts to the Renaissance of case research.     

Contingency research in operations management practices

As operations management (OM) best practices have become mature, research on practices has begun to shift its interest from the justification of the value of those practices to the understanding of the contextual conditions under which they are effective—OM practice contingency research (OM PCR). This article sets out to examine and critique the current state of OM PCR. We review OM PCR studies through the lens of the major theoretical view on contingencies, contingency theory, along a number of relevant dimensions: contingency variables, performance variables, measurement, research design and employed form of fit. In this process, we put forward a number of tasks that need to be accomplished in order to move OM PCR forward and develop more solid conceptual foundations in which to anchor rigorous research in this area. Finally, we reflect on the theoretical arguments that underlie OM PCR (which are based on the contingency approach) and identify its limitations in fully explaining the currently observed patterns of use of OM practices and associated performance outcomes. As a result, we propose that in order to increase our understanding of these patterns, OM scholars need to study in more depth the process of selection of OM best practices by organizations. Accordingly, we put forward a framework to underpin such research integrating contingency theory and other theoretical perspectives.     

Longitudinal LISREL model estimation from incomplete panel data using the EM algorithm and the Kalman smoother

Longitudinal data sets with the structure T (time points) × N (subjects) are often incomplete because of data missing for certain subjects at certain time points. The EM algorithm is applied in conjunction with the Kalman smoother for computing maximum likelihood estimates of longitudinal LISREL models from varying missing data patterns. The iterative procedure uses the LISREL program in the M-step and the Kalman smoother in the E-step. The application of the method is illustrated by simulating missing data on a data set from educational research.     

A framework for the identification of reusable processes

A significant challenge that faces IT management is that of aligning the IT infrastructure of an enterprise with its business goals and practices, also called business-IT alignment. A particular business-IT alignment approach, the foundation for execution approach, was well-accepted by practitioners due to a novel construct, called the operating model (OM). The OM supports business-IT alignment by directing the coherent and consistent design of business and IT components. Even though the OM is a popular construct, our previous research detected the need to enhance the OM, since the OM does not specify methods to identify opportunities for data sharing and process reuse in an enterprise. In this article, we address one of the identified deficiencies in the OM. We present a process reuse identification framework (PRIF) that could be used to enhance the OM in identifying process reuse opportunities in an enterprise. We applied design research to develop PRIF as an artefact, where the development process of PRIF was facilitated by means of the business-IT alignment model (BIAM). We demonstrate the use of the PRIF as well as report on the results of evaluating PRIF in terms of its usefulness and ease-of-use, using experimentation and a questionnaire.     

The relative importance of journals used in operations management research A citation analysis

Journal rankings are important for a variety of reasons, most importantly as the basis of academic tenure and promotion decisions. A common approach in other fields is the citations methodology which is used in this study to determine the relative importance of various operations management (OM) journals. The citations analysis noted the frequency that all other journals are cited in Decision Sciences, Journal of Operations Management, and Management Science during the period 1992 to 1994. Rankings are provided based on total citations, citations per article, and citations per words published. The study shows that the journals with the most importance to OM research are Management Science, Decisions Sciences and Operations Research. Other journals important to OM research are the Harvard Business Review, Journal of Operations Management, and IIE Transactions.     

Taking ‘Don’t Knows’ as Valid Responses: A Multiple Complete Random Imputation of Missing Data

Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts’ awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved scores. In many instances, however, non-response to a stimulus does not result from measurement problems that inhibit accurate surveying of empirical reality, but from the inapplicability of the survey question. In such cases, existing imputation techniques replace valid non-response with counterfactual estimates of a situation in which the stimulus is applicable to all respondents. This paper suggests an alternative imputation procedure for incomplete data for which no true score exists: multiple complete random imputation, which overcomes the biasing effects of missing data and allows analysts to model respondents’ valid ‘I don’t know’ answers.     

On the treatment of persons with disabilities in organizations: A review and research agenda

Human resource practitioners play a crucial role in promoting equitable treatment of persons with disabilities, and practitioner's decisions should be guided by solid evidence‐based research. We offer a systematic review of the empirical research on the treatment of persons with disabilities in organizations, using Stone and Colella's seminal theoretical model of the factors influencing the treatment of persons with disabilities in work organizations, to ask: What does the available research reveal about workplace treatment of persons with disabilities, and what remains understudied? Our review of 88 empirical studies from management, rehabilitation, psychology, and sociology research highlights seven gaps and limitations in extant research: (a) implicit definitions of workplace treatment; (b) neglect of national context variation; (c) missing differentiation between disability populations; (d) overreliance on available data sets; (e) predominance of single‐source, cross‐sectional data; (f) neglect of individual differences and identities in the presence of disability; and (g) lack of specificity on underlying stigma processes. To support the development of more inclusive workplaces, we recommend increased research collaborations between human resource researchers and practitioners on the study of specific disabilities and contexts, and efforts to define and expand notions of treatment to capture more nuanced outcomes.     

Across the great divide: a literature review of management accounting and operations management at the shop floor

Management accounting’s ability to provide relevant information in production environments has long been discussed in the fields of management accounting (MA) and operations management (OM). Researchers from each field play a major part not only in disseminating their research results, but also in channelling their perceptions of management accounting in production environments through journal publications. The thesis of this paper is that via an examination of the paradigms, theories, and methods in the fields of MA and OM our understanding of the prevailing assumptions about management accounting in production environments in the academic community can be enhanced. The review shows a divide between the fields where the field of OM is oriented towards problem-solving, and the field of MA is more theory oriented. The review points out that the understanding of practice is a divider between the fields, but it also suggests that incorporation of practicing production members into research is a promising path forward. The paper then concludes that OM problematizes management accounting in production environments as a starting point for their research agenda and that both fields portrayal of management accounting in production environments need to be nuanced. There is a need to challenge the research expectations and to accept unconventional research methods to enhance knowledge about management accounting in production environments.

     

The evolution of the intellectual structure of operations management—1980–2006: A citation/co-citation analysis

Citation analysis combined with a network analysis of co-citation data from three major operations management (OM) journals is used to reveal the evolution of the intellectual structure of the OM field between 1980 and 2006. This spans the entire time since the beginning of research journals specific to the field. Employing a bibliometric citation/co-citation analysis to investigate the foundations of the discipline enables a robust, quantitative approach to uncovering the evolution of research in OM. The study finds that the intellectual structure of the field made statistically significant changes between the 1980s, the 1990s, and the 2000s and evolved from a pre-occupation with narrow, tactical topics toward more strategic, macrotopics, including new research methods and techniques. A factor analysis identifies the 12 top knowledge groups in the field and how they change over the decades. Illustrations of the structure of the co-citations representing the field are generated from a spring-embedded algorithm that is an improvement over the standard multi-dimensional scaling (MDS) approach to illustrating the knowledge groups.     

Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling

In cross-national longitudinal studies it is often impossible to administer the same measurement instruments at the same occasions to all sample units in all participating countries. This quickly results in large quantities of missing data, due to (a) missing measurement instruments in some countries, (b) missing assessment waves within or across countries, (c) missing data for individual sample units. As compared to cross-sectional studies, the problem of missing values is further aggravated by the fact that missing values are always associated with different time intervals between repeated observations. In the past, this has often been dealt with by the use of phantom-variables, but this approach is limited to simple designs with few missing value patters. In the present paper we propose a new way to think of, and deal with, missing values in longitudinal studies. Instead of conceiving of a longitudinal study as a study with \(T\) discrete time points of which some are missing, we propose to conceive of a longitudinal study as a way to measure an underlying process that develops continuously over time, but is only observed at some selected discrete time points. This transforms the problem of missing values into a problem of unequal time intervals. After a quick introduction to the basic idea of continuous time modeling, we demonstrate how this approach provides a straightforward solution to missing measurement instruments in some countries, missing assessment waves within or across countries, and missing data for individual sample units.     

Methods for the Analysis of Explanatory Linear Regression Models with Missing Data Not at Random

Since the work of Little and Rubin (1987) not substantial advances in the analysisof explanatory regression models for incomplete data with missing not at randomhave been achieved, mainly due to the difficulty of verifying the randomness ofthe unknown data. In practice, the analysis of nonrandom missing data is donewith techniques designed for datasets with random or completely random missingdata, as complete case analysis, mean imputation, regression imputation, maximumlikelihood or multiple imputation. However, the data conditions required to minimizethe bias derived from an incorrect analysis have not been fully determined. In thepresent work, several Monte Carlo simulations have been carried out to establishthe best strategy of analysis for random missing data applicable in datasets withnonrandom missing data. The factors involved in simulations are sample size,percentage of missing data, predictive power of the imputation model and existenceof interaction between predictors. The results show that the smallest bias is obtainedwith maximum likelihood and multiple imputation techniques, although with lowpercentages of missing data, absence of interaction and high predictive power ofthe imputation model (frequent data structures in research on child and adolescentpsychopathology) acceptable results are obtained with the simplest regression imputation.     

Understanding project champions’ ability to gain intra-organizational commitment for environmental projects

A key enabler of environmental projects is the ability of the project champion to gain commitment to the project from other stakeholders in his or her organization. This paper develops a model of commitment-gaining success that is based on intra-organizational influence theory. The model also includes project payback, customer pressure, government regulation, top management support and the project champion's position in the organizational hierarchy. The model was tested using survey data from 241 environmental professionals describing their attempts to gain the buy-in of purchasing managers, operations managers, industrial engineers and others for environmental projects. The results (obtained from hierarchical regression analysis) show that intra-organizational commitment is positively associated with the project champion's influence behavior—in particular, the champion's use of three influence tactics (inspirational appeals, consultation and rational persuasion) and avoidance of a fourth tactic (ingratiation). Commitment is also positively associated with project payback and with top management support for the environment and negatively associated with environmental regulation. The paper contributes to the OM knowledge base on environmental project implementation by bringing new theory to bear on the topic, by focusing on individual-level, rather than organization-level, variables and by taking a confirmatory, large sample approach which complements extant exploratory research. In addition, the paper contributes to the OM field by evaluating various antecedents to cross-functional integration. The results also provide specific guidance to those who champion environmental projects within their companies.     

Creating the competitive edge: A new relationship between operations management and industrial policy

Policy interventions by governments to alter the structure of economic activity have either been dismissed or ignored by operations management (OM) scholars. However, in recent years, such ‘industrial policy’ measures have gained increasing support in developed economies, particularly in relation to manufacturing. This paper argues that contemporary manufacturing in high-cost economies is rooted in technological innovation. As such, it can be enhanced by industrial policy interventions that prevent systems failures in the process of turning technological innovation into commercially viable products. In particular, we argue that this can be achieved by establishing non-firm, intermediate research organizations and by other measures to change the institutional architecture of an economy. We disagree with claims in earlier OM literature that industrial policy is all but irrelevant to manufacturing firms and to OM. Instead, we argue that OM must broaden its conceptual scope so as to encompass active engagement with non-firm network participants such as government-supported intermediate research organizations, and that, as well as learning to be effective users of industrial policy, OM practitioners and academics should engage actively in the development of industrial policy. In this way, high-value, high-productivity manufacturing can be viable in high-cost economic environments.     

A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics, 21(5), 495–509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.     

Demand chain management: an integrative approach in automotive retailing

Recent reviews of the ‘operations management’ (OM) research literature have shown a surprising little shift towards empirical methodologies from traditional modelling solution methods. In addition, there is generally a lack of empirical testing and validation of manufacturing strategies. Coupled to this fact is that empirical work is rarely carried out in an inter-disciplinary manner, with the complete combination of OM, marketing, strategy, lean thinking and accounting approaches particularly poorly researched. Fully cross-functional integrative empirical research is required to help support the understanding of the applicability of OM practices within industry. This paper explores such an integration of approaches developed within the ‘lean thinking, strategic cost management, marketing and policy deployment areas’. In order to investigate the approach a single automotive retailer is used as an instrumental case. The results of the study show that the new approach stands up well as an integrative approach that can prove highly beneficial results. However, the pilot work also identified a number of limitations that are the basis of further refinement and testing of the method.     

Inverse probability weighted estimation for general missing data problems

I study inverse probability weighted M-estimation under a general missing data scheme. Examples include M-estimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect in the linear exponential family, and variable probability sampling with observed retention frequencies. I extend an important result known to hold in special cases: estimating the selection probabilities is generally more efficient than if the known selection probabilities could be used in estimation. For the treatment effect case, the setup allows a general characterization of a “double robustness” result due to Scharfstein et al. [1999. Rejoinder. Journal of the American Statistical Association 94, 1135–1146].