Knowledge dissemination in operations management: Published perceptions versus academic reality

The channels for knowledge generation and dissemination in the business disciplines are many: presenting research at conferences, writing books, distributing working papers, offering insights in society newsletters, giving invited talks, publishing studies in academic journals, and many other venues, including even blogs and perhaps Facebook^®. But the most important venue is probably published research in “top-level” academic journals. In the discipline of Operations Management, many studies and lists have been published that attempt to determine which of these journals are supposedly the “top” according to either citation analyses, the opinion of recognized experts, author affiliations, bibliometric studies, and other approaches. These lists may then, in turn, be used in different degrees to evaluate research. However, what really counts is what the academic institutions actually use for guidance in evaluating faculty research. Based on a new source of ranking data from AACSB-accredited schools, we compare published journal-ranking studies against that of academe to determine the degree to which the studies reflect academic “reality”. We present rankings of OM journals based on this new source of data and on an aggregate of the stream of published studies, and evaluate their consistency.     

Common benchmarking and ranking of units with DEA

This paper develops a common framework for benchmarking and ranking units with DEA. In many DEA applications, decision making units (DMUs) experience similar circumstances, so benchmarking analyses in those situations should identify common best practices in their management plans. We propose a DEA-based approach for the benchmarking to be used when there is no need (nor wish) to allow for individual circumstances of the DMUs. This approach identifies a common best practice frontier as the facet of the DEA efficient frontier spanned by the technically efficient DMUs in a common reference group. The common reference group is selected as that which provides the closest targets. A model is developed which allows us to deal not only with the setting of targets but also with the measurement of efficiency, because we can define efficiency scores of the DMUs by using the common set of weights (CSW) it provides. Since these weights are common to all the DMUs, the resulting efficiency scores can be used to derive a ranking of units. We discuss the existence of alternative optimal solutions for the CSW and find the range of possible rankings for each DMU which would result from considering all these alternate optima. These ranking ranges allow us to gain insight into the robustness of the rankings.     

超效率DEA模型的模糊扩展

在输入、输出数据为模糊数而效率值为准确值的模糊DEA模型中,会出现有两个以上有效单元,从而无法对所有决策单元进行全排序的情形。本文利用模糊数的基于α-截集的比较规则,建立了模糊条件下的超效率DEA模型,有效地解决了模糊决策单元的全排序问题。文末给出了一个实例。     

Stochastic Judgments in the AHP: The Measurement of Rank Reversal Probabilities

This paper presents a methodology for analyzing Analytic Hierarchy Process (AHP) rankings if the pairwise preference judgments are uncertain (stochastic). If the relative preference statements are represented by judgment intervals, rather than single values, then the rankings resulting from a traditional (deterministic) AHP analysis based on single judgment values may be reversed, and therefore incorrect. In the presence of stochastic judgments, the traditional AHP rankings may be stable or unstable, depending on the nature of the uncertainty. We develop multivariate statistical techniques to obtain both point estimates and confidence intervals of the rank reversal probabilities, and show how simulation experiments can be used as an effective and accurate tool for analyzing the stability of the preference rankings under uncertainty. If the rank reversal probability is low, then the rankings are stable and the decision maker can be confident that the AHP ranking is correct. However, if the likelihood of rank reversal is high, then the decision maker should interpret the AHP rankings cautiously, as there is a subtantial probability that these rankings are incorrect. High rank reversal probabilities indicate a need for exploring alternative problem formulations and methods of analysis. The information about the extent to which the ranking of the alternatives is sensitive to the stochastic nature of the pairwise judgments should be valuable information into the decision-making process, much like variability and confidence intervals are crucial tools for statistical inference. We provide simulation experiments and numerical examples to evaluate our method. Our analysis of rank reversal due to stochastic judgments is not related to previous research on rank reversal that focuses on mathematical properties inherent to the AHP methodology, for instance, the occurrence of rank reversal if a new alternative is added or an existing one is deleted.     

The use of qualitative case studies in top business and management journals: A quantitative analysis of recent patterns

The use of case studies as qualitative research strategy in social sciences seems to have increased recently, but there are no studies that empirically verify such claim. By explicitly focusing on the field of business and management studies, we aim to investigate the extent of publication and the main features of qualitative case studies published in the 20 highest impact factor business and management journals. The paper discusses the correlation between a journal's ranking and the extent of case studies it published, and between selected features of case studies (e.g. research purpose, design and data sources). Moreover, we shed light on how the identified features of a case study impact its probability of being published.Methodologically, we analyse by means of correlation and regression statistics, as well as clustering techniques a total of 19 features in the 352 qualitative case studies published between 2002 and 2011 in our sample of top business and management journals.     

Journal Rankings in Business and Management and the 2001 Research Assessment Exercise in the UK

The public availability of detailed data from the 2001 Research Assessment Exercise in the UK allows an analysis of the publications cited in submissions to the Business and Management panel. Eighty per cent of the 9,942 publications submitted were journal articles. Submissions to the RAE can be scored in terms of the number of citations they make to journals that appear on various lists, such as the Financial Times list. The concentration of articles in a minority of journals, with 50% of all citations to just 126 journals, means that a core list of business and management journals can be compiled. The core list presented contains 562 journals out of the 1582 journal titles that were cited in Business and Management submissions. It includes all journals with more than two citations overall at least one citation in a 5^*, 5 or 4 rated submission. It also includes all journals cited in the RAE from Starbuck's ranked lists of journals and the Financial Times list.     

基于混合DEA模型的医药行业上市公司经营效率相对有效性评价

DEA模型作为一种客观的多指标决策方法应用十分广泛.但目前使用频率较高的两种通用模型C²R模型和C²GS²模型,在单独使用时都有比较大的局限性,评判结果往往无法令人满意;针对这一问题,本文提出构建混合DEA模型,并应用混合DEA模型对医药上市公司的经营效率相对有效性评价进行实证分析,阐述了混合DEA模型的使用方法,验证了评判方法的可行性和科学性.     

DEA game cross-efficiency approach to Olympic rankings

A number of studies have used data envelopment analysis (DEA) to evaluate the performance of the countries in Olympic games. While competition exists among the countries in Olympic games/rankings, all these DEA studies do not model competition among peer decision making units (DMUs) or countries. These DEA studies find a set of weights/multipliers that keep the efficiency scores of all DMUs at or below unity. Although cross efficiency goes a further step by providing an efficiency measure in terms of the best multiplier bundle for the unit and all the other DMUs, it is not always unique. This paper presents a new and modified DEA game cross-efficiency model where each DMU is viewed as a competitor via non-cooperative game. For each competing DMU, a multiplier bundle is determined that optimizes the efficiency score for that DMU, with the additional constraint that the resulting score should be at or above that DMU 's estimated best performance. The problem, of course, arises that we will not know this best performance score for the DMU under evaluation until the best performances of all other DMUs are known. To combat this “chicken and egg” phenomenon, an iterative approach leading to the Nash equilibrium is presented. The current paper provides a modified variable returns to scale (VRS) model that yields non-negative cross-efficiency scores. The approach is applied to the last six Summer Olympic Games. Our results may indicate that our game cross-efficiency model implicitly incorporates the relative importance of gold, silver and bronze medals without the need for specifying the exact assurance regions.     

基于交叉评价策略的DEA全局协调相对效率排序模型

在DEA(数据包络分析)研究领域,建构在交叉效率概念基础上的现有决策单元排序方法仅以定义的方式给出了用于决策单元排序的交叉效率评价值。对于这种方法构建方式,分别基于管理学的效率概念和多属性决策理论,分析指出其中的交叉效率评价值从本质上讲既与效率的管理学概念不符,也与决策单元的优劣不存在理性逻辑联系。为克服现有决策单元排序方法所存在的上述问题,基于交叉评价策略和效率的管理学概念内涵给出了DEA全局协调相对效率的新概念,在此基础上利用优化理论给出了可以用于决策单元优劣排序的DEA全局协调相对效率测度模型,并通过理论分析和数值案例验证解释了该模型相对于现有决策单元排序方法所拥有的比较优势。     

Research evaluation and journal quality weights

Research evaluations based on quality weighted publication output are often criticized on account of the employed journal quality weights. This study shows that evaluations of entire research organizations are very robust with respect to the choice of readily available weighting schemes. We document this robustness by applying rather different weighting schemes to otherwise identical rankings. Our unit of analysis consists of German, Austrian and Swiss university departments in business administration and economics.     

RANKINGS OF ACADEMIC JOURNALS AND INSTITUTIONS IN ECONOMICS

We conducted a worldwide ranking of academic institutions that produce research in a list of thirty top research journals in economics. We also computed journal rankings for the same period and hence we do not rely on weights that were computed for research carried out in earlier periods. The United States is clearly the dominant force in the top‐fifty group, but European academic institutions are well represented in the group of the top 200 universities worldwide as are universities from Asia and the Far East in particular. (JEL: A14, A10)     

Behavior-based analysis of knowledge dissemination channels in operations management

One essential requirement for the development and vitality of a discipline is a network of channels for knowledge dissemination. These channels, such as scholarly journals, furnish not only a means for knowledge sharing, but also for knowledge generation by the discipline's community of researchers. In the field of operations management (OM), there have been several studies that have sought to rank journals relevant to OM research, using opinion surveys, citation analyses, and author affiliations. However, each of these methods has some limitations. This paper adopts a new approach for discerning journal publication patterns in the OM field. It is based on an examination of the actual publishing behaviors of all full-time, tenured OM researchers at a sizable set of leading research universities in the US. This behavior-based methodology provides three metrics that individually, and in tandem, give a basis for rating publication outlets for OM research in terms of their relative importance. The ratings can be used by scholars and administrators to assist in monitoring, disseminating, and evaluating OM research outlets.     

Theoretical and Interpretation Challenges to Using the Author Affiliation Index Method to Rank Journals

We formally review the Author Affiliation Index (AAI) method as originally conceived by David Harless and Robert J. Reilly from the Economics Department at the Virginia Commonwealth University School of Business and as subsequently developed and interpreted by Gorman and Kanet in their 2005 article. Through this formal review, we first highlight and discuss two important informational inputs that can impact the stability of the AAI scores for journals in any given set of to‐be‐evaluated journals. We then identify and challenge interpretations related to these scores (one theoretical, one statistical) offered by Gorman and Kanet that result in misleading conclusions about journal quality and that may potentially motivate inappropriate editorial behavior. For important professional decisions of hiring, performance evaluation, promotion, and tenure, we conclude by cautioning against sole reliance on the AAI method for ranking journals and against exclusive interpretation of the score computed via the AAI method as an indicator of journal quality.     

A modified DEA cross efficiency method with negative data and its application in supplier selection

Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.

     

Ranking efficient units in DEA

Super-efficiency data envelopment analysis (DEA) model can be used in ranking the performance of efficient decision making units (DMUs). Because of the infeasibility problem associated with the super-efficiency DEA model, ranking has been restricted to the model where constant returns to scale and proportional changes in all inputs or all outputs are assumed. In fact, when super-efficiency is used as an efficiency stability measure, infeasibility means the highest super-efficiency. However, if super-efficiency is interpreted as input saving or output surplus achieved by a specific efficient DMU, infeasibility does not necessarily mean the highest super-efficiency. In order to obtain a complete ranking of efficient DMUs when the two assumptions are relaxed, a modified super-efficiency DEA model is proposed to overcome the infeasibility problem and to correctly capture the super-efficiency represented by the input saving or the output surplus. The current paper suggests using both input- and output-oriented super-efficiency models to characterize the super-efficiency when infeasibility occurs. As a result, we can rank the efficient DMUs if infeasibility occurs. The approach is applied to 20 largest Japanese companies and 15 US cities, respectively.     

Developing a supplier selection system through integrating fuzzy AHP and fuzzy DEA: a case study on an auto lighting system company in Taiwan

Supplier selection plays a very important role in supply chain management. This study intends to develop a novel performance evaluation method, which integrates both fuzzy analytical hierarchy process (AHP) method and fuzzy data envelopment analysis (DEA) for assisting organisations to make the supplier selection decision. Fuzzy AHP method is first applied to find the indicators’ weights through expert questionnaire survey. Then, these weights are integrated with fuzzy DEA. We use α -cut set and extension principle of fuzzy set theory to simplify the fuzzy DEA as a pair of traditional DEA model. Finally, fuzzy ranking using maximising and minimising set method is able to rank the evaluation samples. A case study on an internationally well-known auto lighting OEM company shows that the proposed method is very suitable for practical applications.     

What Influences Business Academics’ Use of the Association of Business Schools (ABS) List? Evidence From a Survey of UK Academics

The development of the Association of Business Schools (ABS) list in 2007 and its rapid adoption by UK business schools has had a profound effect on the nature of business and management academics’ ways of working. Using a large‐scale survey of UK business academics, we assess the extent to which individuals use the Academic Journal Guide (AJG/ABS) list in their day‐to‐day professional activities. In particular, we explore how their perceptions of the list, the academic influence of their research, academic rank and organizational context drive the varied use. Building on prior research on the importance of univalent attitudes in predicting behaviour, we find those who have either strong positive or negative views of the list are more extensive users than those who are ambivalent. We also find that the extent of use of the AJG/ABS list is greatest among those academics who have lower academic influence, in the middle or junior ranks within business schools and in middle and low‐status universities. We explore the implications of these findings for the value of journal rankings and for the management of business schools.     

Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method

Benchmarking of electricity distribution utilities has been widely used as a means to contribute for the adoption or reinforcement of enhanced competitiveness and innovation practices to optimize costs, increase customer satisfaction, improve corporate image and maximize profits. The purpose of this paper is to present a benchmarking study for the maintenance and outage repair activity carried out by a Portuguese electricity distribution company, EDP Distribuição (EDP-D), using the value-based DEA method, which builds on links between data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA). This study illustrates the impact of the incorporation of managerial preferences in the classification and ranking of 40 network areas served by EDP-D, confronting the results with a previous study based on a BCC DEA model. In order to deal with the underlying uncertainty, the value-based DEA method for performance evaluation is adapted to include the concept of super-efficiency. Besides identifying best practices, sources of inefficiency, gaps relatively to best practices and opportunities for improvement, this analysis supports the introduction of corrective measures and informs decisions about future goals.