Modelling knowledge production performance of research centres with a focus on triple bottom line benchmarking

We demonstrate a portable process for developing a triple bottom line model to measure the knowledge production performance of individual research centres. For the first time, this study also empirically illustrates how a fully units-invariant model of Data Envelopment Analysis (DEA) can be used to measure the relative efficiency of research centres by capturing the interaction amongst a common set of multiple inputs and outputs. This study is particularly timely given the increasing transparency required by governments and industries that fund research activities. The process highlights the links between organisational objectives, desired outcomes and outputs while the emerging performance model represents an executive managerial view. This study brings consistency to current measures that often rely on ratios and univariate analyses that are not otherwise conducive to relative performance analysis

Dynamic network range-adjusted measure vs. dynamic network slacks-based measure

Workshop 2013 on Dynamic and Network DEA (January 29-30, 2013)We formulate weighted, dynamic network range-adjusted measure (D-NRAM) and dynamic network slacksbased measure (D-NSBM), run robustness tests and compare results. To the best of our knowledge, the current paper is the first to compare two weighted dynamic network DEA models and it also represents the first attempt at formulating D-NRAM. We illustrate our models using simulated data on residential aged care. Insight gained by running D-NRAM in parallel with D-NSBM includes (a) identical benchmark groups, (b) a substantially wider range of efficiency estimates under D-NRAM, and (c) evidence of inefficient DMU size bias. D-NRAM is also shown to have the additional desirable technical efficiency properties of translation-invariance and acceptance of data. Managerial implications are also briefly discussed.This workshop is supported by JSPS KAKENHI Grant Number 22310095 under the title “Theory and Applications of Dynamic DEA with Network Structure.

How to better identify the true managerial performance: State of the art using DEA

Our motivation is to detail a potential improvement on the three-stage analysis published by Fried et al. [Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 2002;17:157–74] that can distinguish true performers from those that may be advantaged by favourable environments or measurement errors. The method starts with data envelopment analysis (DEA), and continues with stochastic frontier analysis to explain the variation in organisational performance in terms of the operating environment, statistical noise and managerial efficiency. It concludes with DEA again using adjusted data to reveal a measure of performance based on management efficiency only. Our proposed contributions include (i) a comprehensive approach where total input and output slacks are identified simultaneously for non-radial inefficiencies before levelling the playing field, (ii) identifying percent adjustments attributable to the environment and statistical noise, and (iii) using a fully units-invariant DEA model

An empirical investigation of the influence of collaboration in Finance on article impact

We investigate the impact of collaborative research in academic Finance literature to find out whether and to what extent collaboration leads to higher impact articles (6,667 articles across 2001-2007 extracted from the Web of Science). Using the top 5 % as ranked by the 4-year citation counts following publication, we also follow related secondary research questions such as the relationships between article impact and author impact; collaboration and average author impact of an article; and, the nature of geographic collaboration. Key findings indicate: collaboration does lead to articles of higher impact but there is no significant marginal value for collaboration beyond three authors; high impact articles are not monopolized by high impact authors; collaboration and the average author impact of high-impact articles are positively associated, where collaborative articles have a higher mean author impact in comparison to single-author articles; and collaboration among the authors of high impact articles is mostly cross-institutional

Opening the black box of efficiency analysis: An illustration with UAE Banks

Standard data envelopment analysis (DEA) does not provide adequate detail to identify the specific sources of inefficiency embedded in interacting divisions of an organization. On the other hand, network DEA gives access to this underlying diagnostic information that would otherwise remain undiscovered. As a first study of its kind, the paper illustrates an application of non-oriented network slacks-based measure using simulated profit center data that, in turn, rely on actual aggregate data on domestic commercial banks in the United Arab Emirates (UAE). The study also contributes to a perennial research problem, namely, inability of the outside researcher to access internal data for developing or testing new methods. In addition to these contributions to the Operations Research literature, focusing on UAE contributes to banking literature because this rapidly expanding part of the Middle East seldom appears in frontier efficiency literature

Applications of data envelopment analysis in the service sector

The service sector holds substantial challenges for productivity analysis because most service delivery is often heterogeneous, simultaneous, intangible, and perishable. Nevertheless, the prospects for future studies are promising as we gently push the data envelopment analysis research envelope by using more innovative research designs that may include synergistic partnerships with other methods and disciplines, as well as delve deeper into the sub-DMU network of organizations. This chapter is dedicated to providing a selection of applications in the service sector with a focus on building a conceptual framework, research design, and interpreting results. Given the expanding share of the service sector in gross domestic products of many countries, the twenty-first century will continue to provide fertile grounds for research in the service sector

SELECTING INPUTS AND OUTPUTS IN DATA ENVELOPMENT ANALYSIS BY DESIGNING STATISTICAL EXPERIMENTS Hiroshi Morita Osaka University

Abstract Data envelopment analysis (DEA) is a data oriented, non-parametric method to evaluate relative efficiency based on pre-selected inputs and outputs. In some cases, the performance model is not well defined, so it is critical to select the appropriate inputs and outputs by other means. When we have many potential variables for evaluation, it is difficult to select inputs and outputs from a large number of possible combinations. We propose an input output selection method that uses diagonal layout experiments, which is a statistical approach to find an optimal combination. We demonstrate the proposed method using financial statement data from NIKKEI 500 index