Sensitivity analysis of network DEA illustrated in branch banking

Users of data envelopment analysis (DEA) often presume efficiency estimates to be robust. While traditional DEA has been exposed to various sensitivity studies, network DEA (NDEA) has so far escaped similar scrutiny. Thus, there is a need to investigate the sensitivity of NDEA, further compounded by the recent attention it has been receiving in literature. NDEA captures the underlying performance information found in a firm?s interacting divisions or sub-processes that would otherwise remain unknown. Furthermore, network efficiency estimates that account for divisional interactions are more representative of a dynamic business. Following various data perturbations overall findings indicate positive and significant rank correlations when new results are compared against baseline results - suggesting resilience. Key findings show that, (a) as in traditional DEA, greater sample size brings greater discrimination, (b) removing a relevant input improves discrimination, (c) introducing an extraneous input leads to a moderate loss of discrimination, (d) simultaneously adjusting data in opposite directions for inefficient versus efficient branches shows a mostly stable NDEA, (e) swapping divisional weights produces a substantial drop in discrimination, (f) stacking perturbations has the greatest impact on efficiency estimates with substantial loss of discrimination, and (g) layering suggests that the core inefficient cohort is resilient against omission of benchmark branches. Various managerial implications that follow from empirical findings are discussed in conclusions.

Sensitivity of operational and environmental benchmarks of retail stores to decision-makers' preferences through Data Envelopment Analysis

Within the framework of multi-criteria decision analysis (MCDA), weighting methods are typically used to capture decision-makers' preferences. In this regard, the increasing use of the combined LCA (Life Cycle Assessment) + DEA (Data Envelopment Analysis) methodology as an MCDA tool requires an in-depth analysis of how the preferences of decision-makers could affect the outcomes of LCA + DEA studies. This work revisits a case study of 30 retail stores/supply chains located in Spain by applying alternative weighted DEA approaches to evaluate the influence of decision-makers' preferences (weights) on the final outcomes, with a focus on efficiency scores and operational and environmental benchmarks. The ultimate goal is to effectively capture the view of stakeholders when applying LCA + DEA for the sound, sustainability-oriented management of multiple similar entities. Different weight vectors are separately applied to three types of DEA elements: operational inputs, time terms, and divisions. Besides, preferences from three alternative standpoints are considered: company manager through direct rating, and environmental policy-maker and local community through AHP (analytic hierarchy process). A significant influence on efficiency scores and sustainability benchmarks was found when weighting decision-makers' preferences on operational inputs. Additionally, a moderate influence was observed when weighting divisions according to a policy-maker or local community perspective. Although the results are case-specific, they lead to the general recommendation to enrich LCA + DEA studies by following not only an equal-weight approach but also approaches that include the preferences of the stakeholders effectively involved in the study.publishe

Modelling museum efficiency in producing inter-reliant outputs

The aim of this work was to evaluate the performance of a homogeneous state-run network of museums. Nonparametric models are used to measure relative efficiency in these institutions, and we employ a complex production function embracing a number of inputs and outputs adapted to the various functions which museums fulfil: preservation, research, communication, and exhibition. Our approach considers that managers drive certain outputs, but that others escape their control since they are co-produced by visitors and determined by demand conditions and external factors. Based on this, a network two-stage data envelopment analysis approach is applied to evaluate museums’ overall performance and to distinguish between efficiency in two stages: internal management and external outcomes. The low levels of performance and gaps in the scores from the first to the second stage suggest there are external factors that might determine museum performance. We therefore apply truncated regression models to analyse how and how much certain environmental variables might shape levels of museum efficiency. In this case, we consider indicators such as accessibility, tourism capacity, cultural appeal, museum age and the institutional management model. The application is performed on a sample taken from a Spanish state-run network of museums. Results show that, in general, good levels of efficiency in terms of management do not guarantee success when attracting visitors, and there seems to be a trade-off between the two goals. Variables such as tourism capacity and heritage endowments in the surrounding area, as well as the museum’s management model, may determine museums’ efficiency levels. The research findings may prove useful for running these cultural institutions and for those responsible for public resource allocation in cultural policies as well as for scholars, who may find a fresh approach for modelling museum efficiency and for discussing drivers of museum management success.This research was financed by the Regional Ministry of Education of the Regional Government of Castilla y León (Spain) (Project Ref. VA012G19

Homogeneity and best practice analyses in hospital performance management: an analytical framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals

Three essays on behavioural biases of mutual fund managers: overconfidence, disposition effect and tournaments

Sweepstakes: A network DEA approach to mutual fund tournamentsExtended abstract en españolUn enfoque Network DEA para los torneos de fondos de inversión1. IntroducciónEl afán por obtener rentabilidad de los inversores en fondos de inversión es un fenómeno empírico bien documentado. De hecho, la investigación ha demostrado que los inversores tienden a asignar capital basándose en el rendimiento pasado de los fondos de inversión. Está bien establecido que un rendimiento relativo superior de los fondos de inversión se asocia con mayores entradas de dinero posteriores (Ben-David et al., 2022; Berk & Green, 2004; Ferreira et al., 2012; Sirri & Tufano, 1998). Por este motivo, el importante crecimiento experimentado por el sector de los fondos de inversión en las últimas décadas ha agudizado la competencia entre los gestores de fondos de inversión por las entradas de dinero y las comisiones basadas en los activos. La relación entre el rendimiento de los fondos de inversión y la posterior actitud de los gestores hacia el riesgo ha recibido una atención primordial en la literatura internacional. Varios estudios han documentado que los gestores de fondos de inversión modifican activamente el nivel de riesgo de sus carteras en función de su rendimiento relativo en el pasado. Algunos trabajos fundamentales que aportan pruebas de ello son Brown et al. (1996), Busse (2001), Chevalier & Ellison (1997) y Huang et al. (2011).En su investigación seminal, Brown et al. (1996) llegaron a la conclusión de que los gestores perdedores a medio plazo, al no tener mucho más que perder, apostarán y aumentarán la volatilidad de su cartera de fondos, mientras que los ganadores a medio año intentarán fijar su posición y jugar sobre seguro. Tras este estudio, varios autores llegan a una conclusión similar (Acker & Duck, 2006; Goriaev et al., 2005; Schwarz, 2012).Este comportamiento de torneo de los gestores de fondos se ve reforzado por la relación convexa entre el rendimiento previo y los flujos de dinero: Mientras que un porcentaje desproporcionado de las entradas totales se dedica a los fondos con buenos resultados, los inversores no retiran el dinero de los fondos de inversión con malos resultados en la misma proporción (Chevalier y Ellison, 1997; Gruber, 1996; Huang et al., 2007; Sirri y Tufano, 1998). Además, los gestores de fondos de inversión tienen otras preocupaciones que podrían aumentar su motivación para participar en torneos anuales: proteger su empleo (Kempf et al., 2009; Khorana, 1996; Qiu, 2003), ganar un salario más alto (Farnsworth & Taylor, 2006; Kempf et al., 2009) o labrarse una reputación entre sus colegas (Qiu, 2003).Sin embargo, estudios empíricos han revelado resultados contradictorios con respecto a la expectativa de que los perdedores apuestan mientras que los ganadores indexan. Existen pruebas en la literatura que apoyan la noción de que los ganadores son más propensos a apostar (Busse, 2001; Chevalier & Ellison, 1997; Qiu, 2003; Sheng et al., 2019). En lugar de ver estos hallazgos como contradictorios, podría haber matices que descubrir en la teoría del torneo que ha sido ampliamente estudiada tanto con técnicas paramétricas como no paramétricas. Nuestro enfoque en red pretende captar la dinámica real del torneo sin que exista ninguna forma funcional preestablecida entre los principales impulsores del comportamiento del torneo. Para analizar el torneo, dividimos el comportamiento del torneo en tres etapas: en primer lugar, ¿con qué eficiencia reaccionan los gestores de fondos de inversión a su rendimiento pasado en términos de riesgo de cartera? En segundo lugar, ¿con qué eficacia repercuten estos cambios de riesgo en su rendimiento posterior? Y, por último, ¿con qué eficacia atraen estos cambios de rendimiento entradas de dinero a los fondos? Para analizar mejor estas interacciones entre torneos, empleamos un Análisis Envolvente de Datos (DEA) en red. Dada la complejidad de la modelización de las finanzas comportamentales, el uso de modelos DEA en red, que no requieren el establecimiento a priori de formas funcionales entre los factores explicativos, podría ser especialmente útil en este ámbito. Por este motivo, resulta muy adecuado para modelizar patrones de comportamiento complejos, como el comportamiento en los torneos. El modelo de red de este estudio nos permite dividir esta interacción global en procesos individuales y así evaluar mejor cada etapa. Como resume Kao (2014), un sistema global puede considerarse eficiente, aunque sus procesos individuales no lo sean, en realidad. En cuanto al tema que nos ocupa, muchos modelos de torneos se centran únicamente en la reacción de los fondos de inversión a las clasificaciones de rendimiento anteriores y las consecuencias de rendimiento posteriores, pero omiten las posibles consecuencias en los flujos de dinero posteriores. Nuestro modelo supera esta limitación adoptando un enfoque global para analizar el sistema.Que sepamos, este estudio es el primero que aplica una DEA en red para evaluar el comportamiento de los torneos en el sector de los fondos de inversión. La presente investigación llena el vacío existente en la literatura sobre finanzas conductuales utilizando un modelo DEA en red para proporcionar información sobre los componentes secuenciales y dinámicos del comportamiento de los torneos. En este estudio, el objetivo principal es analizar la interacción entre la reacción al torneo, su recompensa en términos de rendimiento y la recompensa potencial en forma de entradas. 2. Datos y metodologíaLos datos primarios utilizados en este estudio se obtienen de la Comisión Nacional del Mercado de Valores (CNMV). Nuestra base de datos inicial incluye los fondos abiertos domiciliados en España que estuvieron en funcionamiento durante el periodo de estudio (enero de 2010 a diciembre de 2015). Este periodo muestral abarca los años con mayores salidas de dinero de la industria de fondos española en las dos décadas anteriores a 2012, junto a una significativa y fuerte recuperación de las entradas de dinero en 2014-2015 (Inverco, 2016). Esto da lugar a contextos de gestión extremadamente diferentes para identificar las prácticas del torneo a través de nuestro modelo propuesto. La base de datos inicial comprende 551 fondos. En total, se descartan 42 fondos indexados dado que no son de gestión activa y solo los fondos de gestión activa cumplirían los requisitos para el análisis del comportamiento de los torneos. Nuestro análisis se centra en las dos principales categorías de inversión de la industria española de fondos: Fondos de Renta Variable Euro y Renta Variable Nacional, que representan un total de 184 fondos. Obtuvimos datos sobre rendimientos diarios, activos netos totales (TNA) mensuales e informes trimestrales de participaciones en cartera.Finalmente, también excluimos un total de 35 fondos de esta simple porque la información reportada no cumple totalmente con la disponibilidad de datos requerida por nuestro modelo (por ejemplo, fondos terminados antes del 31 de diciembre o fondos que no reportan flujos de dinero posteriores para el primer trimestre porque fueron terminados antes del 31 de marzo). Con el fin de obtener resultados fiables para el análisis del torneo, exigimos que los fondos incluidos en un año determinado en el estudio existan en enero y sobrevivan al menos hasta marzo del año siguiente, cuando se computan los flujos. Nuestra muestra final consta de un total de 149 fondos de renta variable distintos y un total acumulado de 624 observaciones de años de fondos.De acuerdo con la revisión de los modelos DEA en red en Kao (2014), la Figura 1 corresponde a una ampliación de una estructura de red básica de dos etapas a una estructura de red básica de tres etapas. Nuestra estructura de red también incluye un componente dinámico y las distintas variables del modelo corresponden a puntos secuenciales en el tiempo para reflejar el comportamiento dinámico de los torneos de fondos de inversión. El uso de cuatro variables intermedias tanto como salidas de la Etapa de Reacción como entradas de la Etapa de Recompensa podría plantear problemas relacionados con la maldición de la dimensionalidad en nuestra estructura de tres etapas, por lo que debe prestarse especial atención a la convención DEA según la cual el número mínimo de unidades de decisión analizadas, en este caso los fondos de inversión, debe ser superior a tres veces el número de variables (Coelli et al., 2005).En la Etapa de Reacción, el fondo de inversión j reacciona a su clasificación de rendimiento en el periodo anterior, desde el mes t-6 hasta el mes t, modificando su nivel de riesgo a través de tres mecanismos diferentes: 1) el porcentaje de la cartera asignado a activos de renta variable como representante del activo más arriesgado, 2) la beta de la cartera como representante del riesgo sistemático, y 3) la concentración de la cartera como representante del riesgo idiosincrático. Esta cronología es coherente con el trabajo seminal de Brown et al. (1996) y estudios posteriores como Busse (2001) y Goriaev et al. (2005)), por citar algunos. En la Etapa de Recompensa, nuestro modelo evalúa la eficiencia de la gestión activa del riesgo. Esta eficiencia se evalúa en términos del impacto de la respuesta al torneo en las clasificaciones de rendimiento posteriores. Por último, en la fase de retribución, nuestro modelo va más allá y evalúa hasta qué punto el impacto del comportamiento en los torneos ha sido visible en términos de flujos monetarios. La literatura anterior ha aportado numerosas pruebas del fenómeno "el ganador se lo lleva todo", en el que los fondos ganadores captan una parte desproporcionada de las entradas totales (Chevalier & Ellison, 1997; Gruber, 1996; Huang et al., 2007; Qiu, 2003; Sirri & Tufano, 1998). Figura 1. Representación del modelo DEA en red.3. Resultados y ConclusionesEste estudio proporciona un modelo de torneo más matizado para el sector de los fondos de inversión y analiza la eficacia con la que los gestores reaccionan a sus clasificaciones provisionales de rentabilidad, la eficacia con la que modifican su cartera para mejorar sus clasificaciones de rentabilidad a final de año y, por último, la eficacia con la que los inversores recompensan estos cambios en las clasificaciones de rentabilidad a través de los flujos hacia el fondo en el trimestre siguiente. Hasta donde sabemos, este estudio es el primero que emplea el Análisis Envolvente de Datos en red (DEA) para modelizar la dinámica de comportamiento en el sector de los fondos de inversión.La aplicación de nuestro modelo a un mercado real arroja resultados empíricos que corroboran nuestras hipótesis iniciales. Nuestros resultados confirman lo complicado que resulta para los gestores de fondos aplicar una estrategia capaz de mejorar eficientemente sus resultados de fin de año en relación con los de sus homólogos. De hecho, los gestores de fondos pueden adoptar una amplia gama de estrategias y de nuestros resultados se desprende que la Etapa de Reacción no está correlacionada con la Etapa de Recompensa. Esto significa que la modificación eficaz de la exposición a la renta variable, la beta y la concentración de la cartera como resultado de los rangos de rentabilidad provisionales no está correlacionada de forma significativa con los flujos posteriores hacia el fondo. En consonancia con la bibliografía sobre flujos, el grado en que los gestores de fondos mejoran su clasificación de rentabilidad modificando la exposición a la renta variable, la volatilidad y la concentración de su cartera es un factor determinante de su capacidad para atraer flujos en el trimestre siguiente. Así pues, el éxito en la Etapa de Retribución, mejorando con éxito el rendimiento a final de año, es determinante en los resultados finales del torneo. Estas conclusiones refuerzan la validez del modelo que proponemos en este estudio. Nuestros resultados son robustos incluso cuando empleamos especificaciones de variables alternativas. Por último, no encontramos persistencia en la eficiencia de los torneos en las fases individuales ni tampoco en general. Nuestros resultados apoyan la idea de que seguir una estrategia de torneo persistente y sistemáticamente eficiente es difícil y complejo.<br /

Efficiency analysis of maintenance and outage repair in electricity distribution

For several years electricity distribution companies have been using efficiency and productivity analysis in the form of data envelopment analysis and stochastic frontier analysis to analyse their operations. This reflects both market forces and responses to regulatory incentives. However we show that there is a significant difference in purpose and implementation between public regulatory benchmarking and internal company benchmarking. In this paper we use a variety of data envelopment analysis models to examine data on maintenance and outage repair on the electricity distribution system during 2004 -2005 in Portu­gal. In particular we examine the relationship between orientated and non-orientated models, and radial and non-radial analysis. We develop performance measures for the regional electricity networks operated in Portugal by EDP Distribuição, and we discover very close relationships among the performance rankings under different models, fulfilling widely-used consistency conditions for performance modeling. The paper uses the experience of this company example to draw some lessons about how performance measurement can be implemented within a company, in contrast to the usual objective of regulatory benchmarking procedures

Efficiency measurement of cloud service providers using network data envelopment analysis

An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models

A novel multilevel network slacks-based measure with an application in electric utility companies

In this paper, we developed an alternative Network Slacks-Based Data Envelopment Analysis Measure (NSBM) wherein the overall efficiency is expressed as a weighted average of the efficiencies of the individual processes. The advantage of this new model is that both overall efficiency and multi-divisional efficiencies have been calculated with a unified framework. The major merits of the proposed model are its ability to provide appropriate measure of efficiency, obtaining weight of processes from model, simultaneous assessment of intermediate variables considering them as both input and output. Finally, an application in electric power companies shows the practicality of the proposed model

Pre-Evaluating Efficiency Analysis of Mergers and Acquisitions of Full-Service Carriers in Korea

In November 2020, Korean Air signs an agreement to acquire and merges with 63.88% of Asiana Airlines’ shares, which is conditionally approved by the Korea Fair Trade Commission to address exclusivity concerns. The conditions require both airlines to return certain take-off or landing positions and revise their licenses for 26 international and 8 domestic routes within 10 years. This paper collects passenger traffic data from 2009 to 2019 using Korean data analysis, retrieval, and transfer systems employed by both airlines. Data envelopment analysis is utilized to assess their performance assuming the merger and acquisition. The analysis reveals that Korean Air’s super-efficiency performance in 2011 is the highest among all decision making units (DMUs). The best super-efficiency performance is achieved not only by individual companies but also by the combined enterprise in 2019

Advancing efficiency analysis using data envelopment analysis: the case of German health care and higher education sectors

The main goal of this dissertation is to investigate the advancement of efficiency analysis through DEA. This is practically followed by the case of German health care and higher education organizations. Towards achieving the goal, this dissertation is driven by the following research questions: 1.How the quality of the different DEA models can be evaluated? 2.How can hospitals’ efficiency be reliably measured in light of the pitfalls of DEA applications? 3.In measuring teaching hospital efficiency, what should be considered? 4.At the crossroads of internationalization, how can we analyze university efficiency? Both the higher education and the health care industries are characterized by similar missions, organizational structures, and resource requirements. There has been increasing pressure on universities and health care delivery systems around the world to improve their performance during the past decade. That is, to bring costs under control while ensuring high-quality services and better public accessibility. Achieving superior performance in higher education and health care is a challenging and intractable issue. Although many statistical methods have been used, DEA is increasingly used by researchers to find best practices and evaluate inefficiencies in productivity. By comparing DMU behavior to actual behavior, DEA produces best practices frontier rather than central tendencies, that is, the best attainable results in practice. The dissertation primarily focuses on the advancement of DEA models primarily for use in hospitals and universities. In Section 1 of this dissertation, the significance of hospital and university efficiency measurement, as well as the fundamentals of DEA models, are thoroughly described. The main research questions that drive this dissertation are then outlined after a brief review of the considerations that must be taken into account when employing DEA. Section 2 consists of a summary of the four contributions. Each contribution is presented in its entirety in the appendices. According to these contributions, Section 3 answers and critically discusses the research questions posed. Using the Translog production function, a sophisticated data generation process is developed in the first contribution based on a Monte Carlo simulation. Thus, we can generate a wide range of diverse scenarios that behave under VRS. Using the artificially generated DMUs, different DEA models are used to calculate the DEA efficiency scores. The quality of efficiency estimates derived from DEA models is measured based on five performance indicators, which are then aggregated into two benchmark-value and benchmark-rank indicators. Several hypothesis tests are also conducted to analyze the distributions of the efficiency scores of each scenario. In this way, it is possible to make a general statement regarding the parameters that negatively or positively affect the quality of DEA estimations. In comparison with the most commonly used BCC model, AR and SBM DEA models perform much better under VRS. All DEA applications will be affected by this finding. In fact, the relevance of these results for university and health care DEA applications is evident in the answers to research questions 2 and 4, where the importance of using sophisticated models is stressed. To be able to handle violations of the assumptions in DEA, we need some complementary approaches when units operate in different environments. By combining complementary modeling techniques, Contribution 2 aims to develop and evaluate a framework for analyzing hospital performance. Machin learning techniques are developed to perform cluster analysis, heterogeneity, and best practice analyses. A large dataset consisting of more than 1,100 hospitals in Germany illustrates the applicability of the integrated framework. In addition to predicting the best performance, the framework can be used to determine whether differences in relative efficiency scores are due to heterogeneity in inputs and outputs. In this contribution, an approach to enhancing the reliability of DEA performance analyses of hospital markets is presented as part of the answer to research question 2. In real-world situations, integer-valued amounts and flexible measures pose two principal challenges. The traditional DEA models do not address either challenge. Contribution 3 proposes an extended SBM DEA model that accommodates such data irregularities and complexity. Further, an alternative DEA model is presented that calculates efficiency by directly addressing slacks. The proposed models are further applied to 28 universities hospitals in Germany. The majority of inefficiencies can be attributed to “third-party funding income” received by university hospitals from research-granting agencies. In light of the fact that most research-granting organizations prefer to support university hospitals with the greatest impact, it seems reasonable to conclude that targeting research missions may enhance the efficiency of German university hospitals. This finding contributes to answering research question 3. University missions are heavily influenced by internationalization, but the efficacy of this strategy and its relationship to overall university efficiency are largely unknown. Contribution 4 fills this gap by implementing a three-stage mathematical method to explore university internationalization and university business models. The approach is based on SBM DEA methods and regression/correlation analyses and is designed to determine the relative internationalization and relative efficiency of German universities and analyze the influence of environmental factors on them. The key question 4 posed can now be answered. It has been found that German universities are relatively efficient at both levels of analysis, but there is no direct correlation between them. In addition, the results show that certain locational factors do not significantly affect the university’s efficiency. For policymakers, it is important to point out that efficiency modeling methodology is highly contested and in its infancy. DEA efficiency results are affected by many technical judgments for which there is little guidance on best practices. In many cases, these judgments have more to do with political than technical aspects (such as output choices). This suggests a need for a discussion between analysts and policymakers. In a nutshell, there is no doubt that DEA models can contribute to any health care or university mission. Despite the limitations we have discussed previously to ensure that they are used appropriately, these methods still offer powerful insights into organizational performance. Even though these techniques are widely popular, they are seldom used in real clinical (rather than academic) settings. The only purpose of analytical tools such as DEA is to inform rather than determine regulatory judgments. They, therefore, have to be an essential part of any competent regulator’s analytical arsenal