Efficiency evaluation of parallel interdependent processes systems: an application to Chinese 985 Project universities

Data envelopment analysis (DEA) has been widely applied in measuring the efficiency of homogeneous decision-making units. Network DEA, as an important branch of DEA, was built to examine the internal structure of a system, whereas traditional DEA models regard a system as a ‘black box’. However, only a few previous studies on parallel systems have considered the interdependent relationship between system components. In recent years, parallel interdependent processes systems commonly exist in production systems because of serious competition among organisations. Thus, an approach to measure the efficiency of such systems should be proposed. This paper builds an additive DEA model to measure a parallel interdependent processes system with two components which have an interdependent relationship. Then, the model is applied to analyse the ‘985 Project’ universities in China, and certain policy implications are explained

Una investigación de la congestión de productos no deseados en el análisis de envolvente de datos

Congestion is one of the most important subjects in Data Envelopment Analysis (DEA) which helps the Decision Maker (DM) to decide about changing the size of units. The estimation of congestion has attractive advantages from different perspectives. For example, the total cost of a partiular DMU, in which the congestion occurs, can be reduced by the decreases in inputs. On the other hand, the output of units can be increased by the recognizing and eliminating the congestion of DMUs and so, the total profit of decision making units can be increased. Hence, the management is eager to know how to recognize and eliminate the congestion of units. Most of the existing methods to estimation of the congestion in the literature consider only the desirable outputs. This study focuses on the evaluation of congestion in the presence of undesirable outputs and proposes an approach to recognize the congestion of units. The method is demonstrated on a numerical example to illustrate the validity of the proposed method.La congestión es uno de los temas más importantes en el análisis envolvente de datos (DEA) que ayuda al responsable de la toma de decisiones (DM) a decidir sobre el cambio de tamaño de las unidades. La estimación de la congestión tiene atractivas ventajas desde diferentes perspectivas. Por ejemplo, el costo total de una DMU en particular, en la que ocurre la congestión, puede reducirse mediante la disminución de los insumos. Por otro lado, la producción de unidades se puede incrementar reconociendo y eliminando la congestión de las DMU y así, se puede incrementar el beneficio total de las unidades de toma de decisiones. Por lo tanto, la gerencia está ansiosa por saber cómo reconocer y eliminar la congestión de unidades. La mayoría de los métodos existentes para estimar la congestión en la literatura consideran solo los resultados deseables. Este estudio se centra en la evaluación de la congestión en presencia de salidas indeseables y propone un enfoque para reconocer la congestión de unidades. El método se demuestra en un ejemplo numérico para ilustrar la validez del método propuesto

Una investigación de la congestión de productos no deseados en el análisis de envolvente de datos

La congestión es uno de los temas más importantes en el análisis envolvente de datos (DEA) que ayuda al responsable de la toma de decisiones (DM) a decidir sobre el cambio de tamaño de las unidades. La estimación de la congestión tiene atractivas ventajas desde diferentes perspectivas. Por ejemplo, el costo total de una DMU en particular, en la que ocurre la congestión, puede reducirse mediante la disminución de los insumos. Por otro lado, la producción de unidades se puede incrementar reconociendo y eliminando la congestión de las DMU y así, se puede incrementar el beneficio total de las unidades de toma de decisiones. Por lo tanto, la gerencia está ansiosa por saber cómo reconocer y eliminar la congestión de unidades. La mayoría de los métodos existentes para estimar la congestión en la literatura consideran solo los resultados deseables. Este estudio se centra en la evaluación de la congestión en presencia de salidas indeseables y propone un enfoque para reconocer la congestión de unidades. El método se demuestra en un ejemplo numérico para ilustrar la validez del método propuesto

Total Factor Productivity Growth of African Public Universities

The study investigates the Total Factor Productivity Growth of 15 African public universities using the DEA-Based Malmquist Productivity Index. The general model, teaching-only model and research-only model are estimated. The operating environment is adjusted to ascertain the extent to which it affects the productivity of the African public universities. Total factor productivity gains for African public universities averaged at 7.5%, 0.7% and 6.8% for the general model, teaching-only model and research-only model, respectively. Productivity improvements are therefore majorly a result of research productivity gains rather than teaching productivity gains before and after adjusting for the operating environment. The university research productivity gains are, however, mainly attributed to technological progress rather than technical efficiency change. Keywords: African public universities, Malmquist Productivity Index, Technical efficiency change, Technological change, Total factor productivity growth DOI: 10.7176/JESD/10-2-1

Measuring the Efficiency of Turkish Research Universities via Two-Stage Network DEA with Shared Inputs Model

The efficiency of universities, which have a network structure of production process, is an essential component of performance measurement in education. However, most previous studies use traditional Data Envelopment Analysis (DEA), which disregards the network structure of the production process in universities. This study adopts a two-stage Network Data Envelopment Analysis (NDEA) with shared inputs model to assess the overall, teaching and research efficiencies of Turkish research universities. The findings show that only 6 out of 23 research universities are efficient, and some universities with lower world rankings are more efficient than those with higher rankings. On the other hand, no significant difference was found between the efficiency levels of regions with a high level of socio-economic development and regions with a relatively low level of socio-economic development. The study also evaluates the effects of different priority scenarios on efficiency and the optimal allocation of shared inputs between sub-processes. This study provides guidance for universities seeking to improve their performance and for the Council of Higher Education (CHE) in determining incentives for research universities. It also promotes the use of multi-stage NDEA with shared inputs model over traditional DEA for accurate efficiency assessment in the field of education

The effect of ICT on schools’ efficiency: Empirical evidence on 23 European countries

In the last two decades, ICT use in schools grew exponentially. In this paper, the relationship between ICT and school’s efficiency and the mechanisms through which ICT can enhance schools’ productivity are investigated, using PISA 2018 data for about 5400 schools. Empirically, we analyze school’s efficiency in producing ICT-mediated instructional time as well as final educational output, by implementing a network Data Envelopment Analysis (DEA) model. The analysis is complemented by a conditional DEA to account for the presence of possible external drivers of schools’ efficiency. Results show that the average schools’ efficiency in using ICT is relatively low, and that it is mainly driven by the ability of translating ICT-mediated instructional time into learning, rather than by the amount of ICT and human resources. This evidence is consistent across countries. By investigating the role of ICT in schools’ efficiency the paper provides insights to guide the transition of digital technology into learning

Higher education efficiency frontier analysis: A review of variables to consider

The measurement of efficiency in higher education has gained a growing interest in recent years, especially due to the expansion of the university system. This paper provides a review of the literature on efficiency in higher education institutions by covering empirical articles which applied frontier efficiency measurement techniques from 1997 to 2019. We review the methodological approaches used, both parametric and non-parametric techniques, such as Data Envelopment Analysis, Malmquist index and Stochastic Frontier Analysis. Secondly, we list the applied inputs, input prices, outputs, quality, and environment variables and based on the overview, we discuss the advantages and drawbacks of the different empirical proxy variables used. We address the importance of characterizing students and research funding as raw materials of both the teaching and research services, respectively, and we provide suggestions on how to deal with them empirically. We also discuss the difference between quality and environmental variables, and we give some practical indications to distinguish them in doubtful cases.Fil: Ferro, Gustavo Adolfo. Universidad del Cema. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: D'Elia, Vanesa. Universidad del Cema; Argentin

Management characteristics, collaboration and innovative efficiency: evidence from UK survey data

This paper explores the impact of management characteristics and patterns of collaboration on a firmÕs innovation performance in transforming innovation resources into commercially successful outputs. These questions are investigated using a recent firm level survey database for 465 innovative British small and medium enterprises (SMEs) over the years 1998-2001. Both Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are employed to benchmark a firmÕs innovative efficiency against best practice. Quality and the variety of innovations are taken into account by combining Principal Component Analysis (PCA) with DEA. We find evidence suggesting that the innovative efficiency of SMEs is significantly affected by their management characteristics and collaboration behaviour. Collaboration, organisational flexibility, formality in management systems and incentive schemes are found to contribute significantly to a firmÕs innovative efficiency. Managerial share-ownership also shows some positive effect. The importance of these effects, however, varies across different sectors. WE find that innovative efficiency in high-tech SMEs is significantly enhanced by collaboration, formal management structure and training; and that in medium- and low-tech SMEs is significantly associated with managerial ownership, incentive schemes and organisational flexibility.management characteristics, collaboration, innovative efficiency

Investment efficiency of the new energy industry in China

This paper evaluates the investment efficiency of the new energy industry in China and investigates factors that explain variations in investment efficiency across firms and over time. Applying a four-stage semi-parametric DEA analysis framework to a sample of listed new energy firms over the period 2012-2015, we find that the overall investment efficiency of the new energy industry is relatively low, with an average total technical efficiency of 44%, pure technical efficiency of 48%, and scale efficiency of 90%. We also find that new energy firms’ investment efficiency is affected by both macroeconomic conditions and firm-specific characteristics. Our results are robust and have significant implications for policy makers and firm managers