Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach

As significant strategic players in China's economy, high-tech industries need to evaluate and analyze the technological innovation activities from a system point of view to understand and improve their technological innovation efficiency and, thereby, promote their development. Different high-tech industries have different characteristics and thus benefit from different industrial development policies. However, few studies to date have discussed this issue from a systematic perspective. In this study, technological innovation activities are divided into a research and development (R&D) stage and a commercialization stage. A high-tech industrial evaluation framework of technological innovation efficiency based on two-stage network data envelopment analysis (DEA) is constructed with shared inputs, additional intermediate inputs, and free intermediate outputs. Our empirical results indicate that the overall efficiency of most industries is relatively low and the differences between the five high-tech industries (i.e., sub-sectors) we examined are large. The Spearman correlation shows that overall efficiency and R&D efficiency are more correlated than overall efficiency and commercialization efficiency. Additionally, R&D has better average efficiency. The sub-sector with the highest average efficiency is computers and office equipment, and the one with the lowest average efficiency is medicines. These findings indicate the inadequacy but potential for breakthroughs in the evolution of high-tech industries in China. The analysis proves that it is necessary to create different industrial policies to encourage effective progress in certain high-tech industries, and some guidelines for doing so are provided.     

Research performance evaluation of Chinese university: A non-homogeneous network DEA approach

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.     

How does stakeholder engagement through environmental,social, and governance affect eco-efficiency and profitability efficiency? Zooming into Apple Inc.'s counterparts

As global ecological degradation intensifies, a trade-off has arisen between environmental protection and production efficiency to achieve sustainable development for the environment, society, and the company itself. However, the potential reverse causality relationship between environmental, social, and governance (ESG) and corporate efficiency may lead to confusion. This study estimates the eco-efficiency of Apple Incorporated's value-chain counterparts in the first stage and creates values and profitability in the second stage of efficiency evaluation. Results obtained from the (i) directional distance function in the two-stage data envelopment analysis (DEA), (ii) additive efficiency decomposition two-stage network DEA model, and (iii) network slacks-based measure model are consistent. That is, Apple counterparts manage more efficient eco-efficiency than profitability efficiency, implying that eco-efficiency is their competitive advantage. We thus also run a regression analysis to examine how the ESG ratings of Apple counterparts explain their eco-efficiency and profitability efficiency. Although the overall ESG rating positively explains the efficiencies, we found that the individual governance rating shows no statistically significant effect. The regression results provide insight for practitioners on the importance of investing in the three aspects of a firm's collective conscientiousness for societal and environmental governance. This paper integrates companies' eco-efficiency and profitability efficiency to resolve the conflict between environmental issues and production efficiency. It also analyzes in depth the effects of ESG and its three individual factors on eco-, profitability, and average efficiencies. The diversity of research methods also provides new ideas for future research related to firm efficiency.     

中国省际高技术产业效率测度与分解——基于共享投入关联DEA模型

本文基于高技术产业科技成果产出和科技成果转化为生产力两阶段视角,考虑"中间产品产出再投入"和"初始投入在两个子系统间的分配结构",构建共享投入关联DEA模型,利用1999～2010年我国29个省份的面板数据,测算高技术产业系统效率和子系统的纯技术效率,并与关联DEA模型、BCC模型的结果进行比较。结果表明,共享投入关联DEA模型在计算效率水平的同时,还可得到中间产品的转化信息和初始投入的配置信息。     

A novel method for computing single output for DEA with application in hospital efficiency

There are two main methods for measuring the efficiency of decision-making units (DMUs): data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Each of these methods has advantages and disadvantages. DEA is more popular in the literature due to its simplicity, as it does not require any pre-assumption and can be used for measuring the efficiency of DMUs with multiple inputs and multiple outputs, whereas SFA is a parametric approach that is applicable to multiple inputs and a single output. Since many applied studies feature multiple output variables, SFA cannot be used in such cases. In this research, a unique method to transform multiple outputs to a virtual single output is proposed. We are thus able to obtain efficiency scores from calculated virtual single output by the proposed method that are close (or even the same depending on targeted parameters at the expense of computation time and resources) to the efficiency scores obtained from multiple outputs of DEA. This will enable us to use SFA with a virtual single output. The proposed method is validated using a simulation study, and its usefulness is demonstrated with real application by using a hospital dataset from Turkey.     

One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient,root-<Emphasis Type="Italic">n</Emphasis> consistent StoNEZD method

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a two-stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one-stage semi-nonparametric estimator that combines the nonparametric DEA-style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi-nonparametric envelopment of z variables data (StoNEZD). The StoNEZD estimator for the contextual variables is shown to be statistically consistent under less restrictive assumptions than those required by the two-stage DEA estimator. Further, the StoNEZD estimator is shown to be unbiased, asymptotically efficient, asymptotically normally distributed, and converge at the standard parametric rate of order n ^−1/2. Therefore, the conventional methods of statistical testing and confidence intervals apply for asymptotic inference. Finite sample performance of the proposed estimators is examined through Monte Carlo simulations.     

Stochastic leader–follower DEA models for two-stage systems

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). In recent years, it has been widely used to evaluate two-stage systems under different organization mechanisms. This study modifies the conventional leader–follower DEA models for two-stage systems by considering the uncertainty of data. The dual deterministic linear models are first constructed from the stochastic CCR models under the assumption that all components of inputs, outputs, and intermediate products are related only with some basic stochastic factors, which follow continuous and symmetric distributions with nonnegative compact supports. The stochastic leader–follower DEA models are then developed for measuring the efficiencies of the two stages. The stochastic efficiency of the whole system can be uniquely decomposed into the product of the efficiencies of the two stages. Relationships between stochastic efficiencies from stochastic CCR and stochastic leader–follower DEA models are also discussed. An example of the commercial banks in China is considered using the proposed models under different risk levels.     

Shadow Price Approach to Total Factor Productivity Measurement: With an Application to Finnish Grass-Silage Production

This paper explores an intermediate route between the Fisher and the Malmquist productivity indexes so as to minimize data requirements and assumptions about economic behavior of production units and their production technology. Assuming quantity data of inputs and outputs and the behavioral hypothesis of allocative efficiency, we calculate the exact value of the Fisher ideal productivity index using implicit shadow prices revealed by the choice of input–output mix. The approach is operationalized by means of a nonparametric data envelopment analysis (DEA) model. Empirical application to Finnish grass silage farms suggests that the Malmquist and the Fisher productivity indices yield similar results when averaged over firms, but there can be major differences in the results of the two approaches at the level of individual firms.     

Using social media for efficient brand marketing: An evaluation of Chinese Universities using Bilibili

As the higher education market becomes increasingly competitive, Chinese universities are competing to develop brand marketing programmes on social media platforms, with significant differences in performance. This paper divides universities’ social media marketing processes into two stages: the production-operation stage and the marketing communication stage. It constructs a two-stage data envelopment analysis (DEA) model to evaluate the brand marketing efficiency of 296 Chinese universities on the Bilibili video-sharing platform to provide strategic proposals for social media marketing. The results show that 10 universities perform efficiently in the production-operation stage, while 11 universities perform efficiently in the marketing communication stage. The gap in marketing efficiency is relatively large across Chinese universities. Most universities perform unsatisfactorily because of their redundant input or insufficient output. To improve the brand marketing efficiency on Bilibili, universities should pay more effort into quality rather than quantity of input, produce high-quality videos continuously and build and maintain fan loyalty.     

Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis

In this paper we propose a new technique for incorporating environmental effects and statistical noise into a producer performance evaluation based on data envelopment analysis (DEA). The technique involves a three-stage analysis. In the first stage, DEA is applied to outputs and inputs only, to obtain initial measures of producer performance. In the second stage, stochastic frontier analysis (SFA) is used to regress first stage performance measures against a set of environmental variables. This provides, for each input or output (depending on the orientation of the first stage DEA model), a three-way decomposition of the variation in performance into a part attributable to environmental effects, a part attributable to managerial inefficiency, and a part attributable to statistical noise. In the third stage, either inputs or outputs (again depending on the orientation of the first stage DEA model) are adjusted to account for the impact of the environmental effects and the statistical noise uncovered in the second stage, and DEA is used to re-evaluate producer performance. Throughout the analysis emphasis is placed on slacks, rather than on radial efficiency scores, as appropriate measures of producer performance. An application to nursing homes is provided to illustrate the power of the three-stage methodology.     

The demand for energy in Canadian manufacturing: An example of the estimation of production structures with many inputs

A model is presented for the estimation of production structures with many inputs when aggregation into a small number of aggregate inputs is undesirable. The procedure utilized is one of two-stage optimization, valid under the assumption of homothetic separability. A unique feature is the use of duality theory to integrate the two stages through the generation in the first stage of an instrumental variable for the aggregate price index of the separable disaggregated factors. The conceptual model is then applied to an analysis of the demand for energy in Canadian manufacturing, in which six energy components are explicitly included in the set of factors of production.     

Inverse data envelopment analysis without convexity: double frontiers

Decisions in Economics and Finance - In this research, inverse data envelopment analysis (IDEA) approaches are proposed to measure inputs changes for output perturbations made while the convexity...     

Productivity drivers and market dynamics in the Spanish first division football league

This paper analyses efficiency drivers of a representative sample of Spanish football clubs by means of the two-stage data envelopment analysis (DEA) procedure proposed by Simar and Wilson (J Econ, 136:31–64, 2007). In the first stage, the technical efficiency of football clubs is estimated using a bootstrapped DEA model in order to establish which of them are the most efficient; the ranking is based on total productivity in the period 1996–2004. In the second stage, the Simar and Wilson (J Econ, 136:31–64, 2007) procedure is used to bootstrap the DEA scores with a truncated bootstrapped regression. Policy implications of the main findings are also considered.     

Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach

Industrial air pollution control recently becomes a major policy issue in China. Performnce evaluation can examine policy effectiveness and provide decesion support for industrial development. A regional industrial system in China contains production and abatement stages. Within this structure, the capacity of industrial waste gas treatment can be treated as a carry-over variable. More precisely, it is a desirable output of the abatement stage in the previous period but an input to the abatement stage in the current period. Using this framework, this study establishes a dynamic two-stage data envelopment analysis model to explore the efficiencies of regional industrial systems in China. This model provides measures of the overall, period, stage, and period stage efficiencies. Based on empirical data from 2007 to 2015, it is concluded that (1) the carry-over variable capacity of industrial waste gas treatment has significant influences on the operational efficiency scores of regional industrial systems, especially in the abatement stage; (2) there exist distinct geographic characteristics of inefficiencies in the regional industrial systems; (3) the average period efficiency of the abatement stage was lower than that of the production stage during China's 11th Five-Year Plan period (2006–2010); however, the reverse happens during the 12th Five-Year Plan period (2011–2015); and 4) industrial structure and economic development level are the key influencing factors of regional industrial efficiencies. This study entails important implications for policy makers in terms of industrial waste gas treatment and revelant contextual factors.     

Weak congestion in output additive data envelopment analysis

Our earlier work [Wei QL, Yan H. Congestion and returns to scale in data envelopment analysis. European Journal of Operational Research 2004;153:641–60] discussed necessary and sufficient conditions for the existence of congestion together with aspects of returns to scale under an output-oriented DEA framework. In line with this work, the current paper investigates the issue of “weak congestion”, wherein congestion occurs when the reduction of selected inputs causes some, rather than all, outputs to increase, without a worsening of others. We define output efficiency for decision making units under a series of typical DEA output additive models. Based on this definition, we offer necessary and sufficient conditions for the existence of weak congestion. Numerical examples are provided for purposes of illustration.     

Impact of conversion to Critical Access Hospital status on hospital efficiency

This study examines the impact of Critical Access Hospital (CAH) Program on hospital efficiency using a two-stage approach, where data envelopment analysis is used in the first stage to estimate cost, technical, and allocative efficiency scores of a sample of rural hospitals. Densities of efficiency scores of CAHs and prospectively paid rural hospitals are estimated and compared using a nonparametric kernel density estimator and a bootstrap-based test. In the second stage, efficiency scores are regressed on environmental variables using bootstrapped truncated regressions. Density analysis and results from bootstrapped truncated regressions show that CAHs are less cost and allocatively efficient compared to prospectively paid rural hospitals, without being less technically efficient. Relative to their pre-conversion selves, CAHs appear to be slightly less allocatively efficient, while they are slightly more technically efficient and no less cost efficient. Overall, our results suggest that the CAH Program may have decreased the allocative and cost efficiencies of those rural hospitals that converted to CAH status relative to prospectively paid rural hospitals, without significantly increasing their technical efficiency.     

A Stochastic Frontier Production Function with Flexible Risk Properties

This paper considers a stochastic frontier production function which has additive, heteroscedastic error structure. The model allows for negative or positive marginal production risks of inputs, as originally proposed by Just and Pope (1978). The technical efficiencies of individual firms in the sample are a function of the levels of the input variables in the stochastic frontier, in addition to the technical inefficiency effects. These are two features of the model which are not exhibited by the commonly used stochastic frontiers with multiplicative error structures.An empirical application is presented using cross-sectional data on Ethiopian peasant farmers. The null hypothesis of no technical inefficiencies of production among these farmers is accepted. Further, the flexible risk models do not fit the data on peasant farmers as well as the traditional stochastic frontier model with multiplicative error structure.     

Marginal Rates and Elasticities of Substitution with Additive Models in DEA

Marginal rates and elasticities of substitution are derived from the optimal slack values obtained from modified versions of additive DEA models. Projection formulas are used to ensure that all points are on the efficient frontier as required for conformance with assumptions in micro-economics. The models used differ from standard versions in that slack values are allowed to be negative as well as positive in these additive models. This makes movement possible on efficiency frontiers, where improvement in some inputs or outputs requires worsening other inputs or outputs. A new definition is therefore introduced in which efficiency is attained only if the value of the worsenings is exactly offset by the value of the improvements. This includes, but is not restricted to, the case in which all slacks must be zero for full attainment of efficiency—as in standard versions of additive models.     

Improving discrimination in data envelopment analysis: some practical suggestions

In some contexts data envelopment analysis (DEA) gives poor discrimination on the performance of units. While this may reflect genuine uniformity of performance between units, it may also reflect lack of sufficient observations or other factors limiting discrimination on performance between units. In this paper, we present an overview of the main approaches that can be used to improve the discrimination of DEA. This includes simple methods such as the aggregation of inputs or outputs, the use of longitudinal data, more advanced methods such as the use of weight restrictions, production trade-offs and unobserved units, and a relatively new method based on the use of selective proportionality between the inputs and outputs.     

Assessing the productivity of the Italian hospitality sector: a post-WDEA pooled-truncated and spatial analysis

This paper analyses the productivity of the hospitality sector (hotel and restaurants) in Italy at a regional level by using a mix of non-parametric and parametric approaches. A novel pooled-truncated and spatial analysis is employed, based upon a window data envelopment analysis (WDEA), where pure technical efficiency is computed. The WDEA results show that Lombardy is the best relative performer. However, overall Italian regions reveal important sources of inefficiency mostly related to their inputs. As a post-WDEA, the pooled-truncated estimation indicates that the rate of utilisation and regional intrinsic features positively affect hospitality efficiency. Nevertheless, the spatial analysis does not support evidence of spill-over effects amongst Italian regions.