Are multi‐resort ski conglomerates more efficient?

This paper compares the efficiency of large ski resort conglomerates with independent ski resorts using data on four countries (Canada, France, United States, Switzerland). Using the stochastic frontier production approach, I find that ski resorts that are owned and managed by the Intrawest group are significantly more efficient than independent ski resorts. The efficiency gap is about nine percentage points on average. The remaining ski resort conglomerates (American Skiing, Vail Resorts Inc., and Compagnie des Alpes SA) do not operate more efficiently than independent ski resorts. Copyright © 2009 John Wiley & Sons, Ltd.     

基于AHP-DEA的第三方物流企业绩效评价管理

适时、全面、有效地对物流企业绩效进行评估,是确保企业能够有效运行,实现企业自身价值和目标的基础和关键。针对第三方物流企业管理中的绩效评价问题,文章基于层次分析法(AHP)和数据包络分析法(DEA),建立第三方物流企业绩效评价管理体系,构建AHP-DEA组合评价模型。该组合模型首先针对第三方物流企业进行指标层次分析,再将分析结果作为数据包络分析的投入产出指标进行DEA评价,并基于供应链视角为企业提供新思路,实证分析表明该模型能够客观评价企业物流绩效,为企业经营管理提供依据。     

Impulse response function analysis of the impacts of land value-added tax policy on government performance

In this study, an epsilon-based network data envelopment analysis is employed to construct assessment mechanisms for government performance. Moreover, performance indicators of two dimensions of tax collection efficiency and financial effectiveness are measured. We propose a vector autoregression model in which all economic variables are regarded as dependent variables to address the disadvantages of traditional regression model. The conclusions are as follows: (a) measures of tax collection efficiency deteriorated, whereas those of financial effectiveness improved. (b) In an impulse response analysis of the model, an increase in government-published land values produced significantly increased tax collection efficiency.     

基于DEA模型的钢铁企业规模经济有效性研究

数据包络分析方法（DEA）是分析企业相对有效性的一种行之有效的方法,鉴于钢铁经济是我国国民经济的重要组成部分,文中用数据包络分析方法分析我国钢铁企业的规模经济有效性,结果表明,我国大部分钢铁企业的规模经济是无效的,最后,结合研究结果给出一些提高钢铁企业规模经济的建议。     

考虑道路风险的应急物流设施选址问题研究

应急物流设施选址是应急管理中备受关注的研究方向。因选址决策是一个多目标决策问题,故应用数据包络分析方法具有合理性和优越性。文中将路径超期风险和通行能力加入选址决策中,构建了基于DEA交叉效率的选址模型,最后通过一个算例应用了文中的方法,说明了其可行性。     

Combining PCA With DEA to Improve the Evaluation of Project Performance Data: A Taiwanese Bureau of Energy Case Study

This article proposes a new method of project performance evaluation, by which project performance data can be better understood. This article combines principal component analysis (PCA) and data envelopment analysis (DEA) to enhance the efficiency of decision‐making units more accurately. The data used was based on energy projects promoted by the Bureau of Energy at the Ministry of Economic Affairs in Taiwan. The results of this article show that by combining PCA and DEA in evaluating the performance of energy projects, there was an improved evaluation of projects over simply using DEA data alone.     

Resource Commitment,Organizational Diversity,and Research Performance: A Case of the National Telecommunication Program in Taiwan

Traditionally, a project team's resource commitment and organizational diversity are thought to be helpful to project performance; however, this relationship may falter when the project performance is considered in light of both cost and benefit. This paper integrates data envelopment analysis (DEA) and analytical hierarchy process (AHP) to evaluate project performance and explores the relationship between project performance and resource commitment and organizational diversity. The empirical study of research teams in the Taiwan National Telecommunication Program shows mixed results of this relationship. Implications for project management and governmental subsidy policy in dealing with resource commitment and organizational diversity are discussed.     

基于数据包络分析的大学生学习有效性探讨

指出数理统计方法和传统的数据包络分析方法的缺陷所在,提出用模糊的生产前沿面来改造传统的数据包络分析模型,并用该模型分析大学生的学习有效性。     

Firm performance and ownership structure: Dynamic network data envelopment analysis approach

This paper applies dynamic network slacks-based data envelopment analysis to measure financial performance based on the interrelationship among investment, financing, and dividend decisions. The empirical results show that financial performance is determined simultaneously by the efficiency of decisions, and sample firms have good performance in investment stage, but need to improve their financing and dividend policies. The proposal financial performance measure explains the multicriteria of decision-making rather than the single financial ratios. Besides, the research contributes a novel on the significant relationship between firm's ownership structure in the form of managers, government and foreign shareholders, and the firm's financial performance.     

基于DEA与BSC相结合的上市商业银行绩效评价研究

2008年金融风暴给全球的金融市场带来很大的冲击,商业银行在艰难发展中越来越重视绩效管理,本文提出基于平衡计分卡视角的绩效评价体系,运用数据包络分析法对我国上市商业银行2005年至2009年的绩效水平进行评价,并对评价结果进行分析。     

Composition versus decomposition in two-stage network DEA: a reverse approach

A two-stage production process assumes that the first stage transforms external inputs to a number of intermediate measures, which then are used as inputs to the second stage that produces the final outputs. The fundamental approaches to two-stage network data envelopment analysis are the multiplicative and the additive efficiency-decomposition approaches. Both they assume a series relationship between the two stages but they differ in the definition of the overall system efficiency as well as in the way they conceptualize the decomposition of the overall efficiency to the efficiencies of the individual stages. In this paper, we first show that the efficiency estimates obtained by the additive decomposition method are biased, by unduly favouring one stage against the other, while those obtained by the multiplicative method are not unique. Then, we present a novel approach to estimate unique and unbiased efficiency scores for the individual stages, which are then composed to obtain the efficiency of the overall system, by selecting the aggregation method a posteriori. Within the particularity of two-stage processes emerging from the conflicting role of the intermediate measures, we develop an envelopment model to locate the efficient frontier whose derivation from our primal multiplier efficiency assessment model is effectively justified. The results derived from our approach are compared with those obtained by the aforementioned basic methods on experimental data as well as on test data drawn from the literature. Similarities and dissimilarities in the results are rigorously justified.     

The productivity and performance of Australia’s major banks since deregulation

The aim of this study is to present some measures of the performance of banks operating in Australia since the deregulation of the Australian financial system in early 1980s; including the periods of financial market instability (the early 1990s and mid to late 2000s). In undertaking this measurement two approaches will be used. The first simply applies standard financial indicators. The second approach applies data envelopment analysis (DEA), to determine Malmquist indices of the levels of and the changes in the efficiency and productivity of Australian banks. The empirical results demonstrate the effect of deregulation and periodic financial crisis’s on the performance of individual banks, and the major part of the Australian banking sector. Overall the productivity performance of the Australian banks tended to improve considerably in those periods of strongest economic growth (i.e. the mid 1980s and 2000s).     

基于DEA方法的投资基金业绩评价

本文在传统投入产出效率分析———数据包络分析 (DEA)方法的基础上 ,通过采用改进型的投入产出DEA模型 ,对深圳证券市场2002年前上市的25家封闭式证券投资基金在2002年上半年的业绩进行综合评价。结果表明 ,在评价期间内大部分基金表现为相对无效。同时我们给出基金相对效率名次     

Managerial Efficiency,Managerial Succession and Organizational Performance

Data envelopment analysis (DEA) is used to create a measure of managerial efficiency in an attempt to reassess the conflicting theories concerning the impact of organizational performance on manager succession, and the counter-theories concerning the impact of manager succession on organizational performance. The analysis uses data for 147 college basketball teams from 1984 to 1991. The results indicate that winning, not efficiency, is the key criterion used in determining managerial retention. Yet when managers of losing teams are dismissed the teams tend to do even worse. If, however, the efficiency of the new manager is greater than that of the former, the disruptive effect of succession is minimized. Because administrators appear to focus on winning, not efficiency, they will often select new managers who are less efficient than departed managers. These results are unique to this literature and indicate promise for the use of DEA in analyses of the internal efficiencies of organizations. © 1997 John Wiley & Sons, Ltd.     

Measuring the performance of Internet companies using a two-stage data envelopment analysis model

In exploring the business operation of Internet companies, few researchers have used data envelopment analysis (DEA) to evaluate their performance. Since the Internet companies have a two-stage production process: marketability and profitability, this study employs a relational two-stage DEA model to assess the efficiency of the 40 dot com firms. The results show that our model performs better in measuring efficiency, and is able to discriminate the causes of inefficiency, thus helping business management to be more effective through providing more guidance to business performance improvement.     

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.     

A systematic approach for ranking football players within an integrated DEA‐OWA framework

The ranking of players has always been an important input for key decisions in the football industry. This paper introduces a systematic methodology for ranking football players through objective performance factors within a framework that integrates data envelopment analysis and ordered weighted averaging operators. Players from European Premier League Football clubs are selected and ranked under a decision setting that involves the players' desirable and undesirable performance factors alongside the attitude of the decision maker. The robustness of the proposed methodology is evaluated using different levels of optimism of the decision maker.     

What do U.S. biopharmaceutical companies get from patents and research and development spikes for their dynamic corporate performance?

This paper investigates how patents and research and development (R&D) spikes affect the corporate performance of 863 firm-year observations of U.S. biopharmaceutical companies. First, a dynamic data envelopment analysis model is adopted to evaluate the performance of the U.S. biopharmaceutical companies. Then, ordinary least squares regression is used to explore the effects of three patent-related variables (patent counts, citations, and claims) and R&D spikes on corporate performance. This study finds positive impacts of patent counts, citations, and claims on corporate performance. In addition, the results show that R&D spikes have negative contemporaneous effects and time-lagged effects on corporate performance.     

Exploring the operating efficiency of Technology Development Programs by an intellectual capital perspective—A case study of Taiwan

Government supported technological research and development can help the private sector to compete globally. A more accurate evaluation system considering multi-factor performance is highly desired. This study offers an alternative perspective and characterization of the performance of Technology Development Programs (TDPs) via a two-stage process that emphasizes research and development (R&D) and technology diffusion. This study shall employ a sequential data envelopment analysis (DEA) with a non-parametric statistical analysis to analyze differences in intellectual capital variables among various TDPs. The results reveal that R&D performance is better than technology diffusion performance for the TDPs. In addition, the “Mechanical, Mechatronic, and Transportation field” is more efficient than the other fields in both R&D and technology diffusion performance models. The findings of this study point to the importance of intellectual capital in achieving high levels of TDP efficiency. The potential applications and strengths of DEA and intellectual capital in assessing the performance of TDP are also highlighted.     

Comparison of dimension reduction methods for DEA under big data via Monte Carlo simulation

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.