The relationship between bank performance and intellectual capital in East Asia

In this paper, we develop a performance efficiency value by using data envelopment analysis (DEA) to integrate five perspectives of CAMEL (Capital Adequacy, Asset Quality, Management, Earnings, Liquidity), which is used by the Federal Deposit Insurance Corporation to evaluate banking performance. In addition, we utilize a tiered DEA to categorize banks into four groups. One-way ANOVA is used to analyze differences in CAMEL and intellectual capital (IC) variables across the four groups. CAMEL variables are manifested more strongly in highly efficient groups when compared with inefficient groups. The findings also reveal the importance of IC in achieving high levels of bank efficiency.     

基于超效率DEA和Tobit模型的中国上市煤炭企业经营效率现状分析

本文运用数据包络分析（DEA）方法和Tobit回归模型,首先选取2011年沪深两市具有代表性的18家煤炭上市公司为研究对象,以货币资金、流动资产合计、长期股权投资、固定资产和非流动资产合计为投入变量,主营业务收入、主营业务利润、利润总额为产出变量,运用CCR模型从技术效率、纯技术效率和规模效率等方面对其经营效率进行评价和分析;其次运用超效率DEA模型对CCR模型下DEA有效的决策单元计算其超效率值,从而为所有决策单元提供完整的效率值排序;最后,本文运用Tobit模型,分析影响中国上市煤炭企业经营效率的重要因素,为决策者提供提高企业经营效率的方向和政策。     

基于DEA模型的房地产上市企业经营效率研究

本文选取了在沪深两市的A股上市的14家房地产企业2016-2020年的年报数据,运用DEA方法研究了房地产企业的经营效率。本文通过分析发现:房地产经营效率整体水平一般,无论是整体经营水平、技术管理水平,还是规模大小的适宜程度都存在一定的提升空间。     

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.     

基于DEA的我国物流企业绩效评价

以2009年沪深两市24家物流上市公司为研究对象,运用数据包络分析(DEA)方法,对其经营绩效进行了评价。研究发现,我国物流上市公司总体绩效并不理想,平均绩效值只有0.819;造成绩效不高的原因主要是规模无效率。同时结合分析结果,提出了提高企业经营效率的对策。     

Measuring efficiency under fixed proportion technologies

Data Envelopment Analysis (DEA) applications frequently involve nonsubstitutable inputs and nonsubstitutable outputs (that is, fixed proportion technologies). However, DEA theory requires substitutability. In this paper, we illustrate the consequences of nonsubstitutability on DEA efficiency estimates, and we develop new efficiency indicators that are similar to those of conventional DEA models except that they require nonsubstitutability. Then, using simulated and real-world datasets that encompass fixed proportion technologies, we compare DEA efficiency estimates with those of the new indicators. The examples demonstrate that DEA efficiency estimates are biased when inputs and outputs are nonsubstitutable. The degree of bias varies considerably among Decision Making Units, resulting in substantial differences in efficiency rankings between DEA and the new measures. And, over 90% of the units that DEA identifies as efficient are, in truth, not efficient. We conclude that when inputs and outputs are not substituted for either technological or other reasons, conventional DEA models should be replaced with models that account for nonsubstitutability.     

Incorporating quality into the measurement of hospital efficiency: a double DEA approach

This paper uses “double DEA” to assess how accounting for quality influences DEA technical efficiency scores of a sample of 1,074 US hospitals. In the first use of DEA, quality indices are estimated using a variety of process and outcome measures of quality. In the second use of DEA, technical efficiency is assessed while controlling for quality. A variety of DEA quality indices and a DEA variety of efficiency models are compared to determine how the treatment of quality influences findings regarding technical efficiency. Controlling for efficiency does matter, with outcome measures having an apparently greater impact than process measures. Given the call for improved quality and better cost containment, controlling for quality is an important contribution to efficiency analysis.     

Integration of network data envelopment analysis and decision-making trial and evaluation laboratory for the performance evaluation of the financial holding companies in Taiwan

Data envelopment analysis (DEA) has been constantly used to measure the technical efficiency of decision-making units (DMUs). However, the major problem of traditional DEA methods is that they do not consider the possible intermediate effects. Recently, many papers have applied network DEA models to evaluate the efficiency scores. However, the linking activity of DMUs is still hard to be recognized. Hence, we employ DEMATEL to obtain the linking activity of DMUs. Our empirical research shows that the proposed method can soundly deal with the purpose of identifying the relationship between variables and derive the reasonable result in network DEA.     

A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia

Data envelopment analysis (DEA) is generally used to evaluate past performance and multi objective linear programming (MOLP) is often used to plan for future performance goals. In this study, we establish an equivalence relationship between MOLP problems and combined-oriented DEA models using a direction distance function designed to account for desirable and undesirable inputs and outputs together with uncontrollable variables. This equivalence model can be effectively used to support interactive processes and performance measures designed to establish future performance goals while taking into account the preferences of decision makers (DMs). In particular, it allows DMs to consider different efficiency improvement strategies when subject to budgetary restrictions. The applicability of the proposed method and the efficacy of the procedures and algorithms are demonstrated using a case study where the performance of high schools in the City of Philadelphia is evaluated.     

An analysis of operations efficiency in large-scale distribution systems

This research applies Data Envelopment Analysis (DEA) methodology to evaluate the efficiency of units within a large-scale network of petroleum distribution facilities in the USA. Multiple inputs and outputs are incorporated into a broad set of DEA models, yielding a comprehensive approach to evaluating supply chain efficiency. This study empirically separates three recognized, important and yet different causes of performance shortfalls which have been generally difficult for managers to identify. They are: (1) managerial effectiveness; (2) scale of operations and potential for a given market area (and efficiency of resource allocation given the scale); and (3) understanding the resource heterogeneity via programmatic differences in efficiency. Overall, the efficiency differences identified raised insightful questions regarding top management’s selection of the appropriate form and type of inputs and outputs, as well as questions regarding the DEA model form selected.     

Stochastic non-smooth envelopment of data for multi-dimensional output

The proposed method of Stochastic Non-smooth Envelopment of Data (StoNED) for measuring efficiency has to date mainly found application in the analysis of production systems which have exactly one output. Therefore, the objective of this paper is to examine the applicability of StoNED when a ray production function models a production technology with multi-dimensional input and output. In addition to a general analysis of properties required by a ray production function for StoNED to be applicable, we conduct a Monte Carlo simulation in order to evaluate the quality of the frontier and efficiencies estimated by StoNED. The results are compared with those derived via Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). We show that StoNED provides competitive estimates in regard to other methods and especially in regard to the real functional form and efficiency.     

The operating efficiency of financial holding and nonfinancial holding banks—Epsilou‐based measure metafrontier data envelopment analysis model

Previous bank efficiency studies assumed that the same efficient frontier system exists in different types of banks, but the conventional radial data envelopment analysis (DEA) model slacks are not counted in efficiency scores, and nonradial DEA models do not consider radial features. This paper develops an Epsilou‐based measure meta‐DEA approach for measuring the efficiencies and technology gaps of different bank types. The results are as the follows: The average efficiency and technology gaps of nonfinancial holding banks are better than those of financial holding banks. The nonfinancial holding banks are more efficient than financial holding banks in investment and other income.     

Cross redundancy and sensitivity in DEA models

Data envelopment analysis (DEA) measures the efficiency of each decision making unit (DMU) by maximizing the ratio of virtual output to virtual input with the constraint that the ratio does not exceed one for each DMU. In the case that one output variable has a linear dependence (conic dependence, to be precise) with the other output variables, it can be hypothesized that the addition or deletion of such an output variable would not change the efficiency estimates. This is also the case for input variables. However, in the case that a certain set of input and output variables is linearly dependent, the effect of such a dependency on DEA is not clear. In this paper, we call such a dependency a cross redundancy and examine the effect of a cross redundancy on DEA. We prove that the addition or deletion of a cross-redundant variable does not affect the efficiency estimates yielded by the CCR or BCC models. Furthermore, we present a sensitivity analysis to examine the effect of an imperfect cross redundancy on DEA by using accounting data obtained from United States exchange-listed companies.     

Adjusting for cultural differences,a new DEA model applied to a merged bank

The usefulness and application of Data Envelopment Analysis (DEA) efficiency measurements is usually limited by the requirement of consistent operating circumstances. However, in many real world situations this is not the case, so to overcome this problem, this paper reports on a new strategy by inventing a Culturally Adjusted DEA model to benchmark business units that operate under different cultural (business) environments. This is especially useful when these environmental factors are partial causes of inefficiency and can not be simply incorporated into a DEA model as inputs or outputs. A simulation analysis is conducted to examine the effectiveness of the CA-DEA model for controlling these environmental effects. This model is applied to a real life efficiency study of two major financial firms in Canada in 2000, when the two entities started to consolidate and merge their branch networks. Two cultural indices are identified to represent a firm’s unique operating environment, one to capture the nature of a firm’s corporate strategies (Corporate Index), and the other to estimate the effectiveness of a firm’s operational systems (Service Index). The results show that a firm’s corporate culture has a significant influence on its branches’ efficiency and this, we found, is often neglected in such studies. This paper also makes a contribution to the bank merger literature by providing an internal view of the potential benefits that may result from sharing cultural advantages while identifying the true managerial inefficiencies.     

Measurement of innovation efficiency in logistic enterprises: Evidence from China based on the three-stage DEA-Malmquist index model approach

Continuous improvement of technological innovation ability, adjustment of the development strategy, and enhancement of operational performance are of great theoretical and practical significance for logistics enterprises. This paper aims to analyze and evaluate the innovation efficiency of the logistics industry. The study utilizes the static three-stage DEA model and the dynamic Malmquist index model, considering a total of 12 indicators related to innovation input, output, and environmental variables. A dataset of 2940 entries from 49 listed logistics enterprises from 2017 to 2021 was calculated. The analysis provides insights into the innovation efficiency of logistics enterprises from a static perspective and the innovation total factor productivity from a dynamic perspective and decomposition terms. Based on the analysis of environmental variables by the SFA model, it was found that DEA inefficiency is the combined result of environmental factors and management inefficiency. Environmental variables have both positive and negative effects on innovation. The improvement of the economic development level will lead to excess R&D investment. Increased government simple fund subsidies are not conducive to the efficient allocation of innovation resources within enterprises. The expansion of enterprise scale will increase R&D personnel and investment in fixed assets. A thriving technology market can encourage enterprises to improve their own conversion rate of scientific and technological output and give full play to their innovation ability. The dynamic Malmquist model analysis reveals a recution in the overall innovation efficiency of listed logistics enterprises over 5 years. The changes in total factor productivity and technological progress efficiency of all listed logistics enterprises are synchronized, with most enterprises exhibiting higher technological progress efficiency compared to comprehensive technical efficiency. The total factor productivity of logistics enterprise innovation is mainly affected by comprehensive technical efficiency.     

基于数据包络分析的供应链知识共享评价

供应链中的知识共享可以提高知识创新与运用的效率,使供应链节点企业间的知识水平达到协调与优化,从而提高供应链的整体绩效水平。讨论了使用数据包络分析(DEA)方法对供应链间的知识共享水平进行评价,并建立了评价的相关输入输出指标体系。     

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.     

基于网络DEA的供应链网络绩效评价研究

供应链网络绩效评价是供应链管理的核心问题。相对于传统DEA方法,网络DEA能准确描述供应链网络内部的复杂结构对供应链网络绩效的影响。文章在建立合适的输入输出指标体系及其量化方法的前提下,运用两阶段合作网络DEA方法对供应链网络绩效进行评价,最后使用算例进行了计算和比较分析。结果表明,基于网络DEA的供应链网络绩效评价,不仅可以得到供应链整体评价结果,而且还可以发现整体低效原因,因此能够为供应链管理提供更为全面的绩效评价信息。     

Is foreign-bank efficiency in financial centers driven by home or host country characteristics?

This paper investigates whether home or host country factors can explain differences in technical efficiency among foreign banks operating in the Luxembourg financial center. We first address heterogeneity across banks by using the group-wise bootstrap to compare DEA measures of bank efficiency between branches and subsidiaries, focused and diversified banks, and euro area and non-euro area banks. We then control for these factors in a second-stage regression indentifying the impact of country-specific regulatory and macroeconomic variables on individual bank efficiency scores. Our regulatory indicators capture the strictness of capital requirements, private monitoring, official disciplinary power and restrictions on bank activities. Our macroeconomic indicators capture GDP per capita in the home country and its position in the business cycle. Our results carry policy implications for bank regulators in both home and host countries and provide insight into banks’ choice between establishing a branch or a subsidiary to develop cross-border activities through international financial centers.