Performance measurement with classification information: an enhanced additive DEA model

This paper presents an improved measurement tool for evaluating performance of branches within a major Canadian bank. While there have been numerous previous studies of performance in the banking industry, particularly at the branch level, this study is different in a very significant way: specifically two kinds of data are used to develop the model. The first type of data is that related to standard transactions, available from any bank; such have formed the basis of numerous previous studies. The second type of data, obtained from the site studied, is classification information, based on branch consultant/expert judgment as to good and poor performance of branches. The purpose herein is to present a modified version of an existing benchmarking model, data envelopment analysis (DEA), and to show how this tool is applied in the banking industry. The mechanism used herein to incorporate expert knowledge within the DEA framework is to first apply a discriminant or classification tool, to quantify the functional relation that best captures the expert's mental model for performance. The outcome of this first phase is an orientation of variables to aid in the definition of inputs and outputs. The resulting orientation then defines the DEA model that makes up the second phase of the model.     

Two-stage evaluation of bank branch efficiency using data envelopment analysis

There are two key motivations for this paper: (1) the need to respond to the often observed rejections of efficiency studies’ results by management as they claim that a single-perspective evaluation cannot fully reflect the operating units’ multi-function nature; and (2) a detailed bank branch performance assessment that is acceptable to both line managers and senior executives is still needed. In this context, a two-stage Data Envelopment Analysis approach is developed for simultaneously benchmarking the performance of operating units along different dimensions (for line managers) and a modified Slacks-Based Measure model is applied for the first time to aggregate the obtained efficiency scores from stage one and generate a composite performance index for each unit. This approach is illustrated by using the data from a major Canadian bank with 816 branches operating across the nation. Three important branch performance dimensions are evaluated: Production, Profitability, and Intermediation. This approach improves the reality of the performance assessment method and enables branch managers to clearly identify the strengths and weaknesses in their operations. Branch scale efficiency and the impacts of geographic location and market size on branch performance are also investigated. This multi-dimensional performance evaluation approach may improve management acceptance of the practical applications of DEA in real businesses.     

An expanded decomposition of the Luenberger productivity indicator with an application to the Chinese healthcare sector

The objective of this research is to combine productivity analysis at the “firm-level” and the “industry-level” so that a novel, fuller decomposition of the sources of productivity change can be undertaken. Productivity change is decomposed into technological progress, technical, mix, and scale efficiency effects. Specifically, our decomposition allows us to capture changes in productivity due to the reallocation of inputs or outputs across productive units. In practice, such reallocation might take place across plants operated by the same firm, across regions within a country, or via mergers and acquisitions. The new decomposition of the aggregate Luenberger productivity indicator is illustrated using data at both the provincial and regional levels for China's healthcare sector over the period 2009–2014. Our results indicate that the growth of the aggregate Luenberger productivity indicator varied across both time and regions. We find that China's regional productivity growth in healthcare was primarily driven by technological progress, while the contributions of the other components of productivity change were smaller and more varied across regions.     

Integration and efficiency convergence in EU banking markets

Evidence of financial integration and convergence are considered of importance in assessing the outcome of EU deregulation policies aimed at improving the efficiency and performance of banking sectors. This paper evaluates the recent dynamics of bank cost efficiency by means of data envelopment analysis (DEA). Borrowing from the growth literature, we apply dynamic panel data models (GMM) to the concepts of β-convergence and σ-convergence to assess the speed at which banking markets are integrating. We also employ a partial adjustment model to evaluate convergence towards best practice. Results seem to provide supporting evidence of convergence of efficiency levels towards an EU average. Nevertheless, there is no evidence of an overall improvement of efficiency levels towards best practice.     

Risk,Process Maturity,and Project Performance: An Empirical Analysis of US Federal Government Technology Projects

In recent years, there has been increasing pressure on the US federal government to reduce spending and improve the management of its technology projects. Mitigating the adverse impact of risks on the performance of these projects presents a significant challenge for its stakeholders. Our research examines this challenge in two steps. First, we identify and define a set of salient risks in federal technology projects—specifically, complexity risk and contracting risk in the planning process, and execution risk in the execution process. Next, we investigate whether higher levels of process maturity, assessed by the Capability Maturity Model Integration (CMMI) framework, mitigate the negative effect of project risks on project performance. The analysis of time‐series data collected from 82 federal technology projects across 519 quarterly time periods indicates that each of the three types of risks has a significant negative effect on project performance. This finding highlights the practical significance of managing these risks in the federal technology project context. Further, we find that increasing levels of process maturity attenuate the negative effect of project risks on the performance of federal technology projects. However, the attenuation effects are consequential only at high levels of project risks; at low levels of project risk, increasing levels of process maturity can adversely affect project performance. To demonstrate the financial implications of increasing process maturity levels in federal technology projects, we examine the magnitude of project cost savings (and overruns) across different levels of CMMI and project risks. In summary, our study contributes to the sparse literature on public sector operations by addressing the understudied context of federal technology projects, and provides a nuanced examination of the implications of process maturity in managing the risk to performance relationship in such projects.     

商业银行分理处效率对效益的影响程度分析

在运用数据包络分析方法对商业银行分理处进行效益和效率的评价的基础上,提出分析效率对盈利性影响程度的方法,通过对某商业银行支行的12个分理处的实证分析,初步证明了该方法的有效性。     

Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks

The great majority of applications of the popular frontier technique data envelopment analysis (DEA) do not test for the association of efficiency estimates with key performance indicators used by industry observers. Nevertheless, identifying efficiency estimates’ associations with commonly accepted financial measures of performance could guide benchmarking activities, pricing decisions, and regulatory monitoring. Thus, the current paper investigates to what extent bank DEA super-efficiency estimates are associated with key financial ratios. A low correlation may present an opportunity to address inefficiencies that were not obvious in financial ratio analysis, thus enabling an update of inferences drawn from ratios. Regressing ratios on efficiency estimates may also help predict financial ratios. Where an input–output specification is comprised of key financial ratios, DEA can also be used to objectively identify benchmarks for ratio analysis based on actual observed data collected from peers. Nine super-efficiency DEA formulations across two profitability models are systematically tested. The slacks-based measure of DEA with a parsimonious profitability efficiency model emerges as the most significant combination explaining the variation in the two industry ratios, post-tax profit/average total assets and return on average equity.     

Determining an Optimal Penetration Among Weighted Regions in Two and Three Dimensions

We present efficient algorithms for solving the problem of computing an optimal penetration (a ray or a semi-ray) among weighted regions in 2-D and 3-D spaces. This problem finds applications in several areas, such as radiation therapy, geological exploration, and environmental engineering. Our algorithms are based on a combination of geometric techniques and optimization methods. Our geometric analysis shows that the d-D (d = 2, 3) optimal penetration problem can be reduced to solving O(n ^2(d–1)) instances of certain special types of non-linear optimization problems, where n is the total number of vertices of the regions. We also give implementation results of our 2-D algorithms.     

Bioaccessibility and Risk of Exposure to Metals and SVOCs in Artificial Turf Field Fill Materials and Fibers

To reduce maintenance costs, municipalities and schools are starting to replace natural grass fields with a new generation synthetic turf. Unlike Astro‐Turf, which was first introduced in the 1960s, synthetic field turf provides more cushioning to athletes. Part of this cushioning comes from materials like crumb rubber infill, which is manufactured from recycled tires and may contain a variety of chemicals. The goal of this study was to evaluate potential exposures from playing on artificial turf fields and associated risks to trace metals, semi‐volatile organic compounds (SVOCs), and polycyclic aromatic hydrocarbons (PAHs) by examining typical artificial turf fibers (n = 8), different types of infill (n = 8), and samples from actual fields (n = 7). Three artificial biofluids were prepared, which included: lung, sweat, and digestive fluids. Artificial biofluids were hypothesized to yield a more representative estimation of dose than the levels obtained from total extraction methods. PAHs were routinely below the limit of detection across all three biofluids, precluding completion of a meaningful risk assessment. No SVOCs were identified at quantifiable levels in any extracts based on a match of their mass spectrum to compounds that are regulated in soil. The metals were measurable but at concentrations for which human health risk was estimated to be low. The study demonstrated that for the products and fields we tested, exposure to infill and artificial turf was generally considered de minimus, with the possible exception of lead for some fields and materials.     

基于DEA方法的我国商业银行X-效率研究

X-效率分析是银行效率研究的前沿和主流方法,利用X-效率评估的DEA分析法以2001-2003三年14家主要商业银行的投入、产出数据对我国商业银行的X-效率评估的结果显示:整体效率水平74%;国有银行的效率平均72%,股份制银行平均75%,上市银行平均79.5%。从产权、市场、功能和监管等方面寻求适宜的目标结构,或许能为提高银行效率创造深刻、有效的结构条件。     

A learning ladder toward efficiency: Proposing network-based stepwise benchmark selection

Starting from the knowledge-based view of efficiency improvement, we propose a network-based approach to find the optimal stepwise benchmarking paths toward the efficiency frontier. The approach treats the Data Envelopment Analysis system as a network of teaching and learning firms and calculates the overall shortest paths taking into account both input endowment similarity and the efficiency gap covered in each step. In addition, based on network centrality concepts, the method discriminates between efficient and intermediate units, and highlights possible outliers or specialized units. As a real-world example, the method is applied to a network of Canadian bank branches and practical implications are discussed.     

Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture

In this paper we consider the use of data envelopment analysis (DEA) for the assessment of efficiency of units whose output profiles exhibit specialisation. An example of this is found in agriculture where a large number of different crops may be produced in a particular region, but only a few farms actually produce each particular crop. Because of the large number of outputs, the use of conventional DEA models in such applications results in a poor efficiency discrimination. We overcome this problem by specifying production trade-offs between different outputs, relying on the methodology of Podinovski (J Oper Res Soc 2004;55:1311–22). The main idea of our approach is to relate various outputs to the production of the main output. We illustrate this methodology by an application of DEA involving agricultural farms in different regions of Turkey. An integral part of this application is the elicitation of expert judgements in order to formulate the required production trade-offs. Their use in DEA models results in a significant improvement of the efficiency discrimination. The proposed methodology should also be of interest to other applications of DEA where units may exhibit specialization, such as applications involving hospitals or bank branches.     

Partial Ambiguity

We extend Ellsberg's two‐urn paradox and propose three symmetric forms of partial ambiguity by limiting the possible compositions in a deck of 100 red and black cards in three ways. Interval ambiguity involves a symmetric range of 50 − n to 50 + n red cards. Complementarily, disjoint ambiguity arises from two nonintersecting intervals of 0 to n and 100 − n to 100 red cards. Two‐point ambiguity involves n or 100 − n red cards. We investigate experimentally attitudes towards partial ambiguity and the corresponding compound lotteries in which the possible compositions are drawn with equal objective probabilities. This yields three key findings: distinct attitudes towards the three forms of partial ambiguity, significant association across attitudes towards partial ambiguity and compound risk, and source preference between two‐point ambiguity and two‐point compound risk. Our findings help discriminate among models of ambiguity in the literature.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

Efficiency Evaluation in Search Advertising

In this article, we use data envelopment analysis combined with principal component analysis to evaluate the efficiency of online retailers in search advertising. We examine various efficiency model specifications involving several resource and performance‐related variables in search advertising. Our analysis based on 200 retailers suggests different efficiency patterns for multichannel and Web‐only retailers. The results of our efficiency pattern analysis indicate that the performance metrics, impressions, online sales, click‐through rate, and conversion rate together reveal differences in efficiency mainly for multichannel retailers. On the other hand, ad positions in sponsored and organic links reveal differences in efficiency for Web‐only retailers. In terms of overall efficiency, we find that multichannel retailers occupy relatively most of the top positions. These results contribute to organizational level understanding of search advertising practices in online retailing and offer insights into keyword management, resource utilization, and performance metrics in search advertising campaigns.     

Impact of Value‐Added Service Features in e‐Retailing Processes: An Econometric Analysis of Web Site Functions

We examine the impact of three classes of Web site functions (foundational, customer‐centered, and value‐added) upon e‐retailer performance. Using secondary panel data for 2007–2009 on operating characteristics of over 600 e‐retailers, our econometric analysis finds that only the value‐added service functions are positively associated with changes in e‐retail sales revenues across time. We also observe a decreasing marginal impact of deploying additional value‐added service features. To account for possible alternate explanations, we control for firm‐ and time‐specific fixed effects, merchant types, merchandise categories, and order fulfillment strategies. By further decomposing e‐retail sales revenues into Web site traffic, conversion rate, and average order value, we find that Web site functions affect e‐retail sales revenues mainly through their impact on Web site traffic. Our investigation demonstrates the empirical research usefulness of the Voss conceptual e‐service sand cone model. Our results identify for managers where to focus ongoing e‐retailing system development efforts, yet suggest that focusing too many retailing capabilities on exploratory and experimental value‐added service features may backfire, potentially leading to worsening e‐retailer performance.     

A Predictive Model for Survival of Escherichia coli O157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure

This study aimed at developing a predictive model that captures the influences of a variety of agricultural and environmental variables and is able to predict the concentrations of enteric bacteria in soil amended with untreated Biological Soil Amendments of Animal Origin (BSAAO) under dynamic conditions. We developed and validated a Random Forest model using data from a longitudinal field study conducted in mid-Atlantic United States investigating the survival of Escherichia coli O157:H7 and generic E. coli in soils amended with untreated dairy manure, horse manure, or poultry litter. Amendment type, days of rain since the previous sampling day, and soil moisture content were identified as the most influential agricultural and environmental variables impacting concentrations of viable E. coli O157:H7 and generic E. coli recovered from amended soils. Our model results also indicated that E. coli O157:H7 and generic E. coli declined at similar rates in amended soils under dynamic field conditions.The Random Forest model accurately predicted changes in viable E. coli concentrations over time under different agricultural and environmental conditions. Our model also accurately characterized the variability of E. coli concentration in amended soil over time by providing upper and lower prediction bound estimates. Cross-validation results indicated that our model can be potentially generalized to other geographic regions and incorporated into a risk assessment for evaluating the risks associated with application of untreated BSAAO. Our model can be validated for other regions and predictive performance also can be enhanced when data sets from additional geographic regions become available.     

Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models

In this paper we study identification and estimation of a correlated random coefficients (CRC) panel data model. The outcome of interest varies linearly with a vector of endogenous regressors. The coefficients on these regressors are heterogenous across units and may covary with them. We consider the average partial effect (APE) of a small change in the regressor vector on the outcome (cf. Chamberlain (1984), Wooldridge (2005a)). Chamberlain (1992) calculated the semiparametric efficiency bound for the APE in our model and proposed a √N‐consistent estimator. Nonsingularity of the APE's information bound, and hence the appropriateness of Chamberlain's (1992) estimator, requires (i) the time dimension of the panel (T) to strictly exceed the number of random coefficients (p) and (ii) strong conditions on the time series properties of the regressor vector. We demonstrate irregular identification of the APE when T = p and for more persistent regressor processes. Our approach exploits the different identifying content of the subpopulations of stayers—or units whose regressor values change little across periods—and movers—or units whose regressor values change substantially across periods. We propose a feasible estimator based on our identification result and characterize its large sample properties. While irregularity precludes our estimator from attaining parametric rates of convergence, its limiting distribution is normal and inference is straightforward to conduct. Standard software may be used to compute point estimates and standard errors. We use our methods to estimate the average elasticity of calorie consumption with respect to total outlay for a sample of poor Nicaraguan households.     

Computing Optimal Beams in Two and Three Dimensions

The problem of computing an optimal beam among weighted regions (called the optimal beam problem) arises in several applied areas such as radiation therapy, stereotactic brain surgery, medical surgery, geological exploration, manufacturing, and environmental engineering. In this paper, we present computational geometry techniques that enable us to develop efficient algorithms for solving various optimal beam problems among weighted regions in two and three dimensional spaces. In particular, we consider two types of problems: the covering problems (seeking an optimal beam to contain a specified target region), and the piercing problems (seeking an optimal beam of a fixed shape to pierce the target region). We investigate several versions of these problems, with a variety of beam shapes and target region shapes in 2-D and 3-D. Our algorithms are based on interesting combinations of computational geometry techniques and optimization methods, and transform the optimal beam problems to solving a collection of instances of certain special non-linear optimization problems. Our approach makes use of interesting geometric observations, such as utilizing some new features of Minkowski sums.     

Intra-Organizational and inter-organizational resource allocation in two-stage network systems

There are organizational systems, such as bank branches and two-stage supply chains, which are composed of multiple parallel two-stage structures. Resource allocation in these systems is to maximize the benefit of the overall organization from a global viewpoint. In this study, we consider two types of systems at an organizational level: a centralized organizational system treating the whole two-stage production process as a basic unit, and a decentralized organizational system including two sub-organizations (groups) treating one of the two-stage production processes as a basic unit. We propose intra-organizational and inter-organizational resource allocation plans for two different organizational systems, respectively. Specially, two modes of free intermediate resource allocation (Free IRA) and fixed intermediate resource allocation (Fixed IRA) are discussed for the decentralized organizational system. The proposed allocation plans are based on two-stage data envelopment analysis models with bi-level formulations, in which the upper-level model is to maximize the entire organizational effectiveness (total outputs minus total inputs) by determining the optimized input resources and output targets while the lower-level model is concerned with efficiency constraints of all decision-making units simultaneously. The developed methods are illustrated by an application to a real-world problem with 17 city bank branches.