Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization

The performance of a decision making unit (DMU) can be evaluated in either a cross-sectional or a time-series manner, and data envelopment analysis (DEA) is a useful method for both types of evaluation. In order to eliminate the inconsistency caused by using different frontier facets to calculate efficiency, common-weights DEA models have been developed, under which a group of DMUs can be ranked for a specific period. This study proposes a common-weights DEA model for time-series evaluations to calculate the global Malmquist productivity index (MPI) so that the productivity changes of all DMUs have a common basis for comparison. The common-weights global MPI not only has sound properties, but also produces reliable results. The case of Taiwan forests after reorganization shows that the MPIs calculated from the conventional DEA model produce misleading results. The common-weights global MPI approach, on the other hand, correctly identifies districts with unsatisfactory performance before the reorganization and those with unsatisfactory productivity improvement after the reorganization.     

Measurement and decomposition of the Malmquist productivity index for parallel production systems

The conventional Malmquist productivity index (MPI), which ignores the internal structure of a production system when measuring changes in performance between two periods, may produce misleading results. This paper thus takes the operations of the component processes into account in investigating the MPI of parallel production systems. A relational data envelopment analysis (DEA) model is developed to measure the biennial MPIs of the system and internal processes at the same time, and it is shown that the former is a linear combination of the latter. This decomposition helps identify the processes that cause the decline in performance of the system. An example of 39 branches of a commercial bank, with deposits, sales, and services as the three major functions operating in parallel, is used to illustrate this approach.     

Statistical precision of DEA and Malmquist indices: A bootstrap application to Norwegian grain producers

Previous applications of data envelopment analysis (DEA) and its subsequent Malmquist indices to efficiency and productivity measurements have been criticised for not providing statistical inferences regarding the significance of observed results. In this paper, DEA and a Malmquist index are combined with a bootstrap method in order to provide succinct statistical inferences that determine the performance of grain producers in Eastern Norway. The data cover the period between 1987 and 1997. Results reveal: (i) a significant degree of inefficiency (approximately 11%) and an average productivity progress of 38% over the period considered; (ii) the formidable productivity progress observed is primarily explained by technical efficiency changes that enabled producers to catch up with front runners; and (iii) environmental factors, such as weather conditions, impact both efficiency and productivity. Finally, the analysis reveals that using bootstrapping to make statistical inferences suggests that researchers should be careful in making performance comparisons based on conventional DEA methods, as any discovered differences may not be significant.     

Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method

Benchmarking of electricity distribution utilities has been widely used as a means to contribute for the adoption or reinforcement of enhanced competitiveness and innovation practices to optimize costs, increase customer satisfaction, improve corporate image and maximize profits. The purpose of this paper is to present a benchmarking study for the maintenance and outage repair activity carried out by a Portuguese electricity distribution company, EDP Distribuição (EDP-D), using the value-based DEA method, which builds on links between data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA). This study illustrates the impact of the incorporation of managerial preferences in the classification and ranking of 40 network areas served by EDP-D, confronting the results with a previous study based on a BCC DEA model. In order to deal with the underlying uncertainty, the value-based DEA method for performance evaluation is adapted to include the concept of super-efficiency. Besides identifying best practices, sources of inefficiency, gaps relatively to best practices and opportunities for improvement, this analysis supports the introduction of corrective measures and informs decisions about future goals.     

R&D project evaluation: An integrated DEA and balanced scorecard approach

We present and demonstrate a multi-criteria approach for evaluating R&D projects in different stages of their life cycle. Our approach integrates the balanced scorecard (BSC) and data envelopment analysis (DEA) and develops an extended DEA model. The input and output measures for the integrated DEA–BSC model are grouped in “cards” which are associated with a “BSC for R&D projects”. The BSC is embedded in the DEA model through a hierarchical structure of constraints that reflect the BSC balance considerations. We illustrate the proposed approach with a case study involving an industrial research laboratory that selects and executes dozens of R&D projects every year.     

Predicting project approvals: A case of grants from the National Science Council of Taiwan

Project selection is an important task for organizations in achieving their missions using limited budgets and resources. Whether or not a project will be approved is also of primary concern to the applicants. This paper predicts whether a project will be approved for cases where the criteria for evaluating it are known while the scoring system is not. The idea is to construct a frontier function for the approved projects from past performance on the criteria. The relative distance between a proposed project and the frontier serves as an indicator of the possibility that the project will be approved. Data from the Management II Division of the National Science Council of Taiwan in the Topic Research Project are collected to illustrate this approach. From the percentile of the distance measure, an applicant is able to predict the possibility that their project will be approved. Since professors with different levels of experience and different research areas have different research performance, these factors are taken into account in the prediction. A Malmquist productivity index analysis is also conducted to investigate the performance improvement of the applicants in research between two periods.     

Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA

Measuring and improving the efficiency of the Chinese commercial banking system has recently attracted increasing interest. Few studies, however, have adopted the two-stage network DEA to explore this issue in the Chinese context. Because the entire operational process of the banking system could be divided into two sub-processes (deposit producing and profit earning), the evaluation of the sub-process efficiencies could be used to assist in identifying the sources of the inefficiency of the entire banking system. In this study, we utilize the network DEA approach to disaggregate, evaluate and test the efficiencies of 16 major Chinese commercial banks during the third round of the Chinese banking reform period (2003–2011) with the variable returns to scale setting and the consideration of undesirable/bad output. The main findings of this study are as follows: (i) the two-stage DEA model is more effective than the conventional black box DEA model in identifying the inefficiency of banking system, and the inefficiency of the Chinese banking system primarily results from the inefficiency of its deposit producing sub-process; (ii) the overall efficiency of the Chinese banking system improves over the study period because of the reform; (iii) the state-owned commercial banks (SOBs) appear to be more overall efficient than the joint-stock commercial banks (JSBs) only in the pre-reform period, and the efficiency difference between the SOBs and the JSBs is reduced over the post-reform period; (iv) the disposal of non-performing loans (NPLs) from the Chinese banking system in general explains its efficiency improvement, and the joint-equity reform of the SOBs specifically increases their efficiencies.     

Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks

In measuring the overall efficiency of a set of decision making units (DMUs) in a time span covering multiple periods, the conventional approach is to use the aggregate data of the multiple periods via a data envelopment analysis (DEA) technique, ignoring the specific situation of each period. This paper proposes using a relational network model to take the operations of individual periods into account in measuring efficiencies. The overall and period efficiencies of a DMU can be calculated at the same time. Notably, the overall efficiency is a weighted average of the period efficiencies, and the weights are the most favorable ones for the DMU being evaluated. This model, together with two existing ones, is applied to measure the efficiency of 22 Taiwanese commercial banks for the period of 2009–2011. The three-year multi-period analysis shows that the proposed model is more discriminative than the existing ones in ranking the performance of the banks. The period efficiencies for the three years increased steadily, indicating that the performances of the Taiwanese banks examined in this work were improving over this period.     

DEA and ranking with the network-based approach: a case of R&D performance

This study enhances the network-based approach, which is a novel method to increase discrimination in data envelopment analysis. The enhancements include removing the bias caused by a scale difference among organizations and highlighting the approach's ability to identify the strengths and weaknesses of each organization. The former makes the approach applicable to both the constant returns of scale (CRS) and the variable returns of scale (VRS) models. The network-based approach applies the centrality concept developed in social network analysis to discriminate efficient decision making organizations as determined by standard data envelopment analysis (DEA). More specifically, the results of data envelopment analysis are transformed into a directed and weighted network in which each node represents a decision making organization and the link between a pair of node represents the referencing relationship between the pair. The centrality value for each efficient organization provides the base for discrimination and ranking. This network-based approach suggests aggregating DEA results of different input/output combinations such that the merits of each organization under various situations can be considered. The final ranking of this approach favors organizations that have their strengths evenly spread and tends to screen out specialized efficient organizations. As a real world example, the approach is applied to evaluate and rank the R&D (research and development) performance of Taiwan's government-supported research institutes. The cross-organizations and within-organization strengths for each efficient research institute are identified after applying the approach. A two-stage R&D evaluation model separates the R&D process into the technology development and technology diffusion stage. The resulting performance map differentiates the research institutes into four categories—Achievers, Marketers, Innovators, and Underdogs.     

DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment

Using an additive super-efficiency data envelopment analysis (DEA) model, this paper develops a new assessment index based on two frontiers for predicting corporate failure and success. The proposed approach is applied to a random sample of 1001 firms, which is composed of 50 large US bankrupt firms randomly selected from Altman's bankruptcy database and 901 healthy matching firms. This sample represents the largest firms that went bankrupt over the period 1991–2004 and represents a full spectrum of industries. Our findings demonstrate that the DEA model is relatively weak in predicting corporate failures compared to healthy firm predictions, and the assessment index improves this weakness by giving the decision maker various options to achieve different precision levels of bankrupt, non-bankrupt, and total predictions.     

Measuring performance of two-stage network structures by DEA: A review and future perspective

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). An important area of development in recent years has been devoted to applications wherein DMUs represent two-stage or network processes. One particular subset of such processes is those in which all the outputs from the first stage are the only inputs to the second stage. The current paper reviews these models and establishes relations among various approaches. We show that all the existing approaches can be categorized as using either Stackelberg (leader-follower), or cooperative game concepts. Future perspectives and challenges are discussed.     

Non-oriented slack-based decompositions of profit change with an application to Taiwanese banking

A change in profit can originate from the output side and the input side. In the spirit of work by Tone [1] and follows Grifell-Tatjé and Lovell's [2], we propose a non-oriented slacks-based measure (SBM) model to decompose the change in the operating profit into various meaningful components: quantity effect and a price effect. The quantity effect can be decomposed into a productivity effect and an activity effect. The productivity effect is further decomposed into a technical effect and an operating efficiency effect. Both of them include an output side, which will result in a change in revenue and an input side which will result in a change in cost. The activity effect can be decomposed into a product mix effect, a resource mix effect and a scale effect. We illustrate our decompositions to the Taiwanese banking sector during the period 1994-2002 using the average of the base and current prices to evaluate these contributions. We find ignoring input side effects on the decomposition of profit changes would cause misleading results in managerial issues.     

Measurement of tourist hotels׳ productive efficiency,occupancy, and catering service effectiveness using a modified two-stage DEA model in Taiwan

This study develops a modified two-stage model to evaluate productive efficiency, occupancy, and catering service effectiveness of Taiwan׳s international tourist hotels. The difference between the modified and original two-stage model is that the modified two-stage model allows for multiple efficiencies to be calculated in the unique stage and the concept of intermediate input is introduced.The modified model was tested using 58 Taiwanese international hotels and the results show the modified model offers a more efficient and effective approach in calculating all the efficiencies in a single data envelopment analysis (DEA) implementation as opposed to independent efficiency calculations.     

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.     

Measuring strategic decision making efficiency in different country contexts: A comparison of British and Turkish firms

This paper compares the relative efficiency of the strategic decision making (SDM) processes of British and Turkish firms. The technique of data envelopment analysis (DEA) is used to measure the relative efficiency of these firms. The evidence obtained by the DEA analysis reveals that the Turkish firms tend to be superior to the British firms in terms of their SDM efficiency. There is a significant difference in scale returns with respect to the country of origin of the firms. The analysis of the improvement potential of inputs indicates that Turkish firms place more emphasis on managing environmental turbulence to enhance their SDM efficiency, while British firms tend to overly focus on the design of an appropriate organizational structure. However, no significant difference was noted between the two groups of firms with regard to the level of resources and effort exerted on formal strategic planning practices. The analysis of output deficits reveals that there is a significant difference between British and Turkish firms in the sources of inefficiency.     

Economies of scope in research and teaching: A non-parametric investigation

We propose a non-parametric methodology to study the presence of economies of scope between teaching and research (i.e., the teaching–research nexus). In particular, the paper advocates a conditional version of the ‘benefit-of-the-doubt’ approach to estimate the relationship between professors’ overall academic output, measured by a composite measure of multi-dimensional and importance-adjusted scores of teaching effectiveness and research productivity, and the time devoted to teaching and to research. The methodology is illustrated with a dataset of professors working at a Business & Administration department of a university college where the time allocation of teaching and research was assigned exogenously. The outcome of the analysis indicates the presence of limited scope economies for professors with an extensive research time.     

Psychosocial safety climate as an antecedent of work characteristics and psychological strain: A multilevel model

Abstract

Psychosocial safety climate (PSC) refers to a specific organizational climate for the psychological health of workers. It is largely determined by management and at low levels is proposed as a latent pathogen for psychosocial risk factors and psychological strain. Using an extended Job Demands-Control-Support framework, we predicted the (24 month) cross-level effects of PSC on psychological strain via work conditions. We used a novel design whereby data from two unrelated samples of nurses working in remote areas were used across time (N=202, Time 1; N=163, Time 2), matched at the work unit level (N= 48). Using hierarchical linear modelling we found that unit PSC assessed by nurses predicted work conditions (workload, control, supervisor support) and psychological strain in different nurses in the same work unit 24 months later. There was evidence that the between-group relationship between unit PSC and psychological strain was mediated via Time 2 work conditions (workload, job control) as well as Time 1 emotional demands. The results support a multilevel work stress model with PSC as a plausible primary cause, or “cause of the causes”, of work-related strain. The study adds to the literature that identifies organizational contextual factors as origins of the work stress process.