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This longitudinal research examines US symphony orchestra sector organizations to determine individual efficiencies in allocating resources (donations, governmental/private funding, etc.) for desirable outputs (concerts, educational programs, community outreach). It provides researchers and managers with a tool for identifying, assessing and mitigating organizational inefficiencies.

This study assesses relative efficiencies in performing arts organizations using Data Envelopment Analysis (DEA), a widely-used nonparametric data-intensive benchmarking technique that determines an optimal “production frontier” of best-practice organizations among their peers and assesses their abilities to turn multivariate inputs into multivariate desired outputs.

This analysis highlights efficiency differences in a wide range of orchestras in converting available resources into performance-related outputs. It provides individual arts organizations with useful results for developing practical benchmarks to achieve organizational efficiency improvement.

This study provides constructive benchmarking guidance for improving efficiencies of relatively-inefficient organizations. Future analysis can expand the scope to utilize a two-stage DEA model to provide more specific guidance to arts organizations.

This pragmatic analysis enables arts/culture institutions to assess their organizational efficiencies and identify opportunities to optimize resources in producing social outputs for their target markets.

Efficiency improvements enable performing arts organizations to provide additional artistic/social services, with fewer resources, to larger audiences.

This research demonstrates the abilities of DEA analysis to assess both a sector and its individual organizations to determine efficiencies, identify sources of inefficiencies and assess longitudinal efficiency trends.

The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated.

DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances.

All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models.

As we are aware, this is the first survey in this topic.

This study aims to present diverse proposals for the measurement of tourism destination competitiveness that serve as alternatives to the travel and tourism competitiveness index (TTCI).

The proposal includes principal component analysis, the DP2-distance method, goal programming, data envelopment analysis and the Borda count. The study evaluates 17 destinations from Central America and the Caribbean.

These include the feasibility that the methodologies provide reliable competitiveness rankings and the possibility of using less information due to the strength of the statistical methodologies. International tourist arrivals, income from international tourism and travel and tourism contribution to the gross domestic product could be used as approximations of tourism destination competitiveness.

The main limitation is the absence of major destinations from the region that constitutes fierce competitors.

New aggregation methods can build composite indicators for competitiveness measurement and their presentation in a more comprehensible way.

The results serve as an alternative for countries that have yet to be considered in international tourism competitiveness comparisons.

A better explanatory power of the proposed index is given, thanks to their decomposition capacity and the reduction of the limitations of the original TTCI. Moreover, the proposals facilitate the inclusion of external information or the execution of a completely objective methodology.

本研究旨在为衡量旅游目的地竞争力提出多样化的建议, 并作为旅行和旅游竞争力指数的替代方案。

该提案包括主成分分析、DP₂ 距离方法、目标规划、数据包络分析和 Borda 计数。 该研究评估了中美洲和加勒比地区的 17 个目的地。

结果包括这些方法提供可靠的竞争力排名的可行性, 以及由于统计方法的优势而使用较少信息的可能性。 国际旅游人数、国际旅游收入以及旅行和旅游对 GDP 的贡献可以用作旅游目的地竞争力的近似值。

主要局限是该地区没有竞争激烈的主要目的地。

新的聚合方法可以为竞争力测量建立综合指标, 并以更易于理解的方式呈现。

结果可作为国际旅游竞争力比较中, 衡量尚未考虑国家的替代方案。

由于其分解能力和原始 TTCI 限制的减少, 所提出的指数具有更好的解释力。 此外, 这些建议有助于纳入外部信息及执行完全客观的方法。

El presente estudio busca presentar diversas metodologías para medir la competitividad de los destinos turísticos, de modo que sirvan como alternativa al Índice de Competitividad de Viajes y Turismo.

La propuesta incluye Análisis de Componentes Principales, el método de distancia DP₂, Programación por Metas, Análisis Envolvente de Datos y el Recuento de Borda. Se analizan 17 destinos de Centro América y el Caribe.

Estos incluyen la validez de las metodologías para obtener rankings de competitividad fiables y la posibilidad de emplear menor cantidad de información, dadas las fortalezas de los procedimientos estadísticos propuestos. Las Llegadas de Turistas Internacionales, los Ingresos por Turismo Internacional, y la Contribución del Turismo al PIB podrían ser buenas aproximaciones para medir competitividad turística

La principal limitación es la ausencia de destinos importantes de la región, que se consideran importantes competidores.

Novedosos procedimientos de agregación para crear indicadores sintéticos para medir la competitividad turística y su presentación de un modo más comprensible.

Los resultados sirven como alternativa para otros destinos que aún no han sido considerados en comparaciones internacionales de competitividad turística.

Un mejor poder explicativo de los índices propuestos, gracias a su capacidad de descomposición, y la reducción de las limitaciones del índice del WEF. Además, las propuestas facilitan la inclusión de información externa o la ejecución de un método completamente objetivo.

The purpose of the study is to determine the relationship between bank efficiency in terms of corporate governance guidelines and the extent of practice of earnings management (EM).

Archival data of listed private commercial banks of Dhaka Stock Exchange over the period of 2007–2016 relating to corporate governance and earnings management are collected and analyzed using parametric and non-parametric methods (efficiency analysis) and applying panel regression analysis.

The same distribution pattern and have low degree of the correlation (0.248) among them. It is found that private commercial banks of Bangladesh, on average, display efficiency level of 80.84%. The average value of discretionary loan loss provision (i.e. measure of earnings management) is 0.4249 and this indicates the presence of earnings management. The relation between earnings management and efficiency score in both cases of two-step system generalized methods of moments (GMMs) and difference GMM are found to be negative. The negative coefficients (−0.7969 and −0.57) indicate that as the efficiency increases, the practice of earnings management by the private commercial bank reduces. By estimating efficiency based on corporate governance guidelines and detecting the existence of EM, the major contribution of the study is establishing the relationship between bank efficiency based on compliance with corporate governance guidelines and managerial practice of earnings management in Bangladesh. Empirical results of the study have also established the fact that the more efficient the management of the banks are, the less likely it will practice earnings management under the compliance of corporate governance guidelines in Bangladesh.

This research study has some limitations. Only conventional banks are considered for the study, with the exception of Islamic banks. Comparison between conventional banks and Islamic banks could have been done.

Based on the literature study, the effectiveness of corporate governance aligns with decreasing agency conflict, protection of shareholders' interests and restrain management from self-serving activities (i.e. practice of earnings management). The empirical results of the study established these facts. Regulators should give more emphasis on effective implementation of good governance.

To the best of the authors' knowledge, this may be the first to empirically determine the relationship between efficiency estimation based on corporate governance and earnings management in case of listed commercial banks of Bangladesh.

This study has two purposes. The first is to identify the determinants influencing the selection of a container number recognition system via a quantitative method to thereby establish an evaluation structure. The second purpose is to conduct an empirical study to determine the weights of the criteria and alternatives.

The exploratory factor analysis (EFA) and fuzzy analytic hierarchy process (AHP) were applied to determine the evaluation structure and weights of the criteria and alternatives, respectively.

An empirical study based on a dedicated terminal at Keelung Port is conducted. The result demonstrates that the radio-frequency identification (RFID) system is a suitable system for the terminal under consideration in this study.

The value of this study is twofold. First, EFA was applied to extract common factors from a wide questionnaire survey, thereby establishing a hierarchical analysis structure. This method and comprehensive evaluation structure are useful references for both practitioners and researchers to deal with problems of gate automation. Second, fuzzy AHP was used to decide the weights of the hierarchical structure. The weights obtained by this method are more objective and rational as the imprecision expressions in returned samples have been considered and dealt with.

This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

This paper presents an alternative approach to evaluating the overall efficiency and performance of Taiwanese container ports. Specifically, a parallel activity with series structure concept in the form of data envelopment analysis (MNDEA) is used to construct a model that applies to three different activities: harbor management, stevedoring and warehousing operations. We will further divide each activity into two process types, production processes and services processes. We will also adopt a Delphi survey approach and use the Analytic Network Process (ANP) to identify these processes’influence dependence and their degree of importance for the MNDEA model setting. An empirical application demonstrates the performance of Taiwanese container ports by using MNDEA with window analysis techniques via the directional distance functionThe results demonstrate that the application is effective in indicating and/or suggesting resource-adjustments, while considering which undesirable output levels and shared inputs were involved. The results also present directions for possible improvements in workplace efficiency.

Many important constructs of business and social sciences are conceptualized as composites of common factors, i.e. as second-order constructs composed of reflectively measured first-order constructs. Current approaches to model this type of second-order construct provide inconsistent estimates and lack a model test that helps assess the existence and/or usefulness of a second-order construct. The purpose of this paper is to present a novel three-stage approach to model, estimate, and test second-order constructs composed of reflectively measured first-order constructs.

The authors compare the efficacy of the proposed three-stage approach with that of the dominant extant approaches, i.e. the repeated indicator approach, the two-stage approach, and the hybrid approach by means of simulated data whose underlying population model is known. Moreover, the authors apply the three-stage approach to a real research setting in business research.

The study based on simulated data illustrates that the three-stage approach is Fisher-consistent, whereas the dominant extant approaches are not. The study based on real data shows that the three-stage approach is meaningfully applicable in typical research settings of business research. Its results can differ substantially from those of the extant approaches.

Analysts aiming at modeling composites of common factors should apply the proposed procedure in order to test the existence and/or usefulness of a second-order construct and to obtain consistent estimates.

The three-stage approach is the only consistent approach for modeling, estimating, and testing composite second-order constructs made up of reflectively measured first-order constructs.

The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.

A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.

The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.

Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.