Does knowledge management matter? Assessing the performance of electricity distribution districts based on slacks-based data envelopment analysis

This study uses slacks-based data envelopment analysis (DEA) to examine the performance of electricity distribution districts (EDDs) in Taiwan in 2004. It also explores the relationship between a knowledge management system (KMS) and variations in the efficiency of EDDs in the Taiwan Power Company from 2000 to 2004. The findings show that the EDDs performed well in terms of managerial efficiency in 2004, and 75% of the districts exhibited an increasing performance variation when using cross-period analysis for that period. We show that there is a positive relationship between a KMS and variations in organizational efficiency. In addition, we present a decision-making matrix and conceptual map to help EDD managers improve their managerial efficiency and skills. This study contributes to the field of knowledge management research by applying the DEA model to link the performance between KMS and an organization. Several significant findings emerge from the study.     

When does data envelopment analysis outperform a naïve efficiency measurement model?

This paper compares the results from data envelopment analysis (DEA) to a naïve efficiency measurement model, which generates a scalar efficiency score by averaging all output–input ratios. Random data and real-life data are used to test the relative performance of the naïve model against various DEA models. The results suggest that the proposed the naïve model replicates DEA efficiency scores almost perfectly for constant return-to-scales and low heterogeneity in output–input data. It is therefore concluded that heterogeneity in output–input data is important to take advantage of the capability of DEA. It is also shown that heterogeneity is more relevant to efficiency measurement than the number of dimensions.     

Does Bitcoin dominate the price discovery of the Cryptocurrencies market? A time-varying information share analysis

Using a time-varying vector error correction model (VECM), we examine the dynamic information shares of the top four Cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC) over 1/1/2016–31/12/2019. Although steadily decreasing, the information share of BTC is still the largest as of end-2019. The individual dominances of market capitalization and trading volume can explain 20% of variations of the BTC information share but only 6% of those for ETH.     

Do mergers enhance the performance of hospital efficiency?

Due to the wave of mergers that have taken place in the USA, the early 1990s could be labelled as a restructuring era for health care systems. The question of whether mergers have an impact on organizational performance is still an area of interest for health services researchers. In this study, we examined the impacts of horizontal mergers of US hospital's technical efficiency before and after merger using longitudinal Data Envelopment Analysis (DEA). The findings of our study illustrate that mergers do increase a hospital's level of efficiency. Constant returns-to-scale model indicated an overall reduction in input utilisation after merger, compared to variable returns-to-scale model. This indicates the role of scale efficiency as a dominant source of improvement in inefficiency of hospitals involved in horizontal mergers, but not for technical efficiency. Suggestions for future study are provided.     

Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment

This study compares DEA (data envelopment analysis) with DEA–DA (discriminant analysis) in terms of bankruptcy assessment. Recently, many DEA researchers propose a use of DEA as a quick-and-easy tool to assess corporate bankruptcy. Meanwhile, other DEA researchers discuss a use of DEA–DA for bankruptcy-based financial analysis. The two groups are very different from the conventional use of DEA because we have long applied DEA to the measurement of operational performance, or productivity analysis. The two research groups open up a new application area (bankruptcy-based financial assessment) for DEA. This study discusses methodological strengths and weaknesses of DEA and DEA–DA from the perspective of corporate failure. The proposed comparative analysis has the three main criteria: (a) how to handle negative data in financial variables, (b) how to handle data imbalance between default and non-default firms, and (c) how to identify a failure process over time. This study finds that DEA is a managerial tool for the initial assessment of corporate failure and DEA is useful for busy corporate leaders and financial managers. In contrast, DEA–DA is useful for researchers and individuals who are interested in the detailed assessment of bankruptcy and its failure process in a time horizon.     

On the efficiency of the Asmussen–Kroese-estimator and its application to stop-loss transforms

Recently, Asmussen and Kroese [6] proposed efficient stochastic estimators to approximate the probability that a random sum exceeds a certain threshold. This article aims to extend the analysis of some of these estimators: Several questions induced by [6] for the threshold setting are discussed. In particular, it is shown that Asmussen and Kroese provide the first heavy-tailed example with vanishing relative error. Furthermore, estimators of similar type for the approximation of moments of stop-loss transforms are given. Their asymptotic performance is analysed and a numerical illustration is attached at the end.     

An extension of a data assimilation method based on the application of the Fokker–Planck equation

A data assimilation method based on the Kalman filter theory and on the Fokker–Planck equation is extended to assimilate Atlantic Ocean data into a new version of the well-known Modular Ocean Model (MOM_3) from NOAA/GFDL. This extension enables assimilation of non-uniformly distributed data in space and time. Numerical experiments with Levitus atlas data are carried out with the ocean model configured at a low resolution. Some results of these experiments as well as other possible expansions are discussed.     

Ecological modernization in the electrical utility industry: An application of a bads–goods DEA model of ecological and technical efficiency

Newly-developed data envelopment analysis techniques permit simultaneous consideration of ‘good and bad’ outputs in evaluating efficiency. We use these techniques to determine joint ecological and technical efficiencies of the 437 largest fossil-fueled electricity-generating plants in the United States. Utilizing the EPA’s E-Grid and Clean Air Markets databases and drawing on ecological modernization theory we evaluate whether innovations in organizational practices and technological solutions help achieve joint technical and environmental performance efficiencies.     

Mean and bold? On separating merger economies from structural efficiency gains in the drinking water sector

The Dutch drinking water sector experienced two drastic changes over the last 10 years. Firstly, the sector association started a voluntary benchmarking aimed to increase the efficiency and effectiveness of the sector. Secondly, merger activity arose. This paper develops a nonparametric model to dissect and distinguish the effects on efficiency of these evolutions. Parametric corrected ordinarily least squares (Fourier) tests show the robustness of the model with respect to the specification and its variables. Although detecting an efficiency enhancing effect of benchmarking, we find insignificant merger economies due to the absence of scale economies and the absence of increased incentives to fight inefficiencies.     

Reliability and efficiency of algorithms for computing the significance of the Mann–Whitney test

Motivated by recent applications of the Mann–Whitney U test to large data sets we took a critical look at current methods for computing its significance. Surprisingly, we found that the two fastest and most popular tools for exact computation of the test significance, Dinneen and Blakesley’s and Harding’s, can exhibit large numerical errors even in moderately large datasets. In addition, another method proposed by Pagano and Tritchler also suffers from a similar numerical instability and can produce inaccurate results. This motivated our development of a new algorithm, mw-sFFT, for the exact computation of the Mann–Whitney test with no ties. Among the class of exact algorithms that are numerically stable, mw-sFFT has the best complexity: O(m ² n) versus O(m ² n ²) for others, where m and n are the two sample sizes. This asymptotic efficiency is also reflected in the practical runtime of the algorithm. In addition, we also present a rigorous analysis of the propagation of numerical errors in mw-sFFT to derive an error guarantee for the values computed by the algorithm. The reliability and efficiency of mw-sFFT make it a valuable tool in compuational applications and we plan to provide open-source libraries for it in C++ and Matlab.     

On the consistency of the MLE for Ornstein–Uhlenbeck and other selfdecomposable processes

In this paper we give easy to verify conditions for the strong consistency of the maximum likelihood estimator (MLE) in the case when data is sampled from a parametric family of selfdecomposable distributions. The difficulty arises from the fact that standard conditions for the consistency of the MLE are based on the pdf, which, for most selfdecomposable distributions, is not available in a closed form. Instead, our conditions are based on properties of the Lévy triplet (i.e. the Lévy measure, the Gaussian part, and the shift) of the distribution. Further, we extend out results to certain selfdecomposable stochastic processes, and, in particular, we give conditions in the case when the data is sampled from a Lévy or an Ornstein–Uhlenbeck process.     

An analysis of the Prothero–Robinson example for constructing new adaptive ESDIRK methods of order 3 and 4

Explicit singly-diagonally-implicit (ESDIRK) Runge–Kutta methods have usually order reduction if they are applied on stiff ODEs, such as the example of Prothero and Robinson. It can be observed that the numerical order of convergence decreases to the stage order, which is limited to two. In this paper we analyse the Prothero–Robinson example and derive new order conditions to avoid order reduction. New third and fourth order ESDIRK methods are created, which are applied to the Prothero–Robinson example and to an index-2 DAE. Numerical examples show that the new methods have better convergence properties than usual ESDIRK methods.     

On CP, LP and other piecewise perturbation methods for the numerical solution of the Schr?dinger equation

The piecewise perturbation methods (PPM) have proven to be very efficient for the numerical solution of the linear time-independent Schr?dinger equation. The underlying idea is to replace the potential function piecewisely by simpler approximations and then to solve the approximating problem. The accuracy is improved by adding some perturbation corrections. Two types of approximating potentials were considered in the literature, that is piecewise constant and piecewise linear functions, giving rise to the so-called CP methods (CPM) and LP methods (LPM). Piecewise polynomials of higher degree have not been used since the approximating problem is not easy to integrate analytically. As suggested by Ixaru (Comput Phys Commun 177:897–907, 2007), this problem can be circumvented using another perturbative approach to construct an expression for the solution of the approximating problem. In this paper, we show that there is, however, no need to consider PPM based on higher-order polynomials, since these methods are equivalent to the CPM. Also, LPM is equivalent to CPM, although it was sometimes suggested in the literature that an LP method is more suited for problems with strongly varying potentials. We advocate that CP schemes can (and should) be used in all cases, since it forms the most straightforward way of devising PPM and there is no advantage in considering other piecewise polynomial perturbation methods.     

Does employee participation matter? An empirical study on the effects of participation on well-being and organizational performance

Central European Journal of Operations Research - Employability, talent and/or motivation of people can be a source of sustainable competitive advantage; difficult for competitors to imitate. The...     

How Do Cultural Classes Emerge from Assimilation and Distinction? An Extension of the Cucker-Smale Flocking Model

When cultural tastes are not neutral but hierarchically matched to social status, people assimilate themselves to higher status by consuming cultural goods while distinguishing themselves from lower status by developing new tastes. Extending the Cucker-Smale model for mutual influence among agents, we examine when and how many cultural classes emerge from continuous distributions of tastes and what conditions those classes satisfy, through the assimilation-distinction mechanism. We simulate the models with different initial distributions of tastes (uniform, normal, and chi-square), given various ranges of 2 parameters: (a) the strength and (b) the range of distinction relative to assimilation. Tastes are flocking and cultural classes emerge when the range of assimilation is much larger than that of distinction. The number of classes increases with the strength of distinction, whereas the distance between classes equals the range of distinction. Some properties of emergent classes are mathematically proved. First, in a two-class system, the stronger distinction, the larger the upper class. Second, in a three-class system, the middle class is necessarily larger than the lower class and likely larger than the upper class. Third, a 3-class system cannot emerge if distinction is weaker than assimilation. These properties are universal and do not depend on the initial distribution of cultural tastes. This independence predicts homogeneous cultural classes emerging across different social conditions. Also, the cultural middle class as the largest group may explain why subjective class consciousness is often higher than objective position. Unless assimilating efforts can reach an infinite range, there emerges a cultural outcast at the lowest end of the cultural hierarchy.     

What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions

Evaluating the economic attractiveness of large projects often requires the development of large and complex financial models. Model complexity can prevent management from obtaining crucial information, with the risk of a suboptimal exploitation of the modelling efforts. We propose a methodology based on the so-called “differential importance measure (D)$(D)$” to enhance the managerial insights obtained from financial models. We illustrate our methodology by applying it to a project finance case study. We show that the additivity property of D grants analysts and managers full flexibility in combining parameters into any group and at the desired aggregation level. We analyze investment criteria related to both the investors’s and lenders’ perspectives. Results indicate that exogenous factors affect investors (sponsors and lenders) in different ways, whether exogenous variables are considered individually or by groups.