Bo Hsiao

Using fuzzy super-efficiency slack-based measure data envelopment analysis to evaluate Taiwan's commercial bank efficiency

Data envelopment analysis (DEA) mainly utilizes envelopment technology to replace production function in microeconomics. The input and output of decision making units (DMUs) are projected into the attributes to evaluate or measure their performance. However, if the inputs and outputs are linguistically termed or are fuzzy-numbered, conventional DEA can not easily measure the performance. Therefore, we propose the use of a fuzzy super-efficiency slack-based measure DEA to analyze the operational performance of 24 commercial banks facing problems on loan and investment parameters with vague characteristics. After our analysis, we find that the fuzzy slack-based measure of efficiency (Fuzzy SBM)/fuzzy super-efficiency slack-based measure of efficiency (Fuzzy Super SBM) can not only effectively characterize uncertainty, but also have a higher capability to evaluate bank efficiency than the conventional Fuzzy DEA approach.

Performance evaluation with the entropy-based weighted Russell measure in data envelopment analysis

Conventional Data envelopment analysis is based on the Debreu-Farrell optimal solution in evaluating the decision-making units efficiency. Even if Farrell efficiency is achieved, there may exist slacks in individual input or output. To solve this problem, the Russell measure can be used to address the inherent shortcomings of the Farrell measure and devise an optimal solution for the Pareto-Koopmans concept of efficiency. However, the non-proportional radial measure may lead to distorted efficiency measurement of inefficient decision-making units, due to assumptions about its implicit importance of inputs and outputs. Therefore, this paper uses a simple method in calculating the weighting measurements, in order to override this assumption using the concept of entropy. By introducing entropy, the Russell measure easily uses input, output, and system weighting to evaluate performance. In addition, we also use the entropy-concepts applied to slack-based measure. Moreover, we illustrate this entropy-based Russell measure using data gathered from 24 of Taiwans commercial banks in order to rank and compare it with the conventional Russell measure.

Affective mechanisms linking Internet use to learning performance in high school students: A moderated mediation study

Although previous studies have concluded that Internet use can help students in learning and research, a number of empirical investigations have confirmed that Internet addiction or excessive Internet use has negative effect on students. Thus, if the Internet does not always benefit students, under which conditions can Internet use have positive effects? Since students’ beliefs in their academic self-efficacy and their abilities to begin, continue, and complete their studies are as important as their academic successes and performances, this study hypothesizes that academic self-efficacy acts as a mediator for Internet use and academic performance. Based on Social cognitive theory, we argue that student academic performance will be mediated by academic self-efficacy with respect to Internet use. Two kinds of Internet use, general and professional, are considered to be antecedents of academic self-efficacy. Survey data from 212 twelfth-grade vocational high school students in Taiwan indicate that general Internet use has an indirect positive effect on student academic performance, which is also mediated through academic self-efficacy. In contrast, general Internet use has no significant direct impact on students learning performance. This study also shows that Internet anxiety moderates the relationship between academic self-efficacy and learning performance. In students with low Internet anxiety, the relationship is moderated, which results in enhanced learning performance.

Measuring the relative efficiency of IC design firms using the directional distance function and a meta-frontier approach

This paper presents an alternative approach for evaluating the efficiency of integrated circuit (IC) design firms. In doing so, it accounts for differences between technology groups containing one or more design firms, and input and output factors to prevent influences of scale (e.g., firm size). Specifically, we employed a directional distance function approach to data envelopment analysis in order to evaluate inefficiency scores and differences among groups based on input and output factors. We found the efficiency of Taiwans IC design firms to be dependent not only on firm size but also on R&D expenditure and patent revenue. Our findings suggest that these factors significantly influence the technical efficiency of Taiwan IC design. Furthermore, by focusing on technology gaps, we offer some suggestions for the different groups based on group-frontier and meta-frontier analyses. Finally, using the results of these analyses, we extend the global results of this study, presenting ways to further improve efficiency.

Multiphase Assessment of Project Risk Interdependencies: Evidence from a University ISD Project in Taiwan

Project risks evolve dynamically, so variations in risk influences during the life cycle of an information system development project require analyses to devise risk management strategies cost effectively and at the appropriate stages. This study extends the Decision Making Trial and Evaluation Laboratory technique, using network theory, to assess the risk interdependencies for distinct project phases. A multiphase observation of a university information system development project in Taiwan provides a more in-depth understanding of the key risk factors. To enhance risk assessments, this study proposes integrating an interdependency indicator with risk exposure measures.

Measuring the technology gap and logistics performance of individual countries by using a meta-DEA–AR model

This study presents an alternative approach to evaluate the efficiency of the logistics performance index (LPI) of individual countries. This study considers the differences among technology groups by classifying and comparing the countries as high-income Organization for Economic Co-operation and Development (OECD), high-income non-OECD, upper-middle-income, lower-middle-income, and low-income countries. The meta-frontier data envelopment analysis with assurance regions (Meta-DEA–AR) model is proposed to evaluate LPI scores and differences accurately among groups. For conformity with the ranking of original World Bank LPI, the assurance region of each logistics indicator is obtained by a regression model. The results show that the LPI rankings obtained by the proposed model are very comparable to those of World Bank LPI. Without distorting the rankings, the results offer several managerial suggestions for those countries with inefficiency in their LPIs.

An application of centralized data envelopment analysis in resource allocation in container terminal operations

This paper presents an application of centralized data envelopment analysis (CDEA) to analyse the performance of a number of container terminals supervised by one liner shipping company. It provides a systematic and centralized perspective of resource reallocation based on one company’s perspective. The numerical results show that two of the five dedicated terminals are, by and large, efficient, but the other three need resources to be reallocated. Under the minor adjustment scenario, the liner shipping company should reduce the aggregated amount of the labour cost and the hauling equipment should be transferred to different terminals in America. Under the major adjustment scenario, both the labour and hauling equipment could be reduced.

Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis

Where different supply chain planning algorithms are used, generally similar results may pose some challenges on the differentiating powers of evaluating different production schedules because of the increasing complexity of a supply chain network structure. For the comparison purpose, performance evaluation of different supply chain planning algorithms aims to use different supply chains models with different demands, capacities, and commonality through efficiency perspective by using a modified network rational data envelopment analysis (DEA) model. The proposed DEA model has the abilities: (1) to treat only undesirable outputs that exist without normal output, and the situation where input and output are both zero by introducing two new parameters to denote the maximum inventory and amount of delayed demands of a given node in a given time period; and (2) to evaluate the effect of the undesirable outputs/inputs on efficiency with assumption that they leave the system at the end of the current time period and re-enter the system at the beginning of the next time period. To prove the effectiveness of this DEA model, eighteen scenarios with different demands, capacities, and multiple periods are compared. In addition, this study tests the DEA model on a wafer testing/probing operation of a leading global semiconductor manufacturing and testing company in Taiwan by internal supply chain perspective. Results show that the DEA model proposed in this study can be used to assess the efficiency of a real-world operation with undesirable outputs/inputs, such as inventory and delayed demands.

Efficiency Predictions by Fuzzy Piecewise Auto-regression

Predicting productivity and efficiency during the transfer of input to output is a key issue in many manufacturing and service operation applications. Operation research and econometrics acknowledge that efficiency analysis is a major research issue. Data envelopment analysis (DEA) has substantially reshaped the result of information economics in previous years. However, the extent to which asymmetric information is relevant for efficiency prediction has rarely been sought empirically. Efficiency prediction plays a crucial role in many state-of-the-art applications and planning. Forecasting methodologies that can accurately predict efficiency scores can help in strategic decision-making. This study focuses on fuzzy piecewise auto-regression and the catching-up efficiency index (CIE), which supports efficiency prediction. In this study, two regression models were formulated by utilizing data from commercial banks in Taiwan from 2002 to 2005. These models were used to validate banking efficiency scores for 2005 and 2006, and to predict the banking efficiency scores for 2007. The results of a thorough computational analysis provide a range indicating the prediction value for each bank based on fuzzy regression characteristics.

Performance evaluation of the Taiwan railway administration

This study aims to investigate the effects of centralized allocation and optimization of railway resources on overall operational efficiency of the railway industry. Its results are intended to help Taiwan railway industry in resource allocation and reduction of organization resistance caused by resource deployment in Taiwan. For this purpose, this study proposes and divides resource reallocation into long-, middle-, and short-term plans, with three resource adjustment programs. These programs consider different geographical ranges and resource conditions of personnel and equipment in resource allocation by using a two-phase centralized data envelopment analysis. Conducted in 2011, the Taiwan railway data analysis indicated that high overall output that was attributed to efficient allocation of resources induced large-scale organizational changes and adjustments (such as staff reduction) in the railway industry. These changes resulted in widespread organization resistance. Nonetheless, this process enabled the railway industry in Taiwan to achieve balance between output and organization resistance under different environments efficiently.