Abstract Construction is an industry that manages various types of construction, including building, extending, remodeling, reconstructing, and demolishing buildings. There are two major types of construction business: One is public construction, and the other is commercial construction, totaled 391 million dollars. Due to the recession of real estate in recent five years, the demand of the private sector decreases dramatically, which leads to the chain reaction of overall sales decreasing in construction industry in Taiwan. However, the industry still has 255 billions of sales revenues generated from government projects each year as well as hundreds of billion dollars of revenues from high-speed railroad and Kaohsiung subway BOT project. As long as the gross profit reaches 500 million dollars, the profit margin will be above 10 percent. The major source of revenues for a construction company is done through bidding. There is only one winner in the bid. Therefore, to research and evaluate relative performance of each construction company in the bid is an important task. The purpose of this thesis is to evaluation the performance of 12 public construction companies using DEA analysis. The evaluation will focus on areas of liquidity ratios, debt and asset management ratios, profitability ratios, and overall performance of efficiency. Major conclusions from the study are as following: 1. Four financial ratios and overall performance of efficiency were derived from DEA analysis, which eliminate inconsistencies of ratio analysis to make evaluation results more relevant. 2. Among the evaluation criteria, including liquidity, debt and asset management, profitability ratios, evaluations of profitability ratios among firms has the biggest inconsistency, followed by asset management. 3. From the sensitivity analysis, we conclude that companies with stable degrees of efficiency, which means efficiency degrees with small variance, have relatively higher profitability. This indicates the management needs to enhance the overall performance in order to enjoy ample profit. 4. From the study of DEA, we find that DEA analysis pinpoint the slack of each variable to enable decision-makers to see clearly which variables (input or output) need to be increased or decreased. This helps them to plan for the companies¡¦ overall efficiency to be at peak. DEA analysis provides decision-makers insights by relatively comparing the differentiation of statistics. This enables them to make executable strategies. Results of examples in this thesis prove efficiencies for evaluation. The convenience of ratio analysis and the complete perspective offered by DEA analysis are two best advantages of these two evaluation methods.

Abstract The Ministry of Education in Taiwan adopted a funding program in 1991 to encourage private universities and colleges to improve their academic performance through developing their own medium-term plans. This thesis tends to study the effect of the funding program on the academic performance of universities and colleges in Taiwan. A data envelopment analysis is conducted to examine the degree of improvement in faculty and teaching resources for 17 universities and colleges that participated in the funding program. Major conclusions in this study are as follows: 1) The academic performance of private universities and colleges was significantly improved after the implementation of the funding program. 2) The Ministry of Education put a limitation on the use of the fund in 1995. However, the academic performance of universities and colleges improved insignificantly after following the limitation. 3) As a matter of fact, the limitation on the use of the fund distorted the distribution of university resources so that the teacher/student ratio, ¡§the percentage of full-time faculty members with Ph.D.¡¨ and ¡§the average research expenditure per full-time faculty member¡¨ were decreased.

With the initiation of national health insurance, the ecology of medicine and organization of hospitals in Taiwan underwent rapid change. The beginning of a global budget aggravated the impact of hospitals, especially the district hospitals which were already facing most of the pressure. Over half (51.61 %) of the above district hospitals chose to focus on long term care, nursing homes making up the great majority. This research evaluates the efficiency of two district hospital-based nursing homes by Data Envelopment Analysis (DEA). The purpose of this research includes: First, evaluate the efficiency of two district hospital-based nursing homes, including overall efficiency, purely technological efficiency and scale efficiency. Second, assess the relative efficiency relationships of the two nursing homes. Third, probe the differences between relatively efficient and relatively inefficient groups in input variable and output variable. Fourth, with the analysis results, suggest adjustments of input and output to every relative inefficiency groups, as the health care manager's reference. The research approach is to consult relevant documents and the purpose of this study is to choose input and output variables. These variables are screened by Pearson analysis for isotonicity relation. Process the study by DEA for relatively efficient analysis, then direct against the relatively inefficient groups for discussion of efficiency reference set and slack variable analysis. The result of study shows: First, overall efficiency: There are 6 relatively inefficient groups (37.5%) in the first nursing home; There are 8 groups relatively inefficiently attended to (50%) in the second nursing home; There are 18 groups relatively inefficient (56.25%) in the combined first and second nursing home groups. Second, purely technological efficiency: There are 4 groups relatively inefficiently attended to (25.0%) in the first nursing home; There are 5 groups relatively inefficiently attended to (31.25%) in the second nursing home; There are 15 groups relatively inefficient (46.9%) in the combined first and second nursing home groups. Third, scale efficiency: There are 6 groups relatively inefficiently attended to (37.5%) in the first nursing home; There are 8 groups relatively inefficiently attended to (50%) in the second nursing home; There are 18 groups relatively inefficient (56.25%) in the combined first and second nursing home groups. Fourth, the slack variable analysis: the input items that must improve are foreign caretaker labor hours, nurse assist labor hours and nurse labor hours into sequentially, the output one is most important in increasing the income range. In this research, nursing staff and attendants' labor hour can only represent the workforce attending to the unit, but besides personnel figures, personnel quality is a very important factor, too; as regards nursing efficiency, it is not only the number attending to the quantity of service, but also the quality: For example, the incidence of pressure sores, unexpected accidents, patient satisfaction, or staff members' satisfaction, etc. These will all influence the overall efficiency in DEA and remaining research to analyse further in the future. We hope the parameters, such as the make up of resident people in different areas, severity of disease, etc., could continue relevant research to influence the efficiency of nursing homes in the future.

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Performance of Emergency Department (ED) physicians (MDs) is multi-faceted since it impacts multiple dimensions such as health outcomes of patients, utilization of resources, throughput of patients and timeliness of care. Therefore, the assessment of their performance demands the use of a tool that allows considering multiple evaluation criteria. However, commonly used multi-criteria evaluation methods often require assigning weights to dimensions in order to define their relative importance on a final performance score. This feature introduces subjectivity in the development of weights and has the potential to produce biased results. The purpose of this thesis research is to develop a multi-dimensional evaluation tool for evaluating performance of ED MDs. The proposed evaluation tool relies on a mathematical programming model known as Data Envelopment Analysis (DEA). The use of DEA does not ask for subjective weighting assignments for each dimension that describe the ED MDs’ performance. It is capable of considering multiple heterogeneous performance measures to identify benchmark practice and the individual improvements leading to best practice of each evaluated unit. The DEA model described here was developed from real data to assess the performance of 20 PED MDs from the Children’s Hospital of Eastern Ontario (CHEO). Multiple evaluations were run on stratified data in order to identify benchmark practice in each of seven categories of patients’ complaints and to determine the impact of accompanying MD trainees on PED MDs’ performance. For each PED MD, performance scores and improvements in each category of patients’ complaints (i.e. respiratory, trauma, abdominal, fever, gastroenterology, allergy and Ear-Nose-Throat complaints) were determined. This helped identifying the required improvements that would lead PED MDs to achieve benchmark performance. Regarding the influence of MD trainees on PED MDs’ performance, results show that most PED MDs (15 out of 20) perform better when they are not accompanied by a trainee which motivates further research to assess trade-offs between teaching and clinical performance. In summary, DEA proved to be an appropriate tool for performance evaluation of PED MDs because it helped to identify benchmark performers and provided information for performance improvements under a multi dimensional performance evaluation framework.

This thesis examines farm-level efficiency of rice farmers in the High Barind region of Bangladesh by estimating technical, allocative and economic efficiency using farm level cross section survey data. Two contrasting methods for measuring efficiency are applied: the stochastic econometric frontier and Data Envelopment Analysis (DEA). These measures are used to investigate the factors associated with technical, allocative and economic inefficiency. First, technical efficiency is computed by estimating the translog stochastic frontier in which technical inefficiency effects are modelled as a function of socioeconomic, infrastructure and environmental degradation factors in a single stage estimation technique using maximum likelihood method. Technical and scale efficiency are calculated by solving output- and input-oriented constant returns to scale (CRS) and variable returns to scale (VRS) DEA frontiers. A Tobit model is used to evaluate factors associated with technical and scale inefficiency from both input-oriented and output-oriented CRS and VRS frontiers. Same factors are analyzed as in the translog stochastic frontier. The translog stochastic frontier results show that farm households are, on average, 79 per cent technically efficient. The output-oriented DEA frontier results show that the average technical efficiency estimates are 79 and 86 per cent under CRS and VRS assumptions and the average scale efficiency is 92 per cent. The average values for technical efficiency measures and scale efficiency from the input-oriented CRS and VRS frontiers are 79, 85 and 93 per cent respectively. The translog stochastic frontier exhibits decreasing returns to scale, whereas the DEA frontier exhibits decreasing, constant and increasing returns to scale. The technical inefficiency effects model in the translog stochastic frontier and Tobit analysis for DEA frontier show that irrigation infrastructure and environmental degradation are significant factors in determining technical inefficiency. We then measure technical, allocative and economic efficiency by estimating the Cobb- Douglas stochastic frontier following the Kopp and Diewert cost decomposition technique and by running input-oriented CRS and VRS DEA frontier models. We estimate the Tobit model to analyze the factors associated with technical, allocative and economic inefficiency from the DEA frontiers. In addition, we compare the results obtained from both the Cobb- Douglas stochastic frontier and DEA frontiers. The results from the Cobb-Douglas stochastic frontier shows that the average technical, allocative and economic efficiency of farm households are 80, 77, and 61 per cent respectively. The input-oriented CRS frontier results show that farm households have, on average, 86, 91 and 78 per cent technical, allocative and economic efficiency and the corresponding VRS frontier shows that farm households are, on average, 91, 87 and 79 per cent technically, allocatively and economically efficient. An evaluation of factors associated with technical, allocative and economic inefficiency from both the Cobb-Douglas stochastic frontier and DEA frontier reveals that irrigation infrastructure and environmental degradation are the most statistically significant factors affecting technical, allocative and economic inefficiency. This implies that diesel-operated pumps and environmental degradation are not only reducing output from given inputs but are also causing sub-optimal cost-minimizing input decisions. Assessing efficiency suggests that there is a considerable amount of inefficiency among farm households and there is room for enhancing rice production through the improvement of technical, allocative and economic efficiency without resort to technical improvements. Farm households could reduce their variable production costs, on average, between 21 - 31 per cent if they could utilize their inputs in a technically and allocatively efficient manner. An evaluation of factors associated with inefficiency concludes that government electrification programmes which convert diesel pumps into electricity-operated pumps for irrigation in rural areas and policies which lead to reduced environmental degradation would reduce inefficiency, thereby increasing rice production and the welfare of farm households.

Since 2008, the world has been in recession. As daily news outlets report, this crisis has prompted many small businesses and large corporations to file for bankruptcy, which has grave global social implications. Despite government intervention and incentives to stimulate the economy that have put nations in hundreds of billions of dollars of debt, and have reduced the prime rates to almost zero, efforts to combat the increase in unemployment rate as well as the decrease in discretionary income have been troublesome. It is a vicious cycle: consumers are apprehensive of spending due to the instability of their jobs and ensuing personal financial problems; businesses are weary from the lack of revenue and are forced to tighten their operations which likely translates to layoffs; and so on. Cautious movement of cash flows are rooted in and influenced by the psychology of the players (stakeholders) of the game (society). Understandably, the complexity of this economic fallout is the subject of much attention. And while the markets have recovered much of the lost ground as of late, there is still great opportunity to learn about all the possible factors of this recession, in anticipation of and bracing for one more downturn before we emerge from this crisis. In fact, there is no time like today more appropriate for research in bankruptcy prediction because of its relevance, and in an age where documentation is highly encouraged and often mandated by law, the amount and accessibility of data is paramount – an academic’s paradise! The main objective of this thesis was to develop a model supported by Data Envelopment Analysis (DEA) to predict the likelihood of failure of US companies in the retail-apparel industry based on information available from annual reports – specifically from financial statements and their corresponding Notes, Management’s Discussion and Analysis, and Auditor’s Report. It was hypothesized that the inclusion of variables which reflect managerial decision-making and economic factors would enhance the predictive power of current mathematical models that consider financial data exclusively. With a unique and comprehensive dataset of 85 companies, new metrics based on different aspects of the annual reports were created then combined with a slacks-based measure of efficiency DEA model and modified layering classification technique to capture the multidimensional complexity of bankruptcy. This approach proved to be an effective prediction tool, separating companies with a high risk of bankruptcy from those that were healthy, with a reliable accuracy of 80% – an improvement over the widely-used Altman bankruptcy model having 70%, 58% and 50% accuracy when predicting cases today, from one year back and from two years back, respectively. It also provides a probability of bankruptcy based on a second order polynomial function in addition to targets for improvement, and was designed to be easily adapted for analysis of other industries. Finally, the contributions of this thesis benefit creditors with better risk assessment, owners with time to improve current operations as to avoid failure altogether, as well as investors with information on which healthy companies to invest in and which unhealthy companies to short.

Zugl.: Stuttgart, Univ., Diss., 2005

Zugl.: Berlin, Freie Univ., Diss., 2008

Zugl.: Frankfurt (Main), Frankfurt School of Finance & Management, Diss.