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研究生:阮黎黄
研究生(外文):NGUYEN,LE-HOANG
論文名稱:以資料包絡分析法與灰色理論為基礎對亞洲主要航空公司進行績效評估
論文名稱(外文):Performance Evaluation of Major Asian Airline Companies Using DEA and Grey Theory
指導教授:王嘉男王嘉男引用關係
指導教授(外文):WANG,CHIA- NAN
口試委員:王俊惠顧志遠
口試委員(外文):WANG,CHUN-HUYGUH,YUH-YUAN
口試日期:2018-01-06
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:72
外文關鍵詞:Data Envelopment AnalysisDEA Window modelGrey GM(1,1)MalmquistAviation
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In the beginning, airline transportation had only served the needs of the military. However, with the rapid development and innovation, air transport nowadays has been associated with the demand for conveyance of passenger and good, which makes it enhance to become a crucial industry playing important role for the world economy in general and for international trade in particular. In line with that trend, the Asian aviation industry in recent years has also made great leaps. As the Asia’s economy will continue to grow higher than any other region in the world leading more and more passengers will fly over the next 20 years, the competition among airline enterprises are also increasingly harsh. As a result, those companies must continually improve their service quality and performance efficiency if they do not want to be eliminated. Motivated by that reason, the researcher conducted this study to evaluate the performance of 16 major airline carriers in the region, thus providing a more comprehensive picture of Asian aviation industry for the time being. Data Envelopment Analysis (DEA) and Grey model GM(1,1) were employed in this research. Through DEA Window model analysis, it is found that Emirates, Cebu Pacific and SriLankan Airlines are those carriers which have had outstanding performances while Cathay Pacific, Singapore Airlines and Japan Airlines are slowly lagging behind and losing their leading positions in the industry. The buoyant emergence of Chinese airline corporations has also contributed to the new face of this service business. Nevertheless, with Grey GM(1,1) model and Malmquist results, it is apparent that the productivity of 16 aviation companies will improve with optimistic signals in the future.
TABLE OF CONTENTS
ABSTRACT iii
ACKNOWLEDGEMENTS v
LIST OF TABLES xi
LIST OF FIGURES xiii
CHAPTER 1: INTRODUCTION 1
1.1. Research background 1
1.2. Research motivation 3
1.3. Scope of the research 4
1.4. Research organization 4
CHAPTER 2: LITERATURE REVIEW 6
2.1. Data envelopment analysis 6
2.1.1. Introduction of Data envelopment analysis 6
2.1.2. Malmquist Productivity Index 7
2.1.3. Notable DEA researches related to airline industry 7
2.2. Grey theory system 9
2.2.1. Introduction of Grey theory system 9
2.2.2. Notable Grey Forecasting researches 10
CHAPTER 3: RESEARCH METHODOLOGY 11
3.1. Collecting data progress 11
3.1.1. Choosing DMUs 11
3.1.2. Selecting Inputs and Outputs 14
3.2. Research Framework 23
3.3. Malmquist Productivity Index 24
3.4. DEA Window model 26
3.5. Grey GM (1,1) 27
3.6. Forecasting Accuracy MAPE 28
CHAPTER 4: EMPIRICAL STUDY 29
4.1. Pearson Correlation 29
4.2. Grey Forecasting 31
4.3. Testing of GM(1,1) 37
4.4. DEA Window analysis 38
4.5. Performance Efficiency Evaluation 44
4.5.1. Efficiency change Index 44
4.5.2. Technical change Index 46
4.5.3. Malmquist Productivity Index 49
CHAPTER 5: CONCLUSION AND RECOMMENDATION 53
5.1. Research conclusions 53
5.2. Limitations 54
5.3. Suggestions for future studies 55
REFERENCES 56
DATA SELECTION SOURCES 60


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