植基延伸共同邊界架構探索汽車品牌競爭效率分析

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：52

、訪客IP：18.221.28.50

姓名

周漢忠(Han-Chung Chou) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

植基延伸共同邊界架構探索汽車品牌競爭效率分析
(Exploring Competition Efficiency of Automobile Brands: An Extensional DEA-Metafrontier Framework)

相關論文

★ 二氧化鈦技術生命週期之研究	★ 整體後勤業參與同步工程於產品開發績效關係之研究—以中科院為例
★ 筆記型電腦之IFA/PIFA天線技術生命週期分析	★ 國籍航空公司經營績效分析－以資料包絡分析方法分析
★ 從專利分析看3D IC技術與市場發展	★ 影響企業導入電子發票系統成效之因素探討
★ 影響企業導入數位學習成功因素之探討-以個案公司為例	★ 產品生命週期管理系統導入成功要素之探討--以S科技公司為例--
★ 組織創新能力影響因素研究	★ 製造業閒置資產轉售平台製造業閒置資產轉售平台-以廣達電腦股份有限公司為例
★ 供應商先行者優勢探討-以宸鴻科技為例	★ 團隊領導者創新特質與開放式創新專案關係之研究
★ 從商業生態系統談樞紐者策略-以Apple 與Nokia 為例	★ 個人電腦的競爭與發展策略-以台灣電子產業為例
★ 應用兩階段資料包絡分析法評估高級職業學校之經營績效	★ ERP導入的促進因素:使用者觀點

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

本研究使用2016年美國消費者報告中有關汽車的資料，並運用資料包絡分析法探究各類車款效率。研究結果顯示如下，首先，轎車在價格、成本及油耗方面表現最佳，豪華車在道路測試表現方面最佳，休旅車在可靠性預測方面表現最佳，跑車在使用者滿意度方面較高。其次，共同邊界效率表現最佳的車款為轎車，推估可能原因為此車款為多數人擁有，各項技術應用純熟，相對效率較高及油耗較低，技術缺口比率也最接近於1。群組邊界最佳的車款為跑車，顯示此車款專為少數人設計及較高進入門檻，該群表現聚焦也相對良好。再者，油耗表現最佳為轎車，休旅車不僅價格高，且油耗表現最差，顯示該車款對於性能與環保效能提升仍待改善。最後，並進一步運用拔靴法取得統計推論，使研究結果更為嚴謹，並具有可信度。本研究的管理意涵如下，建議政府應訂定更嚴謹之環保、節能與安全等相關法令，車商也應配合上述法規及強化低階車款品質，並加速推動智慧與綠色科技運用於各車款上並以符合全球趨勢。研究所得結論可提升消費者綠色認知及購買決策之參考，提供廠商未來產品設計方向，並進而去改善技術缺口或無效率品牌之標竿改善策略運用。

摘要(英)

Data envelopment analysis was employed to investigate automobile vehicle efficiency by collecting data from Consumer Reports 2016. The results are as follows: (1) general cars exhibited the most favorable performance in terms of price, costs, and mileage; luxury cars had the most favorable road test performance; sports utility vehicles (SUVs) scored highest for predicted reliability; and sports cars scored high for owner satisfaction. (2) Metafrontier efficiency was the highest for general cars, which may be because this automobile type has the highest prevalence; hence, the technical applications were thoroughly developed, corresponding efficiency was relatively high, and fuel consumption was relatively low. The technology gap ratio the closest to 1 was obtained for general cars. Sports cars had the most favorable group frontier, indicating that this automobile type is designed for relatively few people and has a relatively higher entry threshold; the group performance also exhibited relatively favorable focus for this type. (3) General cars had the most favorable performance in terms of miles-per-gallon (MPG). Not only were SUVs found to be expensive but their MPG performance was also the least favorable, indicating that this automobile type requires additional improvements regarding functional performance and environmental efficacy. The management implications of this study are as follows. Governments should formulate more stringent laws related to environmental protection, energy conservation, and safety, and automobile manufacturers should comply with such regulations and enhance the quality of low-end automobile models. Additionally, the application of smart and green technology in various automobile types should be accelerated and made to conform to global trends. The conclusions of this study could enhance consumers’ green cognition and serve as a reference in purchase decision-making; they can also provide manufacturers with directions regarding future product designs, methods to mitigate technology gaps, and benchmark strategies for improving inefficient brands.

關鍵字(中)

★ 汽車類型效率
★ 技術缺口比率
★ 資料包絡分析
★ 共同邊界
★ 拔靴法

關鍵字(英)

★ Auto Models Efficiency
★ Technology Gap Ratio
★ Data Envelopment Analysis
★ Metafrontier
★ Bootstrap

論文目次

摘要 i
Abstract ii
Acknowledgments iv
Table of Contents v
List of Figures vii
List of Tables viii
1. Introduction 1
1.1 Motivation 1
1.2 Research Purposes 3
1.3 Study Process 4
2. Literature Review 6
2.1 Automobile Industry Development Overview 6
2.2 Product Attributes and Consumer Perceived Value 10
2.3 Metafrontier-Related Research 12
3. Research Design 17
3.1. Collection and Description of Data 17
3.2. Concept of Metafrontier 18
3.3. TE measure 19
3.4. Metafrontier Approach 22
3.5. Bootstrap in DEA 23
4. Empirical Results 27
4.1. Auto Function Performance Analysis 27
4.2. Automobile Category Efficiency Analysis 28
4.3. Statistical Variability 35
5. Conclusion 53
6. Managerial Implications 55
7. Limitations and Suggestions 57
References 58
Appendix 65

參考文獻

Aida, K., Cooper, W. W., Pastor, J. T., & Sueyoshi, T. (1998). Evaluating water supply services in Japan with RAM: a range-adjusted measure of inefficiency. Omega, 26(2), 207-232.
Assaf, A. (2009). Accounting for size in efficiency comparisons of airports. Journal of Air Transport Management, 15(5), 256-258.
Assaf, A., Barros, C. P., & Josiassen, A. (2010). Hotel efficiency: A bootstrapped metafrontier approach. International Journal of Hospitality Management, 29(3), 468-475.
Assaf, A., Barros, C. P., & Josiassen, A. (2012). Hotel efficiency: A bootstrapped metafrontier approach. International Journal of Hospitality Management, 31(2), 621-629.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical economics, 20(2), 325-332.
Battese, G. E., & Rao, D. P. (2002). Technology gap, efficiency, and a stochastic metafrontier function. International Journal of Business and Economics, 1(2), 87-93.
Battese, G. E., Rao, D. P., & O′donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis, 21(1), 91-103.
Barros, C. P., & Wanke, P. (2017). Technology Gaps and Capacity Issues in African Insurance Companies: Selected Country Evidence. Journal of International Development, 29(1), 117-133.
Binam, J. N., Gockowski, J., & Nkamleu, G. B. (2008). Technical efficiency and productivity potential of cocoa farmers in West African countries. The Developing Economies, 46(3), 242-263.
Bos, J. W., & Schmiedel, H. (2007). Is there a single frontier in a single European banking market?. Journal of Banking & Finance, 31(7), 2081-2102.
Boshrabadi, H. M., Villano, R., & Fleming, E. (2008). Technical efficiency and environmental‐technological gaps in wheat production in Kerman province of Iran. Agricultural Economics, 38(1), 67-76.
Boskin, M. J., & Lau, L. J. (1992). International and intertemporal comparison of productive efficiency. The Economic Studies Quarterly, 43(4), 298-312.
Brümmer, B. (2001). Estimating confidence intervals for technical efficiency: the case of private farms in Slovenia. European review of agricultural economics, 28(3), 285-306.
Büschken, J. (2007). Determinants of brand advertising efficiency: evidence from the German car market. Journal of Advertising, 36(3), 51-73.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of econometrics, 30(1-2), 91-107.
Cooper, W. W., Li, S., Seiford, L. M., Tone, K., Thrall, R. M., & Zhu, J. (2001). Sensitivity and stability analysis in DEA: some recent developments. Journal of productivity Analysis, 15(3), 217-246.
Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1-4.
De Witte, K., & Marques, R. C. (2009). Capturing the environment, a metafrontier approach to the drinking water sector. International Transactions in Operational Research, 16(2), 257-271.
Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers′ product evaluations. Journal of marketing research, 307-319.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
Färe, R., Lovell, C. A. K., & Zieschang, K. (1982). Measuring the technical efficiency of multiple output technologies. In W. Eichhorn et al. (Eds.), Quantitative studies in production and prices. Wurzburg: Physica-Verlag.
Färe, R., & Grosskopf, S. (1985). A nonparametric cost approach to scale efficiency. The Scandinavian Journal of Economics, 87(4), 594-604.
Fuss, M. A., & Waverman, L. (2006). Costs and productivity in automobile production: The challenge of Japanese efficiency. Cambridge University Press.
Glass, J. C., McKillop, D. G., & Hyndman, N. (1995). Efficiency in the provision of university teaching and research: an empirical analysis of UK universities. Journal of applied Econometrics, 10(1), 61-72.
Gunaratne, L. H. P., & Leung, P. S. (2001). Asian Black Tiger Shrimp Industry: A Productivity Analysis, In P. S. Leung and K. R. Sharma (Eds.), Economics and Management of Shrimp and Carp Farming in Asia: A Collection of Research Papers Based on the ADB/NACA Farm Performance Survey, Bangkok: Network of Aquaculture Centers in Asia-Pacific (NACA), 55-68.
Hall, P. (1986). On the number of bootstrap simulations required to construct a confidence interval. The Annals of Statistics, 1453-1462.
Holbrook, M. B. (1994). The nature of customer value: an axiology of services in the consumption experience. Service quality: New directions in theory and practice, 21, 21-71.
Hayami, Y. (1969). Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics, 51(3), 564-575.
Hayami, Y., & Ruttan, V. W. (1970). Agricultural productivity differences among countries. The American economic review, 60(5), 895-911.
Hayami, Y., & Ruttan, V. W. (1971). Agricultural development: an international perspective. Baltimore, Md/London: The Johns Hopkins Press.
Huang, Y. J., Chen, K. H., & Yang, C. H. (2010). Cost efficiency and optimal scale of electricity distribution firms in Taiwan: An application of metafrontier analysis. Energy Economics, 32(1), 15-23.
Huang, T. H., Chiang, L. C., & Chen, K. C. (2011). An empirical study of bank efficiencies and technology gaps in European banking. The Manchester School, 79(4), 839-860.
Jablonsky, J., Fiala, P., Smirlis, Y., & Despotis, D. K. (2004). DEA with Interval Data: An Illustration Using the Evaluation of Branches of a Czech Bank. Central European Journal of Operations Research, 12(4), 323-337.
Jackson, E. C. (2010). A DEA analysis using quality and advertising as determinants of strategic group membership in the automobile industry. The Review of Business Information Systems, 14(4), 1-14.
Kim, J. I., & Lau, L. J. (1994). The sources of economic growth of the East Asian newly industrialized countries. Journal of the Japanese and International Economies, 8(3), 235-271.
Kontolaimou, A., & Tsekouras, K. (2010). Are cooperatives the weakest link in European banking? A non-parametric metafrontier approach. Journal of Banking & Finance, 34(8), 1946-1957.
Lancaster, K. J. (1966). A new approach to consumer theory. The journal of political economy, 74, 132-157.
Lau, L. J., & Yotopoulos, P. A. (1989). The meta-production function approach to technological change in world agriculture. Journal of Development Economics, 31(2), 241-269.
Lin, S. C., & Yang, Y. H. (2014). An Analysis of the Operational and Management Efficiency of Five-Star Hotels in Taiwan. International Journal of Economics and Finance, 6(4), 12-22.
Liu, Y. C., & Chen, Y. H. (2016). Which One is More Efficient? German or Japanese Automobile Industry: A Meta-frontier with Technology Gap Comparison. International Business Research, 9(10), 13-24.
Lovell, C. K., & Pastor, J. T. (1997). Target setting: An application to a bank branch network. European Journal of Operational Research, 98(2), 290-299.
Lovell, C. K., & Schmidt, P. (1988). A comparison of alternative approaches to the measurement of productive efficiency. In Applications of modern production theory: Efficiency and productivity. Springer, Dordrecht, 3-32
Macdonald, E. K., Kleinaltenkamp, M., & Wilson, H. N. (2016). How business customers judge solutions: Solution quality and value in use. Journal of Marketing, 80(3), 96-120.
McMillan, M. L., & Chan, W. H. (2006). University efficiency: A comparison and consolidation of results from stochastic and non‐stochastic methods. Education economics, 14(1), 1-30.
Moreira, V. H., & Bravo-Ureta, B. E. (2010). Technical efficiency and metatechnology ratios for dairy farms in three southern cone countries: a stochastic meta-frontier model. Journal of Productivity Analysis, 33(1), 33-45.
Mundlak, Y., & Hellinghausen, R. (1982). The intercountry agricultural production function: Another view. American Journal of Agricultural Economics, 64(4), 664-672.
O′Donnell, C. J., & Westhuizen, G. (2002). Regional comparisons of banking performance in South Africa. South African Journal of Economics, 70(3), 224-240.
O’Donnell, C. J., Rao, D. P., & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical economics, 34(2), 231-255.
Oh, I., Lee, J. D., Hwang, S., & Heshmati, A. (2010). Analysis of product efficiency in the Korean automobile market from a consumer’s perspective. Empirical Economics, 38(1), 119-137.
Papahristodoulou, C. (1997). A DEA model to evaluate car efficiency. Applied Economics, 29(11), 1493-1508.
Pastor, J. T., Ruiz, J. L., & Sirvent, I. (1999). An enhanced DEA Russell graph efficiency measure. European Journal of Operational Research, 115(3), 596-607.
Rust, R. T., & Oliver, R. L. (Eds.). (1993). Service quality: New directions in theory and practice. Sage Publications.
Sharma, K. R., & Leung, P. (2000). Technical efficiency of carp pond culture in South Asia: An application of a stochastic meta‐production frontier model. Aquaculture Economics & Management, 4(3-4), 169-189.
Sherman, H. D., & Zhu, J. (2013). Analyzing performance in service organizations. MIT Sloan Management Review, 54(4), 37-42.
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.
Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management science, 44(1), 49-61.
Simar, L., & Wilson, P. W. (2000a). A general methodology for bootstrapping in non-parametric frontier models. Journal of applied statistics, 27(6), 779-802.
Simar, L., & Wilson, P. W. (2000b). Statistical inference in nonparametric frontier models: The state of the art. Journal of productivity analysis, 13(1), 49-78.
Simar, L., & Wilson, P. W. (2007). Statistical inference in nonparametric frontier models: Recent developments and perspectives. In: Fried, H.O., Lovell, C.A.K., Schmidt, S.S. (Eds.), The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press.
Tofallis, C. (2001). Combining two approaches to efficiency assessment. Journal of the Operational Research Society, 52(11), 1225-1231.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509.
Villano, R., Fleming, E. M., & Fleming, P. (2008, February). Measuring regional productivity differences in the Australian wool industry: A Metafrontier approach. In Proceedings of the AARES 52nd Annual Conference.
Wanke, P., & Barros, C. P. (2016). Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach. Economic Modelling, 53, 8-22.
Wirtz, B. W., Pistoia, A., Ullrich, S., & Göttel, V. (2016). Business models: Origin, development and future research perspectives. Long Range Planning, 49(1), 36-54.
Womack, J. P., Jones, D. T., & Roos, D. (2007). The machine that changed the world: The story of lean production, Toyota’s secret weapon in the global car wars that is revolutionizing world industry (New Ed.). New York.
Worthington, A. C., & Lee, B. L. (2008). Efficiency, technology and productivity change in Australian universities, 1998–2003. Economics of education review, 27(3), 285-298.
Yang, C. H., & Chen, K. H. (2009). Are small firms less efficient?. Small Business Economics, 32(4), 375-395.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. The Journal of Marketing, 2-22.
Zhu, J., & Shen, Z. H. (1995). A discussion of testing DMUs′ returns to scale. European Journal of Operational Research, 81(3), 590-596.
Zhuo, C. H. E. N., & Shunfeng, S. O. N. G. (2008). Efficiency and technology gap in China′s agriculture: A regional meta-frontier analysis. China Economic Review, 19(2), 287-296.

指導教授

洪秀婉(Shiu-Wan Hung)

審核日期

2018-7-2

推文