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研究生: 江孟桓
Chiang, Meng-Huan
論文名稱: 探討高鐵自由座服務水準及願付價格之研究
Exploring the Service Quality and Willingness to Pay for High-Speed Rail Passengers with unreserved seats
指導教授: 鄭永祥
Cheng, Yung-Hsiang
學位類別: 碩士
Master
系所名稱: 管理學院 - 交通管理科學系
Department of Transportation and Communication Management Science
論文出版年: 2017
畢業學年度: 105
語文別: 中文
論文頁數: 89
中文關鍵詞: 台灣高鐵自由座服務水準願付價格Rasch模式
外文關鍵詞: Taiwan High Speed Rail, Unreserved seats, Service quality, Willingness to pay, Rasch
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  • 高鐵自由座提供了旅客的搭乘彈性,包括少許的折扣再加上隨到、隨買、隨搭的方便性吸引行程安排較為彈性的旅客搭乘自由座。自由座提供了旅客搭乘的彈性,使得自由座目前在尖峰時段無控管再加上自由座開放站票的情況下,在連假或是通勤的尖峰時段幾乎是一票難求,造成過度擁擠而影響服務品質,也使得自由座的服務水準降低。
    本研究利用Rasch模式量測使用高鐵自由座各服務階段所遭遇的困難,服務階段包含上車前、車廂內以及下車途中此三種過程,同時欲探討不同人格特質是否能夠將其分群,吸引其族群搭乘自由座。此外自由座之折扣無隨著尖峰時段以及離峰階段進行差別取價,本研究希望探討透過差別取價增加離峰自由座之選擇機率,結合個體選擇模式,透過敘述性偏好了解受訪者對於取得座位之願付價格。本研究以台灣高鐵為例。
    研究結果顯示受訪者認為使用自由座「車廂內」服務時最為困難,其中以行李問題以及人潮造成的問題最為嚴重。因此台灣高鐵可以著重在於此部分問題優先解決。根據不同人格特質以及社經變數之分類,可以得知擁有長期運動習慣以及無提前規劃的受訪者對於自由座較有選擇偏好,所得較低的受訪者對於自由座也較有選擇偏好,可以依據分群進行策略的制定。將離峰自由座價格減少5%,可以使得29.7%原本選擇搭乘尖峰自由座的乘客轉移搭乘離峰自由座,原本29.1%原本選擇指定席的乘客轉移搭乘離峰自由座,因此可以認為對於離峰自由座進行差別取價可以有效使得乘客改變其搭乘時段。此外整體情況看來高鐵自由座在尖峰時段,假設旅次為台北至左營,受訪者願意多付180元取得座位。透過本研究之結果可以針對自由座進行尖離峰差別訂價或者是推出加價產品做為改善自由座服務水準的策略,並提供台灣高鐵做為未來制定策略之參考。

    The high-speed rail unreserved seat provides the flexibility of the passengers with a small discount and convenience of taking any shifts in a day. The flexibility of unreserved seats causes the crowding issue during the peak hour decreases the service quality.
    In this study, we use the Rasch model to measure the difficulties that passenger encounter in the stages of using unserved seats service. There are three service stages including service before getting aboard, service in the car and service getting off the car. This study aims to explore whether different personality could be grouped and develop strategies to attract them to use unreserved seats service. In addition, the discounts of unreserved seats are consistent in the peak hour and off peak hour. This study aims to increase the probability of choosing unreserved seats in off peak hour through price discrimination and understand the willing price of seats certainty. This study takes Taiwan high-speed rail as an example.
    The results show that passenger felt that it was most difficult to use the "in-car" service, with the most serious problems caused by baggage problems and crowds. According to the classification of different personality and social and economic variables, it is possible to know that respondents with long-term exercise habits and those who do not plan early are more likely to choose unreserved seats. The passengers with low income are more likely to choose unreserved seats. The price of unreserved seats in the off peak reduce 5% which would allow 29.7% of the passengers who originally choose to take the peak hour unreserved seats change their mind to take the off peak unreserved seats and 29.1% of the passengers who originally choose the reserved seats to take the unreserved seats. In addition, assuming the trip is Taipei to Zuoying, the passengers are willing to pay 180 NT additionally to ensure their seats in the peak hour. We hope this study can provide Taiwan High Speed Rail as a reference for the improvement and future development strategy.

    摘要 i 誌謝 vi 圖目錄 ix 表目錄 ix 第一章 緒論 1 1.1 研究背景與動機 1 1.2研究目的 4 1.3研究範圍 5 1.4研究架構 5 第二章 文獻回顧 7 2.1 服務水準 7 2.2 擁擠度 16 2.3 願付價格 21 2.4 小結 22 第三章 研究方法 23 3.1研究流程 23 3.2研究模型 25 3.3試題反應理論 26 3.3.1二元計分模式 27 3.3.2 多元計分模式 28 3.3.3 Rasch模式假設檢驗 29 3.4 個體選擇模式 30 3.4.1 多項羅吉特模式 30 第四章 調查內容 32 4.1 服務水準問項 32 4.2替代方案屬性水準值設計 35 4.3社經變數問項 37 4.4 調查方式 38 第五章 實證分析 39 5.1 敘述性統計分析 39 5.2 Rasch模式分析 42 5.2.1 困難度分析 43 5.2.2 群體差異評估 48 5.2.3 投入與改善效果 51 5.3多項羅吉特 57 5.3.1 模式變數設定 57 5.3.2 羅吉特模式分析結果 60 5.3.3 願付價格分析 63 5.4彈性分析 64 5.5敏感度分析 67 第六章 結論與建議 69 6.1 結論 69 6.2 建議 72 6.3 研究貢獻 75 6.4 研究限制與方向 76 參考文獻 77 附錄一 問卷 84

    1. 台灣高速鐵路股份有限公司(2014),103年度報表。
    2. 交通部運輸研究所(2012),101年運輸政策白皮書,臺北市:交通局。
    3. 張凱閔(2015),「探討寧靜車廂的應用對於高速鐵路顧客滿意度之影響」,國立成功大學交通管理科學研究所碩士論文。
    4. 陳怡燕(2015),「台灣高鐵售票通路組合策略及願付價格之研究」,國立成功大學交通管理科學研究所碩士論文。
    5. 陳庭歡(2016),「探討通勤者對於高鐵通勤困難度及願付價格之研究」,國立成功大學交通管理科學研究所碩士論文。
    6. 鄭永祥、李治綱(2009),台灣高鐵營收管理模式研發(一),中興工程(105),91-95。
    7. Adams, R. J., Wilson, M., & Wang, W. C. (1997). The multidimensional random coefficients multinomial logit model. Applied psychological measurement, 21(1), 1-23.
    8. Altman, I. (1975). The Environment and Social Behavior: Privacy, Personal Space, Territory, and Crowding.
    9. Arentze, T. A., & Molin, E. J. (2013). Travelers’ preferences in multimodal networks: design and results of a comprehensive series of choice experiments. Transportation Research Part A: Policy and Practice, 58, 15-28.
    10. Barabino, B., & Di Francesco, M. (2016). Characterizing, measuring, and managing transit service quality. Journal of Advanced Transportation.
    11. Batta, R. N. (2000). Tourism and the environment: A quest for sustainability: With special reference to developing countries, and policy analysis on Himachal Pradesh. Indus Publishing.
    12. Bhattacharya, A. K. (2005). Eco-Tourism and Livelihoods: Capacity Building for Local Authorities. Concept Publishing Company.
    13. Bluhm, G., Nordling, E., & Berglind, N. (2004). Road traffic noise and annoyance-An increasing environmental health problem. Noise and Health, 6(24), 43.
    14. Brady, M. K., & Cronin Jr, J. J. (2001). Some new thoughts on conceptualizing perceived service quality: a hierarchical approach. Journal of marketing, 65(3), 34-49.
    15. Carreira, R., Patrício, L., Jorge, R. N., & Magee, C. (2014). Understanding the travel experience and its impact on attitudes, emotions and loyalty towards the transportation provider–A quantitative study with mid-distance bus trips. Transport Policy, 31, 35-46.
    16. Chen, C. F. (2008). Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: Evidence from Taiwan. Transportation Research Part A: Policy and Practice, 42(4), 709-717.
    17. Cheng, Y. H., & Huang, T. Y. (2014). High speed rail passenger segmentation and ticketing channel preference. Transportation Research Part A: Policy and Practice, 66, 127-143.
    18. Chou, J. S., & Kim, C. (2009). A structural equation analysis of the QSL relationship with passenger riding experience on high speed rail: An empirical study of Taiwan and Korea. Expert Systems with Applications, 36(3), 6945-6955.
    19. Chrousos, G. P., & Gold, P. W. (1992). The concepts of stress and stress system disorders: overview of physical and behavioral homeostasis. Jama, 267(9), 1244-1252.
    20. Cunningham, L. F., Young, C. E., & Lee, M. (2002). Cross-cultural perspectives of service quality and risk in air transportation.
    21. Davidson, B., Vovsha, P., Abedini, M., Chu, C., & Garland, R. (2011). Impact of capacity, crowding, and vehicle arrival adherence on public transport ridership: Los Angeles and Sydney experience and forecasting approach.  
    22. Eboli, L., Fu, Y., & Mazzulla, G. (2016). Multilevel comprehensive evaluation of the railway service quality. Procedia Engineering, 137, 21-30.
    23. El-Adly, M. I., & Eid, R. (2016). An empirical study of the relationship between shopping environment, customer perceived value, satisfaction, and loyalty in the UAE malls context. Journal of Retailing and Consumer Services, 31, 217-227.
    24. Fecht, D., Hansell, A. L., Morley, D., Dajnak, D., Vienneau, D., Beevers, S., ... & Gulliver, J. (2016). Spatial and temporal associations of road traffic noise and air pollution in London: Implications for epidemiological studies. Environment international, 88, 235-242.
    25. Fleishman, L., Feitelson, E., & Salomon, I. (2004). The role of cultural and demographic diversity in crowding perception: Evidence from nature reserves in Israel. Tourism Analysis, 9(1-2), 23-40.
    26. Fletcher, G., & El-Geneidy, A. (2013). Effects of fare payment types and crowding on dwell time: fine-grained analysis. Transportation Research Record: Journal of the Transportation Research Board, (2351), 124-132.
    27. Genuit, K. (1996). Objective evaluation of acoustic quality based on a relative approach. PROCEEDINGS-INSTITUTE OF ACOUSTICS, 18, 3233-3238.
    28. Golant, S. M. (1983). The effects of residential and activity behavior. Elderly people and the environment, behavior and natural environment, 7, 239-274.
    29. Graefe, A. R., Vaske, J. J., & Kuss, F. R. (1984). Social carrying capacity: An integration and synthesis of twenty years of research. Leisure Sciences, 6(4), 395-431.
    30. Han, H., & Ryu, K. (2009). The roles of the physical environment, price perception, and customer satisfaction in determining customer loyalty in the restaurant industry. Journal of Hospitality & Tourism Research, 33(4), 487-510.
    31. Haywood, L., & Koning, M. (2015). The distribution of crowding costs in public transport: New evidence from Paris. Transportation Research Part A: Policy and Practice, 77, 182-201..
    32. Hensher, D. A., Greene, W. H., & Li, Z. (2011). Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings. Transportation research part B: methodological, 45(7), 954-972.
    33. Kang, J., & Hyun, S. S. (2012). Effective communication styles for the customer-oriented service employee: Inducing dedicational behaviors in luxury restaurant patrons. International Journal of Hospitality Management, 31(3), 772-785.
    34. Lee, H., & Graefe, A. R. (2003). Crowding at an arts festival: Extending crowding models to the frontcountry. Tourism Management, 24(1), 1-11.
    35. Lin, H. F., & Huang, Y. W. (2015). Factors affecting passenger choice of low cost carriers: An analytic network process approach. Tourism Management Perspectives, 16, 1-10.
    36. Lin, W. B. (2007). An empirical of service quality model from the viewpoint of management. Expert systems with applications, 32(2), 364-375.
    37. Loo, C. (1974). Important issues in researching the effects of crowding on humans. Crowding and Behavior, 4, 133.
    38. Mahudin, N. D. M., Cox, T., & Griffiths, A. (2012). Measuring rail passenger crowding: Scale development and psychometric properties. Transportation research part F: traffic psychology and behaviour, 15(1), 38-51.
    39. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149-174.
    40. Mathe-Soulek, K., Slevitch, L., & Dallinger, I. (2015). Applying mixed methods to identify what drives quick service restaurant's customer satisfaction at the unit-level. International Journal of Hospitality Management, 50, 46-54.
    41. McAlexander, J. H., Kaldenburg, D. O., & Koenig, H. F. (1994). Service quality measurement. Marketing Health Services, 14(3), 34.
    42. McDougall, G. H., & Levesque, T. J. (1995). A revised view of service quality dimensions: An empirical investigation. Journal of Professional Services Marketing, 11(1), 189-210.
    43. McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of applied Econometrics, 447-470.
    44. Merino-Castello, A. (2003). Eliciting consumers preferences using stated preference discrete choice models: contingent ranking versus choice experiment. UPF economics and business working paper, (705).
    45. Morgan, D. J., & Lok, L. (2000). Assessment of a comfort indicator for natural tourist attractions: the case of visitors to Hanging Rock, Victoria. Journal of Sustainable Tourism, 8(5), 393-409.
    46. Münzel, T., Gori, T., Babisch, W., & Basner, M. (2014). Cardiovascular effects of environmental noise exposure. European heart journal, 35(13), 829-836.
    47. Muzet, A. (2007). Environmental noise, sleep and health. Sleep medicine reviews, 11(2), 135-142.
    48. Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of retailing.
    49. O'Reilly, A. M. (1986). Some factors to be considered for the redevelopment of a major international airport, in a tourism-dependent developing country, based on supply and demand analysis. The Tourist Review, 41(1), 25-31.
    50. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. the Journal of Marketing, 41-50.
    51. Parasuraman, A., Zeithaml, V., & Berry, L. (2002). SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. Retailing: critical concepts, 64(1), 140.
    52. Park, J. W., Robertson, R., & Wu, C. L. (2006). The effects of individual dimensions of airline service quality: Findings from Australian domestic air passengers. Journal of hospitality and tourism management, 13(02), 161-176.
    53. Pearlin, L. I., & Schooler, C. (1978). The structure of coping. Journal of health and social behavior, 2-21. 
    54. Pel, A. J., Bel, N. H., & Pieters, M. (2014). Including passengers’ response to crowding in the Dutch national train passenger assignment model. Transportation Research Part A: Policy and Practice, 66, 111-126.
    55. Rust, R. T., & Oliver, R. L. (1994). Service Quality Insight and Management Implications from the Frontier. Service Quality: New Directions in Theory and Practice, Sage, Thousand Oaks, CA.
    56. Ryan, C., & Cessford, G. (2003). Developing a visitor satisfaction monitoring methodology: Quality gaps, crowding and some results. Current Issues in Tourism, 6(6), 457-507.
    57. Saveriades, A. (2000). Establishing the social tourism carrying capacity for the tourist resorts of the east coast of the Republic of Cyprus. Tourism management, 21(2), 147-156.
    58. Shelby, B. (1980). Crowding models for backcountry recreation. Land Economics, 56(1), 43-55.
    59. Shelby, B., & Heberlein, T. A. (1984). A conceptual framework for carrying capacity determination. Leisure Sciences, 6(4), 433-451.
    60. Shelby, B., & Heberlein, T. A. (1987). Carrying capacity in recreation settings. Oregon State University Press.
    61. SHIBATA, M., TERABE, S., & UCHIYAMA, H. (2009). A Seat Class Choice Model on Intercity Rapid Train Passengers for Flexible Seat Class Assignment. In Proceedings of the Eastern Asia Society for Transportation Studies (Vol. 2009, No. 0, pp. 224-224). Eastern Asia Society for Transportation Studies.
    62. Stokols, D. (1972). On the distinction between density and crowding: some implications for future research. Psychological review, 79(3), 275.
    63. Thomas, L. J., Rhind, D. J., & Robinson, K. J. (2006). Rail passenger perceptions of risk and safety and priorities for improvement. Cognition, Technology & Work, 8(1), 67-75.
    64. Tirachini, A., Hensher, D. A., & Rose, J. M. (2013). Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transportation research part A: policy and practice, 53, 36-52 
    65. Tirachini, A., Sun, L., Erath, A., & Chakirov, A. (2016). Valuation of sitting and standing in metro trains using revealed preferences. Transport Policy, 47, 94-104.
    66. Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of marketing research, 204-212.
    67. Vaske, J. J., & Donnelly, M. P. (2002). Generalizing the encounter--norm--crowding relationship. Leisure Sciences, 24(3-4), 255-269.
    68. Vovsha, P., Marcelo, G. S. O., & William, D. (2014). Statistical analysis of transit user preferences including in-vehicle crowding and service reliability. In TRB 2014 annual meeting.
    69. Whelan, G. A., & Crockett, J. (2009, March). An investigation of the willingness to pay to reduce rail overcrowding. In International Choice Modelling Conference 2009.
    70. Xu, X. L., Ma, L. L., Guo, D. G., & Hu, Y. Z. (2014). Discussion on design of egress in underground inter-city railway (UIR) station in China. Procedia engineering, 71, 7-15.
    71. Yildirim, K., & Akalin-Baskaya, A. (2007). Perceived crowding in a café/restaurant with different seating densities. Building and Environment, 42(9), 3410-3417.
    72. Zehrer, A., & Raich, F. (2016). The impact of perceived crowding on customer satisfaction. Journal of Hospitality and Tourism Management, 29, 88-98.
    73. Zijlema, W., Cai, Y., Doiron, D., Mbatchou, S., Fortier, I., Gulliver, J., ... & Key, T. (2016). Road traffic noise, blood pressure and heart rate: Pooled analyses of harmonized data from 88,336 participants. Environmental research, 151, 804-813.

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