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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許文馨 | zh_TW |
dc.contributor.advisor | Wen-Hsin Hsu | en |
dc.contributor.author | 楊筑鈞 | zh_TW |
dc.contributor.author | Chu Chun Yang | en |
dc.date.accessioned | 2025-02-26T16:10:48Z | - |
dc.date.available | 2025-02-27 | - |
dc.date.copyright | 2025-02-26 | - |
dc.date.issued | 2025 | - |
dc.date.submitted | 2025-02-05 | - |
dc.identifier.citation | Agoda. (n.d.). Company information. Retrieved from https://www.agoda.com
Booking Holdings Inc. (n.d.). About Agoda. Retrieved from https://www.bookingholdings.com/our-brands/agoda/ Brehm, J. W. (1966). A theory of psychological reactance. Academic Press. https://doi.org/10.6267/JTLS.2015.21(3)2 Chaw, L. Y., & Tang, C. M. (2019). Online accommodation booking: what information matters the most to users? Information Technology & Tourism, 21(3), 369-390. https://doi.org/10.1007/s40558-019-00146-1 Chen, C. M., & Schwartz, Z. (2013). Timing matters: Travelers’ advanced-booking expectations and decisions. Journal of Travel Research, 52(2), 181-193. https://doi.org/10.1177/0047287512461562 Chen, Xichan. (2023). Estimating the Number of Tourists in Kyoto Based on GPS Traces and Aggregate Mobile Statistics. Lecture notes in mobility, doi: 10.1007/978-981-19-8361-0_14 Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354. https://doi.org/10.1509/jmkr.43.3.345 Chiang, C. F., & Huang, C. W. (2022). Online Reviews on Online Travel Agency: Understanding Tourists' Perceived Attributes of Taipei's Economy Hotels. Journal of Quality Assurance in Hospitality & Tourism, 23(4), 945-959. https://doi.org/10.1080/1528008x.2021.1923107 Chih-Ming Lee, Ye Xuan, Wen-Lung Wan (2018). The Study of Key Factors Affecting Cross-Strait Consumers' Selection of Online Travel Agent. Soochow Journal of Economics and Business, (97), 63-102。https://www.airitilibrary.com/Article/Detail?DocID=02593769-201812-201901040023-201901040023-63-102 Christaller, W. (1966). Central places in Southern Germany. Prentice Hall. Easy World Travels. (2023). Agoda.com user statistics. Retrieved from https://easyworldtravels.com/agoda-com-users-statistics Gu-Shin Tung, Yu-Kai Huang, Hui-Juan Huang (2015). A Cusp Catastrophe Model for Non-Linear Choices of International Tourist Hotels in Resort District. Journal of Tourism and Leisure Studies, 21(3), 249-280. https://doi.org/10.6267/JTLS.2015.21(3)2 Ijsrem, Journal. (2023). Tourism website - travel buddy using web development. Indian Scientific Journal Of Research In Engineering And Management, doi: 10.55041/ijsrem18291 Jan, Y. Y., & Wang, T. H. (2017). Online Hotel Booking Service Quality, Satisfaction and Customer Loyalty: A Case Study Using Agoda. Journal of Sport and Recreation Management, 14(1), 45-66. https://doi.org/10.6214/JSRM.1401.004 Kato, H., & Takizawa, A. (2022). Population Decline through Tourism Gentrification Caused by Accommodation in Kyoto City. Sustainability, 14(18), 11736. https://doi.org/10.3390/su141811736 Kim, M., Lee, S. M., Choi, S., & Kim, S. Y. (2021). Impact of visual information on online consumer review behavior: Evidence from a hotel booking website. Journal of Retailing and Consumer Services, 60. ttps://doi.org/10.1016/j.jretconser.2021.102494 Leung, R., Au, N., Liu, J. W., & Law, R. (2018). Do customers share the same perspective? A study on online OTAs ratings versus user ratings of Hong Kong hotels. Journal of Vacation Marketing, 24(2), 103-117. https://doi.org/10.1177/1356766716679483 Levitt, T. (1965). Exploit the product life cycle. Harvard Business Review, 43(6), 81-94. Lim , J. E., Mohd Zahari, F. ., & Abidin, R. . (2022). Factors Affecting Online Hotel Booking Intention: A Study On Uum Students. Journal of Technology and Operations Management, 17(1), 53–61. https://doi.org/10.32890/jtom2022.17.1.5 Martin-Fuentes, E., Mellinas, J. P., & Parra-Lopez, E. (2021). Online travel review rating scales and effects on hotel scoring and competitiveness. Tourism Review, 76(3), 654-668. https://doi.org/10.1108/Tr-01-2019-0024 McGuire, K. A., & Ho, J. Hotel Pricing in a Social World : Driving Value in the Digital Economy (1st ed.). https://ebookcentral.proquest.com/lib/sinciatw/detail.action?docID=4548095 Mellinas, J. P. (2019). Dependency of Spanish Urban Hotels on Booking.Com. Tourism Analysis, 24(1), 3-12. https://doi.org/10.3727/108354219x15458295631909 Porcu, Elisabetta. (2022). The Gion Festival in Kyoto and Glocalization, Religions 13, no. 8: 689. https://doi.org/10.3390/rel13080689 Roemer, M.K. (2007), Ritual Participation and Social Support in a Major Japanese Festival. Journal for the Scientific Study of Religion, 46: 185-200. https://doi.org/10.1111/j.1468-5906.2007.00350.x Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34-55. https://doi.org/10.1086/260169 Saito, T., Takahashi, A., & Tsuda, H. (2016). Optimal room charge and expected sales under discrete choice models with limited capacity. International Journal of Hospitality Management, 57, 116-131. https://doi.org/10.1016/j.ijhm.2016.06.006 Saito, T., Takahashi, A., & Tsuda, H. (2016). Optimal Room Charge and Expected Sales under Discrete Choice Models with Limited Capacity. Econometrics: Single Equation Models eJournal. https://doi.org/10.2139/ssrn.2723297 Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355-374. https://doi.org/10.2307/1882010 Statista. (2023). Most used online travel agencies in Asia by country in 2023. Retrieved from https://www.statista.com/statistics/1410006/asia-most-used-online-travel-agencies-by-country Statista. (2023). Most used online travel agencies in Thailand in 2023. Retrieved from https://www.statista.com/statistics/1203566/thailand-most-used-online-travel-agencies Takii, A., Iba, C., & Hokoi, S. (2023). Analysis of temperature preference of guests from various countries/regions during summer and winter in a budget hotel in Kyoto, Japan. Building and Environment, 232. https://doi.org/10.1016/j.buildenv.2023.110052 Tanaka, R., Kato, H., & Matsushita, D. (2023). Population Decline and Urban Transformation by Tourism Gentrification in Kyoto City. Sustainability, 15(3), 2247. https://doi.org/10.3390/su15032247 Tian-Shyug Lee, Chi-Jie Lu, Ting-Fang Cheng(2017). Hotel Sales Forecasting Based on Variable Selection Techniques and Extreme Learning Machine。Journal of Quality,24(6),411-430。https://doi.org/10.6220/joq.2017.24(6).02 Tseng, T. H., Chang, S. H., Wang, Y. M., Wang, Y. S., & Lin, S. J. (2021). An Empirical Investigation of the Longitudinal Effect of Online Consumer Reviews on Hotel Accommodation Performance. Sustainability, 13(1). https://doi.org/10.3390/su13010193 Wachyuni, S.S., & Wiweka, K. (2020). Kepuasan Wisatawan Dalam Penggunaan E-Commerce Agoda Dalam Pemesanan Hotel. Tourism, 8, 61-70. https://doi.org/10.35814/TOURISM.V8I1.1366 Weathernews.jp. (n.d.). Cherry blossom (March, April) time of Kyoto. Retrieved from https://weathernews.jp/ Yagi, T.八木透. (2020). Fire, Prayer, and Purification: Early Winter Events and Folk Beliefs in Kyoto. Journal of Religion in Japan, 9(1-3), 195-212. https://doi.org/10.1163/22118349-00901010 Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22. https://doi.org/10.1177/002224298805200302 Zhang, J., Zhang, L., Raveendran, V., Ben-Zuk, Z., & Lu, L.L. (2020). PriceAggregator: An Intelligent System for Hotel Price Fetching. https://doi.org/10.48550/arXiv.2008.02087 | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97040 | - |
dc.description.abstract | 本研究探討影響線上旅行社 Agoda 住宿價格的因素,特別聚焦於日本京都。透過分析 18 間不同星級與地理位置的飯店,涵蓋 32 週的數據,本研究探討星級評等、顧客評價(Agoda 分數與 Google 分數)、地點、季節性變化與房間條件對價格動態的影響,並運用統計方法(如相關性分析)識別關鍵的價格決定因素。研究結果顯示,星級評等與 Agoda 分數對價格的影響最為顯著,較高的評級可帶來更高的房價。地點亦有一定影響,但程度較低,相較於京都車站附近,鄰近四條河原町的飯店價格影響更為明顯。此外,季節性變化顯著影響房價,櫻花季價格達到高峰,而 6 月則呈現需求低迷,房價最低。進一步分析顯示,Agoda 分數與 Google 分數之間存在中等相關性,反映顧客在不同平台上的評價趨於一致。而飯店年齡對價格的影響則較小。整體而言,旅客在決策時更重視評價與評等,凸顯提供高品質服務的重要性。本研究強調飯店經營者應善用星級評等、優化季節性定價策略,並強調地理位置優勢,以制定更精準的價格策略。此外,研究結果亦可幫助旅客獲取最佳住宿優惠。透過這些見解,業者與消費者能更有效應對京都競爭激烈的觀光市場,在獲利與顧客滿意度間取得平衡。 | zh_TW |
dc.description.abstract | This research examines the factors influencing accommodation pricing on the online travel agency Agoda, specifically focusing on Kyoto, Japan. By analyzing data from 18 hotels of varying star ratings and locations over 32 weeks, the study explores the impact of key variables such as star rating, customer reviews (Agoda scores and Google scores), location, seasonality, and room condition on pricing dynamics. Statistical methods, including correlation analysis, were employed to identify significant pricing determinants.
The findings reveal that star ratings and Agoda scores strongly influence pricing, with higher ratings commanding premium prices. Location also plays a role, albeit to a lesser extent, with proximity to Shijo Kawaramachi being more impactful than proximity to Kyoto Station. Seasonal trends significantly affect prices, with notable peaks during cherry blossom season. Conversely, the lowest prices were observed in June, reflecting a seasonal dip in demand. Additional insights include a moderate relationship between Agoda and Google scores, emphasizing consistent customer feedback across platforms, while the hotel's age showed minimal influence on pricing. Travelers prioritize reviews and ratings when making decisions, underscoring the need for high-quality services. These results underscore the importance of leveraging star ratings, optimizing seasonal pricing, and emphasizing location-based advantages to create tailored strategies. The study provides actionable recommendations for hoteliers to refine pricing strategies and for travelers to secure the best deals. By integrating these insights, stakeholders can navigate Kyoto's competitive hospitality market more effectively, balancing profitability and customer satisfaction. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-26T16:10:48Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2025-02-26T16:10:48Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Table of Contents I
List of Figures/ Tables III Abstract IV Ⅰ. Introduction 1 (Ⅰ) Problem Statement 3 (Ⅱ) Research Objectives 4 A. Date of the Year 4 B. Agoda score/ Google score 4 C. Opening Date: Room Condition (New or Old) 4 D. Star Rating 5 E. Location: Near Kyoto Station or Near Shijo Kawaramachi (Kyoto Takashimaya) 5 F. Reason for using all Chain Stores Hotel for Analysis 5 Ⅱ. Literature Review 6 Ⅲ. Theoretical Framework 9 (Ⅰ) Conceptualization of Key Variables 9 (Ⅱ) Theoretical Framework and Relationships 10 Ⅳ. Collect the needed data & secondary sources 14 (Ⅰ) Primary Data Collection: 14 (Ⅱ) Secondary Data Utilization: 14 Ⅴ. Results & Discussion 15 (Ⅰ) Basic Information Analysis 16 A. Hotel Star Ratings and Distribution: 16 B. Location: “Kyoto Station” and “Shijo Kawaramachi (Kyoto Takashimaya)” 17 C. Total 18 Hotels Average, Min, Max & Average of Each Star Rating 21 D. Box Plot of Average Price by Star Rating 23 E. Minimum Price Allocation: The lowest price happens the most in June. 24 (Ⅱ) Price Trend for the 18 hotels 30 A. Total 18 Hotels, Price Trends Line with Markers (Period: 3/15 - 10/15, 2024) 31 B. Monthly Hotel Average Price 34 C. Correlation of price fluctuations among 18 hotels 36 (Ⅲ) Correlation between all factors within all 18 hotels 39 A. Correlation Heatmap for all 18 hotels reflecting the relationships between the various factors and hotel prices 41 B. Correlation Heatmap Focusing on Average Price 44 C. Scatter Plots showing the relationships between individual factors and the average price for all 18 hotels 47 D. Booking Frequency (Sold Out on Agoda Times) Ranking 50 Ⅵ. Conclusion 54 References 58 | - |
dc.language.iso | en | - |
dc.title | 影響線上旅行商Agoda住宿的訂價因素-以日本京都市為例 | zh_TW |
dc.title | Factors Influencing the Pricing of Accommodations on Online Travel Agency Agoda: A Case Study of Kyoto City, Japan | en |
dc.type | Thesis | - |
dc.date.schoolyear | 113-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 堯里昂;劉心才 | zh_TW |
dc.contributor.oralexamcommittee | Leon van Jaarsveldt;Hsin-Tsai Liu | en |
dc.subject.keyword | 線上旅行社,住宿,價格,策略,價格策略,日本,京都,飯店,酒店,各種價格,星級,開業日期,Agoda評分,Google評分,連鎖飯店,地點,京都車站,四条河原町,京都高島屋,旅遊,旅客,飯店業者,住宿產業,預訂行為,需求波動,旅遊經濟學,價格決定因素,收益優化, | zh_TW |
dc.subject.keyword | OTA,Agoda,Online Travel Agency,Accommodation,Price,Strategy,Price Strategy,Japan,Kyoto,Hotel,Stay,Various price,Star Rating,Opening Date,Agoda Score,Google Score,Chain Store,Location,Kyoto Station,Shijo Kawaramachi,Kyoto Takashimaya,Travel,Traveler,Hotelier,Hospitality Industry,Booking Behavior,Demand Fluctuations,Tourism Economics,Price Determinants,Revenue Optimization, | en |
dc.relation.page | 62 | - |
dc.identifier.doi | 10.6342/NTU202500422 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2025-02-06 | - |
dc.contributor.author-college | 管理學院 | - |
dc.contributor.author-dept | 企業管理碩士專班 | - |
dc.date.embargo-lift | 2025-02-27 | - |
顯示於系所單位: | 管理學院企業管理專班(Global MBA) |
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