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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭佳瑋(Chia-Wei Kuo) | |
| dc.contributor.author | Eric C. Su | en |
| dc.contributor.author | 蘇紀瑋 | zh_TW |
| dc.date.accessioned | 2021-06-17T00:21:48Z | - |
| dc.date.available | 2012-07-18 | |
| dc.date.copyright | 2012-07-18 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-06-14 | |
| dc.identifier.citation | References
2009 Publication Annual (Taiwan), Industry Foundation in Taiwan (RIT) “Apple surpasses Wal-Mart as No. 1 music retailer in U.S.” http://www.cbc.ca/news/technology/story/2008/04/04/tech-itunes-numberone.html Brennan, M. and Webster, B. (2011). “Why Concert Promoters Matter”, Scottish Music Review, vol. 2, no. 1, pp. 1-25. Busch, L. and Curry, P. (2004). “Rock Concert Pricing and Anti-Scalping Laws: Selling to an Input”, pp. 1-9 Chase, C. (2007). “How the Band Protects Its Brand: The Use of Trademarks to Protect and Promote the Musical Artist”, pp. 1-12 Connolly, M. and Krueger, A. (2006). “Rockonomics: The Economics of Popular Music,” in Handbook of the Economics of Art and Culture, Vol. 1, pp. 667-719 Diamond, T. (1982). “Ticket Scalping : A New Look at an Old Problem.” University of Miami Law Review 37 : pp. 71-92. Frith, S. (2007). “Live Music Matters”, Scottish Music Review, vol. 1, no. 1 (2007), pp. 1-17. Happel, S. and Jennings, M. (1995). “The Folly of Anti-Scalping Laws.” Cato Journal 15(1) : pp. 65-80. Happel, S. and Jennings, M. (1995). “Herd them together and Scalp them.” The Wall Street Journal (February 23) : Section A pp.14 column 4. Huntington, P. (1993). “Ticket Pricing Policy and Box Office Revenue.” Journal of Cultural Economics 17(1) : pp. 71-87. Krueger, A. (2005): ‘The Economics of Real Superstars: The Market for Rock Concerts in the Material World’, Journal of Labor Economics, vol. 23, no. 1, pp. 1-30. Louvain Economic Review (2000), “An economic guide to ticket pricing in the entertainment industry, Louvain Economic Review 66(1), pp.1-26 Pagliero, M. (2011). “The Pricing of Art and the Art of Pricing: Pricing Styles in the Concert Industry”, pp. 1-97 Phillips, R. (2005). Pricing and Revenue Optimization. Stanford Business Books. pp.1-368 Prynn, J. (2008). “Festival explosion turns live music into £1.9bn big business”, Evening Standard, 10 September. http://www.thisislondon.co.uk/standard/article-23553561-details/Festival+explosion+turns+live+music+into+1.9bn+big+business/article.do Rosen, S. (1974). “Hedonic Prices and Implicit Markets : Product Differentiation in Pure Competition.” Journal of Political Economy 82(1). pp. 35-55. Rosen, S. and Rosenfield, A. (1997). “Ticket Pricing.” Journal of Law and Economics 40(2) : pp. 351-76. Tirole, J. (1988). The Theory of Industrial Organization. Cambridge, Mass. The MIT Press. Williams, A. (1994). “Do Anti-Ticket Scalping Laws Make a Difference?” Managerial and Decision Economics 15(5) (September-October) : pp. 503-09. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66100 | - |
| dc.description.abstract | 在以往的研究中，演唱會的票價與週邊商品這兩項相關互補的產品類總是在研究中被分開來探討。在本篇的研究中，主要是以實證研究(Empirical Study)的方式使用問卷來蒐集資料，來推導出一個可以結合演唱會票價的購買意願與週邊商品的迴歸模型(Regression Model)。由於演唱會最熱門跟最常出現的週邊商品為螢光棒以及T-SHIRT，本研究針對這兩項周邊商品在加上明星光環時的購買意願進行調查。在檢查完迴歸模型適用後，本研究探討的相關係數包含週邊商品加了明星光環後的訂價差異，參加演唱會的民眾對於螢光棒和T-SHIRT的喜愛程度，以及民眾印象中對於其他人在週邊商品上所進行的花費數字。最後，本研究以迴歸模型來推導一場模擬實際現實中演唱會的市場需求(Demand Function)，來進行收益管理(Revenue Management)與訂價的策略。此研究發現，加了明星光環的T-SHIRT比加了明星光環的螢光棒帶來更高的票價購買意願。此外，使用迴歸模型的收益訂價策略的收入會增加51.19%。 | zh_TW |
| dc.description.abstract | Traditionally, concert ticket sales and merchandise sales which are complementary to one other have been examined separately. The following paper conducts an empirical study to develop a model that links the relationship between a consumer’s merchandise preferences and its influence on the individual’s ticket reservation price. Two of the most prevalent concert merchandise items, the Glowstick and the T-Shirt, are selected to represent this category. The merchandise variables investigated include the consumer’s personal interest in the merchandise item, the difference in the reservation price for a merchandise item due to celebrity stardom, and the individual’s perception of the merchandise spending of an average concert attendee. Regression is used to explain the link between ticket reservation price model and these merchandise variables. It is discovered that there is a stronger brand influence to Celebrity T-Shirts when compared to Celebrity Glowsticks on Ticket Reservation Prices. Using the model to forecast a larger population demand through ticket reservation prices, revenue management is applied to a live ticket concert scenario to determine the optimal ticket prices. Compared to the traditional scenario of ticket pricing, the revenue management scheme incorporating the merchandise regression model shows a 51.19% improvement in revenue. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T00:21:48Z (GMT). No. of bitstreams: 1 ntu-101-R99749015-1.pdf: 2665947 bytes, checksum: 37f05a19c40a891b78a8ecdc2cbe4c00 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | Table of Contents
Master Thesis Certification i Acknowledgement and Dedication ii Chinese Abstract iii English Abstract iv Table of Contents v List of Figures vii List of Tables viii Chapter 1: Introduction 1 1.1 Background 2 1.2 Statement of Problem 4 1.3 Research Process 11 Chapter 2: Literature Review 13 2.1 The Supply Side: Vertical Organization of Entertainment Industry 13 2.2 The Demand Side: Concert attendees vs. Arbitragers, Free Riding Merchants 14 2.3 Related Works on Ticket and Merchandise Pricing 15 Chapter 3: The Model 22 3.1 Benefits of Regression Analysis 22 3.2 Formulating the Model 24 3.3 The Model 26 3.4 Explanation and Calculation of Model Variables 26 Chapter 4: Results and Analysis 34 4.1 Correlation Matrix and Residuals Analysis 35 4.2 Multiple Regressions 44 4.3 Finalized Model Equation for Ticket Reservation Price 45 4.4 Interpreting the Model Coefficients 45 4.5 Revenue Management on Ticket Pricing 47 4.6 Multiple Regressions II (Stepwise Regression) 56 4.7 Revenue Management on Ticket Pricing II (Stepwise Regression) 58 Chapter 5: Conclusions and Recommendations 65 5.1 Conclusions 65 5.2 Recommendations on Future Research Direction 68 References 71 Appendix A-Calculated Data from Survey Responses 73 Appendix B-Design Survey for Concert (Chinese Version) 80 Appendix C-Design Survey for Concert (English Version) 83 Appendix D-Design Survey Raw Data 86 List of Figures Figure 1: Overview Structure of Research Paper 2 Figure 2: The Decline of Record Sales in Taiwan (1997-2008) 6 Figure 3: Concert Organization of Zones 27 Figure 4: Survey Design: The Six Pricing Scenarios 28 Figure 5: Scatter Plot of RTICKETSi as a function of RGCELBi 35 Figure 6: Scatter Plot of RTICKETSi as a function of IGLOWSTICKi 36 Figure 7: Scatter Plot of RTICKETSi as a function of RTCELBi 36 Figure 8: Scatter Plot of RTICKETSi as a function of IT-SHIRTi 36 Figure 9: Scatter Plot of RTICKETSi as a function of RPEMSi 37 Figure 10: Combined Scatter Plot 37 Figure 11: Residuals over Time 38 Figure 12: Scatter Plot of RGCELBi Residuals 39 Figure 13: Scatter Plot of IGLOWSTICKi Residuals 39 Figure 14: Scatter Plot of RTCELBi Residuals 39 Figure 15: Scatter Plot of IT-SHIRTi Residuals 40 Figure 16: Scatter Plot of RPEMSi Residuals 40 Figure 17: Predicted Values vs. Residuals 41 Figure 18: Histogram of Standardized Residuals 42 Figure 19: VIF Calculation Formula 42 Figure 20: Ticket Price-Demand Function for Zone 1 49 Figure 21: Ticket Price-Demand Function for Zone 2 50 Figure 22: Ticket Price-Demand Function for Zone 3 50 Figure 23: Ticket Price-Demand Function for Zone 4 51 Figure 24: Ticket Price-Demand Function for Zone 5 51 Figure 25: Ticket Price-Demand Function for Zone 6 52 Figure 26: Ticket Price-Demand Function for Zone 7 52 Figure 27: Ticket Price-Demand Function for Zone 1 (Stepwise Regression) 59 Figure 28: Ticket Price-Demand Function for Zone 2 (Stepwise Regression) 59 Figure 29: Ticket Price-Demand Function for Zone 3 (Stepwise Regression) 60 Figure 30: Ticket Price-Demand Function for Zone 4 (Stepwise Regression) 60 Figure 31: Ticket Price-Demand Function for Zone 5 (Stepwise Regression) 61 Figure 32: Ticket Price-Demand Function for Zone 6 (Stepwise Regression) 61 Figure 33: Ticket Price-Demand Function for Zone 7 (Stepwise Regression) 62 List of Tables Table 1: The Decline of Record Sales in Taiwan (1997-2008) 6 Table 2: Income Contribution of Live Concerts of Taiwanese Artists in 2008 7 Table 3: Taiwan’s Record Companies 14 Table 4: Model Variables 26 Table 5: Ticket Prices based on Zones for Scenarios 1~6 28 Table 6: Example: Determination of Ticket Reservation Price 29 Table 7: Determination of Glowstick Reservation Prices in Lower/Upper Bounds 30 Table 8: Example: Determination of Glowstick Branding Factor 31 Table 9: Determination of T-Shirt Reservation Prices in Lower/Upper Bounds 31 Table 10: Mixed Bundling Options 32 Table 11: Personal Preference Factor on Reservation Price 32 Table 12: Personal Preference/Interest Measurement Evaluation 33 Table 13: Correlation Matrix 35 Table 14: Durbin-Watson Table 41 Table 15: VIF Calculation on Multicollinearity 43 Table 16: Regression Statistics 44 Table 17: Sorted Reservation Price Data by Model Predictions 48 Table 18: Customer Demand by Zone 49 Table 19: Optimal Prices for Revenue Management under Model 53 Table 20: Revenue earned under Prices for Mayday DNA Live 2009 World Tour 54 Table 21: A Comparison of Ticket Prices for both Scenarios 55 Table 22: A Comparison of Revenue Earnings 56 Table 23: Regression Statistics II (Stepwise Regression) 57 Table 24: Customer Demand by Zone (Stepwise Regression) 58 Table 25: Optimal Prices for Revenue Management (Stepwise Regression) 63 Table 26: A Comparison of Ticket Prices of Original vs. Stepwise Regression 63 Table 27: A Comparison of Revenue Earnings of Original vs. Stepwise Regression 64 | |
| dc.language.iso | en | |
| dc.subject | 收益管理 | zh_TW |
| dc.subject | 購買意願 | zh_TW |
| dc.subject | 演唱會票價 | zh_TW |
| dc.subject | 週邊商品 | zh_TW |
| dc.subject | 明星光環 | zh_TW |
| dc.subject | 品牌 | zh_TW |
| dc.subject | 迴歸模型 | zh_TW |
| dc.subject | Concert Ticket Pricing | en |
| dc.subject | Revenue Management | en |
| dc.subject | Multiple Regression | en |
| dc.subject | Celebrity Branding | en |
| dc.subject | Reservation Price (Willingness-to-Pay) | en |
| dc.subject | Merchandising | en |
| dc.title | 現場演唱會之最適訂價研究 | zh_TW |
| dc.title | An Empirical Study of Ticket Pricing at Live Concerts | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊朝龍(Chao-Lung Yang),黃奎隆(Kwei-Long Huang) | |
| dc.subject.keyword | 購買意願,演唱會票價,週邊商品,明星光環,品牌,迴歸模型,收益管理, | zh_TW |
| dc.subject.keyword | Reservation Price (Willingness-to-Pay),Concert Ticket Pricing,Merchandising,Celebrity Branding,Multiple Regression,Revenue Management, | en |
| dc.relation.page | 94 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-06-15 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 企業管理碩士專班 | zh_TW |
| 顯示於系所單位： | 管理學院企業管理專班(Global MBA) | |
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