臺灣博碩士論文加值系統

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%。

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.

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

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