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研究生:孫珮瑜
研究生(外文):Pei-Yu Sun
論文名稱:舞臺劇產業中銷售資訊對銷售速率的影響:藉由台灣劇團資料之實證研究
論文名稱(外文):Impact of Performance Ticket Sales Information on SalesSpeed: An Empirical Study Based on a Taiwanese Theater
指導教授:孔令傑孔令傑引用關係
口試日期:2017-06-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:66
中文關鍵詞:票券銷售銷售資訊揭露購買意願不確定性動態座位分配迴歸分析
外文關鍵詞:Ticket SalesSales Information DisclosureValuation UncertaintyDynamic Seat AllocationRegression Analysis
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本研究資料透過銷售資訊的揭露,期望能幫助決策者最大化獲利。我們藉由迴歸模型分析一個台灣劇團的售票資料,研究消費者購票瞬間觀察到的各票種售票狀況如何影響消費者購買意願,並探討決策者持有此資訊後,能如何透過動態調整各票種的可售數量來最大化預期營收。我們發現票種的銷售量與銷售速度成正相關,但是和其他票種的累積銷售量呈負相關。這樣的現象尤其在票種之間的票價差距越大的時候越趨明顯。我們的研究也證實銷售速度會因為銷售期間的增加而降低。
透過需求預測模型,我們可以根據當前的銷售來計算並調整不同票種最適座位數量。我們也考量了動態決策的情境,使決策者能動態分配不同票種的座位數量,並最佳化獲利。在我們的數值實驗中,透過銷售資訊的揭露,本研究發現考慮銷售狀況對銷售速度影響的動態座位數量調整能使獲利提高4.60%。
The data in this study highlight the lengths that a decision maker can go to in order to maximize its profit through sales information disclosure. By incorporating sales information of price bands into a single regression model, designed to represent the example of Taiwanese theater ticket theoretical framework formalizes how decision makers are able to affect customers’ valuation. It is verified that sales of a price band has a positive relation to its sales speed while the cumulated sales of other price bands have negative effects. The relationships intensify when the price of the price bands are closer to one another. Our analyses further support that the sales speed decreases when the length of selling period escalates.
The estimated demand system allows for the calculation of capacity allocation for each price bands based on its current sales. A scenario in which decision makers can dynamically allocate ticket capacities of various price bands of the same performance to optimize profit is investigated. The profit difference between the model with the sales information disclosure is 4.6% higher than the suboptimal model.
1 Introduction 3
1.1 Background and motivation . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Research plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Literature review 9
2.1 Perishable products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Sales information disclosure . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Ticket selling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Problem Description and Formulation 17
4 Analysis 23
4.1 Data cleansing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 An illustrative example of data processing . . . . . . . . . . . . . . . . . 26
4.2.1 Testing Hypothesis 1 to 3 . . . . . . . . . . . . . . . . . . . . . . 27
4.2.2 Testing Hypothesis 4 . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.3 Exploratory data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.4 Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4.1 Testing Hypothesis 1 to 3 . . . . . . . . . . . . . . . . . . . . . . 33
4.4.2 Testing Hypothesis 4 . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Application: Dynamic Seat Allocation 43
5.1 Exploratory data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 Numerical experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.3 Quantity allocation based on Lagrangian relaxation . . . . . . . . . . . . 50
6 Conclusions and Future Works 55
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
6.2 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
A Figures of Exploratory Data Analysis 59
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