臺灣博碩士論文加值系統

本研究分析口碑與電影票房之關聯性，提供全面性的實證研究。本研究透過全球知名網路電影討論平台，如網路電影資料庫(IMDb)、爛番茄(Rotten Tomatoes)、臉書(Facebook)、雅虎電影(Yahoo Movie)，以及國內青少年最常使用的批踢踢實業坊電影版(PTT Movie)，蒐集正負面電影口碑之評價資料，試圖檢驗網路口碑對消費者之觀影行為。本文以2016年之104部國內外電影為研究樣本，整體而言，研究結果發現正向的網路口碑會提高電影票房，顯示網路口碑會影響電影績效。然而，本研究以電影名稱在Google搜尋結果筆數及秒數為從眾行為代理變數的觀察中，本研究未發現Google搜尋與電影響票房有關之證據。

This paper provides a comprehensive empirical study to analyze the effect of word of mouth on the box office revenue. This study examines the effect of word of mouth by using the world's leading online film discussion platforms, such as the Internet Movie Database (IMDb, Rotten Tomatoes, Facebook and Yahoo Movie), and I also including the most commonly used batch kicks for domestic teenagers, the PTT movies platform, as the data resource. I collect positive and negative rating data on movie Word-of-mouth, trying to investigate that the electronic word-of-mouth how to influence the behavior of consumers/viewers. The final sample of this article includes 104 movies in 2016 box office. Overall, I find that positive-rating word of mouth increases the box office revenue, showing that electronic word of mouth directly affects movie performance. In this paper, I use the number of words and the number of seconds in the Google search results to proxy the herding behavior. However, this study not finds evidence of Google's search and electrical influence on the box office.

目錄
第一章 緒論 1
第一節 研究動機及目的 1
第二節 研究背景與問題 2
第三節 預期結果 3
第四節 研究流程 4
第二章 文獻探討 5
第一節 電影票房 5
第二節 網路口碑 9
第三節 從眾行為 13
第三章 研究方法 16
第一節 研究架構 16
第二節 研究假說推倒 16
第三節 研究變數操作定義 18
第四節 樣本描述 24
第五節 統計方法 24
第四章 資料分析與結果 25
第一節 敘述性統計 25
第二節 相關分析 26
第三節 迴歸分析 26
第五章 結論與建議 38
第一節 研究結果 38
第二節 研究結論 39
第三節 研究建議 40
參考文獻 41
附錄 45

 
表次
表2-1 台灣電影產業 5
表2-2 台灣電影營業商家數量 5
表2-3 2012年至2016年台北市電影票房市場概況 7
表2-4 2016年下半年度(7-12月)全國電影票房市場概況 7
表2-5 口碑正面動機 10
表2-6 口碑負面動機 10
表3-1 變數操作定義 23
表4-1 樣本結構 45
表4-2 相關分析 47
表4-3 PTT好雷與台灣票房 48
表4-4 PTT負雷與台灣票房 49
表4-5 IMDb 評分、IMDb 評分總票數與電影票房 50
表4-6 爛茄茄評分、爛番茄總票數與電影票房 51
表4-7 爛番茄平均評分與電影票房 52
表4-8 爛番茄觀眾評分與電影票房 53
表4-9 爛番茄觀眾平均評分與電影票房 54
表4-10 爛番茄新鮮數量與電影票房 55
表4-11 爛番茄腐爛數量與電影票房56
表4-12 爛番茄腐爛數量佔爛番茄評論總數量比例與電影票房57
表4-13 爛番茄用戶總投票數與電影票房58
表4-14 爛番茄用戶總投票數×爛番茄觀眾平均評分、爛番茄觀眾平均評分與電影票房 59
表4-15 FB按讚人數與電影票房 60
表4-16 FB追蹤人數與電影票房 61
表4-17 Yahoo期待度、Yahoo 投票人數與電影票房 62
表4-18 Yahoo滿意度、Yahoo 投票人數與電影票房 63
表4-19 新聞次數與電影票房 64
表4-20 搜尋時間(秒)與電影票房 65
表5-1 研究結果彙總 38

圖次
圖1-1 研究流程圖 4
圖2-1 電影產業鏈 6
圖3-1 研究架構 16

中文部分
1.IMDb網站：https://www.imdb.com
2.PTT電影版網站：https://www.ptt.cc/bbs/movie/index.html
3.Rotten Tomatoes網站：https://www.rottentomatoes.com
4.文化部影視及流行音樂產業局(2016)-電影產業調查報告
5.余洪亮、蔡儀清、莊懿妃(2012)商管研究資料分析SPSS的應用，台北市:華泰文化
6.李茂興、余伯泉 譯(2001)社會心理學，台北市：弘智文化。
7.法源法律網: http://www.lawbank.com.tw/index.aspx

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