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  • 學位論文

電影對其原著小說的影響:台灣市場

The Effect of Movies on Their Originals: A Case of Taiwan

指導教授 : 馮勃翰

摘要


這篇論文討論電影對其原著小說的影響,近年來以文學小說為藍本的電影劇本越來越受重視,有些小說本身就是暢銷作品,有些則是藉由電影改編而成為暢銷作品,有些則仍然不受關注,這篇論文想回答電影作為一個小說品質確認與宣傳小說的管道,是否對其原著小說的銷量有正面的影響,直觀而言,小說本身擁有好的品質的話,他的銷量應比較差的小說好,並且這能反映在電影內容中,進而顯現在電影票房數字上,而電影也能看作是宣傳小說的手段,這樣的曝光應能使小說銷量增加,我透過蒐集三年間在台灣上映的電影與其對應的原著小說資料,來印證我的猜想。 透過台灣偶像劇場這個網站,我蒐集了從2013年八月至2016年七月在台灣上映的電影基本資料,再藉由IMDB與實際搜尋博客來網站以確認電影的原著小說是否在台灣販售,最後得到112部電影對應201本原著小說作為樣本,由於我無法取得小說的實際銷售數字,我使用的是博客來網站的每週排行榜名次作為衡量依據,實際觀察小說的名次變化發現,名次變化模式大致呈現一個山形,從較低的名次慢慢往上,於高名次的部分停留後再跌至低名次,最後離開榜單,這裡名次的高低與數字相反,第100名為低名次,第1名則為高名次,100名至50名間的銷售數量差異不大,甚至與百名外不在榜單上的其他小說銷售差異不大,但前面的名次想要再往上提高排名是困難的,這樣的現象讓我將衡量名次的最低標準設在50名,不過實際的回歸結果顯示標準的設置不影響結論,前面提到我無法得到實際的銷售數字,因此我使用的替代方案是計算小說的上榜週數,以50名作為標準,小說在的名次在50名內則計算為上榜一次,用這個方式我計算在電影上映後的16週間小說進50名內的次數作為銷售數字的替代方案,電影上映前四週的宣傳期則是以進50名榜兩次以上的標準判斷小說的品質好壞,名次的部分都可以用不同的標準重新測試,我的回歸結果顯示不同的標準對結論的影響不大,了解了上榜週數的概念後,我主要想看的是電影票房對上榜週數的影響,以及小說品質的好壞對電影票房的交互作用,在控制了語言、續作、套書等變數後,回歸結果顯示電影票房對於小說的上榜週數有正面的影響,以50名為基準的平均上榜週數為2.2週,票房的係數則說明票房每增加1%,會使上榜週數增加約0.69週,電影票房對原著的影響是顯著正向的,並且小說品質與電影票房交互作用的係數則顯示好的品質能帶動電影票房對小說本身銷量的影響,我所使用代表品質的變數雖有瑕疵,但他確實能代表部分的小說品質,且結果與直觀相符,好的品質能帶來好的銷量,但不論小說品質如何,改編成電影對小說的銷量確實是有顯著的正面影響。

關鍵字

電影 小說 票房 銷售排行 排行榜

並列摘要


This paper discussed the effect of movies to their originals. Novel-based screenplays have been important to the movie industry in recent years. Some of the novel were bestselling, some of them became bestselling because of the movies, and the rest of them remained unpopular. My paper used the data of movies and novel in Taiwan’s market from 2013/8 to 2016/7 to answer whether movies could give positive impact on their originals. Intuitively, novel with good quality would become bestselling and thus reflected in the content of the corresponding movies, which would bring the sales of the novel better. Also, movies could be regarded as advertisements of their originals’ quality check; in addition, the exposure movies brought for their originals would make the sales of novel greater. In this paper, I tried to prove that this phenomenon was true. By tw.dorama.info, I scrapped the movies’ information from 2013/8 to 2016/7 of Taiwan’s market. Besides, through IMDB and books.com, I checked if movies did have originals and made sure that novel had Chinese versions in Taiwan’s market. Last, I obtained 112 movies corresponding to 201 novel as my sample. Since I couldn’t get the exact sales number, I use the rankings every week on books.com as substitutes of real sales number. The shape of the ranking pattern was like a hill, starting from low rankings, climbing up to high rankings, staying for a while, descending to low rankings, and then dropping out of the sales charts. Here I refer high ranking to small ranking number and vice versa. In addition, the sales in the range of 100 to 50 were quite similar and the rankings around 100 even shared little difference in sales of those not on the sales charts. I set the bottom line at rank 50 due to this observation; however, it didn’t really make great differences when the bottom line was set at a different rankings, which could be seen as robustness. As I mentioned before, I couldn’t get the actual number of sales so I came up with a method of using the total number of weeks on sales charts to be a proxy as the sales. For instance, a novel with rankings of 54, 36, 12, 43, and 86 will got 3 weeks on sales charts, which was based on total number of weeks on sales charts with bottom line at rank 50. Furthermore, I regarded a novel as good quality by the condition of whether it was on sales charts for at least two weeks with bottom line at rank 50 within the period of advertisement, which was 4 weeks before the opening week of corresponding movies. I found this standard reasonable but the standard could be changed and the regression results showed that changing standard wouldn’t make a big impact to the conclusion. Based on the knowledge of total number of weeks on sales charts, what I concern the most was the effect of movies, represented by box office, to their originals, counted in total number of weeks on sales charts, and the interaction between quality of novel and movies’ box office. The regression result showed that, compared to the average total number of weeks on sales charts, 2.2 weeks, with the bottom line at rank 50, 1% increase in box office would lead to 0.69 weeks more on sales charts for novel, which was quite significant. As to interaction, the result indicated that good quality novel could bring in movies with positive impact on the sales of themselves. Though there were flaws in my estimations, the result suited the intuition quite nice. In conclusion, novel with good quality was important to the sales of themselves but the appearance of movies could bring in positive impact to sales of novel whether the quality was good or bad.

並列關鍵字

Movie Novel Box office Sales Ranking Sales charts

參考文獻


Judith A. Chevalier and Dina Mayzlin (2006) “The Effect of Word of Mouth on Sales: Online Book Reviews”, Journal of Marketing Research, August 2006, Vol. 43, No. 3, pp. 345-354.
Judith A. Chevalier and Austan Goolsbee (2003) “Measuring Prices and Price Competition Online: Amazon.com and BarnesandNoble.com”, Quantitative Marketing and Economics, June 2003, Vol. 1, Issue 2, pp. 203–222.
David A. Reinstein and Christopher M. Snyder (2005) “The Influence of Expert Reviews on Consumer Demand for Experience Goods: A Case Study of Movie Critics”, The Journal of Industrial Economics, Vol. 53, Issue 1, March 2005, pp. 27–51.
Karen Clay, Ramayya Krishnan and Eric Wolff (2001)”Prices and Price Dispersion on the Web: Evidence from the Online Book Industry”, The Journal of Industrial Economics, Vol. 49, Issue 4, December 2001, pp. 521-539.
Authur De Vany and W. David Walls (1999) “Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office?”, Journal of Cultural Economics, November 1999, Vol. 23, Issue 4, pp. 285–318.

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