English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51624609      Online Users : 507
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/48949
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/48949


    Title: 應用資料採礦技術於電影市場研究
    Application of Data Mining Techniques to Film Market Research
    Authors: 蔡依庭
    Tsai, Yi-Ting
    Contributors: 鄭宇庭
    蔡依庭
    Tsai, Yi-Ting
    Keywords: 電影
    資料採礦
    決策樹
    市場區隔
    Film
    Data Mining
    Decision Tree
    Market Segmentation
    Date: 2009
    Issue Date: 2010-12-08 01:53:54 (UTC+8)
    Abstract: 就當前電影市場的現況來看,電影發行成本的節節升高,顧客需求的複雜多變,再加上電影消費集中化趨勢越趨明顯的事實,不論是從電影發行公司或是電影映演事業的角度來看,如何透過對於市場顧客需求、行為的解讀,清楚分隔市場,並為不同市場區隔設計不同的產品及行銷組合已經成了電影工業刻不容緩的課題。
    有鑑於此,本研究透過應用資料採礦之技術,選用四個決策樹(C&RT、QUEST、CHAID、C5.0)、邏輯斯迴歸以及類神經網路等方式進行模型建置,由於決策樹CHAID對於「是否去電影院看外片或國片」及「是否去電影院看電影」兩種不同的目標變數,其不論是在整體預測正確率、準確度、反查率,皆是高於其他模型,故最後兩個目標變數皆選擇CHAID此一模型,而目標變數為「是否去電影院看電影」之CHAID模型表現也較好,故主要以其結果為主。
    透過目標變數為「是否去電影院看電影」之CHAID模型,共獲得十三項影響「是否去電影院看電影」之相關變數,並根據分析結果,將電影市場顧客區分為最高貢獻顧客、一般貢獻顧客及低度貢獻顧客三類,將其歸納出並找出三種不同貢獻程度的顧客族群特性,而三種不同貢獻族群在「年齡」、「教育程度」、「娛樂文化支出」、「居住地區」、「是否上網瀏覽資訊網頁」、「是否上網蒐集資訊」、「是否會收看電視外片」、「是否看電視歐美影集」、「是否會說英文」、「是否上網線上觀賞影片」、「經濟富裕」、「即時行樂」均呈現顯著的差異,故本研究以不同貢獻程度族群特性為主,以看外片或國片之族群特性為輔,作為行銷策略建議之依據。
    Considering the current film market, the publication cost of a film is steadily increased. Meanwhile, customers have complicated requirements, and the trend of concentrated film consumption is gradually clear. For the perspective of both film companies and film broadcasting business, clear market segmentation after understanding customers’ needs and interpretation of customer behaviors to design different products and marketing combination for different markets are of great urgency for the general film industry.
    In view of this, the study aims to using four Decision Trees(C&RT, QUEST, CHAID, C5.0), Logistic Regression, and Artificial Neural Network to construct the model by applying Data Mining technology. Since Decision Tree-CHAID is excellent in the forecast accuracy, precision, and recall rate as compared to other models for response variables of going to the movies and going to foreign movies or Taiwan movies, the CHAID is adopted in this research for both response variables. The CHAID is more excellent for the response variable of going to the movies than the other, so use it as the main result.
    Through using Decision Tree-CHAID, this study identified thirteen factors that have greater impact on going to the movies. Based on the analysis results, this study induced the characteristics of three customer groups-the highest contribution customers, regular contribution customers and low contribution customers. Three different contribution groups shows significant differences at age, education, entertainment expenditure, living area, internet surfing, collecting information from internet, watch foreign movies, watch foreign drama, speak English, watch on-lines movies, affluent, and seize the day. This study mainly based on the characteristics of the three different groups, and group characteristic of going to foreign movies or Taiwan movies as auxiliary, to provide the marketing portfolio strategy recommendations.
    Reference: 中文參考文獻
    王東昇(2001),「台北地區電影影城消費行為之研究」,銘傳大學管科所碩士論
    文。
    王清華(2005),「2005年台灣電影總覽」,1999年電影年鑑,臺北:電影資料館。
    王清華(2006),「2006年台灣電影總覽」,1999年電影年鑑,臺北:電影資料館。
    方世榮 譯(1999),原著Philip Kotler,「行銷管理學」(Marketing management:analysis,planning,implementation,and control,9th),台北:東華書局。
    全國意向股份有限公司,http://www.trendgo.com.tw/index2.html。
    行政院主計處,http://www.dgbas.gov.tw/mp.asp?mp=1。
    杜榮瑞(1978),「中國學生電影觀賞行為研究」,國立政治大學企研所碩士論文。
    李克珍(1986),「大學生到電影院看電影的動機與行為研究」,輔仁大學大傳所
    碩士論文。
    李清志(1998),「台北電影院」,台北市:元尊文化。
    周希平(1985),「電影觀賞行為之分析與研究」,國立政治大學企研所碩士論文。
    周建輝(1986),「電影市場之區隔化研究--以台北市區學生為例」,國立交通大
    學管科所碩士論文。
    別蓮蒂(2000),「生活型態白皮書-2000年台灣消費者習慣調查報告」,台北:
    城邦文化。
    林文淇(1997),「電影的社會實踐」,台北:遠流。(原書Turner G.〔1988〕. Film
    as Social Practice.)
    高登第(1998),「票房行銷---菲利浦科特勒談表演藝術行銷策略」,原著Kolter P. & Scheff J. Marketing the Performing Arts,台北:遠流。
    洪育忠 譯(2005),「顧客關係管理-資料庫行銷方法之應用」,原著 Kumar,
    Werner & Reinartz,台北:華泰文化事業股份有限公司。
    陳坤河(1991),「戲院安全一籮筐」,1991電影年鑑,台北:電影資料館。
    郭幼龍(1999),「民眾對臺灣電影的評價與電影消費行為之關係研究」,世新大
    學傳播所碩士論文。
    郭淑雲(2002),「消費者特性與網際網路購物意願關係之研究--以生鮮食品為例」
    ,國立中興大學行銷所碩士論文。
    章柏青、張衛(1994),「電影觀眾學」,北京:中國電影出版社。
    國家電影資料館編,「電影辭典」,台北:國家電影資料館。
    彭佳琪(1999),「電影閱聽人之生活型態分析」,文化大學新研所碩士論文。
    黃美蘭(2005),「從生活型態觀點探討旅遊網站消費者購買決策過程與使用意願之研究」,國立政治大學廣告所碩士論文。
    葉龍彥(1997),「西門町電影發展史1986-1997」,台北:文建會出版,p316~329。
    楊順安(2002),「消費者訊息搜尋模式與消費型態之分析」,國立中興大學行銷所碩士論文。
    電影事業發展基金會(1989),「電影人口意見調查」,台北:電影事業發展基金會。
    新聞局,台灣電影網(2007),「產業情報-市場統計」。
    蓋洛普徵信(1993),「大台北地區民眾觀賞電影行為意見調查」,台北:電影事業發展基金會。
    劉又菁(2002),「從欲求利益觀點探討台灣電影之市場區隔-以美國電影為研究對象」,國立交通大學管理科學所碩士論文。
    盧非易(1997),「台灣電影映演市場的結構分析:以1994 年為例」。
    謝邦昌、鄭宇庭、蘇志雄(2008),「Data Mining概述-以Clementine12.0為例」,中華資料採礦協會。
    簡貞玉(1996),「消費者行為學」,原著Del I. Hawkins & Roger J. Best & Kenneth A. Coney¸,台北:五南。
    英文參考文獻
    Berman, B. and J. R. Evans (1982), ”Marketing”, Macmrillian Publishing Co., pp:189-193.
    Breiman, L., J. H. Friedman, R. A. Olshen and C. J. Stone (1984), “Classification and Regression Tree” Wadsworth, California.
    Biggs, D., B. D. Ville and E. Suen (1991), “A Method of Choosing Multiway Partitions for Classification and Decision Trees”, Journal of Applied Statistics.
    Engle, J. F., R. D. Blackwell and D. T. Kollat (1978), “Consumer Behavior” , 3rd ed. , Prentice Hall inc. , pp:33-37.
    Fish, K. E., J. H. Barnes and M. W. Aiken, “Artificial Neural Networks: A New Methodology for Industrial Market Segmentation,” Industrial Marketing Management, Vol.24, 1995, pp.431-438.
    Hunt, E.B. (1962), “Concept learning: An information processing problem”, New York: Wiley.
    Hunt, E.B., J. Marin and P.J. Stone (1966), “Experiments in induction”, New York: Academic Press.
    Hawkins, D. I., R. J. Best and K.A. Coney (1992), “Consumer Behavior: Implications for Marketing Strategy” , 5th ed., Chicago: Richard D Irwin, pp:326.
    Kolter, P. (1965), “Marketing Management: Analysis, Planning and Control”, 5th ed., Prentice Hall Inc., pp:133, 148-149.
    Kotler, P. (1998), “Marketing Management: Analysis, Planning, Implementation and control”, 9th ed., Prentice-Hall Inc.
    Lazer and William (1963), “Life Style Concept and Marketing”, Toward Scientific Marketing , Stephen Cresyser ed., Chicago AMA, pp:143-151.
    Plummer, J. T. (1967), “The Concept and Application of Life Style Segmentation”, Journal of Marketing, Vol.38, pp: 25-67.
    Perreault, W. D. and H. C. Barksdale (1980), “A model-free approach for analysis of complex contingency data in survey research”, Journal of Marketing Research, Vol. XVII (November), 503-515.
    Quinlan, J. R. (1979), “Discovering rules by induction from large collections of examples”, In D. Michie (Ed.), “Expert Systems in the Micro -Electronic Age”,pp.168-201, Edinburgh University Press, Edinburgh.
    Reynold, F. D. and W. R. Darden(1974), “Constructing Life Style and Psychographic”, Williams D. W. ed , Life Style and Psychographics, Chicago : AMA , pp:71-96.
    Rubin, D. B. (1987), “Multiple Imputation for Nonresponse in Surveys”, New York
    Wiley.
    Shannon, C. E. and W. Weaver (1949), “The Mathematical Theory of Communication”, Urbana: University of Illinois Press.
    Shannon, C. E. (1949), “Communication in the presence of noise”, Proc. IRE, 37, 10-21.
    Vellido, A., P. J. G. Lisboa and J. Vaughan (1999), “Neural Networks in Business: A Survey of Applications”, Expert Systems with Applications, Vol.17, pp.51-70.
    Wendell, R. S. (1956), “Product Differentiation and Market Segmentation as Alternative Marketing Strategies”, Journal of Marketing, Vol.21, pp.3-8.
    Wind, Y. and P. E. Green (1974), “Some Conceptual Measurement and Analytical Problem in Life Style Research”, in William D. Well ed., Life Style and Psychographics, Chicago : AMA , pp:99-126.
    Wind, Y. (1978), “Issues and Advances in Segmentation Research”, Journal of Marketing Research, Vol. 15, pp: 217-337.
    Loh, W. Y. and Y. S. Shih (1997), “Split Selection Methods for Classification Trees”, Statistica Sinica 7, 815-840.
    Description: 碩士
    國立政治大學
    統計研究所
    97354016
    98
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0097354016
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML2334View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback