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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 任立中 | |
dc.contributor.author | Yi-Chen Lin | en |
dc.contributor.author | 林怡辰 | zh_TW |
dc.date.accessioned | 2021-06-17T09:06:54Z | - |
dc.date.available | 2021-01-21 | |
dc.date.copyright | 2020-01-21 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-12-19 | |
dc.identifier.citation | 中文部分
[1]. 李心嵐(2000)。跨國新產品銷售預測模式之研究──以電影為例。國立政治大學國際貿易學系碩士論文。 [2]. 劉小芳(1999)。跨國流行商品擴散模型之比較研究:美國影片在台銷售預測模式的建立。國立暨南國際大學國際企業學研究所碩士論文。 英文部分 [3]. Brooks, S. P., and Gelman, A. (1997), General Methods for Monitoring Convergence of Iterative Simulations, Journal of Computational and Graphical Statistics 7, p. 434-455. [4]. Frank M. Bass (1969), A New Product Growth Model for Consumer Durables, Management Science, Vol. 15, No. 5, p. 215-227. [5]. Galvão, M., & Henriques, R. (2018), Forecasting Movie Box Office Profitability, Journal of Information Systems Engineering & Management, 3(3), 1-9. [22] [6]. Gelman, A., and Rubin, D. B. (1992), Inference from Iterative Simulation using Multiple Sequences'. Statistical Science, 7, p. 457-511. [7]. Green, W. H. (1997), Econometric Analysis, 3rd ed., New Jersey: Prentice-Hall. [8]. Jain and Rao (1990), Effect of Price on the Demand for Durables: Modeling, Estimation and Findings, Journal of Business and Economic Statistic, (April), p. 163-169. [9]. Lenk, P. J. and Rao, A. G. (1990), New Models From Old: Forecasting Product Adoption By Hierarchical Bayes Procedures, Marketing Science, Vol. 9(1), p. 42-53. [10]. Mahajan, Muller, and Bass (1990), New products diffusion models in marketing: a review and direction for research, Journal of Marketing, Vol.54, p. 4. [11]. Mansfield, E. (1961), Technical Change and the Rate of Imitation, Econometrica, 29, (October), p. 741-766. [12]. Neelamegham, R. and P. Chintagunta (1999), A Bayesian Model to Forecast New Product Performance in Domestic and International Markets, Marketing Science, 18 (2), p. 115-136. [13]. Rogers, E. M. (1995), Diffusion of Innovation, 4th ed., NewYork:The Free Press. [14]. Schmittlein, D. and Mahajan, V. (1982), Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance, Marketing Science, Vol. 1(1), (Winter), p. 57-78. [15]. Srinivasan, V. and C. H. Mason (1986), Nonlinear Least Squares Estimation of New Procuct Diffusion Models, Marketing Science, 5 (2),(Spring), p. 169-178. 網路資源 [16]. Box Office Mojo(n.d.), Box Office, Retrieved December, 8, 2018, from https://www.boxofficemojo.com/ [17]. IMDb(n.d.), Movies, TV & showtimes, Retrieved December, 8, 2018, from https://www.imdb.com/ [18]. metacritic(n.d.), metascore, Retrieved January, 20, 2019, from https://www.metacritic.com/ [19]. THE NUMBERS(n.d.), Box Office Star Records, Retrieved February, 11, 2019, from https://www.the-numbers.com/ | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74748 | - |
dc.description.abstract | 現今市場環境變化迅速,企業使用以往的經營方式已無法因應瞬息萬變的動態市場,需隨時調整公司資源以強化其應變能力。開發新產品前,企業首要注重的是新產品的需求規模(市場潛力)以及其擴散速度是否快速,故需制定良好的銷售預測模式,為每項產品找到最適切的模型,將會有效降低公司的投資風險以及協助行銷策略的擬定。
科技進步促使人們改變生活習慣,在生活步調匆忙與高強度的工作環境下,能夠令人放鬆的休閒活動變得不可或缺。觀賞電影對於現代人而言是一種快速、也最有效的都市休閒活動。本研究將使用美國電影作為實證分析的對象,透過創新產品銷售預測模式預測出每部電影的銷售量,期望能提供業界預測新產品銷量之參考。研究目的為提出影響美國電影銷售擴散型態之因素,以此建構出不同的電影銷售預測模型,進而比較不同電影銷售預測模型之預測效度。 近年來Bass擴散模式不斷地發展,配合各種高效度的統計分析方法,若再加入足以影響銷售的各種重要變數,預測模式將更趨於精準穩定。因此本研究以過去文獻所得之最優良模式為基準,加入層級貝氏模式進行分析,在此兩套模式預測體系下,利用創新產品之屬性(製作預算、美國MPAA電影分級、電影類型、前三大主演是否為票房明星、票房明星分數、專家評分、上映前留言數、有無假日效應)來預測銷售量,比較優劣後,找出最佳之預測模式。由最終研究結論可知新產品銷售量之最佳預測模式為使用層級貝氏所建構的模型。 | zh_TW |
dc.description.abstract | Nowadays, the market environment is changing rapidly. Enterprises using traditional operation methods could no longer adapt to the dynamic market, so they need to adjust the resources and strengthen their ability of problem solving. Before launching new products, companies should focus on the scale of demand (market potential) and whether it is spreading rapidly. Therefore, it is necessary to develop an accurate sales forecasting model to find the most consistent product cycle for each product, which will effectively reduce the risk of the company investment and assist in the formulation of marketing strategies.
As the advance in technology makes people change their lifestyles, and with the fast-past life and stressful work environment, leisure activities become indispensable. Among different kinds of leisure activities, seeing movies is the fastest and the most effective way for modern people. This study will use American films as the object of empirical analysis, predicting the sales volume of each film through the innovative product sales forecasting model, and expect to provide a reference for the movie industry to predict the sales of new products. The purpose of this study is to propose factors that influence the diffusion pattern of American film sales, so as to construct different sales forecasting models, and then compare the predictive validity of different models. In recent years, the Bass diffusion model has been continuously developed by using different kinds of effective statistical methods. If we add various important variables that affect the prediction model, the model will become more precise and stable. Therefore, this study is based on the best model which is derived from the previous literature, and adds the hierarchical Bayesian model to do the analysis. Under these two model prediction systems, the attributes of the innovative products (production budget, MPAA rating, film type, main cast, star score, expert rating, number of comments before release, holiday effect) are considered in the models. After comparing the advantages and disadvantages, we can find the best prediction model. According to the research conclusion, the best prediction model for the sales of innovative products is the hierarchical Bayesian model. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:06:54Z (GMT). No. of bitstreams: 1 ntu-108-R06h41006-1.pdf: 2808136 bytes, checksum: 51385f93f0ac3ba77f58d7ef79d81c98 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 摘要 ii ABSTRACT iii 目錄 v 圖目錄 viii 表目錄 ix 第一章 研究介紹 1 第一節 研究背景與動機 1 第二節 研究目的 2 第三節 論文架構 2 第二章 文獻探討 3 第一節 新產品之擴散 3 第一項 Rogers(1962)分法 3 第二項 Bass(1969)分法 5 第二節 擴散模式 6 第一項 Bass(1969)擴散模式 6 第二項 Bass擴散模式參數估計法 8 第三節 似無相關迴歸模型 11 第四節 層級貝氏模式 12 第一項 貝氏統計模式 12 第二項 層級貝氏統計模式 13 第五節 新產品預測模式 14 第六節 影響電影擴散之因素 17 第三章 研究方法 18 第一節 模型建立 18 第一項 模型一:Bass連續擴散模式+迴歸方程式體系 18 第二項 模型二:層級貝氏模型 19 第二節 R語言套件──rjags 21 第三節 層級貝氏模型診斷指標 21 第四節 模型預測效度指標 23 第四章 實證分析 25 第一節 資料來源與整理 25 第二節 實證模型建構 31 第一項 變數說明 31 第二項 模型建構 32 第三節 模型預測效度比較 36 第五章 研究結論與建議 43 第一節 研究結果 43 第二節 研究限制 43 第三節 未來研究方向 44 參考文獻 45 附錄 47 附錄一 前20部美國電影銷售曲線 47 附錄二 NLS法之p、q、m估計結果 48 附錄三 SUR法之p、q、m迴歸係數估計結果 51 附錄四 層級貝氏模型之各迴歸係數估計結果 52 附錄五 層級貝氏模型之各迴歸係數診斷結果 54 附錄六 層級貝氏模型之mi 迴歸係數(η)診斷圖形 56 附錄七 層級貝氏模型之mi 迴歸係數(η)收斂軌跡圖 58 | |
dc.language.iso | zh-TW | |
dc.title | 以層級貝氏模型預測新產品銷售模式:以美國電影為例 | zh_TW |
dc.title | A Hierarchical Bayesian Forecasting Model for Innovative Products–in the case of American Films | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林郁翔,蔡政安 | |
dc.subject.keyword | 電影,新產品預測,Bass擴散模式,層級貝氏模式,似無相關迴歸, | zh_TW |
dc.subject.keyword | Movie,New product prediction,Bass diffusion model,Hierarchical Bayesian model,Seemingly unrelated regression, | en |
dc.relation.page | 58 | |
dc.identifier.doi | 10.6342/NTU201900901 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-12-20 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
顯示於系所單位: | 統計碩士學位學程 |
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