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    Title: 影音串流平台之創新擴散模型研究－以Netflix和Spotify為例
    Innovation Diffusion Models of Streaming Media : A Case Study of Netflix and Spotify
    Authors: 劉湝沂
    Liu, Chieh-Yi
    Contributors: 吳豐祥
    Wu, Feng-Shang
    劉湝沂
    Liu, Chieh-Yi
    Keywords: 影音串流
    串流平台
    創新擴散模型
    成長曲線
    video streaming
    streaming platform
    innovation diffusion model
    growth curves
    Date: 2019
    Issue Date: 2020-03-02 11:36:27 (UTC+8)
    Abstract: 影音串流平台為近五年開始蓬勃發展的服務類型，在網際網路的技術穩定和智慧型行動裝置的普及化下，影音串流服務之接收品質有效提升，且串流平台本身具有高度的跨國性及流通性，使得全球之影音串流訂閱戶在2018年正式超越了有線電視的訂閱戶，電視媒體衰弱而串流服務崛起，影音串流平台逐漸走向媒體服務的主流市場，因此串流平台的用戶擴散及其創新擴散模式已成為現今之重要研究議題。
    本研究透過文獻探討選取了Gompertz Model、Bass Model及將網路使用人數作為基礎擴散的Contingent Diffusion Model作為研究模型，欲探討影音串流平台之用戶擴散是否適用創新擴散模型來探討其成長趨勢，並透過觀察目前發展最為快速，也累積最多用戶數的兩影音串流平台—Netlfix和Spotify，了解影視串流和音樂串流服務是否有不同的創新擴散模式，以及延伸過去學者對於最適解釋模型和最佳預測模型之質疑，探討影音串流平台所適用之解釋模型和預測模型是否如先前學者之結論確實有差異。本研究透過判定係數和修訂Theil不等係數來檢測模型之解釋能力，而模型預測能力則使用MAPE值衡量。
    經實證研究後發現，影音串流平台確實可以透過創新擴散模型觀察其成長趨勢，且如同過去學者針對耐久財之結論，影音串流平台的最適解釋模型和最佳預測模型確實不同，顯示模型的解釋能力和預測能力並不具有絕對的關係；最後，本研究亦發現串流平台中的不同的服務類型差異並不會影響模型的解釋和預測結果，且付費用戶和免費用戶適用於不同的創新擴散模型，付費用戶容易受到口碑效應影響，而廣告用戶成長速度較快，會隨著網路使用人數增加而大幅度成長，因此企業在進行用戶分群時可以針對不同訴求而採取不一樣之行銷行為。
    The video streaming platform is a type of service that has been booming in the past five years. With the stability of internet and popularization of smart mobile devices, the quality of video streaming services has been effectively improved, leading to the global video streaming subscribers officially surpassed the users of cable TV in 2018. The video streaming platforms are gradually moving towards to mainstream market of media services so users` growth of streaming platforms and its diffusion trend have become important topics nowadays.
    This study explored the Gompertz Model, Bass Model, and Contingent Diffusion Model which uses the number of users on the Internet as a basis for research, to explore whether the user growth of the video streaming platform is applicable to the innovation diffusion model. Through observing number of subscribers of Netflix and Spotify, the two fast-growing video streaming platforms, the study discussed whether video streaming and music streaming services has different results on the innovation diffusion models. Besides, according to the query of scholars, the research also discussed whether explanatory and forecast model is the same model. It took the coefficient of determination and the Theil inequality coefficient to determine the goodness-of-fit and used the Mean Absolute Percentage Error to measure forecasting performance of these models.
    In the light of the empirical research, the study considered that video streaming platforms can be observed through the innovation diffusion model, moreover, it found the fittest explanatory model and the best forecast model of video streaming platforms are different, no matter the result is from Netflix or Spotify. It indicated that the goodness-of-fit and forecasting ability of models is not absolutely relevant. Last but not least, this study also found that different service types in the streaming platform will not affect the interpretation and prediction results of the model, and paying users and free users are applicable to different innovation diffusion models. To be precise, paying users are susceptible to word-of-mouth effects, and free users grow significantly with the increase in the number of internet users. Therefore, companies can adopt different marketing strategies for different target audiences.
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    Description: 碩士
    國立政治大學
    科技管理與智慧財產研究所
    1063641331
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G1063641331
    Data Type: thesis
    DOI: 10.6814/NCCU202000347
    Appears in Collections:[Graduate Institute of TIPM] Theses

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