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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136691


    Title: 社群遊戲平台創新擴散模型研究-以Steam為例
    Innovation Diffusion Models of Social Gaming Platform - A Case Study of Steam
    Authors: 黃俐銘
    Contributors: 許牧彥
    黃俐銘
    Keywords: 遊戲平台
    創新擴散模型
    活躍人數
    Social gaming platform
    Innovation diffusion model
    Active users
    Date: 2021
    Issue Date: 2021-08-04 16:28:23 (UTC+8)
    Abstract: 全球電玩市場的規模不斷成長和發展,其中遊戲平台Steam 在2003年推出後功能不斷的跌宕更新,從提供遊戲自動更新到數位遊戲販售和社群功能,用戶在2020年也隨之成長到10億人;而平台為新經濟體系下的新商業模式型態,對於平台來說,如何使用戶增長或是了解用戶成長的關鍵因素是一重要議題。
    本研究透過整理擴散模型相關文獻,以Bass模型和附隨擴散模型作為研究模型,將其簡化並篩選出影響平台及遊戲用戶成長的可能因子,並加入活躍人數變數重新建構回歸模型,進行觀察變數間的關聯性。
    實證研究發現,對於平台來說,活躍人數相對於平台累積人數更能代表平台的真實發展狀態;而影響單一遊戲人數成長因素會依遊戲性質不同而異,三款平台上熱門遊戲CS:GO、DOTA2 和PUBG分別以遊戲累積人數、遊戲月活躍人數、平台月活躍人數及平台累積人數等變數進行實證分析,結果顯示遊戲活躍人數會影響遊戲新增人數成長。
    Global games market is a multi-billion industry and continues to grow. Since 2003, the gaming platform Steam has been providing different services. From automatic game updates to digital game sales and social networking services, Steam hit one billion registered users in 2019. For platform, understand users’ growth trend and the factors affecting users’ growth is an important topic.
    This study explored Bass model and Contingent Diffusion Model as a research model, sort out possible factors that affect the growth of the platform, and adding active users as a variable to develop a regression model to observe the relevance between variables.
    This study shows that for the platform, the number of active users is more representative of the true development status of the platform than the registered users on the platform. Moreover, through observing the most played games on the platforms, CS: GO, DOTA2 and PUBG, the study also found that the factors affect the growth of the players of a single game will vary according to the types of the game. The result indicates that active users have positive affect on the growth of new game players.
    Reference: 英文文獻

    Bass, F. M. (1969). A new product growth for model consumer durables.
    Management Science, 15(5), 215-227.

    Bass, F. M., Krishnan, T. V., & Jain, D. C. (1994). Why the Bass model fits
    without decision variables. Marketing Science, 13(3), 203-223.

    Farrell, J., & Saloner, G. (1985). Economic issues in standardization.

    Griliches, Z. (1957). Hybrid corn: An exploration in the economics of technological. change Econometrica, Journal of the Econometric Society, 501-522

    Gompertz, B. (1825). XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. FRS &c. Philosophical Transactions of the Royal Society of London, 115, 513-583.

    Kiiski, S., & Pohjola, M. (2002). Cross-country diffusion of the Internet.
    Information Economics and Policy, 14(2),297-310

    Meade,N., and Islam, T. (2006). Modelling and forecasting the diffusion of. innovation-A 25-year review. International Journal of Forecasting, 22(3),519-545.

    Newzoo. (2021). Global Games Market to Generate $175.8 Billion in 2021; Despite a
    Slight Decline, the Market Is on Track to Surpass $200 Billion in 2023. Retrieved from https://newzoo.com/insights/articles/global-games-market-to-generate-175-8-billion-in-2021-despite-a-slight-decline-the-market-is-on-track-to-surpass-200-billion-in-2023/

    Rogers, E. M. (2003). Diffusion of Innovations.


    中文文獻

    Betta Chik(2018)。為什麼Fortnite能勝過PUBG 。取自
    https://exp.gg/zh_tw/41767

    王豔(2019)。互聯網信息服務業產品附隨擴散研究-基于網絡外部性視角。重 慶郵電大學管理科學與工程。

    朱文伶(2010)。行動電話擴散研究之模型選用及驅動因子分析。國立政治大學科技管理研究所。

    吳函菂(2018)。Bass模型在P2P網路借貸擴散中的應用。成都電子科技大學 金融學。

    李嘉泰(2019)。手機遊戲成長趨勢之研究-不同產業層級分析之異同。國立政治大學科技管理與智慧財產研究所。

    何藝雯(2019)。短視頻APP創新擴散模型研究-以“抖音“為例。西南財經大 學媒體經營管理。

    知乎(2020)。2012上線,8年後踏入巔峰:為什麼csgo越品越香。取自
    https://zhuanlan.zhihu.com/p/114921066

    郭兵(2019)。社交媒體中用户社會網絡和活躍度對有獎轉發的影響研究 ——以新浪微博為例。南京理工大學。

    張筱祺 (2011) 。新產品擴散模式之探討-以時尚精品為例。國立政治
    大學科技管理與智慧財產研究所。

    曹碩(2017)。微信產品創新擴散模型研究。哈爾濱工業大學。

    資誠聯合會計事務所(2019)。2020-2024臺灣娛樂暨媒體業展望報告。

    霍娟(2008)。基于附隨擴散模型的移動增值業務擴散實證研究。西南交通大
    學技術經濟及管理。

    簡汶恩(2012)。以個別顧客活躍率之隨機選擇模型建立一對一行銷分析。國立交通大學經營管理研究所。

    劉湝沂(2019)。影音串流平台之創新擴散模型研究-以Netflix和Spotify為例。國立政治大學科技管理與智慧財產研究所。

    劉輝輝、柴躍廷、劉義(2010)。基於擴散模型的電子商務平台規模分析與預測。統計與決策,第323期。
    Description: 碩士
    國立政治大學
    科技管理與智慧財產研究所
    108364129
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108364129
    Data Type: thesis
    DOI: 10.6814/NCCU202100748
    Appears in Collections:[科技管理與智慧財產研究所] 學位論文

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