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題名 應用Google Analytics於網站流量及 Web2.0社群網站績效表現之關聯性分析
Utilizing google analytics to study the relationship between operating indexes and the development of Web 2.0 social websites
作者 許嘉文
Hsu, Chia Wen
貢獻者 洪叔民
許嘉文
Hsu, Chia Wen
關鍵詞 Google Analytics
網站流量指標
移動視窗法
多元迴歸分析
社群網站績效評估
社群網站
Google Analytics
Web Metric Analysis
Moving Windows
Multiple Regression Analysis
Performances Assessment
SNSs
日期 2011
上傳時間 30-Oct-2012 10:55:04 (UTC+8)
摘要 網際網路的發展讓人們的生活起了變化，Web2.0的概念更是增加了人們對網際網路的依賴性，我們成為網路內容的生產者、我們在社交網站上發表、追縱朋友的動態，以及取得全球世界各地的資訊。在這無限的虛擬空間中隱含的巨大商機，讓各大企業紛紛而至，因而加速了Web2.0社群網站的發展，維持與增加網站流量更是成為社群網站生存的關鍵與重要的績效指標。但社群網站該如何從流量指標之變化來評斷社群網站之績效呢?這是令我們最好奇之處。
藉由Google Analytics提供的流量分析工具，本研究蒐集了台灣四間社群網站1-3年間的流量資訊進行分析，考量蒐集之資訊具時間序列性質特性，本研究首先採用移動視窗法重新進行資料的整理，並據此概念應用在後續的統計分析。此外，本就以指數加權平均法及多元迴歸分析進行流量異常值之偵測，最後，對照各網站重大事件里程碑並與各網站業主進行一對一深訪。故本研究實際上包含質、量化之分析結果。
本篇研究四間個案網站為例，並依網站創造的服務與使用者互動情形流量將其區分為社交互動型與資訊交換型網站，並歸納其在網站流量指標上不同特徵表現及各自可參考之績效評估指標。同時，本研究採用多元迴歸分析做為社群網站績效評估模型，並企圖建構一績效評估分析流程期以做為後續研究者針對網站流量相關研究之參考。
The development of Internet makes a great influence on human society and the development of Web2.0 enhances human’s dependence on the internet and becomes a channel of social connections. Currently, most contents of the Internet are generated by common users who could retrieve information through the entire network and trace their friends’ actions over the Social Network Sites (SNSs).Owing to the potential business opportunities on the internet, companies try to enter the market causing the prosperities of SNSs. Maintaining or even increasing traffic flows become a critical issue for SNSs to survive in the competitive market. However, how to evaluate the performance of SNSs based on traffic flow indices remains unsolved.This study collected Google Analytics data for 1-3 years from four SNSs’, respectively.Consider the time series charactics, this study applied “Moving Windows“ to organize the data for further statistical analysis.In addition, Exponentially Weighted Moving Average and Multiple Regression Analysis were used to detect the abnormal traffic flows. Finally, these abnormal records were compared with the important events and one-on-one interviewings with the SNSs operators were conducted. The results of this study are based on qualitative and quentitative analysis. This research studiesd four SNSs that were categorized into information-oriented and interaction-oriented services based on their services and users’ interaction. The SNSs at different categories behaved differently following certain characteristics defined previously.A performance evaluation process was developed as a reference for further studies.
參考文獻 中文文獻
林信成、洪銘禪，2010，應用Google Analytics於數位典藏網站計量分析，教育資料與圖書館學，47 (3)，343-369
林惠玲、陳正倉，2011，應用統計學 (第四版修訂版)，台北市:雙葉書廊有限公司.
林震岩，2010，多變量分析-SPSS的操作與應用 (再版)，台北市:智勝出版社。188-225。
邱均平，2007，網路信息計量學導論，國立成功大學圖書館館刊，16。19-25
邱均平，2010，網絡計量學，中國: 科學出版社。22-24。
邱哲修、林卓民、洪瑞成、柯月華，2005，價格跳躍與避險策略之探討-以道瓊工業指數現貨與期貨為例，經營管理論叢，1 (2) 。93-116
馬進，1994，公路客貨運輸量多元線性回歸預測方法探討，汽車運輸研究，1。102-106.
張翔，2009，提綱挈領學統計(第二版)，台北市:鼎茂圖書出版有限公司。427-432，
梅田望夫，2006，網路巨變元年:你必須參與的大未來(蔡昭儀譯)。台北市：先覺出版社。 (原著出版年﹕2006 年)
盛啟峰、游麗誌，1998，SPSS 統計軟體操作手冊。2012年4月20日取自Scribd線上檢索全文網頁，http://www.scribd.com/doc/66926162/13/第六章-相關性分析.
蔡明月，2003，資訊計量學與文獻特性。台北市:國立編譯館。437-440。
謝美華，2005，外匯期貨最適避險比率之估計－EWMA法，高雄第一科技大學財務管理學系碩士論文。
欒斌，陳苡任，羅凱揚，2009，電子商務 (第6版)。台北市：滄海書局。
英文文獻:
Ahn, H. J. (2008). A new similarity measure for collaborative filtering to allevative the new user cold-starting problem. , International Journal of Information Sciences, 178(1), 37-51.
Alimind, T. C. ,Ingwersen, P . (1997). Informatric analyses on the world wide web: methodological approaches to “webometrics”. Journal of Documentation, 53(4), 404-426.
Anderson C. (2006). The long tail: Why the Future of Business is Selling Less of More. NY: Hyperion.
Babcock, B. , Datar, M. , Motwani, R. (2002). Sampling from a moving window over streaming data. Proceeding of 13th SIAM-ACM Symp. on Discrete Algorithms, 633-634.
Bannan, K. J. (2008). RADirect uses web analytics to improve online video success. BtoB: Marketing Metrics, 93, 14.
Boyd, D. M. (2008). Why youth (heart) social network sites: The role of networked publics in teenage social life, Youth, Identity, and Digital Media, Cambridge, MA: MIT Press. 119-142.
Boyd, D. M. , & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13 (1), 210-230.
Braender, L. M. , Kapp, C. M. & Years, J. (2009). Using web technology to teach students about their digital world. Journal of Information Systems Education, 20, 145-15??.
Brooks, C.& Chong, J. (2001). The cross-currency hedging performance of implied versus statistical forecasting model, The Journal of Futures Markets, 21(11), 1043-1069.
Cardon, P. W. , Marshall, B. , Norris, D. T. , Collier, C. , Goreva, V. , Nillson, S. , North, M. , Svensson, L. , Valenzuala, J. P. &Whelan, C. (2009). Online and offline social ties of social network website users: An exploratory study in eleven societies, Journal of Computer Information Systems, 50(1), 54-64.
Castells, M. (2001), The Internet Galaxy: Reflections on The Internet, Business, and Society, NY: Oxford University Press.
Chafkin, M. (2006). Analyze This, Says Google. Inc., 28-30.
Dearstyne, B. W. (2007). Blogs, mashups, & wikis: Oh, My! Information Management Journal, 41, 25-33.
Durbin, J. & Watson, G. S. (1950). Testing for serial correlation in least squares regression, Oxford Journals-Biometrika, 37, 409-428.
Gilbert, E. , Karahalios, K. & Sandvig, C. (2008). The network in the garden: An empirical analysis of social media in rural life. Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems, Florence, Italy, April 5-10.
Goossen, R. J. (2008). E-Preneur: From Wall Street to Wiki, Succeeding as A Crowdpreneur in the New Virtual Marketplace, NJ: Career Press.
Hair, J. F. ,Black, W. C. , Babin, B. J. & Anderson , R. E. (2009). Multivariate Data Analysis (7th ed. ), New Jersey: Prentice Hall, 155-231.
Hennig-Thurau, T. , Gwinner, K. P. , Walsh, G.& Gremler, D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18 (1), 38-52.
Jamali, H. R. , Nicholas, D. & Huntington, P. (2005). The use and users of scholarly e-journals a review of log analysis studies, ASLIB Proceedings, 57 (6), 554-571.
Khoo, M. , Pagano, J. , Washington , A. L. , Recker, M. , Palmer, B. & Donahue, R. A. (2008). Using web metrics to analyze digital libraries. , Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM, New York, 375-384.
Kıcıman, E. & Livshits, B. (2007). AjaxScope: A platform for remotely monitoring the client-side behavior of web 2.0 applications, ACM SIGOPS Operating Systems Review, 41, 17-30.
Ledford, J. L. , Teixeira, J. & Tyler, M. E. (2009). Google Analytics, Indiana:Wileypublishing.
Lokan, C. (2009). Applying moving windows to software effort estimation, Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement, 111-122.
Montgomery, D. C. (2005). Introduction to Statistical Quality Control (5th ed.), New Jersey: John Wiley & Sons, Inc.
Morgan, J.P. (1996). Risk Metrics Technical Document(4th ed. ), New York: J.P.Morgan.
Musser, J. , O’Reilly, T. & O’Reilly Radar Team (2006). Web2.0: Principles and best practices, CA: O’Reilly Media, 1-14.
Nichols, L. A. & Richard, N. (2004). Development of a psychometrically sound internet addiction scale: A preliminary step, Psychology of Addictive Behaviors, 18 (4), 381-384.
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.), New York: McGraw-Hill.
Pempek, T. A. ,Yermolayeva, Y. A. & Calvert, S. L. (2009). College students: Social networking experiences on Facebook, Journal of Applied Developmental Psychology, 30, 227-238.
Plaza, B. (2009). Monitoring web traffic source effectiveness with google analytics: An experiment with time series, Aslib Proceedings, 61, 474-482.
Safko, L. & David, K. B. (2009). The Social Media Bible: Tactics, Tools & Strategies for Business Success. Hoboken, NJ: John Wiley & Sons, Inc.
Shih, C. (2009). The Facebook Era: Tapping Online Social Networks to Build Better Products, Reach New Audiences, and Sell More Stuff. MA: Prentice Hall, Boston, 23-30.
Spar, D. & Jeffrey, B. (1996). Ruling the net, Harvard Business Review, May-June, 125-133.
ThelWell, M. (2001). Web log file analysis: Backlinks and queries, Asilb Proceeding, 53 (6), 217-223.
Toffler, A. & Toffler, H. (1997). Creating a New Civilization: The Politics of the Third Wave, Publisher: Turner Publishing.
Tsai, S. M. & Zhuang, W. Y. (2006). Data mining for library borrowing hisory records based on the weighted sliding window model, Journal of Computers, 17(4), 80-96.
Viswanath, V. , Mislove, A. , Cha, M. , & Gummadi, K. P. (2009). On the evolution of user interaction in Facebook, Proceedings of the 2nd ACM SIGCOMM Workshop on Social Networks, Barcelona, Spain, August 17.
描述 碩士
國立政治大學
企業管理研究所
99355046
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099355046
資料類型 thesis
dc.contributor.advisor 洪叔民zh_TW
dc.contributor.author (Authors) 許嘉文zh_TW
dc.contributor.author (Authors) Hsu, Chia Wenen_US
dc.creator (作者) 許嘉文zh_TW
dc.creator (作者) Hsu, Chia Wenen_US
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 10:55:04 (UTC+8)-
dc.date.available 30-Oct-2012 10:55:04 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 10:55:04 (UTC+8)-
dc.identifier (Other Identifiers) G0099355046en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54387-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所zh_TW
dc.description (描述) 99355046zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 網際網路的發展讓人們的生活起了變化，Web2.0的概念更是增加了人們對網際網路的依賴性，我們成為網路內容的生產者、我們在社交網站上發表、追縱朋友的動態，以及取得全球世界各地的資訊。在這無限的虛擬空間中隱含的巨大商機，讓各大企業紛紛而至，因而加速了Web2.0社群網站的發展，維持與增加網站流量更是成為社群網站生存的關鍵與重要的績效指標。但社群網站該如何從流量指標之變化來評斷社群網站之績效呢?這是令我們最好奇之處。
藉由Google Analytics提供的流量分析工具，本研究蒐集了台灣四間社群網站1-3年間的流量資訊進行分析，考量蒐集之資訊具時間序列性質特性，本研究首先採用移動視窗法重新進行資料的整理，並據此概念應用在後續的統計分析。此外，本就以指數加權平均法及多元迴歸分析進行流量異常值之偵測，最後，對照各網站重大事件里程碑並與各網站業主進行一對一深訪。故本研究實際上包含質、量化之分析結果。
本篇研究四間個案網站為例，並依網站創造的服務與使用者互動情形流量將其區分為社交互動型與資訊交換型網站，並歸納其在網站流量指標上不同特徵表現及各自可參考之績效評估指標。同時，本研究採用多元迴歸分析做為社群網站績效評估模型，並企圖建構一績效評估分析流程期以做為後續研究者針對網站流量相關研究之參考。		zh_TW
dc.description.abstract (摘要) The development of Internet makes a great influence on human society and the development of Web2.0 enhances human’s dependence on the internet and becomes a channel of social connections. Currently, most contents of the Internet are generated by common users who could retrieve information through the entire network and trace their friends’ actions over the Social Network Sites (SNSs).Owing to the potential business opportunities on the internet, companies try to enter the market causing the prosperities of SNSs. Maintaining or even increasing traffic flows become a critical issue for SNSs to survive in the competitive market. However, how to evaluate the performance of SNSs based on traffic flow indices remains unsolved.This study collected Google Analytics data for 1-3 years from four SNSs’, respectively.Consider the time series charactics, this study applied “Moving Windows“ to organize the data for further statistical analysis.In addition, Exponentially Weighted Moving Average and Multiple Regression Analysis were used to detect the abnormal traffic flows. Finally, these abnormal records were compared with the important events and one-on-one interviewings with the SNSs operators were conducted. The results of this study are based on qualitative and quentitative analysis. This research studiesd four SNSs that were categorized into information-oriented and interaction-oriented services based on their services and users’ interaction. The SNSs at different categories behaved differently following certain characteristics defined previously.A performance evaluation process was developed as a reference for further studies.en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 研究方法 3
第四節 研究架構 5
第二章 文獻探討 8
第一節 網路的發展與演進 8
第二節 社群網站的發展 15
第三節 社群網站績效衡量工具 21
第三章 分析工具與分析方法 25
第一節 流量分析工具 26
第二節 統計分析方法 30
第四章 流量指標分析結果 46
第一節 敘述性統計 47
第二節 相關性統計分析 55
第三節 迴歸模型分析 72
第四節 後續訪談命題 77
第五章 結論與研究貢獻 83
第一節 訪談命題探討 83
第二節 研究貢獻 102
第三節 研究限制 107
第四節 未來建議 108
參考文獻 109		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099355046en_US
dc.subject (關鍵詞) Google Analyticszh_TW
dc.subject (關鍵詞) 網站流量指標zh_TW
dc.subject (關鍵詞) 移動視窗法zh_TW
dc.subject (關鍵詞) 多元迴歸分析zh_TW
dc.subject (關鍵詞) 社群網站績效評估zh_TW
dc.subject (關鍵詞) 社群網站zh_TW
dc.subject (關鍵詞) Google Analyticsen_US
dc.subject (關鍵詞) Web Metric Analysisen_US
dc.subject (關鍵詞) Moving Windowsen_US
dc.subject (關鍵詞) Multiple Regression Analysisen_US
dc.subject (關鍵詞) Performances Assessmenten_US
dc.subject (關鍵詞) SNSsen_US
dc.title (題名) 應用Google Analytics於網站流量及 Web2.0社群網站績效表現之關聯性分析zh_TW
dc.title (題名) Utilizing google analytics to study the relationship between operating indexes and the development of Web 2.0 social websitesen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 中文文獻
林信成、洪銘禪，2010，應用Google Analytics於數位典藏網站計量分析，教育資料與圖書館學，47 (3)，343-369
林惠玲、陳正倉，2011，應用統計學 (第四版修訂版)，台北市:雙葉書廊有限公司.
林震岩，2010，多變量分析-SPSS的操作與應用 (再版)，台北市:智勝出版社。188-225。
邱均平，2007，網路信息計量學導論，國立成功大學圖書館館刊，16。19-25
邱均平，2010，網絡計量學，中國: 科學出版社。22-24。
邱哲修、林卓民、洪瑞成、柯月華，2005，價格跳躍與避險策略之探討-以道瓊工業指數現貨與期貨為例，經營管理論叢，1 (2) 。93-116
馬進，1994，公路客貨運輸量多元線性回歸預測方法探討，汽車運輸研究，1。102-106.
張翔，2009，提綱挈領學統計(第二版)，台北市:鼎茂圖書出版有限公司。427-432，
梅田望夫，2006，網路巨變元年:你必須參與的大未來(蔡昭儀譯)。台北市：先覺出版社。 (原著出版年﹕2006 年)
盛啟峰、游麗誌，1998，SPSS 統計軟體操作手冊。2012年4月20日取自Scribd線上檢索全文網頁，http://www.scribd.com/doc/66926162/13/第六章-相關性分析.
蔡明月，2003，資訊計量學與文獻特性。台北市:國立編譯館。437-440。
謝美華，2005，外匯期貨最適避險比率之估計－EWMA法，高雄第一科技大學財務管理學系碩士論文。
欒斌，陳苡任，羅凱揚，2009，電子商務 (第6版)。台北市：滄海書局。
英文文獻:
Ahn, H. J. (2008). A new similarity measure for collaborative filtering to allevative the new user cold-starting problem. , International Journal of Information Sciences, 178(1), 37-51.
Alimind, T. C. ,Ingwersen, P . (1997). Informatric analyses on the world wide web: methodological approaches to “webometrics”. Journal of Documentation, 53(4), 404-426.
Anderson C. (2006). The long tail: Why the Future of Business is Selling Less of More. NY: Hyperion.
Babcock, B. , Datar, M. , Motwani, R. (2002). Sampling from a moving window over streaming data. Proceeding of 13th SIAM-ACM Symp. on Discrete Algorithms, 633-634.
Bannan, K. J. (2008). RADirect uses web analytics to improve online video success. BtoB: Marketing Metrics, 93, 14.
Boyd, D. M. (2008). Why youth (heart) social network sites: The role of networked publics in teenage social life, Youth, Identity, and Digital Media, Cambridge, MA: MIT Press. 119-142.
Boyd, D. M. , & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13 (1), 210-230.
Braender, L. M. , Kapp, C. M. & Years, J. (2009). Using web technology to teach students about their digital world. Journal of Information Systems Education, 20, 145-15??.
Brooks, C.& Chong, J. (2001). The cross-currency hedging performance of implied versus statistical forecasting model, The Journal of Futures Markets, 21(11), 1043-1069.
Cardon, P. W. , Marshall, B. , Norris, D. T. , Collier, C. , Goreva, V. , Nillson, S. , North, M. , Svensson, L. , Valenzuala, J. P. &Whelan, C. (2009). Online and offline social ties of social network website users: An exploratory study in eleven societies, Journal of Computer Information Systems, 50(1), 54-64.
Castells, M. (2001), The Internet Galaxy: Reflections on The Internet, Business, and Society, NY: Oxford University Press.
Chafkin, M. (2006). Analyze This, Says Google. Inc., 28-30.
Dearstyne, B. W. (2007). Blogs, mashups, & wikis: Oh, My! Information Management Journal, 41, 25-33.
Durbin, J. & Watson, G. S. (1950). Testing for serial correlation in least squares regression, Oxford Journals-Biometrika, 37, 409-428.
Gilbert, E. , Karahalios, K. & Sandvig, C. (2008). The network in the garden: An empirical analysis of social media in rural life. Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems, Florence, Italy, April 5-10.
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