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    政大機構典藏 > 商學院 > 企業管理學系 > 學位論文 >  Item 140.119/54387
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/54387


    Title: 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
    Authors: 許嘉文
    Hsu, Chia Wen
    Contributors: 洪叔民
    許嘉文
    Hsu, Chia Wen
    Keywords: 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
    Date: 2011
    Issue Date: 2012-10-30 10:55:04 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    企業管理研究所
    99355046
    100
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0099355046
    Data Type: thesis
    Appears in Collections:[企業管理學系] 學位論文

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