商业视角下的网络社区的用户行为研究
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摘要
自上世纪九十年代起，网络社区随着因特网的飞速发展而产生，其发展速度之快引起很多学者的注意。但是学者们对于网络社区的定义始终没有达成一致，多数著作都认为网络社区是指包括BBS论坛、贴吧、公告栏、群组讨论、在线聊天、交友、个人空间、无线增值服务等形式在内的网上交流空间，同一主题的网络社区集中了具有共同兴趣的访问者。作为用户，我们只要工作学习中需要用到网络，就会不可避免的成为某个或某些个网络社区的成员。网络社区的独特优势及特性使得这个新生事物深入人心，网络社区的形成对于用户来说毫无疑问是有利的，不仅丰富了互联网生活，更为用户拓展了互联网空间，加深了共同兴趣方面的知识深度。
网络社区的个数已经呈指数级增长，面对如此激烈的竞争，研究网络社区中的用户行为对于社区运营者来说是非常必要的，首要原因就是商业利益。DCCI 2009-2010中国互联网市场数据显示，网络社区广告营收规模增速低于受众规模增长，2010年年底中国互联网社区论坛受众规模为1.83亿人，而到2011年年底这一数字大幅增加至2.45亿人，净增6200万人，增幅达33.9%。由此可见，谁能够争取到这部分用户，谁就能够获得更大的利益。因此，必须设置怎样的吸引量来引起用户的注意并留住用户使之产生的行为对网站最为有利。这就需要对用户的行为进行透彻的了解和彻底的分析，课题的研究具有实际意义和必要性。
本文以QQ社区为研究对象，所设计问卷及调查分析均以QQ社区为模版。首先使用结构方程建模的方法对于影响网络社区盈利的因子进行归类，并对其产生的影响程度量化处理。结构方程建模是一种目前管理学研究中常用的数据分析方法。它是一种与多元回归分析关系密切，却在原理和方法上有许多拓展的多变量数据分析方法。它涵盖了多种原有的多变量数据分析方法，适用于定序、定类以及定距定比尺度，在管理、社会科学的实证研究中，逐渐成为与多元回归分析并立的一种主要多变量数据分析方法。
根据用户对调查问卷的反馈，统计各选项出现频率，进而使用关联规则进行数据挖掘以达到对用户归类的目的，继而使用复杂网络中的社团划分算法找到社区中的中心用户和桥用户，对各类用户分别进行讨论，对于不同类型的用户要采取不同的维护策略，最终使QQ社区建设拥有更多更好的经济基础。
复杂网络技术和数据挖掘技术都是比较成熟的技术，把这两种技术同时应用在网络社区的用户行为分析上是一种比较新的研究和应用。本文结合使用两种人工智能技术，为网络社区中用户行为的分析找到了一个新的角度，可以使网络社区网站清晰了解具有何种特征的用户会对网站产生正面影响以及影响的程度如何。
Online community emerged with the rapid development of the Internet since the 90’s of last century,and the speediness of its development has caused many scholars’attention. But the scholars have notagreed on the definition of online community, most of them support the opinion that online communitymeans a community includes a BBS forum, post bar, bulletin boards, discussion groups, online chat, friends,personal space, wireless value-added services and other forms of online communication, space, the sametheme of the online community has focused with common interests the visitor. As the user, as long as ourwork requires network, we will inevitably become a certain or some a member of an Internet community.Unique advantages and features of online community make this newly emerging things to win supportamong the people, formation of online community for the user is undoubtedly beneficial, not only forenriching the Internet life, more for the user to expand the Internet space, to deepened the common interestwith respect to the depth of knowledge.
The number of online community has been growing exponentially, faced with such fierce competition,research in online community user’s behavior for community operator is very necessary, the most importantreason is commercial interests. According to the DCCI 2009-2010 Chinese Internet market data, growthrate of revenue in online community advertising is below the growth produced by audience size. By the endof 2010, Internet community forum audience is 183million people in China, and by the end of 2011 thisnumber increased to 245million people, a net increase of 62million people, an increase of 33.9%. Thus,those can get this part of the user can get more benefits. Therefore, how to attract volume must be set tocause the user's attention and retain customers to produce the most favorable behavior on the web. Thisrequires thorough understanding and thorough analysis on the user's behavior, research has practicalsignificance and necessity.
This paper is based on the QQ community as the object of study, the questionnaire design and surveyanalysis take QQ community as a template. Firstly, use structural equation modeling method to classify thefactors that impact online community’s profit, and quantify the degree of the factors. Structural equationmodeling is a commonly used data analysis method in current management study. It is closely related tomultivariate regression analysis, but its principle and method can be applied to many development multivariate data analysis method. It covers various original multivariate data analysis method, applied tosequencing, and spacing ratio scale, management, social science research, and multiple regression analysisand has gradually become a main multivariate data analysis method.
According to the user of the questionnaire feedback, frequency of the various options can be counted,then association rules data mining is used in order to achieve users classification, and then use a complexnetwork of community partitioning algorithm to find the community of users and user center bridge, and alltypes of users were discussed respectively. For different types of users to adopt different maintenancestrategy, finally makes QQ community construction have more and better economic foundation.
Complex network technology and data mining technology is relatively mature technology, theapplication of these two kinds of technology in online community user behavior analysis is a relatively newresearch and application. In this paper, combined with the use of two kinds of artificial intelligencetechnology, online community user behavior analysis can be researched in a new perspective, can make theonline community website has a clear understanding of users who will have a positive impact on the siteand the degree of their influence.
网络社区的个数已经呈指数级增长，面对如此激烈的竞争，研究网络社区中的用户行为对于社区运营者来说是非常必要的，首要原因就是商业利益。DCCI 2009-2010中国互联网市场数据显示，网络社区广告营收规模增速低于受众规模增长，2010年年底中国互联网社区论坛受众规模为1.83亿人，而到2011年年底这一数字大幅增加至2.45亿人，净增6200万人，增幅达33.9%。由此可见，谁能够争取到这部分用户，谁就能够获得更大的利益。因此，必须设置怎样的吸引量来引起用户的注意并留住用户使之产生的行为对网站最为有利。这就需要对用户的行为进行透彻的了解和彻底的分析，课题的研究具有实际意义和必要性。
本文以QQ社区为研究对象，所设计问卷及调查分析均以QQ社区为模版。首先使用结构方程建模的方法对于影响网络社区盈利的因子进行归类，并对其产生的影响程度量化处理。结构方程建模是一种目前管理学研究中常用的数据分析方法。它是一种与多元回归分析关系密切，却在原理和方法上有许多拓展的多变量数据分析方法。它涵盖了多种原有的多变量数据分析方法，适用于定序、定类以及定距定比尺度，在管理、社会科学的实证研究中，逐渐成为与多元回归分析并立的一种主要多变量数据分析方法。
根据用户对调查问卷的反馈，统计各选项出现频率，进而使用关联规则进行数据挖掘以达到对用户归类的目的，继而使用复杂网络中的社团划分算法找到社区中的中心用户和桥用户，对各类用户分别进行讨论，对于不同类型的用户要采取不同的维护策略，最终使QQ社区建设拥有更多更好的经济基础。
复杂网络技术和数据挖掘技术都是比较成熟的技术，把这两种技术同时应用在网络社区的用户行为分析上是一种比较新的研究和应用。本文结合使用两种人工智能技术，为网络社区中用户行为的分析找到了一个新的角度，可以使网络社区网站清晰了解具有何种特征的用户会对网站产生正面影响以及影响的程度如何。
Online community emerged with the rapid development of the Internet since the 90’s of last century,and the speediness of its development has caused many scholars’attention. But the scholars have notagreed on the definition of online community, most of them support the opinion that online communitymeans a community includes a BBS forum, post bar, bulletin boards, discussion groups, online chat, friends,personal space, wireless value-added services and other forms of online communication, space, the sametheme of the online community has focused with common interests the visitor. As the user, as long as ourwork requires network, we will inevitably become a certain or some a member of an Internet community.Unique advantages and features of online community make this newly emerging things to win supportamong the people, formation of online community for the user is undoubtedly beneficial, not only forenriching the Internet life, more for the user to expand the Internet space, to deepened the common interestwith respect to the depth of knowledge.
The number of online community has been growing exponentially, faced with such fierce competition,research in online community user’s behavior for community operator is very necessary, the most importantreason is commercial interests. According to the DCCI 2009-2010 Chinese Internet market data, growthrate of revenue in online community advertising is below the growth produced by audience size. By the endof 2010, Internet community forum audience is 183million people in China, and by the end of 2011 thisnumber increased to 245million people, a net increase of 62million people, an increase of 33.9%. Thus,those can get this part of the user can get more benefits. Therefore, how to attract volume must be set tocause the user's attention and retain customers to produce the most favorable behavior on the web. Thisrequires thorough understanding and thorough analysis on the user's behavior, research has practicalsignificance and necessity.
This paper is based on the QQ community as the object of study, the questionnaire design and surveyanalysis take QQ community as a template. Firstly, use structural equation modeling method to classify thefactors that impact online community’s profit, and quantify the degree of the factors. Structural equationmodeling is a commonly used data analysis method in current management study. It is closely related tomultivariate regression analysis, but its principle and method can be applied to many development multivariate data analysis method. It covers various original multivariate data analysis method, applied tosequencing, and spacing ratio scale, management, social science research, and multiple regression analysisand has gradually become a main multivariate data analysis method.
According to the user of the questionnaire feedback, frequency of the various options can be counted,then association rules data mining is used in order to achieve users classification, and then use a complexnetwork of community partitioning algorithm to find the community of users and user center bridge, and alltypes of users were discussed respectively. For different types of users to adopt different maintenancestrategy, finally makes QQ community construction have more and better economic foundation.
Complex network technology and data mining technology is relatively mature technology, theapplication of these two kinds of technology in online community user behavior analysis is a relatively newresearch and application. In this paper, combined with the use of two kinds of artificial intelligencetechnology, online community user behavior analysis can be researched in a new perspective, can make theonline community website has a clear understanding of users who will have a positive impact on the siteand the degree of their influence.
引文
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[21]高飞.关联规则挖掘算法研究[D] .西安:电子科技大学.2001.
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[24]Wachter R, Gupta J, Quaddus M. IT takes a village:virtual communities in support of education[J] .International Journal of Information Management.2010,20(6): 473-489.
[25]Wallace J L, Vaux A.Social support network orientation:The role of adult attachment style[J] .Journal of Social and Clinical Psychology.2011,12(3):354-365.
[26]Wasko M M, Faraj S.It is what one does: why people participate and help others in electroniccommunities of practice [J] . Journal of Strategic Information Systems.2009, 9(2-3):155-173.
[27]Wasko M M., Faraj S. Why Should I Share? Examining Knowledge Contribution in ElectronicNetworks of Practice [J] .MIS quarterly, 2005,29(1):35-57.
[28]Williams L J, Anderson S E.Job satisfaction and organizational commitment as predictors oforganizational citizenship and in-role behaviors [J] .Journal of Management, 1991,17(3): 601-617.
[29]侯杰泰,温忠麟,成子娟.结构方程模型及其应用[M] .北京:教育科学出版社.2004.
[30]李怀祖.管理研究方法论(第2版)[M] .西安:西安交通大学出版社.2004.
[31]刘丽群,宋咏梅.虚拟社区中知识交流的行为动机及影响因素研究[J].新闻与传播研究.2007,14(1): 43-51.
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[2] Tristan Henderson，Jon Crowcroft, Saleem Bhatti，Congestion Pricing-Paying your way inCommunication Networks[J]，IEEE Internet Computing，2001，5(5)：85-89.
[3]魏娇龙.基于博弈论的网络资源分配方法研究[D].武汉：华中科技大学，2004.
[4]BicberM，EngelbartD.Virtual community knowledge evolution[A]. Proceedings of the 33rd HawaiiInternational Conference on System Sciences[C].Hawaii，2000.
[5]张志彦，李俊峰.关联规则挖掘在学生成绩分析中的应用[J].科技和产业.2009，5(9)：120-122.
[6]宋冰晨，综合性门户网站服务质量与顾客满意度的关系研究[D] .杭州：浙江大学.2005.
[7]杨晓明，田澎.我国综合性门户网站用户满意形成机理及其影响因素的实证研究[J] .管理评论.2006，18(12)：30-36.
[8]SudhaR，JinsooP.Digital libraries for the next millenniumL Challenges and research directions[J] .Information Systems Frontiers，2009(1)：75-94.
[9]Hagel J，Armstrong A.NetGain：Expanding Markets through Virtual Communities [M] . Boston，MA：Harvard Business School Press.1997.
[10]Rheingold H.The Virtual Community：Table of Content [EB/OL]. http://www.well.com/user/hlr/vcbook/.
[11]Romm C，Pliskin N.Virtual communities and society：Toward anintegrative three phase model [J] .International Journal of Information Management，2007，17(4)：261-270.
[12] Vaux A C，Burba PC，Stewart D.Orientation toward utilization of support resources [J] .Journal ofCommunity Psychology，2006，14(2)：159-170.
[13]Tiwana A，Bush A.Peer-to-Peer valuation as a mechanism for reinforcing active learning in virtualcommunities：Actualizing social exchange theory[A] .Proceedings of the 33rd Hawaii InternationalConference on System Sciences[C].Hawaii，2000.
[14]黄进.关联规则数据挖掘算法的研究[D] .成都：西南交通大学，2003..
[15]GTS Ho.An intelligent information infrastructure to support the streamlining of integrated logisticsworkflow[J] .Expert Systems，2004，21(3)：123-131.
[16]S.N.Eisenstadt.Social evolution and modernity：Some observations on parson’s comparative andevolutionary analysis：Person’s analysis from the perspective of multiple modernities[J] .TheAmerican Sociologist.2004，12(2)：5-24.
[17]探讨数据挖掘.应对金融危机—第二届金融数据挖掘研讨会简述[J] .统计与信息论坛.2009(7)：11-13.
[18] Jiawei Han.Micheline Kamber.数据挖掘概念与技术[M].机械工业出版社.2005.
[19]贺文星，上官文力.基于博弈分析的家族企业职业经理人选择[J].商场现代化.2005(11)：319-320.
[20] Afuah A，Tucci L.Internet business models and strategies-text and cases[M]，New York，McGraw-Hill Press，2001，11-12.
[21]高飞.关联规则挖掘算法研究[D] .西安:电子科技大学.2001.
[22]姜洪涛.虚拟社区组织公民行为影响因素研究[D] .重庆:重庆大学.2009.
[23]周涛,鲁耀斌.基于社会资本理论的移动社区用户参与行为研究[J],管理科学,2005(3):43-50.
[24]Wachter R, Gupta J, Quaddus M. IT takes a village:virtual communities in support of education[J] .International Journal of Information Management.2010,20(6): 473-489.
[25]Wallace J L, Vaux A.Social support network orientation:The role of adult attachment style[J] .Journal of Social and Clinical Psychology.2011,12(3):354-365.
[26]Wasko M M, Faraj S.It is what one does: why people participate and help others in electroniccommunities of practice [J] . Journal of Strategic Information Systems.2009, 9(2-3):155-173.
[27]Wasko M M., Faraj S. Why Should I Share? Examining Knowledge Contribution in ElectronicNetworks of Practice [J] .MIS quarterly, 2005,29(1):35-57.
[28]Williams L J, Anderson S E.Job satisfaction and organizational commitment as predictors oforganizational citizenship and in-role behaviors [J] .Journal of Management, 1991,17(3): 601-617.
[29]侯杰泰,温忠麟,成子娟.结构方程模型及其应用[M] .北京:教育科学出版社.2004.
[30]李怀祖.管理研究方法论(第2版)[M] .西安:西安交通大学出版社.2004.
[31]刘丽群,宋咏梅.虚拟社区中知识交流的行为动机及影响因素研究[J].新闻与传播研究.2007,14(1): 43-51.
[32]卢艳峰.虚拟社区互动沟通对网络购物意向的影响模式研究[D] .杭州:浙江大学. 2005.
[33]秦燕.组织公民行为的问卷编制及现状调查[D] .苏州:苏州大学.2005.
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