基于动态用户画像的学术虚拟社区粘性驱动机制研究
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摘要
[目的/意义]基于动态用户画像探索学术虚拟社区的粘性驱动机制在于用户角色精准定位,有助于提升用户忠诚度、信任度、留存率、回访率。[方法/过程]依据社区属性和用户感知分析学术虚拟社区的粘性驱动因子;以用户自然属性、行为属性、心理特征为用户画像的数据来源,建构包括基础数据、行为建模、服务应用、评价反馈4个模块的动态用户画像结构模型;结合驱动因子与结构模型构建了粘性驱动机制模型。[结果/结论]模型深度刻画了学术虚拟社区用户全貌,为优化系统效能和精准化服务指供指导,以期增强学术信息资源流转与学术影响力。
[Purpose/Significance]The sticky driving mechanism for exploring virtual academic community based on dynamic user profile lies in the precise positioning of user roles,which helps to improve rate of loyalty,reliability,retention and re-access of users.[Method/Process]This paper analysed the sticky driving factors of academic virtual community based on community attributes and user perception,took user's natural attributes,behavioral attributes and psychological characteristics as the data source of user profile,constructed a dynamic user profile structure model including four modules:basic data,behavior modeling,service application and evaluation feedback;The sticky driving mechanism model was constructed by combining the driving factors and the structural model.[Result/Conclusion]The model portrayed the full view of the virtual academic community users,and provided guidance for optimizing system performance and accurate services,in order to enhance the flow of academic information resources and academic influence.
[Purpose/Significance]The sticky driving mechanism for exploring virtual academic community based on dynamic user profile lies in the precise positioning of user roles,which helps to improve rate of loyalty,reliability,retention and re-access of users.[Method/Process]This paper analysed the sticky driving factors of academic virtual community based on community attributes and user perception,took user's natural attributes,behavioral attributes and psychological characteristics as the data source of user profile,constructed a dynamic user profile structure model including four modules:basic data,behavior modeling,service application and evaluation feedback;The sticky driving mechanism model was constructed by combining the driving factors and the structural model.[Result/Conclusion]The model portrayed the full view of the virtual academic community users,and provided guidance for optimizing system performance and accurate services,in order to enhance the flow of academic information resources and academic influence.
引文
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[24]陈志明,胡震云.UGC网站用户画像研究[J].计算机系统应用,2017,26(1):24-30.
[25]张慧敏,辛向阳.构建动态用户画像的四个维度[J].工业设计,2018,(4):59-61.
[26]王庆,赵发珍.基于“用户画像”的图书馆资源推荐模式设计与分析[J].现代情报,2018,38(3):105-109,137.
[27]王凌霄,沈卓,李艳.社会化问答社区用户画像构建[J].情报理论与实践,2018,41(1):129-134.
[28]单晓红,张晓月,刘晓燕.基于在线评论的用户画像研究[J].情报理论与实践,2018,41(4):99-104,149.
[2]360百科.用户粘度[EB/OL].https://baike.so.com/doc/4199686-4400597.html,2018-10-22.
[3]Lu H,Lee M.Demographic Differences and the Antecedents of Blog Stickiness[J].Online Information Review,2010,34(1):21-38.
[4]Li G,Park S T,Jin H.Antecedents of Wechat Group Chatting User’s Stickiness and Organizational Commitment[J].Journal of Engineering and Applied Sciences,2017,12(1):5708-5713.
[5]段菲菲,翟姗姗,池毛毛,等.手机游戏用户粘性影响机制研究:整合Flow理论和TAM理论[J].图书情报工作,2017,61(3):21-28.
[6]赵青,薛君.网络用户粘性行为测评研究[J].统计与信息论坛,2014,29(10):72-78.
[7]高丽.教育技术虚拟社区的社区粘度研究[D].上海:上海师范大学,2011.
[8]赵青,张利,薛君.网络用户粘性行为形成机理及实证分析[J].情报理论与实践,2012,35(10):25-29.
[9]Li D,Browne G J,Wetherbe J C.Why Do Internet Users Stick with A Specific Web Site?A Relationship Perspective[J].International Journal of Electronic Commerce,2006,10(4):105-141.
[10]王海萍.在线消费者粘性研究[M].北京:经济科学出版社,2010.
[11]Delone W H,Mclean E R.The Delone and Mclean Model of Information Systems Success:A Ten-year Update[J].Journal of Management Information Systems,2003,19(4):9-30.
[12]查先进,陈明红.信息资源质量评估研究[J].中国图书馆学报,2010,(3):46-55.
[13]Lin H F.Understanding Behavioral Intention to Participate in Virtual Communities[J].Cyber Psychology & Behavior,2006,9(5):540-547.
[14]Lidwell W,Holden K,Butle J.Universal Principles of Design[M].朱占星,薛江,译.北京:中央翻译出版社,2013.
[15]陈为东,王萍,王益成.网络用户效能负载力的影响因素与优化策略研究[J].情报理论与实践,2018,41(1):111-115.
[16]李宇佳.学术新媒体信息服务模式与服务质量评价研究[D].长春:吉林大学,2017.
[17]Goncaleves H M,Lourenco T F,Silva G M.Green Buying Behavior and the Theory of Consumption Values:A Fuzzy-set Approach[J].Journal of Business Research 2016,69(4):1484-1491.
[18]Moon J W,Kim Y G.Extending the TAM for A World-wide-web Context[J].Information & Management,2001,28(4):217-230.
[19]Venkatesh V,Bala H.Technology Acceptance Model 3 and A Research Agenda on Interventions[J].Decision Sciences,2010,39(2):273-315.
[20]陈为东,王萍,王益成.学术新媒体环境下用户信息偶遇要素及内在机理研究[J].情报理论与实践,2018,41(2):28-33,45.
[21]Hung S Y,Ku C Y,Chang C M.Critical Factors of WAP Services Adoption:An Empirical Study[J].Electronic Commerce Research & Applications,2004,2(1):42-60.
[22]Burnham T A,Frels J K,Mahajan V.Consumer Switching Costs:A Typology,Antecedents,and Consequences[J].Journal of the Academy of Marketing Science,2003,(2):109-126.
[23]牛温佳,刘吉强,石川,等.用户网络行为画像[M].北京:电子工业出版社,2016.
[24]陈志明,胡震云.UGC网站用户画像研究[J].计算机系统应用,2017,26(1):24-30.
[25]张慧敏,辛向阳.构建动态用户画像的四个维度[J].工业设计,2018,(4):59-61.
[26]王庆,赵发珍.基于“用户画像”的图书馆资源推荐模式设计与分析[J].现代情报,2018,38(3):105-109,137.
[27]王凌霄,沈卓,李艳.社会化问答社区用户画像构建[J].情报理论与实践,2018,41(1):129-134.
[28]单晓红,张晓月,刘晓燕.基于在线评论的用户画像研究[J].情报理论与实践,2018,41(4):99-104,149.
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