[Purpose/significance] To obtain the differentiated behavioral characteristics of mobile library users in universities by a method of personas construction, helps to knowing potential demand and the actual behavior of the users more exquisitely and accurately, and plays a very important role as a reference to improve customer service.[Method/process] The paper reviews the application status of personas in the university library, and reconstructs the label description system from a perspective of psychological preference by learning from VALS2 user segmentation attitude scale which is based on the specific situation of mobile library. It designs the questionnaire with the label system questionnaire and makes an empirical study on variables of the demographic characteristics, usage behavior and psychological preference of mobile library users, as well as using factor analysis, cluster analysis and discriminant analysis to select the appropriate number of personas. By using R language wordcloud2 package to make a visualization show on features tag cloud of different personas, it expands the description and discussion detailed, combined with the feature of demographic attributes, use behavior and characteristics labels for each persona.[Result/conclusion] This paper presents a method to construct personas in specific business situations, which provides an empirical research reference for university mobile library to improve accurate user positioning and carry out differentiated service.
Chen Tianyuan
. An Empirical Research on Personas Construction of Mobile Library in Universities[J]. Library and Information Service, 2018
, 62(7)
: 38
-46
.
DOI: 10.13266/j.issn.0252-3116.2018.07.005
[1] COOPER A. About Face 3.0:the essentials of interaction design[M].New York:John Wiley & Sons Inc,2007.
[2] 郝胜宇,陈静仁.大数据时代用户画像助力企业实现精准化营销[J].中国集体经济, 2016(4):61-62.
[3] 王庆福.贝叶斯网络在用户兴趣模型构建中的研究[J].无线互联科技,2016(12):101-102.
[4] 丁伟,王题,刘新海,等.基于大数据技术的手机用户画像与征信研究[J].邮电设计技术, 2016(3):64-69.
[5] 董莉莉. 剖析大数据时代下用户画像及媒介策略[J]. 传媒, 2016(2):82-83.
[6] XU G,ZHANG Y, ZHOU X. Towards user profiling for Web recommendation[C]//AI 2005:Advances in artificial intelligence. Berlin:Springer,2005:415-424.
[7] 景永霞,苟和平,刘强,等.基于内容相似性的社交网络用户行为倾向性研究[J].吉林师范大学学报(自然科学版), 2016, 37(4):135-139.
[8] 王丹.基于主题模型的用户画像提取算法研究[D].北京:北京工业大学,2016.
[9] 辛菊琴,蒋艳,舒少龙.综合用户偏好模型和BP神经网络的个性化推荐[J].计算机工程与应用,2013,49(2):57-60.
[10] 胡明国,大数据时代下客户立体画像在银行业应用研究[J].中国城市金融,2016,355(1):47-48, 42.
[11] 邵雅利,连榕.市场营销中的消费价值观:研究范式及新进展[J].上海商学院学报,2016,17(5):56-64,70.
[12] 沈虹.移动中的90后:90后大学生媒介接触行为、生活形态与价值观研究[M].北京:机械工业出版社,2014:61-90.
[13] 吴垠.关于中国消费者分群范式(China-Vals)的研究[J].南开管理评论,2005,8(2):9-15.
[14] HOLT Z.Using persona descriptions to inform library space design[J]. Advances in library administration and organization,2016,36:335-358.
[15] MARRALLR M R,NORA K,BURMEISTER,DILLON J. Usability & Design Working Group documents[EB/OL].[2017-10-25].http://cedar.wwu.edu/library_udwgdocs/?utm_source=cedar.wwu.edu%2Flibrary_udwgdocs%2F7&utm_medium=PDF&utm_campaign=PDFCoverPages.
[16] HOLT Z, RACKHAM S. Identification and development of patron personas for an academic library[J]. Performance measurement and metrics, 2016,17(2):124-133.
[17] OTTERLO M. Project BLⅡPS:making the physical public library more intelligent through artificial intelligence[J]. Qualitative and quantitative methods in libraries, 2017, 5(2):287-300.
[18] SHBOUL M K A, ABRIZAH A. Modes of information seeking developing personas of humanities scholars[J]. Information development, 2016, 32(5):1786-1805.
[19] LEWIS C, CONTRINO J. Making the invisible visible:personas and mental models of distance education library users[J]. Journal of library & information services in distance learning, 2016, 10(1-2):15-29.
[20] 李业根.基于大数据的图书馆信息营销策略[J].图书馆学刊,2014(10):7-9.
[21] 王登秀,张文德,林熙阳.基于VASL2的高校数字图书馆联盟人物角色[J].图书情报工作, 2011,55(3):83-87.
[22] 裘惠麟,邵波.图书馆精准服务研究现状及发展策略[J].图书馆学研究,2017(16):2-7.
[23] 朱白.图书馆读者用户"脸谱"绘制研究[J].商洛学院学报,2017(5):90-93,99.
[24] 刘速.浅议数字图书馆知识发现系统中的用户画像——以天津图书馆为例[J].图书馆理论与实践,2017(6):103-106.
[25] 王晓文,沈思,崔旭.基于K-Means聚类的学科服务用户市场细分实证研究[J].图书馆学研究,2017(9):77-83.
[26] 活动盒子_APP活动运营工具.构建用户画像的目的很简单:了解你,是为了更好的服务你[EB/OL].[2017-11-25]. http://www.jianshu.com/p/4e7b56c301bb.
[27] 天善智能.用户画像之标签权重算法[EB/OL].[2017-11-25]. http://www.sohu.com/a/160943678_572440.
[28] 大数据部.构建用户画像:5W2H方法(一)[EB/OL].[2017-11-26]. http://blog.csdn.net/bdchome/article/details/53173275.
[29] 麦克莱夫,本森,辛西奇.商业和经济统计学[M].卲学清,张亚涛,刘乐平,等译.北京:中国财政经济出版社,2008.
[30] 全国师范院校图书馆联盟."高等师范类院校大学生阅读调查报告(2016)"发布[EB/OL].[2017-11-23].http://sflm.bnu.edu.cn/templates/qgsflm/newsInfo.cshtml?cid=147&id=385.
[31] 张文彤.IBM SPSS数据分析与挖掘实战案例精粹[M].北京:清华大学出版社,2013.
[32] 南英子.聚类分析结果的有效性辨析[J].统计与决策,2008(20):157-158.
[33] 沈浩.聚类分析——每周一讲多变量分析[EB/OL].[2017-11-23].http://shenhaolaoshi.blog.sohu.com/163197842.html.
