基于K-Means和SEM的消费者互联网保险购买意愿研究——以TPB和TAM为分析框架
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摘要
"互联网+"正颠覆和重塑着中国经济格局,互联网保险借势兴起并酝酿一场"现象级"热潮。消费者的购买意愿是进一步挖掘互联网保险市场潜力的直接推动因素,以消费者的视角研究互联网保险,合理引导消费者作出购买决策对于互联网保险的发展具有十分重要的意义。以ZH财险的互联网车险客户数据和问卷调查数据进行实证分析,运用K-Means聚类技术完成客户细分,并综合TPB和TAM理论构建消费者互联网保险购买意愿新模型,运用结构方程验证该模型。基于研究结论,为互联网保险经营发展提出针对性建议。
Internet insurance has risen and brewed a "phenomenal"craze while "Internet + "is turning and reshaping China's economic pattern. Consumer's purchase intention is the direct driving factor to further the potential of the Internet insurance market. It is of great significance to study Internet insurance from the perspective of consumers and reasonably guide consumers to make purchasing decisions for the development of Internet insurance. Based on the empirical data of ZH Internet auto insurance customer data and questionnaire data,K-Means clustering technology was used to complete customer segmentation,and TPB and TAM theory were combined to construct a new model of consumer Internet insurance purchase intention,and the structural equation was used to verify the model. Based on the research conclusions,this paper put forward suggestions for the development of Internet insurance business.
Internet insurance has risen and brewed a "phenomenal"craze while "Internet + "is turning and reshaping China's economic pattern. Consumer's purchase intention is the direct driving factor to further the potential of the Internet insurance market. It is of great significance to study Internet insurance from the perspective of consumers and reasonably guide consumers to make purchasing decisions for the development of Internet insurance. Based on the empirical data of ZH Internet auto insurance customer data and questionnaire data,K-Means clustering technology was used to complete customer segmentation,and TPB and TAM theory were combined to construct a new model of consumer Internet insurance purchase intention,and the structural equation was used to verify the model. Based on the research conclusions,this paper put forward suggestions for the development of Internet insurance business.
引文
[1]中国保险行业协会.互联网保险行业发展报告[M].北京:中国财政经济出版社,2014.
[2]曲速资本. 2016互联网保险行业研究报告[R].北京:[出版者不详],2016.
[3]张岩.互联网保险合规问题探析[J].法制与经济,2014(10):4-8.
[4]陈小萍.关于中国互联网保险可持续发展面临的问题研究[J].现代经济信息,2014(21):328-329.
[5]何丽新,池骋.互联网保险对于传统保险法律规则的冲击与重塑———以类型化分析为视角[J].兰州学刊,2016(8):170-178.
[6] ADAM K. Insurance on the Internet[J]. Risk Manage-ment and Insurance Review,2000(3):45-62.
[7]何德旭,董捷.中国的互联网保险:模式、影响、风险与监管[J].上海金融,2015(11):64-67.
[8]中国保险行业协会.互联网保险行业发展报告[M].北京:中国财政经济出版社,2014.
[9]陈秀芬,唐宇石.大数据时代我国互联网保险的现状与发展研究[J].改革与战略,2016,32(6):33-37.
[10]李亚光,闫俊花.我国互联网保险的发展基础、过程与逻辑[J].现代管理科学,2017(3):51-53.
[11]王洋.国外互联网保险行业的发展、监管经验及对我国的启示[J].武汉金融,2017(3):53-56.
[12]池骋,陈耀南.台湾地区互联网保险监管法制研究及其经验启示[J].江淮论坛,2017(3):101-107,11.
[13]刘远翔.互联网保险发展对保险企业经营效率影响的实证分析[J].保险研究,2015(9):104-116.
[14]崔宇清,赵明.基于最大诚信原则的我国互联网保险法律风险问题研究[J].金融理论与实践,2017(6):70-74.
[15]乔丽华,傅德印.缺失数据的多重插补方法[J].统计教育,2006(12):4-7.
[16]邓银燕.缺失数据的填充方法研究及实证分析[D].西安:西北大学,2010.
[17]庞新生.缺失数据多重插补处理方法的算法实现[J].统计与决策,2012(11):87-90.
[18]京东. 21世纪经济研究院. 2016中国电商消费行为报告[R]. 2017.
[19] MACQUEEN J. Some methods for classification and anal-ysis of multivariate observations[C]//Proceedings of the5th Berkeley Symposium on Mathematical Statistics andProbability. USA:Berkeley University of California Press,1967:281-297.
[20]吴夙慧,成颖,郑彦宁,等. K-means算法研究综述[J].现代图书情报技术,2011(5):28-35.
[21] FISHBEIN M. A behavior theory approach to the relationsbetween beliefs about an object and the attitude towardthe object[J]. Reading in Attitude Theory and Measure-ment. 2000,22:389-400.
[22]武瑞娟,李东进,吴波.中国农民消费者对下乡家电产品的购买意向分析[J].中国软科学,2010(1):40-52.
[23]邓新明.中国情景下消费者的伦理购买意向研究———基于TPB视角[J].南开管理评论,2012,15(3):22-32.
[24]李晓,屠采撷.中国消费者的延迟购买偏好是如何反转的?———计划行为视角[J].经济管理,2011(12):10-17.
[25] DAVIS F D. Technology acceptance model for empiricallytesting new end-use information system:theory and results[D]. Cambridge MA:MIT Sloan School of Management,1986.
[26] TAYLOR S,TODD P A. Understanding information tech-nology usage:A test of competing models[J]. InformationSystem Research,1995(2):144-176.
[27]叶静,石静,陈小龙.基于附加信任的TAM模型个人互联网保险使用意向研究[J].财经纵览,2014(19):118.
[28] AJZEN I. The theory of planned behavior[J]. Organiza-tional behavior and human decision process,1991,50(2):179-211.
[29]李志倩.基于UTAUT模型的保险电子商务用户接受度研究[D].南宁:广西大学,2014.
[30]吴明隆.结构方程模型———AMOS的操作与应用[M].重庆:重庆大学出版社,2009.
[31]温忠麟,张雷,候杰泰,等.中介效应检验程序及其应用[J].心理学报,2004,36(5):614-620.
(1)Kettle中文名为“水壶”,是一款国外开源的ETL工具,纯java编写,可以在Window\Linux\Unix上运行,数据抽取高效稳定。
[2]曲速资本. 2016互联网保险行业研究报告[R].北京:[出版者不详],2016.
[3]张岩.互联网保险合规问题探析[J].法制与经济,2014(10):4-8.
[4]陈小萍.关于中国互联网保险可持续发展面临的问题研究[J].现代经济信息,2014(21):328-329.
[5]何丽新,池骋.互联网保险对于传统保险法律规则的冲击与重塑———以类型化分析为视角[J].兰州学刊,2016(8):170-178.
[6] ADAM K. Insurance on the Internet[J]. Risk Manage-ment and Insurance Review,2000(3):45-62.
[7]何德旭,董捷.中国的互联网保险:模式、影响、风险与监管[J].上海金融,2015(11):64-67.
[8]中国保险行业协会.互联网保险行业发展报告[M].北京:中国财政经济出版社,2014.
[9]陈秀芬,唐宇石.大数据时代我国互联网保险的现状与发展研究[J].改革与战略,2016,32(6):33-37.
[10]李亚光,闫俊花.我国互联网保险的发展基础、过程与逻辑[J].现代管理科学,2017(3):51-53.
[11]王洋.国外互联网保险行业的发展、监管经验及对我国的启示[J].武汉金融,2017(3):53-56.
[12]池骋,陈耀南.台湾地区互联网保险监管法制研究及其经验启示[J].江淮论坛,2017(3):101-107,11.
[13]刘远翔.互联网保险发展对保险企业经营效率影响的实证分析[J].保险研究,2015(9):104-116.
[14]崔宇清,赵明.基于最大诚信原则的我国互联网保险法律风险问题研究[J].金融理论与实践,2017(6):70-74.
[15]乔丽华,傅德印.缺失数据的多重插补方法[J].统计教育,2006(12):4-7.
[16]邓银燕.缺失数据的填充方法研究及实证分析[D].西安:西北大学,2010.
[17]庞新生.缺失数据多重插补处理方法的算法实现[J].统计与决策,2012(11):87-90.
[18]京东. 21世纪经济研究院. 2016中国电商消费行为报告[R]. 2017.
[19] MACQUEEN J. Some methods for classification and anal-ysis of multivariate observations[C]//Proceedings of the5th Berkeley Symposium on Mathematical Statistics andProbability. USA:Berkeley University of California Press,1967:281-297.
[20]吴夙慧,成颖,郑彦宁,等. K-means算法研究综述[J].现代图书情报技术,2011(5):28-35.
[21] FISHBEIN M. A behavior theory approach to the relationsbetween beliefs about an object and the attitude towardthe object[J]. Reading in Attitude Theory and Measure-ment. 2000,22:389-400.
[22]武瑞娟,李东进,吴波.中国农民消费者对下乡家电产品的购买意向分析[J].中国软科学,2010(1):40-52.
[23]邓新明.中国情景下消费者的伦理购买意向研究———基于TPB视角[J].南开管理评论,2012,15(3):22-32.
[24]李晓,屠采撷.中国消费者的延迟购买偏好是如何反转的?———计划行为视角[J].经济管理,2011(12):10-17.
[25] DAVIS F D. Technology acceptance model for empiricallytesting new end-use information system:theory and results[D]. Cambridge MA:MIT Sloan School of Management,1986.
[26] TAYLOR S,TODD P A. Understanding information tech-nology usage:A test of competing models[J]. InformationSystem Research,1995(2):144-176.
[27]叶静,石静,陈小龙.基于附加信任的TAM模型个人互联网保险使用意向研究[J].财经纵览,2014(19):118.
[28] AJZEN I. The theory of planned behavior[J]. Organiza-tional behavior and human decision process,1991,50(2):179-211.
[29]李志倩.基于UTAUT模型的保险电子商务用户接受度研究[D].南宁:广西大学,2014.
[30]吴明隆.结构方程模型———AMOS的操作与应用[M].重庆:重庆大学出版社,2009.
[31]温忠麟,张雷,候杰泰,等.中介效应检验程序及其应用[J].心理学报,2004,36(5):614-620.
(1)Kettle中文名为“水壶”,是一款国外开源的ETL工具,纯java编写,可以在Window\Linux\Unix上运行,数据抽取高效稳定。