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复杂系统与复杂性科学  2020, Vol. 17 Issue (1): 71-80    DOI: 10.13306/j.1672-3813.2020.01.009
  本期目录 | 过刊浏览 | 高级检索 |
在线顾客购买阵发性的测量和调节作用
卢美丽1, 高宇佳1, 叶作亮2
1.山西财经大学工商管理学院,太原 030006;
2.西南财经大学国际商学院,成都 611130
Measurement and Moderating Effect of Online Customer Purchase Clumpy
LU Meili1, GAO Yujia1, YE Zuoliang2
1.School of Business Administration, Shanxi University of Finance and Economics,Taiyuan 030006, China;
2.School of International Business, Southwestern University of Finance and Economics, Chengdu 611130, China
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摘要 随着对人类行为时空复杂性分析的深入,大量的实证研究发现人类的行为常常不再表现为泊松分布,而是呈现出一段时间内频繁发生,经历长久静默之后再次爆发的“阵发性”特征。针对我国电子商务中顾客购买行为的特点,提出改进的阵发性测量方法,并对一号店及京东平台的顾客购买行为进行测量和实证分析。结果表明,在线顾客最近购买时间R、购买频次F与其活跃的概率正相关;阵发性对最近购买时间R有调节作用,无阵发性的顾客,最近购买时间R和其活跃的概率更相关。
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卢美丽
高宇佳
叶作亮
关键词 在线购买阵发性泊松分布幂律分布Logit回归    
Abstract:With the in-depth analysis of space and time complexity of human behavior, a large number of empirical studies have found that human behavior is no longer considered as Poisson distribution, but shows a "clumpy" phenomenon that occurs frequently after a long silence. This paper proposes an improved method combining the characteristics of current online purchase behavior. By measuring and the purchase clumpy of customers on the YHD and the JD platform, this paper gives empirical research. The results show that the customer's recent purchase time R, the purchase frequency F and the customer's active odds are positively correlated; Clumpy has a moderating effect on the recent purchase time R, and the recent purchase time R is more related to the customer's active odds to the no clumpy customers.
Key wordsonline purchase    clumpy    Poisson distribution    power-law distribution    Logit regression
收稿日期: 2019-08-28      出版日期: 2020-04-29
ZTFLH:  F719  
基金资助:国家教育部人文社科项目(18YJA630071);山西省软科学研究计划项目(2018041069-1);山西省高等学校工商管理优势学科攀升计划项目(晋教研[2018]4号)
作者简介: 卢美丽(1970-),女,山西浑源人,博士,副教授,主要研究方向为电子商务和企业物流。
引用本文:   
卢美丽, 高宇佳, 叶作亮. 在线顾客购买阵发性的测量和调节作用[J]. 复杂系统与复杂性科学, 2020, 17(1): 71-80.
LU Meili, GAO Yujia, YE Zuoliang. Measurement and Moderating Effect of Online Customer Purchase Clumpy. Complex Systems and Complexity Science, 2020, 17(1): 71-80.
链接本文:  
http://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2020.01.009      或      http://fzkx.qdu.edu.cn/CN/Y2020/V17/I1/71
[1]Schmittlein D C, Morrison D G, Colombo R. Counting your customers: Who are they and what will they do next? [J]. Management Science,1987,33(1):1-24.
[2]Fader P S, Hardie B G S, Shang J. Customer-base analysis in a discrete-time noncontractual setting[J]. Marketing Science,2010,29(6):1086-1108.
[3]Oliveira-Castro J M, Ferreira D C S, Foxall G R, et al. Dynamics of repeat buying for packaged food products[J]. Journal of Marketing Management, 2005, 21(21):37-61.
[4]Abe M. "Counting your customers" one by one: A hierarchical bayes extension to the Pareto/NBD model[J]. Marketing Science, 2009, 28(3):541-553.
[5]Jerath K, Fader P S, Hardie B G S. New perspectives on customer “death” using a generalization of the Pareto/NBD model[J]. Marketing Science, 2011, 30(5):866-880.
[6]Buschken J, Ma S. When are your customers active and is their buying regular or random? An erlang mixture state-switching model for customer scoring[J]. Ssrn Electronic Journal, 2012.
[7]Barabási A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439):509-512.
[8]Barabási A L. The origin of bursts and heavy tails in human dynamics[J]. Nature, 2005, 435(7039):207-211.
[9]周涛,韩筱璞,闫小勇,等. 人类行为时空特性的统计力学[J]. 电子科技大学学报, 2013, 42(4):481-540.
Zhou Tao, Han Xiaopu, Yan Xiaoyong, et al. Statistical mechanics on temporal and spatial activities of human[J]. Journal of University of Electronic Science and Technology of China, 2013, 4(4):481-540.
[10] Liu-Thompkins Y, Malthouse E C. A primer on using behavioral data for testing theories in advertising research[J]. Journal of Advertising,2017, 46(1):213-225.
[11] Wang J, Gao K, Li G. Empirical analysis of customer behaviors in Chinese e-commerce[J]. Journal of Networks, 2010, 5(10):1177-1184.
[12] 樊超. 从图书借阅看人类群体和个体行为的动力学机制[D]. 上海理工大学, 2010:19-20.
Fan Chao. Study on dynamic mechanism of human behaviors base on library loans fromcollective and individual perspectives[D].University of Shanghai for Science and Technology, 2010:19-20.
[13] 叶作亮,王雪乔,宝智红,等. C2C环境中顾客重复购买行为的实证与建模[J]. 管理科学学报, 2011, 14(12):71-78.
Ye Zuolinag, Wang Xueqiao, Bao Zhihong, et al. Modeling and empirical research of repeat purchase behavior in C2C ecommerce[J]. Journal of Management Sciences on China, 2011, 14(12):71-78.
[14] Zhang Y, Bradlow E T, Small D S. New measures of clumpiness for incidence data[J]. Journal of Applied Statistics, 2013, 40(40):2533-2548.
[15] Zhang Y, Bradlow E T, Small D S. Predicting customer value using clumpiness: From RFM to RFMC[J]. Marketing Science, 2015, 34(2):21-46.
[16] Gilovich T, Vallone R, Tversky A. The hot hand in basketball: On the misperception of random sequences[J]. Cognitive Psychology, 1985, 17(3):295-314.
[17] Eckmann J P, Moses E, Sergi D, et al. Entropy of dialogues creates coherent structures in e-mail traffic[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(40):14333-7.
[18] Goh K I,Barabasi A L. Burstiness and memory in complex systems[J]. Europhysics Letters, 2008, 81(4): 48002.
[19] 周涛. 在线电影点播中的人类动力学模式[J]. 复杂系统与复杂性科学, 2008, 5(1):1-5.
Zhou Tao. Human activity pattern on on-line movie watching[J].Complex System and Complexity Science, 2008, 5(1):1-5.
[20] Kumar V, Srinivasan K, Rao V R, et al. Commentaries and reply on “predicting customer value using clumpiness: From RFM to RFMC” by Yao Zhang, Eric T. Bradlow, and Dylan S. Small[J]. Marketing Science, 2015, 34(2):209-217.
[21] Tian Y, Ye Z L, Yan Y F, et al. A practical model to predict the repeat purchasing pattern of consumers in the C2C e-commerce[J]. Electronic Commerce Research, 2015, 15(4):571-583.
[22] 吴晓飞. 在线零售市场结构及其演化机制研究[D]. 成都:西南财经大学, 2016:48-56.
Wu Xiaofei. The structure and evolution mechanism research of online retail market[D]. Chengdu: Southwestern University of Finance and Economics, 2016:48-56.
[23] 卢美丽,叶作亮,曹翠珍. 在线零售市场顾客重复购买行为建模与实证研究[J]. 软科学, 2019 (1):134-139.
Lu Meili, Ye Zuoliang, Cao Cuizhen. Modeling and empirical research on customer repeat purchasing behavior in online retail market[J]. Soft Science, 2019 (1):134-139.
[24] 吴明隆. 问卷统计分析实务-SPSS操作与应用[M].重庆:重庆出版社,2009.
[25] 温忠麟,侯杰泰,张雷. 调节效应与中介效应的比较和应用[J]. 心理学报, 2005, 37(2):268-274.
Wen Zhonglin, Hou Jietai, Zhang Lei. A comparison of moderator and mediator and their applications[J]. Acta Psychologica Sinica, 2005, 37(2):268-274.
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