Please wait a minute...
Advanced Search
数据分析与知识发现  2023, Vol. 7 Issue (3): 69-79     https://doi.org/10.11925/infotech.2096-3467.2022.0354
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
混合在线客服对消费者购买转化的影响研究*
李顺,李莉(),陈白雪
南京理工大学经济管理学院 南京 210094
Impact of Hybrid Online Customer Service on Consumer Purchase Conversion
Li Shun,Li Li(),Chen Baixue
School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
全文: PDF (703 KB)   HTML ( 25
输出: BibTeX | EndNote (RIS)      
摘要 

【目的】 探究电子商务中混合在线客服对消费者购买转化的影响。【方法】 以企业数据为样本,利用Logit模型探究人工客服、智能客服及二者交互项对消费者购买转化的影响,并通过分组回归讨论混合在线客服对消费者购买转化的异质性效果。【结果】 在混合在线客服提供模式下,人工客服、智能客服的使用对消费者购买转化有显著的正向作用,人工客服与智能客服的使用之间存在替代关系。【局限】 混合在线客服对消费者购买转化的研究有待扩展,未来可以考虑将消费者收入、消费习惯等因素加入研究模型并对会话内容进行分析。【结论】 本研究关注消费者实际购买转化,为电商企业制定有效的客户服务运营策略提供参考和建议。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
李顺
李莉
陈白雪
关键词 人工客服智能客服购买转化Logit模型    
Abstract

[Objective] This paper investigates the impacts of hybrid online customer service on consumer purchase conversion in e-commerce. [Methods] Using enterprise data as the sample, we employed the Logit model to explore the effect of human customer service, intelligent customer service, and their interaction on consumer purchase conversion. This paper also discussed the heterogeneous effects of hybrid online customer service on consumer purchase conversion with grouping regression. [Results] Under the hybrid online customer service mode, human customer service and intelligent customer service had significant positive impacts on consumer purchase conversion. There was a substitution relationship between human customer service and intelligent customer service. [Limitations] More research is needed to add consumer income and consumption habits to the proposed model and analyze conversation content. [Conclusions] This study focuses on actual consumer purchase conversion. It provides suggestions for e-commerce companies to develop effective customer service operation strategies.

Key wordsHuman Customer Service    Intelligent Customer Service    Purchase Conversion    Logit Model
收稿日期: 2022-04-17      出版日期: 2023-04-13
ZTFLH:  F724  
基金资助:国家自然科学基金项目(71771122);江苏省研究生科研与实践创新计划项目(KYCX22_0554)
通讯作者: 李莉,ORCID:0000-0003-0397-601X,E-mail:lily691111@126.com。   
引用本文:   
李顺, 李莉, 陈白雪. 混合在线客服对消费者购买转化的影响研究*[J]. 数据分析与知识发现, 2023, 7(3): 69-79.
Li Shun, Li Li, Chen Baixue. Impact of Hybrid Online Customer Service on Consumer Purchase Conversion. Data Analysis and Knowledge Discovery, 2023, 7(3): 69-79.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0354      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I3/69
变量类型 变量 定义 均值 标准差 最小值 最大值
被解释变量 Purchaseij 用户i当天是否购买产品j的虚拟变量 0.098 0.297 0 1
解释变量 Arti_Chatij 用户i是否针对产品j咨询人工客服的虚拟变量 0.018 0.131 0 1
Rob_Chatij 用户i是否针对产品j咨询智能客服的虚拟变量 0.009 0.092 0 1
Arti_Chatij ×Rob_Chatij 用户i是否针对产品j咨询人工客服与智能客服的交互项 0.006 0.079 0 1
控制变量 Visit_numij 用户i当天浏览产品j的次数 1.779 1.636 1 77
Visit_time_avgij 用户i当天浏览产品j的平均时长 44.784 78.764 0 900
Genderi 用户i的性别,0表示女性,1表示男性 0.538 0.499 0 1
Salej 产品j当天的页面显示销量,即近一个月的历史销量 1 094.89 2 248.11 0 23 389
Pricej 产品j当天的页面显示价格 59.080 184.396 0.7 4 300
Table 1  变量的描述性统计特征
变量 系数 标准误 z值 P值 风险比率
Arti_Chatij 1.020 5*** 0.093 10.995 0.000 2.774 4
Rob_Chatij 0.453 1** 0.216 2.097 0.036 1.573 1
Arti_Chatij ×Rob_Chatij -0.674 8** 0.263 -2.562 0.010 0.509 3
Genderi -0.274 1*** 0.029 -9.484 0.000 0.760 3
Visit_numij 0.326 7*** 0.007 45.160 0.000 1.386 5
Visit_time_avgij 0.002 5*** 0.000 17.740 0.000 1.002 5
Pricej -0.001 5*** 0.000 -7.365 0.000 0.998 5
Salej 5.399e-05*** 5.55e-06 9.726 0.000 1.000 1
截距项 -2.925 0*** 0.030 -97.764 0.000
Log Likelihood -17 424.0
Pseudo R2 0.087 8
Table 2  总体样本的Logit估计结果
变量 风险比率
Arti_Chatij 2.741 0*** 2.825 4***
Rob_Chatij 1.651 4 1.521 7
Arti_Chatij ×Rob_Chatij 0.502 2* 0.505 4*
Visit_numij 1.358 4*** 1.415 2***
Visit_time_avgij 1.002 2*** 1.002 7***
Salej 1.000 0*** 1.000 1***
Pricej 0.998 8*** 0.998 1***
样本数量 32 099 27 519
Log Likelihood -8 645.1 -8 772.3
Pseudo R2 0.075 7 0.094 3
Table 3  不同性别的分组回归结果
变量 风险比率
浏览次数较多 浏览次数较少
Arti_Chatij 3.506 1*** 4.660 6***
Rob_Chatij 2.124 7*** 1.949 2
Arti_Chatij ×Rob_Chatij 0.474 4*** 0.462 6
Genderi 0.790 2*** 0.771 1***
Visit_time_avgij 1.002 4*** 1.002 6***
Salej 1.000 1*** 1.000 1***
Pricej 0.998 6*** 0.998 7***
样本数量 22 248 37 370
Log Likelihood -10 028 -7 473
Pseudo R2 0.027 2 0.022 9
Table 4  不同浏览次数的分组回归结果
变量 风险比率
大于等于19秒 小于19秒
Arti_Chatij 2.686 7*** 2.473 6***
Rob_Chatij 1.616 6** 0.312 7
Arti_Chatij ×Rob_Chatij 0.496 5** 2.927 4
Visit_numij 1.304 6*** 1.483 8***
Genderi 0.739 4*** 0.817 5***
Salej 1.000 0*** 1.000 1***
Pricej 0.998 1*** 0.998 9**
样本数量 30 220 29 398
Log Likelihood -11 025.0 -6 163.2
Pseudo R2 0.068 1 0.098 9
Table 5  不同浏览平均时长的分组回归结果
变量 风险比率
高价 低价
Arti_Chatij 2.976 5*** 1.850 1***
Rob_Chatij 1.677 2** 1.080 8
Arti_Chatij ×Rob_Chatij 0.476 3** 0.820 1
Visit_numij 1.329 9*** 1.461 6***
Visit_time_avgij 1.002 2*** 1.002 6***
Genderi 0.698 6*** 0.836 8***
Salej 1.000 2*** 1.000 0
样本数量 32 293 27 325
Log Likelihood -9 659.2 -7 670.2
Pseudo R2 0.095 3 0.088 6
Table 6  不同价格的分组回归结果
变量 风险比率
高销量 低销量
Arti_Chatij 2.212 4*** 3.662 1***
Rob_Chatij 1.305 3 1.880 7*
Arti_Chatij ×Rob_Chatij 0.605 6 0.454 9*
Visit_numij 1.401 9*** 1.361 7***
Visit_time_avgij 1.002 5*** 1.002 4***
Genderi 0.755 7*** 0.722 7***
Pricej 1.003 7*** 0.998 1***
样本数量 29 847 29 771
Log Likelihood -10 064.0 -7 215.2
Pseudo R2 0.087 8 0.084 4
Table 7  不同销量的分组回归结果
[1] 汪旭晖, 陈鑫. 用户生成内容的图文匹配对消费者感知有用性的影响[J]. 管理科学, 2018, 31(1): 101-115.
[1] ( Wang Xuhui, Chen Xin. Fit of Graph and Text in User-Generated Contents and Its Effect on the Perceived Usefulness for Consumers[J]. Journal of Management Science, 2018, 31(1): 101-115.)
[2] 袁海霞, 白琳, 陈俊. 在线复合评论:“众口难调”“行合趋同”抑或“金无足赤”——基率信息和偏好差异性的调节效应研究[J]. 南开管理评论, 2019, 22(6): 211-220.
[2] ( Yuan Haixia, Bai Lin, Chen Jun. The Contingent Effect of Composite Online Reviews on Online Sales: Study on Boundary Conditions of Base-Rate Information and Preference Variance[J]. Nankai Business Review, 2019, 22(6): 211-220.)
[3] 中国互联网络信息中心. 第49次中国互联网络发展状况统计报告[R/OL]. [2022-02-25]. http://www.cnnic.cn/n4/2022/0401/c88-1131.html.
[3] ( China Internet Network Information Center. The 49th China Statistical Report on Internet Development[R/OL]. [2022-02-25]. http://www.cnnic.cn/n4/2022/0401/c88-1131.html.)
[4] Suh K S. Impact of Communication Medium on Task Performance and Satisfaction: An Examination of Media-Richness Theory[J]. Information & Management, 1999, 35(5): 295-312.
doi: 10.1016/S0378-7206(98)00097-4
[5] Shawar B, Atwell E. Using Corpora in Machine-Learning Chatbot Systems[J]. International Journal of Corpus Linguistics, 2005, 10(4): 489-516.
doi: 10.1075/ijcl
[6] Chi O H, Denton G, Gursoy D. Artificially Intelligent Device Use in Service Delivery: A Systematic Review, Synthesis, and Research Agenda[J]. Journal of Hospitality Marketing & Management, 2020, 29(7): 757-786.
[7] Pavlou P A, Liang H G, Xue Y J. Understanding and Mitigating Uncertainty in Online Exchange Relationships: A Principal- Agent Perspective[J]. MIS Quarterly, 2007, 31(1): 105-136.
doi: 10.2307/25148783
[8] Tam K Y, Ho S Y. Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes[J]. MIS Quarterly, 2006, 30(4): 865-890.
doi: 10.2307/25148757
[9] Krawczyk-Sokołowska I, Ziołkowska B. Computer-Aided and Web-Based Tools in Customer Relationship Management[J]. Acta Electrotechnica et Informatica, 2013, 13(4): 13-19.
doi: 10.15546/aeei
[10] Ho A, Hancock J, Miner A S. Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations with a Chatbot[J]. Journal of Communication, 2018, 68(4): 712-733.
doi: 10.1093/joc/jqy026 pmid: 30100620
[11] Basso A, Goldberg D, Greenspan S, et al. First Impressions: Emotional and Cognitive Factors Underlying Judgments of Trust E-Commerce[C]// Proceedings of the 3rd ACM Conference on Electronic Commerce. 2001: 137-143.
[12] Tan X J, Wang Y W, Tan Y. Impact of Live Chat on Purchase in Electronic Markets: The Moderating Role of Information Cues[J]. Information Systems Research, 2019, 30(4): 1248-1271.
doi: 10.1287/isre.2019.0861
[13] Jiang Z H, Chan J, Tan B, et al. Effects of Interactivity on Website Involvement and Purchase Intention[J]. Journal of the Association for Information Systems, 2010, 11(1): 34-59.
doi: 10.17705/1jais
[14] Cheng X S, Bao Y, Zarifis A, et al. Exploring Consumers’ Response to Text-Based Chatbots in E-Commerce: The Moderating Role of Task Complexity and Chatbot Disclosure[J]. Internet Research, 2022, 32(2): 496-517.
doi: 10.1108/INTR-08-2020-0460
[15] Flanagin A J. Commercial Markets as Communication Markets: Uncertainty Reduction Through Mediated Information Exchange in Online Auctions[J]. New Media & Society, 2007, 9(3): 401-423.
doi: 10.1177/1461444807076966
[16] 卢云帆, 鲁耀斌, 林家宝. 在线沟通对顾客网上购买决策影响的实证研究[J]. 图书情报工作, 2012, 56(12): 130-137.
[16] ( Lu Yunfan, Lu Yaobin, Lin Jiabao. An Empirical Study: The Influence of Online Communication on Customers’ Online Purchase Intention[J]. Library and Information Service, 2012, 56(12): 130-137.)
[17] Ou C X, Pavlou P A, Davison R M. Swift Guanxi in Online Marketplaces: The Role of Computer-Mediated Communication Technologies[J]. MIS Quarterly, 2014, 38(1): 209-230.
doi: 10.25300/MISQ
[18] 艾瑞咨询. 2021年中国对话机器人ChatBot行业发展研究报告[EB/OL]. [2021-06-30]. https://report.iresearch.cn/report/202106/3808.shtml.
[18] ( iResearch. 2021 China ChatBot Industry Development Report[EB/OL]. [2021-06-30]. https://report.iresearch.cn/report/202106/3808.shtml.)
[19] Daugherty P R, Wilson H J, Michelman P. Revisiting the Jobs Artificial Intelligence will Create[J]. MIT Sloan Management Review, 2019, 60(4): 1-8.
[20] Luo X M, Tong S L, Fang Z, et al. Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases[J]. Marketing Science, 2019, 38(6): 937-947.
[21] 张瑞, 潘鑫, 杨艳妮, 等. 情感介入式智能客户服务系统[J]. 情报理论与实践, 2016, 39(8): 70-74.
[21] ( Zhang Rui, Pan Xin, Yang Yanni, et al. Intelligent Customer Service System with Emotional Intervention[J]. Information Studies: Theory & Application, 2016, 39(8): 70-74.)
[22] 蔡志文, 林建宗. 基于购买意向的移动电子商务智能客服系统[J]. 科技管理研究, 2015, 35(18): 179-183.
[22] ( Cai Zhiwen, Lin Jianzong. An Intelligent Customer Service System for Mobile E-Commerce Based on Purchase Intention[J]. Science and Technology Management Research, 2015, 35(18): 179-183.)
[23] Rese A, Ganster L, Baier D. Chatbots in Retailers’ Customer Communication: How to Measure Their Acceptance?[J]. Journal of Retailing and Consumer Services, 2020, 56: 102176.
doi: 10.1016/j.jretconser.2020.102176
[24] Chung M, Ko E, Joung H, et al. Chatbot E-Service and Customer Satisfaction Regarding Luxury Brands[J]. Journal of Business Research, 2020, 117: 587-595.
doi: 10.1016/j.jbusres.2018.10.004
[25] Tran A D, Pallant J I, Johnson L W. Exploring the Impact of Chatbots on Consumer Sentiment and Expectations in Retail[J]. Journal of Retailing and Consumer Services, 2021, 63: 102718.
doi: 10.1016/j.jretconser.2021.102718
[26] Chen J V, Thi Le H, Tran S T T. Understanding Automated Conversational Agent as a Decision Aid: Matching Agent’s Conversation with Customer’s Shopping Task[J]. Internet Research, 2021, 31(4): 1376-1404.
doi: 10.1108/INTR-11-2019-0447
[27] McLean G, Osei-Frimpong K. Chat Now… Examining the Variables Influencing the Use of Online Live Chat[J]. Technological Forecasting and Social Change, 2019, 146: 55-67.
doi: 10.1016/j.techfore.2019.05.017
[28] Wilson H J, Daugherty P R. Collaborative Intelligence: Humans and AI are Joining Forces[J]. Harvard Business Review, 2018, 96(4): 114-123.
[29] Gudigantala N, Bicen P, Eom M T I. An Examination of Antecedents of Conversion Rates of E-Commerce Retailers[J]. Management Research Review, 2016, 39(1): 82-114.
doi: 10.1108/MRR-05-2014-0112
[30] 张鹏翼, 王丹雪, 焦祎凡, 等. 基于用户浏览日志的移动购买预测研究[J]. 数据分析与知识发现, 2018, 2(1): 51-63.
[30] ( Zhang Pengyi, Wang Danxue, Jiao Yifan, et al. Predicting Mobile Purchase Decisions Based on User Browsing Logs[J]. Data Analysis and Knowledge Discovery, 2018, 2(1): 51-63.)
[31] 周翔, 张鹏翼, 王军. 移动购物用户信息浏览特征及对购买的影响研究——基于移动电商APP点击流日志的分析[J]. 数据分析与知识发现, 2018, 2(4): 1-9.
[31] ( Zhou Xiang, Zhang Pengyi, Wang Jun. Impacts of Information Browsing Behaviors on Mobile Shopping: Case Study of Commerce APP Click Stream Analysis[J]. Data Analysis and Knowledge Discovery, 2018, 2(4): 1-9.)
[32] Park J, Chung H. Consumers’ Travel Website Transferring Behaviour: Analysis Using Clickstream Data-Time, Frequency, and Spending[J]. The Service Industries Journal, 2009, 29(10): 1451-1463.
doi: 10.1080/02642060903026254
[33] Lin X L, Featherman M, Brooks S L, et al. Exploring Gender Differences in Online Consumer Purchase Decision Making: An Online Product Presentation Perspective[J]. Information Systems Frontiers, 2019, 21(5): 1187-1201.
doi: 10.1007/s10796-018-9831-1
[34] 王夏阳, 陈思霓, 邬金涛. 网络预售下消费者购买行为的影响因素分析——基于淘宝2018春夏女装的实证研究[J]. 南开管理评论, 2020, 23(5): 4-15.
[34] ( Wang Xiayang, Chen Sini, Wu Jintao.On the Factors Influencing Online Consumers’ Purchasing Behavior Under Pre-Order Strategy: An Empirical Study Based on Women’s 2018 Spring-Summer Apparels of Tmall[J]. Nankai Business Review, 2020, 23(5): 4-15.)
[35] Ye Q, Cheng Z, Fang B. Learning from Other Buyers: The Effect of Purchase History Records in Online Marketplaces[J]. Decision Support Systems, 2013, 56: 502-512.
doi: 10.1016/j.dss.2012.11.007
[36] Cheung C M K, Xiao B S, Liu I L B. Do Actions Speak Louder than Voices? The Signaling Role of Social Information Cues in Influencing Consumer Purchase Decisions[J]. Decision Support Systems, 2014, 65: 50-58.
doi: 10.1016/j.dss.2014.05.002
[37] Byrnes J P, Miller D C, Schafer W D. Gender Differences in Risk Taking: A Meta-Analysis[J]. Psychological Bulletin, 1999, 125(3): 367-383.
doi: 10.1037/0033-2909.125.3.367
[38] 许博, 邵兵家, 杨海峰. C2C电子商务感知风险影响因素的实验研究[J]. 软科学, 2010, 24(7): 125-128.
[38] ( Xu Bo, Shao Bingjia, Yang Haifeng.Experimental Study of Influencing Factors of Perceived Risk in Online C2C Market[J]. Soft Science, 2010, 24(7): 125-128.)
[39] Sun H S. A Longitudinal Study of Herd Behavior in the Adoption and Continued Use of Technology[J]. MIS Quarterly, 2013, 37(4): 1013-1042.
doi: 10.25300/MISQ
[40] Tucker C, Zhang J J. How Does Popularity Information Affect Choices? A Field Experiment[J]. Management Science, 2011, 57(5): 828-842.
doi: 10.1287/mnsc.1110.1312
[1] 李顺, 李莉, 陈白雪. 混合在线客服对消费者购买转化的影响研究 [J]. 数据分析与知识发现, 0, (): 1-.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn