消费咨询网络中的虚假观点识别与产品采用研究

doi:10.16381/j.cnki.issn1003-207x.2019.08.010

中国管理科学 ›› 2019, Vol. 27 ›› Issue (8): 96-106.doi: 10.16381/j.cnki.issn1003-207x.2019.08.010

消费咨询网络中的虚假观点识别与产品采用研究

邵鹏¹, 胡平²

1. 西安工程大学管理学院, 陕西西安 710048;
2. 西安交通大学管理学院, 陕西西安 710049

收稿日期:2017-11-02 修回日期:2018-04-23 出版日期:2019-08-20 发布日期:2019-08-27
通讯作者: 邵鹏(1987-),男(汉族),陕西蓝田人,西安工程大学管理学院副教授,博士,研究方向:产品扩散与复杂网络,E-mail:shaopengde@sohu.com. E-mail:shaopengde@sohu.com
基金资助:
教育部人文社会科学研究青年基金资助项目（18YJC630139，17YJCZH082）；陕西省自然科学基础研究计划资助项目（2018JQ7001）；西安工程大学博士科研项目（107020338）

Untruthful Opinions Idenfication and Product Adoption in Consumer Advice Network

SHAO Peng¹, HU Ping²

1. School of Management, Xi'an Polytechnic University, Xi'an 710048, China;
2. School of Management, Xi'an Jiaotong University, Xi'an 710049, China

Received:2017-11-02 Revised:2018-04-23 Online:2019-08-20 Published:2019-08-27

摘要/Abstract

摘要： 消费咨询网络是社会化商务环境中的一类特殊社交网络，商家借助消费咨询网络用户传播产品信息从而形成了社交广告。当网络中传播的社交广告传递高于产品品质的评价，或对竞争对手的产品做出低于真实产品品质的评价，称为虚假评价。基于连续观点和离散行为思想构建了产品采用模型，并在模型中设置了用户观点学习规则，即尚未购买产品的用户优先选择诚信指数较高的用户进行观点学习。借助计算实验方法研究了社交广告、观点学习轮次对观点演化和产品采用的影响。结果表明：产品采用模型能够使得被虚假观点误导从而购买产品的用户比例降低；虚假观点识别机制可以有效识别出"低质高评"和"高质低评"，并降低其导向性；观点学习轮次的增加可以提高虚假观点的识别效果。研究克服了现有产品扩散模型在不区分信息扩散与产品购买、不考虑信息真伪性、不考虑不同采用阶段等方面的局限。

关键词: 社会化商务, 产品采用, 虚假观点, 观点学习

Abstract: The consumer advice network is a special type of social network in a social commerce environment. Users are able to establish online social relationship and sharing product evaluation, and search product related information from network members. As merchants start to spread product information through influential users in the consumer advice network, this activity lead to the emergence of social advertising, or user-generated advertising. In this background, merchants may rely on external incentives to stimulate uses to post and diffuse social advertising on the consumer advice network. By using social advertising tools, merchants make higher-than-true product quality evaluations for their products, or make lower-than-true product quality evaluations for competitors' products. Such deceptive evaluations are called untruthful evaluations. Based on the concept of continuous opinion and discrete behavior, the model of product adoption is constructed, and the user opinion learning rules are set up in the model. That is, the users who have not yet purchased the product learning the opinion from those who have higher honest index. The influence of social advertising, opinion learning rounds on opinion evolution and product adoption are studied by means of agent-based computational experiment. The results show that the model can reduce the product adoption proportion of users who are misleaded by the untruthful opinions. The untruthful opinions identification mechanism can effectively identify the "low quality with high evaluation" and "high quality with low evaluation" and reduce the misleading effect from untruthful opinions. The increase of opinion learning rounds can increase the recognition of untruthful opinion. This Research overcome the limitations in existing product diffusion model, such as no distinction between information diffusion and product adoption, regardless of the authenticity of product information, regardless of the different stages of product adoption.

Key words: social commerce, product adoption, untruthful opinions, opinion learning

中图分类号:

F274
F224

邵鹏, 胡平. 消费咨询网络中的虚假观点识别与产品采用研究[J]. 中国管理科学, 2019, 27(8): 96-106.

SHAO Peng, HU Ping. Untruthful Opinions Idenfication and Product Adoption in Consumer Advice Network[J]. Chinese Journal of Management Science, 2019, 27(8): 96-106.

参考文献

[1] Borgatti S P, Mehra A, Brass D J, et al. Network analysis in the social sciences[J]. Science, 2009,323(5916):892-895.
[2] 黄敏学, 王琦缘, 肖邦明, 等. 消费咨询网络中意见领袖的演化机制研究——预期线索与网络结构[J]. 管理世界, 2015, (7):109-121.
[3] 姚曦, 简予繁. 社会化媒体中消费者生成广告的影响因素模型及作用路径[J]. 财经论丛, 2016, (8):86-95.
[4] 郭泽德. 传统广告将死,社交广告崛起——社交媒体时代广告发展趋势及传播策略研究[J]. 编辑之友, 2015, (7):9-13.
[5] 邓莎莎, 张朋柱, 张晓燕, 等. 基于欺骗语言线索的虚假评论识别[J]. 系统管理学报, 2014,23(2):263-270.
[6] 陈燕方, 娄策群. 在线商品虚假评论形成路径研究[J]. 现代情报, 2015,35(1):49-53.
[7] Katz E. The two-step flow of communication:An up-to-date report on an hypothesis[J]. Public Opinion Quarterly, 1957,21(1):61-78.
[8] 江晓东. 什么样的产品评论最有用?——在线评论数量特征和文本特征对其有用性的影响研究[J]. 外国经济与管理, 2015,(4):41-55.
[9] 邵鹏. 消费者网络对试用产品的"商家-平台"合作机制的影响[J]. 中国管理科学, 2016,24(2):76-83.
[10] Ding Zhaogang, Dong Yucheng, Liang Haiming, et al. Asynchronous opinion dynamics with online and offline interactions in bounded confidence model[J]. Journal of Artificial Societies and Social Simulation, 2017,20(4):6.
[11] Martins A C R. Mobility and social network effects on extremist opinions[J]. Physical Review E, 2008,78(3):36104.
[12] Martins A C R. Continuous opinions and discrete actions in opinion dynamics problems[J]. International Journal of Modern Physics C, 2008,19(4):617-624.
[13] 熊熙, 胡勇. 基于社交网络的观点传播动力学研究[J]. 物理学报, 2012,61(15):104-110.
[14] Deng Lei, Liu Yun, Xiong Fei. An opinion diffusion model with clustered early adopters[J]. Physica A:Statistical Mechanics and its Applications, 2013,392(17):3546-3554.
[15] Sussman S W, Siegal W S. Informational influence in organizations:An integrated approach to knowledge adoption[J]. Information Systems Research, 2003,14(1):47-65.
[16] Darke P R, Ritchie R J B. The defensive consumer:Advertising deception, defensive processing, and distrust[J]. Journal of Marketing Research, 2007,44(1):114-127.
[17] Cacioppo J T, Petty R E. Effects of message repetition on argument processing, recall, and persuasion[J]. Basic and Applied Social Psychology, 1989,10(1):3-12.
[18] Calder B J, Sternthal B. Television commercial wearout:An information processing view[J]. Journal of Marketing Research, 1980,17(2):173-186.
[19] Lee K, Lee H, Kim C O. Pricing and timing strategies for new product using agent-based simulation of behavioural consumers[J]. Journal of Artificial Societies and Social Simulation, 2014,17(2):1.
[20] Anderson E W, Sullivan M W. The antecedents and consequences of customer satisfaction for firms[J]. Marketing Science, 1993,12(2):125-143.
[21] 刘波, 叶勇. 顾客购后评价:失望、后悔与不公平[J]. 管理评论, 2004,16(2):54-58.
[22] Fu Feng, Liu Lianghuan, Wang Long. Empirical analysis of online social networks in the age of Web 2.0[J]. Physica A:Statistical Mechanics and its Applications, 2008,387(2-3):675-684.
[23] 汤胤, 欧治花, 陈杏惠, 等. 兴趣社交网络中的供需匹配研究:以豆瓣网为例[J]. 管理工程学报, 2015,29(2):41-50.
[24] Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999,286(5439):509-512.
[25] Jeong H, Neda Z, Barabasi A L. Measuring preferential attachment for evolving networks[J]. Europhysics Letters, 2003,61(4):567-572.
[26] 郭斌, 郭琳, 汪玥琦. 重复购买行为与新产品创新扩散——基于产品复杂性的视角[J]. 浙江大学学报人文社会科学版, 2014,44(3):88-99.
[27] Schenk T A, Löffler G, Rauh J. Agent-based simulation of consumer behavior in grocery shopping on a regional level[J]. Journal of Business Research, 2007,60(8):894-903.
[28] Miao Chunyan, Yang Qiang, Fang Haijing, et al. A cognitive approach for agent-based personalized recommendation[J]. Knowledge-Based Systems, 2007,20(4):397-405.
[29] Roozmand O, Ghasem-Aghaee N, Hofstede G J, et al. Agent-based modeling of consumer decision making process based on power distance and personality[J]. Knowledge-Based Systems, 2011,24(7):1075-1095.
[30] 金淳, 董秋, 吕苗. 基于Agent的网站促销下消费者行为仿真研究[J]. 系统工程理论与实践, 2014,34(4):845-853.
[31] 刘宏鲲, 杨慧, 唐明, 等. 自适应网络中针对疾病传播暂态的局部隔离策略[J]. 中国科学:物理学力学天文学, 2014,44(1):32-41.
[32] Park C, Lee T M. Information direction, website reputation and eWOM effect:A moderating role of product type[J]. Journal of Business Research, 2009,62(1):61-67.

消费咨询网络中的虚假观点识别与产品采用研究

Untruthful Opinions Idenfication and Product Adoption in Consumer Advice Network

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