[Purpose/significance] This paper aims to propose a method to mine the key factors influencing reader satisfaction from a large amount of online commodity review data, and deeply explore the influence mode and degree of each influencing factor on consumer satisfaction, so as to provide a theoretical basis for book publishing enterprises and e-commerce platforms to continuously improve reader satisfaction.[Method/process] On the one hand, naive bayesian classifier was used to classify reader emotion; On the other hand, the high frequency nouns in the evaluation text were clustered to find the main factors that affect reader satisfaction. On this basis, the influence mode and degree of each influencing factor were analyzed based on the principle of minimizing uncertainty.[Result/conclusion] Taking the JD's artificial intelligence book review as an example, an empirical study was conducted. It was found that the six factors including content and price can greatly reflect the reader's satisfaction (83.2%). Therefore, for books and commodities, we can find out the main factors affecting readers' satisfaction by studying a large number of historical review data, and design a simplified reader review framework accordingly, so as to enhance the enthusiasm of readers to participate in the review and improve the quality of the review. ‘Book content’ is the most important factor affecting reader satisfaction. When readers express different emotions on the content of the book, the influence mode and degree of other factors on reader satisfaction are completely different. When readers are satisfied with the content of the book, 89.2% of the overall comments are favorable, while other factors have little influence. When readers think the book content is general, the review tends to be neutral, and readers pay more attention to the service and logistics; When readers think the content of books is not satisfactory, the factors affecting the satisfaction of readers are packaging, service quality and price in order. Based on the influence mode and influence degree of different influencing factors on reader satisfaction, book publishing companies and e-commerce platforms can be improved more specifically to improve reader satisfaction.
[1] 当当,人人书. 2018中国图书阅读市场专题分析报告[EB/OL].[2018-06-10].http://www.sohu.com/a/245466602_100218710.
[2] 易观.中国B2C市场季度监测报告2017年第3季度[EB/OL].[2018-06-10].https://www.analysys.cn/article/analysis/detail/1000995.
[3] UTZ S, KERKHOF P, VAN D, et al. Consumers:how consumer reviews influence perceived trustworthiness of online stores[J]. Electronic commerce research and applications, 2012, 11(1):49-58.
[4] TAX S S, BROWN S W, CHANDRASHEKARAN M. Customer evaluations of service complaint experiences:implications for relationship marketing[J]. Journal of marketing, 1998, 62(2):60-76.
[5] 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.
[6] JENSEN M L, AVERBECK J M, ZHANG Z, et al. credibility of anonymous online product reviews:a language expectancy perspective[J]. Journal of management information systems, 2013, 30(1):293-324.
[7] LUDWIG S, RUYTER D, FRIEDMAN M, et al. More than words:the influence of affective content and linguistic style matches in online reviews on conversion rates[J]. Journal of marketing, 2013, 77(1):87-103.
[8] LI X, HITT L M. Self-selection and information role of online product reviews[J]. Information systems research, 2008, 19(4):456-474.
[9] XIAO S, WEI C P, DONG M. Crowd intelligence:analyzing online product reviews for preference measurement[J]. Information & management, 2016, 53(2):169-182.
[10] HODAC N N. CARSON S J, MOORE W L. The effects of positive and negative online customer reviews:do brand strength and category maturity matter?[J]. Journal of marketing, 2013,77(6):37-53.
[11] 王君珺,闫强.不同热度搜索型产品的在线评论对销量影响的实证研究[J].中国管理科学,2013,21(S2):406-411.
[12] LEE J H, JUNG S H, PARK J H. The role of entropy of review text sentiments on online WOM and movie box office sales[J]. Electronic commerce research and applications, 2017, 22:42-52.
[13] DUAN W, GU B, WHINSTON A B. Do online reviews matter?——an empirical investigation of panel data[J]. Decision support systems, 2008, 45(4):1007-1016.
[14] 彭丽徽,李贺,张艳丰,等.基于品牌声誉感知差异的在线评论有用性影响因素实证研究[J].情报科学,2017,35(9):159-164.
[15] AHMED R R, SEEDANI S K, AHUJA M K, et al. Impact of celebrity endorsement on consumer buying behavior[J]. Ssrn electronic journal, 2015(16):12-20.
[16] KUMAR N, BENBASAT I. Research note:the influence of recommendations and consumer reviews on evaluations of websites[J]. Information systems research, 2006, 17(4):425-439.
[17] FLOH A, KOLLER M, ZAUNER A. Taking a deeper look at online reviews:the asymmetric effect of valence intensity on shopping behavior[J]. Journal of marketing management,2013,29(5-6):646-670.
[18] ZHAO K, STYLIANOU A C, ZHENG Y. Sources and impacts of social influence from online anonymous user reviews[J]. Information & management, 2018,55(1):16-30.
[19] 汪旭晖,张其林,杜航.在线顾客评论对产品销量的影响:品牌强度和产品成熟度的调节作用[J].管理工程学报,2018,32(3):9-18.
[20] HEYMAN J,ARIELY D.Effort for payment a tale of two markets[J]. Psychological science,2004,15(11):787-793.
[21] SWEENEY J C, SOUTAR G N, MAZZAROL T. Word of mouth:measuring the power of individual messages[J].European journal of marketing, 2012, 46(1/2):237-257.
[22] SKOWRONSKI J J, CARLSTON D E. Negativity and extremity biases in impression formation:a review of explanations[J]. Psychological bulletin,1989,105(1):131-142.
[23] 宁连举, 孙韩. 在线负面评论对网络消费者购买意愿的影响[J]. 技术经济, 2014, 33(3):54-59,96.
[24] 宋远征. 网络负面评论对消费者购买意愿影响-产品类型调节作用[D].哈尔滨:哈尔滨工业大学,2016.
[25] CHEVALIER J, GOOLSBEE A. Measuring prices and price competition online:amazon.com and barnesandnoble.com[J]. Quantitative marketing & economics, 2003, 1(2):203-222.
[26] 孙克琳, 张心悦, 林浡夏,等. 电子商务中在线评论对商品销售的影响——以当当网自营图书为例[J]. 电子商务, 2018(2):33-34.
[27] 冀经纬. 在线评论影响图书销量的实证研究[D].北京:中国地质大学,2018.
[28] CARDOZO R N. An experimental study of customer effort, expectation, and satisfaction[J]. Journal of marketing research, 1965, 2(3):244-249.
[29] OLIVER R L. Measurement and evaluation of satisfaction processes in retail settings[J]. Journal of retailing, 1981, 57(3):25-48.
[30] OLIVER R L. A Behavioral Perspective on the Consumer[J]. Asia pacific journal of Management, 1997, 2(2):285-286.
[31] HUNT H K. Consumer satisfaction:discussant comments[J]. Advances in consumer research, 1983, 10(4):262-262.
[32] MOEZZI H. Correlation between the determinants of customer relationship management system, electronic (e-CRM) customer satisfaction[J]. Research bulletin, 2009, 4(22):25-12.
[33] 张圣亮,李小东.网上购物顾客满意度影响因素研究[J]. 天津大学学报(社会科学版), 2013, 15(2):109-115.
[34] 查金祥, 王立生. 网络购物顾客满意度影响因素的实证研究[J]. 管理科学, 2006, 19(1):50-58.
[35] ZHANG L, TAN W, XU Y, et al. Dimensions of perceived risk and their influence on consumers' purchasing behavior in the overall process of B2C[J]. Lecture notes in electrical engineering, 2012, 111:1-10.
[36] 李玉萍, 胡培. 顾客网络购物满意度影响因素研究[J]. 商业研究, 2015, 57(1):160-165.
[37] TANDON U, KIRAN R, SAH A. Analyzing customer satisfaction:users perspective towards online shopping[J]. Nankai business review international, 2017,8(3):266-288.
[38] TAPUSHE R T, IMRAN M, MOHAMED E H, et al. To read or not to read:modeling online newspaper reading satisfaction and its impact on revisit intention and word-of-mouth interdisciplinary[J]. Journal of information, knowledge and management, 2018,13(3):337-359.
[39] SAXON D, PEARSON A T, WU P. Hyperlink-embedded journal articles improve statistical knowledge and reader satisfaction[J]. Journal of graduate medical education, 2015, 7(4):654-657.
[40] 张飞相, 杨扬, 陈敬良. 图书在线消费者忠诚度影响因素的实证研究[J]. 中国流通经济, 2014(11):87-93.
[41] 匡霞,杨扬. 图书在线消费者感知质量对忠诚度影响的实证研究[J].科技与出版,2019,4:151-156.
[42] 陈硕. 基于词性与句法的产品评论特征及情感词挖掘[D]. 河北:河北大学, 2012.
[43] 吴维芳,高宝俊,杨海霞,等.评论文本对酒店满意度的影响:基于情感分析的方法[J].数据分析与知识发现,2017(03):62-71.
[44] 夏名首.基于买家在线评价的购后评价特征数据挖掘[J].中国流通经济,2017(7):30-38.
[45] 李惠富, 陆光. 多类型分类器融合的文本分类方法研究[J]. 计算机应用研究, 2019, 36(03):119-122.
[46] 韩忠明,段大高.数据分析与R[M].北京:北京邮电大学出版社,2014.
