Understanding the formation process of negative customer engagement behaviours: a quantitative and qualitative interpretation

IF 3.6 4区管理学 Q2 MANAGEMENT

Total Quality Management & Business Excellence Pub Date : 2023-11-14 DOI:10.1080/14783363.2023.2277395

Luning Zang, Yuying Liu, Xiaojing Sun, Decheng Wen

{"title":"Understanding the formation process of negative customer engagement behaviours: a quantitative and qualitative interpretation","authors":"Luning Zang, Yuying Liu, Xiaojing Sun, Decheng Wen","doi":"10.1080/14783363.2023.2277395","DOIUrl":null,"url":null,"abstract":"AbstractThe development of mobile internet has made it easier for negative emotions, cognitions and behaviours expressed by customers to be spread, and the damage caused by negative customer engagement behaviours (NCEBs) to the company's brand value and reputation has gradually been amplified. Therefore, this study aims to explore the formation process of NCEBs in online brand community by combining qualitative method with quantitative method. Xiaomi Community was selected as the data source platform, using Python programming language to crawl users’ comments in ‘11Ultra circle’ and machine learning methods to obtain negative emotional polarity comments. The text coding and classification of negative emotion polarity comments are mainly based on manual coding and supplemented by machine learning. Perform binary logistic regression on the classified data to obtain the impact of various factors on NCEBs. The results showed that there were differences in the impact of different factors on NCEBs. This article obtains a different result from existing literature, that is, cognition and emotion are no longer necessary factors for the generation of NCEBs. Company managers should start with pricing, users’ cognition mining, and identifying and solving key issues reported by users to suppress the occurrence of NCEBs.Keywords: Consumer behaviour; negative customer engagementonline communityquality management Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://weibo.com/u/36341487602 https://mp.weixin.qq.com/s/lS9-kBoVAqf3GhQMKD83Zg3 https://chejiahao.m.autohome.com.cn/info/82701024 https://auto.ifeng.com/qichezixun/20200928/1483345.shtml5 https://www.xiaomi.cn/6 https://www.xiaomi.cn/board/25686841Additional informationFundingThis work was supported by National Natural Science Foundation of China [grant number 72072104]; National Office of Philosophy and Social Sciences [grant number 18ZDA079].","PeriodicalId":23149,"journal":{"name":"Total Quality Management & Business Excellence","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Total Quality Management & Business Excellence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/14783363.2023.2277395","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}

引用次数: 0

Abstract

AbstractThe development of mobile internet has made it easier for negative emotions, cognitions and behaviours expressed by customers to be spread, and the damage caused by negative customer engagement behaviours (NCEBs) to the company's brand value and reputation has gradually been amplified. Therefore, this study aims to explore the formation process of NCEBs in online brand community by combining qualitative method with quantitative method. Xiaomi Community was selected as the data source platform, using Python programming language to crawl users’ comments in ‘11Ultra circle’ and machine learning methods to obtain negative emotional polarity comments. The text coding and classification of negative emotion polarity comments are mainly based on manual coding and supplemented by machine learning. Perform binary logistic regression on the classified data to obtain the impact of various factors on NCEBs. The results showed that there were differences in the impact of different factors on NCEBs. This article obtains a different result from existing literature, that is, cognition and emotion are no longer necessary factors for the generation of NCEBs. Company managers should start with pricing, users’ cognition mining, and identifying and solving key issues reported by users to suppress the occurrence of NCEBs.Keywords: Consumer behaviour; negative customer engagementonline communityquality management Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://weibo.com/u/36341487602 https://mp.weixin.qq.com/s/lS9-kBoVAqf3GhQMKD83Zg3 https://chejiahao.m.autohome.com.cn/info/82701024 https://auto.ifeng.com/qichezixun/20200928/1483345.shtml5 https://www.xiaomi.cn/6 https://www.xiaomi.cn/board/25686841Additional informationFundingThis work was supported by National Natural Science Foundation of China [grant number 72072104]; National Office of Philosophy and Social Sciences [grant number 18ZDA079].

查看原文本刊更多论文

了解负面客户参与行为的形成过程:定量和定性的解释

摘要移动互联网的发展使得客户所表达的负面情绪、认知和行为更容易传播，负面客户参与行为(nceb)对企业品牌价值和声誉的损害逐渐被放大。因此，本研究旨在通过定性与定量相结合的方法，探索网络品牌社区中新企业的形成过程。选择小米社区作为数据源平台，使用Python编程语言抓取“11超圈”用户评论，采用机器学习方法获取负面情绪极性评论。负面情绪极性评论的文本编码和分类以人工编码为主，辅以机器学习。对分类数据进行二元逻辑回归，得到各种因素对nceb的影响。结果表明，不同因素对nceb的影响存在差异。本文得出了与已有文献不同的结论，即认知和情感不再是新语言语言产生的必要因素。企业管理者应从定价、挖掘用户认知、识别和解决用户反映的关键问题入手，抑制nceb的发生。关键词:消费者行为;负面的客户参与在线社区质量管理披露声明作者未报告潜在利益冲突。注1 https://weibo.com/u/36341487602 https://mp.weixin.qq.com/s/lS9-kBoVAqf3GhQMKD83Zg3 https://chejiahao.m.autohome.com.cn/info/82701024 https://auto.ifeng.com/qichezixun/20200928/1483345.shtml5 https://www.xiaomi.cn/6 https://www.xiaomi.cn/board/25686841Additional information国家自然科学基金资助[批准号:72072104];国家哲学社会科学办公室[批准号18ZDA079]。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Total Quality Management & Business Excellence MANAGEMENT-

CiteScore

8.90

自引率

12.80%

发文量

期刊介绍： Total Quality Management & Business Excellence is an international journal which sets out to stimulate thought and research in all aspects of total quality management and to provide a natural forum for discussion and dissemination of research results. The journal is designed to encourage interest in all matters relating to total quality management and is intended to appeal to both the academic and professional community working in this area. Total Quality Management & Business Excellence is the culture of an organization committed to customer satisfaction through continuous improvement. This culture varies both from one country to another and between different industries, but has certain essential principles which can be implemented to secure greater market share, increased profits and reduced costs. The journal provides up-to-date research, consultancy work and case studies right across the whole field including quality culture, quality strategy, quality systems, tools and techniques of total quality management and the implementation in both the manufacturing and service sectors. No topics relating to total quality management are excluded from consideration in order to develop business excellence.