Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews

被引:39
|
作者
Kumar, Avinash [1 ]
Chakraborty, Shibashish [1 ]
Bala, Pradip Kumar [2 ]
机构
[1] Indian Inst Management, Area Mkt, Ranchi 834008, Jharkhand, India
[2] Indian Inst Management, Area Informat Syst & Business Analyt, Ranchi 834008, Jharkhand, India
关键词
Topic modelling; Online customer reviews; Text mining; Customer satisfaction; Grocery mobile apps; ON-SHELF AVAILABILITY; USER ACCEPTANCE; E-LOYALTY; SERVICE; DISCONFIRMATION; DISSATISFACTION; ANTECEDENTS; INTENTIONS; EXPERIENCE; AGREEMENT;
D O I
10.1016/j.jretconser.2023.103363
中图分类号
F [经济];
学科分类号
02 ;
摘要
In recent years, there has been proliferation of grocery mobile apps as grocery shopping on mobile has found increasing acceptance among customers accelerated by multiple factors. Maintaining high level of customer satisfaction is important for grocery mobile apps in the highly competitive app market. Online reviews have been a rich source of information to analyze customer satisfaction with a product or service. This paper explores the determinants of customer satisfaction for grocery mobile apps using online reviews. Latent Dirichlet Analysis (LDA), which is a text mining technique, is used to analyze online customer reviews of 27,337 customers to identify determinants of customer satisfaction. The determinants identified were further analyzed using a series of analysis to understand the importance of each determinant. Dominance analysis examined the relative importance of the determinants of customer satisfaction based on the overall rating. Correspondence analysis identified determinants which cause satisfaction separately from the determinants which cause dissatisfaction. The results from this study will provide insights to business managers of grocery mobile apps for decision-making on customer satisfaction management.
引用
收藏
页数:13
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