Text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. (July 2023)

• Record Type: Journal Article
• Authors: Kumar, Avinash; Chakraborty, Shibashish; Bala, Pradip Kumar
• Is Part Of: Journal of retailing and consumer services. Volume 73(2023)
• Publication Date: 2023-07
• Subjects: Topic modelling -- Online customer reviews -- Text mining -- Customer satisfaction -- Grocery mobile apps; Retail trade -- Periodicals; Service industries -- Periodicals; Customer services -- Periodicals; Commerce de détail -- Périodiques; Service à la clientèle -- Périodiques; Customer services; Retail trade; Periodicals; 658.87
• DOI: 10.1016/j.jretconser.2023.103363
• Languages: English
• ISSNs: 0969-6989

Abstract

Abstract: 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.