Online customer reviews: insights from the coffee shops industry and the moderating effect of business types

Abstract

Purpose This study aims to explore the hidden connectivity among words by semantic network analysis, further identify salient factors accounting for customer satisfaction of coffee shops through analysis of online reviews and, finally, examine the moderating effect of business types of coffee shops on customer satisfaction. Design/methodology/approach Two typical major procedures of big data analytics in the hospitality industry were adopted in this research: one is data collection and the other is data analysis. In terms of data analysis, frequency analysis with text mining, semantic network analysis, CONCOR analysis for clustering and quantitative analysis with dummy variables were performed to dig new insights from online customer reviews both qualitatively and quantitatively. Findings Different factors were extracted from online customer reviews contributing to customer satisfaction or dissatisfaction, and among these factors, the brand-new factor “Sales event” was examined to be significantly associated with customer satisfaction. In addition, the moderating effect of business types on the relationship between “Value for money” and customer satisfaction was verified, indicating differences between customers from different types of coffee shops. Research limitations/implications The present study broadened the research directions of coffee shops by adopting online customer reviews through relative analytics. New dimensions such as “Sales event” and detailed categorization of “Coffee quality”, “Interior” and “Physical environment” were revealed, indicating that even new cognition could be generated with new data source and analytical methods. The industry professionals could develop their decision-making based on information from online reviews. Originality/value The present study used online reviews to understand coffee shop costumer experience and satisfaction through a set of analytical methods. The textual reviews and numeric reviews were concerned simultaneously to unearth qualitative perception and quantitative data information for customers of coffee shops.

Satisfaction dimensions influencing consumers’ behavioral intentions through structural topic modeling analysis of restaurant reviews

“…Research has previously considered a six-month time period from several cities in the USA (Li et al , 2020; Luo and Xu, 2021; Tian et al , 2021; Xu, 2021) and the UK (Mathayomchan and Taecharungroj, 2020; Rahimi et al , 2022). Some studies narrow their research down to only fine-dining restaurants (Cassar et al , 2020; Harba et al , 2021; Saydam et al , 2022; Tao and Kim, 2022), green restaurants (Park et al , 2021) or single chain restaurants (DiPietro and Levitt, 2019); a sample may be formed randomly from collected reviews (Oh and Kim, 2021) or filtering and only studying positive ones in depth (Ahmad and Guzmán, 2021). Research has also covered the limited time period of the COVID-19 outbreak (Chen et al , 2020).…”

“…We do not consider any methods based on survey analysis, like structural modeling, where eWOM may not be representative (Jeong and Jang, 2011). Some research study review sentiment (Hajek and Sahut, 2022; Kim et al , 2022; Mathayomchan and Taecharungroj, 2020; Tian et al , 2021), but this method could be misleading and uninformative, because of relying only on the sentiment of each word and not on the context of their usage (Rahimi et al , 2022; Tao and Kim, 2022; Vidal et al , 2015). The authors see case study and content analysis as an alternative for in-depth text analysis (Ng et al , 2021) or emotion labeling surveys (Oh and Kim, 2021), but they are both limited because they cannot cover the amount of existent data.…”

Impact of reputation on hospitality profitability: impact of service failure online exposure on revenue performance – evidence from the hotel industry in China

“…The volume of online reviews is widely used as a proxy to measure response, such as those of consumers and managers (Li et al , 2019; Chen et al , 2019; Xie et al , 2016; Kim et al , 2015). In the hospitality industry, the volume of online reviews positively affects the restaurant performance (Kim et al , 2016; Tao and Kim, 2022), hotel booking transaction values (Torres et al , 2015) and hotel occupancy rates (Viglia et al , 2016). Additionally, a positive effect of the volume of online reviews on RevPAR growth is found in unbranded or independent hotel chains (Raguseo and Vitari, 2017).…”

A new theoretical model for online customer review intention

“…This theory helps to analyze how people are influenced by the opinion of friends, parents or close associates and how the people are influenced by the information relating to consumption of a product or service through internet (Kaur and Medury, 2011;Chatterjee, 2019b). Thus, in terms of socialization theory, internet usage and peer influence help the customers to be easily influenced by the online feedback (Hunter-Jones, 2014;Jayawardena et al, 2021;Tao and Kim, 2022). The Expectation Value Theory (EVT) (Fishbein and Ajzen, 1975) provides an effective theoretical framework to analyze the tendency of the customers to be involved in WOM through online platform (eWOM).…”