"How was your meal?" Examining customer experience using Google maps reviews. (September 2020)

• Record Type: Journal Article
• Authors: Mathayomchan, Boonyanit; Taecharungroj, Viriya
• Is Part Of: International journal of hospitality management. Volume 90(2020)
• Publication Date: 2020-09
• Subjects: Restaurant experience -- Restaurant review -- Online reviews -- Sentiment analysis -- Text analysis -- Google maps; Hotel management -- Periodicals; Restaurant management -- Periodicals; Food service management -- Periodicals; Hôtels -- Gestion -- Périodiques; Restaurants -- Gestion -- Périodiques; Services alimentaires -- Gestion -- Périodiques; Food service management; Hotel management; Restaurant management; Periodicals; 647.94
• DOI: 10.1016/j.ijhm.2020.102641
• Languages: English
• ISSNs: 0278-4319

Abstract

Highlights: 935, 386 Google Maps reviews of 5010 restaurants in the UK were analysed. This study used a combination of review scraping, sentiment, text, and logistic regression analyses. Food, service, and atmosphere positively affect the odds of getting 5-star rating. The lexical analyses of food items in 8 types of restaurant were conducted. Alcohol and a variety of dietary options positively affect the overall customer experience. Abstract: The purpose of this research is to examine the effects of restaurant attributes and the underlying factors impacting overall customer experience within a range of different restaurant types. To understand their experiences, this study analyses online reviews of restaurants which have become important sources of customer experience data. This current research utilises a combination of quantitative analyses to examine 935, 386 Google Maps reviews of 5010 restaurants in London, Birmingham, and Manchester. The authors used the VADER sentiment analysis algorithm to measure the sentiment of four key restaurant attributes: food, service, atmosphere, and value. Logistic regression was conducted to test the relationships between these attributes and a 5-star rating. Furthermore, logistic regression was used to compare the changes of odds at different star rating levels. To understand the factors that drive positive and negative reviews, the top 30 food items of 8 types of restaurants were analysed.