Key Attributes of Michelin 3-star Restaurants Experiences: Evidence from TripAdvisor

Abstract

Consumers can share User Generated Content on social media sites like TripAdvisor, which allows customers to track their contentment and displeasure. These activities are an important form of electronic word-of-mouth that might affect other customers' purchasing decisions. Hence, utilizing User Generated Content from an online platform, this study attempts to investigate guest' experiences in Michelin 3-star restaurants in the United States. Broadly speaking goal of the study was to identify the key aspects of Michelin 3-star restaurant experiences and see if they differed depending on the age and nationality of the travelers. Content analysis approaches were used to examine a sample of 1,032 customer online reviews. After computer-assisted qualitative data analysis identified the dominant themes, subsequent qualitative analysis identified the important narratives connected with Michelin 3-star restaurant experiences using Leximancer software. The findings show that customers are exceedingly pleased with their Michelin 3-star dining experiences. "Food," "chefs," "restaurant," "wine," "staff," and "price" are the major motives that run across the dominant narratives. The findings provide valuable perspectives into guests' overall experiences based on social media data and make it easier to identify the primary themes associated with the guests' gender and nationality

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).…”

Customer experience management in capsule hotels: a content analysis of guest online review

“…Due to the rising prevalence of the Internet and social media, customers increasingly rely on user-generated content to collect information and make their decisions (Abdullah et al , 2022; Saydam et al , 2022a, b, c). As a result of the lack of user-generated content in capsule hotels, the purposive sample approach was used, and the top 10 capsule hotels in the globe based on booking.com reviews were analyzed.…”

“…The top ten capsule hotels were chosen based on their popularity index and the number of reviews per hotel. Thereafter, Leximancer 4.5 was used for analysis due to its exploratory and predictive capabilities (Saydam et al , 2022a, b, c). The locations of the hotels are shown in Table 1.…”

“…This program uses a two-organized analytical approach for dealing with extricating co-event data to conduct theoretical and social research (Dambo et al , 2020). Hence, Leximancer determines the similarity of reformulated ideas and forms where these ideas are linguistically interconnected to create a graphic representation of the results (Saydam et al , 2022a, b, c). In addition, Figure 2 illustrates the research’s visual portrayal in greater detail.…”

Guest service experience in eco-centric hotels: a content analysis

“…In today's highly competitive service environment, understanding true perceptions and meeting the expectations of customers is critical (Malik, Akhtar, Raziq, & Ahmad, 2020;Preko, Gyepi-Garbrah, Arkorful, Akolaa, & Quansah, 2020), especially in the time of pandemic (Saydam, Arici, & Olorunsola, 2022a). The new coronavirus (COVID-19) has resulted in a heightened concern for service safety and a reorganization of service operations for improved segregation and decreased interaction (Jafari, € Ozduran, & Saydam, 2021).…”