Branding luxury hotels: Evidence from the analysis of consumers’ “big” visual data on TripAdvisor

Abstract

The aim of this paper is to understand consumers' perception of luxury hotel brands. To this end, the research evaluates consumers' "big" visual data on TripAdvisor through a machine learning approach. Results shed light on the significant part of non-textual elements of the hotel experience such as pictures, which cannot be explored through traditional methods as content analysis. In particular, the analysis of 7,395 consumers' pictures leads to the identification of the attributes that had the higher impact on their experience. These attributes emerged as specific features of interior elements of the hotels (rooms and restaurant).Finally, the study shows how big data analytics and machine learning algorithms can (i) help monitoring social media and understand consumers perception of luxury hotels through the new analysis of visual data, and (ii) turn into better brand management strategies for luxury hotel managers.

When a luxury brand bursts: Modelling the social media viral effects of negative stereotypes adoption leading to brand hate

“…Finally, drawing upon recent studies considering consumer generated contents on social media as trustable source for consumer research that mainly focused on Twitter ( Aleti et al, 2019 , Athwal et al, 2019 , Dindar and Yaman, 2018 , Giglio et al, in press , Pantano and Stylos, 2020 , Walasek et al, 2018 ), our study adds new knowledge on the evaluation of Instagram posts as data source, by confirming that also this platform might provide useful insights for marketing research. In this way, our study further extends the few works ( Arora et al, 2019 , Casalo et al, 2020 , Riquelme et al, 2018 ), by describing how extracts consumers’ insights Instagram through new metrics and analytics, with emphasis on the specific sector of luxury brands.…”

“…To evaluate the distribution of the popularity across time (from October to December 2018), the software provides a set of machine learning algorithms that can be used to extract information from unstructured data like posts shared on social networks ( Pantano and Stylos, 2020 , Valdivia et al, 2017 ). Machine learning algorithms are considered particularly efficient to extract patterns and make prediction on large data sets (as posts published online and shared among users) ( Giglio et al, in press ); in particular, the software provides the machine learning algorithms as pre-trained methods capable of manipulating and analyzing a wide range of unstructured data from different sources ( Zotos, 2007 ).…”

“…Understanding how to take advantage from information diffusion and social influence in social media has acquired an important role in brand management ( Aleti et al, 2019 , Giglio et al, in press ). For instance, the selection of a certain endorser (celebrity) might influence the (positive) diffusion of advertising messages on social media, due to the higher authenticity and credibility which leads to a lower resistance to the message ( Casalo et al, 2020 , de Vries et al, 2012 ).…”

Cultural Traits in the Consumption of Luxury Hotel Services

“…Extant research already attempted to examine the antecedents and outcomes of luxury hotel guests' satisfaction [20] and dissatisfaction [21], and to examine the nature of ecomplaints in luxury [22] by the mean of online reviews, by focusing on specific small hotel samples. Hotel guests' luxury perceptions have also been explored by focusing on the visual content of online reviews [23]. However, to the best of the authorsḱ nowledge, no study has adopted online reviews data to investigate the influence of cultural traits on guest associations of luxury when staying at hotels.…”

Tourist satisfaction with quality of service, food, atmosphere, and value for money in restaurants of major cities of the Western Balkans

“…TripAdvisor is also becoming more popular by the year (Khorsand, Rafiee, & Kayvanfar, 2020). The platform has more than 455 million visitors -on average monthly -and 630 million restaurant and hotel views (Giglio, Pantano, Bilotta, & Melewar, 2019). Essentially, TripAdvisor should help guests utilise the experiences of previous guests who have already used that service, prior to using the service themselves (Nilashi, et al, 2018).…”