Emphasizing the Digital Shift of Hospitality Towards Hyper-Personalization: Application of Machine Learning Clustering Algorithms to Analyze Travelers

Emphasizing the Digital Shift of Hospitality Towards Hyper-Personalization: Application of Machine Learning Clustering Algorithms to Analyze Travelers

Nuno Gustavo (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Elliot Mbunge (University of Eswatini, Eswatini), Miguel Belo (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Stephen Gbenga Fashoto (University of Eswatini, Eswatini), João Miguel Pronto (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Andile Simphiwe Metfula (University of Eswatini, Eswatini), Luísa Cagica Carvalho (Instituto Politécnico de Setúbal, Portugal), Boluwaji Ade Akinnuwesi (University of Swaziland, Swaziland), and Tonderai Robson Chiremba (University of Swaziland, Swaziland)
DOI: 10.4018/978-1-7998-8306-7.ch001
OnDemand PDF Download:
Available
$29.50
No Current Special Offers
TOTAL SAVINGS: $29.50

Abstract

This chapter aims to review the tech evolution in hospitality, from services to eServices, that will provide hyper-personalization in the hospitality field. In the past, the services were provided by hotels through diligent staff and supported by standardized and weak technology that was not allowed to provide personalized services by itself. Therefore, the study applied K-means and FCM clustering algorithms to cluster online travelers' reviews from TripAdvisor. The study shows that K-means clustering outperforms fuzzy c-means in this study in terms of accuracy and execution time while fuzzy c-means converge faster than K-means clustering in terms of the number of iterations. K-means achieved 93.4% accuracy, and fuzzy c-means recorded 91.3% accuracy.
Chapter Preview
Top

Rethinking Hospitality For Tomorrow

Previous research has shown that small tourism and hospitality businesses have been particularly affected by the pandemic, being pushed for mass lay-offs, temporarily closing, becoming more financially fragile with cash on hand of only one month, and/or seeking support from the government, with problematic difficulties in recovering business (Bartik et al., 2020; Sobaih et al., 2021). Considering that small and medium enterprises constitute 95.4% of the Portuguese entrepreneurial tissue (European Commission, 2019) and that 59.5% of the hospitality is composed of independent hotels (Deloitte, 2020), protecting and securing jobs in Small Hospitality Businesses (SHBs) seems to be of utmost importance for the socio-economic response to COVID-19 in Portugal.

Indeed, micro, small and medium-sized tourist companies are decapitalized financially and in human resources, with no capacity of investment to turn around their businesses on a standalone basis (Gössling et al., 2021). These companies have been facing other challenges for years, like low managerial skills, low technical and, digital skills, which will be exacerbated by the post-pandemic current and future challenges. Moreover, previous research has recognized that tourism firms' innovation efforts are often made individually and independently by various tourism stakeholders, although collaborative networks have been recognized as a competitive advantage for tourism companies' innovativeness (Martínez-Román, 2015).

Complete Chapter List

Search this Book:
Reset