An Analysis of Fostering Intercultural Competence Among Host Communities in Hostels (Case Study: Hostelling International Santa Monica)
The tourism industry is one of the world’s largest industries, contributing over seven trillion U.S. dollars in 2014 (The Statistics Portal, 2014). It is also frequently highlighted as a possible agent in promoting peace, as well as improving cross-cultural understanding. In particular, tourist accommodations can allow for such opportunities. This research analyzes how hostelling, in particular, Hostelling International Santa Monica, serves as an avenue to foster intercultural competence for host communities. This study uses a combination of grounded theory and ethnographic research methods, in order to conceptualize a process model of fostering intercultural competence. Conceptualizing the hostel as an intercultural space, this research identifies the importance of purposeful facilitation to foster engagement, self-reflection and behavioral transformation for improved intercultural competence. This study illustrates hostels as an embassy to improving cross-cultural understanding. It recognizes the potential for hostelling to foster diverse understanding and cultivate intercultural competence. It also serves to solidify Hostelling International USA’s contribution to broadening cultural exchanges, encouraging intercultural dialogue and educating for peace.
AIRBNB Toronto: Exploring Free-Text Review Topics and Sentiment Classification using Machine Learning Techniques
Running a rental property on a short-term hospitality platform such as Airbnb in the age of social media is challenging as a large amount of trust comes from word-of-mouth. Customers in all sectors have learned to ‘Google it’ or read reviews prior to making a purchase to ensure they are receiving the product they are investing in. In this environment, it is important for a host to do everything it takes to ensure a positive experience from the time a guest lands on the listing page, to writing their own review post-stay. This paper aims to first explore the sentiment of reviews per listing in Toronto’s Airbnb market using Natural Language Processing techniques, and second, develop a classification prediction model using machine learning techniques to assist both the rental property owners and rental platform Airbnb given minimal available information. Utilizing data that is scraped from Insideairbnb.com from May 2020, this paper will reveal characteristics of Toronto’s current Airbnb market. A range of methods from regression to tree-based models; specifically Naive Bayes, Random Forest and Logistic Regression will be used for creating the prediction model. The full code and results can be found at https://github.com/jolam08/Capstone.