How crowdsourcing impacts prices and customer satisfactionthe Airbnb case

Resumen

The travel accommodation industry has changed drastically due to the sharing economy. This sharing economy depends significantly on crowdsourcing in the form of quantitative and qualitative reviews. In this thesis, we study the impact of expressed sentiments in the text reviews on listing prices; we explore differences in the factors that lead to satisfaction according to customer origin and the effects of external shocks on Airbnb´s prices. We employ NLP to extract the sentiment and emotion scores to be modeled as a function of different characteristics and geophysical information. Our study provides an important contribute to the research on pricing and customer satisfaction in the sharing economy by finding a significant effect of the expression of positive sentiments on prices, describing the differences in the factors that lead to satisfaction according to customer origin, and revealing that guests moved away from main municipalities and valued listings with more bedrooms during the Covid-19 pandemic.