Date of Award
PhD in Business
Department of Mathematical Sciences: Business Analytics
With the rise of social platforms, consumers started creating value by sharing human and physical resources. As a result, there has been an increase in collaborative consumption. My papers focus on the peer-to-peer travel accommodation service Airbnb, a popular online market for short-term housing rentals.
In paper one, an integrated framework for collaborative consumption pricing is developed by adopting the hedonic demand theory and leveraging the multilevel modeling method that accounts for various factors from nested data. This paper contributes to the research literature by extending the usage of multilevel modeling across cities to consider city effects and broadening the research span of the multilevel research method in the hospitality industry. The results show that collaborative consumption pricing is dependent on product factors, social factors, and economic factors.
Paper two determines the effect of different regulations on listing prices. This paper contributes to the literature 1) by providing a first overview of overarching regulatory practices and their impact on Airbnb prices, 2) by applying a “Difference-in-Difference” (DD) model and synthetic control methodologies together, and 3) by constructing a rich dataset of twenty-four cities over five years from Airbnb, 4) by developing severity indices to evaluate different regulation practices. It is shown that these regulatory changes lead to price increases, indicating a potential downward trend in Airbnb usage.
Paper three explores the influence of reputation mechanisms on pricing as well as the relationship of different ratings by applying panel data, fixed effects, and directed acyclic graphs (DAG) methodologies. An essential aspect of the sharing economy (SE) is the establishment of feedback rating systems to foster trust in peer-to-peer platforms. The fixed-effect model reveals that cleanliness is the only significant attribute that directly impacts the pricing. The DAG analysis shows that all rating factors are associated with each other. This paper is the first example of employing the DAG methodology to examine the relationship among ratings in the SE literature and presents a theoretical model of how ratings affect each other.
Sarican, Funda, "Towards a Better Understanding of the Development of Collaborative Consumption Pricing". 2022. 5.