This paper analyzes the characteristics that influence the revenue of Airbnb listings in Sweden, aiming to uncover the factors driving high competition in the hotel business due to the sharing economy. By investigating the relationship between Consumer Trust The- ory and Hedonic Pricing Theory in the context of Airbnb, the study identifies variables that contribute to the success of hosts in this sector. Utilizing panel data analysis with panels nested in host ID and property ID, the research integrates theoretical concepts and empirical data to offer practical applications for hosts seeking to enhance their listings' performance. The findings reveal that Airbnb revenue positively correlates with a higher number of booked reservations, bedrooms, bathrooms, reviews, and photos. Listings with flexible cancellation policies and those priced in local currency also experience increased revenue. Conversely, long stay duration requests from hosts, shared accommodation rooms, and instant bookings are associated with lower revenue. These results provide valuable insights for optimizing revenue strategies and improving platform design. The implications of this research extend to Airbnb hosts and online platform designers, offer- ing strategies to enhance revenue and user experience. Furthermore, the study contributes to a broader understanding of the tourism sector in Sweden and lays a foundation for future academic research on the economic and sociological effects of home-sharing ser- vices.

Background: The increasing reliance on Artificial Intelligence (AI) in personalized marketing has reshaped consumer interactions in the digital era. With technological advancements, there is a growing need to explore how AI-driven personalization influences consumer behavior, particularly in satisfaction, loyalty, and ethical considerations. Purpose: This thesis investigates the impact of AI-driven personalized marketing on consumer perceptions, attitudes, and behaviors. It aims to understand how trust and ethical considerations such as data privacy and algorithmic bias influence consumer responses and engagement with personalized marketing. Method: Employing a quantitative approach, this study integrates quantitative analysis from surveys conducted on 100 participants. This method provides a comprehensive understanding of the implications of AI-driven personalization. Statistical tools like SPSS were used for data analysis, ensuring rigorous examination of the collected data. Conclusion: The findings reveal a nuanced response to personalized marketing. While AI-driven personalization can enhance consumer engagement and satisfaction, transparency and ethical considerations are critical in securing consumer trust and loyalty. The study underscores the importance of ethical marketing practices and the need for continuous adaptation to technological advancements to align with consumer expectations and ethical standards. This research contributes to academic discussions on personalized marketing and offers strategic insights for integrating technological advancements with consumer-centric approaches in marketing practices.