How do P2P accommodation Hosts Secure the Competitive Advantage through Host Brand Management? : Multi-level Approach Considering Consumer Choice Behavior and Spatial Effects.

P2P accommodation is the most significant player and recognizable platform in sharing economy. However, most hosts as micro-entrepreneurs have limited knowledge and experience in management, marketing, quality control, and customer interaction. The purpose of this dissertation is to support the P2P hosts as micro-entrepreneurs in sharing economy in strategic brand management by 1) conceptualizing the P2P brand concept, 2) exploring host brand impact on consumer behavior and pricing strategies, and 3) empirically testing P2P brand attributes on the performance by considering seasonality (temporal) and geographical pattern (spatial). Three studies were designed to understand the P2P brand and provide managerial implications for P2P hosts to secure competitive advantages. Each study reflects the P2P brand characteristics and P2P customers’ behavioral economic responses. Study 1 conceptualizes the P2P brand. Four databases (EBSCOhost, JURN, Science Direct, and SCOPUS) were used to include major hospitality and tourism journals. In using both Leximancer and Nvivo, this study used auto coding to generate the theme in conceptualizing the P2P brand framework: which includes 16 sub-dimensions for Awareness, Value, Trust, and Loyalty on both host brand and platform brand. Study 2 aims to demonstrate pricing strategies with maximized competitive advantage based on the P2P lodging customer’s willingness to pay on the different choice options of host brand attributes. Data were collected from the Mturk online panel in the United States, and 428 answers were included after screening. Signaling cues that reduce the uncertainty, such as SuperHost badge, interior photo, and Rating, were found to increase customers’ willingness to pay. Study 3 is designed to find the relationship between the P2P host brand management and financial performance. The current study includes three analyses (Hotspot analysis, OLS, and GWR) to test the influence of host brand attributes on host performance considering temporal (seasonality) and spatial (geo-locational pattern) characteristics. Airbnb property data from AirDNA was used, including 12,170 listing information from Miami, FL, in 2019. The study confirmed the significant impact of seasonality and spatial pattern on the host performance and also found significant spatial autocorrelation in each model. Management strategy considering seasonality and the spatial pattern is required for better host performance.