This study examines the importance of tourism clusters in peer-to-peer accommodation. Based on a rich dataset of 112,748 Airbnb listings in Florida, one of the top U.S. tourism destinations, this study uses geographically weighted regression to explore the spatially heterogeneous effects of tourism clusters on Airbnb performance across individual counties (intraregional clusters) and neighboring counties (interregional clusters). The results indicate that overall tourism clusters, especially in the industries of accommodation and food services, lead to superior Airbnb performance, but the tourism clusters-Airbnb performance relationship varies across industry and region, confirming the existence of intraregional and interregional clusters. These findings can help Airbnb hosts and tourism policymakers in other regions implement localized tourism industry strategies for maximizing Airbnb performance.

Introduction

Tourism products and services are characterized by a network or cluster of tourism supply chains involving different service components (Zhang, Song, & Huang, 2009). Regional industry structure, such as the degree of industry clustering, influences a tourism firm’s pricing and other business decisions, which in turn determine economic performance (Scherer & Ross, 1990). Clusters are defined as geographic concentrations of interconnected companies, specialized suppliers and customers, and associated institutions (Porter, 1998). Due to the localized nature of tourism experiences, tourism clusters result from the colocation of complementary tourism industries and firms in a given destination (Chan, Lin, & Wang, 2012; Michael, 2003) and further enable small enterprises to innovate incumbent tourism products (Novelli, Schmitz, & Spencer, 2006). Hence, Airbnb, the largest peer-to-peer accommodation sharing platform, emerged as a transformative innovation (Karlsson, Kemperman, & Dolnicar, 2017) and has been growing rapidly in an environment of tourism clusters through both collaboration and competition (Gutiérrez, García-Palomares, Romanillos, & Salas-Olmedo, 2017). The phenomenal growth of Airbnb has motivated tourism researchers to better understand the nuances of the accommodation sharing economy. Previous research on Airbnb has adopted three levels of analysis: the individual/marketing level (e.g., host behavior, guest-host experience, and pricing decisions), the firm level (e.g., impacts on hotels and housing affordability), and the community/government level (e.g., social impact and regulation) (Cheng, 2016; Prayag & Ozanne, 2018; Sainaghi, Köseoglu, d’Angella, & Mehraliyev, 2019). For example, researchers have investigated the characteristics of peer-to-peer sharing transactions (Tussyadiah, 2015) and Airbnb’s impact on the tourism industry (Fang, Ye, & Law, 2016) and tourist behavior (Tussyadiah & Pesonen, 2016). Researchers have also examined the spatial distribution of Airbnb supply and demand in a single city (Gutiérrez et al., 2017), large cities (Coyle & Yeung, 2017) and regions (Adamiak, 2018). However, little attention to date has been paid to the role of tourism clusters in the peer-to-peer accommodation sharing economy, especially at the community level (Sainaghi et al., 2019).