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By building on cooperation–competition theory, this study aims to investigate the multidimensional flipped effects of neighborhood hotels on Airbnb listings’ popularity, examining the degree to which such impacts are influenced by hotel types and geographical areas.

This study explores the interdependent and competitive relationship between neighborhood hotels and Airbnb from the perspective of effects on Airbnb listings’ popularity by exploring a data set covering 10,492 Airbnb listings and 2,691 hotels from Ctrip.

Results reveal that neighborhood hotels’ number of reviews, review ratings and prices each have positive spillover effects on Airbnb listings’ popularity, while quality assurance labels and negative review topic sentiments exert competitive effects on Airbnb popularity. Moreover, the number of budget chain hotels and high-star hotels have positive and negative effects on Airbnb popularity, respectively. Geographical areas also have a moderating effect on the relationship between various hotel-related influencing factors and Airbnb.

This study can offer hotel managers and Airbnb operators a clearer understanding of these businesses’ coexisting relationship. Findings can also provide Airbnb-specific guidelines for practitioners in terms of site selection, promotional features and development strategies for Airbnb listings.

This study establishes a cooperation–competition relationship model between hotels and Airbnb and considers the flipped effects of hotels on Airbnb for the first time. It expands previous studies by considering the multidimensional effects of hotels on Airbnb listings’ popularity and by examining the influences of hotel types and geographical areas on hotels’ impacts on Airbnb.

The sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa.

Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018.

The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects.

Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances.

While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors.

To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

The purpose of this study is to investigate the effect of the COVID-19 pandemic on key performance metrics of accommodation properties by elaborating on the roles of business models (i.e. franchised, chain-managed and independent hotels, and the sharing economy) and state-level restrictions in the US.

The pandemic is considered a variable interference against the average daily rate, occupancy and revenue per available room, which permits the examination of the before and after effects of the pandemic. The panel data model is used to examine the effect of the recent pandemic on the accommodation sector in the USA.

The results showed that chain-managed hotels were the most adversely impacted by the COVID-19 pandemic, while independent hotels were the least adversely impacted. Interestingly, and consistent with emerging consumer needs suggested by spatial distance theory, the pandemic does not have significant negative effects on Airbnb. The adverse impact of the pandemic on hotels was exacerbated in more restrictive states, while Airbnb remained immune to regulatory differences.

This study addresses the dearth of research on the types, roles and efficacy of business models in the accommodation industry and makes important theoretical contributions to the study of business model resilience in the accommodation industry, leveraging the resource-based theory of the firm and spatial distance theory.

The findings of this study make a significant contribution to the extant literature on the resilience of business models in the accommodation industry and have important implications for hotels, Airbnb owners, accommodation brands and destination and health policymakers. They demonstrate that a lower level of corporate control and greater flexibility in brand and operational standards allow for a more effective response to business disruptions such as a global pandemic.

This study aims to explore the influence of different types of cleanliness information provided on the Airbnb platform (hosts’ sanitation labels, Airbnb cleaning protocol and previous guests’ reviews) on guests’ trust and behavioral intentions.

This study uses an online scenario-based experimental design. A two-step approach was applied to discover the proposed relationships by assessing the measurement model fit and validity of the constructs with confirmatory factor analysis and testing study hypotheses with structural equation modeling.

The results demonstrate that three types of cleanliness information (i.e. provided by Airbnb’s hosts, platform and customer reviews) had statistically significant effects on customers’ trust and behavioral intentions.

The research results provide practical recommendations for Airbnb hosts and peer-to-peer accommodation platforms on using several types of textual and visual cleanliness information to influence guests’ attitudes and behavioral intentions.

This study advances knowledge by introducing new factors affecting guests’ trust and behavioral intentions in peer-to-peer accommodation settings and differentiating the effects of different sources of cleanliness information and different types of guests’ trust.

This study aims to explore the impacts of Airbnb listings on land values in the Austin, Texas, USA area, particularly on single-family homes. The goal of the analysis is to shed light on how greatly and in what direction Airbnb is affecting the housing market, with an emphasis on the spatial distribution of its effects.

The analysis in this paper is performed using three distinct models on a data set of land parcel data within Travis County: an ordinary least squares regression model, a geographically weighted regression (GWR) aimed at detecting the influence of variables at the census tract level, and a Bayesian approach, which describes spatial and temporal effects on the data.

The findings of the analysis indicate that across the years 2013 to 2019, higher numbers of Airbnb listings were associated with lower percentage increases in land value in certain tracts in the northern and eastern parts of the city. Additionally, the results of the Bayesian model indicated that much of the change in land value can be attributed to unobserved factors within census tracts.

The contribution of this study to the existing literature is its analysis of the spatial and temporal analysis of the effects of Airbnb listings on land value using a GWR and a Bayesian model. Also, as the negative correlation found in the study departs from previous research, this paper may provide policymakers insight into the complex spatial distribution and conflicting effects of Airbnb listings across distinct parts of cities.

Taking a global perspective, this paper aims to examine the impact of COVID-19 on Airbnb booking activities through three critical perspectives – the initial Wuhan lockdown, local COVID-19 cases and local lockdowns.

Using Airbnb reviews and cancellations as proxies for Airbnb bookings on a global scale, econometrics was used to examine the impacts of the initial Wuhan lockdown, local COVID-19 cases and local lockdowns on Airbnb bookings.

The authors find that local lockdowns result in a 57.8% fall in global booking activities. Every doubling of newly infected cases is associated with a 4.16% fall in bookings. The sensitivity of bookings to COVID-19 decreases with geographic distance to Wuhan and increases with government stringency of lockdown policies and human mobility within a market.

The empirical evidence from this research can provide governments with insights into more accurate assessment of the financial loss of Airbnb hosts so that proper support can be offered based on the financial needs because of due to sudden lockdown.

This research contributes to new knowledge on peer-to-peer accommodation during a time of crisis and provides much needed global evidence to understand the impacts of COVID-19 on the accommodation industry.

In the past decade, urban tourism has increased worldwide as a result of the development of peer-to-peer (P2P) accommodation platforms such as Airbnb, causing a major disruption to the tourism industry and urban space. The expansion of tourist accommodation in cities has motivated many governments to act, to control its effects and reduce conflicts between tourists and residents. The purpose of this paper is to identify the attractions that have motivated the concentration of P2P accommodation and its effects in specific areas of Valencia different from the historical centre: the Russafa and El Cabanyal-Canyamelar neighbourhoods.

The methodology used includes fieldwork and spatial analysis of factors such as the housing market, tourist attractions, local businesses and urban renewal policies.

The current spatial distribution pattern of tourist housing in Valencia is the result of the convergence of various factors: the initial presence of tourists in some areas; the evolution of certain aspects of the neighbourhood due to urban renewal; the concentration of tourist and leisure activities; the effects of the legal framework

Many researchers have addressed the effects of rising short-term rentals (STRs) in cities, but the causes of their concentration in specific neighbourhoods different from historical centres have not yet been sufficiently investigated. This research looks in depth at the urban causes and effects of the spatial distribution of tourist housing in Valencia, to anticipate possible future concentrations of STRs in other areas and to avoid gentrification. The methodology and results could be applied to other cities. The research implies a detailed and analysis of different aspects that act simultaneously such as the housing market, the evolution of the population and changes in the business.

The demand for Airbnb is at a peak in Malaysia with 137% of yearly growth. As such, it is indeed important to understand what makes tourists to switch to Airbnb. However, little has been known about this issue in the existing literature. Considering this, the present study aims to shed some light on the factors that drive tourists to switch to Airbnb. More specifically, the objectives of this study are to examine the direct effects of price unfairness and alternative attractiveness on psychological engagement, and to examine the direct effect as well as the mediating effect of psychological engagement towards tourists’ switching intention in the context of Malaysian Airbnb.

By considering the attribution theory, this study developed and tested a framework to examine tourists’ switching intentions. A Web-based survey was designed to collect the data which yielded 162 complete and usable responses. Structural equation modelling, more particularly, partial least squares (SmartPLS, version 3) technique was used to analyze the data.

The results revealed that price unfairness negatively affects psychological engagement, which in turn negatively affects tourists’ switching intention. However, no significant relationship was found between alternative attractiveness and psychological engagement. Additionally, psychological engagement mediated the relationship between “price unfairness and tourists’ switching intention”, but not between “alternative attractiveness and tourists’ switching intention.”

It is expected that the findings of this study will enable the hoteliers to better understand the impact of perceived unfairness, alternative attractiveness and psychological engagement in provoking tourists to switch to Airbnb services. It eventually will assist them in improving their offerings and services accordingly.

The discussion on Airbnb is quite new in the tourism literature. This study is among the pioneers to highlight the switching intention towards Airbnb in the Malaysian market. Guided by the attribution theory, this study developed and tested comparatively new linkages. More specifically, no prior study has considered psychological engagement as the antecedent of the switching intention which this study attempted to address. Additionally, this is a prior study that examines the mediating effect of psychological engagement between price unfairness, alternative attractiveness and switching intention.

This research investigates Airbnb’s financial implications in emerging economies and their potential to influence stock market profitability.

Employing a multifaceted approach, the study combines parametric and nonparametric tests, robustness checks, and regression analysis to assess the impact of Airbnb’s announcements on emerging economy stock markets.

Airbnb’s announcements affect emerging economies' stock markets with a distinct pattern of cumulative abnormal returns (CAR): negative before the announcement and positive afterward. Informed investors strategically leverage this opportunity through short selling before the announcement and acquiring positions following it. Regression analysis validates these trends, revealing that stock index returns and inbound tourism affect CAR before announcements, while GDP growth influences CAR afterward. Announcements pertaining to emerging economies exert a more pronounced impact on stock indices compared to city-specific announcements, with COVID-19 period announcements demonstrating greater significance in abnormal returns than non-COVID-19 period announcements.

This study advances existing literature through a comprehensive range of statistical tests, differentiation between emerging countries and cities, introduction of five macroeconomic variables, and reliance on credible primary Airbnb data. It highlights the potential for investors to leverage Airbnb announcements in emerging markets for stock market profits, emphasizing the need for adaptive investment strategies considering broader macroeconomic factors.