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COVID-19-induced travel restrictions have led to a sharp drop in Airbnb bookings. Confronted with a decrease in demand, hosts have implemented heterogeneous price responses. This study evaluates the different price adjustments developed by professional and non-professional hosts. Considering the city of Barcelona as the case study, I exploit monthly longitudinal data for 24,000 different Airbnb listings observed between June 2020 and April 2021. Using hedonic regressions with listing and neighbourhood fixed effects, I show that professional hosts have reduced prices to a greater extent, especially during the worst months of the pandemic. The findings support intertemporal price discrimination among professional hosts, which seem to adjust prices faster to meet demand and better adapt to market conditions.
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PurposeThis study aims to investigate how the COVID-19 pandemic has impacted and changed Airbnb market in the Greater Melbourne area in terms of its temporal and spatial patterns and identify possible shifts in underlying trends in travel activities.Design/methodology/approachA panel data set of Airbnb listings in Melbourne is analysed to compare temporal patterns, spatial distribution and lengths of stay of Airbnb users before and after the COVID outbreak.FindingsThis study found that the COVID disruption did not fundamentally change the temporal cycle of the Airbnb market. Month-to-month fluctuations peaked at different levels from pre-pandemic times mainly because of lockdowns and other restrictive measures. The impact of COVID-19 disruptions on neighbourhood-level Airbnb revenues is associated with distance to CBD rather than number of COVID cases. Inner city suburbs suffered major loss during the pandemic, whereas outer suburbs gained popularity due to increased domestic travel and long stays. Long stays (28 days or more, as defined by Airbnb) were the fastest growing segment during the pandemic, which indicates the Airbnb market was adapting to increasing demand for purposes like remote working or lifestyle change. After easing of COVID-related restrictions, demand for short-term accommodation quickly recovered, but supply has not shown signs of strong recovery. Spatial distribution of post-pandemic supply recovery shows a similar spatial variation. Neighbourhoods in the inner city have not shown signs of significant recovery, whereas those in the middle and outer rings are either slowly recovering or approaching their pre-COVID levels.Practical implicationsThe COVID-19 pandemic has significantly impacted short-term rental markets and in particular the Airbnb sector during the phase of its rapid development. This paper helps inform in- and post-pandemic housing policy, market opportunity and investment decision.Originality/valueTo the best of the authors' knowledge, this is one of the first attempts to empirically examine both temporal and spatial patterns of the COVID-19 impact on Airbnb market in one of the most severely impacted major cities. It is one of the first attempts to identify shifts in underlying trends in travel based on Airbnb data.
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In this research, an effort has been put to develop an integrated predictive modeling framework to automatically estimate the rental price of Airbnb units based on listed descriptions and several accommodation-related utilities. This paper considers approximately 0.2 million listings of Airbnb units across seven European cities, Amsterdam, Barcelona, Brussels, Geneva, Istanbul, London, and Milan, after the COVID-19 pandemic for predictive analysis. RoBERTa, a transfer learning framework in conjunction with K-means-based unsupervised text clustering, was used to form a homogeneous grouping of Airbnb units across the cities. Subsequently, particle swarm optimization (PSO) driven advanced ensemble machine learning frameworks have been utilized for predicting rental prices across the formed clusters of respective cities using 32 offer-related features. Additionally, explainable artificial intelligence (AI), an emerging field of AI, has been utilized to interpret the high-end predictive modeling to infer deeper insights into the nature and direction of influence of explanatory features on rental prices at respective locations. The rental prices of Airbnb units in Geneva and Brussels have appeared to be highly predictable, while the units in London and Milan have been found to be less predictable. Different types of amenity offerings largely explain the variation in rental prices across the cities.
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This study examined the impacts of perceived Airbnb risks, i.e., performance, financial, physical, social, time, and psychological risks on tourists' intention to use Airbnb. The study also explored the moderating effect of the fear of COVID-19 on the relationship between perceived Airbnb risks and the tourist's intention to use Airbnb. The data was collected from 248 customers of Airbnb using a survey approach. The data were analyzed utilizing the Smart PLS V.4. The PLS-SEM results revealed that Airbnb (physical, psychological, time, performance, financial and social risks) had a significant negative effect on the intention to use Airbnb. On the other side, the fear of COVID-19 acted as a moderator between Airbnb's physical, psychological, and social risks and the intention to use Airbnb, indicating that customers tend to tolerate time and performance risks when having a high level of fear of COVID-19 and prefer to use Airbnb regardless of them. This shift in customer behavior towards customers' intention to use Airbnb in light of the fear of COVID-19 gives Airbnb an edge that should be exploited via remedying other risks. It was suggested that the Airbnb hosts' awareness of the importance of Airbnb in the context of tourism in Egypt should be enhanced. Additionally, a legislative framework should govern Airbnb transactions to secure tourists in dealing with Airbnb hosts. Professionalism in providing Airbnb services should also be adopted. Future studies in the context of the current subject could conduct multi-group analyses according to different types of Airbnb accommodation and use a larger sample size.
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In this paper we contribute to the understanding of how failed stakeholder inclusion in times of crisis can lead to distrust of platform organizations. Drawing on the revelatory case of Airbnb, we explore how stakeholder in-clusion was practiced when the platform was confronted with the COVID-19 pandemic, a severe context in which its business practices were disrupted and social interactions uprooted. Our qualitative analysis suggests that platform leaders failed to notice a dissonance between articulated stakeholder meaning and understanding (i.e., what they said) and the crisis response practices implemented (i.e., what they did and did not do). This ulti-mately fostered distrust and selective disengagement as stakeholders re-interpreted Airbnb's claims about their role and how stakeholders were given voice in times of crisis. Drawing on research in psychology, we utilize the concept of inattentional blindness as a novel metaphorical explanation for failed stakeholder inclusion in plat-form organizations in times of crisis.
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The sharing economy represented by Airbnb has evolved rapidly. It is particularly important to identify and understand how consumer concerns change over time. As a result, this study employs structural topic modelling using room type and time as covariates to extract topics from 896,658 Airbnb reviews in London and to observe the variation in the prevalence of topics over time. The findings show that the topic proportion changed relatively sharply in the early years of Airbnb (2010-2013) and during the COVID-19 pandemic (2020-2022), but relatively smoothly in the middle period (2014-2019). This research also discovered that the proportion of topics on customers' special experiences has been decreasing while the proportion of topics on their overall experience has been increasing. This shift could be attributed to an increase in the number of professional hosts, which has accelerated the standardisation of the Airbnb service.
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The current COVID-19 pandemic has caused a significant decline in human mobility during the past three years. This may lead to reconfiguring future tourism flows and resulting transformations in the geographic patterns of economic activities and transportation needs. This study empirically addresses the changes in tourism mobility caused by the pandemic. It focuses on the yet unexplored effects of the destination type on tourism volume change. To investigate this, 1426 metropolitan, urban/resort and dispersed destinations were delimited based on Airbnb offers. Airbnb reviews were used as the proxy for the changes in tourist visits in 2019–2022. Linear mixed-effects models were employed to verify two hypotheses on the differences between the effects of the pandemic on three kinds of tourism destinations. The results confirm the tourism de-metropolisation hypothesis: metropolitan destinations have experienced between −12.4% and −7.5% additional decreases in tourism visits compared to secondary cities and resorts. The second de-concentration hypothesis that urban/resort destinations are more affected than dispersed tourism destinations is not supported. The results also confirm that stricter restrictions and destination dependence on international tourism have negatively affected their visitation. The study sheds light on post-pandemic scenarios on tourism mobility transformations in various geographic locations.
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PurposeApplying the value creation of corporate social responsibility (CSR), this study aims to investigate the direct effect of preventive measures in the postpandemic world on Airbnb consumers' attitudes toward the host, which may further impact their behavioral intentions. It also examined the mediating role of perceived risk and perceived motives, as well as the moderating role of message strategies and risk tolerance in this process.Design/methodology/approachA 2 (preventive measures: basic versus enhanced) × 2 (message strategies: promotional social cause message [PSC] versus partake-in-our-cause message [PIOC]) between-subjects factorial design was conducted with a sample of 476 US Airbnb consumers through an online survey. PROCESS macro was used for hypothesis testing.FindingsResults demonstrated that enhanced preventive measures had more positive impact on Airbnb consumers' attitudes toward the host, which was positively related to positive word of mouth and booking intention. Perceived risk and perceived motives mediated the relationship between preventive measures and attitude toward Airbnb hosts. Positive relationships between attitude toward the host and behavioral intentions were strengthened when PIOC was used than PSC. The negative relationship between preventive measures and perceived risk was enhanced when PIOC was used than PSC. Risk tolerance did not interact with messages strategy to impact the relationship between preventive measures and perceived risk.Originality/valueThe study uncovered the mechanisms by which consumers form their responses toward different safety information of Airbnb accommodations postpandemic and the role of message strategies in the process. It provided implications for the Airbnb platform and hosts in the postpandemic period.
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The production of services in the accommodation sharing industry is heterogeneous in the sense that listings with different strategic management could adopt distinct technologies. This paper analyses the time-varying efficiency of the peer-to-peer accommodation sector using the input distance stochastic frontier model with random coefficients to accommodate both multi-input and multi-output technology and the technological heterogeneity among listings. An empirical analysis is conducted based on data from Airbnb and HomeAway listings in the Canary Islands (Spain), before, during and after the COVID-19 lockdown (source: AirDNA), in the period January 2019-September 2020 (monthly data). The results show technological heterogeneity between listings and time-varying inefficiency which negatively depends on productivity. Moreover, multi-unit hosts are clearly more efficient than single-unit hosts. A mean efficiency of around 78% during the study period was estimated. © 2023 The Authors
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Purpose: To provide a dynamic view on accommodation choice behaviors during the pandemic, this study aims to examine the impact of recent trends on prospective travelers' preferences for hotels and Airbnb. Design/methodology/approach: The paper adopts a mixed methods approach that incorporates three independent studies (experimental analysis, online search pattern analysis and an econometric event study) to understand customer decision-making behaviors. Findings: The findings indicate that travelers prefer Airbnb entire flats/apartments to hotels when the pandemic is trending upward. This result externally validates travelers' preference toward Airbnb during periods of high risk. Interestingly, when the trends go downward, however, the same behavioral pattern was not identified. Research limitations/implications: This study provides important empirical insights into how the evolution of health crises influence customer decision-making for hotels and Airbnb. Future research needs to consider the role of socio-demographic factors in accommodation selection behaviors and examine how travelers react to cleanliness levels between Airbnb and hotels. Originality/value: As one of initial studies that empirically examine Airbnb customers' decision-making behaviors in the context of the COVID-19 pandemic's trends, this study provides a dynamic view on how the evolution of the pandemic influences accommodation choice behaviors. © 2023, Emerald Publishing Limited.
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PurposeThe purpose of this study is to examine the impact of key decision-making attributes on consumers' choice of accommodation among and between hotels and Airbnb.Design/methodology/approachThe study used a choice-based conjoint approach using 21 key decision-making factors that impact consumers' choice of accommodation across five segments ranging from economy to luxury. Latent class estimation was used to identify segments of respondents who tend to have similar preferences for accommodation.FindingsThe results showed the presence of a consistent pattern of decision-making across the five accommodation segments, culminating in a hierarchy of importance in accommodation choice. The 21 key decision-making attributes comprised three tiers in order of decreasing importance: quality and service, amenities, and accessibility and safety. Further, latent class analysis indicated the presence of a hotel group and an Airbnb group of customers, which allowed us to identify how both types of providers might maximize the value of their offers to encourage customer switch.Research limitations/implicationsThe accommodation landscape is extremely dynamic (particularly as the COVID-19 pandemic unfolds) and complex. The present study cannot capture all of its intricacies but provides an invaluable foundation for future research on the topic of consumer choice in an evolving and competitive accommodation market.Originality/valueExtant research on accommodation choice has focused on hotels or Airbnb only. Moreover, research that has considered both types of accommodation simultaneously is limited in its conceptual and methodological scope. The present study synthesizes the fragmented literature on consumers' accommodation choices and offers a holistic and coherent schematic – the hierarchy of importance in accommodation choice – that can be used by future researchers and practitioners alike.
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The unbridled propagation of the sharing economy halted abruptly with the onset of the pandemic, forcing sharing economy companies to respond to the new set of challenges faced by them. There were marked differences between the pre- and post-COVID challenges. Post-COVID, sharing economy companies designed novel response strategies including tempering ambitions, adapting business models, designing new standard operating procedures, displaying empathy towards employees and partners, building trust, and sharing information with stakeholders. This signified a reorientation of sharing economy companies in the hyperconnected world where there is a blurring of boundaries as brand experiences and meanings are cocreated by stakeholders. Sharing economy companies would need to focus on different set of values to attain competitive advantage in the post-COVID world – partnership instead of confrontation, nurturing instead of destructive, open and sharing instead of closed, and empathetic instead of uncaring. This can help create meaningful differentiation and provide competitive advantage.
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Fully online tourist experiences remain a largely unexplored area of inquiry, as a valuable and meaningful traveling modality per se, beyond any connection with conventional tourism. This explorative study aims to advance knowledge on the role of human and technology dimensions in shaping the online tourist experience during the pandemic. A sample of 9,300 Airbnb Online Experience reviews of thirty-four online destination experiences was analyzed. A typology of four fully online tourism experiences is presented, such as individual escape and personalized learning, meeting the destination, meeting the host and social bonding. The research reveals the dynamic evolution of the fully online tourist experience during the pandemic, highlighting the strengthening role of the human dimension and the human-to-human interaction, despite the digital mediation of the in-remote destination visit. Avenues for destination management and marketing research and practice spring from the analysis of fully online tourist experiences as a form of virtual tourism. 完全在线的旅游体验仍然是一个很大程度上未被探索的领域, 作为一种有价值和有意义的旅游方式, 超越了传统旅游的任何联系. 这项探索性研究旨在增进对人类和技术维度在大流行期间塑造在线旅游体验中的作用的认识. 对34个在线目的地体验的9300个Airbnb在线体验评论样本进行了分析. 介绍了四种完全在线旅游体验的类型, 如个人逃生和个性化学习、与目的地见面、与主人见面和社会联系. 这项研究揭示了疫情期间完全在线旅游体验的动态演变, 突出了人的层面和人与人的互动的强化作用, 尽管远程目的地访问是数字媒介. 目的地管理和营销研究与实践的途径来源于对作为虚拟旅游形式的完全在线旅游体验的分析. © 2023 Taylor & Francis Group, LLC.
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The COVID-19 pandemic has negatively affected the tourism and services sector. Using the example of Airbnb's recent layoff of 25% of its workforce, we focused on the role of organizational justice in mitigating the negative psychological impacts of layoffs. Based on a unique survey of Airbnb employees who survived the layoffs, as well as those who left, we employed an ordinary least squares regression to show that employees' perceptions of organizational justice were positively related to their job satisfaction and trust in management, while being negatively related to their emotional exhaustion and cynicism. We discovered the crucial importance of interactional justice (i.e., interpersonal and informational justice). The respect, dignity, and politeness shown by management (i.e., interpersonal justice), as well as truthful and adequate communication about the procedure (i.e., informational justice) were pivotal to successfully conducting layoffs, especially during unprecedented economic uncertainty. © 2023 by the authors.
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PurposeThis 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.Design/methodology/approachThis 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.FindingsThe 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.Practical implicationsThe 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.Originality/valueThis 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.
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The COVID-19 pandemic had serious economic effects, forcing businesses to adjust their strategies to survive. The tourism sector was one of the most affected due to the imposed travel restrictions;both traditional units and people providing services to tourists through the sharing economy platforms had to adapt. The objective of this paper is to build a hedonic price model to identify the attributes that significantly influence the price of the room, using the WLS regression model. Despite the general negative trend, there were also hosts who considered that opening a new listing during these times may represent a business opportunity. Therefore, the focus of the article is on newly opened listings on the Airbnb platform during 2020. The approach used included the main categories of variables discussed previously in the literature: listing attributes, listing location, listing reputation, rental policies, and hosts characteristics/involvement. The maximum number of guests accepted in the property, the type of listing, and the availability of air conditioning (AC) and parking represent the main characteristics that influence the prices. Changes in travel behaviour due to increased health and safety concerns made hosts aware that guests are willing to pay more to stay in central locations or in places owned by Superhosts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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An underdeveloped theme in scholars' understanding of the personal services sector of the platform economy—also known as the "sharing economy”—is change. Most research on ride-hail, food delivery, accommodations, and other personal services has offered largely essentialist accounts. In this paper, we focus on how platforms have become increasingly "commercialized.” In labor-intensive services, commercialization occurs as a growing fraction of the work is done by a core of full-time, dedicated workers. However, platforms that rely primarily on capital may display similar dynamics, in which a small number of participants account for the majority of activity and capture the largest share of value. In this paper, we present the first comprehensive account of commercialization of a major platform. We analyze how Airbnb markets in the 10 largest short-term rental markets in the United States changed between 2015 and 2019. We find considerable evidence of commercialization, as a rising majority of properties are rented on a very frequent basis, and casual listings, while still present, are a small and falling percentage. Relying on an original database of regulations, we show that enactment of even the strictest regulations has not durably reduced the number of listings and has had limited success in altering the mix of commercialized and casual listings over this period. We also consider the impact of COVID-19 on this platform and the sharing sector. We conclude that the short-term rental market on Airbnb has become a fairly conventional one, with little of the peer-to-peer character of its earlier days. [ FROM AUTHOR] Copyright of American Behavioral Scientist is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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While researchers recognize the impact of tourism on restaurants, the impact of peer-to-peer accommodations on restaurants has been under-researched. While staying at a peer-to-peer accommodation is inherently choosing local over corporate business, does this behavior hold through to the choice of restaurant? COVID-19's influence on peer-to-peer accommodation guests' dining behaviors is also yet to be studied. Does this accommodation mode affect restaurant employment? Thus, this study examines the impact of peer-to-peer accommodations on restaurant employment, the antecedents of restaurant choice (chain vs. local restaurant) of peer-to-peer guests, and the impact of COVID-19 regulations on this choice. We used ten years of AirDNA and publicly available restaurant employment earnings data in the U.S. to measure the economic impact of peer-to-peer accommodations on restaurant employment. We surveyed 479 peer-to-peer accommodations guests who had traveled in the last two years and analyzed that data using SEM. Authenticity, perceived value, and star ratings impact the choice of chain or local restaurant. Adherence to COVID-19 regulations impacted this choice and risk perception mediated this decision. Guests were more likely to choose chain restaurants after COVID-19. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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The present investigation takes a qualitative approach to explore Airbnb's Online Experience as a new virtual tourism initiative through the lens of attention restoration theory. Data were collected from tourist reviews at Airbnb's platform with an emphasis on touring services such as sightseeing and cultural immersion sessions. Our findings first point to a multitude of virtual tour experiences germane to deep immersion, authenticity, nostalgia, hedonism, past-present resonance, novelty, learning, social interaction, and escape. These stay-at-home virtual restorative experiences were infused by Airbnb's state-of-the-art platform with features such as a super host, storytelling, personalization, virtual connection, stay-at-home indulgence, and technology enablement, which ultimately transcend home as the center stage for virtual attention restoration under the COVID-19 new normal. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Arising as an efficient and flexible model of the rental business amidst the rising asset economy, short-term-rental (STR) platforms such as Airbnb are prevalent globally and have induced neighborhood changes in many aspects. Debates on Airbnb-induced gentrification concern scholars and policymakers worldwide. Nonetheless, most existing studies consider it a unidirectional process, and the dynamic interactions and mutual influence between Airbnb and gentrification remain unexamined. To address this salient lacuna, this study unravels the changing dynamic of Airbnb-gentrification interactions in central Beijing during the COVID-19 pandemic. Through matching housing transaction records in the secondary market and Airbnb's data, we develop two indexes and employ a series of regression models, as well as difference-in-difference estimation to unravel the variegated Airbnb-gentrification patterns, their interrelation, and the impacts brought by the pandemic. Results reveal a general pattern of intensifying gentrification caused by clustering Airbnb. Meanwhile, in neighborhoods experiencing different stages of gentrification, heterogeneous outcomes of Airbnb development are unveiled concerning impacts on rentals and housing prices during the pandemic. Our findings provide a more nuanced understanding of the dynamic Airbnb-gentrification interrelation and add to the ongoing debates on "fifth-wave gentrification”. © 2023 Elsevier Ltd