Search results

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

This paper aims to investigate the role of the service provider in determining customer satisfaction in sharing economy services. The authors sought to examine how the intrinsic and extrinsic cues along with their interactions influence customer satisfaction.

This research uses a mixed-methods design to test the hypotheses. Study 1 uses secondary data from Inside Airbnb. Study 2 uses a 2 × 2 × 2 between-subject experimental design.

Both studies support the confirmation bias perspective over the expectancy-confirmation perspective in explaining the interplay among different cues in determining customer satisfaction. In the context of Airbnb, in the absence of a Superhost badge, if hosts adopt a reactive communication style, physical presence has a greater impact on customer satisfaction compared to virtual presence.

This study extends the services marketing literature and cue utilization theory by investigating the dynamic interactions among multiple intrinsic and extrinsic service cues. It shed new light on how a combination of these cues may become additive or redundant in determining customer satisfaction. This study contributes to the services marketing literature by addressing the interactive nature of sharing economy services and the neglected role of service providers.

The sharing economy enables apartment owners to generate income from their assets. “Agoda Homes” is an online travel agent (OTA) that directly competes with Airbnb. A destination has to discover its competitiveness, but few studies have provided an overview of accommodation attributes in each destination, which are crucial to shaping its brand image. This paper aims to illustrate firm-generated content or attributes that apartment owners list about their properties on an OTA platform to comprehend factual information about apartments in each destination with various star ratings and user ratings and to formulate a research model for future studies.

Informational content and accommodation attributes for apartments are automatically collected using a Web scraping tool (the Data Miner). Descriptive statistics and text analysis (word cloud and word frequency) are used to analyze data.

Findings reveal the primary location, facilities, cleanliness and safety attributes for all apartments in each destination, along with star ratings and user ratings. A research framework for scholars is also suggested. Guidelines for stakeholders in the tourism industry are additionally furnished.

This work concentrates on apartments, which have received less attention in the tourism literature. The study gathers factual data from a website to mitigate respondent bias issues inherent in the traditional survey methods.

Despite a growing body of research focusing on the dark side of sharing economy development, arguments are fragmented and incomplete. This study aims to address the gap by integrating existing viewpoints based on a provider’s perspective.

This study conducted a bibliometric analysis using text mining and clustering algorithm techniques to measure the scope of scientific output on this topic and identify the main research themes.

Through the bibliometric analysis, this study developed an integrative framework based on the platform providers’ internal management issues and external conflicts with consumers, society, government regulations and traditional business. It also identified significant gaps within each research theme and proposed a future research agenda.

Sharing economy development has not yet been fully understood and regulated, leading to unprecedented challenges to existing business systems. The study addresses knowledge gaps and advances the understanding of the dark side of the sharing economy based on the provider’s internal management and interplay with external forces. It offers a roadmap for future research to advance understanding of the “hidden” dark side of the sharing economy.

The success of sharing economy (SE) platforms has made it attractive for many firms to adopt this business model. However, the inherent weaknesses of these platforms, such as their unstandardized service quality, the burden of maintenance on resource owners and the threat of multi-homing, have become increasingly apparent. Previous prescriptions for addressing these weaknesses, however, are limited because they do not account for factors such as compliance costs and information asymmetry, and tend to solve the problem on only one side of the platform at the expense of the others. By exploring the strategies deployed and actions undertaken across the development of Xbed, a successful accommodation-sharing platform in China, this study aims to explore an alternative solution that would overcome the aforementioned weaknesses without the corresponding compromises.

The authors conducted a case study consisting of secondary data and interviews with 15 informants who were representatives of Xbed's top management, organizational IT functions and its various business units.

The authors identified three inherent weaknesses that may be found in SE business models and how these weaknesses can be overcome without compromising other stakeholders through an auxiliary platform. The authors also discuss the advantages, characteristics, deployment and nature of auxiliary platforms.

This model contributes an in-depth view of establishing and nurturing auxiliary platforms to complement a primary SE platform. Owners and managers of SE platforms may use our model as the basis of guidelines for optimizing their platforms' development, thereby extending the benefits of SE to more stakeholders.

The purpose of this study is to propose and test an integrated model to explain how trust is built in sharing economy (SE) transactions.

Initially, prior literature was systematically selected and synthesized to develop a comprehensive framework applicable to multiple trust-building perspectives and categories of SE platforms. Then, a survey was conducted to validate the constructs and test the model with Airbnb guests. A sample of 351 responses was collected and analyzed using structural equation modeling.

The results indicate that the cues an individual assesses to infer their counterpart’s trustworthiness and the reasons the individual has for engaging in the SE transaction can explain a large variance in their trust in the counterpart. In addition, the individual’s propensity to trust moderates this relationship.

The proposed model can help identify the most effective trust-building mechanisms. It can be taken as a common knowledge base for scholars to compare the four trust-building perspectives and different categories of SE platforms, as well as to investigate the subject over time and across cultures.

This research can also help practitioners understand the complexity of building trust and design platform features to do so.

A unified model clarifies trust in the SE, aiding platform growth and community bonding. This insight guides platforms in feature enhancement and policymakers in drafting balanced regulations.

To the best of the authors’ knowledge, for the first time, there is a comprehensive and parsimonious model applicable to the four trust-building perspectives and different categories of SE platforms.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

The purpose of this study is to explore how past and future temporal focus of CEOs in the hospitality industry influence their intention to invest in metaverse technology and the underlying mechanism under boundary conditions of perceived competitive pressure.

This multi-informant study collected data over three waves from a sample of 235 CEOs and their subordinates in India’s hospitality industry. A PLS-SEM was applied to the study data. Further, the study also used phenomenological interviews to capture CEOs’ perspectives on the study’s conceptual model.

Findings suggest that the past temporal focus of CEOs decreases technology orientation, and future temporal focus increases the technology orientation of firms, consequently impacting the intention to invest in the metaverse. CEOs’ perceived competitive pressure moderates the mediating relationship, such that the negative impact of past temporal focus on technology orientation is decreased and that of future temporal focus on the CEO is increased.

By exploring the role of a CEO’s past and future temporal focus on influencing technology orientation and, hence, adoption of new technology, the study extends upper-echelon theory to the field of metaverse adoption in the hospitality industry and responds to scholars’ calls to explore the industry’s technology adoption from the lens of the upper echelon.

The study has significant implications for the success of the adoption of metaverse technology in the hospitality industry. Findings imply that the board members should encourage CEOs to have future temporal focus.

The study provides novel insights into the adoption of metaverse technology by the hospitality industry, where CEO attributes such as their temporal focus influence intention to invest in metaverse.