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The research purpose is to conceptualize competitive productivity (CP) in the peer-to-peer (P2P) accommodation businesses. This study aims to conceptualize the four driving forces of P2P hosts’ CP and to empirically capture guest-based equity that supports such conceptual hosts’ CP model.

The goal of this paper is to apply Bauman’s Firm competitive productivity (FCP) model to the P2P accommodation business to conceptualize the CP of micro-entrepreneurial hosts. Four areas of the FCP model were reviewed to find how each of them contributes to the P2P hosts’ CP maximization.

Host talent, host resource management, value and host branding were conceptualized as key drivers of P2P hosts’ CP. The study also filled a gap in current literature by empirically analyzing online reviews to successfully capture key guest-based equity as satisfiers contributing to host talent, resource and branding.

Based on the hosts’ CP model, customer-generated resources play a significant role in the managerial implications, so that guest reviews with needs and wants and ratings can be empirically used to strengthen hosts’ CP under specific market circumstances.

This study is the first attempt to conceptualize a P2P host as a micro-entrepreneurial firm in the sharing economy platform for CP. This study looked at how the unique characteristics of the P2P accommodation industry and guest-based equity affect the P2P hosts’ CP.

Based on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic.

A guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19.

This research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed; perceived emotional values associated with peer-to-peer stays were more complex; perceived social values decreased, host–guest interactions were reduced and online communication became a stronger trend; tourist preferences for types of experiences changed, and people changed their destination selections; perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased.

This research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19.

To the best of authors’ knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.

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.

Presents an historic model of technologic maturation and examines five emerging information technologies projected to achieve marketplace pre‐eminence during the next three to five years that will pose transformational implications for traditional classroom‐based teacher‐mediated education.

The purpose of this paper is to show the potential of self‐organized semantic storage services. The semantic web has provided a vision of how to build the applications of the future. A software component dedicated to the storage and retrieval of semantic information is an important but generic part of these applications. Apart from mere functionality, these storage components also have to provide good performance regarding the non‐functional requirements scalability, adaptability and robustness. Distributing the task of storing and querying semantic information onto multiple computers is a way of achieving this performance. However, the distribution of a task onto a set of computers connected using a communication network is not trivial. One solution is self‐organized technologies, where no central entity coordinates the system's operation.

Based on the available literature on large‐scale semantic storage systems, the paper analyzes the underlying distribution algorithm, with special focus on the properties of semantic information and corresponding queries. The paper compares the approaches and identify their shortcomings.

All analyzed approaches and their underlying technologies were unable to distribute large amounts of semantic information and queries in a generic way while still being able to react on changing network infrastructure. Nonetheless, as each concept represented a unique trade‐off between these goals, the paper points out how self‐organization is crucial to perform well at least in a subset of them.

The contribution of this paper is a literature review aimed at showing the potential of self‐organized semantic storage services. A case is made for self‐organization in a distributed storage system as the key to excellence in the relevant non‐functional requirements: scalability, adaptability and robustness.

This paper aims to purposely place community and its stakeholders at the forefront of an investigation of positive and negative social, economic and environmental impacts of the sharing economy in the specific context of Airbnb by drawing upon the triple bottom line (TBL) framework of sustainability.

A qualitative enquiry through a “scoping approach” with the search of relevant electronic databases identified a range of conceptual and empirical studies in the period from 2008 to 2018 informing a profile focussed on the triple bottom line impacts.

The criteria limited search yielded 23 focal articles, which investigated or discussed Airbnb impacts on local communities. Analysis of these articles informed a three-pillar tabulation of positive and negative impacts, which are presented against four key stakeholder groupings.

The study is exploratory, and further research, especially confirmatory research, is recommended.

The study’s value extends to praxis. Guided by findings, real-time planning and policy-making are already underway within the authors’ community. Additionally, an extension project, as requested by the community, is now investigating direct traditional accommodation provider impacts.

Understanding of the social issues concerning Airbnb and indeed, the wider sharing economy, is broadened through identified need for further social impact research.

To the best of authors’ knowledge, this is one of the first studies to apply a “scoping approach” to holistically illuminate the positive and negative impacts of Airbnb at the micro-level in each of the three domains of sustainability. The research methodology is shown to be effective, with positive community impact, and will easily adapt to other destinations grappling with policy decisions.

The emergence of peer-to-peer accommodation (P2PA) introduces new values and meanings to the hospitality experience. Focusing on the diverse dimensions of the tourist experience, the purpose of this paper is to identify and assess the relative importance of the main dimensions of guests’ P2PA experience and its relationships with perception of authenticity, place attachment and loyalty to both the visited destination and the P2PA.

A qualitative netnographic approach with content analysis permitted the analysis of 250 reviews taken from the Airbnb platform, specifically focusing on P2PAs where guests stay with hosts in the same space.

The results of the study suggest that guests’ P2PA experiences are particularly influenced by the experience dimensions “aesthetic/sense”, “relate/social interaction”, “escape”, “act” and “feel”. P2PA experiences also result in loyalty intentions, to both the visited destination and the particular P2PA. The dimensions “aesthetic/sense”, “relate/social interaction” and “escape” stand out as most influential in determining perceived authenticity. In turn, place attachment is most influenced by the dimensions “feel” and “relate/social interaction”.

Limitations of this research need to be acknowledged: the P2PA guest experience is explored from the restricted perspective of online reviews using passive netnography. Therefore, some criteria of data collection, for instance, gathering only reviews written in English and with more than 80 words, may be limitative in a more comprehensive assessment of the P2PA experience. Another point is, although P2PA platforms such as Airbnb encourage their guests to review the experience, some people are not inclined to do so; therefore, the published reviews may not reflect all possible experiences at these accommodations inclined to do so; therefore, the published reviews may not reflect all possible experiences at these accommodations.

This study suggests a comprehensive analytical framework for assessing the “holistic multidimensional tourist experience”, integrating Pine and Gilmore’s (1999) and Schmitt’s (1999) approaches, thus deepening the conceptual and methodological debate on the tourist experience. It further contributes to a better understanding of the dimensionality of the tourist experience in the context of shared accommodation. The dimensions under analysis and their association with perceived authenticity, loyalty and place attachment are both of theoretical and practical interest, suggesting approaches to improve the P2PA experience as well as the image and success of the destinations where these units are located.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

This study aims to give a glimpse of the existing blockchain applications across industries and add to a complete knowledge of the blockchain’s properties.

Systematic literature review is used as the research strategy for this investigation and other aspects of the preferred reporting items for systematic reviews and meta-analyses framework have been incorporated to create a scholarly publications evaluation of the blockchain-based application in the financial arena and its future. The research looks at 86 studies published between 2018 and 2022.

There has been a steady but noticeable increase in the study of blockchain’s potential in many application domains over the past few of years. This rising tendency illustrates the newness and potential of blockchain technology, as well as the increasing attention from academics. According to the findings, blockchain is an appropriate solution for processing transactions using cryptocurrencies; nevertheless, it still has significant technical issues and limits that require to be exploring and solving before it can be considered a viable option. It is therefore, necessary to have a high level of reliability for payments and confidentiality, in addition to maintaining the anonymity of nodes, to stop assaults and efforts to disrupt transactions in the blockchain.

This study has several important theoretical and practical implications. First, it adds to the body of knowledge on blockchain and Fintech, focusing on the transaction side. While much blockchain research has focused on how the technology may affect strategic choices, this study has shed light on its potential from the perspective of financial reporting. Second, by highlighting the importance of the demand for the prompt identification of losses, this work adds to the body of knowledge on the factors that influence transaction frauds involving paper money. Additionally, by establishing the link between transparency and virtual transactions, the author backs up the asymmetric responses of investors to different investment possibilities. It looks at the evolution of financial technology (Fintech) and shows how it can be used to take the advantage of unique opportunities.

The study is different and novel from the previously published literature on this topic mainly because of its comprehensiveness, as it revolves around all industrial and commercial areas. The three main lines of research have been outlined, namely, classifying the many blockchain-based innovations that will alter the financial landscape in many industries; identifying whether these industries are a good fit for blockchain’s wealth creation potential; and directing researchers by outlining prospective study pathways.