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To promote the success of peer-to-peer accommodation, this study examines the effects of online host–guest interaction as well as the interaction's boundary conditions of listing price and reputation on listing popularity.

Using 330,686 data collected from Airbnb in the United States of America, the authors provide empirical evidence to answer whether social-oriented self-presentation and response rate influence listing popularity from the perspective of social exchange theory (SET). In addition, the authors investigate how these two kinds of online host–guest interactions work with listing price and reputation to influence listing popularity.

The results reveal the positive association between online host–guest interaction and listing popularity. Notably, the authors find that listing price strengthens but listing reputation weakens the positive effects of online host–guest interactions on listing popularity in peer-to-peer accommodation.

This study is the first attempt to adopt SET to explain the importance of online host–guest interactions in influencing listing popularity as well as examine the moderating role of listing price and reputation on the above relationship.

Listing popularity indicates the public’s interest in a listing on peer-to-peer (P2P) accommodation platforms. Although listing popularity is crucial to the survival and development of the P2P accommodation platform, this issue has received limited attention in the tourism management discipline. Drawing upon the heuristic-systematic model and uncertainty reduction theory, this study aims to examine the impacts of host and property attributes on listing popularity.

The model was empirically validated using a data set of 6,828 listings on a popular P2P accommodation platform called Airbnb. This study chooses a hierarchical regression analysis to perform the model validation.

The findings reveal that host self-disclosure, host reputation and host identity verification are key host attributes in promoting listing popularity. Meanwhile, property visual description, property photo verification and property visual appeal are important property attributes in facilitating listing popularity.

The study adds useful insights on understanding on determinants of listing popularity. Future researchers are recommended to empirically verify the underlying psychological mechanism by which host attributes and property attributes influence listing popularity.

The P2P accommodation platform should promote the listing popularity by taking advantage of the host attributes and providing property attributes.

First, to the best of the authors’ knowledge, this study is one of the few studies to explore the formation of the listing popularity. Second, this study examines how the host and property attributes promote the listing popularity through the heuristic and systematic information processing modes.

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.

Sharing accommodation has lowered the threshold for digital entrepreneurship in the accommodation industry, prompting entrepreneurs to join this industry. However, digital micro-entrepreneurs have been ignored by previous studies. To bridge the gap, this study aims to explore the impact of reputational asset (host popularity) and host’s human capital (entrepreneurial entry speed and managerial seniority) on host expansion grounded on the resource-based theory.

This study obtained crawler data by python from Airbnb.com, locking the time range to the past five years from 2013 to 2018 in Beijing of China. This study finally has 348 hosts’ balanced panel data to estimate the ordinary least squares regression model with fixed-year effect.

Results demonstrate that host popularity has a significant positive effect on host expansion. Furthermore, entrepreneurial entry speed strengthens the positive effect of host popularity on host expansion, whereas managerial seniority weakens the positive effect. The three-way interaction analysis reveals that the positive impact of host popularity on host expansion is strongest when managerial seniority is smaller and entrepreneurial entry speed is faster.

The research has important implications to how the platforms interfere with the implementation of host expansion strategy and adds valuable insights to understand the transformation process of host expansion from nonprofessional to professional.

The research has expanded the literature related to the micro-entrepreneurship of the sharing economy and verified the application of resource-based theory under sharing economy.

Mission statements are considered to be one of the most popular management tools in the world – and also one of the most frustrating. To secure their success, it has been recommended that mission statements be communicated and disseminated to as many internal and external stakeholders as possible. One means for doing this is through the Internet and the posting of an organization’s mission statement on its company Web site. But who is doing this? What types of organizations are using the World Wide Web to advertise their missions? Where is a mission statement typically located in a corporate Web site? And what are some of the motivations that an organization has for posting or not posting its mission? These are questions which have not yet been addressed by previous research and which this preliminary study sought to answer.

This paper aims to conceptualize two patterns of user recognition mechanisms and two kinds of user contribution behavior in enterprise-hosted online product innovation community and explain their relationships between user recognition mechanisms and user contribution behavior of online product innovation community.

A Chinese enterprise-hosted online innovation community and an American enterprise-hosted online innovation community are selected as research objects. Four Logit models are developed and some hypotheses are supposed from the perspective of prosocial behavior theory. Objective user data with three months from two online product innovation communities are collected to test with Logit regression analysis.

Findings show that there are obvious correlations between user recognition mechanisms and user contribution behavior, and there is also an obvious difference in community user activity level between the quantity-based user recognition mechanism community and the quality-based user recognition mechanism community. More specifically, in the online product innovation community with quantity-based recognition mechanism, both variables of peer recognition and community image motivation significantly affect user proactive contribution behavior. In the online product innovation community with quality-based recognition mechanism, the variable of peer recognition significantly affects both user proactive contribution behavior and user responsive contribution behavior; the variable of community image motivation significantly affects both user proactive contribution behavior and user responsive contribution behavior.

Although it is voluntary, online user voluntary contribution behavior still need to be presented, recognized and affirmed by community. For enterprise-hosted online community managers, they should pay more attention to design the reasonable online community user recognition mechanism with the coexistence of quantity and quality.

The theoretical contribution in this study is to enrich the existing research theme about enterprise-hosted online product innovation community. First, it conceptualizes two patterns of user recognition mechanisms. Second, it regards the variable of user contribution behavior as the co-existence of proactive contribution and responsive contribution. Third, from the perspective of prosocial behavior theory, it is an important supplement to explain the mechanism of user contribution behavior in enterprise-hosted online product innovation community. Fourth, it deepens the overall understanding of the relationship between user recognition mechanism and user contribution behavior. This study provides theoretical guidance for enterprises how to design reasonable and efficient online product innovation community platform. The theoretical contribution in this study is to enrich the existing research theme about enterprise-hosted online product innovation community. First, it conceptualizes two patterns of user recognition mechanisms. Second, it regards the variable of user contribution behavior as the co-existence of proactive contribution and responsive contribution. Third, from the perspective of prosocial behavior theory, it is an important supplement to explain the mechanism of user contribution behavior in enterprise-hosted online product innovation community. Fourth, it deepens the overall understanding of the relationship between user recognition mechanism and user contribution behavior. This study provides theoretical guidance for enterprises how to design reasonable and efficient online product innovation community platform.

This paper examines the categories of property attributes that are important to UK university students in their online accommodation search. It also analyses the volume of information displayed regarding the property attributes and explores the influence of some of the information provided on the attractiveness and by extension, the booking potential of the property.

The authors use data from an online student accommodation listing platform – student.com – which contain tangible and non-tangible property attributes, and the data are analysed using a hedonic regression model.

The results show that purpose-built student accommodation's (PBSA) tangible and intangible attributes are important to students in their online accommodation search, although, these attributes vary in impact. The study also reveals that failure to display key information of a PBSA may reduce the attractiveness of the property.

The empirical evidence on student accommodation ex ante preferences and choices is limited, particularly as it relates to online accommodation search in a UK context. The authors' approach to identify the application of the search theory to the student accommodation search process is particularly unique.

Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.

A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.

Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.

This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

This study aims to investigate a sharing economy context, where vacation rental units that are owned and operated by individuals throughout the world are rented out through a common website: vrbo.com. It is posited that gross domestic product (GDP) per capita, a common indicator of the level of economic development of a nation, will impact the likelihood that prospective travelers will choose to book accommodations in the sharing economy channel (vs traditional hotels). The role of online customer reviews in this process is investigated as well, building upon a significant body of extant research which shows their level of customer decision influence.

An empirical analysis is conducted using data from the website Vacation Rentals By Owner on 1,940 rental listings across 97 countries.

GDP per capita serves as risk deterrent to prospective travelers, making the sharing economy an acceptable alternative to traditional hotels for the average traveler. It is also found that the total number of online customer reviews (OCR volume) is a signal of popularity to prospective travelers, while the average star rating of those online customer reviews (OCR valence) is instead a signal of accommodation quality.

This study adds to a growing agenda of research investigating the effect of online customer reviews on consumer decisions, with a particularly focus on the burgeoning sharing economy. The findings help to explain when the sharing economy may serve as a stronger disruptive threat to incumbent offerings. It also provides the following key insights for managers: sharing economy rental units in developed nations are more successful in driving booking activity, managers should look to promote volume of online customer reviews and positive online customer reviews are particularly influential for sharing economy rental booking rates in less developed nations.

This study aims to elicit the preferences of potential travelers for different property listings' attributes (online review number, positive valence rate of reviews and discount strategy) when selecting hotels and peer-to-peer (P2P) accommodation sharing on online booking platforms.

A discrete choice experiment (DCE) was conducted with 291 respondents with accommodation needs. They were asked to choose between pairs of listings.

The authors found that when booking accommodation online, complex discount strategies were not determinant both in selecting hotels and P2P accommodations. Positive valence rate of reviews has a higher impact on the selection of traditional hotels than P2P accommodations, while the number of online reviews has a higher impact on the selection of P2P accommodations than traditional hotels. The authors further discuss the effect of each attribute on online accommodation selection in terms of price ranges of the property listings.

The findings provide suggestions for platform operators and product/service providers to improve their marketing strategies and optimize their management efforts.

To the best of the authors’ knowledge, this is one of the first studies that investigate the role of property listings' attributes on the selections between hotels and P2P accommodations. The findings from this research study could be generalized to other online platforms and electronic commerce-related transactions.