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This study aims to explore how attribute performance and hosts’ service quality attributes affect room sales on peer-to-peer (P2P) platforms from the cue congruence perspective.

More than 9.53 million reviews concerning 258,473 listings located in 35 major cities worldwide were collected from Airbnb. Data was collected from December 2019 to December 2020 and was analysed using a generalised linear model.

Results show that when attribute performance and hosts’ service quality attributes give positive signals, Airbnb room sales are significantly higher than when the two kinds of cues give inconsistent or negative signals; when attribute performance gives positive signals and hosts’ service quality attributes give negative signals, room sales are higher than when the former gives negative signals and the latter give positive signals; surprisingly, when both kinds of cues give negative signals, room sales are higher than when attribute performance gives positive signals and hosts’ service quality attributes give negative signals.

This paper adds useful insights on understanding of cue congruence (incongruence) effect on room sales of P2P accommodation platforms. This study has practical implications for hosts, online platform managers and guests regarding how to use online strategies and promotions on the Airbnb platform.

This study is an early attempt to explore how the combination of attribute performance and hosts’ service quality attributes affects Airbnb room sales under the conditions of consistency and inconsistency.

This paper aims to identify a wide array of utility-based attributes of Airbnb listings and measures the effects of these attributes on consumers’ valuation of Airbnb listings.

A hedonic price model was developed to test the effects of a group of utility-based attributes on the price of Airbnb listings, including the characteristics of Airbnb listings, attributes of hosts, reputation of listings and market competition. The authors examined attributes as they relate to the price of Airbnb listings and, therefore, estimated consumers’ willingness to pay for the specific attributes. The model was tested by using a dataset of 5,779 Airbnb listings managed by 4,602 hosts in 41 census tracts of Austin, Texas in the USA over a period from Airbnb’s launch in Texas up until November 2015.

The authors found that the functional characteristics of Airbnb listings were significantly associated to the price of the listings, and that three of five behavioral attributes of hosts were statistically significant. However, the effect of reputation of listings on the price of Airbnb listings was weak.

This study inspires what they call a factor-endowment valuation of Airbnb listings. It shows that the intrinsic attributes that an Airbnb listing endows are the primary source of consumer utilities, and thus consumer valuation of the listing is grounded on its functionality as an accommodation. This conclusion can shed light on the examination of competition between Airbnb and hotel accommodations that are built on the same or similar intrinsic attributes.

With the prevalence of the sharing economy phenomenon, there are an increasing number of hosts on Airbnb who manage more than one listing. Managing more listings likely makes hosts more seasoned in terms of serving guests, but it may undermine host quality due to hosts’ constrained capability. This paper aims to examine the effects of host quality attributes and the number of listings per host on the reservation performance of these listings.

Using a large-scale but granular data set of 5,805 active listings of 4,608 Airbnb hosts in Austin, Texas, this study estimates the effects of host attributes (host quality and listing quantity) on the performance of the hosts’ Airbnb listings through a blend of regression models.

This study evidences that host quality attributes significantly influence listing performance through cue-based trust. In addition, this study finds a “trade-off” between host quality and the quantity of their listings. As the number of listings managed by a host increases, the performance effects of host quality diminish.

The business implications of this study include the suggestion that sharing economy businesses such as Airbnb should sustain service quality through incentivizing hosts to improve host quality while balancing the quantity of listings managed.

This study contributes to the literature through its meaningful theoretical extension in the sharing economy context and unique data-driven insights enabled by an analytical approach. It addresses the critical but less researched topic of host quality and listing quantity and generates important practical business and policy implications.

Trust has been widely recognized as the crucial factor of consumer purchase intention when shopping on peer-to-peer short-term rental platforms where hosts and renters are strangers. However, the specific attributes of hosts that help build trust with potential renters and drive their purchase of short-term rentals remain unknown. This study aims to explore the effects of host attributes on renter purchases made on Xiaozhu.com, one of the top short-term rental platforms in China, while controlling for short-term rental characteristics.

A crawler program was developed by Python to collect the host attributes and their short-term rental characteristics of 935 hosts in Beijing from November 18, 2015 to February 14, 2016. The authors use Poisson regression models to estimate the effects of host attributes on renter reservations. They also conduct a series of robustness checks for the estimated results.

The authors found that host attributes such as the time of reservation confirmation, the acceptance rate of renter reservations, the number of listings owned, whether a personal profile page is disclosed and gender of the host significantly affect renter reservations, whereas the response rate of the host does not influence renters when purchasing short-term rentals online.

This study identifies which host attributes are perceived as trustworthy and affect renters’ purchase decisions, a topic of both theoretical and practical importance but currently less researched. The findings add to emerging literature by providing insights on trust-building in the peer-to-peer economy. Useful suggestions are also provided on strengthening the trust mechanism on short-term rental platforms to facilitate peer-to-peer transactions. Notably, the study is the first attempt to examine the perception of Chinese users toward short-term rentals despite its global prevalence. The analytical insights revealed from large scale but granular online observations data of host attributes and actual renter reservations greatly supplement findings of extant literature using survey and experiment approaches.

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.

The purpose of this study is to examine the effects of host attributes and travelers’ frequency of past stays and their interaction on the likelihood of repeat purchase of home-sharing services at both the host and listing levels.

A combination of econometrics analyses using a large-scale, granular online observational data set collected from a home-sharing platform was performed.

Travelers exhibit salient loyalty to home-sharing services. At the host level, host attributes including acceptance rate and listing capacity positively affect travelers’ likelihood of repeat purchase; such effects diminish as travelers’ frequency of past stays with a host/listing increases. At the listing level, confirmation efficiency and acceptance rate are critical, and travelers’ frequency of past stays matters.

Responding to the call for more research on customer loyalty of sharing economy, this study instantiated on a home-sharing website in China and adds a unique perspective to the research domain, but its findings may not be generalized in other settings.

This study identifies the factors affecting customers’ repeat purchase behaviors at both the host and listing levels, allowing the hosts, webmasters of home-sharing websites and even hoteliers to advance specific tactics to promote repeat purchase among travelers.

Loyalty was measured with real-time internet-enabled observational data about travelers’ actual repeat purchase behavior on a home-sharing website, rather than assessing consumers’ behavioral intentions through the conventional survey method. Two specific levels of customer loyalty were analyzed, including the ones towards a service provider (host) and a service product (listing).

The purpose of this paper is to investigate the relationship between consumers' alliance encounter satisfaction (AES) and their behavioral intentions toward standalone platforms of host and guest partners, and the moderating effects of consumer AES attributions on AES-to-behavioral intention relationships.

This paper extends attribution theory and satisfaction literature to the brand alliance context. The study used 1,470 survey responses from consumers, each having had purchase experiences with one of 16 brand alliances, to test hypotheses.

AES spills over to favorable behavioral intentions toward each alliance partner as a standalone brand. This spillover effect is strengthened by a carryover effect. Intriguingly, if a partner outshines the other and solely receives AES attributions, there is a significant contrast effect adversely affecting the spillover effect for the non-attributed partner.

The findings provide advice on partner selection and alliance resource commitments. Choosing partners well and working synergistically to attain AES is essential for gaining behavioral intention uplift for alliance partners. Brand managers are advised to select partners with comparable rather than superior capabilities. Also, since outcomes of alliance encounters lead to greater gains (or losses) for host brands, hosts are called to be particularly thorough when making alliance resource commitments.

There are few studies of actual consumer experiences with alliances, since most consumer-focused alliance research uses experimental designs. This paper is among the first to examine the effects of actual consumer alliance encounters on behavioral intentions toward each alliance partner as a standalone firm.

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.

The purpose of this study is to examine the effectiveness of ingredient advertising. Specifically, the authors consider the question as to whether ingredient advertising can increase derived demand and favorably influence purchase intentions and attitudes toward the host product that incorporates the advertised B2B ingredient.

They conduct experiments in two host product categories using a three-group, between-subjects experimental design.

The findings of the study are revealing about the impact of ingredient advertising on the demand for host products, in which the advertised ingredients are incorporated. It is demonstrated that consumer advertising positively affects the attitude and purchase intention toward the host brand that incorporates the advertised industrial product. It is also found that the higher the importance of the advertised ingredient as an attribute of the host product, the greater the advertising effects on the consumer brand.

The findings imply that ingredient advertising can help marketers to stimulate derived demand in the sense that it makes consumer brands incorporating the advertised industrial product more attractive to consumers. The positive influence of ingredient advertisements is greater for industrial products that are perceived by consumers as very important ingredients of the final product as consumers are more prone to search for and process ingredient-related information and are also more likely to respond to it.

Despite the implementation of ingredient advertising campaigns by many B2B brands and the vast literature on conventional B2C advertising, there has been no previous attempt to investigate this issue in the empirical literature. This empirical study shows how ingredient advertising works and how it can benefit both buyers and suppliers of the advertised B2B products.

The purpose of this paper is to map the service quality (SQ) of Airbnb, to provide additional insight for such top player of short-stay accommodation in the sharing economy context.

A mixed-method approach is used in two phases. In the qualitative phase, 112,138 online review comments of Airbnb guests were analyzed to generate the service attributes. In the quantitative phase, an online survey (n = 814) was conducted to calculate the performance and importance values of extracted attributes to plot them in an Importance-Performance Analysis (IPA) matrix.

A holistic image of the Airbnb extracted service attributes was presented through the IPA plot. Four types of SQ strategies were proposed, considering the actions priority. “Price reasonability” was the most important service attribute of Airbnb for guests, whereas “Check-in flexibility” was the best performed one.

The results shed light on the most relevant SQ attributes of Airbnb and proposed suitable strategies that can prioritize relevant stakeholders’ actions and decisions. The study significantly contributes to all decision makers involved in the short-stay accommodation sharing industry to further understand and develop SQ.

This research, using a comprehensive hybrid method, opens a lens to see more clearly the positioning of different attributes of Airbnb service from importance and performance viewpoints. As a contribution, the SQ of Airbnb was mapped by conducting an IPA for the first time in the literature.