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Crack sensor based on RFID tag has become a research hotspot in the field of metal structural health monitoring for its significant benefit of passive wireless transmission. While in practice, crack location will impact the performance of crack depth-sensing tag. The purpose of this paper is to provide a method for reducing disturbance of crack location on crack depth-sensing tag.

The effect analysis of crack location on crack depth-sensing tag is presented first to find disturbance reason and disturbance law. On the basis of that, a miniaturized tag is proposed to improve the current distribution and reduce the disturbance introduced by crack location.

The degree of crack location disturbance is closely related to the current distribution in the coverage area of tag. Because sensing tag performs better when crack locates in the high current density area, miniaturization of sensing tag is exploited to expand the high current density area and make the area more symmetrical. The simulated and experimental results demonstrate that tag miniaturization can enhance the performance of crack depth-sensing tag.

This paper provides a method to enhance the performance of crack depth-sensing tag.

RFID tags for sensing are available to operate and transmit sensing data to measurement equipment without battery and wires, which is a great advantage in establishing IoT environment. For crack sensing tags, however, the short service life of tags restricted their application. This paper aims to introduce a method of surface crack detection and monitoring based on RFID tag, which makes it possible for tags to be reused.

Metal plate to be monitored, acting as the ground plane of microstrip patch antenna, is underneath the crack sensing tag. The propagating surface crack in metal plate will change the electric length of tag’s antenna that is directly proportional to the crack depth and length. Thus, the deformation of sensing tag introduced by the load on metal structure is no longer a prerequisite for crack sensing.

The simulated and experimental results show that the proposed crack sensing tag can sense the change of surface crack with mm-resolution and sense surface crack propagation without a deformation, which means the proposed crack sensing tag can be reused.

The key advantage of the proposed method is the reusability of the RFID tags.

Despite the importance of hosts who contribute to the success of accommodation sharing through sharing underutilized space with guests, current literature sheds little light on what exactly incentivizes hosts to grow their properties. The purpose of this study is to investigate the effects of multifaceted motivations including financial benefits, online social interaction and membership seniority and their interplay on hosts’ multiple listing behavior.

The study is instantiated on real-world business data collected from an accommodation-sharing platform in China. The data set includes 3,199 observations of 252 multi-listing hosts in Beijing who managed 815 properties from September 2012 to October 2016.

The study discloses that financial benefits, online social interaction and membership seniority significantly incentivize hosts to list multiple properties on the accommodation-sharing platform. In particular, the social incentive is the most important driver among the three. With a 1 per cent increase in online social interactions, the number of properties operated by a host would increase by 13.5 per cent. While the financial benefits and online social interaction motivate hosts to engage in the multi-listing behavior, such effects are significantly mitigated as the membership seniority increases.

This study adds to the extant literature a unique yet less researched perspective of supply expansion driven by hosts. It also provides important practical implications for managing multiple properties for a healthy and viable accommodation-sharing community.

While a majority of the extant research on the sharing economy primarily takes a consumer-related perspective, this study addresses a different and original topic about hosts’ multiple-listing behavior that drives the supply of accommodation sharing. It is a first empirical investigation of the increase of accommodation sharing supply with host motivations explained.

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).

Buyers (renters) and sellers (hosts) on peer-to-peer (P2P) room-sharing websites make purchasing/selling decisions based on each other’s demographic information published in the cyber marketplace. Nevertheless, how this reciprocal selection based on the similarities between renters and hosts may lead to a successful P2P transaction of such services has not yet been discussed. Building on the similarity–attraction paradigm, this study assessed the similarity effects between renters and hosts on the likelihood of a P2P room-sharing transaction.

A logistical regression model was employed in analysis, using a large-scale, granular online observational data set collected from Xiaozhu.com, a primary home-sharing platform in China.

Renter–host similarities in age and education significantly affect the likelihood of a P2P room-sharing transaction. As the number of listings managed by a host increases, the effect of age similarity decreases. While a renter’s experience with a room-sharing website negatively moderates the similarity effect of age, it is a factor positively moderating the similarity effect of education.

Other possible host–renter similarities were not analyzed due to the limitation of the data source. The reciprocal selection process for room-sharing services was acknowledged by integrating buyers’ and sellers’ data into one analysis.

Implications are advanced for the stakeholders of room-sharing business, including entrepreneurs running a room-sharing website, operators of short-term residential rentals and hoteliers.

This study represents a first attempt to research the buyer–seller similarity effects on the likelihood of a P2P transaction in sharing economy.

This study aims to focus on peer-to-peer accommodation services in the sharing economy. Adopting construal level theory as the theoretical foundation, this study investigates the main and interaction effects of social and spatial distances on guest loyalty toward peer-to-peer accommodation.

This study uses a secondary source of online observational data archived on Xiaozhu, a leading peer-to-peer accommodation sharing platform in China. It consists of 2,612 observations of 1,304 unique travelers who stayed at 559 listings managed by 281 hosts in four major metropolitan areas of China over four years from August 2012 to August 2016. Non-linear binary choice panel models of probability regressions were used to estimate the effects of psychological distances (social and spatial) between hosts and guests on the likelihood of repeat purchase. The software used for the econometric analyses is STATA 14.

The results indicate that social distance negatively affects guest loyalty toward the listing hosts, while spatial distance has a positive influence on guest loyalty. The results also show significant interactions between the two psychological distance dimensions in influencing loyalty. The findings provide important insight into the influences of psychological distances on travelers’ repeat purchase behavior toward peer-to-peer accommodation providers.

This study contributes to the literature by providing empirical evidence that supports the importance of psychological distances in forming a loyal relationship between hosts and guests in the peer-to-peer accommodation sector of the sharing economy.

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.

Due to their very different contexts, the responses made by property hosts to online reviews can differ from those posted by hotel managers. Thus, the purpose of this study is to investigate the determinants of the responding behavior of hosts on peer-to-peer property rental platforms.

This study applied a comprehensive framework based on the theory of planned behavior. Empirical models are constructed based on 89,967 guest reviews with their associated responses to reveal the responding pattern of property hosts.

Unlike hotel managers, property hosts are more likely to reply to positive than to negative reviews; moreover, when they do choose to respond to negative reviews, they are likely to do so negatively, in a “tit-for-tat” way. This study also finds that one reason for the difference of responding patterns between property hosts and hotel managers is the hosts’ lack of experience of consumer relationship management and service recovery.

This study provides a good start point for future theoretical development regarding effective responding strategy on peer-to-peer property rental platforms, as well as some useful implications for practitioners.

This study is an early attempt to analyze the impact of the particularity of emerging platforms on the responding behavior of service providers based on a comprehensive conceptual framework and empirical model thus provides a good starting point for the further investigation of effective response strategies on these emerging platforms.

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.

Although room-sharing platforms such as Airbnb have become globally prevalent business phenomena, no cross-cultural research has been conducted to examine how travelers outside the “Western Bubble” perceive and respond to this sharing economy. The purpose of this paper is to explore factors that affect Chinese travelers’ behavioral intentions toward room-sharing platforms in the sharing economy.

Based on theories of motivation, the influences on travelers of utilitarian and hedonic motivation, perceived trust and past experience were investigated. Considering the characteristics of room-sharing, utilitarian motivation is measured by service experience, information acquisition, cost saving and resource efficiency. Hedonic motivation is measured by adventure, gratification, sharing and friend-seeking. Data were collected in online survey from 445 valid samples and analyzed by partial least squares (PLS) regression approach using SmartPLS 3.0.

The findings indicate that although intensity varies, utilitarian motivation, hedonic motivation and perceived trust do have positive effects on tourists’ behavioral intentions. Past experience with room-sharing moderates these effects.

There is scant research about consumers’ behaviors toward a sharing economy in Western countries and no empirical research in this field with analyses of Chinese consumers, although China is an emerging market in the hospitality industry. None of the previous research was based on theories of motivation and trust, especially without a consideration of the moderating effect of past experience. Therefore, this study aims to narrow these gaps and to combine, organize and extend previous studies to improve the analysis of behavioral intention in the sharing economy. The value of this study lies in the research model and analysis with the backdrop of China.