In Sharing Economy We Trust: The Effects of Host ...

1. Wu, Jiang, Ma, Panhao, & Xie, Karen (2017). In sharing economy we trust: The effects of host attributes on short-term rental purchases. International Journal of Contemporary Hospitality Management, 29(9). The Special Issue of Sharing Economy. Forthcoming

In Sharing Economy We Trust: The Effects of Host Attributes on Short-term Rental Purchases Abstract Purpose Trust has been found as the key determinant of consumer decisions when shopping on peer-topeer 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 shortterm rentals remain unknown. In this paper, we investigate the effects of host attributes on renter purchases made on Xiaozhu.com, a leading short-term rental platform in China, while controlling for short-term rental characteristics. Design/Methodology/Approach We developed a crawler program by Python to collect the host attributes and their short term rental characteristics of 935 hosts in the city of Beijing from November 18, 2015 to February 14, 2016. We use Poisson regression models to estimate the effects of host attributes on renter reservations. We also conduct a series of robustness checks for the estimated results. Findings We 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. Originality/Value 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 in order 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. Keywords: Trust, Sharing economy, Short-term rental, Host attributes, Gender

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1. Introduction A few years ago, the thought of staying at a stranger’s home merely by making a couple of quick mouse clicks or taps on a mobile app would be inconceivable. Now, however, it is increasingly common for people to share access to underutilized resources, such as spare rooms, with their peers while receiving income. The so-called “sharing economy” has become prevalent in recent years, with global revenue estimated at $15 billion in 2013 and anticipated to reach $335 billion in 2025 (PWC 2015). The proliferation of the sharing economy has mirrored the growth of scalable platforms such as Airbnb.com, which act as a matchmaker linking individuals with underused accommodation space and others who will pay for using them (Belk 2010). As more and more users participate in sharing economy activities, such as renting a room through Airbnb, hitching a ride through Uber, and buying a dress from Poshmark or Rent the Runway, researchers have frequently cited cost savings (Bardhi and Eckhardt 2012), a sense of community (Tussyadiah 2014), technology innovations (Rick 2013), and environmental concerns (Gansky 2010) as the drivers of the emergence of the sharing economy. However, trust is the primary enabling factor inherent within all sharing economy services, which keeps the sharing economy spinning and growing (Finley 2013; PWC 2015). Lack of trust is one of the greatest barriers between sellers and buyers who are not familiar with each other (Shankar et al. 2002), especially in peer-to-peer sharing economy platforms. For example, Mike Silverman, an Airbnb renter, was attacked by his host during his stay in Salta, Argentina in 2015 (Ert et al. 2016). Incidents like this highlight the centrality of trust, a crucial factor that short-term rental services rely on (Finley 2013). Research indicates that in electronic commerce markets, buyers’ reviews of sellers and other evaluation indexes can arguably build trust (Yu and Singh 2002). However, caution has been urged when relying solely

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on online consumer reviews as the indicator of host trustworthiness. Zervas et al. (2015) found that about 95% of Airbnb listings boast a rating of either 4.5 or 5 stars (the maximum rating is 5 stars), which means that ratings lack variance, making it less psychologically effective for buyers to differentiate the trustworthiness of sellers. To facilitate better trust-building in peer-to-peer sharing economy platforms, buyers may need to refer to other relevant information about hosts that can increase transparency and reduce the worry and friction that may occur when strangers participate in the sharing economy (Ufford 2015). However, the specific host attributes that are perceived as essential in building trust with renters—and more importantly, driving purchases on peer-to-peer short-term rental platforms—are less researched. To bridge the research gap, this study examines which host attributes disclosed online are important to consumers when making their purchase decision of short-term rental products. We collect a unique dataset of 935 hosts in the city of Beijing from November 18th, 2015 to February 14th, 2016 on Xiaozhu.com, a major peer-to-peer short-term rental platform in China. Founded in August 2012, Xiaozhu.com now offers 80,000 online listings in more than 250 domestic destinations to 3 million active users. It provides information about hosts, including their response rate, the time of reservation confirmation, the acceptance rate of renter reservations, the number of listings owned, whether the host has a personal profile page, and gender of the host, which are further categorized into benevolence, ability, and integrity by adopting the trustworthiness framework of Mayer and Schoorman (1995). Since the number of renter reservations is a count variable, we use Poisson regression models to estimate the effects of these host attributes on renter reservations. The rest of this paper is organized as follows. Section 2 presents the theoretical framework and hypothesis development. Section 3 describes the research methodology, followed

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by discussions of estimation results in Section 4 and robustness checks in Section 5. Section 6 discusses theoretical and practical implications of our empirical results. Section 7 draws conclusions and introduces future research directions. 2. Theoretic Framework and Hypotheses Academic interest in trust can be traced back fifty years (Finley 2013). Trust has been delineated as a critical foundation of civic behavior (Stolle 2002) and the basis of social connectedness (Delhey and Welzel 2011), and also as a bridging mechanism connecting people with unfamiliar parts (Stolle and Hooghe 2004). Trust is essential for business transactions and product exchanges (Doney and Cannon 1997) because it provides consumers with high confidence and may produce more customer satisfaction with sellers and products (Pavlou 2003). The ecommerce marketplace in particular is full of uncertainty: users face privacy risks that their personal information may be utilized by others, and financial risks in transacting with unreliable parties (Golbeck et al. 2009). Trust is of great importance in facilitating successful transactions, and makes it easier for consumers to make sound decisions despite the existence of information asymmetry between buyers and sellers (Finley 2013). In Bussiness to Customer transactions, lack of trust has been seen as a significant impediment to the adoption of online shopping (Chang et al. 2013). In the sharing economy— where people must trust strangers who drive their cars or stay in their apartments—trust building among buyers toward sellers is much more critical than that of the traditional ecommerce marketplace. In sharing economy transactions, people must manage risk involved in their interactions without any presence of trusted authorities or third parties (Xiong and Liu 2003). Moreover, what buyers pay for is more likely to be a service or an experienced product rather than a commodity. Buyers may predict whether or not sellers can offer high-quality service from

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estimating trustworthiness transmitting from sellers. Many researchers have proved that consumers only buy products or services from those who are trustworthy (Gefen 2002; Kim et al. 2008). Therefore, hosts who exhibit more trustworthiness have a better chance to get their listings reserved. An emerging group of researchers have explored important seller attributes that can potentially facilitate trust-building in commercial settings such as eBay, Amazon, and Taobao. For example, Melnik and Alm (2005) discussed sellers’ overall reputations on eBay, finding that customers’ evaluation of sellers has a significantly positive impact on buyer’s trust and willingness to purchase. Gefen (2002) found that trust in the ability of Amazon.com to provide excellent service significantly influences consumers’ trust towards the website. By collecting data from users of Taobao, Lu et al. (2010) found that trust in a vendor’s benevolence, ability, and integrity will positively affect the purchase intention. Similar findings have also been discussed regarding characteristics of online vendors that signal trust (Chen and Dhillon 2003; McKnight et al. 2002; Connolly and Bannister 2007). In traditional ecommerce marketplaces, researchers mostly use online consumer reviews and rating scores as important elements to encourage and build trust between traders. However, in sharing economy websites such as Airbnb, property ratings are uniformly high, with an average score of 4.7 stars, and ratings tend to be overwhelmingly positive, leading to a J-shaped distribution (Hu et al. 2009; Zervas et al. 2015). In addition, some researchers found that sellers’ photos on Airbnb may help consumers reduce anonymity and increase social presence. Consumers will speculate on sellers’ trustworthiness from their photos, and this judgment affects their decisions more than other visual attributes (Ert et al. 2016); Moreover, sharing economy companies understand the importance of trust. In April of 2013, Airbnb added identity

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verification to its platform, enhancing transparency and reducing the worry and friction that will occur when strangers conduct business. In a peer-to-peer marketplace, verifying user identity develops trust, and sellers pay attention to building their online reputations (Ufford 2015). In short-term rental platforms, since online reviews and rating scores are not valuable enough for decision-making, renters depend on additional information—such as host attributes— for justification. Just as Ert et al. (2016) argue, host photos may impact a renter’s decision. With the rise in identity authentication, hosts show more personal information on Xiaozhu.com. Thus, we consider that multiple pieces of information about hosts disclosed online can arguably signal the trustworthiness of hosts and consequently affect renters’ choice. A trusting relationship is contingent upon two elements: the trustees with good intentions (Freitag and Traunmüller 2009) and technical competence to implement those intentions (Yamagishi and Yamagishi 1994). Integrity or other similar constructs have also been discussed as antecedents of trust by a number of theorists (Lieberman 1982; Sitkin and Roth 1993). Similarly, adapting viewpoints of Mayer (1995), we propose a theoretical framework of the relationship between trust-building and host attributes, which is anchored in benevolence, ability, and integrity. Specifically, (1) benevolence means that a trustee wants to do good to the trustor with altruism (Mayer and Schoorman 1995). The response rate and the time of reservation confirmation both represent a host’s effectiveness in managing short-term rental listings. The effectiveness, unlike on other hotel booking websites, is not compulsively required on short-term rental platforms. If a host actively replies to renters’ questions and confirms their reservations in a timely fashion, it can show his strong intentions to renters. Thus these two host attributes reflect benevolence trustworthiness. (2) Ability indicates that the trustee demonstrates certain skills, competencies, and characteristics in a specific domain to gain trust. The acceptance rate of

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renter reservations and the number of listings owned by hosts (reflecting host’s ability to accept reservations) represent ability trustworthiness. (3) Several researchers consider that openness, integrity, and fairness are significant factors in trust (Hart et al. 1986). Integrity shows that a trustee is honest and open to the trustor. In short-term rental platforms, a host who has a personal profile page shows his or her transparency, since not every host is willing to share personal information (age, educational background, etc.) and trading information (reservation history). Therefore, this attribute reflects integrity trustworthiness. Mayer and Schoorman (1995) argue that benevolence, ability, and integrity are common to many previous works on trust, and concisely explain the within-trustor variation in trust for others. Furthermore, host attributes on short-term rental platforms reflect these three dimensions of trust. Moreover, trustworthiness may be different for men and women (Orbell et al. 1994), so we will consider gender as an influencing factor of trustworthiness. Based on the above theoretical foundation, we further propose that if renters are convinced of these attributes of potential hosts, they are more likely to make a reservation with that host, controlling for specific listing characteristics such as the price and location of the listing. Figure 1 presents the theoretical framework of this study, and the detailed theoretical foundations as well as hypotheses are introduced as follows. (Insert Figure 1 about here) 2.1 Benevolence in Trust-building Benevolence is the extent to which a trustee is believed to voluntarily do good to the trustor, aside from an egocentric profit motive. For example, a trustee may offer a trustor additional help or service, even though he is not required to do so and there is no extrinsic reward for the trustee (Mayer and Schoorman 1995; Schoorman and Davis 2007). Some researchers also consider that trust can be described as the belief that the trustee with good

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intentions will behave in a socially responsible manner to fulfill the trustor’s expectations (Freitag and Traunmüller 2009; Gefen 2002). Other characteristics, similar to benevolence, such as good intentions and motives, are also taken as the crux of trust (Cook and Wall 1980). A host who makes an effort to respond to every consumer question, and who confirms reservation in a timely manner, can show his benevolence or kindness. Specifically, consumers can chat online with hosts before making a reservation to ask questions about check-in time, detailed addresses, local weather, culture, and so on. However, this website feature is optional for hosts; they can choose how often and how fast they wish to respond to renter inquires. Hosts who are dedicated to responding to renters’ inquires in a timely manner signal their good intentions. Similarly, if hosts can confirm reservations quickly, which can be seen as altruistic behavior, consumers won’t spend much time waiting for a reply. By doing so, hosts show goodwill and offer renters a better service experience so as to meet renters’ expectations and gain their trust. A kind host is more likely to be trusted. Similar findings have also been discussed in the literature. Ahn et al. (2004) discuss that service quality is an important dimensionality of increasing trust and encouraging purchases. Service quality refers to the availability of multiple communication channels for accepting consumers’ complaints in timely fashion, assisting consumers, providing complementary service, and solving their problems. Based on social presence as well as social exchange theory, Mcknight et al. (2002) argue that trust between sellers and buyers is built during the process of interacting, and that active interaction indicates sellers’ benevolent attitude, which contributes to developing trust. Therefore, the response rate and the time of reservation confirmation stand for benevolence trustworthiness in our research. We hypothesize that: H1a: The response rate has a positive influence on reservation rates. H1b: The time of reservation confirmation has a negative influence on reservation rates.

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2.2 Ability in Trust-building Ability is considered an essential element of trust (Sitkin and Roth 1993; Cook and Wall 1980; Lieberman 1982). Ability trustworthiness increases as a trustee’s skills and competence increase (Sitkin and Roth 1993). For example, a buyer is likely to believe that a supplier will offer high-quality products if that supplier has previously delivered a satisfying performance (Mayer and Schoorman 1995). Unlike the booking process at traditional hotels, consumers can check in as long as rooms are available. However, their reservations may be rejected by hosts after booking through sharing economy websites. Therefore, consumers face the risk of being rejected. In room-sharing platforms, each host can own more than one listing. The capacity to accommodate renters and provide room-sharing services is a unique measure of host trustworthiness. Having multiple listings available for renters to book enables hosts to send a strong signal of trust. For example, if a consumer wants to reserve a listing on a certain day, but that listing has already been reserved by other renters, the host can still accommodate this renter’s reservation if he/she has other available listings. Essentially, the acceptance rate of renter reservations and the number of listings owned by hosts both represent his ability to offer adequate products and provide service. The more capable a host is, the more likely he/she can trustfully deliver the promised room-sharing services. Similar findings have also been discussed in the literature. For example, Gefen (2002) found that trust in the ability of Amazon.com to provide excellent service significantly influences consumers’ trust in the website. Lu et al. (2010) found that trust in a vendor’s ability positively influences the purchase intention. Ahn et al. (2004) also found that a service provider’s competence is one important measurement of service quality and contributes to smooth transactions. A host with a higher acceptance rate of renter

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reservations or who possesses multiple listings deserves to be trusted since he has the ability to accept multiple reservations. So we propose that: H2a: The acceptance rate of renter reservations has a positive influence on reservations. H2b: The number of listings owned by hosts has a positive influence on reservations. 2.3 Integrity in Trust-building Integrity is defined as the extent of seller’s intention to share his true information, showing his or her openness and authenticity (Schoorman and Davis 2007; Genfen 2002). As authentication and legitimate-confirmation processes are gradually implemented across sharing platforms, consumers tend to believe a trustee who has a strong sense of integrity and honesty, as well as someone who shows transparency (Mayer and Schoorman 1995). Especially in peer-topeer markets where information asymmetry exists between buyers and sellers, both sides desire more factual information. At Xiaozhu.com, hosts are not required to show their detailed personal information to renters. There are two kinds of hosts: one is individuals who provide a personal profile page that shares personal information such as age, education background, hometown, job, reservation history and so on; the other type of hosts do not share their personal information. We argue that hosts that have a personal profile page are more likely to be trusted than those who do not, since they are willing to share their personal information and transaction records with renters. Similar findings have also been discussed in the literature. For example, Lutz et al. (2013) argue that competitive information shared via transparent design is beneficial to increasing trust. Connolly and Bannister (2007) provide evidence that consumers’ trust in online shopping is influenced by a vendor’s perceived integrity. Consequently, we propose that hosts who are willing to publish a personal profile page and share their individual information convey more integrity trustworthiness. We hypothesize that:

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H3: Host with a personal profile page get more reservations than those who do not. In addition, it is interesting to find that individuals may prefer women to men as hosts (Ert et al. 2016). Nevertheless, the study wasn’t able to validate that female hosts receive more reservations than male hosts. It is worth exploring whether gender plays a role in short-term rental platforms. In other fields, Blanco’s experiment in social dilemma showed that differences between males and females do influence people’s choices (Blanco et al. 2014). The influence shows up because gender information may produce stereotypes about trustworthiness, which is confirmed as a leading and important motivating element in interactions (Fehr and Gächter 2000). Numerous early research showed that men are more likely to trust others (Snijders and Keren 1999; Chaudhuri and Gangadharan 2007; Dittrich 2015), while women are more likely to be trusted (Croson and Buchan 1999; Chaudhuri and Gangadharan 2007; Buchan et al. 2008). Croson and Buchan (1999) found that women exhibit significantly more reciprocity by returning a higher proportion of their wealth in the game, and found that it is more valuable to be trusted . Innocenti and Pazienza (2006) observed that women show a higher degree of altruism than men for both trust and trustworthiness, but significantly more for trustworthiness. When taking security and trustworthiness into consideration, it’s very likely that gender impacts consumers’ intentions when choosing a host. Thus, we argue that female hosts are more trustworthy than male and suppose that: H4: Female hosts receive more reservations than male hosts.

3. Methods 3.1 Data and Measures

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Our study used a unique dataset collected from Xiaozhu.com, the most prominent sharing economy platform of short-term room rentals in China. Founded in August 2012, Xiaozhu.com now offers over 80,000 online listings in 250 domestic destinations to more than 3 million active users. Using a Python-based software procedure, we developed auto-parsing crawlers to collect online observational data of 935 hosts and their 2,461 listings in Beijing from Xiaozhu.com over three dates (November 18, 2015, December 18, 2015 and February 14, 2016). Such large-scale but granular data covers all hosts and their listings as shown on the Xiaozhu.com website for the city of Beijing. We focused on hosts and their listings in Beijing because Beijing is the leading short-term rental market in China (Beijing Times, 2016). For robustness check purposes, we collected three-period data about host attributes, including response rate, number of minutes it takes a host to confirm a reservation, the acceptance rate of renter reservations, the number of listings owned by a host, whether the host has a personal profile page, and gender of the host, as shown on the host profile page. In addition, we also collected information about each listing owned by a host, such as listing price, listing type (whole apartment, single room, sofa, or bed), and its general location. Because the number of hosts who rent a sofa or bed is very small— accounting for 3 percent—we mainly focus on hosts renting entire apartments or single rooms. Table 1 presents a variable description and summary statistics from our study. (Insert Table 1 about here) 3.2 Model Specification The goal of our estimation is to empirically investigate the influence of host attributes on reservations and control listings’ attributes – price, location, and listing type at the same time. Because our dependent variable, ResNum, is the count variable, we use Poisson regression to estimate the reservation effects of host attributes. Regression models are shown in Equation 1,

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wherein the price (skewness = 6.22) and number of listings a host owns (skewness =5.85) both with skewed distribution are taken log transformation (Field 2009; Trochim and Donnelly 2006). ResNum = β0 + β1log(Price) + β2Location + β3Listing_Type + β4Response_Rate + β5Confirm_Time + β6Accept_Rate +

(1)

β7log(Listing_Num) + β8Has_Page + β9Gender +ε 4. Estimation Results Before performing a regression analysis, we also performed a multicollinearity test and found that no VIF (variance inflation factor) statistic for the variable is greater than 1.9, as shown in Table 2, which indicates the absence of multicollinearity. The estimation results of Equation (1) are reported in Table 2. All of the three control variables, namely Price (β=-0.967, p=0.000), Location (β=0.506, p=0.000) and Listing_Type (β=0.587, p=0.000), have significant influence on reservations. The higher the price, the fewer reservations a host receives, which conforms to market rules. Listings in urban areas receive more reservations than those in the suburbs, since urban areas generally have more convenient transportation. The number of reservations an entire apartment receives is larger than that of a single room, which conforms with an official report released by Xiaozhu.com that family renters (3-5 people per reservation) constitutes the majority and the supply of whole apartments is booming. (Insert Table 2 about here) To our surprise, Response_Rate (β=0.211, p>0.05) fails to pass the significance test, so H1a is not supported. We performed further analysis and found that the mean, median, and upper quartile of Response_Rate are respectively 0.88, 0.96, 1.00, which shows that most of the values are close to 1. Therefore, we suppose that Response_Rate does not vary significantly across hosts, and it may make no psychological difference on guests’ decision-making. Confirm_Time (β=-

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0.004, p=0.000) has a significantly negative effect on reservations, meaning that a host receives more reservations when a shorter amount of time elapses between reservation request and confirmation, supporting H1b. We further find that Accept_Rate (β=1.177, p=0.000) and Listing_Num (β=1.550, p=0.000) have significantly positive effects on reservations. These two variables reflect a host’s ability to offer enough rooms and accept reservations, which helps consumers to perceive the host’s reliability and trustworthiness. Reservations increase with the increased acceptance rate of renter reservations (supporting H2a) and the number of listings owned by hosts (supporting H2b). Moreover, we argue that the more information provided by hosts to consumers, the more trust consumers will feel about hosts (Schoorman and Davis 2007). We also found that hosts who have personal profile pages get more reservations than those who do not (β=0.367, p=0.000), supporting H3. Finally, Gender (β=-0.225, p=0.000) has a significantly negative effect on reservations, which means that compared to male hosts, female hosts receive more reservations, supporting H4. A summary of the results of all hypotheses tests are provided in Table 3. (Insert Table 3 about here) 5. Robustness Check We doubt whether reservations differ according to the type of listing, and therefore ran robustness checks to verify that the effect of host attributes is consistent across different types of listings. As shown in Models 1 and 2 of Table 4, the results are consistent with what is shown in Table 2. To test the appropriateness of lag length of the dependent variable, we ran multiple lag specifications of 2 and 3 months in robustness check. We respectively subtracted the third-period reservations and the second-period reservations, and of the third-period reservations and the first-

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period reservations. The results are shown in Models 3 and 4 of Table 4. Above all, the results of our robustness checks further support the consistency of our estimated effects. (Insert Table 4 about here) 6. Discussions and Implications In this paper, we conducted an empirical study based on trust theory to assess the influence of host attributes on reservations made on Xiaozhu.com while accounting for product attributes (price, location, and listing type). As a result, we found that the time of reservation confirmation—representing benevolence trustworthiness—has a significantly influence on reservations, while the response rate was not significant. Further analysis suggests that due to the lack of variance, this attribute may not be obvious enough for renters to feel the difference of benevolence trustworthiness. Additionally, the acceptance rate of reservations and the number of listings owned by the host—representing the host’s ability trustworthiness—also have a significantly positive effect on reservations. In addition, hosts who have a personal profile page show integrity trustworthiness and earn more reservations than those who do not. Finally, motivated by previous research into trustworthiness of different genders (Ert et al. 2016; Croson and Buchan 1999; Chaudhuri and Gangadharan 2007; Buchan et al. 2008), we attempt to study the effect of gender and found that, compared with male hosts, female hosts receive more reservations. 6.1 Theoretical Implications This study makes some important theoretical contributions. First, based on trust theory, we theorized that host attributes represent their trustworthiness and would affect renters’ choices. Trust is essential for business transactions (Doney and Cannon 1997) and can help consumers

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make sound decisions despite the information asymmetry between buyers and sellers (Finley 2013). In the sharing economy—where people must trust a stranger and stay in their apartments—trust building among buyers toward sellers is much more critical. Buyers judge whether sellers offer higher-quality service by estimating sellers’ trustworthiness. In a peer-topeer marketplace, verifying user identity and building online reputations builds trust (Ufford 2015). However, online reviews and ratings are not valuable enough for decision-making, since property ratings are uniformly high on websites like Airbnb, making it hard for renters to differentiate effectively (Hu et al. 2009; Zervas et al. 2015). Ert et al. (2016) found that sellers’ photo on Airbnb affect consumers’ choices. Consumers will infer a seller’s trustworthiness from his photo, and this judgment affects their decisions more than other visual attributes. Besides photos, Finley (2013) also investigated hosts’ honesty, previous performance, gender, and profile to assess whether these elements are considered important when renters decide which host they can trust. Based on these studies, it’s easy to state that trust plays a pivotal role in the short-term rental marketplace, and we argue that renters likely make decisions depending on host attributes which can reflect a host’s benevolence, ability, and integrity trustworthiness. The results of this research prove that host attributes do influence reservations, which conforms with Ert’s and Finley’s conclusions. Moreover, we investigate additional valuable host attributes that renters can view on websites than were included in previous research. Thus, this paper is beneficial for researchers to attach importance to the impact of host attributes on trustworthiness, and that trust building is vital to healthy and long-term development of short-term rental platforms. Second, the analytical insights—revealed from large-scale but granular online observational data of host attributes and actual renter reservations—greatly supplement findings of extant literature using survey and experimental approaches. For example, Stors and Kagermeier (2015)

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conducted qualitative and quantitative research using questionnaire and interview methods to investigate motives for using Airbnb. Finley (2013) analyzed data collected from interviews as well as document review to conduct an exploratory study, and investigated trust in the sharing economy. Ert et al. (2016) also investigated trust and reputation in the sharing economy using experimental methods. Compared with these research methods, our method analyzed the “electronic trails” of renter actions using real world data on a digital platform, revealing new insights about actual renter decision-making based on host trustworthiness attributes, while adding a unique technology-enabled analytics to extant literature of the sharing economy. Third, this study is the first attempt to examine the perception of Chinese users toward short-term rentals rather than focus on Airbnb, which has been widely studied by numerous researchers (Ert et al. 2016; Finley 2013; Stors and Kagermeier 2015; Zervas et al. 2015) for its global dominance in peer-to-peer accommodations. However, the sharing economy is developing rapidly in China. With the explosive growth of active users as well as gross merchandise volume of Xiaozhu.com, we can witness the thriving development of the short-term rental industry in China, which is poised to become a steady marketplace of the world’s sharing economy. Thus, our research fills gaps in research into China’s sharing economy platforms, and makes contributions to helping researchers have a clearer insight into China’s sharing economy. 6.2 Practical Implications Besides the theoretical implications mentioned above, this study also offers important practical implications. First, on short-term rental platforms, hosts can improve their performance—such as quickly replying to consumer questions and accepting reservations—to demonstrate their benevolence and kindness (Mcknight and Kacmar 2002). Hosts should also provide more detailed information to potential renters (Ahn et al. 2004), otherwise renters will

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likely feel that the host is not enthusiastic or benevolent enough and may seek out a different rental. Second, it’s better for hosts to share more personal information on their profile pages to demonstrate openness, integrity, and gain trust from renters. If hosts disclose detailed personal information and transaction records, renters will know more about their host and his/her reservation history (rather than just the total number of transactions), which contributes to building trust, eliminating risk, and ultimately leads to more reservations (Lutz et al. 2013; Connolly and Bannister 2007). Third, the gender effect on trustworthiness should be further investigated to help male hosts improve their service quality or take other measures to increase their trustworthiness. Furthermore, since females are perceived as more trustworthy than males (Croson and Buchan 1999; Innocenti and Pazienza 2006; Dittrich 2015) and receive more reservations as shown through our analysis, we suggest that it would be beneficial for couples to let the female act as the primary host. These findings are beneficial to the improvement of shortrental platforms, and can be also applied to other sharing economy domains. 7. Conclusions In this paper, we conducted a data-driven study using real world data from Xiaozhu.com to estimate the effects of host attributes on short-term rental purchases from the perspective of benevolence, ability, and integrity trustworthiness, while accounting for product attributes. As a result, we found that except for the response rate, other host attributes—the time of reservation confirmation, the acceptance rate of renter reservations, the number of listings owned by hosts, whether a host has a personal profile page, and host’s gender—all have significant effects on reservations. However, this study does have a few limitations. First, we only focused on the short-term rental services in a specific regional market. Thus, the findings have not been generalized to

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other cities in China. In the future, we will test our conclusions in more cities. Second, although we attempted to include all factors that are disclosed on a host’s profile page, there might be other information that influences trust-building not included in our study due to data unavailability. For example, host’s age, educational background, hometown, and so on can be included in future research as well. Third, we will keep crawling data from Xiaozhu.com periodically to construct a panel of data for further research.

References

Ahn, T., Ryu, S. and Han, I. (2004), "The impact of the online and offline features on the user acceptance of Internet shopping malls", Electronic Commerce Research & Applications, Vol.3 No.4, pp. 405-420. Bardhi, F. and Eckhardt, G. M. (2012), "Access-based consumption: the case of car sharing", Journal of Consumer Research, Vol.39 No.4, pp. 881-898. Beijing Times. (2016), "The 2015 annual trend report of Xiaozhu.com", available at: http://epaper.jinghua.cn/html/2016-02/03/content_278669.html (accessed 3 February 2016). Belanger, F., Hiller, J. S. and Smith, W. J. (2002), "Trustworthiness in electronic commerce: the role of privacy, security, and site attributes", Journal of Strategic Information Systems, Vol.11 No.3, pp. 245-270. Belk, R. (2010), "Sharing", Journal of consumer research, Vol.36 No.5, pp. 715-734. Blanco, M., Engelmann, D., Koch, A. K. and Normann, H. T. (2014), "Preferences and beliefs in a sequential social dilemma: a within-subjects analysis", Games & Economic Behavior, Vol.87 No.4624, pp. 122-135.

20

Buchan, N. R., Croson, R. T. A. and Solnick, S. (2008), "Trust and gender: an examination of behavior and beliefs in the investment game", Journal of Economic Behavior & Organization, Vol.68 No.3, pp.466-476. Chang, M. K., Cheung, W. and Tang, M. (2013), "Building trust online: interactions among trust building mechanisms", Information & Management, Vol.50 No.7, pp. 439-445. Chaudhuri, A. and Gangadharan, L. (2007), "An experimental analysis of trust and trustworthiness", Southern Economic Journal, Vol.73 No.73, pp. 959-985. Chen, S. C. and Dhillon, G.S. (2003). "Interpreting Dimensions of Consumer Trust in ECommerce", Information Technology and Management, Vol. 4 No.3, pp. 303-313. Connolly, R. and Bannister, F. (2007), "Consumer Trust in Electronic Commerce: Social & Technical Antecedents", World Academy of Science, Engineering and Technology, Vol. 34, pp. 239-248. Cook, J. and Wall, T. (1980), "New work attitude measures of trust, organizational commitment and personal need non-fulfilment", Journal of Occupational & Organizational Psychology, Vol.53 No.1, pp. 39–52. Croson, R. and Buchan, N. (1999), "Gender and culture: international experimental evidence from trust games", American Economic Review, Vol.89 No.2, pp.386-391. Delhey, J. and Welzel, C. (2011), "How general is trust in 'most people'? Solving the radius of trust problem", American Sociological Review, Vol.76 No.5, pp. 786-807. Dittrich, M. (2015), "Gender differences in trust and reciprocity: evidence from a large-scale experiment with heterogeneous subjects", Applied Economics, Vol.47 No.36, pp.1-14. Doney, P. M. and Cannon, J. P. (1997), "An examination of the nature of trust in buyer-seller relationships", Journal of Marketing, Vol.61 No.2 , pp. 35-51.

21

Ert, E., Fleischer, A. and Magen, N. (2016), "Trust and reputation in the sharing economy: the role of personal photos on airbnb", Tourism Management, Vol.55, pp. 62–73. Fehr, E. and Gächter, S. (2000), "Fairness and retaliation: the economics of reciprocity", Journal of Economic Perspectives, Vol.14 No.3, pp. 159-181. Field, A. (2009). Discovering Statistics Using SPSS, SAGE, London. Finley, K. (2013), "Trust in the sharing economy: an exploratory study", available at: https://www2.warwick.ac.uk/fac/arts/theatre_s/cp/research/publications/madiss/ccps_a4_ ma_gmc_kf_3.pdf (accessed September 2013). Freitag, M. and Traunmüller, R. (2009), "Spheres of trust: an empirical analysis of the foundations of particularised and generalised trust", European Journal of Political Research, Vol.48 No.6, pp. 782–803. Gansky, L. (2010), The Mesh: Why the Future of Business is Sharing, Portfolio Trade , New York, NY. Gefen, D. (2002), "Reflections on the dimensions of trust and trustworthiness among online consumers", ACM Sigmis Database, Vol.33 No.3, pp. 38-53. Golbeck, J., Marsh, S., Briggs, P., Castelfranchi, C., Falcone, R., Lorini, E., Seigneur, J. M., Levien, R., Ziegler, C. N. and Golbeck, J. (2009), Introduction to Computing with Social Trust, Springer, London. Hart, K. M., Capps, H. R., Cangemi, J. P. and Caillouet, L. M. (1986), "Exploring organizational trust and its multiple dimensions: a case study of general motors", Organization Development Journal, Vol.135 No.1, pp. 31-39. Hu, N., Zhang, J. and Pavlou, P. A. (2009), "Overcoming the J-shaped distribution of product reviews", Communications of the Acm, Vol.52 No.10, pp. 144-147.

22

Innocenti, A. and Pazienza, M. G. (2006) "Altruism and gender in the trust game", Department of Economics, University of Sienna, Sienna, 5 April. Kim, D. J., Ferrin, D. L. and Rao, H. R. (2008), "A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents", Decision support systems, Vol.44 No.2, pp. 544-564. Lieberman, J. K. (1982), "The litigious society", Michigan Law Review, Vol.80 No.4, pp. 216227. Lu, Y., Zhao, L. and Wang, B. (2010), "From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers' purchase intention", Electronic Commerce Research & Applications, Vol.9 No.4, pp. 346-360. Lutz, C., Hoffmann, C. P. and Kaenel, A. V. (2013), Perception is Reality: The Impact of Buyer and Seller Attributes on Online Trust, Power of trust - perspectives and current challenges in the entrepreneurial context, Springer, Wiesbaden. Mayer, R. C. and Schoorman, F. D. (1995), "An integrative model of organizational trust", Academy of Management Review, Vol.20 No.3, pp. 709-734. Mcknight, D. H. and Kacmar, C. (2002), "Developing and Validating Trust Measures for eCommerce: An Integrative Typology", Information Systems Research, Vol.13 No.3, pp. 334-359. Melnik, M. I. and Alm, J. (2005), "Seller reputation, information signals, and prices for heterogeneous coins on eBay", Southern Economic Journal, Vol.72 No.72, pp. 305-328. Orbell, J., Dawes, R. and Schwartz-Shea, P. (1994), "Trust, social categories, and individuals: the case of gender", Motivation & Emotion, Vol.18 No.2, pp. 109-128. Pavlou, P. A. (2003), "Consumer acceptance of electronic commerce: integrating trust and risk

23

with the technology acceptance model", International Journal of Electronic Commerce, Vol.7 No.7, pp. 101-134. PWC (2015), "The sharing economy – sizing the revenue opportunity", available at: http://www.pwc.co.uk/issues/megatrends/collisions/sharingeconomy/the-sharingeconomy-sizing-the-revenue-opportunity.html (accessed 27 April 2015). Rick, T. (2013), "The drivers behind the rise of the collaborative economy", available at: http://www.torbenrick.eu/blog/strategy/the-drivers-behind-the-rise-of-the-collaborativeeconomy (accessed 23 August 2013). Schoorman, F. D. and Davis, J. H. (2007), "An integrative model of organizational trust: past, present, and future", Academy of Management Review, Vol.32 No.2, pp. 344-354. Shankar, V., Urban, G. L. and Sultan, F. (2002), "Online trust: a stakeholder perspective, concepts, implications, and future directions", Journal of Strategic Information Systems, Vol.11 No.3, pp 325–344. Sitkin, S. B. and Roth, N. L. (1993), "Explaining the limited effectiveness of legalistic 'remedies' for trust/ distrust", Organization Science, Vol.4 No.3, pp. 367-392. Snijders, C. and Keren, G. (1999), "Determinants of trust", Games & Human Behavior Essays in Honor of Amnon Rapoport, Vol. 40 No.4, pp. 375. Stolle, D. (2002), "Trusting strangers: the concept of generalized trust in perspective", Osterreichische Zeitschrift Für Politikwissenschaft, Vol.4 No.2, pp. 397-412. Stolle, D. and Hooghe, M. (2004), "The roots of social capital: attitudinal and network mechanisms in the relation between youth and adult indicators of social capital", Acta Politica, Vol.39 No.4, pp. 422-441. Stors, N. and Kagermeier, A. (2015), "Motives for using airbnb in metropolitan tourism – why

24

do people sleep in the bed of a stranger ?", Regions Magazine, Vol.299, No.1, pp 17-19. Taylor, P., Parker, K., Morin, R., Patten, E. and Brown, A. (2014), "Millennials in adulthood", available

at

:

http://www.pewsocialtrends.org/2014/03/07/millennials-in-adulthood

(accessed 7 March 2014). Trochim, W. M. and Donnelly, J. P. (2006), The Research Methods Knowledge Base (3rd ed.), Atomic Dog, Cincinnati, OH. Tussyadiah, I. P. (2014), "Toward a theoretical foundation for experience design in tourism", Journal of Travel Research, Vol.53 No.5, pp. 543-564. Ufford, S. (2015), "The future of the sharing economy depends on trust", available at: http://www.forbes.com/sites/theyec/2015/02/10/the-future-of-the-sharing-economydepends-on-trust/#5ea6884758ff (accessed 10 February 2015). Xiong, L. and Liu, L. (2003), "Building trust in decentralized peer-to-peer electronic communities", in Proceedings of the 5th International Conference on Electronic Commerce in Pittsburgh, Pennsylvania, USA, 2003, ACM International Conference Proceeding Series 50. Yamagishi, T. and Yamagishi, M. (1994), "Trust and commitment in the United States and Japan", Motivation & Emotion, Vol.18 No.2, pp. 129-166. Yu, B. and Singh, M. P. (2002), "Distributed reputation management for electronic commerce", Computational Intelligence,Vol.18 No.4, pp. 535-549. Zervas, G., Proserpio, D. and Byers, J. (2015), "A first look at online reputation on airbnb, where every stay is above average", available at SSRN: https://ssrn.com/abstract =2554500 (accessed 28 January 2015).

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Host attributes

Trustworthiness Product’s attributes (Control variables)

Benevolence trustworthiness

Ability trustworthiness

Integrity trustworthiness

Host attributes Response rate Price

H1a

Confirm time H1b

Accept rate

Number of listings

H2a

H2b

Has page or not H3

Gender

H4

Location Reservation of listings

Figure 1. Research Framework

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Dimensions Dependent variable

Control variable (Product’s attributes)

Independent variable

Table 1 Variable Description and Summary Statistics Variable Definition Mean SD Min Max Reservations that a host ResNum 6.60 11.33 0 119 receives in one month Price of single room or Price whole apartment (unit: 342.41 422.49 55 6588 yuan) Listing’s location, listings in urban = 1, listings in suburban = 0 Location (Listings within the Fifth 0.79 0.40 0 1 Ring Road of Beijing are defined as urban, others being suburban) Listing type – whole Listing_Type apartment = 1, single 0.53 0.50 0 1 room = 0 Number of host responses versus number of renter inquiries (i.e., Response_Rate 0.88 0.23 0 1 the rate that a host responds to questions asked by renters) Number of minutes it takes for a host to Confirm_Time 9.57 31.34 0 664 confirm the renter reservation The acceptance rate of Accept_Rate 0.71 0.32 0 1 renter reservations The number of listings Listing_Num 2.63 3.72 1 49 owned by hosts Host who has a personal profile page = 1, do not Has_Page 0.21 0.41 0 1 have personal profile page = 0 Gender of the host, male Gender 0.42 0.49 0 1 = 1, female = 0

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Table 2. Estimation Result of Host Attributes on Reservations Dependent Variable: Reservations Coefficient Standard Error p-value Log(Price) -0.967 0.074 0.000 Location 0.506 0.041 0.000 Listing_Type 0.587 0.036 0.000 Response_Rate 0.211 0.111 0.056 Confirm_Time -0.004 0.001 0.000 Accept_Rate 1.177 0.072 0.000 Log(Listing_Num) 1.550 0.030 0.000 Has_Page 0.367 0.028 0.000 Gender -0.225 0.027 0.000 (Constant) 1.774 0.190 0.000 LR chi2(10) 4363.34 ( p = 0.000) Log likelihood -4628.64

VIF 1.82 1.02 1.81 1.38 1.02 1.44 1.06 1.07 1.02

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Hypothesis H1a H1b H2a H2b H3 H4

Table 3. Results of Hypotheses Testing Host Attributes Hypothesis Testing The response rate The time of reservation confirmation The acceptance rate of renter reservations The number of listings owned by hosts Whether the host has a personal profile page Gender of the host

Not Supported Support Support Support Support Support

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Table 4. Robustness Check Results Dependent variable: Reservations

Log(Price) Location

(1) Whole apartment -0.713*** (0.081) 0.496*** (0.052)

(2) Single room -1.935*** (0.165) 0.574*** (0.068)

-0.081 (0.167) -0.008** (0.003) 1.148*** (0.116) 1.853*** (0.044) 0.296*** (0.041) -0.149*** (0.041) 3.984 (0.372) 2518.00 ( p = 0.000)

(3) Two month lag -0.741*** (0.064) 0.327*** (0.035) 0.613*** (0.032) 0.278*** (0.097) -0.003*** (0.001) 0.980*** (0.063) 1.571*** (0.028) 0.372*** (0.025) -0.172*** (0.024) 1.639*** (0.166) 5092.24 ( p = 0.000)

(4) Three month lag -0.839*** (0.049) 0.405*** (0.027) 0.600*** (0.024) 0.249*** (0.073) -0.004*** (0.001) 1.066*** (0.047) 1.561*** (0.020) 0.370*** (0.019) -0.196*** (0.018) 2.384*** (0.125) 9425.96 ( p = 0.000)

0.351*** (0.017) -0.003*** (0.001) 1.133*** (0.093) 1.304*** (0.042) 0.425*** (0.038) -0.286*** (0.035) 1.757*** (0.250) 1890.32 ( p = 0.000) -2835.88

-1728.67

-5706.89

-8427.21

Listing_Type Response_Rate Confirm_Time Accept_Rate Log(Listing_Num) Has_Page Gender (Constant) LR chi2(10) Log likelihood

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