How Guests Develop Trust in Hosts: An Investigation of Trust Formation in P2P Accommodation

Abstract

As peer-to-peer (P2P) accommodation service often involves multistage interactions between hosts and guests in online and offline settings, trust between the parties involved is of the utmost importance. In particular, the possibility of interacting offline in P2P service delivery highlights the significance of interpersonal trust between hosts and guests. Accordingly, this study examines the formation of trusting beliefs in hosts, comprising prospective guests’ perception of the ability, benevolence, and integrity of the hosts. This study estimated the effects of two antecedents—propensity to trust and trust in P2P platform—on trusting beliefs, as well as the consequence of trusting beliefs, that is, behavioral intention to book from the host. Important implications for trust formation in a P2P accommodation marketplace are provided.

Keywords

sharing economy peer-to-peer accommodation interpersonal trust trusting beliefs

Introduction

The tourism and hospitality industry has been experiencing the disruptive development of peer-to-peer (P2P) accommodation over the last decade, as the new business model emerged within the growing phenomenon of the sharing economy. Today, private individuals are able to easily connect with each other to share temporary access to underutilized spaces for a fee via Airbnb, HomeAway, and other similar peer platforms. According to a report by Smith Travel Research, Airbnb has more than three million listings worldwide, which is almost three times the size of Marriott International and four times more than that of Hilton Worldwide (Haywood et al. 2017). Bloomberg suggests that Airbnb has penetrated about 20% of the market in the accommodation industry (Verhage 2016). To that end, researchers have argued that the accelerated growth in Airbnb’s market share may be attributed to the benefits that address the fundamental needs of travelers with regard to economic and societal considerations (Tussyadiah 2016). That is, the platform enables travelers to seek low-cost accommodation and desirable authentic experiences through more meaningful social interactions with the local community while visiting a travel destination.

The sharing economy is defined as a socioeconomic system that allows private individuals to engage in temporary disposition and acquisition of objects with peers via online-based network (Philip, Ozanne, and Ballantine 2015). Transactions in the sharing economy are carried out in a “triadic relationship,” involving peers (i.e., hosts and guests), underlined products (i.e., spare rooms), and platforms (e.g., Airbnb, HomeAway) (Benoit et al. 2017). Since trading in a P2P marketplace is conducted between strangers, consumers (and P2P accommodation hosts) face information asymmetry as well as various risks, including economic and security risks (Ert, Fleischer, and Magen 2016). More importantly, the sharing economy often involves multistage interactions that occur not only online, but also in offline environments, as opposed to typical retail websites where consumers simply communicate with sellers online—a single-stage interaction (Ellison and Hancock 2013). Therefore, trust is considered a unique feature of the sharing system and, thus, is vital in P2P accommodation research (Hawlitschek, Teubner, and Weinhardt 2016; Tussyadiah and Park 2018). Indeed, connecting people and creating trust are considered the fundamental components in shaping a reliable environment of collaboration (Mazzella et al. 2016; Wu, Ma, and Xie 2017). However, while tourism studies have focused on identifying the drivers and inhibitors of using P2P systems (e.g., Gansky 2010; Tussyadiah and Pesonen 2016; Zekanovic-Korona and Grzunov 2014), studies on understanding how trust is formed in the sharing economy are still limited (Cheng 2016).

The intensity of social interactions between peers in the sharing economy highlights the significance of interpersonal trust—trust between guests and P2P accommodation hosts. While the general e-commerce marketplace does not necessarily require buyers and sellers to meet in person, the settings of the sharing economy often facilitate both digital (computer-mediated) and face-to-face interactions (Möhlmann 2016). Thus, trust in hosts plays an important role in consumer decision making in the context of P2P accommodation. This study will, therefore, examine trust in peer-to-peer accommodation hosts based on the concept of trust as interconnected components, including trusting beliefs, which reflects consumer evaluation of the hosts’ integrity, benevolence, and ability, and behavioral intention, which is the consumer’s willingness to depend on the hosts.

Further, drawing on the concept of institutional trust (Liang, Choi, and Joppe 2018; Zucker 1986), it has been suggested that trust in online platforms and trust in sellers who provide services using the platform are of distinct types. Trust in platforms has been considered a catalyst for encouraging online transactions, allowing consumers to construct high expectations of a satisfying exchange relationship with sellers (Pavlou 2003). In addition to the element directly associated with the P2P platform, an individual’s propensity to trust other people as a form of personality trait shaped through enduring experiences is another important element in explaining trusting beliefs (Koufaris and Hampton-Sosa 2004). Thus, this research will assess trust in P2P accommodation platforms as well as the propensity to trust, as the antecedents of trust in P2P accommodation hosts, along with behavioral intention as a consequence.

Literature Review

Trust in Sharing Economy

Research has found that engagement in monetary/nonmonetary transactions is less likely to take place between two strangers if they do not trust one another (Ert, Fleischer, and Magen 2016; Huurne et al. 2017). Understanding the role of trust is especially salient in the sharing economy, where two strangers (buyers and sellers) interact through an online platform to share access to specific objects. Indeed, the principal element of service in a P2P system is provided by private individuals using online network platforms, allowing the service providers to be exposed to potential user opportunism (Huurne et al. 2017). This involves asymmetric information as well as various risks to both buyers and sellers. In this respect, the intensity of social interactions among peers is stronger in the sharing economy than in other conventional retailing platforms, such as Amazon.

Mayer, Davis, and Schoorman (1995) defined trust as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (p. 712). In other words, in a relationship or an exchange, trust exists when one (the trustor) is willing to rely on (and abandon control over) the actions of the other (the trustee); it can thus be considered willingness to take a risk. This type of relationship resembles the transactional environment in the sharing economy, in which both parties (P2P hosts and guests in this study) present some kind of vulnerability to, and form a specific expectation about, the behavior of the other party (Huurne et al. 2017; Resnick and Zeckhauser 2002). In particular, the sharing economy in tourism involves several social interactions between guests and hosts. In addition to online interactions to initiate and complete the transaction, face-to-face interaction often occurs, for instance, when hosts hand over keys to the apartment or provide information and recommendations about destinations. Thus, this study highlights interpersonal trust—an orientation of one actor to other individual(s)—between e-commerce consumers (guests) and e-vendors (hosts) (see McKnight and Chervany 2001). The interpersonal (mutual) trust highlights the notion of the psychological state representing a guest’s willingness to be vulnerable to a host with the consideration of the P2P accommodation host’s characteristics. A guest and a host exchange information about their identities and previous staying/hosting experiences online (i.e., though Airbnb’s website platform) before interacting in person. This implies that a certain level of trust occurs at this stage, as the privacy of this information may place them at risk. It is associated with the generic definition of interpersonal trust proposed by Rotter (1967), noting that trust is the expectation held by people that they can depend on the word, promise, or verbal, vocal, or written statement of another individual. Indeed, previous studies on consumer-to-consumer platforms have demonstrated the importance of interpersonal trust in an online community (Chen, Zhang, and Xu 2009; Lu, Zhao, and Wang 2010; Ridings, Gefen, and Arinze 2002). The interpersonal trust built by community members leads to repeated economic exchanges in the long term (Chen, Zhang, and Xu 2009).

Literature on trust in information technology suggests that there are three dimensions of trusting beliefs in online service providers: ability, benevolence, and integrity (Gefen 2002; Gefen and Straub 2004; Hawlitschek, Teubner, and Weinhardt 2016; Mayer, Davis, and Schoorman 1995; Wu, Ma, and Xie 2017). In this study, the dimensions of trusting beliefs in accommodation hosts are defined as ability, benevolence, and integrity. Ability refers to the trustor’s perception of the trustee’s competence and knowledge salient to the expected behavior (Lu, Zhao, and Wang 2010; Mayer, Davis, and Schoorman 1995). Indeed, consumers evaluate such perception of P2P accommodation hosts based on (a) whether the hosts are competent and sufficient to fulfill the intended behavior, and (b) whether the hosts have the knowledge and skills required to pursue the behavior in a proper manner (Bhattacherjee 2002). Benevolence refers to the extent to which a trustee is believed to care about and have good intentions toward the trustor, beyond its own profit motive (Lu, Zhao, and Wang 2010; Mayer, Davis, and Schoorman 1995). A benevolent trustee would help the trustor with beneficial motives, even if the trustee receives no reward for being helpful. Benevolence introduces faith and altruism into a relationship, which places it against opportunistic behaviors. Integrity refers to the trustor’s perception that the trustee will act in accordance with social norms and stand by a set of principles that the trustor accepts during and after the exchange, such as credibility and dependability (Lu, Zhao, and Wang 2010; Mayer, Davis, and Schoorman 1995).

While it is recognized that several scholars have proposed integrating benevolence with integrity (Lu, Zhao, and Wang 2010; Ridings, Gefen, and Arinze 2002), this study postulates that the three dimensions are distinctive constructs in forming trusting beliefs in Airbnb hosts. This is because benevolence reflects the trustee’s motives associated with altruism, whereas integrity stands for constant commitments and honesty, or reliability, which may be practiced for utilitarian rather than altruistic motivations (McKnight, Cummings, and Chervany 1998). Based on the conceptualizations of trusting beliefs in Airbnb hosts, this study hypothesized:

Hypothesis 1: Trusting beliefs in Airbnb hosts consist of integrity, benevolence, and ability.

Antecedents and Consequence of Trusting Beliefs in P2P Accommodation Hosts

Adopting the framework of online trust proposed by Shankar, Urban, and Sultan (2002), this study employs the “antecedents–trust–outcomes” model to investigate the role of trusting beliefs in P2P accommodation hosts. Specifically, two antecedents, including propensity to trust and trust in platform, and behavioral intention as a consequence of trusting beliefs, will be discussed (McKnight, Choudhury, and Kacmar 2002a; McKnight, Cummings, and Chervany 1998). Propensity to trust (also known as disposition to trust) refers to an individual’s dispositional willingness to rely on others (Ridings, Gefen, and Arinze 2002). Individuals’ propensity to trust has been regarded as a personality trait that is shaped through ongoing experiences and socialization, which potentially influence trust building (Schlaegel 2015). If an individual is likely to trust others in general, this trust creates consistent willingness to form trusting beliefs in others in different contexts (e.g., Airbnb hosts) (Chen, Lai, and Lin 2014). Ridings, Gefen, and Arinze (2002) suggested that propensity to trust is particularly effective when two parties are unfamiliar. This condition particularly applies to tourism, which often involves geographical, temporal, social, and cultural distances, creating a potential information asymmetry between guests and hosts at the destination. Extant research related to e-commerce identified that trust propensity has positive influences on developing trust in websites (Yaobin and Tao 2007) and trust in e-vendors (Gefen 2000). Therefore, it is hypothesized that

Hypothesis 2: Propensity to trust has a positive effect on trust in a platform.

Hypothesis 3: Propensity to trust has a positive effect on trusting beliefs in hosts.

Trust transfer theory denotes that people’s trust in familiar targets can transfer to other individual entities, because of their relationships with the source of trust (Stewart 2003). In this respect, trust transference can take place between an online platform and a certain e-vendor based upon the virtue of their linked websites (Mou, Shin, and Cohen 2017). When buyers trust an online intermediary, they form an association with the intermediary, and then their trust in the platform is cascaded to the online sellers (Chen, Lai, and Lin 2014), which is labeled a hierarchical order of trust transfer (Möhlmann 2016). Indeed, Hong and Cho (2011) have demonstrated the positive effect of a buyer’s trust in the platform on trust in a seller in an online marketplace. Thus, consumers’ positive beliefs and attitudes toward the accommodation sharing platform Airbnb will transfer to the Airbnb hosts as a trustworthy transaction partner. Therefore, it is suggested that:

Hypothesis 4: Trust in a platform has a positive effect on trusting beliefs in hosts.

Trust alleviates the perceptions of uncertainty and risk and encourages engagement in trust-related behaviors with e-vendors, including sharing personal information and purchasing products (McKnight, Choudhury, and Kacmar 2002b). It is suggested that lack of trusting beliefs is the primary reason for shoppers to leave a website without purchasing (e.g., Johnson 2007). According to the transaction theory, trust can reduce the transaction cost in the interactions between online buyers and sellers (Lu, Zhao, and Wang 2010; Pavlou and Dimoka 2006). This is consistent with the Theory of Reasoned Action, which posits that beliefs are a strong predictor of the corresponding intentions (Davis, Bagozzi, and Warshaw 1989). Indeed, the link between trusting beliefs (i.e., perception of the ability, benevolence, and integrity of the vendor) and behavioral intention (willingness to depend, i.e., a decision to make oneself vulnerable to the vendor) has been made clear in the literature as two interrelated components of trust (McKnight, Choudhury, and Kacmar 2002a). In this study, it is suggested that consumer perception of the hosts’ ability, benevolence, and integrity will create the intention to transact with, and hence be vulnerable to, the hosts. Therefore, it is hypothesized that:

Hypothesis 5: Trusting beliefs in hosts have a positive effect on behavioral intention.

Methods

In order to test the hypotheses, an online questionnaire was created to collect data from travelers who are familiar with P2P accommodation. The questionnaire consists of three parts. The first section asked respondents about their past travel behaviors and P2P accommodation experiences. Then, respondents were asked to imagine a scenario where they look for an accommodation on Airbnb. In order to manipulate respondents’ mindset to cope with a key concept of this research (emphasizing interpersonal trust between guests and hosts), respondents were randomly assigned to preview host profiles randomly selected from Airbnb (Ert, Fleischer, and Magen 2016; Hawlitschek, Teubner, and Weinhardt 2016). Only the name and profile description of the hosts (i.e., without a picture, review, or property-related information) were presented to respondents. In order to minimize bias from positive or negative sentiments attached to certain names, Neil and Marjorie, which scored neutral in a previous study on sentiment classification (Dodds et al. 2011), were used to represent male and female hosts, respectively.

Furthermore, respondents were asked to rate their agreement to six types of trust concepts. The measurements were adopted from previous studies and were modified to fit the study context when necessary. More specifically, there were four items measuring propensity to trust, which reflects a dispositional willingness to rely on others (Ridings, Gefen, and Arinze 2002). Respondents were asked to answer six questions about trust in platform (Walsh and Beatty 2007) and a question of behavioral intention (Tanford and Montgomery 2015). Measurement for trusting belief in Airbnb hosts containing the three subconcepts of ability, benevolence, and integrity, were derived from Colquitt and Rodell (2011) and Ridings, Gefen, and Arinze (2002). Lastly, respondents’ demographic information was collected. The detailed survey questions are provided in the appendix.

The questionnaire was distributed to American travelers who are familiar with P2P accommodation via Amazon Mechanical Turk (AMT), a crowd marketplace for tasks requiring human intelligence. Previous studies have shown that users with an approval rating of 95% or higher (i.e., high reputation) typically produce high-quality data (e.g., Peer, Vosgerau, and Acquisti 2014). Therefore, the link to the questionnaire was only made available to AMT users (workers) with approval rating above 98%. All respondents received US$0.40 (forty cents) on completion of the survey. In order to facilitate a consistent understanding of peer-to-peer accommodation, a definition has been provided in the survey: “Peer-to-peer accommodation rentals are accommodation services where you pay a fee to stay at someone’s property (such as Airbnb), but excluding free accommodation services (such as Couchsurfing).” A total of 969 respondents completed the survey.

The data analysis comprises three steps. First, descriptive/frequency analysis was used to understand the profiles of respondents and assess the distribution of the collected data. Then, partial least squares (PLS) was utilized to test the proposed hypotheses by verifying the measurement and structural models. Note that the main purposes of this study are to verify the hierarchical construct of trusting beliefs in hosts and to identify the factors that account for the variances of the trusting beliefs rather than confirming a theoretical model. Thus, PLS approach is a suitable type of analysis to address the research questions (Urbach and Ahlemann 2010). More specifically, this study assessed the measurement model by checking the cross-loadings resulting from the confirmatory factor analysis (CFA), the average variance extracted (AVE) with cut-off value over 0.50, and the latent correlation analysis (Chin 2010). Then, Cronbach’s alpha and composite reliability were estimated with a cut-off level of 0.80 (Hair, Ringle, and Sarstedt 2011). Given the confirmation of the measurement model, a structural model estimating the hypothetical relationships was tested with consideration of p values and R-squares.

A potential for common method error is critical to consider when the data for all constructs are collected using the same measurement approach. Thus, two estimations were tested to identify the existence of common method bias in the results of this research, including Harman’s single-factor test and the correlation matrix (Podsakoff et al. 2003).

Then, in order to verify the findings derived from the structural model estimation of PLS, the effect size of the path model, f² (Cohen 1992), and predictive validity, Stone-Geisser’s Q², were checked (Tenenhaus et al. 2005). Indeed, the Stone-Geisser’s Q² method claims that the model is capable to suggest a prediction of the endogenous variable and shows a synthesis of function fitting and cross validation. When the value of Q² about a certain endogenous variable is larger than zero, its explanatory variables demonstrate predictive relevance.

Results

Profiles of Respondents

The profiles of respondents in this research are presented in Table 1. Forty percent of respondents were female and about 90% were younger than 45 years. Participants in this study have relatively higher levels of education: approximately 57% received a college degree or higher. About 86% of respondents indicated their annual income was below $100,000. In terms of past travel and P2P accommodation experiences, more than half (61.1%) of respondents indicated that they have traveled a few times a year. Most respondents (74%) have stayed in P2P accommodations before, as indicated by their response to the following question in the survey: “Have you stayed in peer-to-peer accommodation rental (such as Airbnb) before?” Those who have no experience in P2P accommodation showed moderate levels of familiarity with the Airbnb.

Table 1.

Profiles of Respondents.

Variables	Frequency	%
Demographics
Gender (Female)	395	40.8
Age
15–24 years	179	18.5
25–34 years	497	51.3
35–44 years	198	20.5
45–54 years	58	6.0
55–64 years	33	3.4
≥65 years	3	0.3
Highest level of education
Less than high school	1	0.1
High school / GED	69	7.1
Some college	254	26.2
Two-year college degree	94	9.7
Four-year college degree	426	44.0
Master’s degree	93	9.6
Doctoral degree	12	1.2
Professional degree (JD, MD)	19	2.0
Annual household income
<$20,000	106	10.9
$20,000–49,999	340	35.1
$50,000–99,999	390	40.2
$100,000–149.999	97	9.9
$150,000+	36	3.7
Travel and P2P accommodation experiences
How often do you travel?
About once every other year or less	89	9.2
About once a year	214	22.1
A few times a year	592	61.1
Monthly	67	6.9
Weekly	7	0.7
Have you stayed in P2P accommodations? (Yes)	717	74.0
	Mean	SD
Familiarity with P2P accommodations	2.89	0.99

Note: GED = General Educational Development; JD = Juris Doctor; MD = Doctor of Medicine; P2P = peer-to-peer.

Estimation of the Measurement Model

A CFA was used to initially check indicator variances of the six latent constructs. The result shows that all factor loadings are statistically significant and larger than the cut-off points (0.70). In addition, the factor loadings reflecting the corresponding constructs are much higher than those with other principal constructs. These findings confirmed the discriminant validity of the constructs (see Table 2).

Table 2.

Results of Confirmatory Factor Analysis.

	Ability	Benevolence	Integrity	Propensity to Trust	Trust in Platform	Intention
Ability_1	0.847	0.400	0.466	0.107	0.309	0.421
Ability_2	0.849	0.377	0.491	0.191	0.333	0.439
Ability_3	0.839	0.535	0.484	0.127	0.334	0.457
Ability_4	0.866	0.515	0.575	0.145	0.345	0.470
Benevolence_1	0.463	0.866	0.552	0.214	0.390	0.395
Benevolence_2	0.482	0.908	0.597	0.198	0.380	0.418
Benevolence_3	0.504	0.896	0.585	0.205	0.376	0.444
Benevolence_4	0.504	0.896	0.585	0.205	0.376	0.444
Integrity_1	0.468	0.470	0.852	0.194	0.401	0.351
Integrity_2	0.512	0.599	0.864	0.166	0.387	0.424
Integrity_3	0.484	0.494	0.742	0.232	0.372	0.374
Propensity_1	0.146	0.219	0.233	0.932	0.351	0.222
Propensity_2	0.172	0.236	0.243	0.939	0.372	0.239
Propensity_3	0.154	0.204	0.208	0.900	0.349	0.190
Propensity_4	0.138	0.190	0.185	0.897	0.334	0.178
Trust in Platform_1	0.325	0.338	0.373	0.326	0.859	0.352
Trust in Platform_2	0.314	0.372	0.400	0.354	0.886	0.381
Trust in Platform_3	0.334	0.374	0.399	0.344	0.888	0.371
Trust in Platform_4	0.330	0.367	0.400	0.287	0.831	0.318
Trust in Platform_5	0.363	0.384	0.448	0.343	0.855	0.352
Trust in Platform_6	0.355	0.383	0.424	0.338	0.882	0.332
Intent	0.527	0.463	0.468	0.227	0.405	1.000

Table 3 shows the results of the inter-correlation analysis across the first-order constructs. AVEs (the mean-squared loadings) of the individual constructs are not only larger than 0.82 but also higher than the cross-correlations of other constructs. This implies that the extent to which the latent variables explain their indicators are larger than the error variance (i.e., supporting convergent validity). The constructs are distinctively different across the six first-order constructs (i.e., supporting discriminant validity). In terms of reliability tests, estimated results of Cronbach’s alpha are over 0.75 and the values of composite reliability are beyond 0.86 which meets the acceptable level of reliability. There is no variable with a correlation value higher than 0.90, implying restricted collinearity across the six constructs (see Table 3).

Table 3.

Intercorrelations of the Latent Variables for First-Order Constructs.

	Cronbach’s Alpha	Composite Reliability	1	2	3	4	5	6
1. Ability	0.872	0.913	0.850
2. Benevolence	0.906	0.934	0.541	0.884
3. Integrity	0.756	0.861	0.595	0.638	0.821
4. Propensity to Trust	0.937	0.955	0.167	0.232	0.238	0.917
5. Trust in Platform	0.934	0.948	0.389	0.427	0.471	0.384	0.867
6. Intention	1.000	1.000	0.527	0.463	0.468	0.227	0.405	1.000

Note: Bold refers to square root of the average variance extracted on the diagonal.

Then, a hierarchical model of trusting beliefs in Airbnb hosts was assessed (see Table 4). The validity (AVE = 0.729) and reliability tests (Cronbach’s alpha = 0.911; composite reliability = 0.925) provide evidence of a reliable measure. As shown in Table 4, the loadings of the first-order latent variables, including ability, benevolence, and integrity, on the second-order factors exceed 0.80; all loadings are also significant at α = 0.001. These results support the statistical justification of trusting beliefs as a form of a second-order latent variable based on a series of guidelines suggested by Wetzels, Odekerken-Schröder, and Van Oppen (2009). Thus, hypothesis 1 is supported. Importantly, the finding indicating benevolence as the most influential form of trust is consistent with previous studies in the field of information technology (Lewicki and Bunker 1995; Pavlou and Dimoka 2006).

Table 4.

Assessing the Hierarchical Model of Trusting Beliefs in Airbnb Hosts.

	Trusting Beliefs
Cronbach’s alpha	0.911
Composite reliability	0.925
AVE^a	0.729
Ability	0.840 [0.814, 0.862]^b
Benevolence	0.874 [0.855, 0.890]^b
Integrity	0.839 [0.818, 0.860]^b

Square root of average variance extracted.

Percentile estimate of the 97.5% confidence interval.

Assessing the Structural Model

The proposed model to estimate the hypothetical relationships was tested using SmartPLS with a bootstrap resampling method (5,000 sample generation). Propensity to trust positively affects trust in platform (b = 0.384, p < 0.001) and trusting beliefs (b = 0.066, p < 0.05), supporting hypotheses 2 and 3, respectively. To better understand the role of propensity to trust in trust formation, additional indirect and total effects on trusting beliefs were assessed. They show statistically significant indirect effect (b = 0.181) and total effect (b = 0.247) of propensity to trust in explaining the variation in trusting beliefs. As a result, it can be suggested that the variable of propensity to trust directly and indirectly (mediated by trust in platform) affects trusting beliefs (see Table 5).

Table 5.

Direct, Indirect, and Total Effect of Propensity to Trust on Trusting Beliefs.

	Direct Effect	Indirect Effect (via Trust in Platform)	Total Effect
Propensity to trust → Trusting beliefs	0.066	0.181	0.247

Trust in platform also has a positive influence on trusting beliefs (b = 0.473, p < 0.001) supporting hypothesis 4. As a result, about 25% of variance in trusting beliefs in hosts can be explained by the two constructs. Finally, trusting beliefs positively influences behavioral intention (b = 0.571, p < 0.001; R² = 0.326), supporting hypothesis 5 (see Figure 1).

Next, the effect size of the structural models, focusing on exogenous variables, was tested to explain the trusting belief based on Cohen f² approach (Cohen 1992). The effect sizes (f²) of propensity to trust was 0.01 (indicating a very small effect) and of trust in platform was 0.25 (indicating medium effect). Furthermore, a series of blindfolding procedures was conducted to estimate Stone-Geisser’s Q² to test the model’s predictive validity. The values of Q² (i.e., the relative impact of predictive relevance) are all above zero (ability = 0.480, benevolence = 0.560, integrity = 0.452, trust in platform = 0.103, trusting beliefs = 0.124, and behavioral intention = 0.318), which demonstrate the predictive relevance of the estimated model for each exogenous construct.

Testing Common Method Bias and Multicollinearity

It is suggested that when the data for exogenous and endogenous constructs were collected from the same respondents using the same study design, there is a potential for common method error (Podsakoff et al. 2003). To that end, two approaches to estimating common method bias were applied: Harman’s factor loading and latent correlation analysis. Harman’s single-factor test, on the basis of exploratory factor analysis, showed that the Kaiser-Meyer-Olkin’s value was 0.923 (p < 0.001); the unrotated principal components analysis containing the six constructs accounts for 40.578% of the total variance, which is below the cut-off of 50%. Then, as presented in Table 3, no extreme high correlation values (r > 0.90) were found across the constructs being investigated. Thus, based on the results of these two estimations, it can be suggested that there are limited potentials for common method errors in the analytical model. In order to check for multicollinearity issue, variance inflation factor was calculated. The values were below 10, that is, 1.173 for both propensity to trust and trust in platform. Accordingly, this result suggests a limited concern over collinearity between the exogenous variables (Belsley, Kuh, and Welsch 2004).

Discussion and Conclusion

Trust has been regarded as one of the most important elements in producing engagement between people and thus encouraging purchases online (Agag and El-Masry 2017; Ladhari and Michaud 2015). The significance of trust has been emphasized in the sharing economy, which involves not only transactions between peers, but also multiple interactions in online and offline settings (Luo and Zhang 2016). In this respect, recognizing the importance of trust building between buyers and sellers, interpersonal trust (Rachel and Rogers 2010), this research aims to identify consumer trust formation in P2P accommodation hosts by verifying the structure of trusting beliefs, which are composed of the perception of hosts’ ability, benevolence, and integrity. Further, this research tested a model of trust formation by assessing the relationships between trusting beliefs with their two antecedents (propensity to trust and trust in platform) and consequences (behavioral intention) (McKnight, Choudhury, and Kacmar 2002b).

The findings of this research have a number of theoretical implications. This study provides support for interpersonal trust formation in the context of the peer-to-peer economy within the tourism field. While there have been several studies that emphasized the importance of institutionalized trust by focusing on legal institutional arrangements and regulatory security (e.g., Wu, Ma, and Xie 2017), this research suggests the importance of interpersonal trust based on the particular features of the sharing economy in which peers interact in mixed-mode settings (i.e., online and offline). The results reveal a hierarchical order model of interpersonal trust formation, in that trusting beliefs toward hosts are reflected in guests’ perception of hosts’ ability, benevolence, and integrity (Mayer, Davis, and Schoorman 1995; McKnight, Choudhury, and Kacmar 2002a).

This study also found the significance of individual characteristics related to propensity to trust (Lee and Turban 2001), as well as the hierarchical nature of the trust constructs associated with the concept of trust transfer (Stewart 2003). More interestingly, trust in platforms plays a more important role in explaining trusting beliefs toward hosts when compared to trust propensity (Möhlmann 2016). In particular, it is identified that the indirect effect of propensity to trust, mediated by trust in platform, is stronger than its direct effect on trusting beliefs. This finding suggests the importance of an integrated approach to trust research, by considering both institutional (e.g., structural assurance) and interpersonal (e.g., host attributes) trust in the context of a sharing economy, which enables researchers to gain a more context-specific view of how trust is established (Huurne et al. 2017; McKnight and Chervany 2001). As expected, this research determined that people with a higher trust in hosts are more likely to engage in trusting behavior (i.e., more likely to book from the hosts). It can be explained that trust helps the trustors to decrease transactional cost and, as a result, encourage accomplishing the goal-directed behavior (Pavlou and Fygenson 2006).

Based on these findings, it can be suggested that the dimensions of ability, benevolence, and integrity are of equal importance for P2P accommodation hosts in developing trust in prospective guests. In describing themselves online, the hosts should not only portray their capability for hosting but also try to appear more helpful and motivated to provide recommendations about the destination. For example, references to efforts of making guests feel at home and willingness to respond to consumers’ enquiries in a timely manner could be a driver to enhance the perceived benevolence of the hosts. Evidences of hosts’ honesty and sincerity should be included in host profiles, especially in the host description, in order to shape stronger perceptions of hosts’ integrity among consumers. Finally, regular updates of host profiles to form positive impressions of the hosts’ identities (Tussyadiah and Park 2018), as well as updated information about facilities and room availability, are important in increasing the level of trust toward hosts (Wu, Ma, and Xie 2017).

From the perspective of trust transfer, this study provides important implications for platform providers (e.g., Airbnb) as well as service providers (i.e., hosts). The influence of trust in platform on trusting beliefs toward hosts is significantly higher in magnitude compared to the consumers’ disposition to trust. This suggests the important role of the platform provider’s trustworthiness in consumer decision making toward hosts. From a practical viewpoint, it is important that hosts use sharing platforms that are trusted by consumers to allow trust transference to occur. In other words, being associated with a trusted platform will lead to a higher perception of trustworthiness. In the marketplace, attempts from platform providers such as Airbnb to increase trust in platforms have been evidenced. For example, in addition to having transparent practices of trust and safety, Airbnb also established the “Airbnb Trust Advisory Board,” which is made of trusted experts in data security. Future research should examine how different trust-enhancing efforts such as these will play a role in trust formation in general and trust transference (i.e., trust in platforms leading to trust in hosts) in particular.

In order to minimize the confounding effects of host characteristics and property characteristics, this study only used textual descriptions from host profiles. Therefore, the results of this study complement other studies on consumers’ reactions to hosts based on images and other characteristics of the hosts (Ert, Fleischer, and Magen 2016; Karlsson, Kemperman, and Dolnicar 2017). However, to better understand how the different aspects of host characteristics (e.g. appearance, indicators of social status, and indicators of intelligence or education) would lead to perception of trustworthiness, future studies should be conducted to include more variables in the stimuli. For example, the degree to which host descriptions may give away cues of capability by measures of the readability of the description, as well as benevolence and integrity, can be included as variables in studies with an experimental design.

This study focuses on P2P accommodation rentals—a specific type of peer-to-peer business model—and excludes free networked accommodation such as CouchSurfing. In order to count for unique factors that may influence trust formation in other types of accommodation-sharing systems, future research should compare trust formation between rental and free sharing systems to incorporate other trust factors. For example, a study conducted by Rosen, Lafontaine, and Hendrickson (2011) on CouchSurfing has emphasized perceived belonging and connectedness as important factors related to trust formation; a higher sense of belonging generates trust toward the sharing platform. Finally, this study focuses on consumers in the United States, where Airbnb was first introduced. Further studies conducted in different markets (and cultures) and/or with different platforms will assist in verifying the consistency, and thus generalizability, of the results.

Footnotes

Appendix

Appendix.

Measurements of Trust.

Construct and Definition	Scale	Literature
Propensity to Trust: a dispositional willingness to rely on others.	Propensity_1: It is easy for me to trust a person/thing. Propensity_2: My tendency to trust a person/thing is high. Propensity_3: I tend to trust a person/thing, even though I have little knowledge of them. Propensity_4: Trusting someone or something is not difficult.	Ridings, Gefen, and Arinze (2002)
Trust in Platform: the expectations that the peer-to-peer accommodation platform can be trusted.	Trust in Platform_1: Peer-to-peer accommodation rental services can generally be trusted. TrustinPlatform_2: I trust peer-to-peer accommodation rental services. TrustinPlatform_3: I have great confidence in peer-to-peer accommodation rental services. TrustinPlatform_4: Peer-to-peer accommodation rental services have high integrity. TrustinPlatform_5: I can depend on peer-to-peer accommodation rental services to do the right thing. TrustinPlatform_6: Peer to-peer rental services can be relied upon.	Walsh and Beatty (2007)
Integrity: the expectation that hosts adhere to a set of principles that guests perceive to make the hosts dependable and reliable.	Integrity_1: The host is honest with his/her guests.	Colquitt and Rodell (2011) and Ridings, Gefen, and Arinze(2002)
	Ntegrity_2: The host acts sincerely in dealing with his/her guests.
	Integrity_3: The host has sound principles.
Benevolence: the expectation that hosts feel interpersonal care and concern and are willing to act good things to guests beyond an egocentric profit motive.	Benevolence_1: The host is concerned about the welfare of his/her guests. Benevolence_2: The host genuinely cares about his/her guests’ needs. Benevolence_3: The host looks out for what is important to his/her guests. Benevolence_4: The host goes out of his/her way to help his/her guests.	Colquitt and Rodell (2011) and Ridings, Gefen, and Arinze (2002)
Ability: the expectation that hosts have the required skills/knowledge and characteristics that enable them to be perceived as competent within the peer-to-peer accommodation domain.	Ability_1: The host is qualified. Ability_2: The host is skilled. Ability_3: The host is experienced. Ability_4: The host is capable.	Colquitt and Rodell (2011) and Ridings, Gefen, and Arinze (2002)
Behavioral Intention: guests’ behavioral intention to book from the host	How likely are you to book from this host?	Tanford and Montgomery (2015)

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Sangwon Park

Iis P. Tussyadiah

Author Biographies

Sangwon Park is an Associate Professor in the School of Hotel and Tourism Management at The Hong Kong Polytechnic University. His research interests include decision making, Big Data, information and technology.

Iis P. Tussyadiah is Professor of Intelligent Systems in Service and Head of Department of Hospitality in the School of Hospitality and Tourism Management at University of Surrey. Her research interests lie in the intersection of technology and human behavior.

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