The Role of Perceived Technology and Consumers ’ Personality Traits for Trust Transfer in Airbnb

. Airbnb is widely popular among tourists around the world and in the hospitality industry. With Airbnb being a sharing economy and a type of e-commerce platform, consumers ’ trust in it is an important issue. This study proposed three information technology factors affecting trust in Airbnb from positive and negative aspects. Personality traits affecting trust in Airbnb and its hosts are also put forward. Using data collected from Chinese Airbnb users, this study applied the structural equation modeling (SEM) to test the proposed hypotheses. Results suggest various implications for Airbnb and similar sharing economy platforms.


Introduction
The sharing economy is emerging as an important part of the global economy. The basic elements of the sharing economy include suppliers, consumers, and platforms. Airbnb is a representative example of this sharing economy. Airbnb's main participants consist of hosts and guests, who respectively provide and use shared services through the Airbnb platform. In this situation, Airbnb's hosts and guests need to communicate and form a basic understanding of one another. Therefore, trust plays an important role in people's sharing behavior. When consumers use Airbnb services, they are faced with two types of trust: trust in the Airbnb platform and trust in the host [1]. Existing research has focused mainly on finding factors that affect trust in the Airbnb platform or This paper was based on a master's dissertation by the first author (Cirenzhuoga, 2019) and was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A3A2098438).
factors that affect trust in hosts [2]. However, the mechanisms by which guests build trust in the Airbnb platform and hosts have yet to be extensively studied. Despite the fact that guests may have varying dispositions and preferences, we still do not fully understand how a guest's trust disposition toward the other party affects the formation of a trust mechanism. Therefore, the main purpose of this study is to understand how the trust transfer mechanism works when people use Airbnb.
Perceived risk refers to the uncertainty about possible negative effects on expected outcomes. In particular, perceived website risk negatively affects the process of making a purchase decision for a product or service on a website [5]. It stems from the fear/concerns about problems that will arise during the use of a website. Thus, the concern reflected in the risks in using a website negatively affects consumers' trust in that website. If a consumer is fearful and concerned about using a website, they are less likely to trust the website and buy a product or service through it. Therefore, perceived risk refers to a guest's belief in all potential negative consequences they may encounter while using Airbnb [6]. On the basis of these findings, the following hypothesis is proposed: Website quality can also affect consumers' use of a website. Perceived website quality, such as the quality of the information provided by the website and its interface, enhances the formation of consumers' trust in that website [5]. If the Airbnb site provides high-quality information or is structured so that guests can find the information they want quickly and easily, guests will be able to build higher trust in the Airbnb site [2]. Therefore, the following hypothesis is formulated: Reputation refers to public opinion, which is the collective assessment of an entity or person [7]. In previous studies, reputation has been shown to play an important role in establishing trust [8]. As new users with no website experience can rely on the reputation or experience of others, reputation has an impact on building initial trust in a product or service provider [5]. Thus, the reputation perceived by customers can be an important trust-building factor for web vendors. On the basis of these findings, the following hypothesis is derived: Disposition to trust is a personality-based trust [2] that reflects the degree to which a person shows "a consistent tendency to be willing to depend on others across a broad spectrum of situations and persons" [9, p. 477]. It refers to an individual's tendency to trust others in general on the basis of his or her personal trait rather than the direct experience of certain trusted parties [10]. In the context of a website, it can also affect trust in others or in web-based vendors [2,10]. According to Kim et al. [4], personalityoriented antecedents, such as consumer disposition to trust, exert a significant influence on consumers' trust in the website. Thus, the next two hypotheses are proposed: H4a: Consumers' Disposition to Trust Will Positively Affect Their Trust in Airbnb.

H4b: Consumers' Disposition to Trust Will Positively Affect Their Trust in Hosts.
Trust transfer theory explains that trust can be transferred from a well-known target to an unknown target [11]. The trust transfer process is a cognitive process that describes how one person's trust can be transferred to another through some associations [12]. In the case of a well-known object of trust, such as a platform, trust in this platform can be transferred to an unknown object associated with the platform, such as the platform's seller. In other words, trust in the platform can positively affect trust in the seller [3], and the formation of this trust can ultimately affect consumers' purchase intention or attitude [13]. Therefore, the following hypothesis is proposed:

H5: Trust in Airbnb Will Positively Affect Consumers' Trust in Hosts.
Perceived trust has been identified as an important antecedent for consumer intention (e.g., [5]). In particular, perceived trust in sellers is positively related to the willingness-to-purchase intention in e-commerce [3,9]. On the basis of these findings, the following hypothesis is formulated: H6: Trust in Hosts Will Positively Affect Consumers' Continuance Intention to Use Airbnb.

Research Methodology and Results
For the development of the measurements, items from relevant studies were applied, and some of them were modified for this study. Survey items were measured using a five-point Likert scale (i.e., 1 = strongly disagree, 5 = strongly agree). The questionnaire was generated in two steps. First, the questionnaire was written in English and then translated into Chinese by experts who are proficient in Chinese and English. Second, two scholars with a good understanding of the research topic reviewed the validity of each item in the questionnaire. The Chinese version of the questionnaire was uploaded to https://www.wjx.cn, one of the largest online survey sites in China, and the data were collected in May 2019.
A total of 512 responses were collected. A final 228 valid questionnaires were used in this study through a review of the responses to the screening questions. Confirmatory factor analysis (CFA) was performed using AMOS 25.0 to examine convergent validity and discriminant validity. All analysis results were found to support the overall measurement quality. The convergent validity was checked by applying three criteria [14]. First, the standardized path loading value of each item should be statistically significant and must be greater than 0.7. Second, the composite reliability (CR) for each construct should be greater than 0.7. Third, the average variance extracted (AVE) for each construct should be greater than 0.5. The result of the analysis showed that the standardized path loading value of each item was 0.776-0.941. CR showed values ranging from 0.878 (for perceived Airbnb site quality) to 0.960 (for continuance intention). AVE values ranged from 0.706 (for perceived Airbnb site quality) to 0.844 (for trust in Airbnb). As all three conditions were satisfied, convergence validity was supported for each construct. For each construct to have discriminant validity, the square root of the AVE for each construct must be greater than the correlations between that construct and the other constructs. The analysis results were shown to satisfy the condition of discriminant validity for each construct. As shown in Fig. 2, the structural model analysis results indicate that all hypotheses were supported.

Discussion and Conclusion
The analysis of 228 survey responses collected from Chinese Airbnb users showed that all the proposed hypotheses exert significant effects. However, among the factors influencing trust in Airbnb, the influence of perceived Airbnb reputation was found to be the largest. This result suggests that despite the risk factors of the platform, the Airbnb brand itself is the biggest foundation for trust formation. Therefore, companies that provide shared accommodation platform services, such as Airbnb, should pay initial attention to their brand marketing. In addition, the results show that a guest's trait of trust (i.e., disposition to trust) positively affects trust in Airbnb and trust in the host. However, guests' trait of trust has a greater impact on trust in the Airbnb platform than on trust in the host. This result suggests that people have a higher level of trust in a particular service platform than in other people. Moreover, the result also indicates that trust in the platform is transferred to trust in hosts. Trust in hosts will ultimately have a positive impact on continuous intention to use Airbnb. Therefore, companies that provide shared accommodation platform services, such as Airbnb, should secure loyal customers by enhancing their image through brand marketing. Providing loyalty programs that can give loyal customers a variety of incentives could be a good strategic option. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.