Resumo

Over the past decade, we have changed the way we deal with people we do not know. Using sharing economy platforms, like Airbnb and Uber, many of us have been sleeping in strangers’ houses, welcoming strangers into our homes and ridesharing with strangers. Therefore, the question of how trust is built in sharing economy transactions is of great interest. However, prior studies have remained scattered, exploring antecedents of trust from different perspectives: demanders toward suppliers, demanders toward platforms, suppliers toward demanders, and suppliers toward platforms.

Despite the increasing relevance of the sharing economy and the key role of trust in driving this phenomenon, prior research on how trust is built in sharing economy transactions has remained scattered. Therefore, the overall aim of this study is to propose an integrated model, applicable to multiple trust-building perspectives and categories of sharing economy platforms, and to start testing it on a specific perspective of a specific platform. By doing so, this study aims to address the following research question: which factors antecedes trust in sharing economy transactions?

This study defines sharing economy as a group of markets in which digital platforms mediate transactions between individuals who demand and individuals who supply access to resources. Before carrying out a transaction, demanders and suppliers must trust each other and the platform. Trust is the subjective perception of a trustor that their counterpart will perform as anticipated[1,2]. In order to explain how trust is built, prior studies have explored specific antecedents of trust, from different perspectives, within different categories of platforms. Therefore, an integrated model is needed.

Initially, prior literature was systematically selected and synthesized to develop a comprehensive framework applicable to multiple trust-building perspectives and categories of sharing economy platforms. Then, a survey was conducted to validate the constructs and test the model with Airbnb guests. A sample of 351 responses was collected and analyzed using Structural Equation Modeling.

The proposed model organizes the prior literature, integrating 142 factors into nine high-level constructs that explain the formation of trust in sharing economy transactions. In its first test, its constructs proved to be reliable and valid, and the model itself showed a good fit with data and explanatory power. As a result, this study provides evidence that Third-Party Evaluation, First-Party Interaction, and Social Motivation do have a significant role in driving Trust. In addition, it was found that the strength of these effects is moderated by the respondent's Propensity to Trust.

This study proposes and tests an integrated and parsimonious model to explain how trust is built in sharing economy transactions. The proposed model deals with different trust-building perspectives and categories of sharing economy platforms. The results indicate that the cues an individual assess to infer their counterpart’s trustworthiness and the reasons the individual has for engaging in the sharing economy transaction can explain a large variance of its trust in the counterpart. In addition, the individual’s propensity to trust moderates this relationship.

[1]Ert, E., Fleischer, A., & Magen, N. 2016. Trust and reputation in the sharing economy: The role of personal photos in Airbnb. Tourism Management, 55: 62–73. [2]Tussyadiah, I. P., & Park, S. 2018. When guests trust hosts for their words: Host description and trust in sharing economy. Tourism Management, 67: 261–272.