Antecedents and intervention mechanisms of institutional distrust of P2P accommodations during COVID-19 in China

PurposeCOVID-19 affects the peer-to-peer (P2P) accommodation industry. With regard to prospect theory, individuals' negative emotions, such as institutional distrust, are easily evoked and impede consumption intention in an environment of permeating uncertainty and risks. While existing research indicates the negative effects of institutional distrust, scant research has explored its antecedents and intervention mechanisms. This study thus aims to unveil the influencing factors and explore mitigating mechanisms of customers' institutional distrust of P2P accommodations. Design/methodology/approachOnline reviews data were used to identify the underlying critical issues. The authors developed a model to depict how institutional distrust is formed under the boundary condition of subjective norm by the results. The model was verified using a questionnaire survey. Finally, in-depth semi-structured interviews were conducted to ensure its robustness. FindingsThe external environment and internal platform effectiveness are two critical aspects affecting institutional distrust of P2P accommodations. The external environment influences institutional distrust through perceived threat, explaining the formation mechanism of customers' institutional distrust through customers' internal psychology. Furthermore, the authors found subjective norm moderating the effect of perceived threat on customers' institutional distrust. Research limitations/implicationsThis is one of the first studies, to the best of the authors' knowledge, to explore institutional distrust of P2P accommodations after COVID-19. The finding contributes to studies on P2P accommodation by uncovering the contingent role of subjective norm in influencing customers' institutional distrust. Originality/valueThis is a pioneering study that explores the antecedents and mitigating mechanisms of institutional distrust of P2P accommodations during the new normal of COVID-19.

Recommendation agents and information sharing through social media for coronavirus outbreak.

The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers' decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers' decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers' decision making. We conclude with the implications of our study for future research and practice.