ABSTRACT
The global pandemic, COVID-19, has dealt a heavy blow to the tourism industry. Therefore, exploring the mechanisms influencing travel intention in the post-epidemic era can help provide management insights for the recovery of the travel market. Relying on the logic of social cognition theory, we conducted an empirical analysis from the perspective of trust and found that institutional trust and interpersonal trust can positively predict travel intention in the context of the epidemic, while travelers' health risk perception and safety self-efficacy mediate the relationship between trust and travel intention. Moreover, we verified the moderating role of tourists' psychological resilience. Further, the study confirms that China's active prevention policy not only reduces the physical health harm caused by the epidemic, but also effectively increases individuals' institutional trust in a proactive government. Through China's active anti-epidemic policy, individuals were able to counteract the negative impact of the COVID 19 epidemic on their travel intention. Further, theoretical and practical implications are discussed.
ABSTRACT
The global pandemic, COVID-19, has dealt a heavy blow to the tourism industry. Therefore, exploring the mechanisms influencing travel intention in the post-epidemic era can help provide management insights for the recovery of the travel market. Relying on the logic of social cognition theory, we conducted an empirical analysis from the perspective of trust and found that institutional trust and interpersonal trust can positively predict travel intention in the context of the epidemic, while travelers’ health risk perception and safety self-efficacy mediate the relationship between trust and travel intention. Moreover, we verified the moderating role of tourists’ psychological resilience. Further, the study confirms that China’s active prevention policy not only reduces the physical health harm caused by the epidemic, but also effectively increases individuals’ institutional trust in a proactive government. Through China’s active anti-epidemic policy, individuals were able to counteract the negative impact of the COVID 19 epidemic on their travel intention. Further, theoretical and practical implications are discussed.
ABSTRACT
With the rapid development of machine learning and artificial intelligence, hotel service robots are widely used, but there are many problems to be solved in the scheduling scheme of hotel service robots. In this study, the Pareto optimal definition is used to model the problem, and a nondominated sorting heuristic method including genetic algorithm and differential evolution algorithm is designed to solve this problem. Experimental results show the effectiveness and stability of our algorithm. In addition, compared with the previous methods, the method proposed in this paper can provide a more personalized and reasonable service robot scheduling scheme for hotels. Finally, the hotel can optimize its management and operation and further deepen the degree of hotel intelligence.