Influence of COVID-19 on the Tourism Industry in China: An Artificial Neural Networks Approach

Abstract

Prior to COVID-19, the tourism industry was one of the important sectors of the world economy. This study intends to measure the perception of Chinese tourists concerning the spread of COVID-19 in China. The crowding perception, xenophobia, and ethnocentrism are the measurement indicators of the study. A five-point Likert scale is used to predict the perception of the tourists in various destinations. The Kaiser–Mayer–Olkin test and Cronbach’s alpha are conducted to ensure the validity and reliability of the corresponding items. SPSS version 21 is used to obtain factor loading, mean values, and standard deviation. Regression analysis is used to measure the strength of the constructs’ relationship and prove the hypotheses. Questionnaires have been filled from 730 Chinese respondents. Artificial neural networks and confusion matrices are used for validation and performance evaluation, respectively. Results show that crowding perception, xenophobia, and ethnocentrism caused the spread of COVID-19 during the epidemic. Hence, the tourism industry in China is adversely affected by COVID-19. The crisis management stakeholders of the country need to adopt policies to reduce the spread of COVID-19. The tourism sector needs to provide confidence to the tourists. It will provide ground for the mental strength of the tourists in China.