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Based on the BPN and MARS model to discuss how hotels influence operational performance through intellectual capital

Hotels are facing the greatest challenge since the outbreak of SARS and coronavirus disease, and researchers discovered that intellectual capital plays a crucial role at such times. This study applies the concept of artificial neural networks to investigate the effects of hotel intellectual capital with operating performances. This research select 145 international hotels listed in Asian countries’ stock markets in the U.S. Compustat database to investigate the association between the value of intellectual capital and operating performances. This study proposes a two-stage construction procedure. First, The multivariate adaptive regression splines forecasting(MARS) results show that Z-score, Economic Added Value of Employees, Turnover Rate of Fixed Assets, Revenue Growth Rata, Turnover Rate of Accounts Receivable, and Employee Productivity play crucial roles in the intellectual capital dimension on operating performance. Second, the research use the obtained MARS variables as input variables of the back-propagation neural network model(MARS-BPN) not only to shorten the time required for model training but also provides a preferable prediction. The MARS-BPN forecasting has important implications for the implementation of appropriate operating performance decisions or strategies.