Economics in game theory for hotel unemployment

This study uses the game theory to find a Nash equilibrium of price elasticities of hotel demand in the United States before and during the COVID-19 pandemic to interpret the decrease in hotel unemployment rate. The sample selected is the Oahu, Hawaii market due to its higher room rate and higher unemployment rate compared to those in the mainland US. Findings indicate that to increase hotel revenue and decrease unemployment rate, the price elasticity of hotel demand in the mainland US would be higher than the one in Oahu, Hawaii. While the government has built more value into hotels by financially supporting unemployed hotel employees, established hotel brands have maintained their excellent service for their guests during the pandemic.Journal ArticlePublishe

Investigation on influence factors of high impact practices

The purpose of the study is to examine the impact factors of 11 high impact practices (HIPs) of the Association of American Colleges and Universities to identify their influences on the learning experience of college students. A research model is developed based on the theory of planned behaviour to link beliefs of high impacts to the practice of learning behaviours. Structure equation modelling analysis was conducted on the performance metrics of 11 American universities from 2014-2021. Findings indicate that undergraduate research can predict the success of other high-impact practices in Florida. The results will guide administrators in higher education to make financial decisions with a reference to factors of high impact practices. Theoretical and practical implications are also presented.Journal ArticlePublishe

A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels—An Application to Ho Chi Minh City, Vietnam

State-of-the-art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded subannually by high tide, rainfall, and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and cooccurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02 B (95th annual damage percentile of$2.15 B). We find no dependence for most months and large differences in expected damage across months ($36–166 M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72–83%. This is because high-probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments.Water Resource