Student well-being is essential for academic achievement and personal growth. Fostering happiness among university students is crucial for individual development, strong family bonds, a harmonious society, and national progress. This study aimed to identify key determinants of student happiness in higher education. Eight factors, including GPA, workload, family support, university environment, attitude, motivation, time management, and social relationships, were examined among 388 Thai students using an online survey. Students were categorized into distinct groups based on these factors using k-means clustering. ANOVA was employed to assess whether these factors significantly differentiated the groups, and significant factors were further analyzed using regression analysis to confirm their impact on student happiness. A neural network analysis was also utilized to evaluate the relative importance of each factor. The results revealed that attitude, GPA, and time management significantly affected student happiness. A positive attitude fosters a sense of opportunity and achievement, a high GPA reflects academic success and enhances self-confidence, and effective time management reduces stress while allowing more time for enjoyable activities.

Digital marketing significantly influences travellers’ decision-making processes. This research aims to analyze the specific digital marketing strategies affecting Gen-Z travellers’ decisions. Machine learning techniques were employed to uncover key insights. Data were collected through a survey using a convenience random sampling of 346 university students in Thailand. Correlation analysis and machine learning approaches, such as logistic regression and neural network analysis, were used to explore the relationships between various digital marketing strategies and their impact on Gen-Z travellers’ destination choices. The findings indicate that Online Travel Agency (OTA) websites exert the most significant influence on Gen-Z travellers’ decision-making processes, with an odds ratio of 1.6879. This is followed by the use of social media marketing strategies and businesses having their own websites. To validate these results, an Artificial Neural Network (ANN) technique was also employed, confirming the importance of rankings derived from the logistic regression analysis. This research provides valuable insights into which digital marketing strategies most influence Gen-Z travellers’ decision-making processes. These insights can help tourism businesses make informed investments in digital marketing strategies, prioritizing those that align with the preferences of Gen-Z travellers.

Tourists frequently use online search engines for travel planning, making search data a valuable predictor of future tourism volume. This study employs machine learning to analyse the predictive power of keyword search data for forecasting tourist arrivals, incorporating a lag time between searches and arrivals. The dataset is collected and prepared from two sources: a search engine and government agencies, covering the years 2014-2019, to be analysed by machine learning. The SARIMA model effectively forecasts trends in keyword searches and tourist numbers, while SVM (Support Vector Machine) and Random Forest outperform other methods in predicting arrivals. This research supports tourism operators and stakeholders in planning for future tourists, utilising the obtained keywords to enhance visibility in tourist searches through SEO.

The purpose of this study is threefold: to investigate motivational factors influencing high school students’ decisions to enroll in university science programs, to assess satisfaction and loyalty among undergraduate students and alumni, and to examine the relationship between undergraduate student satisfaction and loyalty. Participants comprised three groups, and data were collected via questionnaires. Structural equation modeling and logistic regression in R were employed to examine associations among latent variables and identify key predictors. Among secondary students, intentions to pursue higher education were significantly predicted by university reputation, tuition and fees, and curriculum and academics, with curriculum and academics being the most influential factor (OR = 1.478, 95% CI; 1.256–1.740). For undergraduate students, overall satisfaction was primarily determined by university reputation, and satisfaction was positively correlated with loyalty, which increased with the year of study. University reputation also influenced students’ likelihood of recommending the institution to others. The importance of motivation, satisfaction, and loyalty in higher education has been demonstrated. Motivational factors influence high school students’ enrollment in science programs, while satisfaction predicts student and alumni loyalty. Alumni engagement strengthens the university’s reputation and fosters long-term loyalty, contributing to sustainable growth and lasting connections with graduates.