The novel coronavirus (COVID-19) outbreak has impacted the aviation industry worldwide. Several restrictions and regulations have been implemented to prevent the virus’s spread and maintain airport operations. To recover the trustworthiness of air travelers in the new normality, improving airport service quality (ASQ) is necessary, ultimately increasing passenger satisfaction in airports. This research focuses on the relationship between passenger satisfaction and the ASQ dimensions of airports in Thailand. A three-stage analysis model was conducted by integrating structural equation modeling, Bayesian networks, and artificial neural networks to identify critical ASQ dimensions that highly impact overall satisfaction. The findings reveal that airport facilities, wayfinding, and security are three dominant dimensions influencing overall passenger satisfaction. This insight could help airport managers and operators recover passenger satisfaction, increase trustworthiness, and maintain the efficiency of the airports in not only this severe crisis but also in the new normality.

Autonomous vehicles (AVs) are receiving attention in many countries, including Thailand. However, implementing an intelligent transport system has many challenges, such as safety and reliability and the lack of policy supporting such technology use, leading to hazards for passengers and pedestrians. Hence, factors affecting the adoption of autonomous vehicles require better understanding. This research proposes and employs an extended Technology Acceptance Model (TAM) by integrating ethical standards, legal concerns, and trust to predict the intended use of autonomous vehicles by Thai citizens. A total of 318 questionnaires were collected from online panel respondents. Research hypotheses were tested using a structural equation modelling approach. The study results suggest that ethical standards have a significant positive effect on the intention to use the technology. Meanwhile, the intention was negatively affected by perceived usefulness, perceived ease of use and legal concerns. On the other hand, the results indicate that perceived ease of use directly affected trust, leading to AV adoption. However, other factors influenced trust insignificantly. This study demonstrates the vital role of trust in AV adoption. The study also suggests ideas for further study and discusses the implications for the government and autonomous vehicle companies. The article aims to forecast a success factor that the Thai government should use to consider the policy for autonomous vehicle adoption in Thailand. This paper relies on the technology acceptance model to assess and forecast autonomous vehicle adoption. The theoretical model also includes ethical issues, legal concerns and trust in technology. The model was analysed using the structure equation modelling technique to confirm the factor affecting Thailand’s successful autonomous vehicle adoption. This research confirmed that ethical standards, legal concerns, and trust in technology are the factors significantly affecting the intention to use an autonomous vehicle in Thailand. On the other hand, the perceived ease of use significantly affects the trust in autonomous vehicle technology. This research found that such social factors as ethical standards, legal concerns, and trust in technology affect technology adoption significantly, especially technology related to AI operation. Therefore, the technology acceptance model could be modified to confirm technology adoption in terms of social factors. The government could use the research results to develop a public policy for the regulation and standard supporting autonomous vehicle adoption in Thailand.

In the digital age, social media technology has an important role as a communication platform for interpersonal interactions in the online virtual world. In addition, social media has impacted product exchange behavior in both vendors and buyers, with a shift from the traditional sales model to communication between parties via social media. Social media marketing, an online means of buying, selling, and exchanging goods and services, is increasingly popular due to convenience, speed, and greater choices. This trend has grown rapidly and is set to expand, leading to increased interest in research which analyzes and processes social media marketing data to gain a new integrated body of knowledge to better serve online business transactions. This research covers a new field, which may cause research and development limitations requiring background knowledge in several areas, such as digital technology, data analytics, and business analysis. This research aims to develop a framework to search for association rule mining of demand and supply data on social media platforms. Data is collected from Twitter and underwent cleansing and labeling for separating into five groups. Hashtag data from tweets is then extracted and transformed to input attributes of the framework. Next, association rule mining is performed using the Apriori algorithm in order to determine frequent items and extract candidate association rules. The last stage is rule selection, which uses Twitter-specific statistical attributes, that is, number of retweets and likes, to select highly effective association rules. The findings are that it is possible to mine association rules relating to demand and supply on Twitter. Based on an analysis of the association rule results, the content of those rules reflects trending activities and events at different times. Such information can be analyzed in further research to design improvements in social media marketing.

Airports encompass a range of service touchpoints that directly impact passenger satisfaction and, consequently, the likelihood of service recommendation. This study investigates the service quality of Southeast Asian airports by applying five supervised machine learning classification models — decision trees, random forests, support vector machines, neural networks, and gradient boosting machines — on passenger satisfaction data extracted from the Skytrax website. The dataset includes evaluations of various service dimensions, such as staff behaviour, queuing time, and overall experience. This study incorporates cross-validation and hyperparameter tuning to identify the most suitable model for classifying passenger satisfaction. Among the models tested, the random forest classifier achieved the highest accuracy (0.91), demonstrating strong robustness and interpretability. Model performance was assessed using confusion matrices, balanced accuracy, the Matthews correlation coefficient (MCC), and ROC curves. Furthermore, SHAP values were used to identify the most influential service touchpoints, highlighting airport staff performance and queue management as key factors. These findings align with existing literature emphasising the pivotal role of well-trained airport employees and efficient queuing systems in shaping positive passenger experiences. Studies have shown that courteous staff interactions, efficient conflict resolution, and reduced waiting times significantly contribute to customer satisfaction and loyalty. Additionally, the integration of smart technologies such as self-service kiosks, automated security systems, and touchless check-in and baggage solutions enhances operational efficiency and aligns with sustainability initiatives. This study offers a data-driven approach for airport managers to optimise service delivery, increase passenger experiences, and tailor improvements to specific airport environments.