During the pandemic, artificial intelligence was employed and utilized by students around the globe. Students' conduct changed in a variety of ways when schooling returned to regular instruction. This study aimed to analyze the student's behavioral intention and actual academic use of communicational AI (CAI) as an educational tool. This study identified the variables by utilizing an integrated framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and self-determination theory. Through the use of an online survey and Structural Equation Modeling, data from 533 respondents were analyzed. The results showed that perceived relatedness has the most significant effect on the behavioral intention of students in using CAI as an educational tool, followed by perceived autonomy. It showed that students use CAI based on the objective and the possibility of increasing their productivity, rather than any other purpose in the education setting. Among the UTAUT2 domains, only facilitating conditions, habit, and performance expectancy provided a significant direct effect on behavioral intention and an indirect effect on actual academic use. Further implications were presented. Moreover, the methodology and framework of this study could be extended and applied to educational technology-related studies. Lastly, the outcome of this study may be considered in analyzing the behavioral intention of the students as the teaching-learning environment is still continuously expanding and developing.

The COVID-19 pandemic had brought changes to individuals, especially in consumer behavior. As the government of different countries has been implementing safety protocols to mitigate the spread of the virus, people became apprehensive about traveling and going out. This paved way for the emergence of third-party logistics (3PL). Statistics have proven the rapid escalation regarding the use of 3PL in various countries. This study utilized Artificial Neural Network and Random Forest Classifier to validate and justify the factors that affect consumer intention in selecting a 3PL service provider during the COVID-19 pandemic integrating the Service Quality Dimensions and Pro-Environmental Theory of Planned Behavior. The findings of this study revealed that attitude is the most significant factor that affects the consumers' behavioral intention. Other factors such as customer satisfaction, customer perceived value, perceived environmental concern, assurance, responsiveness, empathy, reliability, tangibility, perceived behavioral control, subjective norm, and perceived authority support, are all contributing factors that affect behavioral intention. Machine learning algorithms, specifically ANN and RFC, resulted to be reliable in predicting factors as they obtained accuracy rates of 98.56% and 93%. Results presented that consumers’ attitude, satisfaction, perceived value, assurance by the 3PL, and perceived environmental concerns were highly influential in choosing a 3PL package carrier. It was seen that people would be encouraged to use 3PL service providers if they demonstrate availability and environmental concerns in catering to the customers' needs. Subsequently, 3PL providers must assure safety and convenience before, during, and after providing the service to ensure continuous patronage of consumers. This is considered to be the first study that utilized a machine learning ensemble to measure behavioral intention for the logistic sector. The framework, analysis tools, and findings of this study could be extended and applied among other behavioral intentions regarding transportation worldwide. Managerial insights among service providers are discussed.

Mental health problems have emerged as one of the biggest problems in the world and one of the countries that has been seen to be highly impacted is the Philippines. Despite the increasing number of mentally ill Filipinos, it is one of the most neglected problems in the country. The purpose of this study was to determine the factors affecting the perceived usability of mobile mental health applications. A total of 251 respondents voluntarily participated in the online survey we conducted. A structural equation modeling and artificial neural network hybrid was applied to determine the perceived usability (PRU) such as the social influence (SI), service awareness (SA), technology self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEOU), convenience (CO), voluntariness (VO), user resistance (UR), intention to use (IU), and actual use (AU). Results indicate that VO had the highest score of importance, followed by CO, PEOU, SA, SE, SI, IU, PU, and ASU. Having the mobile application available and accessible made the users perceive it as highly beneficial and advantageous. This would lead to the continuous usage and patronage of the application. This result highlights the insignificance of UR. This study was the first study that considered the evaluation of mobile mental health applications. This study can be beneficial to people who have mental health disorders and symptoms, even to health government agencies. Finally, the results of this study could be applied and extended among other health-related mobile applications worldwide.

COVID-19 contact-tracing mobile applications have been some of the most important tools during the COVID-19 pandemic. One preventive measure that has been incorporated to help reduce the virus spread is the strict implementation of utilizing a COVID-19 tracing application, such as the MorChana mobile application of Thailand. This study aimed to evaluate the factors affecting the actual usage of the MorChana mobile application. Through the integration of Protection Motivation Theory (PMT) and Unified Theory of Acceptance and Use of Technology (UTAUT2), latent variables such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), perceived risk (PCR), self-efficacy (SEF), privacy (PR), trust (TR), and understanding COVID-19 (U) were considered to measure the intention to use MorChana (IU) and the actual usage (AU) of the mobile application. This study considered 907 anonymous participants who voluntarily answered an online self-administered survey collected via convenience sampling. The results show that IU presented the highest significant effect on AU, followed by HB, HM, PR, FC, U, SEF, PE, EE, TR, and SI. This is evident due to the strict implementation of using mobile applications upon entering any area of the vicinity. Moreover, PCR was not seen to be a significant latent factor affecting AU. This study is the first to have evaluated mobile contact tracing in Thailand. The integrated framework can be applied and extended to determine factors affecting COVID-19 tracing applications in other countries. Moreover, the findings of this study could be applied to other health-related mobile applications worldwide.

Volcanic eruption activity across the world has been increasing. The recent eruption of Taal volcano and Mt. Bulusan in the Philippines affected several people due to the lack of resources, awareness, and preparedness activities. Volcanic eruption disrupts the sustainability of a community. This study assessed people’s preparedness for volcanic eruption using a machine learning ensemble. With the high accuracy of prediction from the ensemble of random forest classifier (93%) and ANN (98.86%), it was deduced that media, as a latent variable, presented as the most significant factor affecting preparedness for volcanic eruption. This was evident as the community was urged to find related information about volcanic eruption warnings from media sources. Perceived severity and vulnerability led to very high preparedness, followed by the intention to evacuate. In addition, proximity, subjective norm, and hazard knowledge for volcanic eruption significantly affected people’s preparedness. Control over individual behavior and positive attitude led to a significant effect on preparedness. It could be posited that the government’s effective mitigation and action plan would be adhered to by the people when disasters, such as volcanic eruptions, persist. With the threat of climate change, there is a need to reevaluate behavior and mitigation plans. The findings provide evidence of the community’s resilience and adoption of mitigation and preparedness for a sustainable community. The methodology provided evidence for application in assessing human behavior and prediction of factors affecting preparedness for natural disasters. Finally, the results and findings of this study could be applied and extended to other related natural disasters worldwide.