COVID 19 pandemic has changed learning delivery and services. Although many universities adapted to postpandemic needs, students view on preferences of learning modalities were not comprehensively examine, particularly, the Philippines. This study aims to understand students' preference of learning modalities after COVID 19 pandemic, given some changes. The study conducted a survey of university students taking Information Technology program. Results show that students generally prefer a learning modality setup that balances flexibility with more emphasis on in-person instruction to improve hybrid learning modality. Upperclass prefer face-to-face classes, particularly those in the upper years (3rd and 4th year) due to better interaction and focused learning. Hands-on courses like capstone and programming are seen as essential to be delivered in a faceto-face setup for practical experience. This study presents implications for improving the delivery of hybrid learning modality and research.

Travelers are choosing eco-friendly accommodations, and the attention is drawn to sustainable tourism. Nevertheless, there is a gap between travelers' proenvironmental intentions on sustainable accommodation options using e-commerce platform, and it should be understood what influences decisions for eco-tourism. This study aims to explain ecommerce for sustainable accommodation adoption intention. The data used were from survey responses collected from travelers in the National Capital Region (NCR) of the Philippines to predict adoption intention. The performance of Support Vector Machine and Decision Tree in predicting sustainable accommodation adoption intention are found accurate and reliable. Awareness of consequences (AC), ascription of responsibility (AR), personal norms (PN) with attitude (AT), subjective norms (SN), perceived behavioral control (PBC) are key influencing factors, and all of these under the Theory of Planned Behavior (TPB). Finally, the Norm Activation Model (NAM) explains the role of trust (TR) and perceived value (PV) in the model. It shows knowledge of the intention behavior gap, and hence for the need of AI driven recommendations and policy incentives for the promotion of sustainable tourism. Overall, this study indicates the potential of e-commerce for sustainable accommodations behavior Intention using predictive models. Research and practical implications are presented.

Artificial intelligence (AI) is increasingly integrated into smartwatches to enhance functionality, user experience, personalization, and health monitoring capabilities. Despite the rapid expansion of the global smartwatch market, empirical research on their long-term use remains limited, especially in developing countries. This study extends the Technology Continuance Theory (TCT) by incorporating AIspecific constructs to examine the antecedents of continuance usage intention of AI-powered smartwatches among Generation Z users in the Philippines. Data were collected from 410 users and analyzed using partial least squares structural equation modeling (PLS-SEM). The results revealed that trust in AI is the strongest predictor of continuance usage intention, followed by satisfaction and cognitive load reduction. In contrast, attitude and perceived usefulness were found to have no significant direct effects on continuance usage intention, suggesting that among Filipino Generation Z users, continued use is driven more by trust-related and cognitive factors than by traditional utilitarian evaluations. These findings offer valuable theoretical and managerial implications for the sustainable growth of the AI-powered smartwatch industry.

Local artisan products are essential in preserving cultural heritage and supporting community livelihoods. They contribute to e-commerce initiatives aiming to address sustainability through ethical consumption, reduced environmental impact, and inclusive economic growth. This paper examines the roles of various factors in online shopping intention of local artisan products. The data were collected through surveys and analyzed using various machine learning models such as Decision Trees, Gradient Boosting, Random Forests, XGBoost, K-Nearest Neighbors, and Support Vector Machines to predict consumer behavior and market trends. Results show that support vector machine outperforms the rest of the models in predicting online shopping intention of local artisan products. The findings provide insights into e-commerce strategies for sustainable economic development and cultural preservation in the Philippines.

Artificial intelligence (AI) has transformed smartphone use and enhanced the entire mobile experience. AI integration in mobile phones has become a game changer for the future of technology. This paper evaluates the factors that influence AI smartphone continuance usage intention. The study is anchored to technology continuance theory (TCT) with additional factors. Data were obtained from 745 Gen Z from various cities in Metro Manila, Philippines, and examined using partial least squares structural equation modeling (PLS-SEM). The study found that utilitarian and hedonic value, confirmation, and perceived usefulness had a positive influence on satisfaction, which was the most significant predictor of AI smartphone continuance usage intention. Further, personal innovativeness and attitude of users had a significant influence on continuance usage intention. Surprisingly, perceived usefulness and AI smartphone continuance usage intention had an insignificant relationship. The study contributes as the first empirical investigation of the continuance usage intention of AI smartphones among Gen Z in a developing nation like the Philippines.