Upcycling is a sustainable solution that mitigates environmental changes, focusing on climate action, by extending the lifecycle of products and reducing wastage. This study investigates Filipino consumers’ behavioral intention to use an upcycling e-marketplace, highlighting the intersection of sustainability, consumer psychology, and digital platforms. Despite growing interest in circular economy models, adoption drivers in this domain remain underexplored and are rarely modeled with predictive analytics. To address this gap, the study collected data from 500 Filipino participants capturing environmental knowledge and concern, perceived ease of use and usefulness, attitude toward use, perceived behavioral control, subjective norms, user demand, and intention, then analyzed using multiple machine learning algorithms, namely, Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Machine. Among these, the Decision Tree model demonstrated the best balance of predictive accuracy (95%), precision (96%), and recall (91%), suggesting strong classification capability. Analysis of the importance of features revealed that Perceived Usefulness, Attitude Toward Use, and Subjective Norms were the most influential predictors of adoption, outweighing traditional environmental concerns. These findings underscore the importance of designing upcycling platforms that emphasize practical value, user convenience, and social validation. The study concludes that sustainable behavior is more likely when aligned with personal benefits and peer influence, rather than relying solely on environmental appeals.

Freight forwarding plays a crucial role in international trade, facilitating the movement of goods through transportation, customs clearance, and documentation management. However, manual processing of transportation transactions in freight forwarding poses various challenges, including inaccurate documentation, inefficient routing and carrier selection, and delayed shipment booking, which can result in disruptions, higher costs, and customer dissatisfaction. To address these issues, the development of a Freight Forwarding Management System is essential by digitizing and automating transactions. Freight forwarding companies can improve accuracy, efficiency, and visibility in their operations. The integration of web and mobile applications enables streamlined order handling, accurate quotations, and an organized inventory. The system's performance was evaluated using the ISO 9126 model, and it received an “Excellent” rating, demonstrating its functionality, reliability, usability, portability, efficiency, and maintainability. Moreover, the inclusion of a best-fit algorithm in the scheduler system allows for both automatic and manual selection of drivers and vehicles by intelligently matching the most suitable drivers and vehicles to specific shipments, the scheduler system optimizes resource allocation, ensuring efficient utilization of assets and improved delivery performance. Through technological innovation, freight forwarding can overcome manual processing challenges and enhance operational performance to meet customer needs effectively.

This study utilized purposive random sampling during beta testing of the game “O Ektos,” a 3D-PC real-time strategy game aimed at raising awareness about clean water and sanitation. Over 100 respondents, including BSIT students specializing in game development, web management, and digital arts, as well as executives and staff from the MWF organization, participated in the evaluation. Respondents tested the gameplay, promotional website, and overall aesthetics, assessing aspects such as mechanics, graphics, user interface, sound design, and storyline. Results showed that the game was well-received across all categories, highlighting its effective design and alignment with the United Nations' Sustainable Development Goal No. 6. The game's combination of contemporary technology, engaging gameplay, and meaningful content positions it as a feasible tool for raising awareness about water pollution and sanitation issues.

This study seeks to predict the intention to adopt greener last-mile delivery in telemedicine supply chain using machine learning-based approach. Data used in the study were acquired from 349 respondents in the Philippines, and examined using different machine learning techniques, namely, Gradient Booting, Random Forests, Support Vector Machines, K-Nearest Neighbor, XGBoost, and Decision Tree further validated using various performance metrics. Results demonstrated that more than 80% of machine learning models' performance accurately predict intention to adopt greener last-mile delivery in telemedicine. Moreover, RF, SVM, and XGB attained optimal prediction performance. Attitude towards greener delivery, perceived behavioral control, perceived usefulness, were the most significant factors influencing the intention to adopt greener last-mile delivery, followed by subjective norms and trust in technology. Interestingly, perceived ease of use ranks the lowest, indicating that intention to adopt greener last-mile delivery among individuals is mostly affected by attitude to support green logistics and their perceived benefits rather than the ease and trust of using this technology. Lastly, theoretical and practical implications, together with effects in telemedicine supply chain are presented at the end of the study.

This paper seeks to predict a consumer's green purchase intention and classify them as green consumers or not through a set of cognitive and behavioral factors. Data were obtained from 526 Generation Z in the National Capital Region (NCR), Philippines, and evaluated through various machine learning techniques, namely, Decision Trees, Random Forests, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Machines. Various performance metrics were used to validate these models. The findings show that most of the models achieved above 80% classification performance. Further, the study revealed that perceived behavioral control, green perceived value, and social media impact were the most crucial factors of green purchase intention, followed by environmental consciousness, green perceived quality, and environmental knowledge. Interestingly, green self-identification achieved the lowest rank, which suggests that green purchase intention among Filipino Generation Z is driven more by practical and psychological factors than just environmental awareness and identity. Finally, theoretical and practical implications are offered at the end of the study.