Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

This paper discusses that the Digital marketplaces play a crucial role in connecting startups with potential investors, yet their adoption success depends on understanding the key factors influencing user intention. Predicting adoption behaviors accurately can help improve engagement and ensure platform sustainability. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to identify key adoption factors including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Trust (TR), and Government Support (GS).and this has been widely applied to study technology adoption, limited research integrates this framework with machine learning models to predict adoption intention in e-marketplaces for startups. This study aims to develop machine learning-based prediction models for StartSmart an e-marketplace linking startups and investors and identify the most influential factors affecting adoption intention based on the UTAUT framework. Data from 542 respondents were analyzed using six machine learning techniques: Decision Trees (DT), Random Forests (RF), Gradient Boosting (GRB), XGBoost (XGB), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).Results indicate that DT achieved the highest accuracy (0.93) and precision (0.94), while RF obtained the highest AUC-ROC score (0.98). Analysis of feature importance revealed that PE and EE were the most significant predictors of adoption, followed by TR and GS. These findings provide valuable insights for platform developers to prioritize usability and performance improvements, and for policymakers to strengthen trust and government support. The study also highlights the potential of combining UTAUT with machine learning to enhance predictive accuracy in digital adoption research.

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.

Despite the potential for e-commerce to boost productivity and market access for farmers, adoption remains low, particularly in rural areas of developing countries. This study addresses the research gap by predicting farmers' adoption of digital platforms in the National Capital Region, Philippines, using the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Based on a survey of 615 farmers and analysis with various machine learning models, with XGBoost as the top performer, the study found that perceived usefulness, trust, and price value are the most significant factors influencing adoption. Social influence and ease of use also play important roles. The findings provide guidance for policymakers and platform developers, highlighting the need to improve digital literacy, build trust, and ensure affordability to accelerate the digital transformation of the agricultural sector.

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.

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.

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts.

Early disease identification and detection have been an interest of experts to enhance productivity and performance in agriculture. This study aims to use deep learning algorithms to classify sugarcane diseases using leaf images. Deep learning algorithms are implemented to create models that can classify sugarcane diseases using 16,800 images of training data, 4,800 images for validation tasks, and 2400 images for testing. Results show that the InceptionV4 algorithm outperforms other models in classifying sugarcane leaf diseases at 99.61 accuracy. Different models such as VGG16, ResnetV2-152, and AlexNet achieve high accuracies of 98.88%, 99.23%, and 99.24%, respectively. Hence, this study provides evidence that deep learning models can perform better in classification problems. This study suggests some improvements to further its contribution.