As the world progresses into the peak of the Fourth Industrial Revolution, the adoption of smart and sustainable technologies, including electric vehicles (EVs), has gained significant momentum. However, the widespread acceptance of EVs is hindered by several unresolved barriers. This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). Specifically, artificial neural networks (ANNs) and SEM are employed to analyze data from online surveys and the existing literature, identifying the critical obstacles that impact consumer acceptance. The findings reveal that the availability of charging stations, range anxiety, and vehicle costs are the primary deterrents to EV adoption. By incorporating a sustainability perspective, this study underscores the crucial role of electric vehicles in reducing environmental impacts and achieving carbon reduction targets. The hybrid methodology presented offers new insights to guide policymakers in promoting electric vehicle usage, thereby contributing to the global sustainable development goals.

In our increasingly digital world, grasping consumer preferences for internet service providers (ISPs) is paramount. This study was conducted in Occidental Mindoro, Luzon, the Philippines, and surveyed 280 respondents across ten municipalities with 81 in-person and online questionnaires. The research focused on twelve latent variables: internet speed, assurance, tangibility, responsiveness, reliability, empathy, data privacy, service quality, value-added services, price, customer satisfaction, and customer loyalty. Methods such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Structural Equation Modeling (SEM), and an Artificial Neural Network (ANN) were integrated for the analysis. The study utilized TOPSIS to rank ISPs based on Service Quality (SERVQUAL) parameters, followed by SEM to delve into variable connections affecting preferences, and through as ANN, consumer behavior and loyalty were predicted. The SEM–ANN results revealed that assurance, responsiveness, empathy, and data privacy directly impacted service quality. Moreover, a significant correlation was found between customer satisfaction and service quality, influencing further customer loyalty alongside pricing. By integrating these methodologies, the study provides a comprehensive understanding of ISP preferences and emphasizes service quality as the most significant factor in industry decision-making.

The shift to remote work has increased demand for virtual assistants (VAs), especially in the e-commerce sector. This study investigated how physical, cognitive, and macro-ergonomic factors affect VAs' work motivation and performance using Partial Least Squares Structural Equation Modeling (PLS-SEM) on responses from 418 VAs in the National Capital Region (NCR), Philippines. Results showed that workstation design and perceived temperature significantly influenced physical ergonomic appraisal, which in turn positively affected work performance (β = 0.338, p = 0.022) but not motivation. Cognitive ergonomic appraisal, driven by task engagement and autonomy, had significant positive effects on both work motivation (β = 0.342, p = 0.001) and performance (β = 0.294, p = 0.002). Macro-ergonomic factors, including reward systems, supervision, and organizational support, also significantly boosted both motivation (β = 0.484, p < 0.001) and performance (β = 0.390, p = 0.001). Work motivation strongly predicted work performance (β = 0.678, p < 0.001), explaining 63 % of its variance. These findings emphasize that while physical ergonomics enhance efficiency, cognitive and organizational resources are more critical for sustaining motivation. Grounded in Self-Determination Theory and the Job Demands-Resources model, this study offers practical insights for improving remote work design. Recommendations include ergonomic investments, promoting autonomy, and structured organizational support. Future research should explore longitudinal effects and sector-specific nuances.

This study aims to determine the human-likeness design combinations that would characterize a chatbot as human-like and non-human-like. The factors considered in the study are as follows: visual cue, identity cue, and conversational cue. Visual cue pertains to the chatbot avatar displayed in the chat, while identity cue pertains to how the chatbot introduces itself. Conversational cue focuses on the manner of the chatbot imitating human communication. Participants were tasked to rate a total of 8 design combinations from 1 (not human-like) to 5 (very human-like). To analyze the data gathered, Minitab tools such as full factorial design, ANOVA, and response optimization technique (with a 95% confidence level) were utilized. Based on the results, a high human-likeness chatbot should include the following characteristics: (1) human avatar, (2) introduces herself as a sales associate, and (3) acknowledges and sympathizes with the customer's previous message whereas a low human-likeness chatbot should have (1) a human avatar, (2) introduces herself as an automated customer service chatbot, and (3) acknowledges and sympathizes with the customer's previous message.