Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, the field grew, and development flourished. However, concerns have been expressed that the techniques are limited in terms of to whom they are applicable and how their effect can be leveraged. Currently, most XAI techniques have been designed by developers. Though needed and valuable, XAI is more critical for an end-user, considering transparency cleaves on trust and adoption. This study aims to understand and conceptualize an end-user-centric XAI to fill in the lack of end-user understanding. Considering recent findings of related studies, this study focuses on design conceptualization and affective analysis. Data from 202 participants were collected from an online survey to identify the vital XAI design components and testbed experimentation to explore the affective and trust change per design configuration. The results show that affective is a viable trust calibration route for XAI. In terms of design, explanation form, communication style, and presence of supplementary information are the components users look for in an effective XAI. Lastly, anxiety about AI, incidental emotion, perceived AI reliability, and experience using the system are significant moderators of the trust calibration process for an end-user.

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.

In recent years, the revolutionary impact of Artificial Intelligence (AI) cannot be overstated. This groundbreaking technology has radically transformed how we perform our daily tasks, thereby redefining the very fabric of our society. However, as our reliance on AI systems continues to grow, the need for calibrated trust becomes increasingly pressing. To address this concern, the concept of Explainable AI (XAI) has been introduced to provide human-level explanations. The primary goal is to offer cognitive information that prompts informed trust decisions. Nevertheless, it is essential to recognize that trust is a multidimensional construct, involving various means of processing beyond mere explanations. To fully understand and explore these dimensions within the context of XAI, this research aims to uncover and comprehend the additional facets of trust. Through an exploratory survey, it was confirmed that XAI serves a vital purpose in facilitating trust, and it can be effectively processed through affective means. Furthermore, the presentation of information beyond the depth of explanation was found to play a significant role in moderating trust formation during human-AI interactions.

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.

Managing electronic waste (e-waste) in the Philippines is a critical challenge, no with roughly 80% handled by an informal sector using hazardous methods. This study develops a context-specific Circular Economy (CE) framework for urban Manila by quantifying the behavioral, institutional, and socio-economic factors influencing recycling efficiency. Using a hybrid methodology, quantitative data were collected from 435 informal recyclers. Structural Equation Modeling (SEM) supported 16 of 18 hypothesized pathways from the Theory of Planned Behavior (TPB), though Perceived Behavioral Control did not directly affect Intention. An Artificial Neural Network (ANN) sensitivity analysis identified economic factors, Income Level (84.01%) and Financial Incentives (82.86%), as the dominant predictors of behavior, followed by the Cultural–Cognitive Pillar (80.98%). This necessitates modifying the TPB for subsistence economies, where economic survival acts as a super-moderator. The resulting CE framework mandates inclusive policies, prioritizing “Economic First Interventions” like buy-back schemes to equitably integrate informal recyclers into formal Extended Producer Responsibility systems.

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.