Scopus ID: 105000235910
TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON), (2024), pp. 1533-1536
Conference Paper | Published: January 1, 2024
Abstract
Social relationships are important to human well-being and interaction. Recently, there has been growing interest in how they are formed in various digital media, including social media and video games. Amid the rise of digital engagement, the metaverse has emerged as an essential virtual environment for social interaction. Unfortunately, there is limited understanding of how social relationships are developed and maintained within the metaverse. In this study, we explored the dynamics of social relationship development in a metaverse world. Using a one-shot case study, we assessed the roles of immersion and embodiment, as well as the moderating effect of copresence, in building social relationships through multiple regression analysis. Our findings show that with higher levels of immersion and embodiment, the formation of social relationships in the metaverse is significantly improved. Copresence further intensifies these effects, which is indicative of its crucial role in virtual social interactions. These results indicate that enhancing the immersion, embodiment, and copresence elements in metaverse environments can encourage stronger social bonds among users. Overall, this study advances our understanding of online social relationship formation in the metaverse environments and its design and development.
Scopus ID: 105000268427
TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON), (2024), pp. 940-943
Conference Paper | Published: January 1, 2024
Abstract
Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Limited research on Cebuano has hindered linguistic documentation and understanding of its grammar and vocabulary. This study introduces a Cebuano POS tagger using the Hidden Markov Model (HMM) to improve Cebuano text processing. The researchers also propose a method for handling unfamiliar words. Results show the algorithm performs well on a news text corpus of 25,000 datasets, with an accuracy of 84 %, precision of 80%, recall of 81.52%, and F1-score of 82%. These outcomes demonstrate the algorithm's effectiveness in addressing language challenges in specific genres. Additionally, the research contributes to the Sustainable Development Goals (SDGs) by promoting linguistic diversity and fostering inclusive language technologies. The study provides insights into Cebuano's linguistic traits and grammatical structures, offering a foundation for further research in natural language processing.
Scopus ID: 86000023442
2024 9th International Conference on Business and Industrial Research (ICBIR), (2024), pp. 0841-0846
Conference Paper | Published: January 1, 2024
Abstract
Energy consumption significantly increased in recent decades, notably at the household level, due to economic development, rising population, and technological advancements. To address this sustainability concern, peer-to-peer energy sharing service (P2PESS) is introduced as a solution to household level energy needs. However, P2PESS has yet to be fully explored in terms of development and adoption. As such, this study attempts to provide an understanding of the adoption intention on artificial intelligence (AI) in P2PESS a developing country. This study is realized by developing an extended adoption intention model analyzed through partial-least squares - structural equation modeling (PLS-SEM). Results show that attitude is the most significant predictor of AI in P2PESS adoption intention. This study also reveals that the trust dimension has the strongest effect on attitude, while attitude toward use has the strongest effect on behavioral intention. Also, this study confirms ease of use and usefulness as critical factors in adoption intention. Meanwhile, AI-anxiety is the least significant predictor in the model. Finally, this study is the first evidence of AI in P2PESS adoption intention from the perspective of household level users.
Scopus ID: 86000010175
2024 9th International Conference on Business and Industrial Research (ICBIR), (2024), pp. 0823-0828
Conference Paper | Published: January 1, 2024
Abstract
Sustainability is a present concern in many developing countries, where the role of the household is pivotal in realizing its benefits. This study aims to explore artificial intelligence in green energy technologies (AIGET) adoption intention among household-level respondents selected in the National Capital Region (NCR), Philippines. The study has 446 respondents and analyzed using partial least squares and structural equation modeling approaches (PLS-SEM). Among the factors tested, results revealed that perceived usefulness is the strongest predictor of AIGET adoption intention. Factors such as usefulness, ease of use, subjective norms, and perceived risk have a positive effect on attitude. This confirms that attitude has a positive impact on behavioral intention on AIGET. Finally, this study shows that household-level participants have a positive interest in adopting AIGET, considering its usefulness and ease of use. This study presents useful theoretical and practical contributions to further its uptake in the Philippines and other developing countries.
Scopus ID: 86000025454
2024 9th International Conference on Business and Industrial Research (ICBIR), (2024), pp. 0835-0840
Alexander A. Hernandez, Victor James C. Escolano, ... Darrel Cardaña
Conference Paper | Published: January 1, 2024
Abstract
Sustainability in transportation technologies is growing in all parts of the world through electric and micro-mobility sharing services. As such, there is a need to explore the factors that influence its adoption and use behavior. However, this is relatively underexamined in many developing countries. This study attempts to understand the intention and use behavior of micro-mobility as a service (MaaS) in the Philippines, a developing country. This study used survey data, and analysis was performed using partial least squares and structural equation modeling (PLS-SEM). Results show that performance expectancy is the strongest predictor of intention, while satisfaction is the least significant predictor. Factors such as social influence, price value, and habit have a positive effect on intention. Overall, the predictive model is explained by the coefficient of determination, revealing that behavior intention, satisfaction, and use behavior have large predictive relevance. This study provides theoretical and practical implications for further micro-mobility research in the future.
Scopus ID: 105030194141
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2024), pp. 1-5
Conference Paper | Published: January 1, 2024
Abstract
This study aims to identify the most critical attributes influencing retention and dropout risk in the Alternative Learning System (ALS) by analyzing various demographic, socio-economic, academic, and behavioral factors. Using Gradient Boosting Decision Trees (GBDT) for predictive modeling, the research explores feature importance scores to rank and prioritize the key attributes. The researcher used Knowledge Discovery in Databases as analytics methodology. Using principal component analysis, it was identified that regular attendance, availability, financial support, parental cohabitation (living together), and internet access positively influence retention. Furthermore, attending public schools, having a widowed parent, and possibly other features like distance to school are linked to increased dropout risk. The results provide insights into the main factors affecting student success, enabling more focused and data-driven interventions. The findings can help ALS administrators and educators develop personalized support plans for at-risk students and allocate resources more effectively.
Scopus ID: 105030199608
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2024), pp. 1-6
Conference Paper | Published: January 1, 2024
Abstract
This study presents an e-commerce platform with customer analysis using hybrid collaborative filtering, developed specifically for the EveryDay streetwear brand to enhance their online presence and improve their business operations. With the rise of online shopping and social commerce in the Philippines, the platform aims to enhance user experience by providing tailored product suggestions using hybrid collaborative filtering. It integrates features like order, payment, inventory management, and product customization, allowing users to personalize their shopping experience, while customer behavior analysis and monthly reporting help improve decision-making and operational efficiency. To guide the development of the platform, the team used the SCRUM methodology. The system's architecture emphasizes data security, user privacy, and reliability through the ISO 25010 Software Quality Model. Surveys were conducted with a total of 75 respondents to measure the system's performance based on the model's parameters. The system scored a weighted mean of 4.81 from customers, 5.00 from both staff and admin, and 4.88 from IT experts, resulting in an overall rating of “Excellent.”. This study highlights the potential of hybrid recommender systems in enhancing e-commerce platforms and driving customer engagement and sales.
Scopus ID: 105030188922
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2024), pp. 1-5
Conference Paper | Published: January 1, 2024
Abstract
Forest devastation is a critical environmental issue with severe ecological consequences. The Philippines, in particular, faces significant challenges in conservation efforts, compounded by a lack of public awareness about the effects of deforestation. To address this gap, we developed “Leshy: A 3D Action-Adventure Game Using Character Switching Mechanics for Promoting Awareness on Combating Forest Devastation.” This game immerses players in a virtual environment that reflects the real-world impacts of deforestation, leveraging character-switching mechanics to enhance engagement and education. “Leshy” aligns with Sustainable Development Goal (SDG) No. 15 - Life on Land, offering a unique and interactive approach to inspire action against forest devastation. The game's effectiveness was evaluated based on Gameplay, Aesthetics, User Interface (UI), Sound Design/Audio, and Storyline, achieving an overall average score of 4.44, rated as “Excellent.” This high rating indicates the game's success in engaging players with its captivating gameplay, appealing visuals, intuitive interface, immersive sound, and compelling narrative. The accompanying website, assessed using the FURPS model (Functionality, Usability, Reliability, Performance, and Supportability), received an overall average score of 4.50, also rated as “Excellent.” This demonstrates the website's user-friendly interface, dependable reliability, and strong performance, ensuring a seamless user experience. In conclusion, “Leshy” effectively addresses the issue of forest devastation by combining educational content with an engaging gaming experience. This project highlights the potential of interactive media in raising environmental awareness and inspiring conservation efforts. Further development and promotion of similar educational games are recommended to enhance their impact on environmental conservation.
Scopus ID: 105030149087
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2024), pp. 1-6
Conference Paper | Published: January 1, 2024
Abstract
The Technology Acceptance Model (TAM) serves as a foundational framework for assessing user acceptance of emerging technologies, particularly in organizational settings. In education, digital governance has become a transformative tool for enhancing operations within State Universities and Colleges (SUCs). However, many SUCs face challenges due to their reliance on commercial applications that lack the capacity for comprehensive and accurate reporting. To address this issue, the researcher developed an enterprise-level information system tailored to integrate research, teaching, and extension functions for SUCs in the Philippines. Utilizing the Agile Development Model, which emphasizes iterative progress through continuous improvement, the study employed a descriptive developmental approach to system creation and evaluation. The TAM criteria guided the evaluation process, resulting in an overall weighted mean of 3.55, interpreted as “Acceptable.” While the system has been positively received, the findings highlight the need for further refinement to optimize its effectiveness. Despite this, the system is strongly recommended for deployment in SUCs, as it offers a comprehensive, customizable solution for enhancing digital governance in higher education. This study underscores the importance of applying frameworks such as TAM to evaluate and refine technological innovations, ensuring their alignment with organizational needs and their contribution to improving digital governance within SUCs.
Scopus ID: 105030148706
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2024), pp. 1-7
Amitav M. Swapnil, Inna Sosunova, ... Ari Happonen
Conference Paper | Published: January 1, 2024
Abstract
The study investigates the role of Augmented Reality (AR) for business operations enhancement, overcoming technological challenges, and advancing industrial sustainability. A systematic literature review was conducted, analyzing AR applications across sectors such as healthcare, education, e-commerce, manufacturing, maintenance, libraries, and museums. Academic databases like Google Scholar, IEEE Xplore, and Scopus were used. AR's effectiveness was assessed based on its ability to improve workflows, reduce errors, and enhance user engagement. Findings indicate that AR facilitates immersive training, reduces product return rates through virtual previews, and personalizes user interactions, leading to increased operational agility and innovation. Also, AR supports sustainability by promoting eco-friendly behaviors, optimizing resource usage, and enabling lean production practices. However, widespread adoption remains limited due to high implementation costs, technical complexity, and a shortage of skilled professionals. We offer a set of targeted recommendations to mitigate growth barriers: investing in scalable, cost-effective AR solutions, improving technical infrastructure, developing industry-specific AR applications, and offering specialized training to build AR expertise within the workforce. These strategies are essential for full AR's potential realization to drive sustainable and transformative business practices across industries. By addressing these current limitations, organizations can leverage AR not only as a tool for operational improvement but also as a strategic asset for advancing sustainability and long-term innovation.