Journal of Orange Technology (JOT) is an international, quarterly, open-access journal. It is positioned to be indexed by Scopus in the categories of Computer Science (Applications), Engineering (Biomedical), Psychology (Applied), and Social Sciences (Health). Aspiring to achieve a Q1 ranking in Scopus across these interdisciplinary fields, JOT aims to establish itself as a premier global platform for humanistic innovation. The journal delivers groundbreaking, high-quality research at the dynamic crossroads of technology, empathy, and societal care. Designed for academicians, graduate students, industry practitioners, policymakers, and interdisciplinary researchers in fields like engineering, psychology, and ethics, JOT provides an unparalleled venue for sparking transformative ideas and collaborative discussions. Every submission undergoes a meticulous double-blind peer-review process, ensuring rigor, integrity, and credibility, with all accepted works made freely accessible online as open-access content to inspire a worldwide audience and maximize global impact.
Humanizing Data Science Through Domain Expertise for Ethical and Trustworthy Artificial Intelligence
The rapid adoption of Artificial Intelligence (AI) has transformed data-driven decision making across healthcare, finance, education, and public services. De spite these advances, AI systems continue to face challenges related to algorithmic bias, limited contextual understanding, insufficient transparency, and declining user trust, highlighting the need to integrate human expertise throughout the data science process. This study aims to systematically examine the role of domain expertise in Humanizing Data Science for the development of Ethical and Trustworthy Artificial Intelligence. A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Peer-reviewed studies published be tween 2021 & 2026 were identified, screened using predefined inclusion and exclusion criteria, and analyzed through thematic synthesis. The review identifies five major themes, namely Human-Centered Artificial Intelligence, Domain Expertise Integration, Ethical and Trustworthy AI, Explainability and Human Oversight, and Humanizing Data Science. Based on these findings, this study proposes a Humanizing Data Science Framework integrating domain expertise throughout the AI lifecycle. The framework demonstrates that combining tech nical capabilities with human knowledge supports transparent, fair, accountable, trustworthy, and human-centered AI, providing valuable practical and theoretical guidance for researchers, practitioners, organizations, policymakers, and future interdisciplinary innovation initiatives worldwide.
Copyright (c) 2026 Marviola Hardini, Nur Azizah, Syahla Naurah, Rendhika Adyatama, Souza Nurafrianto Windiartono Putra https://creativecommons.org/licenses/by/4.0
Explainable AI Supporting Responsible Human AI Interaction in Intelligent Decision Support Systems
The rapid adoption of Artificial Intelligence (AI)-enabled Intelligent Decision Support Systems (IDSS) has transformed decision-making across multiple sectors. However, increasing AI complexity often limits users’ understanding of recommendation processes, creating challenges for responsible human-AI interaction. Existing studies mainly emphasize explainability and transparency from technical perspectives while providing limited evidence regarding their influence on responsible interaction and decision quality. This study investigates the effects of Perceived Explainability and Perceived Transparency on Responsible Human-AI Interaction and Decision Quality, including the mediating role of Responsible Human-AI Interaction. Grounded in the Human-AI Teaming perspective and the Responsible AI Framework, this quantitative study employs a survey of 180 respondents with experience using AI-enabled Intelligent Decision Support Systems. Data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study is expected to demonstrate that explainability and transparency strengthen responsible human-AI interaction, which subsequently enhances decision quality. These findings are expected to enrich responsible AI literature and provide practical guidance for designing transparent, human-centered Intelligent Decision Support Systems that promote trustworthy decision-making and reinforce the humanistic values underpinning intelligent technologies
Copyright (c) 2026 Cut Amalia Saffiera, Takumi Sase, Qurotul Aini, Danny Manongga, Harry Agustian https://creativecommons.org/licenses/by/4.0
Enhancing Human Trust Through Explainable AI Communication for Nontechnical Stakeholders
The rapid adoption of Artificial Intelligence (AI) in data driven decision making has increased the complexity of analytical models, creating significant communication barriers between technical experts and nontechnical stakeholders. Limited understanding of AI generated insights often reduces trust, delays decision making, and restricts the effective adoption of AI supported recommendations. As organizations increasingly rely on explainable and human centered AI systems, effective communication has become essential to ensuring that AI out- puts are accessible, transparent, and meaningful for diverse stakeholder groups. This study aims to identify the key challenges in communicating complex AI model results and to develop practical communication strategies that enhance human trust among nontechnical stakeholders. A qualitative research design was employed using case studies, open ended surveys, expert interviews, and focus group discussions involving data scientists and nontechnical decision makers from business organizations. The collected data were analyzed through thematic analysis to identify recurring communication barriers and effective explanatory practices. The findings reveal that technical jargon, cognitive overload, and limited contextual explanations are the primary factors reducing stakeholder trust in AI generated insights. Conversely, explainable AI communication supported by intuitive data visualization, contextual storytelling, simplified summaries, and audience centered messaging significantly improves understanding, transparency, and stakeholder confidence across organizational contexts. The study proposes a human centered communication framework that strengthens trust in AI assisted decision making while promoting more inclusive and responsible technology adoption. These findings contribute to explainable AI research by demonstrating how effective communication can bridge the gap between technical complexity and human understanding, thereby generating meaningful humanistic impacts in organizational decision making and sustainable organizational innovation.
Copyright (c) 2026 Michael Christian, Christian Haposan Pangaribuan, Dwi Apriliasari, Bintang Nandana Henry, Alexander Williams https://creativecommons.org/licenses/by/4.0
Human Centered Artificial Intelligence Through GDPR Compliant Data Science Workflow Implementation
The rapid advancement of Artificial Intelligence (AI) and data science technologies has transformed organizational decision-making across healthcare, finance, and digital services. Despite these advancements, the increasing use of personal data has intensified concerns regarding privacy, transparency, ac countability, and public trust. Building trustworthy Human-Centered AI requires data science workflows that integrate regulatory compliance, ethical principles, and responsible governance throughout the AI lifecycle. This study aims to examine how GDPR-compliant data science workflow implementation supports the development of trustworthy Human-Centered AI through a qualitative and practice-oriented research approach. The study synthesizes evidence from recent literature, industry case studies, expert perspectives, and governance oriented analytical frameworks to identify effective strategies for integrating privacy by design, data minimization, transparency, accountability, and privacy-preserving techniques, including anonymization, pseudonymization, and differential privacy, into data science workflows. The findings indicate that successful implementation depends not only on technical safeguards but also on strong organizational governance, continuous compliance monitoring, and cross-functional collaboration among legal, technical, and managerial stakeholders. Furthermore, the integration of explainable and governance-aware machine learning models improves transparency, strengthens stakeholder trust, and supports responsible human-centered AI without significantly reducing analytical performance. This study proposes a structured GDPR-compliant data science workflow framework that enables organizations to balance analytical effectiveness, regulatory compliance, and human-centered principles while fostering trustworthy, transparent, and sustainable Artificial Intelligence for real-world digital innovation.
Copyright (c) 2026 Nuke Puji Lestari Santoso, Dewi Khairani, Siti Ummi Masruroh, Agung Rizky, Aman Jaiswal https://creativecommons.org/licenses/by/4.0
Leveraging Public APIs for Human Centered Strategic Decision Making in Digital Organizations
Public Application Programming Interfaces (APIs) have become essential com- ponents of digital ecosystems, enabling organizations to access external data and transform it into meaningful information for strategic decision making. While previous studies have primarily emphasized the technical and operational roles of Public APIs, limited attention has been given to their contribution to human centered strategic decision making within digital organizations. This study aims to explore how Public APIs support strategic decision making by enhancing managerial interpretation, organizational responsiveness, and responsible digital innovation while promoting human centered organizational practices. A qualitative multiple case study approach was employed involving organizations operating in data intensive digital environments. Data were collected through semi structured interviews with managers and analysts, complemented by document analysis and limited non participant observation. The collected data were analyzed using thematic analysis to identify recurring patterns related to strategic decision support, organizational adaptability, and human centered technology adoption. The findings demonstrate that Public APIs function not only as technical connectors but also as strategic information resources that strengthen managerial judgment, improve organizational agility, and support evidence based decision making. The study further reveals that integrating external data through Public APIs encourages more transparent, inclusive, and responsible decision making, thereby contributing to organizational resilience and stakeholder value. However, challenges related to data quality, governance, external dependency, and trust remain important considerations for sustainable implementation. This study contributes to the growing body of knowledge on human centered digital transformation by demonstrating how Public APIs can support responsible strategic decision making beyond purely technical applications. The findings offer practical insights for organizations seeking to align data driven innovation with organizational objectives while fostering transparency, inclusiveness, and broader humanistic impact within digital ecosystems.
Copyright (c) 2026 Fransisca Rahardja https://creativecommons.org/licenses/by/4.0
Blockchain for Financial Identity in Developing Nations
The absence of formal financial identity remains a major barrier to financial inclusion in developing nations. Existing centralized identity systems are often vulnerable to fraud, data breaches, and third-party control, leading to low public trust and limited access to financial services. Blockchain technology offers a decentralized alternative through the concept of Self-Sovereign Identity (SSI), which enables individuals to own and control their personal data securely. This study aims to examine how blockchain-based identity systems can address the weaknesses of traditional identity frameworks and to identify the key technical and social challenges in their implementation within developing economies. Using a qualitative, case study-based approach, the research analyzes several blockchain-driven digital identity projects across diverse regions. The findings indicate that blockchain-enabled SSI improves data security, transparency, and user trust, while enhancing access to financial services. However, challenges related to infrastructure limitations, scalability, and regulatory uncertainty re- main significant obstacles. Overall, blockchain has the potential to serve as a foundational technology for creating inclusive, secure, and sustainable financial identity systems in developing nations, while offering practical insights for policymakers, regulators, and financial institutions worldwide.
Copyright (c) 2026 Sri Watini, Ora Plane Maria Daeli, Syahyono, Ramzi Zainum Ikhsan, Oliver Sauntos https://creativecommons.org/licenses/by/4.0
Microfinance 2.0 and Digital Lending Platforms for Human Well-Being and Financial Inclusion
The microfinance industry is undergoing a significant transformation driven by the emergence of digital lending platforms, a shift often referred to as ”Microfinance 2.0.” Traditional microfinance models, characterized by group-based lending and intensive personal interaction, are increasingly being replaced by technology-enabled systems that offer lower operational costs, broader outreach, and enhanced data analytics capabilities. This study aims to empirically exam- ine the impact of digital lending platform adoption on the operational performance, portfolio quality, and financial inclusion capacity of MFIs. This research adopts a quantitative approach using secondary financial and operational data collected from MFIs that have implemented digital lending platforms. Key performance indicators analyzed include cost per loan, loan portfolio at risk (PAR), processing time, and number of clients served. The data are analyzed using descriptive statistics, correlation analysis, and multiple regression modeling to evaluate the relationship between digital platform adoption and institutional performance outcomes. The findings indicate that the adoption of digital lending platforms is significantly associated with reduced operational costs, shorter processing times, and expanded client outreach. Statistical results also suggest improvements in portfolio monitoring efficiency, although variations in default risk patterns are observed across institutions. The study concludes that digital transformation contributes positively to operational efficiency and financial inclusion when supported by appropriate risk management mechanisms. Strategic integration of technology can enhance financial sustainability while maintaining the social mission of microfinance institutions in the era of Microfinance 2.0.
Copyright (c) 2026 Wisnu Wira Atmadja Effendi, Dwi Apriliasari, Muhtarom, Aulia Khanza, Zeze Nanle https://creativecommons.org/licenses/by/4.0
Designing Inclusive Companion Robots to Mitigate Bias and Enhance Empathy in AI-Driven Care Systems
This study adopts a qualitative research approach using a semi-structured interview design. Data were collected from individuals with diverse physical, sensory, and cognitive disabilities who have prior experience interacting with AI-based care technologies. The data were analyzed using thematic analysis to identify recurring patterns and key experiential dimensions related to ethical perception, inclusivity, trust, emotional support, and well-being. The findings reveal that ethical AI design, particularly in terms of transparency, fairness, and privacy, plays a crucial role in fostering user trust and a sense of security. Inclusive design features, such as accessibility, adaptability, and personalization, were found to enhance user comfort, independence, and emotional engagement. Furthermore, emotional support emerged as a central theme, indicating that users perceive AI care agents not only as assistive tools but also as sources of psychological reassurance and companionship. This study contributes to the growing body of human-centered AI research by providing qualitative insights into how ethical and inclusive design principles shape meaningful user experiences and well-being outcomes among people with disabilities. The findings highlight the importance of integrating humanistic values into AI care development to promote equitable, responsible, and sustainable digital care solutions.
Copyright (c) 2026 Karnawati, Dwi Nur Ramadhan, Tiurida Lily Anita, Risma Nurmala, Lily Maria https://creativecommons.org/licenses/by/4.0
Ethical and Inclusive Design of AI Care Agents for People with Disabilities
The increasing use of Artificial Intelligence (AI) in care services offers significant potential to improve the quality of life for people with disabilities, while also raising important ethical and inclusivity concerns. This study examines the ethical and inclusive design of AI care agents, emphasizing human-centered principles that support autonomy, dignity, and emotional well-being. This study employs a quantitative research approach using a cross-sectional survey design. Data were collected from individuals with diverse physical, sensory, and cognitive disabilities who have experience interacting with AI care agents. Measurement instruments were adapted from established ethical AI, inclusive design, and technology acceptance frameworks. Humanistic well-being was assessed using the WHO-5 Well-Being Index. The data were analyzed using Structural Equation Modeling (SEM) to examine the relationships between ethical AI design, inclusive features, user trust, technology acceptance, emotional support, and well-being outcomes. The findings indicate that ethical AI design significantly enhances user trust and perceived emotional support, while inclusive design features positively influence technology acceptance and emotional engagement. User trust, technology acceptance, and emotional support significantly contribute to humanistic well-being, with emotional support emerging as the strongest predictor. This study contributes a quantitative empirical model demonstrating how ethical and inclusive AI design principles translate into measurable well-being outcomes for people with disabilities. The findings highlight the importance of integrating humanistic values into AI care agent development to ensure equitable, responsible, and sustainable digital care innovation.
Copyright (c) 2026 Vivi Meilinda, Umi Narimawati, Thomas Sumarsan Goh, Irene Apriani Widjaya, Kamal Arif Al-Farouqi https://creativecommons.org/licenses/by/4.0
Emotion-Aware Digital Health Platforms Supporting Psychological Well-Being through Positive Computing Approaches
The rapid advancement of digital health technologies creates new opportunities to enhance psychological well-being beyond symptom reduction. Emotion-aware systems, grounded in positive computing approaches, enable digital platforms to respond adaptively to users’ affective states and promote sustained well-being rather than merely addressing negative symptoms. This study examines the development and quantitative evaluation of an emotion-aware digital health platform designed to support psychological well-being through positive computing principles. A quantitative research design was employed to assess the effectiveness of the platform. The system integrates self-reported emotional scales and behavioral interaction data to detect users’ emotional states in real time. Psychological well-being was measured using validated instruments, including the WHO-5 Well-Being Index, administered before and after platform usage. Statistical analysis was conducted to evaluate changes in well-being indicators and emotional awareness levels. The findings demonstrate a statistically significant improvement in users’ psychological well-being, positive affect, and emotional awareness after interacting with the platform. These results provide empirical evidence that integrating emotion-awareness into digital health systems can generate measurable humanistic impact and contribute to sustainable psychological well-being through positive computing approaches.
Copyright (c) 2026 Risma Haris, Ardi Kho, Efa Ayu Nabila, Nova Syahrani Arasid, Abdullah Arif Kamal https://creativecommons.org/licenses/by/4.0
The Impact of Gamified Mobile Applications on Happiness Levels Based on User Data
The rapid growth of gamified mobile applications has transformed how users engage with digital technologies, particularly in promoting positive emotional experiences. This study investigates the impact of gamified mobile applications on happiness levels based on user data. A quantitative approach was employed using survey data collected from active users of gamified mobile applications across diverse demographic backgrounds. Happiness levels were measured using a validated well-being scale, while gamification features such as rewards, challenges, feedback, and social interaction were assessed as key predictors. Data were analyzed using structural equation modeling to examine the relationships between gamification elements and users’ happiness. The results indicate that gamified mobile applications have a significant positive effect on users’ happiness levels, with reward mechanisms and social interaction emerging as the strongest contributors. The findings suggest that thoughtfully designed gamification can enhance emotional well-being by fostering enjoyment, motivation, and sustained engagement. This study contributes to the field of affective and positive computing by providing empirical evidence of the humanistic impact of gamified digital technologies. Practically, the results offer insights for developers and designers to create user-centered applications that prioritize emotional well-being alongside functional performance. Overall, the study highlights the potential of gamified mobile applications as effective digital interventions for enhancing users’ happiness and advancing human-centered innovation within positive computing environments.
Copyright (c) 2025 Dwi Andayani, Antonius Ary Setyawan, Aulia Rahma Dina, Lista Meria, Omar Arif Al-Kamari https://creativecommons.org/licenses/by/4.0
Human Centered Analysis of Digital Technology and Community Social Well Being
The increasing integration of gamification into mobile applications has transformed digital platforms into interactive environments that shape users emotional experiences. Within the framework of positive computing, gamified mobile applications are increasingly designed not only to enhance engagement but also to foster psychological well-being. This study investigates how gamified mobile applications function as drivers of user happiness in positive computing environments. A quantitative research design was employed using survey data collected from 120 active users of gamified mobile applications across diverse demographic backgrounds. Key gamification elements rewards, challenges, feedback mechanisms, and social interaction were modeled as predictive constructs of user happiness. Happiness levels were measured using a validated well-being scale, and the proposed relationships were examined through structural equation modeling (SEM). The findings indicate that gamified mobile applications exert a significant positive influence on user happiness, with reward systems and social interaction emerging as the strongest contributors. These results suggest that gamification operates as an affective design mechanism that enhances enjoyment, intrinsic motivation, and perceived social connectedness. The study contributes to the positive computing literature by providing empirical evidence that thoughtfully designed gamified systems can promote emotional well-being beyond functional performance outcomes. Practically, the findings offer strategic guidance for developers and policymakers seeking to design mobile applications that prioritize sustainable happiness and human-centered digital innovation.
Copyright (c) 2025 Fitra Putri Oganda, Pratibha Pandey, Syifa Wulandari, Sipah Audiah, Yasir Mustafa Kareem https://creativecommons.org/licenses/by/4.0
Anxiety Prediction Model Based on Smartwatch Activity Data and Self-Reported Affect Scale in Adolescents
This study proposes an anxiety prediction model based on smartwatch activity data integrated with a self-reported affect scale in adolescents. Anxiety among adolescents is a growing public health concern, often underdetected due to subjective assessment and limited continuous monitoring. To address this gap, this research combines objective physiological and behavioral indicators collected from smartwatches, including heart rate variability, sleep duration, physical activity intensity, and daily movement patterns, with subjective emotional states measured through a validated affect scale. Data were collected longitudinally from adolescent participants over several weeks to capture temporal variations in activity and mood. Machine learning techniques were applied to develop and evaluate predictive models capable of identifying anxiety levels with high accuracy. Model performance was assessed using standard metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that the integration of wearable sensor data with self-reported affect significantly improves anxiety prediction compared to single-source data models. The proposed model offers a scalable, non-invasive, and real-time approach for early anxiety detection, supporting timely intervention and personalized mental health monitoring for adolescents. This study contributes to the development of human-centered digital health technologies and highlights the potential of wearable-based analytics in preventive mental healthcare systems for future applications.
Copyright (c) 2025 Po Abas Sunarya, Mohd Faiz Hilmi, Nesti Anggraini Santoso, Sondang Visiana Sihotang, Ramiro Santiago Ikhsan https://creativecommons.org/licenses/by/4.0
Measuring the Humanistic Impact of Digital Technologies on Happiness A Cross-Cultural Analysis
The rapid advancement of digital technologies has transformed social interaction, daily activities, and individual well-being across societies. While prior studies emphasize economic and technological outcomes, limited attention has been given to the humanistic impact of digital technologies on happiness across cultural contexts. This study aims to measure the humanistic impact of digital technologies on happiness through a cross-cultural analysis. A quantitative research design was employed using survey data collected from multiple countries representing diverse cultural, social, and economic backgrounds. Digital technologies were conceptualized as a multidimensional construct encompassing digital access, usage intensity, digital inclusion, and human-centered digital engagement, while happiness was measured using subjective well-being indicators. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS, including measurement model evaluation, structural model assessment, and multi-group analysis to examine cross-cultural differences. The findings demonstrate that digital technologies have a positive effect on happiness when they support humanistic values such as social connectedness, autonomy, and inclusivity. However, the strength of these relationships varies across cultural groups, indicating the moderating role of cultural norms. This study contributes to the literature by integrating a humanistic perspective into digital well-being research and offers implications for policymakers and technology designers globally applicable.
Copyright (c) 2025 Nuke Puji Lestari Santoso, Migunani, Lukita Pasha, Muhammad Rifat Syauqy, Noah Rangi https://creativecommons.org/licenses/by/4.0
Inclusive AI Interaction Framework for Enhancing Happiness and Care within Orange Technology Applications
This study introduces an Inclusive AI Interaction Framework designed to enhance happiness, emotional well-being, and compassionate digital care through the integration of emotion-aware algorithms, adaptive conversational models, and accessibility-centered interface design. The framework prioritizes inclusivity by addressing diverse user abilities, cultural backgrounds, and emotional states, ensuring that AI interactions remain fair, empathetic, and supportive for broad user groups, including vulnerable populations. The study aims to examine how personalized and affectively responsive AI interactions influence emotional comfort, perceived empathy, trust, satisfaction, emotional stability, and sustained engagement among diverse users. A quantitative research design was employed, using structured questionnaires, multimodal sentiment-based rating scales, and standardized World Health Organization WHO-5 well-being measures to evaluate user well-being. SEM was applied to validate the relationships among core constructs and assess the impact of inclusive AI design on affective outcomes. The analysis revealed significant positive effects of inclusively designed, emotion-sensitive AI systems on affective outcomes, including trust, satisfaction, emotional stability, and overall emotional comfort. The findings demonstrate that inclusively designed, emotion-sensitive AI systems can meaningfully elevate user well-being. This research provides empirically grounded design principles and practical insights for developing emotionally supportive, ethically aligned, and universally accessible AI solutions that contribute to more humane digital experiences.
Copyright (c) 2025 Adam Faturahman, Hamdan, Muhammad Faizal Fazri, Untung Rahardja, Adele Vallery https://creativecommons.org/licenses/by/4.0
Grant Submission Monitoring System Based on Orange Technology Using Laravel 12 and Vue.js
The development of effective digital systems for managing research and community service grants is essential to ensure transparency, administrative efficiency, and user comfort within academic environments. However, many institutions still rely on manual processes or outdated platforms, resulting in unclear progress tracking, slow information updates, and increased emotional stress for lecturers as proposal authors. This study proposes a Grant Submission Monitoring System based on Orange Technology, an approach that emphasizes human-centered design, emotional well-being, and socially responsible innovation. The system was developed using the SDLC Waterfall model, employing Laravel 12 as a secure and high-performance backend framework and Vue.js to deliver an interactive Single Page Application (SPA) interface. Functional validation was conducted through Black Box Testing to ensure accuracy and reliability across all modules. The resulting system provides real-time notifications, structured visualization of proposal stages, and faster administrative processing, reducing delays by up to 40% compared to traditional methods. The SPA architecture enables a responsive experience with an average content rendering time of approximately 150 ms after initial load, supporting seamless user interaction. Feedback from lecturers indicates reduced uncertainty, increased trust, and greater emotional comfort due to clearer information flow and improved interface intuitiveness. Overall, the system demonstrates how Orange Technology principles can be integrated into academic information systems to create solutions that are not only efficient and transparent but also supportive of user well-being. This approach offers a scalable foundation for enhancing institutional governance and strengthening digital services across diverse educational contexts.
Copyright (c) 2025 Indira Puspa Gustiah, Ninda Lutfiani, Muhamad Rapidan Kusuma, Etty Puji Lestari, Thomas Green https://creativecommons.org/licenses/by/4.0
Examining the Effects of Innovation Access Safety and Sustainability Practices on Global Health Outcomes
Pharmaceutical innovation plays a critical role in improving global health outcomes by enabling the development of advanced medicines, vaccines, and healthcare technologies. However, the benefits of innovation are not solely determined by technological advancement but are also influenced by access, safety, and sustainability practices within pharmaceutical systems. This study aims to quantitatively examine the effects of innovation adoption, access and equity, pharmaceutical safety, and sustainability practices on global health impact, as well as to investigate the mediating role of pharmaceutical safety in the relationship between innovation adoption and global health outcomes. Using secondary data obtained from international health and development organizations, this study applies PLS-SEM with SmartPLS to test the proposed research model. The findings indicate that innovation adoption, access and equity, pharmaceutical safety, and sustainability practices each have a positive and significant effect on global health impact. Furthermore, pharmaceutical safety is found to partially mediate the relationship between innovation adoption and global health impact, highlighting the importance of robust safety mechanisms in translating innovation into meaningful health benefits. This study contributes to the literature by integrating innovation, equity, safety, and sustainability into a unified empirical framework and offers policy-relevant insights for strengthening global pharmaceutical systems to achieve equitable and sustainable health outcomes.
Copyright (c) 2025 Marviola Hardini, Magda Siahaan, Ihsan Nuril Hikam, Tuti Nurhaeni, Kristina Vaher https://creativecommons.org/licenses/by/4.0
Enhancing Academic Wellbeing through Empathic Digital Attendance Systems using UTAUT Approach
The increasing integration of information technology in higher education has transformed various academic processes, including attendance monitoring. Online attendance systems are gaining traction as institutions seek greater accuracy, transparency, and operational efficiency in managing student participation. This study aims to analyze how the use of an online attendance system influences student attendance performance within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). It explores key determinants that shape user acceptance and behavioral responses toward the system. A quantitative case study was conducted at a higher education institution, involving students and academic staff who actively used the system. Data were collected through structured questionnaires and analyzed using statistical techniques to examine the relationships among system usage, perceived usefulness, ease of use, and attendance performance. The findings reveal that the online attendance system significantly improves student attendance performance by enhancing consistency, reducing manual errors, and encouraging timely participation. Perceived usefulness and ease of use were found to mediate the relationship between system usage and attendance outcomes, demonstrating the system’s practical and motivational impact. This study underscores the vital role of digital solutions in strengthening attendance management and promoting user compliance. The results offer valuable guidance for institutions seeking to optimize online attendance systems. Future research should involve broader samples and comparative institutional settings to generate more comprehensive insights.
Copyright (c) 2025 Oscar Jayanagara, Ika Yuni Purnama, Meri Mayang Sari, Sheila Aulia Anjani, Carlos Perez https://creativecommons.org/licenses/by/4.0
Design and Evaluation of Emotionally Adaptive Chatbots to Promote Positive Mental Well-Being in Young Adults
The increasing prevalence of mental health challenges among young adults has driven growing interest in affective computing technologies that foster emotional support and psychological well-being. This study aims to design and evaluate an emotionally adaptive chatbot that promotes positive mental well-being through empathetic and user-centered interactions. Grounded in affective and positive computing frameworks, this research examines the influence of Emotion-Adaptive Capability, Perceived Empathy of the Chatbot, and Usability & Interaction Quality on Positive Mental Well-Being. A quantitative approach was employed by distributing an online questionnaire to young adult respondents who had interacted with emotion-aware chatbot systems. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to test the hypothesized relationships. The results are expected to demonstrate that chatbots with higher emotional adaptability, greater perceived empathy, and better usability significantly enhance users’ psychological well-being. This study contributes to the development of human-centered affective computing by providing empirical evidence on how emotionally intelligent chatbot design can positively influence mental health outcomes. The findings offer practical implications for designers and developers aiming to create AI systems that are not only functional but also emotionally supportive and aligned with humanistic technology values.
Copyright (c) 2025 Novi Indah Susanthi, Muhammad Fadheel Djamaly, Anandha Fitriani, Mardiana, Chua Toh Hua https://creativecommons.org/licenses/by/4.0
Sentiment Aware Chatbots as Companions for Reducing Loneliness through Positive Computing
Loneliness has increasingly emerged as a global mental health concern, particularly among vulnerable populations such as the elderly, students in remote learning environments, and individuals experiencing social isolation in urban societies. Advances in affective and positive computing offer promising solutions for addressing these challenges by creating empathetic digital companions capable of responding to human emotions in real time. This study aims to evaluate the effectiveness of sentiment-aware chatbots as digital companions in reducing loneliness and enhancing emotional well-being through positive computing principles. A quantitative experimental approach was employed, integrating sentiment analysis algorithms with Natural Language Processing (NLP) to detect emotional cues from user input and generate empathetic responses. The chatbot system was tested with 150 participants over a six-week period using standardized psychometric instruments, including the UCLA Loneliness Scale and the WHO-5 Well-Being Index. Statistical analysis using paired-sample t-tests, ANOVA, and Structural Equation Modeling (SEM) revealed significant improvements in loneliness reduction and psychological well-being among participants interacting with the sentiment-aware chatbot. Furthermore, perceived empathy and user satisfaction were found to mediate these effects, highlighting the emotional quality of human AI interaction as a crucial determinant of positive outcomes. Findings provide empirical evidence that sentiment-aware chatbots can function as effective digital companions, reducing loneliness and fostering psychological resilience. By integrating affective and positive computing principles, this study contributes to the advancement of compassionate AI systems designed to promote human well-being and support broader societal goals.
Copyright (c) 2024 Heni Nurhaeni, Sandy Kosasi, Made Bunga Thalia, Elisa Ananda Natalia, John Edwards https://creativecommons.org/licenses/by/4.0
Human Centered Affective Computing Models for Positive Emotional Health
The increasing prevalence of stress, anxiety, and emotional imbalance in digital society highlights the importance of designing computational systems that not only process information but also support psychological resilience, forming the background of this study. With this in mind, the objective of the research is to explore and develop affective and positive computing algorithms and interfaces capable of fostering emotional wellbeing through adaptive interaction strategies. To achieve this, the method combines literature review, algorithmic design, and prototype evaluation using affective data such as facial expression, voice tone, and physiological signals, which are analyzed through machine learning models and integrated into interactive interface prototypes. The results indicate that the proposed algorithms successfully recognize emotional states with higher accuracy than baseline models, while the interfaces provide feedback and adaptive interventions that enhance users’ sense of calmness, engagement, and positive affect during interaction sessions. Moreover, experimental validation suggests that the system can dynamically adjust its responses to individual emotional patterns, leading to more personalized and effective support for wellbeing. In conclusion, this research demonstrates that affective and positive computing can be meaningfully integrated into algorithmic frameworks and interface design to promote emotional health, offering not only theoretical contributions to the field of human-computer interaction but also practical implications for digital mental health tools that encourage resilience and positive experiences in everyday life.
Copyright (c) 2024 Agung Dharmawan Buchdadi, Sri Wahyuningsih, Yessy Oktavyanti, Ester Ananda Natalia, Henry Zainarthur https://creativecommons.org/licenses/by/4.0
Smart Urban Mental Health Mapping through IoT Sensor Networks and AI Analysis
Urban mental health is increasingly challenged by environmental stressors such as noise pollution, high population density, and air quality degradation. This study proposes an integrated framework combining Internet of Things (IoT) sensor networks with artificial intelligence (AI) analytics to monitor and predict mental health outcomes across metropolitan districts. A total of 300 participants from three urban areas contributed self-reported psychological data, which were combined with real-time environmental measurements including noise, air quality, temperature, humidity, and pedestrian density. Quantitative analyses, including correlation, multiple regression, and AI-based predictive modeling, revealed that noise and crowd density were the strongest predictors of elevated stress, while green spaces and improved air quality were positively associated with mood. The predictive models achieved 15–20% higher accuracy than survey-only models, and mapping of high-risk zones aligned with actual mental health service usage. These findings demonstrate the potential of IoT and AI-driven approaches to provide actionable insights for policymakers, urban planners, and healthcare providers. Future research should expand longitudinal and cross-city validation, integrate additional environmental and social indicators, and explore real-time interventions to create resilient and human-centered urban environments.
Copyright (c) 2024 Laras Sitoayu, Muhamad Viktor A Sin, Sugeng Riyadi, Mardaleni Daeli, Jonathan Parker https://creativecommons.org/licenses/by/4.0
Beyond the Algorithm A Review of User Experiences and Attitudes Toward Emotional Wellbeing Technologies
The escalating prevalence of stress, anxiety, and emotional dysregulation in an increasingly digitized society underscores the critical need for technological interventions that support psychological well-being. While the field of affective and positive computing has made significant strides in algorithmic development and technical efficacy, a holistic understanding of user perception and lived experience remains fragmented within the existing body of literature. This paper addresses this gap by presenting a narrative synthesis of ten seminal studies that explore the human centric dimensions of engaging with emotional well-being technologies. Our review reveals that user perceptions are intricately shaped by three recurring themes including the perceived credibility of a system’s emotion-recognition capabilities, concerns surrounding data privacy and control, and the fundamental distinction between viewing technology as a tool for personal empowerment versus a replacement for human connection. These themes collectively highlight a critical divergence between the technical promise and the psychological realities of affective technology. In conclusion, this synthesis provides a nuanced understanding of user attitudes that transcends purely algorithmic metrics. It offers valuable theoretical contributions to the field of human-computer interaction and provides practical implications for designing more empathetic, transparent, and ethically-aligned digital mental health tools that genuinely foster resilience and positive human experience.
Copyright (c) 2024 Richardus Eko Indrajit, Iswara Gandhi, Untung Rahardja, Meriyana Sunengsih, Kgomotso Moyo https://creativecommons.org/licenses/by/4.0
Empirical Studies on the Relationship Between Wearable Stress Detection and Workplace Productivity
Workplace stress has been widely recognized as a critical factor influencing employee health, performance, and organizational outcomes. Recent advancements in wearable technologies provide real-time physiological data that open new opportunities for monitoring and managing stress in professional settings. This study aims to empirically investigate the relationship between wearable-based stress detection and workplace productivity, focusing on how continuous monitoring can enhance well-being and performance. A quantitative approach was employed with 250 participants across three corporate sectors, where wearable devices measured physiological indicators such as heart rate variability and skin conductance, while productivity was assessed through task completion rates and self-reported efficiency. Statistical analyses, including correlation, regression, and moderation analysis, were conducted to examine the strength of associations. Findings reveal a significant negative correlation between elevated stress levels and productivity metrics, while participants using wearable feedback interventions demonstrated improved stress awareness and a 15% increase in task efficiency compared to the control group. In conclusion, wearable stress detection presents a promising tool for enhancing workplace productivity by enabling proactive stress management, highlighting the importance of integrating technology, psychology, and organizational practices to foster healthier and more effective work environments.
Copyright (c) 2024 Kristoko Dwi Hartomo, Muhammad Zaki, Gilang Kartika Hanum, Nur Silawati, Adele Vallery https://creativecommons.org/licenses/by/4.0