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Strengthening Artificial Intelligence Governance through Ethical Handling of Sensitive Data: An Applied Study on Text Classification and Differential Privacy
Get PDFThis research develops a comprehensive hybrid framework to enhance Artificial Intelligence governance by ethically managing sensitive textual data through advanced classification techniques. Focusing on natural language processing (NLP) applications, the study integrates rule-based systems, logistic regression, and transformer-based models, notably BERT, to address the challenges of identifying and handling sensitive information within complex and ambiguous linguistic contexts. Experimental results demonstrate that the hybrid model attains an overall classification accuracy of 91%, with precision and recall scores of 89% and 94%, respectively, achieving an F1-score of 92%. These metrics reflect the model’s robustness in real-world scenarios where explicit textual indicators are often lacking. Individually, the rule-based approach excels in precision (98.6%) for clearly identifiable sensitive content, logistic regression ensures perfect recall (100%), capturing all sensitive instances albeit with increased false positives, and the BERT model achieves perfect precision, effectively minimizing false alarms. The hybrid approach synergizes these strengths, resulting in a balanced and reliable classification system. The study further explores the integration of differential privacy via a differentially private logistic regression model using the diffprivlib library, assessing privacy-utility trade-offs at varying privacy budgets (ε = 3, 5, 6). Results reveal that stronger privacy guarantees (lower ε) reduce classification accuracy (78% at ε=3), while looser privacy constraints (ε=6) approach non-private model performance (97% accuracy). These findings underscore the potential of combining hybrid NLP models with differential privacy to deliver scalable, trustworthy, and privacy-preserving AI systems. The proposed framework holds significant relevance for sensitive domains such as healthcare, public administration, and corporate governance, where balancing data privacy and AI performance is critical. Future research should extend these findings by exploring additional privacy configurations and validating the approach against diverse real-world datasets to optimize the equilibrium between privacy protection and analytical effectiveness
Singapore Role in Advancing Global Low-Carbon Economy: A Joint Effort for Sustainability and Climate Commitments
Get PDFThis study examines Singapore’s proactive role in advancing a low-carbon economy by analyzing its policies, innovations, and global partnerships in addressing climate change and promoting sustainability. Despite being a small, densely populated island nation with limited natural resources, Singapore has emerged as a global leader in sustainable development. Its approach integrates forward-thinking policies, cutting-edge technologies, and strategic collaborations. This study employs a structured literature review with a qualitative approach, systematically analyzing key policy documents and case studies of successful initiatives. Initiatives such as the Singapore Green Plan 2030 and the Carbon Pricing Mechanism drive energy efficiency, promote renewable energy adoption, and reduce carbon emissions. Additionally, Singapore actively participates in global climate action, contributing to regional and international platforms. This study highlights Singapore’s efforts in fostering a low carbon economy, encouraging collaboration among policymakers, research institutions, and stakeholders to build a sustainable and resilient global future
The Moderating Role of Emotional Intelligence in the Relationship Between Employee Resilience, Perceived Organizational Support, and Work Engagement: A Multi-Sector Study in Saudi Arabia
Get PDFEmployee engagement plays a crucial role in organizational success, influencing productivity, retention, and overall workplace performance. This study examines the impact of employee resilience and perceived organizational support (POS) on work engagement, with emotional intelligence (EI) as a moderating factor, across multiple sectors in Saudi Arabia. Grounded in the Job Demands-Resources (JD-R) model, the study hypothesizes that resilience and POS positively influence engagement, while EI moderates these relationships by enhancing employees’ ability to leverage resilience and support effectively. A quantitative research approach was employed, using a structured survey distributed to 450 full-time employees across industries such as healthcare, education, finance, manufacturing, and IT. Data were analyzed through structural equation modeling (SEM) to assess the relationships among the variables. The findings confirm that employee resilience and POS significantly enhance work engagement, supporting the direct effects. Additionally, EI moderates these relationships, indicating that employees with higher emotional intelligence are better equipped to utilize resilience and organizational support to sustain engagement. These findings contribute to Saudi Vision 2030, emphasizing workforce development and employee well-being. The study provides practical insights for HR professionals on fostering engagement through resilience training, supportive workplace policies, and emotional intelligence development programs
Enhancing Market Reach and Profitability in the Indian Aquaculture Industry
Get PDFThe Indian aquaculture industry, a global leader, faces persistent challenges in marketing, pricing, and supply chain management that limit profitability and market expansion. This study investigates how marketing channels, pricing strategies, and supply chain practices influence commercial success, focusing on West Godavari (Andhra Pradesh), Hooghly (West Bengal), and Kollam (Kerala). Semi-structured interviews with 45 stakeholders, including farmers, marketers, and supply chain managers—reveal that using online platforms and targeting export markets significantly enhances reach and profitability. Value-based pricing improves margins by aligning prices with product quality and customer perception. Efficient supply chain management, particularly through blockchain and automation, is vital for maintaining product integrity and meeting market demands. However, high implementation costs, lack of technical expertise, and resistance to change hinder adoption, especially among smaller operators. The study concludes that sustainable growth requires integrating diversified marketing strategies, value-driven pricing, and tech-enabled logistics. Key recommendations include investing in digital tools, embracing innovation, and fostering stakeholder collaboration to address operational barriers and strengthen the industry’s economic impact
Business Model Innovation in the Trading Card Grading Industry: Cross-National Insights from Pokémon Trading Card Game and Non-fungible Tokens
Get PDFThis study examines how firms in the Pokémon Trading Card Game (PTCG) grading industry adapt their business models in response to digital disruption. We employ a qualitative multiple-case design, investigating three leading grading companies – PSA (United States), CCIC (China), and SQC (Thailand) – through 30 in-depth interviews and supplemental document analysis. The findings reveal divergent strategies shaped by both dynamic capabilities and institutional contexts. PSA leverages scale and AI technology to enhance efficiency, CCIC focuses on legitimacy and incremental improvements under regulatory constraints, and SQC pursues exploratory digital initiatives (e.g., NFT-linked trials) to co- create value with its community. These patterns highlight the ambidexterity required for business model innovation in a digitizing niche service sector. The study contributes to business model innovation and digital transformation literature by demonstrating how national institutions and customer engagement influence innovation paths. Practical implications include lessons for balancing core business sustainability with transformative innovation in different regulatory environments
Activating Building Information Modeling Using Artificial Intelligence: An Applied Analytical Study
Get PDFThis study introduces the development of an intelligent, cost-effective, and replicable system for the classification and analysis of Building Information Modeling (BIM) data through supervised machine learning. The primary aim is to enhance the interpretability and functional value of BIM metadata by embedding artificial intelligence (AI) techniques into the design evaluation process. The research focuses on classifying BIM elements using structured attributes—such as dimensions, materials, fire ratings, and load-bearing status—and contextualizing these classifications within specific application domains, including residential, industrial, and healthcare environments. To identify the most effective classification strategy, four machine learning algorithms were evaluated: Logistic Regression, XGBoost, Neural Network (MLP), and Random Forest. Among these, the Random Forest model demonstrated superior performance with 99% accuracy, 0.99 precision, 0.98 recall, and a 0.99 F1-score, and was thus adopted as the core model for the proposed system. Unlike conventional BIM tools that depend on manual labeling, the proposed system autonomously predicts element categories using raw numerical and categorical data, showcasing a practical approach to semantic enrichment and intelligent automation in digital design workflows. The application, developed using Streamlit, features an interactive interface that accepts BIM data in CSV format, processes and classifies elements, assesses compliance with intended use contexts, and calculates associated design risk scores. It also generates simplified 3D-like visualizations to support user comprehension. In addition to classification, the system provides descriptive feedback and actionable suggestions, thereby facilitating informed decision-making during early design stages. By bridging the gap between static, IFC-based BIM data and AI-powered design intelligence, this research presents a novel tool for automated classification, risk evaluation, and context-aware assessment. The findings underscore the feasibility and utility of integrating AI into BIM environments to support more efficient, intelligent, and responsive architectural and structural planning
Examining Determinants of Real Estate Appraisal Accuracy in Property Business
Get PDFThis study investigates the factors influencing real estate appraisal accuracy, focusing on market dynamics, technological integration, appraiser expertise, and the regulatory framework. The research aims to explore how these factors impact the accuracy of property valuations performed by real estate appraisers in Saudi Arabia. A cross-sectional survey was conducted with 161 licensed real estate appraisers, using a convenience sampling method. Data was collected through a structured questionnaire, and the responses were analyzed using structural equation modeling (SEM). The study found that market dynamics, technological integration, appraiser expertise, and regulatory frameworks significantly influence real estate appraisal accuracy. The findings highlight the importance of these factors in improving the reliability of property valuations, providing valuable insights for real estate professionals, regulators, and policymakers. The findings suggest that real estate appraisers should stay informed about market trends, enhance their technological skills, and continuously develop their expertise to improve appraisal accuracy. Regulatory bodies should strengthen guidelines and standards to ensure consistency in the appraisal process. Policymakers can use these insights to develop strategies that promote trust and stability in the real estate market
The Mediating Role of Green Innovation in the Relationship Between Strategic Green Marketing Orientation and Marketing Performance
Get PDFThis study investigates the relationships between strategic green marketing orientation (SGMO), green innovation, and marketing performance within the manufacturing sector in Saudi Arabia. Employing a cross-sectional research design, data was collected from mid-level employees in the marketing departments of manufacturing firms. A total of 357 responses were gathered using a structured questionnaire, which measured the three key constructs using validated scales adapted from prior studies. Structural equation modeling (SEM) was utilized to analyze the data, enabling the examination of both direct and indirect relationships among the variables. Results revealed that SGMO has a strong positive effect on green innovation, which in turn significantly enhances marketing performance. Additionally, green innovation was found to mediate the relationship between SGMO and marketing performance, highlighting its pivotal role in translating green marketing strategies into improved market outcomes. The study underscores the importance of integrating environmental concerns into marketing strategies and fostering green innovation to achieve sustainable business success. These findings offer valuable insights for managers and policymakers in the manufacturing sector, emphasizing the need to align green marketing practices with innovation to drive both environmental and economic benefits
Transforming Export Competitiveness: Technological Upgradation and Digitalization in the Indian Heating, Ventilation, and Air Conditioning Industry
Get PDFThis study investigates the impact of technological upgradation and digitalization on the export competitiveness of India’s Heating, Ventilation, and Air Conditioning (HVAC) industry. Drawing on a 23-year panel dataset from 51 low-and-lower-middle-income countries, the research employs econometric analysis using high-tech exports and broadband subscriptions as proxies. The findings reveal that technological upgradation—measured through medium and high-tech exports—has a statistically significant positive impact on export competitiveness. In contrast, digitalization, proxied by broadband subscriptions, shows no significant effect, suggesting that mere infrastructure is insufficient without deeper operational integration. The Indian HVAC sector, though poised for growth amid global demand and sustainability mandates, faces challenges such as limited R&D investment, inadequate digital adoption, and scale inefficiencies. The study proposes a theoretical framework linking technological advancement and digital readiness with competitive export performance, offering insights for policymakers and industry stakeholders. It underscores the need for strategic investments in innovation, sector-specific digital tools, and workforce development. By aligning macroeconomic data with sectoral realities, the research contributes to a nuanced understanding of how emerging economies like India can leverage technological transformation to boost global trade competitiveness
Evaluating Green Investment Performance in Morocco: An Empirical Study on Policy, Environmental, and Economic Drivers
Get PDFThis study investigates the performance of green investments in Morocco, focusing on their financial returns, environmental impact, and social contributions within the framework of the country’s sustainability and economic development goals. Drawing on Sustainable Finance Theory and the Triple Bottom Line (TBL) framework, the research examines five key influencing factors: government policies and incentives, investment challenges, environmental impact, investor confidence, and job creation. Primary data were collected through a structured survey administered to 225 participants, including policymakers, investors, and industry experts involved in green investment projects. The survey utilized a Likert scale format to assess perceptions of investment effectiveness and barriers. Quantitative methods, including descriptive statistics, multiple regression, and correlation analysis, were employed to analyze the relationships between these variables and green investment performance. The results reveal that environmental impact (β = 0.181, p < 0.01), job creation and economic impact (β = 0.155, p < 0.01), and investment factors (β = 0.119, p < 0.01) significantly enhance investment performance. In contrast, investor confidence has a negative effect (β = −0.322, p < 0.01), and government policies do not show a statistically significant impact. The model explains 74.7% of the variation in green investment performance (R² = 0.747). These findings underscore the need for stronger and more consistent policy implementation, targeted investment incentives, and a greater focus on job-generating sustainable projects. The study offers practical insights for policymakers and stakeholders aiming to advance Morocco’s green transition and promote sustainable development through more effective green investment strategies