This 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.

Aquaculture has become a cornerstone of rural livelihoods and food security in India, particularly in Andhra Pradesh where districts such as West Godavari have evolved into high- density shrimp and fish farming zones. Despite considerable economic gains in export revenues and rural employment, questions remain regarding the ecological costs and long- term economic resilience of small and medium-scale farmers. Building on prior studies, this paper addresses gaps in micro-regional sustainable-aquaculture research by (i) explicitly stating and testing hypotheses grounded in ecosystem and resilience theory, (ii) validating our survey instrument (content validity index = 0.89; Cronbach’s α = 0.87), (iii) applying advanced quantitative techniques (exploratory factor analysis, structural equation modeling) alongside rigorous thematic analysis, and (iv) offering an integrated framework for policy and practice. Data were collected January–March 2023 via a cross-sectional survey of 200 stakeholders (farmers, input suppliers, institutional actors) and 30 in-depth interviews across five mandals in West Godavari. Structural equation modeling confirms that intensive input use (chemicals, high stocking densities) significantly predicts environmental degradation (β = 0.52, p < 0.001), which in turn negatively affects farm profitability (β = –0.65, p < 0.001). Adoption of sustainable practices (water recycling, polyculture) mitigates these effects (indirect standardized effect = +0.22, p < 0.05). Qualitative analysis yielded three major themes—“Ecological Risk Awareness,” “Economic Vulnerability,” and “Adaptive Innovation”—each illustrated by farmers’ verbatim statements. We discuss theoretical contributions to triple-bottom-line sustainability models, outline practical extension and credit mechanisms, and propose policy reforms including aquaculture zoning, zero-discharge mandates, and sustainability-linked finance. This study provides a robust empirical foundation for steering India’s aquaculture toward long-term ecological integrity and economic resilience.

This 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.

This 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.

In today's dynamic and competitive business environment, organizations adopt supply chain management to boost innovation and improve performance. This study investigates the linkbetween supply chain management and firm performance and the mediating role of innovation capability. A cross-sectional survey research design was employed; 5 textile firms in Lagos State were conveniently selected for the study. 353 structured questionnaires were administered to top-, middle-, and lower-level managers of the selected firms, of which only 328 were returned. After data screening, only 312 responses were valid for analysis. The study employed SPSS statistical software to perform both descriptive and inferential statistical analysis. The mediation aspect of the analysis was done using Model 4 of the SPSS PROCESS macro. The study found that supply chain management has a significant positive effect on firm performance and innovation capability. Also, innovation capability significantly influences firm performance and plays a mediating role in improving the relationship between supply chain management and firm performance. The study recommends that leaders from industry should prioritize investments in SCM practices that can bring supply chain partners together to build a strong IC for performance improvement.

This 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.

This 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.

This study explores the influence of sustainable business strategies and facilitative policy frameworks on ASEAN companies' upgrading and integration into global supply chains (GSCs). The study employs a qualitative document analysis approach to critically examine 12 national and regional policy reports, 6 international institutional reports, and 12 firm-level sustainability and annual reports. Thematic coding and cross-case comparative analysis were conducted using ATLAS.ti software to determine main drivers of sustainable GSC involvement at firm and policy levels. There are suggested and argued conceptual frameworks: the Sustainable Value Chain Upgrading Framework and the Policy-Driven Sustainable Trade Framework. Evidence indicates that firms embracing environmental, social, and governance (ESG) values, circular economy approaches, and innovation investment possess higher market positioning and supply chain resilience. Meanwhile, green growth policies at the national level and international efforts promoting sustainable trade assist in guaranteeing main compliance reduction and corporate sustainability uptake incentives. Cross-country analysis reveals growing convergence towards ASEAN global ESG standards among ASEAN firms, with differences in social impact strategies localized. The article contributes to the existing literature by integrating firm- and policy-level perspectives and offers practical recommendations to policymakers and business managers interested in pursuing economic upgrading through sustainability in the region. The findings call for dynamic firm-policy interactions to attain resilient, inclusive, and sustainable engagement in global value chains.

Hybrid work environments that blend human labor with artificial intelligence (AI) systems reconceptualize assumptions about identity, agency, and value creation within the firm. Grounded in the philosophical postulate that organizational reality is a social construct, this study analyses how human capital interprets AI integration in companies located in the Sierra de Zongolica, Veracruz. A phenomenological design was employed; twenty-five semi structured interviews and two focus groups were conducted with service, production, and administrative workers who interact daily with human–AI systems. Thematic coding revealed four interrelated constructs: AI as an operational enabler, perceived occupational well-being, enhanced professional autonomy, and holistic job satisfaction. Participants reported that AI lightens repetitive tasks, shortens cycle times, and broadens decision-making scope, thereby reducing stress and improving work–life balance. Concurrently, concerns arose regarding the loss of human interaction and job stability, particularly among longer-tenured employees. The findings indicate that AI functions as a contingent complement to human expertise; its value depends on transparent algorithms, upskilling programmes differentiated by age cohorts, and change management sensitive to the cultural context. The study concludes that corporate strategies and public policies must align technological efficiency with ethical governance so that digital transformation simultaneously fosters productivity and human development.

The 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.