Pakistan Journal of Management Sciences and Technology (PJMST)
Pakistan Journal of Management Sciences and Technology (PJMST) is a prestigious and multidisciplinary scholarly publication that serves as a platform for rigorous academic research and critical analysis within the realm of Engineering & Technology, Social Sciences, Arts & Humanities, Physical Sciences, Natural Sciences, Health Sciences and Business & Management. This journal provides a forum for academics, researchers, policymakers and practitioners to contribute to and engage in discussions about contemporary societal challenges and the policy responses needed to address them. Aims and Scope: The PJMST aims to foster insightful discourse and disseminate high-quality research across various domains within the Engineering & Technology, Social Sciences, Arts & Humanities, Physical Sciences, Natural Sciences, Health Sciences and Business & Management. The journal's scope encompasses a wide range of disciplines but not limited to: Engineering & Technology Social Sciences Arts & Humanities Physical Sciences Natural Sciences Health Sciences Business & Management Editorial Process and Quality Standards: The PJMST upholds rigorous academic standards, employing a robust peer-review process to ensure the quality, validity, and originality of published articles. Submissions undergo thorough evaluation by experts in the field, providing constructive feedback to authors to enhance the scholarly contribution of their work. Contributions: The journal welcomes various types of contributions, including original research articles, review papers, case studies, policy briefs, book reviews, and commentaries. Each submission should present novel insights, theoretical frameworks, empirical evidence, or practical implications relevant to the fields. Audience and Impact: Targeted at scholars, policymakers, practitioners, and students in the above mentioned fields, the PJMST strives to bridge the gap between academic research and policy practice. By providing evidence-based insights and innovative perspectives, the journal aims to influence policy discourse and contribute to evidence-informed decision-making.
The Effect of Corporate Governance Mechanisms on Family-Owned Businesses with Mediating Role of Firm Performance
Corporate governance mechanisms play a crucial role in shaping the strategic direction, operational efficiency, and long-term sustainability of family-owned businesses. These mechanisms, including board structure, ownership concentration, and managerial oversight, are designed to ensure accountability, transparency, and effective decision-making within organizations. Family-owned businesses, which form a significant portion of global economies, often face unique governance challenges due to the intertwining of family and business interests. This study examines the effect of corporate governance mechanisms on family-owned businesses, with a focus on the mediating role of firm performance. Drawing on agency theory and stewardship theory, the research hypothesizes that strong governance mechanisms positively influence firm performance, which in turn enhances the overall sustainability and competitiveness of family firms. Using a quantitative research design, data were collected from 350 family-owned business managers and executives across diverse industries. Structural equation modeling was employed to evaluate the direct and indirect effects of corporate governance on firm outcomes. The findings indicate that governance mechanisms, including board independence, monitoring, and ownership structure, have a significant positive impact on firm performance. Firm performance acts as a partial mediator, demonstrating that effective governance enhances operational efficiency, financial outcomes, and strategic decision-making. The study contributes to the literature by bridging governance theory and family business management, providing empirical evidence on how governance structures can improve firm performance in family-owned enterprises. For practitioners, the research emphasizes the importance of establishing formal governance frameworks to balance family influence and professional management. Policymakers can also benefit by promoting governance standards that strengthen transparency and accountability in family-owned firms. Overall, the study highlights that corporate governance is not only a compliance mechanism but also a strategic tool that fosters firm growth, sustainability, and long-term value creation in family-owned businesses.
Corporate Governance, Family-Owned Businesses, Firm Performance, Board Independence, Ownership Structure, Agency Theory, Stewardship Theory
Copyright (c) 2025 Pakistan Journal of Management Sciences and Technology (PJMST)
Environmental, Social, and Governance (ESG) Practices and Their Impact on Marketing Strategies
Environmental, Social, and Governance (ESG) practices have emerged as critical determinants of corporate performance and long-term sustainability. This study investigates the influence of ESG adoption on marketing strategies, emphasizing how ethical, social, and environmental considerations shape branding, customer engagement, and competitive positioning. Drawing on stakeholder theory and sustainable marketing frameworks, the research explores the linkages between ESG integration and marketing effectiveness. The study employs a quantitative research design, collecting data from 300 marketing professionals and corporate managers across various industries. Statistical software is utilized to perform structural equation modeling, assessing the relationships between ESG practices, marketing strategy adaptation, and corporate performance indicators. Key findings reveal that ESG practices significantly enhance marketing strategies by improving brand reputation, customer trust, and differentiation in competitive markets. Moreover, environmental initiatives positively affect green marketing campaigns, social responsibility drives customer loyalty, and governance practices strengthen credibility and transparency in marketing communications. The study highlights the mediating role of marketing strategy innovation in translating ESG initiatives into tangible business outcomes. These insights contribute to the literature on sustainable business practices and marketing management by providing empirical evidence of the practical benefits of ESG integration. The study also offers actionable guidance for marketing managers and policymakers seeking to align corporate sustainability goals with strategic marketing objectives. Overall, the research underscores the necessity for firms to adopt ESG frameworks not only for compliance or ethical reasons but also as a strategic lever to enhance market positioning and achieve long-term competitive advantage. This study bridges the gap between corporate social responsibility and marketing strategy, emphasizing the role of ESG as a driver of sustainable value creation in contemporary business environments.
Environmental Social Governance, ESG practices, Marketing Strategy, Sustainable Marketing, Brand Reputation, Stakeholder Theory
Entrepreneurial Orientation and Startup Growth: Moderating Role of Market Turbulence
Entrepreneurial orientation has been widely acknowledged as a key determinant of startup success; however, its effectiveness varies across different environmental conditions. This study investigates the relationship between entrepreneurial orientation and startup growth, with market turbulence serving as a moderating variable. Drawing on data collected from 260 startup founders and managers; the study employs Structural Equation Modeling (SEM) using to test the proposed relationships. The findings reveal that entrepreneurial orientation significantly and positively influences startup growth. Moreover, market turbulence significantly moderates this relationship, such that the positive impact of entrepreneurial orientation on growth is stronger under conditions of high market turbulence. These results suggest that entrepreneurial behaviors such as innovativeness, proactiveness, and risk-taking become more valuable when markets are dynamic and unpredictable. The study contributes to entrepreneurship literature by highlighting the contingent role of environmental factors in shaping entrepreneurial outcomes. Practically, the findings emphasize that startups operating in turbulent markets should adopt strong entrepreneurial orientations to exploit emerging opportunities and sustain growth. The study concludes that entrepreneurial orientation is not universally effective but becomes a critical strategic asset under conditions of high market uncertainty.
Entrepreneurial Orientation, Startup Growth, Market Turbulence, Entrepreneurship
Digital Health Records, Data Security, and Ethical Governance in Healthcare Systems
Digital health records (DHRs) have transformed healthcare systems by improving patient care, streamlining administrative processes, and enhancing data accessibility. However, the adoption of digital health technologies introduces significant challenges related to data security, privacy, and ethical governance. This study investigates the interplay between digital health record implementation, data security measures, and ethical governance practices in healthcare systems. Drawing on information systems and healthcare governance frameworks, the research examines how robust security protocols and ethical governance influence the effective adoption and utilization of digital health records. A quantitative research design was employed, with data collected from 350 healthcare professionals, administrators, and IT specialists across hospitals and clinics. Structural equation modeling using was applied to analyze relationships between digital health records, data security practices, ethical governance, and healthcare system efficiency. Results indicate that strong data security measures and ethical governance practices significantly enhance the reliability, accessibility, and patient trust in digital health records. Ethical governance mediates the relationship between data security practices and successful digital record adoption, emphasizing the role of transparent policies, accountability frameworks, and regulatory compliance in healthcare. The study contributes to the literature by providing empirical evidence on the importance of integrating technical and governance strategies to maximize the benefits of digital health technologies. Findings offer actionable insights for healthcare policymakers, IT professionals, and hospital administrators in designing secure, ethically governed digital health systems. Overall, the research underscores that the integration of digital health records requires simultaneous investment in technical security and governance frameworks to ensure patient data protection, compliance, and sustainable healthcare delivery. The study provides a roadmap for balancing technological innovation with ethical responsibility in modern healthcare systems.
Digital Health Records, Data Security, Ethical Governance, Healthcare Systems, Patient Privacy, Information Technology, Healthcare Administration
Determinants of Vaccine Hesitancy and Strategic Public Health Communication Interventions
Vaccine hesitancy has emerged as a critical public health concern, undermining immunization coverage and increasing the risk of preventable disease outbreaks worldwide. Understanding the determinants of vaccine hesitancy and designing effective public health communication interventions are essential to improving vaccination uptake. This study investigates the individual, social, and systemic factors influencing vaccine hesitancy, including risk perception, misinformation, trust in healthcare authorities, cultural beliefs, and social norms. Drawing on the Health Belief Model and the Theory of Planned Behavior, the study explores how these determinants impact vaccination decisions and evaluates the role of strategic public health communication in addressing hesitancy. A quantitative research design was employed, collecting data from 400 participants across diverse demographics and regions. Structural equation modeling using was utilized to examine relationships between determinants, communication interventions, and vaccine acceptance. The results indicate that perceived vaccine risks, misinformation exposure, and low trust in healthcare authorities significantly increase vaccine hesitancy, while effective communication strategies including targeted messaging, transparency, and engagement campaigns—significantly reduce hesitancy and improve vaccination intent. The study highlights the mediating role of public health communication in translating knowledge and awareness into positive behavioral outcomes. These findings provide actionable insights for public health policymakers, healthcare providers, and communication specialists seeking to design interventions that counter vaccine hesitancy. By addressing psychological, social, and informational barriers through evidence-based communication strategies, vaccination coverage can be improved, contributing to global health security. The research contributes to the literature on health communication and behavioral science by empirically validating the determinants of vaccine hesitancy and the efficacy of strategic interventions, emphasizing the critical role of communication in public health outcomes.
Vaccine Hesitancy, Public Health Communication, Risk Perception, Misinformation, Health Belief Model, Theory of Planned Behavior, Vaccination Uptake
Virtual Museums and Cultural Learning Outcomes: Mediation of Emotional Connection and Moderation of Prior Cultural Exposure
Virtual museums (VMs) have emerged as innovative platforms for cultural education, providing interactive, immersive experiences that allow users to explore cultural heritage without geographical constraints (Sylaiou et al., 2009). This study examines the impact of virtual museums on cultural learning outcomes, focusing on the mediating role of emotional connection and the moderating effect of prior cultural exposure. A quantitative survey of 250 participants who regularly engaged with virtual museum platforms was conducted, and data were analyzed using to indicate that virtual museums significantly enhance cultural learning outcomes, with emotional connection partially mediating this relationship. Users who experience stronger emotional engagement are more likely to retain information, reflect on cultural content, and demonstrate appreciation for cultural heritage. Additionally, prior cultural exposure moderates the effect of virtual museum use on learning outcomes, suggesting that individuals with higher baseline cultural knowledge derive greater benefits from VM interactions. The findings extend research on digital cultural education by integrating affective and experiential factors and provide practical guidance for educators, museum curators, and designers to optimize virtual museum experiences by enhancing emotional engagement and tailoring content based on users’ prior cultural experiences.
Virtual Museums, Cultural Education, Emotional Connection, Cultural Heritage
Copyright (c) 2024 Pakistan Journal of Management Sciences and Technology (PJMST)
Smart Manufacturing Systems and Operational Efficiency: Mediation of Data Integration Capability and Moderation of Digital Maturity
The rise of Industry 4.0 has brought smart manufacturing systems (SMS) to the forefront of operational efficiency in manufacturing organizations. This study investigates the impact of SMS on operational efficiency, examining the mediating role of data integration capability (DIC) and the moderating effect of digital maturity. A quantitative survey was conducted among 250 manufacturing professionals, and data were analyzed using PLS-SEM. Findings indicate that SMS adoption significantly enhances operational efficiency, with DIC partially mediating this relationship by enabling effective utilization of integrated data for informed decision-making. Furthermore, digital maturity positively moderates the SMS–operational efficiency link, suggesting that firms with higher digital readiness derive greater benefits from SMS implementation. The study contributes to the literature by integrating technological and organizational factors in explaining operational performance improvements and offers practical guidance for managers to invest in data integration and digital transformation initiatives to maximize the effectiveness of smart manufacturing systems in enhancing efficiency.
Smart Manufacturing Systems SMS, (DIC), Digital Maturity, Efficiency.
Entrepreneurial Orientation and Startup Growth: Mediation of Innovation Capability
Entrepreneurial orientation has been widely recognized as a critical driver of startup success in dynamic and competitive markets. However, the mechanisms through which entrepreneurial orientation influences startup growth remain insufficiently explored. This study examines the effect of entrepreneurial orientation on startup growth, with innovation capability serving as a mediating variable. Using a quantitative research design, data were collected from 260 startup founders and managers operating in emerging business environments. Structural Equation Modeling (SEM) was employed to test the hypothesized relationships. The results reveal that entrepreneurial orientation has a significant positive effect on startup growth. Furthermore, innovation capability partially mediates this relationship, indicating that entrepreneurial firms grow faster when their strategic orientation is supported by strong innovation capabilities. The findings highlight that innovativeness, proactiveness, and risk-taking foster the development of new products, services, and processes, which in turn enhance firm growth. This study contributes to entrepreneurship literature by empirically validating the mediating role of innovation capability and extending the resource-based view in the context of startups. Practically, the findings emphasize the importance of nurturing innovative capabilities alongside entrepreneurial behaviors to achieve sustainable growth. The study concludes that entrepreneurial orientation alone is insufficient unless supported by continuous innovation.
Entrepreneurial orientation, startup growth, innovation capability, entrepreneurship
Digital Transformation and Firm Performance: Mediating Role of Organizational Agility and Moderating Role of Leadership Capability
Digital transformation has emerged as a critical driver of organizational competitiveness in today’s rapidly evolving business environment. However, empirical evidence suggests that the relationship between digital transformation and firm performance is complex and influenced by organizational factors. This study investigates the impact of digital transformation on firm performance, examining organizational agility as a mediating variable and leadership capability as a moderating variable. Using a quantitative research approach, data were collected from 250 managerial-level respondents working in digitally active firms. Structural Equation Modeling (SEM) using SmartPLS was employed to test the proposed relationships. The results indicate that digital transformation has a significant positive effect on firm performance. Organizational agility partially mediates this relationship, demonstrating that firms achieve better performance when digital initiatives enhance flexibility, responsiveness, and adaptability. Furthermore, leadership capability significantly moderates the relationship between digital transformation and organizational agility, suggesting that capable leadership strengthens the effective implementation of digital initiatives. The findings contribute to the dynamic capability literature by highlighting the importance of organizational and leadership factors in realizing digital value. Practically, the study offers insights for managers and policymakers to prioritize leadership development and agile practices alongside digital investments. The research concludes that digital transformation alone is insufficient unless supported by agile structures and competent leadership.
Digital Transformation, Firm Performance, Organizational Agility, Leadership Capability
Climate Modeling Accuracy and Public Trust: Mediation of Communication Clarity and Moderation of Scientific Literacy
Climate modeling plays a crucial role in predicting climate change impacts and guiding public policy. However, public trust in climate science depends not only on the accuracy of climate models but also on the clarity of communication and the audience’s scientific literacy. This study investigates the effect of climate modeling accuracy (CMA) on public trust (PT), emphasizing the mediating role of communication clarity (CC) and the moderating effect of scientific literacy (SL). A quantitative survey of 260 respondents from diverse demographics was conducted, and data were analyzed using SmartPLS. Findings indicate that CMA significantly enhances PT, with communication clarity partially mediating this relationship. Clear explanations and transparent reporting of modeling assumptions increase public comprehension and confidence. Scientific literacy moderates the CMA–PT link, with individuals possessing higher scientific literacy demonstrating stronger trust in accurate climate models. The results highlight the combined importance of technical precision, effective communication, and audience understanding in fostering trust in climate science. Practically, scientists and policymakers should prioritize transparent, accessible communication strategies and consider varying levels of scientific literacy to enhance public engagement and confidence in climate projections.
Climate Modeling, Public Trust, Communication Clarity, Scientific Literacy
Renewable Energy Innovation and Technology Adoption: Mediating Role of Cost Perception and Moderating Role of Policy Incentives
The transition to renewable energy is critical for sustainable development and reducing carbon emissions. Renewable energy innovation (REI) enhances the efficiency, reliability, and affordability of renewable technologies, but adoption rates remain influenced by stakeholders’ perceptions and policy frameworks. This study investigates the impact of REI on renewable energy technology (RET) adoption, emphasizing the mediating role of cost perception and the moderating effect of policy incentives. A quantitative survey was conducted with 260 respondents, including energy companies, technology developers, and consumers. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess reliability, validity, and the significance of direct, mediating, and moderating paths. Findings indicate that REI positively influences technology adoption, with cost perception partially mediating this relationship. Lower perceived costs increase adoption likelihood, while policy incentives, such as subsidies, tax credits, and regulatory support, significantly moderate the REI–adoption relationship, amplifying adoption rates. These results highlight the interplay of technological, economic, and policy factors in renewable energy adoption. Practically, governments and organizations should design cost-effective innovations and implement supportive policies to accelerate technology uptake, contributing to sustainable energy transitions.
Renewable Energy Innovation, Stakeholders, RET, PLS-SEM, Policies
Green Supply Chain Management and Competitive Advantage: Mediating Role of Operational Efficiency and Moderating Role of Regulatory Pressure
The growing emphasis on environmental sustainability has compelled organizations to adopt green supply chain management (GSCM) practices as a strategic necessity rather than a regulatory obligation. This study examines the impact of green supply chain management on competitive advantage, with operational efficiency serving as a mediating variable and regulatory pressure as a moderating variable. Using a quantitative research design, data were collected from 260 professionals working in manufacturing and service-oriented organizations engaged in environmentally responsible practices. Structural Equation Modeling (SEM) using SmartPLS was employed to analyze the proposed relationships. The findings reveal that GSCM significantly enhances competitive advantage by improving operational efficiency, reducing waste, and optimizing resource utilization. Operational efficiency partially mediates the relationship between GSCM and competitive advantage, indicating that environmental practices translate into superior performance through improved operational processes. Furthermore, regulatory pressure strengthens the relationship between GSCM and operational efficiency, suggesting that stricter environmental regulations encourage firms to adopt more effective green practices. The study contributes to sustainability and operations management literature by integrating environmental and strategic perspectives. Practically, the findings highlight the importance of aligning environmental initiatives with operational goals and regulatory requirements to achieve sustainable competitive advantage.
Green Supply Chain Management, Competitive Advantage, Operational Efficiency, Regulatory Pressure.
Digital Storytelling and Reader Engagement: Mediating Role of Narrative Immersion and Moderating Role of Digital Literacy
Digital storytelling has transformed the way audiences engage with narratives by integrating multimedia elements such as audio, video, animations, and interactive components. This study investigates the impact of digital storytelling on reader engagement, focusing on the mediating role of narrative immersion and the moderating effect of digital literacy. A quantitative survey was conducted among 250 readers who regularly consume digital narratives, and data were analyzed. Results indicate that digital storytelling significantly enhances reader engagement, with narrative immersion partially mediating this relationship. Readers who experience higher immersion are more likely to exhibit sustained attention, emotional involvement, and cognitive engagement with the story. Furthermore, digital literacy strengthens the effect of digital storytelling on reader engagement, suggesting that technologically adept readers can better navigate, interpret, and interact with multimedia narratives. The findings extend the literature on digital media and user engagement by integrating cognitive and technological factors, offering insights for content creators, educators, and digital marketers. Practically, enhancing narrative immersion through well-designed digital stories and promoting digital literacy can optimize engagement outcomes in educational, entertainment, and marketing contexts.
Digital Storytelling, Audiences Engage, Digital Literacy, Technological Factors
Development of Sustainable Nanocomposite Construction Materials Using Industrial Waste for Low Carbon Infrastructure
The construction industry is one of the largest contributors to global carbon emissions due to extensive cement production, resource extraction, and energy intensive manufacturing processes. The urgent need for sustainable infrastructure development has accelerated research into alternative materials that reduce environmental impact while maintaining structural performance. This study investigates the development of sustainable nanocomposite construction materials using industrial waste for low carbon infrastructure applications. The research integrates supplementary cementitious materials derived from industrial waste such as fly ash, silica fume, ground granulated blast furnace slag, and nano silica to produce advanced nanocomposites with enhanced mechanical and durability properties. A conceptual model is proposed to examine the relationships between industrial waste utilization, nanomaterial integration, mechanical performance, durability enhancement, and carbon emission reduction. A quantitative research design was employed, and data were collected from 210 professionals including civil engineers, material scientists, and construction managers. Soft tools evaluate the structural model and test hypotheses through partial least squares structural equation modeling. Measurement model assessment confirmed reliability and validity through factor loadings, composite reliability, average variance extracted, and discriminant validity. Structural model results indicated significant positive relationships between industrial waste utilization and mechanical performance, nanomaterial integration and durability enhancement, and durability enhancement and carbon emission reduction. Mediation analysis demonstrated that mechanical performance and durability enhancement partially mediate the relationship between waste utilization and low carbon infrastructure outcomes. The findings reveal that incorporating industrial waste and nanomaterials significantly improves compressive strength, reduces permeability, enhances microstructural densification, and decreases lifecycle carbon emissions. The study contributes to sustainable construction theory by integrating circular economy principles with nanotechnology innovation. Practically, it provides evidence-based guidance for policymakers and engineers to promote industrial symbiosis and low carbon material development. The research supports the transition toward climate resilient and environmentally responsible infrastructure systems.
Sustainable Construction, Nanocomposite Materials, Industrial Waste Utilization, Low Carbon Infrastructure, Structural Equation Modeling, Circular Economy, Green Concrete
AI Adoption and Engineering Project Success: Mediating Role of Trust in AI and Moderating Role of Organizational Culture
Artificial Intelligence (AI) is increasingly being integrated into engineering projects to improve efficiency, innovation, and decision-making. This study examines the impact of AI adoption on engineering project success, focusing on the mediating role of trust in AI and the moderating role of organizational culture. Using survey data from 250 engineering professionals and analyzed through Smart-PLS, the results reveal that AI adoption positively influences project success, with trust in AI significantly mediating this relationship. Organizational culture strengthens this effect, highlighting the importance of supportive work environments in AI integration. Findings provide actionable insights for engineering firms aiming to enhance project performance through AI-driven tools.
Artificial Intelligence, Ai Adoption, Organizational Culture, Project Success
The Impact of Entrepreneurial Ecosystems and Innovation Policy on New Startup Sustainability
Entrepreneurial ecosystems and innovation policies are increasingly recognized as critical drivers of startup sustainability in dynamic economic environments. Entrepreneurial ecosystems encompass networks of actors, institutions, and resources that facilitate entrepreneurial activity, including access to finance, mentorship, human capital, infrastructure, and market linkages. Innovation policy, defined as governmental strategies and regulatory frameworks supporting research, development, and technology commercialization, further shapes the ability of startups to survive and scale. This study investigates the combined impact of entrepreneurial ecosystems and innovation policy on the sustainability of new startups, emphasizing how ecosystem quality, policy incentives, and institutional support contribute to long-term viability. A mixed-methods approach was employed, integrating survey data from 350 startup founders across multiple regions with semi-structured interviews of policymakers and ecosystem stakeholders. Structural equation modeling using SmartPLS was applied to examine the relationships between ecosystem factors, innovation policy support, and startup sustainability outcomes. Results indicate that strong entrepreneurial ecosystems (β=0.71, p<0.001) and effective innovation policies (β=0.68, p<0.001) positively influence startup sustainability, including financial performance, market penetration, and survival rates. Access to mentorship, venture capital, research infrastructure, and regulatory support mediates these effects, highlighting the importance of both systemic and policy-level interventions. Findings suggest that startup survival is contingent not only on individual entrepreneurial capabilities but also on the enabling environment created by coordinated ecosystem actors and innovation-focused policies. Policy implications emphasize the need for integrated ecosystem development, supportive innovation legislation, funding mechanisms, and knowledge-sharing platforms to enhance startup resilience. This study contributes to entrepreneurship theory by linking ecosystem quality and innovation policy to tangible sustainability outcomes, providing empirical evidence for governments, incubators, investors, and academic institutions seeking to strengthen startup performance. Future research should explore longitudinal impacts of ecosystem maturity, sector-specific dynamics, and regional variations in policy effectiveness.
Entrepreneurial Ecosystems, Innovation Policy, Startup Sustainability, Startup Survival, Ecosystem Quality, Policy Incentives, Venture Capital, Mentorship, Innovation Infrastructure
Social Media Polarization and Its Implications for Democratic Institutions and Political Engagement
The proliferation of social media platforms has fundamentally transformed political communication, public discourse, and citizen engagement, but it has also intensified social and political polarization. Social media polarization refers to the phenomenon in which individuals’ political opinions and social networks become increasingly homogeneous, reinforcing ideological divides and echo chambers. This study investigates the impact of social media polarization on democratic institutions and political engagement, exploring how exposure to ideologically aligned content, algorithmic personalization, and selective information consumption shape civic participation and trust in democratic processes. A mixed-methods approach was employed, integrating survey data from 350 social media users with network analysis of content sharing patterns to capture the breadth and intensity of polarization. Structural equation modeling using SmartPLS was applied to examine the relationships between social media polarization, political knowledge, institutional trust, and political engagement. Results indicate that social media polarization negatively affects trust in democratic institutions (β=-0.68, p<0.001) and moderates political engagement behaviors (β=-0.62, p<0.001), while exposure to diverse perspectives partially mitigates these effects (β=0.41, p<0.01). Highly polarized users exhibit increased affective polarization, lower deliberative participation, and selective exposure to ideologically consistent information, undermining constructive political discourse. These findings underscore the complex interplay between social media algorithms, user behavior, and democratic functioning, highlighting the need for policy interventions and media literacy programs to reduce polarization and promote informed civic engagement. The study contributes to theoretical understanding of digital political polarization, providing empirical evidence of its implications for institutional trust and participation. Future research should explore platform-specific mechanisms, cross-national variations, and the effectiveness of interventions designed to foster exposure to diverse viewpoints. By integrating empirical analysis with policy recommendations, this research provides insights for scholars, practitioners, and policymakers seeking to mitigate the adverse effects of social media polarization on democratic institutions and political engagement.
Social Media Polarization, Democratic Institutions, Political Engagement, Institutional Trust, Algorithmic Influence, Civic Participation, Echo Chambers, Deliberative Democracy, Affective Polarization,
Religious Pluralism and Interfaith Dialogue in South Asian Societies
Religious pluralism, the coexistence of multiple religious traditions within a society, plays a crucial role in shaping social cohesion, intercommunal relationships, and political stability in South Asian societies. Interfaith dialogue, defined as constructive engagement and communication between different religious communities, has emerged as a key mechanism to promote tolerance, mutual understanding, and peaceful coexistence. This study examines the dynamics of religious pluralism and interfaith dialogue in South Asian contexts, focusing on how pluralistic attitudes, institutional support, and community engagement influence social harmony and reduce religious conflict. A mixed-methods approach was employed, combining survey data from 400 participants across India, Pakistan, Bangladesh, Nepal, and Sri Lanka with in-depth interviews of religious leaders, scholars, and community organizers. Structural equation modeling using was applied to analyze relationships between religious pluralism, interfaith dialogue participation, social cohesion, and tolerance outcomes. Results indicate that pluralistic attitudes (β=0.73, p<0.001) and institutional support for interfaith initiatives (β=0.68, p<0.001) significantly predict active engagement in interfaith dialogue, which in turn enhances social cohesion (β=0.71, p<0.001) and tolerance (β=0.69, p<0.001). Participation in interfaith dialogue mediates the relationship between pluralistic attitudes and social outcomes, demonstrating the importance of dialogue-based interventions. The findings underscore that fostering religious pluralism through education, community programs, and institutional backing can strengthen intercommunal harmony, mitigate conflict, and promote inclusive governance. Policy implications highlight the need for targeted interfaith programs, legal protections for minority religious communities, and public campaigns promoting religious literacy. By integrating empirical analysis with theoretical insights, this study contributes to the understanding of religious pluralism as a mechanism for social stability and democratic resilience in multi-religious South Asian societies. Future research should explore longitudinal effects of interfaith programs, the role of digital media in shaping pluralistic attitudes, and comparative studies across urban and rural contexts.
Religious Pluralism, Interfaith Dialogue, South Asia, Social Cohesion, Tolerance, Community Engagement, Institutional Support, Religious Literacy, Conflict Mitigation
Digital Inequality, Socioeconomic Stratification, and Social Mobility in Developing Economies
Digital technologies have transformed social, economic, and political landscapes, yet the benefits of digital access and literacy remain unevenly distributed, particularly in developing economies. Digital inequality the disparity in access to information and communication technologies (ICTs), digital skills, and online opportunities intersects with existing socioeconomic stratifications, influencing individuals’ capacity for social mobility. This study investigates the relationships between digital inequality, socioeconomic stratification, and social mobility in developing countries, exploring how digital exclusion reinforces structural inequalities and limits upward mobility. A mixed-methods approach was employed, combining survey data from 300 participants across urban and rural regions with semi-structured interviews to capture contextual nuances. Structural equation modeling using SmartPLS examined the direct and mediated effects of digital access, digital literacy, and ICT usage on social mobility, considering socioeconomic status as a moderating factor. Results indicate that digital access (β=0.72, p<0.001) and digital literacy (β=0.69, p<0.001) positively influence social mobility, with socioeconomic stratification mediating these relationships (β=0.65, p<0.001). Individuals from higher socioeconomic strata derive greater benefits from digital engagement, while marginalized populations face compounding barriers due to limited infrastructure, skills, and resources. The study highlights that addressing digital inequality requires both policy interventions to expand ICT access and educational initiatives to develop digital competencies, particularly for vulnerable communities. By integrating digital inclusion strategies with socioeconomic development policies, governments and civil society can enhance opportunities for social mobility, reduce structural inequalities, and foster equitable economic growth. These findings contribute to the theoretical understanding of the digital divide as a multidimensional phenomenon and offer practical insights for policymakers, educators, and technology developers in developing economies. Future research should explore longitudinal impacts of digital inclusion programs and the role of emerging technologies such as mobile internet, e-learning platforms, and digital finance in facilitating social mobility.
Digital Inequality, Social Mobility, Socioeconomic Stratification, Digital Literacy, ICT Access, Developing Economies, Structural Inequality, Digital Divide, Socioeconomic Development.
Deep Reinforcement Learning-Based Autonomous Navigation Systems for Intelligent Transportation Networks
The rapid advancement of autonomous vehicles and intelligent transportation networks (ITNs) has transformed modern mobility systems, promising improved safety, efficiency, and sustainability. Deep reinforcement learning (DRL), an intersection of deep learning and reinforcement learning, has emerged as a powerful computational paradigm for enabling autonomous navigation, traffic optimization, and adaptive decision-making in dynamic urban environments. This research investigates DRL-based autonomous navigation systems, focusing on their integration into intelligent transportation networks to enhance vehicle routing, collision avoidance, and traffic flow management. A hybrid methodology combining simulation-based experiments and computational modeling was employed to evaluate the performance of DRL agents in multi-agent traffic scenarios. Structural equation modeling using SmartPLS was applied to examine the relationships between algorithm design parameters, environmental perception accuracy, decision-making efficiency, and navigation outcomes. Results indicate that DRL algorithms significantly enhance autonomous navigation performance (β=0.74, p<0.001) by optimizing route selection, reducing collisions, and improving travel time reliability. Environmental perception accuracy (β=0.68, p<0.001) and decision-making efficiency (β=0.71, p<0.001) mediate the relationship between DRL algorithm sophistication and navigation outcomes, emphasizing the importance of sensor fusion, real-time data processing, and adaptive reward mechanisms. The study demonstrates that DRL-based navigation systems provide robust, scalable, and adaptive solutions for autonomous vehicles operating within complex ITNs, outperforming traditional rule-based and classical path-planning methods. Implications include enhanced traffic efficiency, reduced congestion, and increased safety for autonomous transportation networks. Future research should explore integration with vehicle-to-everything (V2X) communication, edge computing, and multi-agent reinforcement learning to further optimize real-time traffic coordination. The findings provide both theoretical and practical insights for researchers, transportation planners, and policymakers seeking to deploy DRL-driven autonomous navigation systems for next-generation intelligent transportation networks.
Deep Reinforcement Learning, Autonomous Navigation, Intelligent Transportation Networks, Multi-Agent Systems, Traffic Optimization, Sensor Fusion, Real-Time Decision-Making, Path Planning, Adaptive Algorithms
The Impact of Green Marketing Strategies and Consumer Trust on Sustainable Brands
Sustainable brands are increasingly recognized as essential contributors to environmental conservation and socially responsible business practices. Green marketing strategies have emerged as vital tools for promoting eco-friendly products and enhancing brand image. Simultaneously, consumer trust plays a crucial role in shaping perceptions, purchase intentions, and loyalty toward sustainable brands. This study investigates the impact of green marketing strategies and consumer trust on the performance and acceptance of sustainable brands. Employing a mixed-methods approach, data were collected from 250 consumers across multiple metropolitan areas through structured questionnaires. The study operationalizes green marketing strategies through eco-labeling, environmental advertising, and corporate sustainability communication, while consumer trust is measured through perceived brand credibility, transparency, and reliability. Structural equation modeling conducted to examine direct and mediating effects between green marketing, consumer trust, and sustainable brand outcomes. Results indicate that green marketing strategies positively influence consumer trust (β=0.71, p<0.001) and directly enhance sustainable brand perception (β=0.64, p<0.001). Consumer trust significantly mediates the relationship between green marketing strategies and sustainable brand acceptance (β=0.67, p<0.001), demonstrating that trust is a critical mechanism for translating marketing efforts into brand loyalty and consumer engagement. The findings underscore the importance of authentic, transparent, and consistent green marketing communications in fostering consumer confidence and promoting sustainable consumption. By integrating marketing strategies with trust-building measures, organizations can strengthen sustainable brand positioning, increase market competitiveness, and encourage responsible consumer behavior. Future research should explore the role of digital green marketing, social media engagement, and cross-cultural perceptions in enhancing trust and promoting sustainable brands globally. This study contributes to the theoretical understanding of green marketing efficacy while offering actionable insights for managers seeking to cultivate environmentally responsible brands and long-term consumer relationships.
Green Marketing, Consumer Trust, Sustainable Brands, Eco-Labeling, Environmental Advertising, Brand Loyalty, Sustainability, Corporate Social Responsibility, Purchase Intention
Sustainable Supply Chain Management under Climate Risk and Regulatory Pressures
Sustainable Supply Chain Management has become a strategic imperative in the context of escalating climate risks and intensifying regulatory pressures. Organizations across industries are increasingly exposed to physical climate disruptions such as floods, heatwaves, and resource scarcity, as well as transition risks arising from carbon pricing, environmental regulations, and disclosure mandates. This study investigates how climate risk and regulatory pressures influence the adoption of Sustainable Supply Chain Management practices and how these practices enhance environmental performance and supply chain resilience. Drawing upon stakeholder theory, institutional theory, and the resource-based view, a conceptual model is developed linking climate risk and regulatory pressures to Sustainable Supply Chain Management practices, which in turn affect environmental performance and supply chain resilience. The study further examines the mediating role of Sustainable Supply Chain Management in these relationships. A quantitative research design was employed using survey data collected from 312 supply chain managers in manufacturing and logistics firms. Structural equation modeling using SmartPLS was applied to assess the measurement and structural models. The results demonstrate that climate risk significantly influences the adoption of Sustainable Supply Chain Management practices, while regulatory pressures exert an even stronger effect. Sustainable Supply Chain Management practices show a positive and significant impact on both environmental performance and supply chain resilience. Mediation analysis confirms that Sustainable Supply Chain Management partially mediates the relationship between climate risk and performance outcomes and fully mediates the effect of regulatory pressures on environmental performance. The findings highlight that firms responding proactively to climate risk and regulatory demands through strategic integration of sustainability into procurement, logistics, and supplier collaboration achieve improved environmental and operational outcomes. This study contributes to the literature by integrating climate risk considerations into Sustainable Supply Chain Management theory and providing empirical evidence from an emerging economy context. The research offers managerial implications for enhancing supply chain adaptability and regulatory compliance while supporting global climate mitigation efforts.
Sustainable Supply Chain Management, Climate Risk, Regulatory Pressure, Environmental Performance, Supply Chain Resilience, Institutional Theory
Genomic Adaptation Mechanisms in Crops under Drought and Heat Stress Conditions
Climate change has intensified abiotic stresses such as drought and heat, which significantly threaten global crop productivity and food security. Plants employ complex genomic adaptation mechanisms to survive and maintain yield under these stress conditions. This study investigates the genetic and molecular responses of major crop species under drought and heat stress, focusing on key genomic traits, regulatory networks, and adaptive pathways. High-throughput genomics, transcriptomics, and genome-wide association studies (GWAS) reveal that drought and heat tolerance involve multi-layered interactions among stress-responsive genes, transcription factors, signaling molecules, and epigenetic modifications. Specific genes such as DREB, HSP, NAC, and WRKY play pivotal roles in activating stress-responsive pathways that regulate stomatal conductance, osmolyte accumulation, antioxidant defense, and cellular homeostasis. The integration of omics approaches with phenotypic selection allows for the identification of quantitative trait loci (QTLs) and candidate genes responsible for stress resilience. Moreover, modern molecular breeding and gene-editing tools, including CRISPR/Cas9, enable the precise modification of stress-adaptive genes to develop high-yielding and climate-resilient crops. Statistical modeling using SmartPLS demonstrates the significant influence of gene expression and regulatory networks on phenotypic traits such as drought tolerance index and heat stress survival. The findings underscore the importance of understanding genomic adaptation mechanisms for sustainable agriculture under changing environmental conditions. This study provides a framework for breeders and researchers to develop stress-tolerant crop varieties by integrating molecular genetics, functional genomics, and predictive modeling. Future research should focus on multi-omics integration, gene network modeling, and field validation under combined drought and heat stress to enhance the accuracy of selection strategies.
Genomic Adaptation, Drought Stress, Heat Stress, Crops, Stress-Responsive Genes, Transcription Factors, Molecular Breeding, CRISPR/CAS9, Climate Resilience.
Experimental and Computational Approaches to Dark Matter Detection in High-Energy Physics
Dark matter constitutes approximately 27% of the universe's mass-energy content, yet its nature remains one of the most profound mysteries in modern physics. Direct and indirect detection of dark matter particles, as well as computational modeling, have become central to advancing our understanding of the cosmos. This research examines both experimental and computational approaches used in high-energy physics to detect dark matter, focusing on the interplay between theoretical predictions, collider experiments, and simulation frameworks. Experimental techniques include direct detection via recoil signals in cryogenic and liquid noble detectors, indirect detection through gamma rays and neutrinos, and collider searches at facilities such as the Large Hadron Collider (LHC). These methods aim to identify Weakly Interacting Massive Particles (WIMPs), axions, sterile neutrinos, and other hypothesized dark matter candidates. Computational approaches employ advanced simulations, Monte Carlo methods, and statistical modeling to predict interaction cross-sections, background noise, and detector responses, enhancing the sensitivity of experiments. Structural equation modeling using SmartPLS was applied to evaluate the relationships between experimental parameters, computational predictions, and detection outcomes. Results indicate significant correlations between optimized detector parameters and the likelihood of dark matter signal detection, demonstrating the value of integrating computational modeling with experimental design. The study highlights the necessity of multi-modal approaches, where experimental data and computational simulations complement one another to refine dark matter models and reduce uncertainties. This integrated strategy not only improves detection sensitivity but also informs theoretical frameworks, narrowing the parameter space for viable dark matter candidates. Future research should focus on next-generation detectors, enhanced computational algorithms, and global collaboration for cross-validation of results. These efforts are critical for resolving fundamental questions about the composition of the universe and for guiding new physics beyond the Standard Model.
Dark Matter Detection, High-Energy Physics, Wimps, Axions, Collider Experiments, Direct Detection, Indirect Detection, Computational Modeling, Monte Carlo Simulations,
Civil Society Engagement in Climate Justice Movements: Advocacy, Policy Influence, and Social Change
Climate justice movements have emerged as pivotal mechanisms for addressing the disproportionate impacts of climate change on vulnerable populations. Civil society organizations (CSOs) play a central role in advocating for equitable climate policies, influencing public and governmental decision-making, and mobilizing communities toward sustainable practices. This study examines the dynamics of civil society engagement in climate justice movements, focusing on the relationships between advocacy strategies, policy influence, and social change outcomes. Utilizing a mixed-methods approach, the research integrates survey data from 200 CSOs across diverse geographic contexts with qualitative interviews of climate activists. Structural equation modeling using SmartPLS was applied to assess the direct and mediated effects of advocacy efforts on policy influence and community-level social change. Results indicate that strategic advocacy, including public campaigns, lobbying, and digital activism, significantly enhances policy influence (β=0.68, p<0.001) and fosters social change (β=0.63, p<0.001). Policy influence serves as a mediator between civil society advocacy and social change, highlighting the importance of institutional engagement for achieving long-term climate justice outcomes. The findings underscore that civil society engagement is not only a vehicle for policy reform but also a catalyst for societal transformation, fostering awareness, community participation, and behavioral shifts toward sustainability. The study emphasizes the need for collaborative networks among CSOs, governmental agencies, and international organizations to maximize impact. By integrating advocacy, policy influence, and social change, this research provides a conceptual and empirical framework for understanding effective civil society strategies in climate justice movements. Future research should explore the role of digital platforms, transnational coalitions, and youth-led movements in amplifying civil society influence and sustaining climate justice initiatives over time.
Civil Society, Climate Justice, Advocacy, Policy Influence, Social Change, Climate Activism, Structural Equation Modeling, Community Engagement, Sustainability
The Impact of Artificial Intelligence Adoption on Labor Market Structures, Skills Demand, and Income Inequality
The rapid adoption of artificial intelligence across industries has transformed production processes, organizational structures, and labor market dynamics. While AI driven automation enhances productivity, efficiency, and innovation, it simultaneously raises concerns regarding job displacement, occupational polarization, skills mismatch, and widening income inequality. This study investigates the structural impact of AI adoption on labor market restructuring, evolving skills demand, and income inequality using an empirically validated structural equation modeling approach. The research develops a comprehensive framework integrating technological change theory, skill biased technological change theory, and labor market polarization theory to examine direct and mediated relationships between AI adoption intensity, skills transformation demand, labor market restructuring, employment polarization, and income inequality. A quantitative research design was employed with survey data collected from 412 respondents including industry managers, policy analysts, human resource professionals, and technology specialists across manufacturing, services, finance, and digital sectors. Data were analyzed to assess measurement reliability, validity, structural relationships, and mediation effects. The findings reveal that AI adoption significantly increases demand for advanced digital and analytical skills while simultaneously accelerating labor market restructuring. Results indicate strong positive effects of labor market restructuring on employment polarization and income inequality. Skills transformation demand partially mediates the relationship between AI adoption and inequality outcomes, while labor market restructuring serves as a dominant mediating mechanism. The model explains 61 percent of variance in employment polarization and 54 percent in income inequality, demonstrating substantial predictive capability. The study contributes theoretically by integrating technological adoption and labor economics perspectives into a unified empirical framework. Practically, the findings highlight the urgent need for policy interventions in education reform, reskilling initiatives, inclusive innovation strategies, and social protection systems to mitigate inequality risks associated with AI driven transformation. The research concludes that AI adoption is not inherently inequality inducing but becomes distributive disruptive when institutional adaptation, workforce reskilling, and regulatory mechanisms fail to keep pace with technological advancement. Strategic policy design and proactive labor market governance are essential to ensure equitable outcomes in the AI driven economy.
Artificial Intelligence Adoption, Labor Market Restructuring, Skills Demand, Employment Polarization, Income Inequality, Technological Change, Workforce Transformation
Copyright (c) 2023 Pakistan Journal of Management Sciences and Technology (PJMST)
Digital Transformation Strategies in Small and Medium Enterprises Drivers, Barriers, and Performance Outcomes
Digital transformation has become a strategic imperative for small and medium enterprises seeking competitiveness, innovation, and long term sustainability in dynamic markets. Unlike large corporations, small and medium enterprises often operate with limited financial resources, technological expertise, and managerial capacity, making digital transformation both a significant opportunity and a substantial challenge. This study examines the drivers and barriers influencing digital transformation strategies in small and medium enterprises and evaluates their impact on organizational performance outcomes. Drawing upon the Technology Organization Environment framework and Dynamic Capabilities Theory, the research proposes and empirically validates a structural model linking technological readiness, leadership commitment, competitive pressure, financial constraints, organizational resistance, digital capability development, and firm performance. A quantitative research design was employed using survey data collected from 421 owners and managers of small and medium enterprises across manufacturing, retail, and service sectors. structural equation modeling was used to assess measurement reliability, convergent validity, discriminant validity, and structural relationships, including mediation effects. The findings reveal that technological readiness, leadership commitment, and competitive pressure significantly drive digital transformation initiatives, while financial constraints and organizational resistance act as major barriers. Digital capability development partially mediates the relationship between transformation drivers and firm performance. The structural model explains 67 percent of variance in digital transformation adoption and 62 percent in firm performance, indicating strong predictive power. Results demonstrate that enterprises that strategically align digital initiatives with organizational capabilities achieve higher operational efficiency, innovation performance, and market competitiveness. This study contributes theoretically by integrating contextual drivers and internal capabilities within a unified empirical framework. Practically, the findings provide actionable insights for policymakers and managers seeking to enhance digital maturity among small and medium enterprises. The research concludes that effective digital transformation requires strategic leadership, resource optimization, and continuous capability development to overcome structural barriers and realize sustainable performance gains.
Digital Transformation, Small and Medium Enterprises, Drivers, Barriers, Firm Performance, Dynamic Capabilities, Technology Adoption
Media Representation, Cultural Politics, and Identity Formation in the Digital Age
The digital age has fundamentally transformed the production, circulation, and consumption of media, reshaping how identities are constructed, negotiated, and politicized. Social media platforms, streaming services, algorithmic recommendation systems, and participatory digital cultures have disrupted traditional media gatekeeping structures and enabled new forms of representation. At the same time, these technologies have intensified cultural politics by amplifying ideological polarization, identity-based mobilization, and contested narratives. This study examines the structural relationships between media representation, digital platform engagement, cultural politics, and identity formation in contemporary digital environments. Drawing upon theories of representation, cultural politics, and social identity, this research develops and empirically tests a conceptual model in which media representation diversity, algorithmic amplification, and digital participation influence identity formation directly and indirectly through cultural politics engagement. A quantitative research design was employed using survey data collected from 436 respondents including university students, media professionals, digital content creators, and general social media users. Data were analyzed using to assess measurement reliability, convergent validity, discriminant validity, structural relationships, and mediation effects. The findings indicate that diverse and inclusive media representation positively influences identity affirmation and identity hybridity. Algorithmic amplification significantly strengthens engagement in cultural politics, which in turn intensifies identity salience and political identification. Cultural politics engagement partially mediates the relationship between media representation and identity formation. The structural model explains 63 percent of variance in identity formation and 58 percent in cultural politics engagement, demonstrating substantial predictive power. This study contributes theoretically by integrating media representation theory with digital platform studies and social identity frameworks into a unified empirical model. Practically, the results highlight the critical role of digital media ecosystems in shaping cultural narratives and identity politics. The research underscores the need for ethical platform governance, inclusive representation policies, and digital literacy initiatives to promote balanced identity formation in increasingly mediated societies
Media Representation, Cultural Politics, Identity Formation, Digital Media, Algorithmic Amplification, Social Identity Theory, Digital Participation
Design and Optimization of AI Driven Smart Grid Systems for Large Scale Renewable Energy Integration and Grid Stability Enhancement
The rapid transition toward renewable energy sources has significantly transformed modern power systems, introducing variability, intermittency, and bidirectional power flows that challenge conventional grid stability mechanisms. Large scale integration of solar, wind, and distributed energy resources requires advanced coordination, forecasting, and real time control strategies. Artificial intelligence driven smart grid systems have emerged as a promising solution to enhance operational efficiency, reliability, and stability while maximizing renewable penetration. This study develops and empirically validates an AI driven smart grid optimization framework that integrates machine learning based load forecasting, predictive maintenance, dynamic demand response, and intelligent energy management to enhance grid stability and renewable energy integration. The research proposes a conceptual model where AI predictive analytics, real time monitoring capability, demand side management, and distributed energy resource coordination function as key determinants of renewable integration performance and grid stability enhancement. Grid flexibility and adaptive control mechanisms are examined as mediating variables. Structural Equation Modeling using SmartPLS is employed to evaluate relationships among constructs using survey data collected from power system engineers, grid operators, and renewable energy specialists. The measurement and structural models are validated through reliability, convergent validity, discriminant validity, path coefficients, and mediation analysis. Results indicate that AI predictive analytics significantly improves renewable integration performance and indirectly enhances grid stability through adaptive control mechanisms. Demand side management demonstrates strong mediation effects, while distributed energy resource coordination positively influences grid flexibility. The findings confirm that AI driven optimization significantly reduces frequency deviations, voltage instability, and energy curtailment. This study contributes theoretically by integrating artificial intelligence, smart grid theory, and renewable integration frameworks into a unified optimization model. Practically, it provides a roadmap for utilities and policymakers seeking to develop resilient, low carbon, and digitally intelligent energy infrastructures. The research concludes that AI driven smart grid systems are essential for achieving large scale renewable energy integration while maintaining system reliability, efficiency, and long-term sustainability.
Artificial Intelligence, Smart Grid Systems, Renewable Energy Integration, Grid Stability, Predictive Analytics, Demand Response, Distributed Energy Resources,
Carbon Capture and Storage Technologies Chemical Innovations and Environmental Impact Assessment
Carbon capture and storage technologies have emerged as critical mitigation strategies for reducing anthropogenic carbon dioxide emissions and achieving global climate targets. As fossil fuel dependent energy systems continue to contribute significantly to greenhouse gas emissions, innovative chemical solutions for carbon capture, transport, utilization, and long term storage have gained substantial attention. This study investigates the role of chemical innovations in advancing carbon capture efficiency and examines their environmental impacts using a comprehensive structural assessment framework. The research integrates perspectives from green chemistry, environmental impact assessment, and sustainable energy transition theory to evaluate how solvent innovation, sorbent material advancement, process optimization, and regulatory compliance influence environmental performance and sustainability outcomes. A quantitative research design was employed using survey data collected from 368 engineers, environmental scientists, energy policy experts, and industrial practitioners engaged in carbon capture initiatives. SmartPLS 4 structural equation modeling was used to evaluate measurement reliability, structural relationships, and mediation effects. The conceptual model proposes that chemical innovation intensity, process efficiency optimization, and regulatory compliance directly influence environmental impact reduction and long term storage safety, with lifecycle assessment practices functioning as a mediating construct. The findings demonstrate that advanced solvent systems and novel sorbent materials significantly enhance capture efficiency while reducing energy penalties. Process optimization shows a strong positive effect on environmental impact reduction. Lifecycle environmental assessment partially mediates the relationship between chemical innovation and sustainability performance. The model explains 65 percent of variance in environmental impact reduction and 59 percent in storage safety performance, indicating strong predictive capacity. This study contributes theoretically by integrating chemical engineering innovation with environmental governance frameworks in an empirically validated model. Practically, the findings highlight the importance of green solvent development, energy efficient regeneration processes, and robust monitoring mechanisms for sustainable deployment of carbon capture systems. The research concludes that while carbon capture technologies offer substantial climate mitigation potential, their environmental effectiveness depends on continuous chemical innovation and comprehensive lifecycle evaluation.
Carbon Capture and Storage, Chemical Innovation, Environmental Impact Assessment, Solvent Technology, Sorbent Materials, Lifecycle Analysis, Sustainable Energy Transition