Advanced Journal of Management, Humanity and Social Science

Abstract Cooling systems in thermal power plants play a decisive role in maintaining steam cycle efficiency, enhancing equipment reliability, and reducing maintenance costs. The chemical quality of water in these systems is one of the most critical factors affecting corrosion rates, scale formation, and heat transfer degradation. In the present study, the water chemistry and corrosion behaviour in the hybrid (dry-wet) cooling system of a sample thermal power plant were investigated. Sampling was conducted on demineralised water, recirculating water of the dry tower, inlet water to heat exchangers, wet tower water, and makeup water. General parameters including pH, electrical conductivity, TDS, temperature, and salinity were measured. Additionally, the concentrations of metallic elements were determined using ICP-MS. The results indicated that the demineralised water exhibits exceptionally high purity and is virtually devoid of metal ions, whereas in the dry cycle, a significant increase in aluminium concentration up to approximately 260 ppb was observed, indicative of active corrosion in the aluminium radiators of the dry tower. In contrast, in the wet tower, the concentrations of calcium, sodium, potassium, silica, copper, and zinc increased markedly, attributable to evaporation, concentration of dissolved solids, and the use of wastewater as make-up water. Comparison of the results with EPRI guidelines revealed that although iron and copper levels fall within acceptable ranges, the elevated aluminium concentration may be regarded as the primary indicator of heat transfer equipment degradation. Accordingly, the revision of the water chemistry control programme, continuous monitoring of dissolved aluminium, and reassessment of the dry tower operating conditions are proposed as the most critical management strategies. The findings of this research can serve as a basis for the development of intelligent corrosion monitoring programmes in power plants equipped with hybrid cooling systems.

Abstract This study examines the impact of microfinance services on the financial sustainability and performance of medium-sized enterprises (SMEs) in Mazar-e-Sharif, Afghanistan, using data from 100 firms selected through systematic random sampling. The primary objective is to assess how microcredit access, savings services, and entrepreneurial development training affect return on assets (ROA) and financial sustainability (FS), while controlling for firm age and size. Data were collected via structured questionnaires administered to SME owners/managers, with reliability confirmed by Cronbach's alpha of 0.780 (N=100 items) and content validity established through review by 10 Afghan microfinance experts. Analysis employed descriptive statistics, Pearson correlation matrices, and multiple linear regression models following established SME research methodologies. The first regression model revealed that internal finance (β=0.012, t=10.78, p<0.001) and trade credit (β=0.008, t=7.23, p<0.001) significantly enhance ROA (R²=0.61, F=24.37, p<0.001), while non-institutional finance shows a negative effect (β=-0.130, p=0.017). The second model demonstrated strong positive impacts of microcredit (β=0.287, t=5.02, p<0.001), savings services (β=0.214, t=3.67, p=0.01), and entrepreneurial training (β=0.176, t=3.11, p=0.002) on financial sustainability (R²=0.65, Adj. R²=0.62, F=28.45, p<0.001), with larger/younger firms benefiting most. Nine of ten hypotheses were supported, underscoring microfinance's comprehensive role (financial + non-financial services) in fragile economies. Policy recommendations include government investment in energy infrastructure, transportation networks, and SME training programs to enhance competitiveness. These findings offer actionable insights for post-conflict development strategies.

Abstract This expanded article examines whether cognitive flexibility and emotional regulation predict post-traumatic growth (PTG) among adult survivors of natural disasters. Drawing on post-traumatic growth theory, the organismic valuing perspective, and the regulatory-flexibility framework, the manuscript argues that growth after disaster depends not only on the severity of exposure but also on survivors’ capacity to revise assumptions, tolerate emotional distress, and select regulation strategies that fit changing demands. A cross-sectional design is proposed with 328 adult survivors recruited 6 to 36 months after an earthquake, flood, landslide, storm, wildfire, or related natural hazard. The study uses the Posttraumatic Growth Inventory-Short Form, the Cognitive Flexibility Inventory, the Emotion Regulation Questionnaire, and the Difficulties in Emotion Regulation Scale, together with demographic, loss-severity, displacement, and social-support variables. Descriptive statistics, Pearson correlations, hierarchical regression, and assumption checks are specified in detail. The editable results show that cognitive flexibility and cognitive reappraisal are significant positive predictors of PTG, whereas expressive suppression and broader emotion-regulation difficulties are significant negative predictors after controlling for age, gender, education, injury, property loss, displacement, time since disaster, and perceived social support. The model explains a meaningful proportion of variance in PTG and highlights the need for integrated disaster mental-health interventions that combine cognitive flexibility training, reappraisal skills, emotional awareness, culturally sensitive expression, and community-based support. The manuscript is prepared in the AJMHSS style and includes APA in-text citations and an APA-formatted reference list. Statistical values, affiliation details, ORCID, and ethics information finalized with the author’s actual dataset before submission.

Abstract Effective communication is increasingly recognized as the critical determinant of success in complex project environments. Yet, despite decades of empirical research, communication, documentation, and knowledge management remain fragmented constructs within project management theory. This study proposes the Communicative Infrastructure Model CIM, a conceptual and mathematical framework that reconceptualizes project communication as a dynamic control system. The CIM integrates documentation usability D, knowledge integration K, risk communication efficiency R, and communication network quality CQ into a unified model based on systems theory, information entropy, and control stability analysis. Using a mathematical foundation derived from Shannon’s information theory and Lyapunov stability criteria, the model defines a Communicative Infrastructure Function CIF that quantifies how communication reduces uncertainty, mitigates risk, and stabilizes decision-making processes. The proposed framework positions communication not as a soft skill but as a measurable, strategic infrastructure that governs project equilibrium. By demonstrating that project systems achieve stability when communication efficiency exceeds a calculable threshold, this study establishes formal criteria linking communicative quality to project performance outcomes. A comparative analysis of recent studies (2020–2025) illustrates how existing empirical models of Agile collaboration, cross-functional coordination, and documentation practices can be mathematically integrated within the CIM. This theoretical advancement offers both researchers and practitioners a quantitative foundation for assessing and optimizing communicative effectiveness in project environments. It opens pathways for future empirical calibration through data-driven simulations, AI-enabled communication analytics, and real-time performance monitoring. The CIM thus redefines project communication as a cybernetic infrastructure of project management, capable of transforming risk, uncertainty, and knowledge flow into measurable system stability.

Abstract Ethical consumerism varies widely across global markets, yet its socio-cultural foundations remain insufficiently theorized. This study conducts a structured qualitative, systematic literature review, guided by PRISMA principles, to integrate two decades of research on socio-cultural drivers of ethical consumption. The thematic synthesis identifies six recurring mechanisms—cultural value systems, social norms, identity expression, moral responsibility, religious frameworks, and the global–local interpretation of ethical narratives.In recent decades, the global marketplace has witnessed a striking rise in ethical consumerism—forms of consumption guided not only by personal preference but by concerns for social responsibility, environmental stewardship, and moral integrity. Ethical consumerism spans a wide spectrum of behaviours: choosing fair-trade goods, prioritizing eco-friendly and cruelty-free products, supporting companies with transparent labour practices, and rejecting brands associated with exploitation or environmental harm.

These mechanisms collectively explain how consumers assign meaning to ethical claims and why ethical behaviors diverge across cultural contexts. Drawing on these insights, the study proposes a novel conceptual model in which socio-cultural drivers operate through a meaning-making filter that mediates global ethical messages and generates culturally specific marketing implications.

This model reframes ethical consumerism as a culturally embedded interpretive process rather than a preference-driven outcome, offering actionable guidance for policymakers and global marketers aiming to design culturally adaptive, norm-responsive, and identity-aligned ethical strategies.

Abstract The rapid advancement of digital technologies has fundamentally transformed the landscape of public governance. In modern governments and digital societies, big data and intelligent systems have emerged as critical instruments for improving the design and evaluation of public policies. This study examines how the integration of large-scale data analytics, artificial intelligence, and intelligent decision-support systems enhances evidence-based policymaking, policy effectiveness, and governmental responsiveness. Big data, derived from diverse sources such as administrative records, social media, sensors, and digital platforms, enables policymakers to better understand complex social dynamics, identify emerging policy problems, and design targeted interventions. Intelligent systems further support this process by applying machine learning and predictive analytics to forecast policy outcomes, optimize resource allocation, and reduce uncertainty in decision-making. The article highlights the role of these technologies across different stages of the public policy cycle, particularly in policy formulation and evaluation. In policy design, data-driven insights facilitate more precise problem definition and scenario analysis, while in policy evaluation, continuous data streams allow for real-time monitoring, performance measurement, and impact assessment. This shift from traditional, retrospective evaluation methods to dynamic and adaptive evaluation frameworks enhances accountability and institutional learning. Moreover, the use of big data and intelligent systems contributes to digital governance by increasing transparency and enabling greater citizen participation through open data initiatives and digital feedback mechanisms. However, the study also emphasizes key challenges, including data privacy risks, algorithmic bias, ethical concerns, and limitations in institutional capacity. Addressing these challenges requires robust data governance frameworks, ethical guidelines, and investment in technical and human capabilities. Overall, the article argues that while big data and intelligent systems hold significant potential to transform public policymaking, their successful adoption depends on balancing technological innovation with democratic values, social equity, and public trust.

Abstract In today’s highly volatile and uncertain environment, organizations are increasingly exposed to crises that are complex, systemic, and largely unpredictable. Traditional managerial approaches based on linear planning and stability assumptions are often inadequate for addressing such disruptions. This article examines the critical role of strategic thinking in crisis management and its contribution to enhancing organizational resilience under conditions of uncertainty. Drawing on interdisciplinary literature from strategic management, crisis management, organizational behavior, and systems theory, the study conceptualizes strategic thinking as a dynamic cognitive capability that integrates long-term vision, holistic understanding, adaptability, and learning orientation. The paper argues that strategic thinking enables organizations to better anticipate emerging threats, respond effectively during crises, and engage in post-crisis recovery and renewal. Key dimensions of strategic thinking—including systems perspective, strategic intent, intelligent opportunism, thinking in time, and hypothesis-driven learning—are analyzed in relation to the main phases of crisis management: pre-crisis preparedness, crisis response, recovery, and organizational learning. Through this analysis, the study highlights how strategic thinking supports sense-making under ambiguity, enhances strategic flexibility, and facilitates informed decision-making during high-pressure situations. Furthermore, the article positions strategic thinking as a foundational element of organizational resilience. It demonstrates that resilience is not solely dependent on structural resources or formal procedures, but also on how leaders and organizations interpret uncertainty, reframe adversity, and mobilize collective capabilities. An integrative conceptual framework is proposed to illustrate the linkages between strategic thinking, crisis management processes, and resilience outcomes. The study offers practical implications for managers and leaders by emphasizing the need to cultivate strategic thinking capabilities at both individual and organizational levels. It also outlines directions for future research aimed at empirically testing the proposed relationships across diverse organizational contexts

Abstract The article examines the role of crisis management and management models in improvingthe resilience of government organizations against the crises caused by climate change. The main purpose of this study is to analyze the different stagesof resilience of organizations, including risk identification, preparedness and planning, rapid response to crises, and reconstruction. It is after the crisis. The research method was qualitative and based on the content analysis of the collected data through structured interviews with managers and experts of government organizations in vulnerable areas. The data were analyzed using thematic analysis to identify the main patterns inresilience processes. should be identified and analyzed. This research also identified the weaknesses and strengths of the existing management models and concluded that organizations that have proper planning and preparation to deal with climate crises are able to respond more quickly and effectively in crisis situations. The findings showed that accurate identification of climate risks and threats as the first step in the resilience process has a great impact on increasing the response capacity of organizations. Also, training and empowerment of human resources is one of the important factors in increasing the resilience of organizations. The active participation of local communities and cooperation between different institutions were also factors that helped to improvethe resilience of organizations. Finally, rapid reconstruction of infrastructures and the use of past experiences in improving future processes make organizations more resilient to future crises. The conclusion of this study shows that organizational resilience in the faceof climate crises requires a comprehensive and integrated approach that includes careful planning, continuous training, cooperation with various institutions, and the use of new technologies. In general, organizations with To deal with climate crises, strengthen their resilience and use past experiences to improve future processes.

Abstract Small and Medium-sized Enterprises (SMEs) represent the backbone of the global economy, yet they are disproportionately vulnerable to global economic fluctuations, supply chain disruptions, and sustainability pressures. In today’s uncertain environment, resilient supply chain management (RSCM) and the integration of sustainable practices have become strategic imperatives for SMEs striving to maintain competitiveness and continuity. This paper explores how SMEs can enhance resilience by diversifying suppliers, leveraging digital technologies, and strengthening collaborative networks while simultaneously adopting sustainability measures such as circular economy models, renewable energy integration, and ethical sourcing. The study emphasizes the dual role of resilience and sustainability as mutually reinforcing concepts: resilient practices ensure operational continuity during crises, while sustainable initiatives reduce long-term vulnerabilities and improve stakeholder trust. Case studies from SMEs across diverse industries demonstrate that adopting these strategies not only mitigates risks but also provides access to global markets, enhances brand reputation, and improves resource efficiency. However, SMEs face significant barriers, including limited financial resources, technological gaps, regulatory complexities, and knowledge deficits. Overcoming these challenges requires supportive policies, financial incentives, and capacity-building programs, alongside SME-driven innovation.

Abstract This study develops a comprehensive and integrative framework for agile leadership in knowledge-based organizations (KBOs), designed to address the unique challenges of operating in volatile, uncertain, complex, and ambiguous (VUCA) environments. Drawing from the knowledge-based view (KBV) of the firm, dynamic capabilities theory, and contemporary agile leadership models, the framework emphasizes the interplay between leadership behaviors, organizational culture, and knowledge management processes. Specifically, it integrates the “align–empower” leadership paradigm, which balances strategic alignment with team empowerment, and incorporates transformational leadership as a catalyst for innovation, trust, and resilience. The framework operationalizes Nonaka and Takeuchi’s SECI model of knowledge creation (Socialization, Externalization, Combination, Internalization) within agile contexts, highlighting the role of leaders in creating an enabling environment (“Ba”) for knowledge sharing, learning, and application. By fostering psychological safety, autonomy, and shared purpose, agile leaders can accelerate both the creation and utilization of organizational knowledge, enhancing adaptability, responsiveness, and innovation. This synthesis offers theoretical contributions by bridging agile leadership and KBV perspectives, while providing practical guidelines for leadership development, digital transformation, and cultural change in KBOs. Implications extend to small and medium-sized enterprises (SMEs) and large-scale knowledge-intensive organizations, with recommendations for context-specific adaptation. The proposed model serves as a roadmap for scholars seeking to empirically test agile leadership mechanisms and for practitioners aiming to embed agility into the DNA of knowledge-driven enterprises. Ultimately, this framework underscores that in knowledge-based economies, agility in leadership is not optional—it is an imperative for sustained competitive advantage.

Abstract The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) technologies is reshaping educational landscapes worldwide. Personalized learning, characterized by tailored educational experiences based on individual student needs, preferences, and performance, is one of the most promising applications of these technologies. This paper explores the theoretical, technical, and practical aspects of integrating AI and ML into educational systems to create adaptive, student-centered learning environments. We begin by examining the foundational theories that link AI/ML to cognitive and instructional science. The paper then defines personalized learning and analyzes its necessity in 21st-century classrooms. Various applications, such as intelligent tutoring systems, recommendation engines, automated feedback systems, and predictive analytics, are presented as case studies. Benefits of AI-based personalization—such as improved student engagement, better learning outcomes, and optimized teaching strategies—are examined alongside challenges including data privacy, algorithmic bias, infrastructure disparities, and resistance to technological change. Furthermore, the study investigates real-world implementations of AI in diverse contexts, including K-12 education, higher education, and online learning platforms. Ethical implications and policy frameworks are addressed to ensure that AI-driven personalization is inclusive, transparent, and equitable. Finally, the article identifies current research gaps and proposes future directions for educators, developers, and policymakers. This comprehensive review underscores the transformative potential of AI and ML in fostering individualized education, while cautioning against unchecked adoption without critical evaluation of pedagogical and ethical consequences.

Abstract In this study, human-centered supply chain management has been examined. Supply chain management simplifies activities related to the supply of goods, increases customer satisfaction and is considered a competitive advantage for a business. In simple terms, supply chain management is a network of people, businesses, resources, technology and specialized knowledge that work together in a coherent manner to produce, distribute and sell products. The supply chain structure usually begins with the producers of raw materials and ends with the supply of the final product to customers by various retailers. Each implementation of the supply chain entails costs for a business. Therefore, it is natural that an optimal supply chain reduces waste of resources and additional costs. The supply chain begins to work when the manufacturer receives the order from the customer. Therefore, its basic functions include product development, marketing, sales, operations, distribution networks, finance and customer service. The entities involved in the supply chain include manufacturers, sellers, warehouses, transportation companies, distribution centers, and retailers. The goal of supply chain management is to improve the efficiency of the supply chain process so that the right product reaches the customer on time and at the lowest cost. Shafiee et al. (2009) have stated the factors driving organizations towards supply chain management as follows: The need for improvement activities, increasing levels of outsourcing, supply chain complexity, the importance of global trade, increasing globalization, competitive pressures, increasing transportation costs, and the need for inventory management.

Abstract This study examines the effect of financial risk management practices on the financial performance of steel milling companies operating in Afghanistan. Using a quantitative, explanatory research design, primary data were collected from 32 finance-related staff across five steel companies through a structured Likert-scale questionnaire, while secondary financial data were obtained from audited statements to compute return on assets (ROA). Descriptive statistics, Pearson correlation analysis, and multiple linear regression were employed to evaluate the relationships between financial performance and four dimensions of financial risk management: understanding of risk and risk management, risk identification, risk analysis and assessment, and risk monitoring. The findings reveal that all four dimensions exhibit positive and statistically significant effects on ROA, indicating that firms with stronger and more structured risk-management systems achieve higher profitability. The regression model explains 84.3% of the variation in financial performance, demonstrating substantial predictive power. While firm size and capital structure show positive but statistically insignificant effects, the results emphasize that managerial capability in applying risk-management practices outweighs structural firm characteristics in determining profitability. The study concludes that effective financial risk management is essential for enhancing financial performance in Afghanistan’s steel industry, which operates within a highly volatile and uncertain environment. Strengthening internal controls, broadening risk-identification processes, and improving monitoring systems are recommended to support long-term financial sustainability.

Abstract The integration of agile methodologies into digital transformation initiatives within public sector organizations represents a critical yet challenging endeavor in contemporary governance. This article examines the paradoxical relationship between Agile principles—emphasizing flexibility, iterative delivery, and self-organizing teams—and the inherently bureaucratic nature of public administration, characterized by hierarchical structures, rigid procurement frameworks, and risk-averse cultures. Through a systematic synthesis of empirical studies published between 2014 and 2025, this research identifies four primary categories of implementation challenges: institutional and regulatory barriers, procurement and contractual misalignments, cultural and resistance factors, and resource and capability constraints. The findings reveal that while agile adoption can significantly enhance transparency, responsiveness, and citizen-centered service delivery, successful implementation requires fundamental adaptations rather than wholesale methodological transplantation. A multidimensional framework proposed, integrating legal-procedural adaptations, hybrid governance models, tailored procurement mechanisms, cultural transformation strategies, and iterative implementation roadmaps. This research contributes to both public administration theory and digital government practice by providing evidence-based guidance for navigating the inherent tensions between agility and accountability in democratic governance contexts.