Pakistan Journal of Management and Social Issues (PJMSI)
Pakistan Journal of Management and Social Issues (PJMSI) is a prestigious and multidisciplinary scholarly publication that serves as a platform for rigorous academic research and critical analysis within the realm of Business & Management, Social Sciences, Arts & Humanities, Physical Sciences, Natural Sciences, Engineering & Technology and Health Sciences. 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 PJMSI aims to foster insightful discourse and disseminate high-quality research across various domains within the Social Sciences, Arts & Humanities, Physical Sciences, Natural Sciences, Engineering & Technology, Health Sciences and Business & Management. The journal's scope encompasses a wide range of disciplines but not limited to: Social Sciences Arts & Humanities Physical Sciences Natural Sciences Engineering & Technology Health Sciences Business & Management Editorial Process and Quality Standards: The PJMSI 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 PJMSI 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.
Measuring Trust in Artificial Intelligence Systems: A Multidimensional Framework and Empirical Assessment
The rapid integration of artificial intelligence (AI) systems into high-stakes decision environments has intensified concerns regarding trust, reliability, and ethical governance. Despite growing scholarly attention, existing research on trust in AI remains fragmented, often focusing on isolated technical or psychological factors without integrating ethical and institutional dimensions. This study develops and empirically validates a multidimensional framework for measuring trust in AI systems. Drawing on theories of trust in automation, technology acceptance, and responsible AI governance, the framework conceptualizes trust as a composite construct comprising perceived competence, transparency, reliability, fairness, and accountability. A structured survey instrument was developed using validated measurement scales and administered to respondents with prior exposure to AI systems. Reliability and validity analyses confirm the robustness of the proposed constructs. Structural modeling results demonstrate that all five dimensions significantly influence overall trust, with transparency and fairness emerging as particularly strong predictors. Furthermore, trust significantly predicts user reliance intentions, underscoring its central role in AI adoption and operational integration. The findings contribute to the literature by providing an integrated measurement model that bridges technical performance attributes with ethical and governance considerations. The study also offers practical implications for AI developers and policymakers seeking to design trustworthy systems and regulatory frameworks. By operationalizing trust as a measurable and multidimensional phenomenon, this research advances empirical approaches to responsible AI deployment and contributes to ongoing efforts to strengthen public confidence in intelligent systems.
Artificial Intelligence; Trust in AI; Trustworthiness; Transparency; Algorithmic Fairness; Accountability; Reliability; Technology Acceptance; AI Governance; Explainable Ai; Human–AI Interaction; Structural Equation Modeling
Copyright (c) 2025 Pakistan Journal of Management and Social Issues (PJMSI)
Online Education Adoption, Self-Regulation and Learning Satisfaction: Moderating Role of Internet Accessibility
The rapid expansion of digital technologies has significantly transformed educational systems, leading to increased adoption of online education across the globe. This study examines the relationship between online education adoption, self-regulation, and learning satisfaction, with a particular focus on the moderating role of internet accessibility. Online education adoption refers to the extent to which students accept and utilize digital learning platforms, while self-regulation encompasses learners’ ability to plan, monitor, and control their learning processes. Learning satisfaction reflects students perceived quality and effectiveness of their learning experience. Drawing upon the Technology Acceptance Model and self-regulated learning theory, this study proposes that online education adoption positively influences both self-regulation and learning satisfaction. Empirical studies indicate that self-regulation plays a critical role in enhancing students’ satisfaction with online learning environments. Furthermore, research suggests that higher levels of self-regulation skills significantly increase the adoption of online learning systems. Internet accessibility is introduced as a moderating variable, as access to reliable and high-speed internet determines the effectiveness of online education. Limited connectivity can hinder participation, reduce engagement, and negatively affect satisfaction levels. Conversely, high internet accessibility enhances the learning experience by facilitating uninterrupted access to digital resources. A quantitative research design was employed using survey data collected from 340 university students. Smart PLS was used to test the proposed relationships. The findings reveal that online education adoption significantly enhances self-regulation and learning satisfaction. Additionally, self-regulation positively influences learning satisfaction. The moderating effect of internet accessibility is significant, indicating that students with better internet access experience stronger relationships among the variables. This study contributes to the literature by integrating technological, behavioral, and infrastructural factors into a unified framework. The findings provide valuable insights for educational institutions and policymakers aiming to improve online learning experiences by enhancing both technological infrastructure and students’ self-regulation skills.
Online Education Adoption, Self-Regulation, Learning Satisfaction, Internet Accessibility, E Learning, Higher Education
Digital Learning Tools and Student Performance Engagement: Moderating Role of Teacher Competence
The integration of digital learning tools has transformed modern education by enhancing instructional delivery and student engagement. This study examines the relationship between digital learning tools and student performance engagement, with teacher competence serving as a moderating variable. Digital learning tools include technologies such as learning management systems, virtual simulations, gamified platforms, and artificial intelligence-based applications that facilitate interactive and personalized learning experiences. Student performance engagement refers to the level of students’ cognitive, behavioral, and emotional involvement in learning activities. Drawing upon constructivist learning theory and technology acceptance theory, this study argues that digital learning tools positively influence student engagement by creating interactive and student-centered learning environments. Empirical evidence indicates that digital tools significantly improve student motivation, participation, and academic performance by offering real time feedback and collaborative learning opportunities. However, the effectiveness of these tools largely depends on teacher competence, which includes digital literacy, pedagogical skills, and the ability to integrate technology effectively into teaching practices. Teacher competence is proposed as a moderating variable that strengthens or weakens the relationship between digital tools and student engagement. Research shows that teachers with strong digital competencies enhance classroom interactivity and improve learning outcomes, whereas inadequate skills limit the effectiveness of digital tools. Furthermore, studies highlight that teacher preparedness and training are essential for maximizing the benefits of digital technologies in education. This study employs a quantitative research design using Partial Least Squares Structural Equation Modeling. Data are collected through structured questionnaires measuring digital tool usage, teacher competence, and student engagement. The findings reveal that digital learning tools significantly enhance student performance engagement, while teacher competence positively moderates this relationship. The study contributes to the literature by integrating technological and pedagogical perspectives and provides practical implications for improving digital education strategies.
Digital Learning Tools, Student Engagement, Teacher Competence, Educational Technology
Human Rights, Cultural Norms, Awareness and Activism: Moderating Role of Responsibility
This study examines the complex relationship between human rights, cultural norms, awareness, and activism, with a specific focus on the moderating role of responsibility. Human rights are universally recognized principles aimed at safeguarding dignity and equality; however, their implementation often varies across cultural contexts. Cultural norms significantly shape individuals’ perceptions of rights, influencing both awareness and participation in activism. This research investigates how awareness mediates the relationship between cultural norms and activism, and how responsibility strengthens or weakens these associations. Drawing upon critical theory and socio-cultural frameworks, the study conceptualizes human rights awareness as a cognitive mechanism that transforms normative beliefs into actionable activism. Responsibility, both individual and collective, is hypothesized as a moderating variable that enhances the translation of awareness into active engagement. Empirical analysis was conducted using Smart PLS to test the structural relationships among constructs. The findings indicate that cultural norms have a significant influence on awareness and activism, but their effects are context dependent. Awareness positively mediates the relationship between cultural norms and activism. Moreover, responsibility plays a critical moderating role by strengthening the impact of awareness on activism. These results highlight that individuals with a stronger sense of responsibility are more likely to engage in human rights activism when they are aware of social injustices. The study contributes to the literature by integrating cultural, cognitive, and behavioral dimensions of human rights engagement. It also provides practical implications for policymakers, educators, and activists seeking to promote inclusive and effective human rights advocacy.
Human Rights, Cultural Norms, Awareness, Activism, Responsibility, Moderation, Social Change
The Effect of Domestic Violence on Women’s Mental Health: Moderating Role of Cultural Norms
Domestic violence is a pervasive global issue that significantly affects women’s physical, psychological, and emotional wellbeing. It encompasses physical, emotional, sexual, and economic abuse, often occurring within intimate relationships. Among its many consequences, the impact on women’s mental health is particularly severe, leading to long term psychological distress. This study investigates the effect of domestic violence on women’s mental health, with a specific focus on the moderating role of cultural norms. Drawing on feminist theory, ecological theory, and social learning theory, the study posits that domestic violence negatively affects women’s mental health outcomes, including depression, anxiety, stress, and post-traumatic stress disorder. Empirical evidence indicates that women exposed to domestic violence frequently experience significant psychological disorders such as anxiety, depression, and PTSD. Cultural norms, including patriarchal beliefs, gender roles, and societal acceptance of violence, are expected to influence the strength of this relationship. A quantitative research design was employed, and data were collected from 300 women through structured questionnaires. Structural Equation Modeling using Smart PLS was used to test the proposed hypotheses. The findings reveal that domestic violence has a significant negative effect on women’s mental health. Furthermore, cultural norms significantly moderate this relationship, such that in societies where violence is normalized or stigmatized, the psychological impact is more severe. The study concludes that while domestic violence directly harms mental health, cultural norms play a crucial role in either exacerbating or mitigating these effects. Addressing domestic violence therefore requires not only legal and institutional interventions but also cultural transformation.
Domestic Violence, Mental Health, Women, Cultural Norms, Psychological Wellbeing
The Impact of Social Inequality Outcomes on Digital Welfare Delivery Systems
Digital welfare delivery systems, including online social assistance platforms, e-government portals, and mobile welfare applications, have emerged as critical tools for improving accessibility, efficiency, and transparency in public service provision. However, social inequality—defined in terms of income, education, digital literacy, and geographic disparities may significantly influence both access to and outcomes of digital welfare programs. This research investigates the impact of social inequality on the effectiveness and inclusivity of digital welfare delivery systems. A quantitative survey approach was adopted, collecting responses from 380 beneficiaries, welfare administrators, and policy experts across urban and rural regions. Structural Equation Modeling using SmartPLS examined the relationships between social inequality indicators, system accessibility, user engagement, and perceived welfare effectiveness. Results indicate that higher social inequality adversely affects access, utilization, and engagement with digital welfare platforms. Digital literacy and infrastructure availability emerged as significant mediators, with disparities in these areas amplifying inequality in program outcomes. Geographic location and income levels were strongly correlated with barriers to digital system use, while user engagement and feedback mechanisms moderated the relationship between inequality and perceived system effectiveness. The study contributes to the literature on digital governance and welfare economics by highlighting the complex interplay between social inequality and technology-mediated service delivery. Practically, findings underscore the need for policymakers and platform designers to implement inclusive strategies, such as simplified user interfaces, digital literacy programs, and infrastructure expansion, to ensure equitable access. Limitations include reliance on self-reported data and cross-sectional design. Future research should incorporate longitudinal studies and behavioral analytics from system logs to better understand long-term equity impacts
Social Inequality, Digital Welfare Systems, Accessibility, Digital Literacy, User Engagement, E-Government, Welfare Effectiveness
The Impact of Ethical, Legal, and Social Implications of Gene Editing in Financial Institutions
Advances in gene editing technologies, particularly CRISPR-Cas9, have raised profound ethical, legal, and social concerns (ELSI), which extend beyond biomedical applications into broader societal domains. Financial institutions, as custodians of capital, insurance, and investment decisions, are increasingly confronted with the implications of gene editing for risk assessment, insurance underwriting, investment ethics, and corporate governance. This study explores the impact of ethical, legal, and social considerations of gene editing on financial institutions’ operational, strategic, and regulatory decisions. A quantitative survey was conducted among 360 professionals in banking, insurance, and investment sectors, assessing perceptions of ethical risk, legal compliance, social responsibility, and organizational response to emerging gene editing technologies. SmartPLS Structural Equation Modeling was applied to examine relationships between awareness of ethical, legal, and social implications, organizational policies, risk mitigation strategies, and stakeholder trust. Results indicate that ethical awareness and perceived social impact significantly influence institutional policies, while legal compliance mediates the relationship between perceived risks and stakeholder trust. Financial institutions that proactively integrate ELSI considerations into governance frameworks are more likely to maintain regulatory compliance, stakeholder confidence, and reputational resilience. This research contributes to interdisciplinary literature bridging bioethics, finance, and corporate governance. Findings highlight the need for institutional frameworks that address the societal and ethical ramifications of gene editing technologies, while mitigating regulatory and reputational risks. Limitations include reliance on self-reported data and cross-sectional design; future research should incorporate longitudinal studies and scenario-based simulations to evaluate institutional responses under diverse regulatory and social contexts.
Gene Editing, Crispr, Ethical Implications, Legal Compliance, Social Responsibility, Financial Institutions, Risk Management, Stakeholder Trust, Corporate Governance
Optimizing Green Hydrogen Production Through Techno-Economic and Lifecycle Analysis
Green hydrogen, produced via water electrolysis using renewable energy, is a cornerstone of the global transition to low-carbon energy systems. Optimizing green hydrogen production requires a comprehensive assessment of techno-economic performance, energy efficiency, and environmental sustainability. This study investigates the optimization of green hydrogen production through combined techno-economic and lifecycle analysis (TEA-LCA), focusing on cost, energy consumption, carbon emissions, and system design parameters. A quantitative modeling approach was applied, integrating process simulation, cost estimation, and lifecycle assessment. Key parameters analyzed include electrolyzer technology (PEM, alkaline, SOEC), renewable energy input, storage, and distribution. SmartPLS Structural Equation Modeling was used to examine the relationships between techno-economic indicators, environmental performance, operational efficiency, and overall system sustainability. Results indicate that system efficiency, energy source cost, and electrolyzer technology significantly influence both production cost and carbon intensity. Lifecycle analysis reveals that renewable energy mix and electrolyzer durability are critical factors in minimizing environmental impact. TEA-LCA integration identifies optimal operating strategies, highlighting trade-offs between cost reduction and emissions mitigation. The study contributes to energy systems literature by providing empirical insights into the drivers of cost-effective and environmentally sustainable green hydrogen production. Findings inform policymakers, energy developers, and investors on strategies to accelerate the adoption of green hydrogen while achieving carbon neutrality targets. Limitations include modeling assumptions on energy prices and technology performance; future research should incorporate real-time operational data and stochastic modeling to enhance robustness.
Green Hydrogen, Techno-Economic Analysis, Lifecycle Assessment, Electrolyze Technology, Renewable Energy, Energy Efficiency, Sustainability, Carbon Neutrality, Production Optimization
Measuring the Psychological Effects of Algorithmic News Consumption on Political Polarization
The rise of algorithm-driven news recommendation systems on social media platforms has reshaped information consumption patterns, raising concerns about political polarization. Algorithmic curation, designed to maximize engagement by tailoring content to user preferences, may reinforce ideological biases, create echo chambers, and amplify selective exposure. This study investigates the psychological effects of algorithmic news consumption on political polarization, examining cognitive, emotional, and behavioral responses to personalized news feeds. A quantitative survey was conducted with 400 social media users, spanning diverse age groups, education levels, and political affiliations. Constructs measured include algorithmic exposure, selective perception, cognitive bias, emotional response, and political polarization. Structural Equation Modeling using SmartPLS was employed to test the relationships between algorithmic news consumption and polarization outcomes. Results indicate that higher exposure to algorithmically tailored content significantly increases selective perception and cognitive bias, which mediate the relationship between algorithmic exposure and political polarization. Emotional responses, including anger and moral outrage, further exacerbate polarization effects, suggesting that algorithmic curation not only shapes information exposure but also amplifies affective drivers of political division. The study contributes to media psychology and political communication literature by providing empirical evidence linking algorithmic news curation with psychological mechanisms that drive polarization. Findings highlight the role of cognitive and emotional mediators, emphasizing the need for platforms, policymakers, and users to recognize and mitigate the effects of algorithmic filtering. Limitations include reliance on self-reported survey data and cross-sectional design. Future studies should incorporate longitudinal and behavioral tracking methods to capture real-time interaction patterns and causal mechanisms.
Algorithmic News, Political Polarization, Social Media, Cognitive Bias, Selective Perception, Emotional Response, Echo Chambers, Digital Media Psychology
Evaluating Corporate Carbon Disclosure Practices and Their Impact on Investor Confidence
Climate change has intensified the scrutiny of corporate environmental practices, making carbon disclosure an essential component of sustainable business reporting. Carbon disclosure refers to the systematic reporting of greenhouse gas emissions, climate-related risks, and mitigation strategies. Transparent carbon reporting can enhance corporate accountability and influence investor decision-making by signaling environmental responsibility and long-term sustainability. This study evaluates the effectiveness of corporate carbon disclosure practices and examines their impact on investor confidence. A quantitative survey was conducted among 340 investors, financial analysts, and sustainability officers to assess perceptions of carbon disclosure comprehensiveness, reliability, and transparency. Structural Equation Modeling was employed to examine the relationships between disclosure quality, reporting consistency, stakeholder trust, and investor confidence. Results indicate that higher quality and more consistent carbon disclosure positively influence investor confidence. Stakeholder trust mediates this relationship, demonstrating that transparent and reliable reporting enhances the credibility of firms’ environmental commitments. Firms with comprehensive reporting frameworks, such as adherence to the Carbon Disclosure Project (CDP) and international standards, are perceived as more accountable, reducing perceived environmental risk and fostering investor confidence. The study contributes to the literature on environmental, social, and governance (ESG) reporting by providing empirical evidence linking carbon disclosure practices to financial market perceptions. Practically, findings emphasize the need for firms to adopt standardized disclosure frameworks, improve data reliability, and communicate emissions performance clearly to maintain investor trust. Limitations include reliance on self-reported perceptions and cross-sectional survey data. Future studies should incorporate longitudinal analyses and examine actual market reactions to carbon disclosure.
Carbon Disclosure, Investor Confidence, Corporate Transparency, Stakeholder Trust, Smartpls, Esg Reporting, Greenhouse Gas Reporting, Sustainability Communication
The Effect Formation and Spread of Digital Misinformation Networks on Social Media
Digital misinformation on social media has become one of the defining challenges of contemporary communication ecosystems. This study analyzes how misinformation networks are formed, how they spread across platforms, and how they influence user perceptions and behaviors. The research integrates theories from network science, social psychology, and information systems to examine the structural and cognitive mechanisms that enable false content to travel faster than verified information. A quantitative design using survey data from 380 active social media users was employed. Structural Equation Modeling through SmartPLS tested relationships among algorithmic exposure, social endorsement, emotional appeal, network centrality, and belief adoption. Findings reveal that algorithmic amplification and peer endorsement are the strongest predictors of misinformation diffusion. Emotional content significantly mediates the relationship between exposure and belief acceptance, suggesting that affective triggers override rational evaluation. The study demonstrates that misinformation networks operate as self reinforcing systems where highly connected nodes accelerate cascade effects. Digital literacy shows a negative relationship with misinformation acceptance, yet its moderating effect is weaker than expected, indicating structural platform factors dominate individual capabilities. This research contributes to theoretical understanding by proposing an integrated model that links technological affordances with psychological vulnerabilities. Practical implications emphasize the need for platform design reforms, transparent recommendation systems, and community based verification practices. The study further highlights ethical responsibilities of social media companies in mitigating coordinated inauthentic behavior. Limitations include cross sectional design and reliance on self reported measures. Future studies should incorporate behavioral trace data and cross platform comparisons.
Digital Misinformation, Social Media Networks, Algorithmic Amplification, Emotional Contagion, Network Centrality, Belief Formation, Digital Literacy
Copyright (c) 2024 Pakistan Journal of Management and Social Issues (PJMSI)
Measuring the Role of Fintech Adoption in Enhancing SME Financial Performance in Emerging Markets
This study investigates the role of financial technology (Fintech) adoption in improving the financial performance of small and medium enterprises (SMEs) in emerging markets. Fintech adoption represents the integration of innovative digital financial services such as mobile payments, digital credit, blockchain-based solutions, and cloud-based accounting into business operations. Emerging markets face structural challenges such as limited access to formal finance, high transaction costs, and inadequate financial infrastructure, which constrain SME growth and sustainability (Beck & Demirgüç-Kunt, 2006; Lee & Shin, 2018). SMEs that adopt Fintech are hypothesized to achieve enhanced financial performance measured through profitability, operational efficiency, liquidity, and growth. The research explores how these technological capabilities affect SME outcomes through empirical modeling and structural equation modelling (SEM). The study draws on the Technology-Organization-Environment (TOE) framework to explain Fintech adoption by SMEs, while the Resource-Based View (RBV) informs the link between Fintech resources and superior performance (Tornatzky & Fleischer, 1990; Barney, 1991). A quantitative survey was administered to 400 SME owners across multiple emerging market contexts (e.g., Pakistan, Kenya, Nigeria, Indonesia). A structured questionnaire measured constructs of Fintech adoption, organizational readiness, environmental support, and financial performance. Partial Least Squares SEM was used to estimate relationships and test hypothesized paths. Results indicate that Fintech adoption significantly influences SME financial performance (β = 0.48, p < .01), with mediating effects from organizational readiness and customer digital engagement (p < .05). Fintech usage mitigates traditional financing constraints, improves cash flow management, and supports faster transactions. Findings underscore the importance of regulatory support and digital infrastructure in maximizing benefits. The study contributes to academic literature by empirically validating the strategic importance of Fintech for SMEs in challenging environments. It offers practical implications for policy makers to facilitate digital financial inclusion, for Fintech providers to customize offerings for SMEs, and for SME managers to invest in digital capabilities. Future research should explore longitudinal impacts, sectoral differences, and qualitative insights into adoption barriers.
Fintech Adoption, Small And Medium Enterprises, Financial Performance, Emerging Markets, Digital Finance, SME Growth
Examining the Cultural Impact of Algorithmic Curation on Artistic Production
Algorithmic curation has become a central force in shaping contemporary artistic production, mediating how art is discovered, disseminated, and consumed across digital platforms. This research investigates the cultural effects of algorithmically curated recommendations on creative processes, artistic visibility, and audience reception. Drawing upon theories of cultural production, media sociology, and platform studies, the study examines how algorithmic filtering influences artistic diversity, genre innovation, and cultural hierarchies. A mixed-method approach was employed, combining survey data from 320 professional artists and cultural producers with content analysis of streaming and social media platforms. Structural Equation Modeling using SmartPLS assessed the relationships between algorithmic exposure, visibility, creative autonomy, audience feedback, and cultural impact. Results indicate that algorithmic curation significantly affects the visibility of certain genres and artist profiles, favoring mainstream and algorithmically optimized content. Creative autonomy mediates the effect of algorithmic visibility on perceived cultural influence, with artists adapting production strategies to align with algorithmic preferences. Audience engagement amplifies these effects, reinforcing patterns of cultural reinforcement and selective exposure. The study contributes to understanding the interplay between technological infrastructures and cultural production by demonstrating that algorithmic curation not only redistributes attention but actively shapes the creative choices of artists. Practically, findings suggest that platform governance, transparency, and support for diverse artistic expressions are essential to mitigating homogenization risks. Limitations include self-reported measures and platform-specific sampling. Future research should examine cross-platform dynamics and longitudinal changes in artistic practices under evolving algorithmic architectures.
Algorithmic Curation, Artistic Production, Cultural Impact, Creative Autonomy, Visibility, Audience Engagement, Digital Platforms
Assessing the Effectiveness of Blockchain-Based ESG Reporting on Corporate Transparency
Environmental, Social, and Governance (ESG) reporting has become a critical tool for enhancing corporate accountability, investor confidence, and sustainable business practices. Traditional ESG reporting suffers from limitations such as lack of verifiability, selective disclosure, and low trust among stakeholders. Blockchain technology offers the potential to address these challenges by providing a secure, immutable, and transparent system for recording ESG-related data. This research investigates the effectiveness of blockchain-based ESG reporting in improving corporate transparency. A quantitative survey design collected data from 360 corporate managers, sustainability officers, and investors across multiple industries actively implementing or evaluating blockchain-enabled ESG reporting systems. Structural Equation Modeling using SmartPLS was applied to examine relationships among blockchain adoption, data reliability, stakeholder trust, reporting accuracy, and perceived transparency. Results indicate that blockchain adoption significantly enhances data reliability, which in turn positively influences stakeholder trust and perception of corporate transparency. Reporting accuracy mediates the relationship between blockchain adoption and transparency, suggesting that the technological infrastructure improves not only accessibility but also the quality of disclosed ESG data. The study contributes to ESG literature by providing empirical evidence on the value of blockchain in addressing reporting inefficiencies and enhancing accountability. Findings highlight that firms integrating blockchain into ESG processes are perceived as more transparent, trustworthy, and accountable by investors and other stakeholders. Practically, the research informs managers, regulators, and policymakers on the benefits and limitations of blockchain-based reporting frameworks, while identifying critical success factors such as standardization, interoperability, and governance protocols. Limitations include reliance on self-reported measures and cross-sectional data. Future studies should incorporate longitudinal analysis and behavioral data from actual blockchain transactions
Blockchain, ESG Reporting, Corporate Transparency, Stakeholder Trust, Reporting Accuracy, , Sustainable Governance, Data Reliability
Analyzing Organizational Resilience Strategies During Global Economic Shocks
Organizational resilience has emerged as a critical capability that enables firms to withstand, adapt, and thrive amid global economic shocks. This research investigates the strategies organizations deploy to sustain operations during sudden downturns and prolonged financial distress, including diversification, innovation, digital transformation, stakeholder engagement, and strategic resource allocation. The study synthesizes multiple theoretical lenses, linking resilience to dynamic capabilities theory, adaptive cycle theory, and systems thinking. Drawing upon primary survey data from senior managers across manufacturing, services, and technology sectors, Structural Equation Modeling through SmartPLS was used to examine relationships among strategic resilience dimensions and firm performance under economic shocks. Results indicate that strategic flexibility and knowledge management significantly mediate the relationship between environmental turbulence and resilience outcomes, while digital transformation serves as a critical enabler of adaptive capacity. This research contributes to resilience theory by clarifying mechanisms through which organizational strategies mitigate the adverse impacts of global shocks. Practical implications suggest that firms prioritizing learning cultures, cross-functional collaboration, and real-time data analytics are better positioned to navigate economic volatility. The study further identifies that leadership commitment and agile governance frameworks amplify the effectiveness of resilience strategies. Findings reveal that resilience is not merely reactive but a proactive, strategically embedded capability requiring continuous investment. This research advances a comprehensive model that policymakers and leaders can use to assess organizational preparedness and response efficacy.
Organizational Resilience, Economic Shocks, Strategic Flexibility, Adaptive Capacity, Dynamic Capabilities, Digital Transformation, Performance Sustainability
Optimizing Renewable Energy Storage Systems Using Advanced Control Algorithms
The global transition toward renewable energy has intensified the need for efficient and intelligent energy storage systems capable of balancing intermittent generation with fluctuating demand. Solar and wind resources fluctuate with weather and time, creating instability in power grids that were designed for predictable fossil fuel plants. Advanced control algorithms offer a pathway to optimize charging, discharging, and degradation management of batteries, supercapacitors, and hybrid storage technologies. This study evaluates the effectiveness of model predictive control, reinforcement learning, and adaptive fuzzy logic in improving technical and economic performance of renewable energy storage systems. A comprehensive framework was developed that integrates real time forecasting of renewable generation, state of charge estimation, and grid demand signals. Simulation experiments were conducted using a microgrid model containing photovoltaic arrays, lithium ion batteries, and residential loads. Performance indicators included energy efficiency, lifecycle cost, depth of discharge, and grid stability metrics. SmartPLS structural modeling was employed to validate relationships between algorithm sophistication, forecasting accuracy, and overall system optimization. Results demonstrate that advanced control algorithms significantly enhance storage utilization compared with rule based strategies. Model predictive control achieved the highest energy efficiency while reinforcement learning provided superior adaptability under uncertain conditions. The analysis further reveals that forecasting accuracy mediates the effect of algorithm type on economic outcomes. The proposed evaluation index explains 72 percent of variance in system performance, confirming the suitability of the conceptual model. The research contributes to sustainable energy engineering by offering quantitative evidence for selecting control strategies in distributed grids. Practical implications include guidelines for integrating artificial intelligence with battery management systems and recommendations for real time implementation on embedded hardware. Future work should examine cybersecurity risks and scalability to national grids. By aligning algorithmic intelligence with physical storage constraints, the study supports resilient and affordable renewable power systems.
Renewable Energy Storage, Advanced Control Algorithms, Model Predictive Control, Reinforcement Learning, Microgrid Optimization
Evaluating Thermal and Electrical Properties of Quantum Materials for Energy Applications
Quantum materials have emerged as promising candidates for next generation energy technologies because their electronic states, lattice vibrations, and magnetic excitations can be engineered in ways impossible for classical solids. This research evaluates the thermal and electrical properties of selected quantum materials with the aim of determining their suitability for energy conversion and storage applications. Materials such as topological insulators, layered transition metal dichalcogenides, and strongly correlated oxides exhibit unusual carrier mobility, tunable band structures, and low thermal conductivity that could improve thermoelectric generators, solid state batteries, and superconducting power devices. However, systematic assessment methods that connect microscopic quantum behavior to macroscopic energy performance remain limited. The present study develops an integrative evaluation framework combining experimental characterization, theoretical modeling, and multivariate statistical validation. Samples of bismuth telluride alloys, molybdenum disulfide thin films, and lanthanum strontium copper oxide ceramics were synthesized using controlled deposition and sintering techniques. Electrical conductivity, Seebeck coefficient, carrier concentration, and thermal diffusivity were measured across temperature ranges relevant to energy systems. SmartPLS modeling was employed to validate relationships between crystal quality, defect density, phonon scattering, and overall energy efficiency indicators. Results demonstrate that quantum confinement and interface engineering can simultaneously enhance electrical transport while suppressing lattice thermal conductivity, leading to significant improvement in the thermoelectric figure of merit. The study further reveals that microstructural stability mediates the relationship between intrinsic quantum properties and device level performance. The research contributes to energy materials science by offering a quantitative index that links fundamental quantum descriptors with application-oriented metrics. Practical implications include guidelines for material selection in waste heat recovery modules and high efficiency power electronics. The findings emphasize that successful energy deployment requires balancing competing properties rather than maximizing a single parameter. Future work should extend the framework to two dimensional heterostructures and explore machine learning assisted discovery of novel compounds. By uniting quantum physics with engineering evaluation, this study supports the transition toward cleaner and more efficient energy technologies.
Quantum Materials, Thermoelectric Properties, Electrical Conductivity, Thermal Transport, , Energy Applications
Evaluating AI-Assisted Methods for Large-Scale Literary Text Analysis
The rapid expansion of digitized literary archives has transformed the study of literature from a predominantly close reading discipline into a data-intensive field supported by artificial intelligence and computational linguistics. This research evaluates AI-assisted methods for large-scale literary text analysis and investigates their validity, interpretability, and contribution to literary scholarship. While machine learning, topic modeling, and natural language processing enable the examination of millions of pages, scholars continue to debate whether these techniques capture the nuanced aesthetic and cultural dimensions traditionally explored through human interpretation. The study develops a framework that compares three families of AI methods: semantic modeling, stylistic pattern detection, and narrative structure extraction. The objective is to determine how effectively these approaches support thematic discovery, authorship attribution, and genre classification. A mixed methodological design was employed using a corpus of 12,000 English novels from the nineteenth and twentieth centuries. Quantitative indicators of algorithmic performance were combined with expert evaluations from literary scholars. SmartPLS structural modeling was used to validate relationships between methodological transparency, interpretive usefulness, and scholarly acceptance. Results indicate that AI methods achieve high accuracy in pattern recognition tasks but vary considerably in their capacity to support meaningful interpretation. Semantic models based on transformer architectures demonstrated superior thematic coherence, while traditional topic models showed limitations in representing metaphor and irony. The research further reveals that interpretability mediates the relationship between technical performance and scholarly adoption. The study contributes to digital humanities by offering an evaluative index for AI-assisted literary analysis that balances computational efficiency with hermeneutic value. Practical implications include guidelines for corpus preparation, model selection, and collaborative workflows between data scientists and literary experts. The findings suggest that AI should be treated as an exploratory partner rather than a replacement for critical reading. Future research should extend the framework to multilingual corpora and investigate ethical concerns surrounding algorithmic bias and copyright. By grounding technological enthusiasm in methodological reflection, the study supports a more thoughtful integration of artificial intelligence into literary scholarship.
Artificial Intelligence, Literary Analysis, Digital Humanities, Text Mining, Interpretability, Large-Scale Corpora
Developing and Validating Social Trust Indices in Digitally Mediated Societies
Social trust has become a fragile and contested resource in societies increasingly organized through digital platforms, algorithmic mediation, and networked communication. Traditional measures of trust were developed for face-to-face communities and institutional interactions, yet contemporary life is shaped by online reputation systems, platform governance, and data-driven personalization. This study develops and validates comprehensive social trust indices suitable for digitally mediated societies. The research integrates perspectives from sociology, information systems, and behavioral economics to conceptualize trust as a multidimensional construct consisting of interpersonal digital trust, institutional digital trust, algorithmic trust, and data privacy confidence. A quantitative design was employed using survey responses from 420 participants drawn from education, commerce, and public service sectors. Measurement scales were adapted from established trust literature and extended to capture digital specific dimensions. SmartPLS structural equation modeling was used to assess reliability, validity, and predictive relevance of the proposed indices. Findings demonstrate that digital social trust is not a single attitude but an ecosystem of related beliefs. Algorithmic transparency and perceived data protection emerged as the strongest predictors of overall social trust, while frequency of online interaction alone showed limited explanatory power. The validated index achieved composite reliability values above .87 and average variance extracted above .60 across all dimensions, indicating strong psychometric properties. The study contributes a practical measurement tool that governments, researchers, and platforms can use to monitor the health of social trust in online environments. Theoretically, it bridges classical trust theory with digital governance debates, proposing that trust in code and platforms now functions alongside trust in people and institutions. Implications include the need for transparent platform design, stronger data ethics frameworks, and public digital literacy initiatives. The research offers policymakers a diagnostic instrument to detect erosion of trust before it manifests as polarization or disengagement. Future studies should apply the index across cultures and longitudinally to observe how crises and technological changes reshape trust dynamics. By translating abstract concerns about digital life into measurable indicators, this work supports evidence-based strategies for sustaining social cohesion in networked societies.
Social Trust, Digital Society, Algorithmic Trust, Smartpls, Index Validation, Data Privacy, Online Platforms
Assessing the Strategic Value of Data-Driven Innovation in Competitive Advantage Creation
This study investigates the strategic value of data-driven innovation in creating competitive advantage within contemporary organizational ecosystems. As businesses increasingly leverage data analytics, digital platforms, and advanced algorithms, data-driven innovation has emerged as a core capability for strategic differentiation. Despite its widespread adoption, there is limited empirical consensus on how data-driven innovation translates into measurable competitive advantage and what internal organizational factors moderate this relationship. This research addresses this gap by developing and validating a theoretical model linking data acquisition and analytics capabilities to innovation outcomes and, ultimately, to competitive advantage. Drawing on dynamic capabilities theory, the study hypothesizes that data acquisition capability, analytics capability, and data governance positively influence innovation performance and competitive advantage. Data were collected from 350 senior managers and data professionals across technology, finance, and manufacturing firms through a structured survey. Structural equation modeling using SmartPLS was employed to assess the relationships among constructs. The results indicate that data analytics capability significantly predicts innovation performance (β = .45, p < .001) and that both innovation performance and data governance significantly mediate the relationship between data acquisition capability and competitive advantage (β = .32, p < .01; β = .28, p < .05). Notably, data governance emerged as a stronger predictor of competitive advantage than raw data acquisition alone, suggesting that organizational processes and governance structures enable firms to extract strategic insight from data assets. Empirical findings underscore the role of internal alignment, technological infrastructure, and managerial commitment in realizing the strategic value of data-driven initiatives. This research enriches both academic and practitioner understanding by articulating a comprehensive framework that links data capabilities to sustainable competitive advantage. The findings offer actionable implications for managers seeking to allocate resources toward data-centric innovation and highlight areas for policy refinement and strategic investment. Key recommendations include strengthening cross-functional data governance, investing in analytics talent, and aligning data strategy with broader business objectives. This study contributes to the literature on strategic management, innovation studies, and information systems by clarifying the mechanisms through which data-driven innovation fosters competitive differentiation
Data-Driven Innovation, Competitive Advantage, Data Governance, Analytics Capability, Strategic Management
Modeling the Relationship Between Leadership Styles and Employee Productivity in Hybrid Work Settings
The rapid expansion of hybrid work arrangements has fundamentally reshaped how organizations manage employees, coordinate tasks, and sustain productivity. Hybrid work settings, characterized by a combination of remote and on-site work, introduce new leadership challenges related to communication, trust, autonomy, and performance monitoring. Leadership styles that were effective in traditional office environments may not yield similar outcomes in hybrid contexts. This research examines how different leadership styles influence employee productivity in hybrid work settings, focusing on transformational, transactional, and servant leadership. Drawing upon leadership theory and organizational behavior literature, this study proposes a conceptual framework in which leadership styles directly affect employee productivity and indirectly influence it through work engagement and perceived autonomy. Hybrid work amplifies the importance of leadership behaviors that foster motivation, psychological safety, and alignment with organizational goals. Transformational leadership emphasizes vision, inspiration, and intellectual stimulation, which may enhance engagement in geographically dispersed teams. Transactional leadership relies on rewards and monitoring, which may have mixed effects in hybrid environments where direct supervision is limited. Servant leadership prioritizes employee well-being and empowerment, potentially strengthening trust and sustained productivity. A quantitative research design is employed using survey data collected from employees working in hybrid arrangements across large organizations. Structural equation modeling using SmartPLS is applied to test the hypothesized relationships among leadership styles, work engagement, perceived autonomy, and employee productivity. Results demonstrate that transformational and servant leadership have significant positive effects on productivity, both directly and indirectly through engagement and autonomy. Transactional leadership shows a weaker direct effect and a non-significant relationship with engagement, suggesting limited effectiveness in hybrid contexts. The findings contribute to leadership and hybrid work literature by empirically demonstrating that leadership effectiveness is contingent on work structure. Practically, the study provides guidance for managers and organizations transitioning to hybrid models by identifying leadership behaviors that enhance productivity without excessive control. The research underscores the need for adaptive leadership development programs aligned with flexible work arrangements.
Hybrid Work, Leadership Styles, Employee Productivity, Transformational Leadership, Servant Leadership, Work Engagement
Examining Climate Change Adaptation Policies Through Social Vulnerability Indicators
Climate change adaptation policies are increasingly critical as global climate impacts intensify social and economic risks for vulnerable populations. Effective adaptation requires not only technical and environmental responses but also integration of social vulnerability indicators that reveal how different groups experience and respond to climate hazards. Social vulnerability indicators such as income, age, health status, education, and access to infrastructure influence adaptive capacity and exposure to climate perturbations. This research examines how climate change adaptation policies incorporate social vulnerability indicators to improve policy effectiveness, equity, resilience, and sustainability. Utilizing a mixed quantitative and qualitative approach, we develop and test a conceptual framework linking vulnerability indicators with adaptation policy outcomes using structural equation modeling (SEM) via SmartPLS. Scholars argue that adaptation policies often emphasize physical infrastructure and technological solutions while insufficiently addressing social determinants that shape risk exposure and adaptive capacities (Adger, 2006; Füssel & Klein, 2006). Social vulnerability frameworks provide a lens to assess how demographic, economic, and institutional factors influence community capacity to adapt to climate shocks (Cutter et al., 2003). Incorporating such indicators into policy design helps target resources to high‑vulnerability groups and align adaptation with social justice and sustainability goals. In this study we collect data from policy documents, stakeholder surveys, and expert evaluations across multiple regions. We operationalize key constructs including policy comprehensiveness, social vulnerability integration, institutional responsiveness, and adaptation efficacy. Using SmartPLS, we evaluate hypothesized relationships showing that policies which integrate social vulnerability indicators more fully are associated with higher perceived adaptation efficacy, greater stakeholder satisfaction, and improved resilience outcomes. Results demonstrate significant positive paths from vulnerability integration to adaptation outcomes, mediated by institutional responsiveness and community participation. The measurement model shows good reliability and convergent validity as indicated by composite reliability and average variance extracted. Structural path assessment reveals robust standardized path coefficients with significance at p < .01, supporting the importance of social vulnerability indicator incorporation. This research contributes both theoretically and practically by offering an empirically tested model that highlights the central role of social vulnerability in shaping adaptation policy effectiveness. Findings recommend systematic inclusion of social vulnerability assessments in policy cycles, capacity building for local implementers, and participatory mechanisms that elevate marginalized voices. Implications extend to climate governance, disaster risk management, and sustainable development planning.
Climate Change Adaptation, Social Vulnerability, Policy Effectiveness, Smartpls, Resilience, Adaptive Capacity
Designing Nanostructured Materials for Enhanced Carbon Dioxide Capture Efficiency
Rising concentrations of atmospheric carbon dioxide (CO2) are a principal driver of climate change, necessitating advanced carbon capture technologies that are efficient, scalable, and cost effective. Nanostructured materials, with their high specific surface areas, tunable pore structures, and unique physicochemical properties, offer promising strategies for improving CO2 adsorption, separation, and utilization. This research examines the design principles, performance optimization, and efficiency of nanostructured CO2 capture materials such as porous carbons, metal organic frameworks (MOFs), hybrid nanocomposites, and functionalized nanomaterials. Understanding how nanostructure design influences CO2 uptake, selectivity, and regeneration energy is critical for developing next generation capture systems that can mitigate emissions from industrial sources and direct air capture processes. This thesis integrates literature on nanomaterial synthesis, adsorption mechanisms, pore structure optimization, and functionalization strategies, drawing on recent advances in porous carbon technologies and hybrid systems. Porous carbons with hierarchical nanostructures can achieve high surface areas and selective adsorption through controlled pore sizes and surface chemistries. MOFs with tunable frameworks provide opportunities for targeted CO2 binding and rapid kinetics, while hybrid nanocomposites combine the advantages of multiple material classes. Functionalization with amine or other active groups further enhances selective CO2 affinity. Using a mixed methodological approach, this study operationalizes constructs such as surface area, pore volume, functional group density, adsorption capacity, and regeneration efficiency. Data are obtained from synthesis experiments, adsorption isotherms, and performance tests under varying temperature and pressure conditions. SmartPLS structural equation modeling (SEM) is applied to assess relationships among nanostructural design features and CO2 capture efficiency metrics. Measurement model assessments show high reliability and validity for latent constructs such as nanostructure quality and capture performance. Structural model results indicate substantial positive effects of surface area and functionalization on adsorption capacity, while pore structure optimization significantly influences selectivity and regeneration energy. Hybridization effects are mediated through enhanced interaction sites and improved mass transport dynamics. Findings support design frameworks that prioritize hierarchical porosity, tailored surface chemistries, and multifunctional hybrid structures to maximize capture efficiency while minimizing energy penalties. The results have implications for industrial implementation, suggesting pathways for synthesizing scalable, high performance nanostructured adsorbents. Future work should explore long term stability under cyclic capture release conditions and techno economic analyses for real world deployment.
Nanostructured Materials, Co2 Capture, Adsorption Efficiency, Porous Carbon, Metal Organic Frameworks, Functionalization
Critically Examining Post-Colonial Identity Construction in Global Visual Art
Post-colonial theory provides critical tools for understanding how former colonial contexts continue to shape cultural, social, and political identities. In global visual art, artists negotiate historical legacies of colonization, hybrid cultural expressions, and contemporary socio-political realities. This study examines how post-colonial identity is constructed, represented, and contested in global visual art, analyzing the intersection of visual aesthetics, historical narrative, and socio-political critique. Using a mixed-methods approach, the research evaluates visual artworks from multiple continents, including Africa, South Asia, the Caribbean, and Oceania, drawing on post-colonial theoretical frameworks to interpret how identity is encoded, challenged, and reimagined. The research identifies three primary dimensions of post-colonial identity in visual art: hybridity, representation of historical trauma, and negotiation of cultural authenticity. Hybridity refers to the fusion of indigenous, colonial, and global artistic forms that challenge essentialist notions of identity (Bhabha, 1994). Historical trauma is represented through visual motifs, symbolism, and narrative strategies that critique colonial violence and its lingering impact (Hall, 1990). Cultural authenticity emerges as artists navigate local traditions while engaging with global art markets, raising questions about commodification and self-representation. Data collection includes archival research, visual content analysis, and semi-structured interviews with artists, curators, and art critics. Constructs are operationalized as thematic presence (e.g., hybridity, colonial critique, identity assertion), aesthetic strategies, and audience reception. Using SmartPLS structural equation modeling, relationships between artistic strategies and perceived identity impact are assessed. The measurement model demonstrates high internal consistency and validity across constructs. Structural path analysis indicates that hybridity and historical trauma representation significantly predict audience perception of post-colonial identity, while debates over cultural authenticity mediate this effect. Findings suggest that global visual art functions as both a site of resistance and negotiation, reflecting multi-layered identities that challenge monolithic narratives. Artists’ engagement with historical, cultural, and political contexts facilitates identity reconstruction that is dialogic, performative, and transformative. This research contributes to post-colonial studies and visual culture scholarship by providing empirical evidence on identity construction in artistic practices. It offers critical insights for art institutions, curators, and policymakers to foster inclusive representation and critical engagement with post-colonial narratives in global art.
Post-Colonial Identity, Visual Art, Hybridity, Cultural Authenticity, Historical Trauma, Audience Perception
Analyzing the Impact of Explainable AI on Strategic Decision Quality in Large Enterprises
The rapid evolution of artificial intelligence (AI) has transformed strategic decision-making across industries, yet concerns persist regarding the transparency and interpretability of AI models in enterprise contexts. Explainable AI (XAI) refers to methods and algorithms that make AI decisions understandable to human stakeholders. This thesis investigates the impact of XAI on strategic decision quality in large enterprises, focusing on operational efficiency, managerial trust, risk mitigation, and decision agility. Drawing on current literature from AI, organizational decision-making, and information systems research, this study proposes a conceptual framework linking XAI adoption to enhanced strategic decisions mediated by trust, perceived usefulness, and accountability. Large enterprises increasingly rely on complex machine learning models for forecasting, resource allocation, and competitive strategy. However, the “black-box” nature of many AI systems reduces user trust and limits managerial uptake of AI recommendations, potentially undermining decision quality (Doshi-Velez & Kim, 2017). Explainability is thus positioned as both a technical and organizational imperative. By providing interpretable insights into AI outputs, XAI can reduce cognitive barriers, improve stakeholder comprehension, and align model reasoning with enterprise objectives (Arrieta et al., 2020). This research integrates both qualitative and quantitative approaches. A cross-sectional survey of AI users and decision makers in large enterprises is utilized, with constructs measured via validated scales. Structural equation modeling (SEM) using SmartPLS tests hypotheses on how explainability affects trust, perceived usefulness, risk perception, and strategic decision quality. Results indicate significant positive relationships between XAI satisfaction and trust, trust and perceived usefulness, and perceived usefulness and decision quality. Notably, risk perception negatively moderates the relationship between trust and decision quality, highlighting the complexity of AI acceptance. The findings suggest that XAI enhances strategic decision quality by building managerial trust and facilitating clearer understanding of AI logic. Practical implications include the need for enterprise investment in explainable models, transparent analytics dashboards, and decision support training. Additionally, the research contributes to theory by delineating mechanisms through which XAI affects high-level strategic outcomes. This thesis underscores the importance of transparent AI systems for effective enterprise decision making, offering guidance for both AI developers and organizational leaders. Further research should explore longitudinal effects of XAI adoption and cross-industry comparisons.
Explainable AI, Strategic Decision Quality, Trust, Perceived Usefulness, Risk Perception, Large Enterprises
Impact of Promotional Activity on Consumer Perception in FMCG Sector in Lahore reign
This research delves deep into the complex ways in which promotional strategies affect how consumers perceive products in the Fast-Moving Consumer Goods (FMCG) sector. In an arena marked by fierce competition and swift product cycles, grasping the impact of these marketing tactics is vital for a brand's sustained relevance and success. Initially, the study sets the stage by exploring the foundational theories of consumer perception, elucidating the ways it is formed and influenced. Subsequently, it investigates a variety of promotional methods commonly used in the FMCG industry, including advertising, price reductions, loyalty initiatives, and in-store promotions. The study evaluates the effects of these promotional techniques in shaping aspects like consumer awareness, the perceived value of products, brand image, and ultimately, consumer purchasing decisions. The findings reveal that while promotional activities do have a significant effect on how consumers perceive products, the extent of their impact varies depending on factors such as the type of product, the demographic characteristics of the consumer, and how well the promotion aligns with the brand's image and core values. A notable discovery is the potential for a negative reaction when promotions are seen as too aggressive or not in sync with what consumers expect from the brand. Moreover, the research underscores a noticeable shift from traditional to digital promotional methods, emphasizing the increasing significance of online interactions in molding consumer perceptions
Fast-Moving Consumer Goods (FMCG) Employee Motivation, Job Performance, Salaries, Bonuses, Motivation
Evaluating the Impact of Employee Motivation on Job Performance: A Study of Organization in Lahore
This research explores the complex connection between employee motivation and job performance, aiming to unravel how different motivational elements play a crucial role in improving employee productivity. The paper commences by exploring the fundamental premise that financial remuneration, including salaries, bonuses, and other monetary rewards, is a primary driver of employee motivation, as underscored by theories from Houran, J., Sara et al., and Frederick Taylor. It further examines the role of non-monetary incentives, like job satisfaction, recognition, and personal growth opportunities, in fostering employee engagement and productivity, drawing upon the findings of Kalimullah et al. and Herzberg's motivation theory. In conclusion, this comprehensive analysis affirms that a multifaceted approach to employee motivation, encompassing both financial and non-financial incentives, leadership quality, trust, empowerment, and organizational support, is crucial for optimizing job performance. This research provides essential knowledge for managers and businesses seeking to cultivate a motivated team, leading to increased productivity and efficiency
Employee Motivation, Job Performance, Salaries, Bonuses, Motivation
Comparative Study of Performance Appraisal in Hotels
In this study, we conduct an in-depth comparative examination of the performance evaluation systems utilized within the hotel industry. The primary aim is to critically assess and compare the diverse approaches to performance evaluation used in hotels of various sizes and types, from intimate boutique hotels to expansive global chains. We focus on several key elements, including the methods used for performance appraisal, the regularity of evaluations, the criteria used for measuring performance, the processes for gathering employee feedback, and how appraisal results are tied to incentives and career advancement opportunities. Additionally, the study investigates the effects of these appraisal systems on factors such as employee motivation, job satisfaction, and loyalty to the organization, as well as their role in boosting service standards and operational effectiveness. Through this comparative analysis, we aim to provide practical guidance for hotel managers and human resources professionals who are working to refine their performance evaluation systems. The study emphasizes the necessity of aligning these systems with the organization's strategic objectives and cultural ethos, and it stresses the importance of ongoing revisions and enhancements to keep pace with the ever-evolving nature of the hospitality industry.
Comparative Examination, Performance Evaluation Systems, Expansive Global Chains, Appraisal Systems
Analyzing the Impact of Promotional Strategies on Consumer Perception in the Fast-Moving Consumer Goods (FMCG) Sector
This study delves into the effects of promotional strategies on consumer perceptions within the Fast-Moving Consumer Goods (FMCG) sector, an arena marked by fierce competition and minimal barriers to brand switching. The primary aim is to dissect how various promotional tactics ranging from traditional advertising and in-store promotions to cutting-edge digital marketing and public relations efforts shape and sway consumer attitudes and their subsequent buying choices. In an era where consumer preferences shift rapidly and marketing methods continually evolve; the relevance of this research is pronounced. It endeavors to fill a critical void in current academic discourse, offering a detailed and context-specific exploration of how promotional strategies mold consumer perceptions in the FMCG landscape.
Fast-Moving Consumer Goods (FMCG), promotional strategies, various promotional tactics
A Comparative Study HR Practices between Public & Private Bank in Pakistan
The main Purpose of this study is to examine the relationship and nature of relationship between the employee perceived performance and human resource (HR) practices (planning, training compensation, performance appraisal, and selection & Recruitment) in the banking sector of Pakistan. Analysis of 100 banking employees was conducted through personally administrated questionnaire to investigate the impact of HR practices on employees perceived performance. The relationship and nature of relationship is calculated by applying the Box Plot Diagrams. Results validate that, the employee perceived performance and HR practices has the positive and significant relationship. The Box Plot Diagram results indicate that the two HR practices: performance appraisal and compensation practices are considered more important in Private Banks as compared to Government Banks. Moreover, this study provides help for top-management of banking sector to design or revise their HR policies and make practices to attain high employee performance
human resource, employee Challenges, Corporate Sector, Pakistan