Big data analytics capability has emerged as a key strategic asset, enabling organizations to generate data-driven insights, improve decision-making, and enhance supply chain outcomes. For clarity, 'sustainable competitive advantage' refers to an organization's ability to maintain superior performance over time by leveraging unique resources and capabilities. 'Digital capabilities' encompass organizational skills and technologies, such as big data analytics, that support strategic objectives. Despite growing scholarly attention, the mechanisms through which these capabilities contribute to sustainable supply chain performance remain poorly understood. This study adopts the resource-based view to develop a framework that clarifies how big data analytics capability supports sustainable supply chain performance through specific organizational capabilities, particularly supply chain resilience and agility. The framework identifies these as critical mechanisms through which big data analytics capability enhances sustainable competitive advantage, ultimately leading to superior supply chain performance. By integrating big data analytics capability, resilience, agility, sustainable competitive advantage, and sustainable supply chain performance into a unified model, this study offers a comprehensive explanation of how digital capabilities generate sustainable supply chain outcomes. The proposed framework addresses gaps in the literature and establishes a foundation for future research.
Financial sustainability remains a persistent challenge for non-governmental organizations (NGOs), which frequently depend on government grants and donor funding. In the absence of stable and diversified income streams, NGOs struggle with strategic planning, staff retention, and consistent service delivery. This systematic review synthesizes current scholarship on financial sustainability in the non-profit sector, focusing on key obstacles and the strategies used to address them. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and NVivo 12, peer-reviewed studies were systematically identified, screened, and analyzed. The findings indicate that the most significant barriers are related to financial and resource constraints, organizational capability gaps, and external contextual challenges. Key strategies to overcome these barriers include revenue diversification, partnership and resource mobilization, financial resource management, and technology use. In addition to revenue diversification, the thematic analysis highlights the importance of partnerships and resource mobilization, financial resource management, and integrating artificial intelligence as complementary strategies. The analysis also identifies four organizational enablers of financial sustainability: organizational capacity, leadership and governance, stakeholder trust, and volunteer capacity. These results emphasize the need for context-specific, multidimensional approaches to strengthen NGOs' financial resilience and ensure the long-term sustainability of their programs. By consolidating insights from diverse studies, this review provides a comprehensive overview of financial sustainability research in the nonprofit sector, identifies critical gaps for further investigation, and offers practical guidance for practitioners and policymakers.
This study aims to conduct a systematic literature review (SLR) of the key factors influencing the investment intention and decision of individual investors in emerging economies. Using NVivo 12 and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically examines 46 peer-reviewed articles published in Scopus and Web of Science (WOS) databases covering the period 2016-2025. To present the findings, the theory–context–characteristics–methods (TCCM) framework is used. The findings reveal trends in publications, widely used theories, dominant research methodologies, and influential journals and countries. The findings identify five main themes of investment intention and decision, including psychological and behavioral factors, financial competence, social influence, individual factors, and ESG-related factors. This systematic review provides a roadmap for further scholarly endeavors in investment intention-decision research by identifying gaps and key research themes. The findings offer insights for theoretical advancement and practical implementation, enabling business entities, financial institutions, and policymakers to better interpret investor behavior.
This study investigates the influence of public employee trust on organizational commitment in Jinan, China, with a particular focus on the mediating role of burnout. A total of 204 public employees participated in the research, selected through purposive sampling. Using a quantitative research design, the study empirically confirmed that public employee trust has a significant positive impact on organizational commitment. Moreover, burnout was found to partially mediate this relationship, indicating that while higher trust directly enhances organizational commitment, it also reduces burnout, which in turn strengthens employees’ commitment. Despite these contributions, the study is limited by its single-city sample, cross-sectional design, and reliance on self-reported data. Future research should employ larger and more diverse samples, longitudinal designs, and additional mediators or moderators to provide a more comprehensive understanding of how trust shapes commitment in the public sector. The findings have important implications, suggesting that building trustful work environments and reducing burnout can enhance employee commitment, while policymakers and administrators should adopt strategies that foster trust and promote employee well-being to strengthen organizational performance.
Against the backdrop of China’s carbon peaking and carbon neutrality goals, population aging and green technology innovation have become key factors influencing carbon emissions. This study investigates the impact of population aging on carbon emissions using a fixed-effects model with panel data from 30 Chinese provinces for 2015–2024. To address endogeneity, we employ 2SLS with lagged population aging as the instrumental variable. Results show that population aging significantly reduces carbon emissions. Green technology innovation strengthens this reduction effect. Heterogeneity analysis reveals that the mitigation effect of aging is significant only in developed and low-aging regions, while in deeply aging regions, carbon reduction effects may be offset by rising energy demand. These findings provide empirical implications for formulating differentiated low-carbon policies under population aging and green transition.
