Diversity-focused Human Resource Management (DHRM) practices and team knowledge-sharing behavior (KSB) are crucial for enhancing performance and fostering inclusive workplaces. They develop positive employee perceptions and help shape an organization's culture. Therefore, this study applies a relational perspective to investigate the relationship between DHRM practices and team KSB through employee involvement climate and shared leadership as serial mediators. It uses a multi-level and multi-wave dataset of 248 employees and their team leaders (n = 55) nested within 21 organizations. The authors followed a 2-2-1-1 research framework in which organizational DHRM practices (a level 2 variable) influenced team KSB (a level 1 outcome variable). This relationship was mediated by employee involvement climate within the organization (a level 2 variable) and shared leadership within teams (a level 1 variable). The findings support our hypotheses that DHRM practices enhance teams' KSB via serial mediation of involvement climate and shared leadership. This study enriches our understanding of team-level knowledge sharing by empirically testing a multi-level model and emphasizes that prioritizing DHRM practices foster positive aspects of teamwork. Organizations should value the unique characteristics of diverse workforce and encourage employee involvement in decision-making. This involvement nurtures shared leadership abilities, which in turn enhance knowledge-sharing tendencies within teams.
PurposeThis study examines the interrelationships among AI-enabled digital ecosystems, digital maturity, organizational resilience, sustainable value co-creation, and sustainable performance within Small and Medium-Sized Enterprises (SMEs) in Pakistan's manufacturing sector. By doing so, it addresses critical gaps in the literature on how these constructs collectively shape resilience and long-term sustainability in information-intensive business environments.Design/methodology/approachA quantitative research design was employed, with data collected from 476 SMEs using structured questionnaires. Structural Equation Modeling using SmartPLS was applied to test the hypothesized relationships and assess the mediating and moderating roles of organizational resilience, sustainable value co-creation, and digital maturity.FindingsThe results show that AI-driven digital ecosystems have a significant positive effect on both organizational resilience and sustainable value co-creation, which in turn drives sustainable performance. Digital maturity acts as both an enabler and a moderator, amplifying the benefits of AI ecosystems. Among the predictors, sustainable value co-creation emerges as the strongest driver of sustainable performance, while the reciprocal relationship between resilience and co-creation highlights their self-reinforcing nature.Practical implicationsThe study extends the Resource-Based View, Dynamic Capability Theory, and Stakeholder Theory by integrating AI-driven ecosystems and sustainability into a unified framework. Digital maturity is reconceptualized as a multi-dimensional construct encompassing technological, behavioral, and strategic readiness, offering a more nuanced understanding of its role in enabling resilience and Value Co-Creation. Practically, the findings provide guidance for SMEs to pursue phased digital transformation, build collaborative stakeholder networks, and embed sustainability-oriented practices. Policy recommendations include targeted programs to enhance SMEs' digital readiness and resilience.Originality/valueThis research advances knowledge by redefining SME performance to include economic, social, and environmental dimensions. It offers a holistic pathway for how AI ecosystems and digital maturity can foster resilience and sustainability, enabling SMEs in resource-constrained contexts to remain competitive while contributing to global sustainability goals such as the SDGs and ESG metrics.
PurposeThis article discusses how a sustainable Career Ecosystem in Technical and Vocational Education and Training (TVET) has changed in promoting the graduates' transition in the workforce beyond academia. The study adopts a narrative literature review approach, which synthesizes the available information to gain insight into how sustainable career ecosystems in TVET contribute to employability, career preparation and career sustainability. The article will give a comprehensive insight into this process of transition, besides educating teachers, policymakers and industry stakeholders on how to better match education with the requirements of the labour market.Design/methodology/approachThe literature review was a narrative, with the analysis of international and national literature, peer-reviewed articles, policy reports, labour market analysis and institutional frameworks concerning career ecosystems of TVET and graduate career transitions. This methodology facilitated a thorough qualitative synthesis of trends, gaps and contradictions in the literature, which allowed a contextual investigation of complicated issues like the role of Higher Education Institutions, employer demands, socio-economic factors in career paths and gender-based issues in career progression.FindingsThe results show that TVET offers sustainable Career Ecosystems that are pivotal in determining the employability of graduates and their long-term career path. Using systematic transition approaches like internships, mentorship and industry partnerships is also a major contributor to increasing the preparedness of graduates to the labour market. More so, sustained career success is also caused by self-regulatory actions encompassing constant skill growth, networking and adaptability in one's career. Nevertheless, there is unequal access to career, and women are offered fewer opportunities and resources, which is becoming an ongoing challenge.Practical implicationsThe article provides practical recommendations to Higher Education Institutions, employers and policymakers through the identification of the relevance of curriculum-industry congruency, work-integrated learning, inclusive labour policies and specific mentorship programs. To ensure an equal and robust career ecosystem, it is necessary to address the structural inequalities, such as gender differences and the digital divide.Originality/valueThe research study adds novel value to the existing body of knowledge by theoretically incorporating sustainable Career Ecosystems in TVET with the body of literature on graduate career transition on a global scale. It gives a general template to use in future research, policy, and practice in ensuring sustainable graduate careers by making crucial gaps and synthesizing different views.
This study investigates the feedback loop between Economic Policy Uncertainty (EPU) and academic knowledge production, assessing whether scholarly inquiry reflects or actively mitigates policy volatility. Using 35,000 Scopus-indexed publications (1985-2023) through bibliometric analysis and time-frequency methods, we find that elevated EPU stimulates research activity, while high-impact scholarly outputs contribute to stabilizing volatile environments by reducing informational frictions. This relationship intensifies during systemic shocks, including the 2008 financial crisis and COVID-19. Findings show a significant cross-national divergence: advanced economies demonstrate proactive, sustained research responses, whereas emerging economies appear reactive and fragmented. Theoretically, this research extends endogenous growth and research-utilization frameworks to uncertainty-driven knowledge creation. Practically, it demonstrates the necessity of resilient funding, institutional capacity, and international collaboration. We conclude that academic research into policy uncertainty functions as an endogenous stabilizing mechanism within volatile global systems, rather than a secondary effect of economic turbulence.
Rainfall accuracy is a critical aspect of climate modeling, agricultural design, disaster management, and urban water resource allocation. However, successful forecasting is a problematic issue due to the non-linear, non-stationary, and complex nature of rainfall time series. Classical statistical algorithms do not identify long-term correlations and time variance, whereas the individual machine learning (ML) and deep learning (DL) algorithms have certain weaknesses, including noise, vanishing gradient, and hyperparameter optimization. To overcome such weaknesses, this study introduces a hybrid architecture that integrates both Variational Mode Decomposition (VMD) and Particle Swarm Optimization (PSO) together with both ML and DL models in a systematic way. VMD breaks down raw rainfall signals into intrinsic mode functions (IMFs), improving the quality of temporal features by isolating short-term variations, seasonal cycles, and long-term trends and reducing noise. PSO optimizes the hyperparameters, including thresholds, learning rate, dropout, sequence window, and balances underfitting and overfitting. The model is tested using rainfall data of 34 cities of five Pakistani provinces that are varied in terms of climatic regimes, including arid deserts and highlands with monsoon. In experiments, a 3-fold TimeSeriesSplit with 3 random seeds (41, 42, 43) was used, resulting in 95
