In the current global competitive landscape, knowledge management is widely acknowledged as the main strategy by which businesses may gain a competitive edge, enabling them to outperform their rivals and deliver better customer-satisfied operations. Drawing on the KBV (Resource-Based View) and RBV (Knowledge-Based View), the purpose of the research is to evaluate the practices of knowledge management on the performance of workers. The study used to determine the small and medium-sized firms in the research setting. A questionnaire was created and distributed to the target demographic, which consists of 428 personnel of SMEs in India. Two statistical techniques were selected to evaluate the hypotheses: structural equation modeling (SEM) using AMOS 24 and confirmatory factor analysis. Because it allows for both objective analysis of the relationship between components and prediction, a quantitative approach was used. The outcome revealed that knowledge acquisition and knowledge codification positively affect performance. Also, indicates that organisational commitment (OC) affects workers’ performance. These findings underscore how crucial it is for organisations to commit to increasing worker efficiency through efficient knowledge management (KM) and capacity building, offering valuable insights for SMEs aiming to leverage knowledge management to improve overall operational effectiveness and competitiveness.
This study aims to systematize and map the literature on knowledge and technology transfer (KTT) between universities and industry (U-I), identifying facilitating factors, barriers, impacts and good practices, based on an integrative and multilevel approach. To develop this study, a bibliometric-systematic literature review (B-SLR) of 350 articles from the Scopus and Web of Science databases (1978–2025) was carried out, combining co-citation, productivity and thematic analysis techniques at three levels of analysis: micro, meso and macro. The results identified two thematic clusters and cross-cutting factors (organizational, relational and contextual) that influence the effectiveness of KTT. The economic, social and academic impacts, as well as good operational practices, were systematised. This study proposes an integrative and multilevel conceptual framework of U-I KTT, which addresses the fragmentation of the literature, guides institutional practices and defines priorities for future research.
This paper examines the direct and indirect effects of geopolitical risk (GPR) and economic policy uncertainty (EPU) on the relationship between innovation and economic growth in the European Union, a topic that has been underexplored in the literature. To this end, it employs the Prais–Winsten estimator with Panel-Corrected Standard Errors, accounting for conventional growth determinants and structural breaks. The empirical results indicate that innovation, measured by patent applications per capita, does not exert a statistically significant contemporaneous effect on economic growth. This finding is robust across linear, nonlinear, and medium-term specifications. Lagged innovation variables provide only limited evidence of delayed effects, suggesting that changes in patenting activity are not strong predictors of growth dynamics. In contrast, when innovation is proxied by a patent stock measure, changes in the stock are positively and significantly associated with growth, underscoring the importance of knowledge accumulation even in the short run. Regarding uncertainty, EPU has a consistently negative and statistically significant impact on growth, while GPR exhibits weaker and less robust effects across different model specifications. Interaction results provide only weak evidence that EPU dampens the growth effects of innovation, whereas GPR does not appear to significantly moderate the innovation–growth relationship. Similarly, interactions between innovation and crisis-period dummies suggest that macroeconomic shocks do not substantially alter the impact of patent-based innovation on growth.
At the backdrop of encroachments on democratic values, including academic freedom, global environmental challenges, and a growing patent output, this paper investigates the effect of academic freedom on green patenting. We zoom into the green patent filings generated by different participants of the national innovation system: universities, government funded research institutes, individual inventors and companies. By incorporating corruption as a moderator, we explore whether corruption amplifies or dampens the positive impact of academic freedom on green invention. Our results reveal that academic freedom generally increases the number of green patent applications, but the effect is different for different types of applicants. Individuals, universities and research institutions overall seem to benefit from greater academic freedom especially in the models containing the interaction between academic freedom and corruption. Our main conclusion is that academic freedom is conducive to green patenting especially accompanied by low levels of corruption. However, companies respond to a complex set of incentives and engage in diverse innovation strategies. We offer our explanations and shed light on the complexity of factors involved and their interactions.
This study has two main objectives, first, to investigate the unconditional effects of research productivity on economic complexity, and second, to examine the synergistic relationship between financial development and research productivity in bolstering Africa’s economic complexity. The estimation framework is designed to analyze how research productivity (i.e., quantity and quality of scholarly work produced) is moderated by both financial depth and the size of financial intermediaries, thereby positively influencing economic complexity. The analysis employs the dynamic system generalized method of moments (GMM) and the static Fixed Effects estimation with Driscoll–Kraay standard errors (DK), using data from 36 African economies over the period 2010–2023. The baseline estimation approach (i.e., System GMM) is designed to ensure valid model selection, mitigate variable omission bias, and prevent instrument proliferation. The study reveals several key findings: First, financial development consistently moderates the negative impact of research productivity on economic complexity, emphasizing its role as a potent channel for enhancing economic complexity in Africa. Second, factors such as renewable energy consumption and ICT diffusion are critical in boosting the region’s economic complexity. The policy implications of these findings are also discussed in line with the Sustainable Development Goals (SDGs).
This paper presents the first integrated and efficiency-based assessment of the Brain Economy within the European Union by constructing a unified Brain Economy Index (BEI) that measures the efficiency with which countries transform human capital, innovation capability, and digital-cognitive infrastructure into brain-economic performance. Grounded in a synthesis of endogenous growth theory, national innovation systems scholarship, human capital theory, and intangible capital research, the BEI offers an analytical framework that captures not what cognitive endowments countries possess but how efficiently they convert those endowments into observable knowledge-based outcomes. The index is derived through a two-step, output-oriented slack-based measure of data envelopment analysis (SBM-DEA) under variable returns to scale, formally grounded in the Benefit-of-the-Doubt composite indicator approach (Cherchye et al., 2007), and applied to 27 EU member states over the period 2004–2024. In the initial stage, three subindexes—Human Capital and Skills, Innovation and Knowledge Creation, and Digital-Cognitive Infrastructure—are estimated; these are subsequently combined into an overall BEI. The results reveal a persistent North–South and West–East divide within the Union. Finland, Denmark, Sweden, Germany, France, and the Netherlands consistently form the leading group of brain-economy performers, whereas Bulgaria, Romania, Greece, Malta, Croatia, and Cyprus remain at the lower end of the distribution. The temporal evolution of the index indicates substantial long-run improvement and notable catch-up among the Central and Eastern European economies, though innovation capability remains the most asymmetric and structurally constrained dimension. These findings constitute the first EU-wide efficiency appraisal of the Brain Economy and provide guidance for policies aimed at strengthening skills formation, innovation systems, and digital infrastructures across the Union.
This study explores the opportunities created by the COVID-19 pandemic for enhancing Green Supply Chain Management (GSCM) and supply chain resilience (SC resilience). It examines the relationships among pandemic-induced uncertainty-anxiety, GSCM, and SC resilience, while investigating the moderating effect of adopting novel technologies such as Big Data Analytics (BDA). A survey of 446 managers from small and medium-sized enterprises (SMEs) in Egypt was conducted, with hypotheses tested using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, controlling for gender, age, education level, firm size, business sector, and management level. The results indicate that pandemic-induced uncertainty-anxiety significantly promotes the adoption of GSCM. Furthermore, BDA moderates the relationship between uncertainty-anxiety and GSCM; the negative interaction coefficient reveals that BDA attenuates (weakens) this relationship. Specifically, firms with higher BDA capabilities rely less on anxiety-driven motivation to adopt green practices, suggesting that data analytics enables more deliberate, less reactive decision-making during crises. GSCM is found to have a significant positive effect on SC resilience and mediates the relationship between uncertainty-anxiety and SC resilience. Control variables showed no significant effects, suggesting that these relationships hold across the demographic characteristics examined in this sample of Egyptian SMEs. These findings underscore the importance of consistently leveraging GSCM and BDA to provide transparent information, meet stakeholder expectations, and enhance SC resilience. The study offers critical insights for managing SMEs during and beyond the pandemic.
This paper examines the key factors that determine institutional quality in the Indian subcontinent region. The investigation focuses on the four major economies of this region, i.e., India, Nepal, Bangladesh, and Pakistan. Collectively, these countries account for nearly 98 per cent of the region’s total GDP, thereby representing the economic core of the subcontinent. Based on the Worldwide Governance Indicators of the World Bank, the study constructs a PCA-weighted index of institutional quality. The findings reveal that after experiencing a significant deterioration in the institutional quality, the sample countries observed an improvement in the years following 2012. Per capita GDP, Years of schooling, and income distribution are significant determinants of institutional quality in the Indian subcontinent. Based on the findings, we draw policy implications for strengthening the regional institutional ecosystem.
This study examines the influence of social capital on financial development, utilizing panel data from 144 countries spanning the period from 2010 to 2020. Our results show that social capital is positively and significantly associated with financial development. Disaggregated analysis results reveal that social capital enhances both financial institutions and financial markets. However, its influence weakens at higher levels of financial development, suggesting a substitutive role in less developed systems and a complementary role in advanced ones. Further decomposition shows that civic engagement, institutional and interpersonal trust, and social networks positively influence financial development, whereas strong family ties have a negative impact. We also find that the strength of the relationship depends on broader economic and regulatory conditions. These findings underscore the systemic importance of social capital for financial development and its conditional effectiveness across diverse institutional contexts.
Limited digital access remains a persistent constraint on educational opportunity in sub-Saharan Africa, yet the mechanisms linking connectivity to learning conditions are often underspecified. This study examines how digital readiness is patterned by social position and enabling infrastructure, and how these patterns relate to national education indicators relevant to participation in the knowledge economy. We integrate Afrobarometer Round 8 microdata from 34 sub-Saharan African countries (N = 47,430) with country-level ICT and education indicators from the ITU, UIS, and related sources. At the national level, internet penetration and mobile data affordability are associated with youth literacy, completion, and enrollment indicators, capturing cross-country patterning rather than causal effects. At the household and individual level, we operationalize digital readiness using internet use frequency and internet-capable device availability and estimate multinomial logit models with country-clustered standard errors. Results show strong readiness gradients by rural residence, gender, education, and lived poverty, with electricity access emerging as a key enabling condition associated with substantially higher odds of being well prepared. A sequential modeling exercise indicates attenuation in the rural coefficient after accounting for electricity, a pattern consistent with electricity functioning as a partial conversion bottleneck rather than a demonstrated causal mediator. The findings support a digital capital conversion perspective in which devices and connectivity are insufficient without reliable electricity and affordable conditions of use. By specifying where conversion appears constrained, the study contributes to research on digital inequality and education by clarifying how unequal digital environments can limit the accumulation and translation of human capital into digitally supported learning and, ultimately, knowledge-intensive participation.
Knowledge exchange (KE) is essential for driving sustainable performance among Small and Medium Enterprises (SMEs), particularly in developing economies. In Nigeria’s informal economy, SMEs rely heavily on external sources of knowledge to navigate complex market challenges. However, the existing literature provides limited insights into how KE contributes to the sustainability of SMEs in Nigeria’s informal economy. To address this gap, our research investigated the direct and indirect relationships of KE dimensions, in achieving the sustainable performance of SMEs in Nigeria’s informal economy. Our data from 387 SMEs owner/owner-managers was analyzed using PLS-SEM. We find that KE from social networks (KESN) directly relates to all three dimensions of sustainable performance; environmental, economic, and social dimensions. On the other hand, KE from Research and Development (KERD) relates to the economic and social dimensions of the firms’ sustainable performance but not to environmental performance. Meanwhile, KERD mediates the relationship between KESN and the economic and social dimensions of sustainable performance but not the environmental dimension. This study highlights the importance of both internal and external knowledge sources as critical capabilities that SMEs must develop to achieve sustainable performance.
From the perspective of endogenous growth, foreign direct investment (FDI) constitutes an important source of economic development, particularly through its interaction with domestic human capital. This study examines the role of FDI in economic growth dynamics, as well as the effect of its interaction with human capital, using panel data covering 18 Latin American countries over the period 1995–2023. The results of dynamic panel regressions, estimated using the generalized method of moments (GMM), initially show that neither FDI nor human capital has a significant effect on economic growth. However, after introducing an interaction variable between FDI and human capital, the results reveal: (i) a significant positive effect of FDI on growth, and (ii) a significant negative effect of the interaction. This last result suggests that Latin American countries do not have a sufficiently skilled workforce to fully absorb and disseminate the positive impacts of FDI. To identify the level of human capital required for FDI to contribute effectively to growth, the study uses a dynamic threshold approach. It establishes a critical human capital threshold of 43.74
New-type infrastructure, characterized by 5G technology, artificial intelligence, and the Internet of Things, is undergoing rapid growth; however, its environmental performance has not been adequately assessed. The motivation of this article is to study the carbon reduction effect of new-type infrastructure, to provide empirical support for the government to formulate reasonable infrastructure policies. Different from existing literature, this article uses a non-parametric additive model to investigate the nonlinear impact of new-type infrastructure on industrial carbon intensity. The findings show that the impact of new-type infrastructure on industrial carbon intensity follows an N-shaped pattern. This indicates that apart from the prominent carbon reduction effect in the mid-term stage, the carbon reduction effect of new-type infrastructure becomes weaker in the early and later stages. Heterogeneity analysis indicates that, from the perspective of resource abundance, new-type infrastructure also has a U-shaped impact on carbon intensity in resource-rich provinces. In contrast, it exhibits an N-shaped effect in non-resource-rich provinces. From a technical perspective, the new-type infrastructure exerts a positive U-shaped influence on industrial carbon intensity in low-tech provinces. In contrast, its nonlinear effects on high-tech provinces are not statistically significant. From a regional perspective, the carbon reduction effect of new-type infrastructure in the eastern region displays an inverted U-shaped pattern; conversely, this effect has exhibited an opposite trend in the western region (a U-pattern). The impact in the central region is also positively U-shaped, but it is not statistically significant. Mechanism analysis shows that renewable energy, industrial upgrading, and green technology innovation all exert nonlinear effects on industrial carbon intensity.
Collaborative innovation initiatives increasingly rely on multi-actor networks to mobilise and coordinate distributed knowledge. Yet, empirical evidence on how inter-organisational network structures shape innovation outcomes remains limited, particularly in policy-driven contexts. This study examines how partner network configurations influence perceived project outcomes within the European Innovation Partnership for Agricultural Productivity and Sustainability (EIP-AGRI) in Italy. Adopting a relational perspective of the knowledge economy, partner networks are conceptualised as knowledge coordination structures that shape learning processes and innovation effectiveness. Social network analysis is applied to a nationwide dataset of partners from EIP-AGRI Operational Groups to examine network embeddedness, brokerage positions, and the emergence of latent interregional networks. Their association with perceived project outcomes is assessed using a beta regression model with random effects. The findings indicate that embeddedness in the core of the partner network, captured by eigenvector centrality, is positively associated with this outcome measure, whereas brokerage positions characterised by high betweenness centrality are negatively associated with it, pointing to coordination costs in intermediary roles. Membership in latent interregional networks is also negatively associated with perceived project outcomes, suggesting additional coordination challenges in geographically dispersed and weakly institutionalised collaboration structures. Overall, the study shows that the position actors occupy in collaborative networks matters not only for accessing knowledge, but also for managing the coordination burdens that accompany it. These findings have practical implications for the design and governance of EIP-AGRI and similar multi-actor innovation policies.
Thematic investing has emerged as a strategic investment approach that transcends traditional asset allocation based on firms, sectors, or countries by emphasizing long-term megatrends aligned with investors’ sustainability, innovation, and risk preferences. Despite its growing importance, the intellectual structure and strategic evolution of thematic investing remain fragmented. This study combines bibliometric analysis and qualitative synthesis to examine the intellectual structure, thematic development, and geographic evolution of thematic investing research. The analysis covers 518 Scopus-indexed articles and reviews published from 1996 to 2025. This study identifies three dominant thematic clusters: (i) sustainable and ESG-oriented investments, (ii) disruptive and digital technological innovation, and (iii) portfolio construction and risk optimization. The first cluster connects thematic investing with sustainable development through climate action, resource conservation, the circular economy, and responsible investment. The second captures the growing prominence of artificial intelligence, big data analytics, blockchain, machine learning, the Internet of Things, and robotics as long-term investment themes. The third centers on diversification, portfolio optimization, and risk management, while also showing that narrowly defined themes can create concentration risk. Over time, the thematic trajectory demonstrates broader geographic participation beyond advanced economies and increasing research attention to climate change, climate risk, green bonds, carbon emissions, FinTech, and digital finance. Overall, this study contributes by showing how thematic investing research has brought sustainability transitions, technological change, and portfolio design into a common field of inquiry focused on long-term economic and societal transformation.
The intersection of knowledge economy theory and international logistics has emerged as a critical domain for understanding the complex dynamics of global trade corridors. However, existing analytical frameworks uniformly treat exogenous factors as external shocks imposed upon transport systems, rather than as knowledge objects that institutional agents within those systems actively process, interpret, and transform. The study’s central contribution is an extension of organizational knowledge creation theory: it demonstrates that SECI (Socialization, Externalization, Combination, Internalization) model operates beyond organizational boundaries in inter-organizational corridor systems, and that the knowledge objects traversing SECI cycles include geopolitical artifacts alongside the technical knowledge conventionally examined in knowledge management research. On this basis, the paper develops the Knowledge-Exogenous Factor (KEF) framework, a novel theoretical synthesis that integrates the SECI model with agent-based modeling approaches for international transport corridor analysis. The KEF framework reconceptualizes exogenous factors as knowledge objects processed through multi-level SECI cycles, structured across a three-tier architecture spanning macro, meso, and micro analytical levels. Classification of the multi-level agent taxonomy yields a structural result: 11 of the 23 agent types (47
The current scoping review synthesises conceptual and empirical evidence published between 2010 and 2025 on the impact of Social Capital (SC) on Small and Medium-sized Enterprises (SMEs) in Ghana and Sub-Saharan Africa (SSA). By distinguishing between network structure and SC, this study examines how relational resources complement or substitute for weak institutions and how they facilitate or constrain SME performance. The Arksey and O’Malley framework and PRISMA-ScR checklist for reporting systematic reviews were used for this study. A systematic search across five databases (Scopus, JSTOR, SAGE Journals, Web of Science, and EBSCOhost) and grey literature identified 24 relevant studies that met the inclusion criteria for this review. A thematic synthesis was used to synthesise the findings in accordance with Social Capital Theory, Resource-Based Theory (RBV), and Network Theory. The evidence base for this review is predominantly centred in Ghana (15/24 studies), with additional studies in South Africa, Uganda, Kenya, Nigeria, Burkina Faso, and SSA-wide studies. Overall, SC is seen to enhance access to finance, innovation/capability building, market access, and performance in SSA settings. However, contingency factors indicate that bridging and linking ties are more consistently associated with growth and competitiveness than bonding ties or politically mediated ties, which may lead to exclusionary effects and dampen innovation. This review offers a framework for understanding how SC may facilitate or constrain SME performance in settings characterised by weak institutions via key mechanisms (finance, innovation/capability building, market access) and boundary conditions (leadership orientations and institutional interaction). The policy implications highlight the need for inclusive business associations and institutions that enhance access to networks and maintain the flexibility inherent in informal relational mechanisms.
Despite the recorded increase in output growth in most sub-Saharan African countries, growth has largely remained non inclusive. This study examined the effect of health investment and education on inclusive growth in SSA. Data for the study considered 20 SSA countries from 2000 to 2021. Based on a cross-section dependence test, the study employed the feasible generalized least square estimation method and the estimation was conducted for the aggregate sample and a sub samples of 17 countries. Two measures of inclusive growth used in the study are the inclusive growth index and GDP per person employed. The results showed that the effect of health investment and education on the inclusive growth index was positive. This was obtained in both the aggregate sample and the sub sample estimates except for life expectancy at birth, which had a negative effect in the sub sample when either corruption or rule of law was controlled for. Health investment and education also maintained a positive effect on GDP per person employed in both the aggregate and sub sample estimates except for public expenditure on health, which had a negative effect in the sub sample when either corruption or rule of law was controlled for. The study concluded that substantial improvement in health investments and education is critical for achieving inclusive growth in SSA. However, such improvements should be supported with strengthening the fight towards reducing corruption and upholding the rule of law.
As Industry 5.0 advances, artificial intelligence (AI) is increasingly positioned as a driver of sustainability in knowledge-based economies; however, empirical outcomes remain uneven and frequently symbolic. Addressing this paradox, this paper examines AI-driven sustainability through the lens of knowledge creation, governance, and application, rather than technological capability alone. Using a Critical Interpretive Synthesis, the paper systematically analyses interdisciplinary literature on AI, Industry 5.0, sustainability, and leadership to move beyond descriptive aggregation toward theory development. The findings reconceptualise AI as a knowledge infrastructure whose sustainability value depends on how AI-generated knowledge is governed, interpreted, and applied across systems. The paper further advances theory by reframing responsible leadership as a knowledge-governance mechanism, explaining how leadership shapes the prioritisation and diffusion of AI-enabled knowledge across micro (individual), meso (organisational/industry), and macro (institutional) levels. Building on these insights, the paper develops an integrative framework that explains why AI-enabled sustainability initiatives often result in performative environmental, social, and governance (ESG) compliance rather than substantive environmental and social impact. By linking responsible leadership with AI knowledge governance, the paper contributes to the knowledge-economy literature by explaining variability in sustainability outcomes beyond technological adoption. The paper concludes by outlining implications for organisational governance and identifying directions for future empirical research to test and extend the proposed framework.
This article examines the performance of the Partnership for Research and Innovation in the Mediterranean Area (PRIMA) after seven years of implementation (2018–2024), situating it as a case study of regional science diplomacy and knowledge governance. Drawing on data from funded projects, social network analysis, and project-level Key Performance Indicators (KPIs), the study explores how PRIMA has shaped collaborative structures, advanced sustainability objectives, and reconfigured power relations across the Euro-Mediterranean region. The findings show that PRIMA has created dense North–South research networks linking Southern Europe with Mediterranean Partner Countries (MPCs), while the 2023 eligibility reform catalysed new South–South collaborations, particularly between Tunisia, Morocco, Egypt, and Jordan. Contributions to the United Nations Sustainable Development Goals (SDGs) were substantial, with projects delivering innovations in drought-tolerant crops (SDG 2), wastewater reuse (SDG 6), circular agri-food value chains (SDG 12), and climate adaptation (SDG 13). Beyond technological outputs, PRIMA strengthened the social and institutional fabric of cooperation: more than 16,000 individuals benefitted from training and outreach, 41 open datasets were produced, and Southern Mediterranean institutions assumed increasing roles as coordinators and innovation developers. These outcomes demonstrate how transnational programmes can function simultaneously as drivers of sustainability transitions and as diplomatic mechanisms for regional integration. The article argues that PRIMA represents a distinctive model of Euro-Mediterranean science diplomacy, in which co-ownership, inclusivity, and alignment with sustainability objectives support both regional knowledge integration and sustainable agri-food innovation. The findings also suggest that a future PRIMA2 phase should build on these achievements by prioritising the upscaling, replication, and wider adoption of project results, thereby strengthening the transition from research outputs to long-term territorial impact.
