This study investigates the impact of innovation networks on knowledge diffusion, aligning with the Knowledge Spillover Theory of Entrepreneurship (KSTE). It explores how these networks promote sustainability-oriented innovation and enhance corporate performance across industries, focusing on the Ghanaian context. A theoretical framework was developed using a two-dimensional fixed effects model and instrumental variable estimations. The analysis utilized longitudinal data from the World Intellectual Property Organization (WIPO) via the PENTSCOPE database from 2000 to 2023. The study reveals that robust innovation networks significantly enhance knowledge diffusion. Absorptive capacity plays a crucial mediating role, enabling firms to assimilate and apply external knowledge effectively. Additionally, organizational culture is a critical moderator, with adaptive and collaborative cultures fostering more efficient knowledge integration. The results highlight the pivotal role of innovation networks in transforming technological advancements into measurable performance outcomes, driving sustainable innovation and competitive advantage. This research extends the KSTE framework by integrating the network theory to examine how firms exploit external knowledge. It provides empirical evidence from the Ghanaian context, offering novel insights into how innovation networks can foster knowledge diffusion and sustainable development. This study’s findings contribute to theoretical discourse and practical applications, emphasizing the importance of strategic engagement in innovation networks and adaptive organizational cultures for long-term success. This research provides insights applicable to other emerging economies, highlighting how robust innovation networks can drive knowledge diffusion and sustainable development beyond the Ghanaian context.

This study examines how artificial intelligence (AI)-enhanced dynamic supply chains can accelerate renewable energy deployment in Africa's frontier markets, addressing critical gaps in both adoption strategies and policy frameworks for sustainable energy transitions. The research employs a mixed-methods approach combining dynamic panel generalized method of moments estimation (20 countries, 2015–2024), machine learning analysis (XGBoost with SHAP values on 5214 firm-quarter observations), and policy simulation modeling. Robustness is ensured through seven validation procedures including alternative measurement approaches, subsample analyses, and placebo tests. The theoretical framework integrates Dynamic Capabilities Theory with the Technology-Organization-Environment model. Results demonstrate a 42.8% increase in renewable capacity per unit AI adoption, with optimal outcomes at 70% AI penetration and 90% digital infrastructure coverage. Supply chain disruptions reduce by 46% under coordinated implementation. The study identifies mobile broadband penetration and regulatory quality as critical enablers, while revealing asymmetric effects where positive AI shocks have 1.9× greater impact than negative ones. This research makes three novel contributions: (1) Quantification of non-linear thresholds for AI adoption in frontier energy markets, (2) empirical validation of the AI-governance-digital infrastructure nexus through advanced machine learning techniques, and (3) development of a policy simulation framework that accounts for spatial and temporal heterogeneities specific to African renewable supply chains. The study bridges theoretical rigor with practical implementation insights for sustainable energy transitions.

This study investigates how firms in emerging economies integrate artificial intelligence (AI) with environmental, social, and governance (ESG) practices to enhance biodiversity conservation and circular economy outcomes. It examines the mediating role of corporate social responsibility (CSR) governance and the moderating effect of institutional strength in shaping this relationship. Using longitudinal data from 4320 firm‐year observations across India, Brazil, and Ghana (2018–2024), a multilevel panel structural equation model (SEM) was employed with extensive robustness and endogeneity diagnostics. Findings show that AI adoption is positively associated with ESG integration and environmental performance, mediated by CSR governance. Institutional strength strengthens the ESG–performance link, with the strongest effects in high‐governance contexts. Cross‐country analyses reveal the AI–ESG–performance pathway is most pronounced in India, followed by Brazil and Ghana. The study advances an integrated AI–ESG–CSR framework, illustrating how digital intelligence translates into measurable ecological sustainability in emerging economies. It also highlights the need for ethical AI governance and social equity considerations.

Background: In today’s volatile and highly competitive business environment, the importance of knowledge capital and innovation management for multinational manufacturing firms has been widely recognised by both academia and industry. Aim: This article aims to explore the impact of transboundary investment and assimilative capacity on the association between knowledge capital and innovation management in this specific context. Setting: A total of 217 managers and employees were selected from 19 multinational manufacturing firms listed on the Ghana stock exchange to participate in the survey. Method: Using a theoretical-based association existing research model, data were collected through an online electronic questionnaire survey. The collected data were then analysed using path analysis, utilising SPSS 22.0 and AMOS 24.0 software to test the formulated hypotheses. Results: The study found that an organisation-wide unified system, technological, organisational resource and knowledge capital-enabling structures strongly influence multinational manufacturing firms. Besides, knowledge capital and innovation management have a positive mediating role and a significant impact on multinational manufacturing firms. However, there was no significant impact between knowledge of empirical capital and innovation management. Conclusion: The findings of this study highlight the importance of knowledge capital in enhancing innovation management and emphasise the role of transboundary investment and assimilative capacity in mediating this relationship. Contribution: This research contributes to the theoretical understanding of how knowledge capital and innovation management are interconnected, as well as the relationship between transboundary investment and assimilative capacity, and their respective roles in mediating this relationship.

Purpose: This study explores how firms in emerging economies engage in digital innovation, leverage internal and external knowledge and achieve their full potential through digital initiatives. It highlights the pivotal role of knowledge management in facilitating technology-driven transformation, specifically within Ghana’s evolving economic landscape.Design/methodology/approach: Using a firm knowledge-based view framework, the study employs a qualitative research approach. Semi-structured, in-depth interviews were conducted with 150 managers across eight industries in Ghana. This method captures detailed insights into how firms utilize digital innovation and knowledge management to gain a competitive edge.Findings: The research reveals that engagement in digital innovation enhances productivity and communication by effectively utilizing internal and external knowledge. Proactive involvement in innovation is strengthened through mentorship, firm support and external consultation, which bolster technological and knowledge capabilities. The study also underscores the importance of managing the human element – termed “human-aided utilization” – as critical for the execution of digital innovation, emphasizing the need for a balanced integration of technology and human capital.Originality/value: This study offers a comprehensive framework for understanding the synergy between digital innovation and knowledge management, particularly in emerging economies. It provides practical insights for managers and decision-makers, emphasizing mentorship, collaboration and effective knowledge utilization as pathways to enhancing digital capacity. The findings are relevant to academics and practitioners seeking to understand knowledge-driven innovation mechanisms and to firms aiming to strengthen their competitiveness in digitally evolving markets.