Call for papers

In this Special Issue, we consider sustainable innovation ecosystems as complex adaptive systems, in which many autonomous and heterogeneous actors leverage their resources and capabilities and create new ones, by interacting with each other at multiple levels, to allow innovative solutions to new problems to emerge, in a very short time.

Background and Motivation

Artificial Intelligence (AI) stands at the forefront of disruptive technologies, driving transformative changes across the business world (Almansour, 2023). Sachs (2023) highlights a significant surge in market interest in AI, noting that investments could reach $100 billion in the United States and $200 billion globally by the end of 2025. As firms increasingly invest in AI-based technologies, recent studies further confirm AI’s positive influence on innovation management. Wael AL-Khatib (2023) and Khan et al. (2024) affirm that AI based tools has a positive influence on both exploitable and exploratory innovation. Indeed, the capacity of AI to quickly create new content is completely in line with the concept of exploratory innovation, which focuses on experimenting with new ideas, technologies, and markets to create disruptive advancements. On the other side, its ability to analyze data patterns and generate tailored solutions can support exploitable innovation, which emphasizes refining and optimizing existing processes, products, or services to maximize efficiency and profitability (Jansen et al., 2006). Furthermore, Xu et al. (2024) identify the quality of AI-generated content as a key factor in stimulating enterprise innovation behavior.

While AI adoption is undoubtedly accelerating innovation, sustained organizational success in this domain also depends on humans’ interaction dynamics and knowledge-related processes. Human interactions are essential for enhancing innovation, as effective interpersonal relationships positively influence the innovation process (Lee and Wu, 2010). Collaboration creates an environment where new ideas can emerge and be refined (Walsh et al., 2016), while teamwork and cooperation serve as key drivers of innovation (West and Hirst, 2005). Additionally, effective conflict management has been shown to positively impact innovation performance (Chen et al., 2012). Furthermore, trust plays a crucial role in encouraging innovative behaviors among employees and strengthening organization’s overall capacity for innovation (Lee, 2008).

Beyond human interactions, knowledge management processes also serve as a cornerstone of innovation (Costa and Monteiro, 2016; Idrees et al., 2023; Mubarak et al., 2025). Acquiring, creating, sharing, and applying knowledge enable organizations to engage in open innovation which leads to improved innovation performance (Laursen and Salter, 2006). Organizations that implement structured knowledge-sharing practices can accelerate the innovation process (Wang and Wang, 2012). Additionally, according to Soomro et al. (2024) team knowledge-sharing encourages team members to look for innovative solutions.

The interconnection between human interactions and knowledge management is also fundamental to driving innovation. In fact, human interactions serve as the foundation for effective knowledge exchange (He et al., 2009) and successful knowledge management (Jiarui et al., 2022), both of which are critical for enhancing innovation (Corvello et al., 2023).

Within this research framework, it is necessary to recognize that AI adoption in the workplace is also fundamentally transforming these two interconnected factors. On the one hand, AI exerts a significant influence on critical aspects of human interactions (Zimmerman et al., 2023; Fahad et al., 2024). On the other hand, AI is reshaping knowledge-related processes (Al Mansoori et al., 2021; Taherdoost and Madanchian, 2023; Fahad et al., 2024). A thorough understanding of how AI influences these factors is essential for organizations aiming to leverage AI’s capabilities while promoting innovation and continuous learning.

Despite the acknowledged importance of AI adoption, human interactions, and knowledge management in advancing innovation, a significant gap remains in the academic literature regarding the interconnections among these factors. While AI is widely acknowledged as a powerful enabler of innovation, its broader implications for human interactions and knowledge-related processes within organizations are still insufficiently explored.

Therefore, this special issue seeks to examine the interplay between AI adoption, human interactions, and knowledge-related processes to advance innovation within organizational contexts. The special issue will benefit scholars, practitioners, and policymakers. Academics in innovation management, AI, human interaction, and knowledge management will gain insights into the complex relationships between AI, human interactions and knowledge processes. Managers and executives will find practical guidance on leveraging AI to drive innovation and develop strategies for sustainable growth. Additionally, policymakers and organizational leaders will understand how AI can be integrated to optimize innovation processes and address its broader impact on the workforce and organizational culture.

Topics and Research Questions

The special issue aims at examining the impact of AI adoption on both human interactions and knowledge management processes for advancing innovation. To achieve this, the issue will focus on two primary objectives. First, it aims to explore the impact of AI on human relationships within organizations, specifically assessing its effects on key interpersonal dynamics such as teamwork, collaboration, trust, cooperation, conflict resolution, and coordination. As these social and relational factors are central for enhancing innovation, it is essential to investigate whether AI acts as an enabler or a disruptor of these processes. Second, the special issue will examine AI’s role in shaping knowledge-related processes within organizations, with particular emphasis on how AI affects knowledge acquisition, creation, transfer, sharing, retention, application, and evolution. The goal is to explore how AI can enhance or hinder these processes in a way that drives innovation. Moreover, it is essential to recognize that human interactions and knowledge-related processes are not isolated factors but are deeply interconnected, both playing a vital role in driving innovation. Therefore, examining AI’s impact on these factors may also lead to a deeper understanding of the nature of their interconnections.

We invite researchers to contribute to the following subtopics, including but not limited to:

AI and human interactions in innovation-driven workplaces

• The impact of AI on teamwork, collaboration, and coordination in the workplace and its effects on firms’ innovation capabilities
• AI’s influence on trust-building and social capital as enablers of an innovative workplace culture
• AI-driven conflict resolution and its role in enhancing creative problem-solving
• AI as a catalyst or barrier to inclusive decision-making in innovation-driven teams
• Ethical considerations in AI-human interactions and their implications for sustainable innovation

AI and knowledge management for continuous innovation

• AI’s role in accelerating knowledge acquisition, creation, and retention to support continuous innovation
• AI-driven knowledge-sharing practices and their influence on collaborative innovation
• The impact of AI on tacit knowledge exchange and its implications for creative ideation
• The risks and challenges of AI-driven knowledge management

AI enhanced decision making and open innovation ecosystems

• AI-based decision support systems for innovation management
• AI’s role in developing open innovation ecosystems and cross-industry knowledge collaboration
• The integration of AI into open innovation frameworks

The interplay between AI, human interactions, and knowledge sharing

• How AI transforms the relationship between human interactions and knowledge-management practices for innovation
• AI’s role in strengthening social capital and knowledge related processes to advance innovation ecosystems
• The impact of AI adoption on the social capital of organizations and its effects on knowledge-based innovation
• The risks of over-reliance on AI in human-driven knowledge exchange and creativity

Measuring and evaluating AI’s long-term impact on human relationships and knowledge management for innovation

• Metrics and frameworks for assessing the impact of AI on human relationships and knowledge-management processes over time
• Longitudinal studies examining the evolution of AI-driven interactions and knowledge-sharing practices in innovation-driven organization

This special issue seeks to attract high-quality contributions that explore the complexity of the proposed research topic. Furthermore, we welcome a diverse range of submissions, including qualitative studies, empirical research, theoretical contributions, conceptual and methodological papers, as well as longitudinal, interdisciplinary, and case studies.

The research topic proposed in the special issue allows advancing knowledge regarding the interrelationship between Circular Economy (CE) and Open Innovation (OI), aiming to promote the green transition. The environmental emergency requires the transition to new models of sustainable development. Consequently, governments worldwide are taking action to develop green policies (i.e., The European Green Deal, the United Nations 2030 Agenda, etc.). Specifically, the UN organization has compiled a list of 17 Sustainable Development Goals, among which can be found to ensure sustainable production and consumption patterns. Underlying this goal is the concept of the CE, which is considered the economic approach to be embraced to reduce the pressure on natural systems.

For this reason, implementing CE principles supports the green transition. This subject particularly appeals to governments, practitioners, and academics, justifying an in-depth study of how organizations can effectively implement CE principles to accomplish a green transition. In this context, OI represents a viable approach to foster and speed up the implementation of circular initiatives. Specifically, the literature (elucidated in the following paragraphs) highlights how the ecosystem approach enabled by OI facilitates this process, thus deserving attention. For the reasons stated here and for relevance to the journal’s scope, the research topic related to the transition from Linear to CE through OI deserves an entire issue.

Global financial and governance systems are increasingly critical in achieving sustainable development. The United Nations Sustainable Development Goals (SDGs) highlight the interconnectedness of economic growth, social equity, and environmental stewardship, urging institutions and decision-makers to align their practices with broader societal objectives (United Nations, 2015; Ahmad et al., 2024). In parallel, Environmental, Social, and Governance (ESG) frameworks have gained traction among investors, businesses, and policymakers, recognizing that non-financial factors significantly influence long-term financial performance and social welfare (Kwilinski et al., 2025; Cai et al., 2022). This evolution underscores the importance of knowledge-sharing, innovation, and stakeholder collaboration in reimagining finance and governance for more equitable and sustainable outcomes (UNEP FI, 2022). Integrating SDGs and ESG objectives into financial decision-making, risk management, and regulatory frameworks can support resilient business models while fostering broader societal benefits (Delgado-Ceballos et al., 2023). This special issue explores how forward-thinking strategies and practices can transform traditional finance and governance, supporting the global transition toward sustainable and inclusive development.

Background and Motivation of the Special Issue

Historically, financial markets have prioritized short-term profitability, often overlooking long-term social and environmental consequences (Elkington, 1999). However, mounting evidence indicates that sustainable investing and stronger governance practices can drive financial performance and broader value creation (Lins et al., 2017). Many organizations now recognize the urgency of integrating SDGs into their strategic agendas, focusing on issues such as climate action (SDG 13), responsible consumption and production (SDG 12), and reduced inequalities (SDG 10), among others (Fallah Shayan et al., 2022). Simultaneously, ESG considerations have become integral to corporate disclosure requirements, emphasizing transparency, stakeholder engagement, and risk mitigation (Cai et al., 2022). Innovative digital technologies—such as blockchain, big data analytics, and artificial intelligence—create opportunities for new financial instruments (e.g., green bonds, carbon offsets) and governance models (e.g., decentralized autonomous organizations), helping entities meet ESG benchmarks and SDG targets more effectively (Umar et al., 2022; Kolk, 2021; UNEP FI, 2022). As the global community seeks mechanisms to address pressing challenges like climate change, biodiversity loss, and social inequality, integrating knowledge and innovation into financial and governance spheres is a critical priority (World Economic Forum, 2023).

The motivation for this Special Issue stems from the growing consensus that finance and governance systems must undergo transformative change to meet 21st-century challenges (Helfaya & Bui, 2025; United Nations, 2015). Scholars, practitioners, and policymakers are increasingly called upon to design innovative frameworks and methodologies that integrate SDG and ESG imperatives into financial decision-making, corporate reporting, and public policy. This evolution hinges on interdisciplinary collaborations that fuse technology, data science, organizational behavior, and policy studies (Fallah Shayan et al., 2022).

By providing a dedicated platform in the Journal of Innovation & Knowledge (JIK), we aim to foster research that advances finance and governance innovations, ultimately positioning sustainability at the forefront of global economic development. We hope that contributions to this Special Issue will serve as a blueprint for these transformative efforts.

Topics and Research Questions

We invite original research articles that provide novel insights and significant contributions at the nexus of finance, governance, knowledge, innovation, and sustainability. Possible topics of interest include but are not limited to:

• How learning systems support the integration of ESG and SDG targets in corporate strategy
• Developing and assessing green bonds, sustainability-linked loans, and other impact-oriented instruments
• Financing models to accelerate progress on specific SDGs (e.g., social impact bonds for education, health, or clean water)
• The role of financial innovation in sustainable development
• Digital transformation in financial systems: AI, blockchain, and fintech applications
• Big data analytics and AI tools to measure and manage climate-related financial risks
• The role of AI and automation in financial risk management and governance
• Knowledge-based governance and policy frameworks for sustainability
• Green finance, ESG investing, and sustainable financial decision-making
• Empirical studies on the financial impacts of ESG-driven investment strategies
• Corporate governance reforms to enhance ESG performance and transparency
• Applications of blockchain in ESG data transparency and decentralized financing
• Role of fintech in widening access to finance for underserved communities, aligning with SDGs
• Strategies for institutionalizing sustainability knowledge and best practices within financial organizations
• Cross-sector knowledge-sharing platforms and networks for collaborative governance
• National and international regulations encouraging responsible finance and corporate accountability
• Role of central banks and financial regulators in promoting climate-resilient economic policies
• Comparative studies of policy interventions aimed at fostering sustainable financial markets (e.g., EU Green Deal, Asia-Pacific green finance frameworks)
• Community-based models for inclusive finance, co-governance, and socio-economic development
• Mechanisms for integrating citizen voices in financial and governance decision-making
• Integrating climate scenarios and stress-testing into enterprise risk management systems
• Corporate resilience frameworks addressing supply chain vulnerabilities and socio-environmental disruptions
• Insurance innovations and financial derivatives that hedge climate, social, and operational risks