Innovation policies are essential for driving economic growth, competitiveness, and societal progress (Edler & Fagerberg, 2017). By setting clear priorities and providing targeted support, they create the conditions for research, experimentation, and technological advancement to flourish (Voß et al., 2009). These policies include incentives for R&D, technology transfer initiatives, startup support, tax incentives, grants, and public–private partnerships (Borrás & Edquist, 2013). Effective innovation policies encourage investment, reduce market barriers, and foster collaboration between public and private actors. Moreover, they align innovation with public goals such as environmental sustainability, social inclusion, and digital transformation, ensuring that new ideas generate broad societal benefits.

Amidst the current technological revolution, driven by the rise of artificial intelligence and digital technologies, the role of innovation policy is increasingly crucial for promoting flourishing innovation ecosystems that foster accountability and mitigate negative societal impacts. The growing concentration of technological development in the hands of a few actors risks premature lock-in and inequality. Thus, innovation policy must ensure that technological change, particularly in cutting-edge domains such as AI, genomics, quantum computing, and advanced robotics, serves the public interest rather than reinforcing dystopian inequalities (Archibugi, 2017).

A contemporary challenge for innovation policy is to integrate insights from managerial and organizational theories—including strategy, organizational behavior, and systems thinking—into policy design and implementation. Managerial research offers a nuanced understanding of how organizations learn, adapt, and coordinate under uncertainty. Concepts such as dynamic capabilities, organizational ambidexterity, sensemaking, emotions, and collective learning can enrich innovation policy frameworks by explaining how actors organize experimentation, build resilience, and manage tensions between stability and change (Haddad et al., 2022; Vedel et al., 2025).

Bridging these domains advances innovation policies by acknowledging that systemic change depends not only on institutions and markets but also on organizational processes—how public agencies, firms, and communities coordinate transformative action (Schot and Steinmueller 2018). Overcoming resistance from established stakeholders underscores the need to manage R&D through alliances and co-creation, supported by green transition approaches that combine long-term vision, adaptive policy learning, and coordinated instruments to overcome lock-ins and market failures (Kemp & Never, 2017). The rapid emergence of new technologies calls for an innovation policy perspective that addresses societal challenges while fostering new ecosystems that transcend path dependencies (Schot & Steinmueller, 2018).

The track welcomes conceptual and empirical papers—qualitative, quantitative, or mixed-methods—that examine the intersection of innovation policy and organizational/managerial scholarship. Suggested areas of interest include, but are not limited to:
1. Innovation Strategies for Sustainable Development:
How can innovation policies advance sustainability moonshots (e.g., biodiversity protection, green and digital transitions)? What managerial capabilities support these transformations?
2. Policy Frameworks and Governance Models:
How can innovation policy foster collaboration, ethical research, and innovation for grand challenges?
3. Demand-side Innovation Policy:
How can tools such as public procurement or standards stimulate innovation? What organizational insights enhance their effectiveness?
4. Knowledge Transfer:
How are innovations disseminated and absorbed across actors? What roles do learning, absorptive capacity, and network management play?
5. Impact Evaluation:
How can policy impacts on knowledge transfer, SME performance, or societal outcomes be measured and used to inform future directions?
6. Innovation Systems:
How do national systems shape technological adaptation and diffusion? How can they be reconfigured for more inclusive and sustainable innovation?