Share this content
Introduction
Artificial Intelligence (AI) is defined as the “frontier of computational advancements that references human intelligence in addressing ever more complex decision-making problems” (Berente et al., 2021:1435). A key development is Generative Artificial Intelligence (Gen AI), particularly Large Language Models (LLMs) like ChatGPT, which can process and generate human-like language, extract insights, and produce creative outputs (Bouschery et al., 2023). Gen AI has enormous potential to significantly boost national economies. Its widespread adoption could increase GDP in the EU region by +8% (EUR 1.2–1.4 trillion) over the next ten years, provided innovators are equipped with the necessary skills and capabilities (Implement Consulting Group, 2024).
Yet, adoption remains uneven. For instance, European countries average a 54 percent adoption rate, below the global average of 61 percent (Kirvelä, Axelsson & Mäkelä, 2025). This uneven adoption is compounded by the global competition for skilled talent and persistent workforce shortages, particularly in digital and AI-related fields (Green, 2024; OECD, 2024, Rigley, Bentley, Krook, & Ramchurn, 2024). McKinsey (2023) estimates that up to 12 million job transitions may be required due to AI, underscoring the urgent need for reskilling and entrepreneurial innovation.
This Special Issue (SI) call aims to advance empirical understanding of how, when, and why humans engage with Gen AI in innovation. We focus on Gen AI as an innovation enabler, and invite submissions to explore the themes, including but not limited to innovation processes, human capital resources, and organizational capabilities. We welcome single- and multi-level studies that examine Gen AI’s role in shaping innovation within and across organizations and encourage contributions that take an interdisciplinary perspective. More importantly, submissions should address managers' pressing challenges in finding new ways to support and improve innovation by investigating how managers can effectively enhance how employees and executives use Gen AI.
List of Topic Areas
Theme 1: Gen AI and Innovation Processes
Technological advancements have made Gen AI more accessible and affordable across industries (Bouschery et al., 2023; Gama & Magistretti, 2023). Gen AI’s impacts are not limited to what organizations innovate but it also changes how they innovate (Mariani & Dwidedi, 2024). Gen AI can be used to replace existing innovation processes, reinforce them, or reveal unforeseen ways of developing new products and services (Gama & Magistretti, 2023). It may, for example, assist in the identification of opportunities, idea generation, and evaluation of innovation potential, reshaping the front-end of innovation processes (Pescher & Tellis, 2025), and enable more rapid experimentation and improved customer understanding (Füller et al., 2022; Roberts & Candi, 2024). Yet, experiences and use cases remain evolving and fragmented, creating uncertainty for managers. As Roberts and Candi (2024) note, “the possibility of using Gen AI in the innovation process is something that firms may not have explored yet.”
Furthermore, Gen AI transforms innovation processes at multiple interrelated levels, whereas existing research has mostly examined them in isolation (Roberts & Candi, 2024; Brem et al., 2023; Füller et al., 2022). Consequently, this Special Issue encourages a multi-level perspective, including individual, team, and organizational level processes. To advance this theme, potential authors are invited to make empirical submissions that explore questions such as:
- How is Gen AI being used in innovation processes at different levels?
- How do organizations apply Gen AI to replace, reinforce, or reveal innovation activities, and what are the resulting effects on, for example, process efficiency, novelty, and responsiveness to market needs?
- How do varied patterns of Gen AI use across process phases and organizational levels influence the dynamics and management of innovation processes?
Theme 2: Gen AI and Human Capabilities
Microfoundations research shows that innovation is fundamentally driven by people, not organizations, since it is individuals who identify opportunities, generate solutions, and implement them (Felin et al., 2015). Equally, to understand how Gen AI enhances innovation outcomes, such as faster development of better-quality products, services, and processes, it is necessary to examine how individual innovators use Gen AI, what influences its use, and what results emerge from these AI-augmented efforts (Amankwah-Amoah & Appiah, 2025; Annosi et al., 2024; Weiss et al., 2022). Team-level dynamics are equally important, as teams have become the core unit of modern organizations. Virtual teams, in particular, require attention to build resilience and achieve innovative outcomes (Degbey & Einola, 2020). Gen AI can support team innovation by sparking dynamic discussions and promoting diverse thinking in design processes (Bouschery et al., 2023), ethical decision-making in human resource management processes (Rodgers, Murray, Stefanidis, Degbey, & Tarba, 2023), and by helping overcome challenges in group decision-making (Metcalf et al., 2019). Finally, scholars have called for more research on how leadership can support AI adoption and digital transformation (Gilli, Lettner & Guettel, 2024; Tursunbayeva & Chalutz-Ben Gal, 2024). Leaders must build new capabilities as AI implementation is often challenging, human-AI interaction may involve complications, and a new range of ethical concerns must be considered (Aziz et al., 2025).
To advance this theme, potential authors are invited to make empirical submissions that explore questions such as:
- How do individual innovators use Gen AI in their work, what influences this usage, and what are the measurable outcomes of AI-augmented innovation efforts in terms of product, service, or process quality and speed?
- In what ways does Gen AI influence innovation team dynamics, including collaboration, resilience, and decision-making, particularly in virtual or hybrid team environments?
- What psychological and behavioral effects emerge from using Gen AI in innovation settings, such as changes in employee engagement, collaboration dynamics, or perceptions of autonomy?
- How do managerial competencies influence the successful implementation of Gen AI in organizational innovation efforts?
Theme 3: Gen AI and Organizational Capabilities
The rapid evolution of Gen AI demands not only technical adaptation but also new approaches to manage the associated processes, tensions, and frustrations. As Gen AI profoundly alters work design, skill requirements, and professional identities, employees face both new opportunities and psychological challenges. To ensure they can develop the AI-related skills needed for the evolving roles, organizational and managerial support mechanisms have to be developed. Yet, there is limited understanding of the organizational capabilities required to implement this change successfully. Effective support may involve building Gen AI literacy, fostering experimentation, and promoting continuous learning (Sabbah & Li, 2025; Füller et al., 2022). At its best, AI is strategically deployed to generate business value and competitive advantage, but without targeted support, organizations risk de-skilling, employee resistance, and ineffective adoption that undermines innovation goals.
Organizational practices for managing innovation are also being challenged by Gen AI, especially in terms of ethical governance. While the benefits of Gen AI in innovation are promising, ethical concerns remain underexplored (Asante et al., 2025; Mäntymäki et al., 2022). Key principles include respect for intellectual property, truthfulness, robustness, recognition of malicious uses, sociocultural responsibility, and human-centric design (Laine et al., 2025).
To advance this theme, potential authors are invited to make empirical submissions that explore questions such as:
- What organizational capabilities need to be developed to successfully implement, integrate, and support the use of Gen AI in innovation efforts?
- How are organizations supporting continuous learning and skill development for employees working with Gen AI, and what mechanisms are most effective in enabling the strategic use of Gen AI?
- What ethical challenges do organizations face when deploying Gen AI in innovation activities, and how are they addressing issues such as intellectual property, transparency, and sociocultural responsibility?
Overall, we seek papers that provide empirical contributions on the evolving role of Gen AI in innovation to deepen psychological and social insights into management practices to support, improve, and offer practical guidance to help managers navigate Gen AI adoption and integration. Submissions that pertain to the three identified themes are welcome for this Special Issue. The listed themes are neither exhaustive nor exclusive; we encourage authors to explore other themes aligned with the aims of the Special Issue and JMP. Further, we seek to bring together scholars of innovation, organizational behavior, management, HR, strategy, technology, and practitioners to contribute research that aligns with JMP’s scope.
Submissions Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available here.
Author guidelines must be strictly followed. Please see here.
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Key Deadlines
Opening date for manuscripts submissions: 15/01/2026
Closing date for manuscripts submission: 31/08/2026
Guest Editors
William Y. Degbey, University of Vaasa, Finland, william.degbey@uwasa.fi
Maria Pajuoja, University of Vaasa, Finland; Esade, Spain, maria.pajuoja@esade.edu; maria.pajuoja@uwasa.fi
Matti Pihlajamaa, VTT Technical Research Centre of Finland, Finland, matti.pihlajamaa@vtt.fi
Baniyelme D. Zoogah, McMaster University, Canada, zoogahb@mcmaster.ca
Waymond Rodgers, University of Texas, USA, wrodgers@utep.edu
Share this content