Call for papers: Disruptive technology in the workplace: the impact of AI on human interactions and knowledge-related processes for advancing innovation

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.

Guest editors:

Antonio Cimino, University of Messina, Department of Engineering, Messina, Italy. antonio.cimino@unime.it

Vincenzo Corvello, University of Messina, Department of Engineering, Messina, Italy. vincenzo.corvello@unime.it

Moustafa Elnadi, Mansoura University, Faculty of Commerce, Business Administration Department, Mansoura, Egypt. m.elnadi@mans.edu.eg

Mohamed Hani Gheith, Al Qasimia University, College of Economics and Management, Sharjah, United Arab Emirates. mgheith@alqasimia.ac.ae

Vittorio Solina, University of Calabria, Department of Mechanical, Energy and Management Engineering, Rende, Italy. vittorio.solina@unical.it 

Manuscript submission information:

The timeline of this special issue is as follows:

Submission dates:

• Submission deadline: 30^th November 2025
• First desk decision: within 10 days from submission
• Review Process: on rolling basis and no later than 2 months from submission

Publication: This is a VSI; accepted papers will be published online immediately once accepted and will be included in the next available issue of the journal.

Before submitting a manuscript, please read carefully the Journal of Innovation & Knowledge Guide for authors.

In particular, authors should disclose in their manuscript the use of AI and AI-assisted technologies and a statement will appear in the published work. Declaring the use of these technologies supports transparency and trust between authors, readers, reviewers, editors, and contributors and facilitates compliance with the terms of use of the relevant tool or technology. Plagiarism in all its forms constitutes unethical behaviour and is unacceptable.

More information can be found here

References:

Abrokwah-Larbi K. & Awuku-Larbi, Y. (2023). The impact of artificial intelligence in marketing on the performance of business organizations: evidence from SMEs in an emerging economy. Journal of Entrepreneurship in Emerging Economies, 16(4), 1090-1117. https://doi.org/10.1108/JEEE-07-2022-0207

Al Mansoori, S., Salloum, S. A., & Shaalan, K. (2021). The Impact of Artificial Intelligence and Information Technologies on the Efficiency of Knowledge Management at Modern Organizations: A Systematic Review. In: Al-Emran, M., Shaalan, K., Hassanien, A. (Eds), Recent Advances in Intelligent Systems and Smart Applications. Studies in Systems, Decision and Control, Springer, Cham. https://doi.org/10.1007/978-3-030-47411-9_9

Almansour, M. (2023). Artificial intelligence and resource optimization: A study of Fintech start-ups. Resources Policy, 80, 103250. https://doi.org/10.1016/j.resourpol.2022.103250

Chen, X., Zhao, K., Liu, X. & Wu, D. (2012). Improving employees’ job satisfaction and innovation performance using conflict management. International Journal of Conflict Management, 23(2), 151-172. https://doi.org/10.1108/10444061211218276

Corvello, V., Felicetti, A. M., Steiber, A., & Alänge, S. (2023). Start-up collaboration units as knowledge brokers in Corporate Innovation Ecosystems: A study in the automotive industry. Journal of Innovation & Knowledge, 8(1). https://doi.org/10.1016/j.jik.2022.100303

Costa, V., & Monteiro, S. (2016). Key knowledge management processes for innovation: a systematic literature review. VINE Journal of Information and Knowledge Management Systems, 46(3), 386-410. https://doi.org/10.1108/VJIKMS-02-2015-0017

Fahad, S. A., Salloum, S. A., Shaalan, K. (2024). The Role of ChatGpt in Knowledge Sharing and Collaboration Within Digital Workplaces: A Systematic Review. In: Al-Marzouqi, A., Salloum, S. A., Al-Saidat, M., Aburayya, A., Gupta, B. (Eds) Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom. Studies in Big Data. Springer, Cham. https://doi.org/10.1007/978-3-031-52280-2_17

He, W., Qiao, Q., & Wei, K.-K. (2009). Social relationship and its role in knowledge management systems usage. Information & Management, 4(3), 175-180. https://doi.org/10.1016/j.im.2007.11.005

Idrees, H., Xu, J., Heider, S. A., Tehseen, S. (2023). A systematic review of knowledge management and new product development projects: Trends, issues, and challenges. Journal of Innovation & Knowledge, 8(2), 100350. https://doi.org/10.1016/j.jik.2023.100350

Jansen, J. J., Van Den Bosch, F. A., & Volberda, H.W. (2006). Exploratory innovation, exploitative innovation, and performance: effects of organizational antecedents and environmental moderators. Management Science, 52(11), 1661-1674. https://doi.org/10.1287/mnsc.1060.0576

Khan, S., Mehmood, S., & Khan, S. U. (2024). Navigating innovation in the age of AI: how generative AI and innovation influence organizational performance in the manufacturing sector. Journal of Manufacturing Technology Management, ahead-of-print. https://doi.org/10.1108/JMTM-06-2024-0302

Laursen, K., & Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27, 131-150. https://doi.org/10.1002/smj.507

Lee, H. W., & Yu, C. F. (2010). Effect of relationship style on innovation performance. African Journal of Business Management, 4(9), 1703-1708.

Lee, S. H. (2008). The effect of employee trust and commitment on innovative behavior in the public sector: An empirical study. International Review of Public Administration, 13(1), 27-46.

Mubarak, M. F., Jucevicius, G., Shabbir, M., Petraite, M., Ghobakhloo, M., & Evans R. (2025). Strategic foresight, knowledge management, and open innovation: Drivers of new product development success. Journal of Innovation & Knowledge, 10(2), Article 100654. https://doi.org/10.1016/j.jik.2025.100654

Soomro, M. A., Ali, A., Memon, A. H., Khahro, S. H., & Memon, Z. A. (2024). Improving innovation in construction projects: Knowledge-sharing, open-mindedness and shared leadership. Journal of Innovation & Knowledge, 9(4), 100629. https://doi.org/10.1016/j.jik.2024.100629

Taherdoost, H., & Madanchian, M. (2023). Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation. Computers, 12(4). https://doi.org/10.3390/computers12040072

wael AL-khatib, A. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: a TOE framework. Technology in Society, 75, Article 102403. https://doi.org/10.1016/j.techsoc.2023.102403

Walsh, J. P., Lee, Y-N., & Nagaoka, S. (2016). Openness and innovation in the US: Collaboration form, idea generation and implementation. Research Policy, 45(8), 1660-1671. https://doi.org/10.1016/j.respol.2016.04.013

West, M. A., & Hirst, G. (2005). Cooperation and teamwork for innovation. In: West, M. A., Tjosvold, D., Smith, K. G. (Eds), International Handbook of Organizational Teamwork and Cooperative Working. John Wiley & Sons Ltd. https://doi.org/10.1002/9780470696712.ch15

Xu, H., Xu, R., Lin, H., & He, X. (2024). The impact of generative artificial intelligence on organizational innovation performance: roles of AI generated content quality, AI experience, and AI usage environment. In Proc. ICETSIS, Kingdom of Bahrain, 2024, 1802-1807.

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