The Next Wave of Innovation Management: Human, Enterprise, and AI united for Impactful Change

Submissions open October 30th 2024

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The increasing use of artificial intelligence (AI) in various organisational practices has significantly transformed modern management (Appio et al., 2023; Khvatova et al., 2023; Holmström, 2022; Shrestha et al., 2019). These developments have paved the way for innovative applications in a wide range of sectors (Kim et al., 2022), including manufacturing (Zeba et al., 2021), banking (Fares et al., 2023) and healthcare (Secinaro et al., 2021; Ali Mohamad et al., 2023).  

The rise of AI technologies is transforming business strategies, reshaping competition and revolutionising organisational structures (Haefner et al., 2023). This impact can be observed through three key lenses: competition, strategy formulation, and organisational structure. In terms of competition, AI enables innovative business models and value creation by leveraging rich data, but as data becomes less unique, maintaining a competitive advantage becomes challenging. In strategy formulation, AI assists strategists by extracting insights from external sources, predicting market developments and automating resource allocation. In organisational structure, AI influences the division and integration of tasks, offering opportunities for increased productivity but requiring attention to biases in machine learning results. Balancing the benefits and challenges of AI is critical for effective integration and governance in diverse business environments (Johnson et al., 2022; Appio et al., 2023;). Researchers and policy makers emphasise the development of human-centred AI, leading to changes in managerial decision-making (Duan et al., 2019) and determining the rethinking of management and innovation management (Garbuio et al. 2021; Agostini, et al., 2020; Schiavone et al., 2022). 

Therefore, AI solutions with profound changes in the organisational paradigms of economies and societies call for a pivotal reflection. Firstly, it calls for a comprehensive reassessment of the acceptance and adoption of AI by individuals and companies. Secondly, it encourages a critical examination of the distinction between different classifications of AI. Finally, and as a consequence, it generates a scholarly reassessment of how the prevailing state of AI can catalyse a metamorphosis in both the pragmatic and theoretical dimensions of innovation, alongside its acceptance and adoption. 

At the individual level, the acceptance and adoption of AI are influenced by various factors, such as personal characteristics, perceived usefulness, perceived ease of use, social influence, perceived intelligence and anthropomorphism. Previous studies have investigated how these factors affect users’ intention to use and satisfaction with different types of AI applications, such as social (Henkel et al., 2023) and enterprise (Eißer et al., 2020) chatbots, recommendation agents (Komiak & Benbasat, 2006), intelligent personal assistants (Lee et al., 2021), service robots (Osei & Cheng, 2023), intelligent autonomous vehicles (Baccarella et al., 2020), and intelligent tutoring systems (Bilquise et al., 2023). However, there is a lack of understanding of how these factors interact with each other and vary across different contexts and domains, such as education, healthcare, entertainment, e-commerce, finance, transportation, and public services. There is also a need to explore how individuals' acceptance and adoption of AI affects their innovative behaviour, such as the development of soft skills and learning outcomes. An interesting area for research is the potential role of AI in the development of skills and capabilities. AI could be a detriment to human nature, but it could also be a boost to the transformation of individuals into Human+ entities. The transformation derived from Human+ capabilities requires not only theoretical reflection, but also the need to find new ways of innovating and thinking about our own society and its transition to a Society 5.0 (Bartoloni et al. 2022; Gravili et al., 2023). 

While the Human+ era promises increased efficiency and productivity, it also brings with it numerous risks. The training and development of AI solutions is susceptible to bias, which often raises ethical concerns (Tsamados et al., 2021). When combined with other technologies, AI tools enable the enhancement or restoration of human senses, raising new ethical questions about human-machine interaction and the need to reassess skills and capabilities in the evolving labour market, innovation management operations and market relationships (Cannavale et al., 2022; Frey & Osborne, 2023). Therefore, it is imperative to explore these ethical concerns in greater depth and provide additional examples to highlight the potential consequences of underestimating the ethical dimension in the use of generative AI tools. However, the mechanisms by which AI can facilitate the development of capabilities and its subsequent impact on innovation management processes are still poorly understood and require further research. 

For example, Kopalle et al. (2022) found that the culture of a nation influences the adoption of AI by companies. However, what exactly motivates companies to adopt AI systems and the potential differences in adoption drivers between companies of different types, sizes and sectors remain unclear (Chatterjee et al., 2021). The adoption of AI could lead to new methods of adoption by firms and restructure innovation adoption characteristics (Kapoor et al., 2014) as well as knowledge management practices (Santoro et al., 2018). As noted by Sharma et al. (2022), knowledge is scarce in terms of AI adoption by companies, as well as in terms of impact on performance, sustainable activities and business models (Ancillai et al., 2023; Cucari et al., 2023). The integration of AI has a profound impact on strategic functions and corporate governance (Hilb, 2020), requiring the attention of top management teams and boards of directors. The impact goes beyond operational efficiency, encompassing strategic decision-making processes and the ethical dimensions of AI implementation. The role of the board of directors becomes crucial in overseeing AI-related decisions, ensuring alignment with organisational values and addressing potential risks. At the same time, there is little focus on entrepreneurship and the processes, practices and outcomes of new ventures (Baraldi et al., 2020; Chalmers et al., 2021; Tran and Murphy, 2023). AI has the potential to revolutionise entrepreneurship research (Giuggioli and Pellegrini, 2023). Understanding how entrepreneurship researchers can strategically use AI is key to enhancing the relevance of research without compromising integrity, making it imperative to explore this transformative intersection of AI and entrepreneurship research (Lévesque et al., 2022). These considerations call for further exploration. 

Following the discussion on AI adoption at the individual and enterprise level, it is crucial to delve into the distinction between traditional (non-generative) and generative AI and its implications for innovation management (Sætra, 2023). Traditional AI, often rule-based, excels in structured environments with clear decision-making processes. However, its application can be limited when it comes to the complex, dynamic and uncertain scenarios often encountered in innovation management. On the other hand, generative AI, using techniques such as deep learning, can generate new data or models, offering novel solutions and fostering creativity, a key aspect of innovation. However, the complexity and unpredictability of generative AI behaviour (e.g. hallucinations) can pose challenges in terms of interpretability and control, which are critical for ethical and responsible AI adoption (Ooi et al., 2023). However, the differences between these two types of AI and their unique contributions to innovation management are not fully understood. Finally, the implications of AI acceptance, adoption, use and diffusion towards a new concept of Innovation 5.0 (Troisi et al. 2023) could enrich and redefine theoretical and robust frameworks. 

This call for papers invites contributions that explore the impact of AI on these three strands, Human, Enterprise, and AI. We welcome all empirical contributions, both qualitative and quantitative, with a clear and solid methodological basis, as well as new theoretical conceptualisations. Papers should shed light on the opportunities and challenges of AI adoption and use at a managerial and methodological level, as well as in terms of innovation management practices, actors and theories.  

List of Topic Areas

Research questions of interest include, but are not limited to: 

How does individuals’ acceptance and adoption of AI influence their innovative behaviour and outcomes? 
What are the main drivers and barriers to individuals’ acceptance and adoption of AI in different contexts and domains? 
How generative AI use can affect innovation and who is the real innovator?  
What are the ethical implications of innovation sparked by AI? 

What are the crucial factors driving the acceptance and adoption of AI in organisations as enterprises from different sizes, sectors or third sector and social enterprise?  
What is the role of the entrepreneur in adapting and leading organizations through AI adoption? Are they ready to innovate? 
How can AI be applied to top management level of enterprise and what are the ethical implications of such application? 

How can AI transform the Human in Human+ and what could be the opportunities and challenges of this transformation and its impact on Innovation management operations? 
How do AI types affect decision-making processes? 
Are there sector-specific considerations for adopting one type over the other? 
What are the discrepancies of using the two types of AI? How can we assess and monitor them? 

Submissions Information

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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: 30th October 2024
Closing date for manuscripts submission: 30th March 2025


Agostini, L., Galati, F., & Gastaldi, L. (2020). The digitalization of the innovation process: Challenges and opportunities from a management perspective. European journal of innovation management, 23(1), 1-12. 
Ali Mohamad, T., Bastone, A., Bernhard, F., & Schiavone, F. (2023). How artificial intelligence impacts the competitive position of healthcare organizations. Journal of Organizational Change Management, 36(8), 49-70. 
Ammirato, S., Sofo, F., Felicetti, A.M. & Raso, C. (2019), A methodology to support the adoption of IoT innovation and its application to the Italian bank branch security context, European Journal of Innovation Management, 22(1), 146-174. 
Ancillai, C., Sabatini, A., Gatti, M., & Perna, A. (2023). Digital technology and business model innovation: A systematic literature review and future research agenda. Technological Forecasting and Social Change, 188, 122307. 
Appio, F. P., La Torre, D., Lazzeri, F., Masri, H., & Schiavone, F. (Eds.). (2023). Impact of Artificial Intelligence in Business and Society: Opportunities and Challenges. 
Baccarella, C. V., Wagner, T. F., Scheiner, C. W., Maier, L., & Voigt, K.-I. (2020). Investigating consumer acceptance of autonomous technologies: The case of self-driving automobiles. European Journal of Innovation Management, 24(4), 1210–1232. 
Baraldi, E., Guercini, S., Lindhal, M., & Perna, A. (2020). Passion and entrepreneurship. Contemporary Perspectives and New Avenues for Research, Cham: Springer. 
Bartoloni, S., Calo, E., Marinelli, L., Pascucci, F., Dezi, L., Carayannis, E., ... & Gregori, G. L. (2022). Towards designing society 5.0 solutions: The new Quintuple Helix-Design Thinking approach to technology. Technovation, 113, 102413. 
Bilquise, G., Ibrahim, S., & Salhieh, S. M. (2023). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies. 
Cannavale, C., Esempio Tammaro, A., Leone, D. & Schiavone, F. (2022), "Innovation adoption in inter-organizational healthcare networks – the role of artificial intelligence", European Journal of Innovation Management, 25(6), 758-774.  
Chalmers, D., MacKenzie, N. G., & Carter, S. (2021). Artificial intelligence and entrepreneurship: Implications for venture creation in the fourth industrial revolution. Entrepreneurship Theory and Practice, 45(5), 1028-1053. 
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880. 
Cucari, N., Nevi, G., Laviola, F., & Barbagli, L. (2023). Artificial Intelligence and Environmental Social Governance: An Exploratory Landscape of AI Toolkit. Available at SSRN :  
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71. 
Eißer, J., Torrini, M., & Böhm, S. (2020). Automation Anxiety as a Barrier to Workplace Automation: An Empirical Analysis of the Example of Recruiting Chatbots in Germany. Proceedings of the 2020 on Computers and People Research Conference, 47–51.   
Fares, O. H., Butt, I., & Lee, S. H. M. (2023). Utilization of artificial intelligence in the banking sector: A systematic literature review. Journal of Financial Services Marketing, 28(4), 835-852. 
Frey, C. B., & Osborne, M. (2023). Generative AI and the Future of Work: A Reappraisal. Brown Journal of World Affairs, 1-12. 
Garbuio, M., & Lin, N. (2021). Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence. Journal of Product Innovation Management, 38(6), 701–725. 
Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837. 
Gravili, G., Hassan, R., Avram, A., & Schiavone, F. (2023). Big data and human resource management: paving the way toward sustainability. European Journal of Innovation Management, 26(7), 552-590. 
Haefner, N., Parida, V., Gassmann, O., & Wincent, J. (2023). Implementing and scaling artificial intelligence: A review, framework, and research agenda. Technological Forecasting and Social Change, 197, 122878. 
Henkel, T., Linn, A. J., & van der Goot, M. J. (2023). Understanding the Intention to Use Mental Health Chatbots Among LGBTQIA+ Individuals: Testing and Extending the UTAUT. In A. Følstad, T. Araujo, S. Papadopoulos, E. L.-C. Law, E. Luger, M. Goodwin, & P. B. Brandtzaeg (A c. Di), Chatbot Research and Design, 83–100. Springer International Publishing.   
Hilb, M. (2020). Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance. Journal of Management and Governance, 24, 851-870. 
Holmström, J. (2022). From AI to digital transformation: The AI readiness framework. Business Horizons, 65(3), 329–339. 
Johnson, P. C., Laurell, C., Ots, M., & Sandström, C. (2022). Digital innovation and the effects of artificial intelligence on firms’ research and development–Automation or augmentation, exploration or exploitation?. Technological Forecasting and Social Change, 179, 121636. 
Kapoor, K., K. Dwivedi, Y., & D. Williams, M. (2014). Innovation adoption attributes: a review and synthesis of research findings. European Journal of Innovation Management, 17(3), 327-348. 
Khvatova, T., Appio, F. P., Ray, S., & Schiavone, F. (2023). Exploring the Role of AI in B2B Customer Journey Management: Towards an IPO Model. IEEE Transactions on Engineering Management. 10.1109/TEM.2023.3284532 
Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 1-19. 
Komiak, S. Y. X., & Benbasat, I. (2006). The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents. MIS Quarterly, 30(4), 941–960.   
Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2022). Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522-540. 
Lee, K. Y., Sheehan, L., Lee, K., & Chang, Y. (2021). The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): The influence of personal traits. Internet Research, 31(5), 1899–1939.  
Lee, Y. S., Kim, T., Choi, S., & Kim, W. (2022). When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy. Technovation, 118, 102590. 
Lévesque, M., Obschonka, M., & Nambisan, S. (2022). Pursuing impactful entrepreneurship research using artificial intelligence. Entrepreneurship Theory and Practice, 46(4), 803-832. 
Malodia, S., Mishra, M., Fait, M., Papa, A., & Dezi, L. (2023). To digit or to head? Designing digital transformation journey of SMEs among digital self-efficacy and professional leadership. Journal of Business Research, 157, 113547. 
Meissner, D., & Shmatko, N. (2019). Integrating professional and academic knowledge: The link between researchers skills and innovation culture. The Journal of Technology Transfer, 44(4), 1273–1289. 
Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., ... & Wong, L. W. (2023). The potential of generative artificial intelligence across disciplines: perspectives and future directions. Journal of Computer Information Systems, 1-32. 
Osei, B. A., & Cheng, M. (2023). Preferences and challenges towards the adoption of the fourth industrial revolution technologies by hotels: A multilevel concurrent mixed approach. European Journal of Innovation Management, ahead-of-print(ahead-of-print). 
Papa, A., Dezi, L., Gregori, G.L., Mueller, J. & Miglietta, N. (2020), Improving innovation performance through knowledge acquisition: the moderating role of employee retention and human resource management practices, Journal of Knowledge Management, 24(3), 589-605. 
Sætra, H. S. (2023). Generative AI: Here to stay, but for good?. Technology in Society, 75, 102372. 
Santoro, G., Vrontis, D., Thrassou, A., & Dezi, L. (2018). The Internet of Things: Building a knowledge management system for open innovation and knowledge management capacity. Technological forecasting and social change, 136, 347-354. 
Schiavone, F., Pietronudo, M. C., Sabetta, A., & Bernhard, F. (2022). Designing AI implications in the venture creation process. International Journal of Entrepreneurial Behavior & Research, 29 (4), 838-859 
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making, 21, 1-23 
Sharma, S., Singh, G., Islam, N., & Dhir, A. (2022). Why do smes adopt artificial intelligence-based chatbots?. IEEE Transactions on Engineering Management. 
Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83. 
Tran, H., & Murphy, P. J. (2023). Generative artificial intelligence and entrepreneurial performance. Journal of Small Business and Enterprise Development, 30(5), 853-856. 
Trittin-Ulbrich, H., Scherer, A. G., Munro, I., & Whelan, G. (2021). Exploring the dark and unexpected sides of digitalization: Toward a critical agenda. Organization, 28(1), 8–25. 
Troisi, O., Visvizi, A. & Grimaldi, M. (2024), "Rethinking innovation through industry and society 5.0 paradigms: a multileveled approach for management and policy-making", European Journal of Innovation Management, 27(9),22-51.  
Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2021). The ethics of algorithms: key problems and solutions. Ethics, Governance, and Policies in Artificial Intelligence, 97-123. 
Wang, J., Zhang, Q., Chen, R., Li, J., Wang, J., Hu, G., Cui, M., Jiang, X., Song, B., & He, Y. (2021). Triple-layer unclonable anti-counterfeiting enabled by huge-encoding capacity algorithm and artificial intelligence authentication. Nano Today, 41, 101324. 
Zeba, G., Dabić, M., Čičak, M., Daim, T., & Yalcin, H. (2021). Technology mining: Artificial intelligence in manufacturing. Technological Forecasting and Social Change, 171, 120971.