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Introduction
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:
Human:
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?
Enterprise:
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?
AI:
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
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: 30th October 2024
Closing date for manuscripts submission: 30th March 2025
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