Call for Papers

While easily delegated to technology management, the human–machine interface represents a phenomenon of growing interest to organization scholars. The nature of transactions between humans and machines is expanding to cover almost every area of human life. The relevance of these transactions can hardly be overstated starting from embodiment of machine components of the cyborg (Haraway, 1985) and immersive gaming environments to medical technology and marketplaces.
 
Human–machine transactions result in the emergence of metahuman systems (Lyytinen et al., 2021) where machines and humans learn with and from each other. The notion of the metahuman system is a promising start and invites a redefinition of organizational roles (Berente et al., 2021), identities (Faulconbridge et al., 2023), and expertise (Jia et al., 2024). In these emergent systems, innovation and creativity assume a special place as they are often thought of as exclusive to human agency (Runco, 2023), all while learning machines steadily become better at imitating human intelligence (Ciardo et al., 2022) and challenge human creative capacity and uniqueness (Haslam et al., 2008; Schweitzer & De Cremer, 2024). Individuals and groups embedded in and relating to machines, that is entities that are ontologically distinct from humans (Guzman, 2020), likely experience the creative process differently from being among humans only.
 
For example, although AI has the potential to increase productivity and provide inspiration, it might negatively affect creativity by reducing employee autonomy and lowering intrinsic motivation (Amabile, 1993). Moreover, the degree of centralization and formalization concerning the use of machines might negatively affect creativity if employees have fewer opportunities to contribute original ideas while their behaviour and decision-making processes are regulated (Hirst et al., 2011) by AI. In addition, even though AI is trained on past data, scholars expect that its output can be both useful and novel (Amabile, 2020), thus creative by definition (Harvey & Barry, 2023). Such data-driven creativity comes in conflict with the social, subjective process of selection and evaluation of creative ideas (Ebbers & Wijnberg, 2012), in which managers are less successful than creatives (Berg, 2014).
 
In this sub-theme we seek to enrich theorizing around the human–machine interface and deepen our awareness of organizational processes that include and are determined by the relationship between human actors and machines (see Bailey et al., 2022). We particularly invite scholars to consider the wide theoretical implications for creativity, “the pinnacle of [human] intelligence” (Das & Varshney, 2022: 85), and by extension innovation. We suggest exploring the nexus of human-machine interactions as the starting point and consequence for organizational creativity and innovation processes: Does the human–machine interface require us to re-evaluate creativity as a prominent human quality? How does subsequent reconfiguration of creative processes (Farmer et al., 2003), practices (Harrison et al., 2022), and values (Harvey & Berry, 2023) proceed in organizations?
 
We are open to a wide range of ontological and epistemological outlooks and encourage bold thinking along the philosophical and ethical challenges that the evolving relationship between humans and machines engenders. We invite submissions from a range of theoretical perspectives and methodologies and are particularly interested in empirical settings in creative industries and knowledge work, but also welcome other contexts. The list of issues we are interested in, and questions below are by no means exhaustive:
 
Redefinition of roles in the creative process:

• How does the emergent human–machine interface influence the perception of creativity as distinctly human characteristic? What does this imply for organizational dynamics?

• Does an AI-augmented creative process shift the human role from creative agent to executor? How does this change the distribution of responsibility over the creative result?

• How can organizations balance human-centric creativity and AI-driven innovation? How do organizations foster environments where humans and AI innovate in synergy?

• How does the human–machine interface impact the wellbeing of creative workers and their emotional coping?

 
Redefinition of creativity:

• How are novelty and usefulness assessed and prioritized in metahuman systems? Does the importance of these factors shift as machines and people get entangled in joint learning?

• How does the presence of machines in the creative process change the methods and criteria for evaluation and selection of its results?

 
Relationships in the creative processes:

• How does human desire for control over the process shape organizational approaches towards creativity and innovation when AI is involved?

• How does the shift from human to AI-mediated feedback impact ideation and implementation of innovation ideas in organizational settings?

• What are the strategies that organizations implement to leverage AI creativity for innovation? Do they affect organizational dehumanization?

• What are the factors that predict acceptance of machine input in the creative process? What are the processes through which humans adopt, adapt, and reinterpret this input?

 
Consequences for the organization of innovation and creative processes:

• What types of human–machine interfaces are supportive and conducive to creative outcomes?

• How are creative practices and supportive technologies adopted and diffused throughout the organization and across organizations?

• How do the economics of organizing for innovation change with the strong involvement of automated processes and learning machines?

References

• Amabile, T.M. (1993): “Motivational synergy: Toward new conceptualizations of intrinsic and extrinsic motivation in the workplace.” Human Resource Management Review, 3 (3), 185-201.

• Amabile, T.M. (2020): “Creativity, Artificial Intelligence, and a World of Surprises.” Academy of Management Discoveries, 6 (3), 351–354.
• Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021): “Managing Artificial Intelligence.” MIS Quarterly, 45 (3), 1433–1450.
• Berg, J.M. (2016): “Balancing on the creative highwire: Forecasting the success of novel ideas in organizations.” Administrative Science Quarterly, 61 (3), 433–468.
• Das, P., & Varshney, L.R. (2022): “Explaining Artificial Intelligence Generation and Creativity: Human interpretability for novel ideas and artifacts.” IEEE Signal Processing Magazine, 39 (4), 85–95.
• Ebbers, J.J., & Wijnberg, N.M. (2012): “Nascent ventures competing for start-up capital: Matching reputations and investors.” Journal of Business Venturing, 27 (3), 372–384.
• Farmer, S.M., Tierney, P., & Kung-McIntyre, K. (2003): “Employee creativity in Taiwan: An application of role identity theory.” Academy of Management Journal, 46 (5), 618–630.
• Faulconbridge, J., Sarwar, A., & Spring, M. (2023): “How Professionals Adapt to Artificial Intelligence: The Role of Intertwined Boundary Work.” Journal of Management Studies, first published online on May 31, 2023; https://doi.org/10.1111/joms.12936.
• Guzman, A.L. (2020): “Ontological boundaries between humans and computers and the implications for human-machine communication.” Human-Machine Communication, 1, 37–54.
• Haraway, D. (1985): “A Manifesto for Cyborgs. Science, Technology, and Socialist Feminism in the 1980s.” First published in the Socialist Review.
• Harrison, S.H., Rouse, E.D., Fisher, C.M., & Amabile, T.M. (2022): “The turn toward creative work.” Academy of Management Collections, 1 (1), 1–15.
• Harvey, S., & Berry, J.W. (2023): “Toward a meta-theory of creativity forms: How novelty and usefulness shape creativity.” Academy of Management Review, 48 (3), 504–529.
• Haslam, N., Loughnan, S., Kashima, Y., & Bain, P. (2008): “Attributing and denying humanness to others.” European Review of Social Psychology, 19 (1), 55–85.
• Hirst, G., Van Knippenberg, D., Chen, C., Sacramento, C.A. (2011): “How Does Bureaucracy Impact Individual Creativity? A Cross-Level Investigation of Team Contextual Influences on Goal Orientation–Creativity Relationships.” Academy of Management Journal, 54 (3), 624–641.
• Jia, N., Luo, X., Fang, Z., & Liao, C. (2024): “When and How Artificial Intelligence Augments Employee Creativity.” Academy of Management Journal, 67 (1), 5–32.
• Lyytinen, K., Nickerson, J.V., & King, J.L. (2021): “Metahuman systems = humans + machines that learn.” Journal of Information Technology, 36 (4), 427–445.
• Runco, M.A. (2023): “AI can only produce artificial creativity.” Journal of Creativity, 33 (3), https://doi.org/10.1016/j.yjoc.2023.100063.
• Schweitzer, S., & De Cremer, D. (2024): “When Being Managed by Technology: Does Algorithmic Management Affect Perceptions of Workers’ Creative Capacities?” Academy of Management Discoveries, 10 (3), 375–392.
•