Call for Papers

The concept of democracy was ideated in Athens a few millennia ago. Fast forward to this day and age: will generative AI (GenAI) now democratise ideation? Over the past decade, scholars have debated if AI will lead to massive job losses or a shift in the type of employment, which we have previously seen in manufacturing with the increased use of production robots. The main difference is that GenAI touches on human creativity by means of its generative capacities.
 
This development is especially intriguing when it touches on the core of new business development: i.e. new venture and/or product ideation. The debate is, however, still open to what extent GenAI will automate or augment human creativity (Bouschery, 2023). For example, one recent study found ChatGPT to generate new consumer product ideas faster and cheaper than elite university product design students while also generating most of the best ideas (Girotra et al., 2023). Another recent study with strategy consultants found more mixed results depending on the current capability of GenAI (Dell’Aqua et al, 2023). Mainly, creative business problem-solving was still found to be outside GenAI’s capability. We might conclude that university-level ideation comes within reach of the entire workforce.
 
However, the nature of GenAI raises various questions in relation to accountability, explainability and, ultimately, (organizational) democracy. GenAI’s large language models are trained on existing large data sets and then improved via reinforcement learning by interacting with humans. The AI becomes better at guessing the expected ‘correct’ response, but the output is not perse correct. On the other end of the spectrum, can such a response be considered a creative jump of imagination (see, Frederiks et al., 2019)? Moreover, is the result biased or bounded by the training set, leading to suboptimal results but of a very different kind? Is AI-facilitated ideation constraining what we understand as ideation, or is this the new ideation? Can we still argue that “technology does nothing, except as implicated in the actions of human beings”? (Giddens & Pierson, 1998, p.82). In other words, what are the repercussions for our concept of agency (see Emirbayer & Mische, 1998), for example, in the realm of entrepreneurial action (Townsend & Hunt, 2019)? Not to mention individual differences, how does GenAI, for instance, impact the narcissism-creativity link (Chang & Gong, 2023)? Finally, will generative AI partly substitute real-life creative practice, like social media has partly substituted real-life social interaction? How would this impact the well-being of entrepreneurs and new product developers alike?
 
For our sub-theme, we invite empirical papers that explore a broad range of theoretical, empirical and methodological aspects of the link between ideation and generative AI. The possible topics we are interested in include, but are not limited to:

• In which business contexts is GenAI better at identifying entrepreneurial opportunities and new products than human beings? Does GenAI help creative workers make better decisions, and if so, how? How does working in a highly automated environment, enhanced by GenAI, affect individuals' perception of their own creativity?

• What are the implications of GenAI for organizational design? What organizational structures and practices most effectively integrate GenAI into the innovation process? How does the introduction of GenAI impact the division of work in innovation processes within organizations? Does it lead to more specialised roles or a more integrated approach among team members?

• Will GenAI democratise ideation and facilitate individuals and groups low in resources, or will advanced capabilities be monopolised by a select few? For example, will GenAI enable multinationals to claim all relevant patent options in a domain, or will it support workers becoming globally active entrepreneurs who, with little effort, finetune and exploit their new venture ideas?

• How do organizations address ethical considerations and accountability in AI-driven innovation processes? How does the use of GenAI in distributed innovation settings impact employee autonomy regarding work division and decision-making? In what way will GenAI affect power structures between creative workers and organizations? Will this lead to higher or lower levels of entrepreneurship?

• What is the influence of AI on creative workers’ well-being? How do the mental health issues of creative workers and AI relate? How do individual differences explain variations in the successful use of GenAI?

• What does the development of GenAI mean for our conceptions of creativity, ideation, and imagination and related existing theories in entrepreneurship and innovation studies?

References

• Bouschery, S.G., Blazevic, V., & Piller, F.T. (2023): “Augmenting human innovation teams with artificial intelligence: Exploring transformer‐based language models.” Journal of Product Innovation Management, 40 (2), 139–153.

• Chang, L., & Gong, Z. (2023): “The relationship between narcissism and creativity: A chain/serial mediation model. Personality and Individual Differences, 205; https://doi.org/10.1016/j.paid.2022.112070.
• Dell’Acqua, F, McFowland, E., Mollick, E.R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K.R. (2023): Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013; https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf.
• Emirbayer, M., & Mische, A. (1998): “What is agency?” American Journal of Sociology, 103 (4), 962–1023.
• Frederiks, A.J., Englis, B.G., Ehrenhard, M.L., & Groen, A.J. (2019): “Entrepreneurial cognition and the quality of new venture ideas: An experimental approach to comparing future-oriented cognitive processes.” Journal of Business Venturing, 34 (2), 327–347.
• Girotra, K., Meincke, L., Terwiesch, C., & Ulrich, K.T. (2023): Ideas are Dimes a Dozen: Large Language Models for Idea Generation in Innovation. The Wharton School Research Paper (forthcoming); https://dx.doi.org/10.2139/ssrn.4526071.
• Giddens, A., & Pierson, C. (1998): Conversations with Anthony Giddens. Cambridge, UK: Polity.
• Townsend, D.M., & Hunt, R.A. (2019): “Entrepreneurial action, creativity, & judgment in the age of artificial intelligence.” Journal of Business Venturing Insights, 11; https://doi.org/10.1016/j.jbvi.2019.e00126.
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