Agency Configurations in Generative AI Ideation: How Textual and Visual Idea Concretizations Shape Idea Creativity and Ideator Effort

References

• Ågerfalk PJ (2020) Artificial intelligence as digital agency. Eur. J. Inform. Systems 29(1):1–8.Crossref, Google Scholar
• Aggarwal V, Hwang EH, Tan Y (2021) Learning to be creative: A mutually exciting spatiotemporal point process model for idea generation in open innovation. Inform. Systems Res. 32(4):1214–1235.Link, Google Scholar
• Baird A, Maruping LM (2021) The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quart. 45(1):315–341.Crossref, Google Scholar
• Barsalou LW (2008) Grounding symbolic operations in the brain’s modal systems. Semin GR, Smith ER, eds. Embodied Grounding: Social, Cognitive, Affective, and Neuroscientific Approaches (Cambridge University Press, Cambridge, UK), 9–42.Crossref, Google Scholar
• Bick A, Blandin A, Deming DJ (2026) The rapid adoption of generative AI. Management Sci., ePub ahead of print January 20, https://doi.org/10.1287/mnsc.2025.02523.Google Scholar
• Bilgram V, Laarmann F (2023) Accelerating innovation with generative AI: AI-augmented digital prototyping and innovation methods. IEEE Engrg. Management Rev. 51(2):18–25.Crossref, Google Scholar
• Boussioux L, Lane JN, Zhang M, Jacimovic V, Lakhani KR (2024) The crowdless future? Generative AI and creative problem-solving. Organ. Sci. 35(5):1589–1607.Link, Google Scholar
• Carroll JM (1999) Five reasons for scenario-based design. Proc. Hawaii Internat. Conf. System Sci. (IEEE, Piscataway, NJ).Google Scholar
• Ceylan G, Diehl K, Wood W (2023) From mentally doing to actually doing: A meta-analysis of induced positive consumption simulations. J. Marketing 88(2):21–39.Crossref, Google Scholar
• Chatterji A, Cunningham T, Deming DJ, Hitzig Z, Ong C, Shan CY, Wadman K (2025) How people use ChatGPT. NBER Working Paper No. 34255, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Chen Z, Chan J (2024) Large language model in creative work: The role of collaboration modality and user expertise. Management Sci. 70(12):9101–9117.Link, Google Scholar
• Compeau D, Marcolin B, Kelley H, Higgins C (2012) Research commentary—Generalizability of information systems research using student subjects: A reflection on our practices and recommendations for future research. Inform. Systems Res. 23(4):1093–1109.Link, Google Scholar
• Davis FD (1989) Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quart. 13(3):319–340.Crossref, Google Scholar
• Dean DL, Hender JM, Rodgers TL, Santanen EL (2006) Identifying quality, novel, and creative ideas: Constructs and scales for idea evaluation. J. Assoc. Inform. Systems 7(10):646–699.Google Scholar
• De Freitas J, Nave G, Puntoni S (2025) Ideation with generative AI—In consumer research and beyond. J. Consumer Res. 52(1):18–31.Crossref, Google Scholar
• Ezzat H, Agogué M, Le Masson P, Weil B, Cassotti M (2020) Specificity and abstraction of examples: Opposite effects on fixation for creative ideation. J. Creative Behav. 54(1):115–122.Crossref, Google Scholar
• Finke RA, Ward TB, Smith SM (1996) Creative Cognition: Theory, Research, and Applications (MIT Press, Cambridge, MA).Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2022) Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.Link, Google Scholar
• George T, Wiley J (2020) Need something different? Here’s what’s been done: Effects of examples and task instructions on creative idea generation. Memory Cognition 48(2):226–243.Crossref, Google Scholar
• Giddens A (1984) The Constitution of Society (University of California Press, Berkeley).Google Scholar
• Gochermann J, Nee I (2019) The idea maturity model—A dynamic approach to evaluate idea maturity. Internat. J. Innovation Tech. Management 16(5):1950030.Google Scholar
• Goldschmidt G (1991) The dialectics of sketching. Creativity Res. J. 4(2):123–143.Crossref, Google Scholar
• Gonsher I (2023) The dialectics of creativity: The abstract and the concrete. Jones D, Borekci N, Clemente V, Corazzo J, Lotz N, Merete Nielsen L, Noel L-A, eds. 7th Proc. Internat. Conf. Design Ed. Res. (Design Research Society, London).Google Scholar
• Gopal RD, Li J, Riemer K, Sarker S, Singh PV, Susarla A, Bichler M, Thatcher JB (2025) Inventing with machines: Generative AI and the evolving landscape of IS research. Inform. Systems Res. 36(4):1949–1967.Link, Google Scholar
• Hofstetter R, Dahl DW, Aryobsei S, Herrmann A (2021) Constraining ideas: How seeing ideas of others harms creativity in open innovation. J. Marketing Res. 58(1):95–114.Crossref, Google Scholar
• Hou JJ, Wang L, Wang G, Wang HJ, Yang S (2025) The double-edged roles of generative AI in the creative process: Experiments on design work. Inform. Systems Res., ePub ahead of print October 3, https://doi.org/10.1287/isre.2024.0937.Link, Google Scholar
• Huang S, Long L, Zhu Y, Zhu JNY (2026) Human-GenAI collaboration across creative phases: Cognitive mechanisms shaping novelty and usefulness. Internat. J. Inform. Management 86:102986.Crossref, Google Scholar
• Jansson DG, Smith SM (1991) Design fixation. Design Stud. 12(1):3–11.Crossref, Google Scholar
• Jussupow E, Spohrer K, Heinzl A, Gawlitza J (2021) Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Inform. Systems Res. 32(3):713–735.Link, Google Scholar
• Just J, Ströhle T, Füller J, Hutter K (2024) AI-based novelty detection in crowdsourced idea spaces. Innovation 26(3):359–386.Crossref, Google Scholar
• King G, Tomz M, Wittenberg J (2000) Making the most of statistical analyses: Improving interpretation and presentation. Amer. J. Political Sci. 44(2):347–361.Crossref, Google Scholar
• Kuss P, Meske C (2025) From entity to relation? Agency in the era of artificial intelligence. Comm. Assoc. Inform. Systems 56:633–674.Google Scholar
• Lawson B (2006) How Designers Think (Routledge, London).Crossref, Google Scholar
• Leonardi PM (2011) When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quart. 35(1):147–167.Crossref, Google Scholar
• Lim Y-K, Stolterman E, Tenenberg J (2008) The anatomy of prototypes: Prototypes as filters, prototypes as manifestations of design ideas. ACM Trans. Comput. Human Interaction 15(2):1–27.Crossref, Google Scholar
• Lin Z (2024) How to write effective prompts for large language models. Nature Human Behav. 8(4):611–615.Crossref, Google Scholar
• Lu T, Zhang Y (2025) 1+ 1 > 2? Information, humans, and machines. Inform. Systems Res. 36(1):394–418.Link, Google Scholar
• Meincke L, Girotra K, Nave G, Terwiesch C, Ulrich KT (2024) Using large language models for idea generation in innovation. Preprint, submitted August 2, https://dx.doi.org/10.2139/ssrn.4526071.Google Scholar
• Memmert L, Soroko D, Bittner E (2025) From effort reduction to effort management: An expectancy theory perspective on professionals’ work practices with generative AI. Bus. Inform. Systems Engrg. 67(5):615–635.Crossref, Google Scholar
• Miaskiewicz T, Kozar KA (2011) Personas and user-centered design: How can personas benefit product design processes? Design Stud. 32(5):417–430.Crossref, Google Scholar
• Nijstad BA, Stroebe W (2006) How the group affects the mind: A cognitive model of idea generation in groups. Personality Soc. Psych. Rev. 10(3):186–213.Crossref, Google Scholar
• Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Science 381(6654):187–192.Crossref, Google Scholar
• Oppenlaender J, Milland K, Visuri A, Ipeirotis P, Hosio S (2020) Creativity on paid crowdsourcing platforms. Proc. Conf. Human Factors Comp. Systems (ACM, New York), 1–14.Google Scholar
• Paivio A (2007) Mind and Its Evolution: A Dual Coding Theoretical Approach (Psychology Press, New York).Google Scholar
• Palmiero M, Nori R, Aloisi V, Ferrara M, Piccardi L (2015) Domain-specificity of creativity: A study on the relationship between visual creativity and visual mental imagery. Frontiers Psych. 6:1870.Google Scholar
• Pieters R, Wedel M (2004) Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. J. Marketing 68(2):36–50.Crossref, Google Scholar
• Rafner J, Beaty RE, Kaufman JC, Lubart T, Sherson J (2023) Creativity in the age of generative AI. Nature Human Behav. 7(11):1836–1838.Crossref, Google Scholar
• Rafner J, Zana B, Bang Hansen I, Ceh S, Sherson J, Benedek M, Lebuda I (2025) Agency in human-AI collaboration for image generation and creative writing: Preliminary insights from think-aloud protocols. Creativity Res. J., ePub ahead of print December 1, https://doi.org/10.1080/10400419.2025.2587803.Crossref, Google Scholar
• Raisch S, Krakowski S (2021) Artificial intelligence and management: The automation-augmentation paradox. Acad. Management Rev. 46(1):192–210.Crossref, Google Scholar
• Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Chen M, Sutskever I (2021) Zero-shot text-to-image generation. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn., vol. 139 (PMLR, New York), 8821–8831.Google Scholar
• Rosengren S, Dahlén M, Modig E (2013) Think outside the ad: Can advertising creativity benefit more than the advertiser? J. Advertising 42(4):320–330.Crossref, Google Scholar
• Shavit Y, Agarwal S, Brundage M, Adler S, O’Keefe C, Campbell R, Lee T, Mishkin P, Eloundou T, Hickey A (2023) Practices for governing agentic AI systems. Research paper, OpenAI, San Francisco.Google Scholar
• Speier C, Morris MG (2003) The influence of query interface design on decision-making performance. MIS Quart. 27(3):397–423.Crossref, Google Scholar
• Tan JK, Benbasat I (1990) Processing of graphical information: A decomposition taxonomy to match data extraction tasks and graphical representations. Inform. Systems Res. 1(4):416–439.Google Scholar
• Vessey I (1991) Cognitive fit: A theory‐based analysis of the graphs versus tables literature. Decision Sci. 22(2):219–240.Crossref, Google Scholar
• Vessey I, Galletta D (1991) Cognitive fit: An empirical study of information acquisition. Inform. Systems Res. 2(1):63–84.Link, Google Scholar
• Yeomans M (2021) A concrete example of construct construction in natural language. Organ. Behav. Human Decision Processes 162:81–94.Crossref, Google Scholar
• Zhao M, Hoeffler S, Zauberman G (2011) Mental simulation and product evaluation: The affective and cognitive dimensions of process versus outcome simulation. J. Marketing Res. 48(5):827–839.Crossref, Google Scholar
• Zheng L, Chiang W-L, Sheng Y, Zhuang S, Wu Z, Zhuang Y, Lin Z, Li Z, Li D, Xing E (2023) Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, eds. Adv. Neural Inform. Processing Systems, vol. 36 (Curran Associates, Inc., Red Hook, NY), 46595–46623.Crossref, Google Scholar
• Zhou E, Lee D (2024) Generative artificial intelligence, human creativity, and art. PNAS Nexus 3(3):pgae052.Crossref, Google Scholar