The Crowdless Future? Generative AI and Creative Problem-Solving

Published Online:https://doi.org/10.1287/orsc.2023.18430

References

  • Abernathy WJ, Rosenbloom RS (1969) Parallel strategies in development projects. Management Sci. 15(10):B-486–B-505.LinkGoogle Scholar
  • Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, Almeida D, et al. (2023) GPT-4 technical report. Preprint, submitted March 15, https://arxiv.org/abs/2303.08774.Google Scholar
  • Agrawal A, Gans J, Goldfarb A (2018) Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, Boston).Google Scholar
  • Allen R, Choudhury P (2022) Algorithm-augmented work and domain experience: The countervailing forces of ability and aversion. Organ. Sci. 33(1):149–169.LinkGoogle Scholar
  • Amabile TM (1983) The social psychology of creativity: A componential conceptualization. J. Personality Soc. Psych. 45(2):357–376.CrossrefGoogle Scholar
  • Anderson BR, Shah JH, Kreminski M (2024) Homogenization effects of large language models on human creative ideation. Preprint, submitted February 2, https://arxiv.org/abs/2402.01536.Google Scholar
  • Anthony C, Bechky BA, Fayard AL (2023) “Collaborating” with AI: Taking a system view to explore the future of work. Organ. Sci. 34(5):1672–1694.LinkGoogle Scholar
  • Ash E, Hansen S (2023) Text algorithms in economics. Annual Rev. Econom. 15:659–688.CrossrefGoogle Scholar
  • Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, Goodman AM, Longhurst CA, Hogarth M (2023) Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine 183(6):589–596.CrossrefGoogle Scholar
  • Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. Preprint, submitted September 1, https://arxiv.org/abs/1409.Google Scholar
  • Bai Y, Kadavath S, Kundu S, Askell A, Kernion J, Jones A, Chen A, et al. (2022) Constitutional AI: Harmlessness from AI feedback. Preprint, submitted December 15, https://arxiv.org/abs/2212.08073.Google Scholar
  • Barr PS, Stimpert JL, Huff AS (1992) Cognitive change, strategic action, and organizational renewal. Strategic Management J. 13(S1):15–36.CrossrefGoogle Scholar
  • Baumol WJ (1993) Formal entrepreneurship theory in economics: Existence and bounds. J. Bus. Venturing 8(3):197–210.CrossrefGoogle Scholar
  • Battle R, Gollapudi T (2024) The unreasonable effectiveness of eccentric automatic prompts. Preprint, submitted February 9, https://arxiv.org/abs/2402.10949.Google Scholar
  • Becker GS (1994) Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education, 3rd ed. (The University of Chicago Press, Chicago).Google Scholar
  • Bell JJ, Pescher C, Tellis GJ, Füller J (2024) Can AI help in ideation? A theory-based model for idea screening in crowdsourcing contests. Marketing Sci. 43(1):54–72.LinkGoogle Scholar
  • Bellemare-Pepin A, Lespinasse F, Thölke P, Harel Y, Mathewson K, Olson J, Bengio Y, Jerbi K (2024) Divergent creativity in humans and large language models. Preprint, submitted May 13, https://arxiv.org/abs/2405.13012.Google Scholar
  • Benner MJ, Tushman ML (2003) Exploitation, exploration, and process management: The productivity dilemma revisited. Acad. Management Rev. 28(2):238–256.CrossrefGoogle Scholar
  • Boudreau KJ, Lacetera N, Lakhani KR (2011) Incentives and problem uncertainty in innovation contests: An empirical analysis. Management Sci. 57(5):843–863.LinkGoogle Scholar
  • Boudreau KJ, Guinan EC, Lakhani KR, Riedl C (2016) Looking across and looking beyond the knowledge frontier: Intellectual distance, novelty, and resource allocation in science. Management Sci. 62(10):2765–2783.LinkGoogle Scholar
  • Brand J, Israeli A, Ngwe D (2023) Using GPT for market research. Preprint, submitted March 30, https://doi.org/10.2139/ssrn.4395751.Google Scholar
  • Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, et al. (2020) Language models are few-shot learners. Preprint, submitted May 28, https://arxiv.org/abs/2005.14165.Google Scholar
  • Brynjolfsson E, Li D, Raymond LR (2023) Generative AI at work. NBER Working Paper No. 31161, National Bureau of Economic Research, Cambridge, MA.CrossrefGoogle Scholar
  • Bubeck S, Chandrasekaran V, Eldan R, Gehrke J, Horvitz E, Kamar E, Lee P, et al. (2023) Sparks of artificial general intelligence: Early experiments with GPT-4. Preprint, submitted March 22, https://arxiv.org/abs/2303.12712.Google Scholar
  • Che YK, Gale I (2003) Optimal design of research contests. Amer. Econom. Rev. 93(3):646–671.CrossrefGoogle Scholar
  • Chen M, Tworek J, Jun H, Yuan Q, Pinto HP de O, Kaplan J, Edwards H, et al. (2021) Evaluating large language models trained on code. Preprint, submitted July 7, https://arxiv.org/abs/2107.03374.Google Scholar
  • Choudhury P, Allen RT, Endres MG (2021) Machine learning for pattern discovery in management research. Strategic Management J. 42(1):30–57.CrossrefGoogle Scholar
  • Choudhary V, Marchetti A, Shrestha YR, Puranam P (2023) Human-AI ensembles: When can they work? J. Management, ePub ahead of print October 3, https://doi.org/10.1177/01492063231194968.Google Scholar
  • Cyert RM, March JG (1963) A Behavioral Theory of the Firm (Prentice-Hall, Englewood Cliffs, NJ), 169–187.Google Scholar
  • Dahan E, Mendelson H (2001) An extreme-value model of concept testing. Management Sci. 47(1):102–116.LinkGoogle Scholar
  • DAIR.AI (2024) Prompt chaining. Prompting guide. Retrieved March 21, 2024, https://www.promptingguide.ai/techniques/prompt_chaining.Google Scholar
  • Dell’Acqua F, McFowland E, Mollick ER, Lifshitz-Assaf H, Kellogg K, Rajendran S, Krayer L, Candelon F, Lakhani KR (2023) Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Working Paper No. 24-013, Harvard Business School, Boston.Google Scholar
  • Dewar RD, Dutton JE (1986) The adoption of radical and incremental innovations: An empirical analysis. Management Sci. 32(11):1422–1433.LinkGoogle Scholar
  • Doshi AR, Hauser O (2023) Generative artificial intelligence enhances creativity. Preprint, submitted August 14, https://doi.org/10.2139/ssrn.4535536.Google Scholar
  • Felin T, Holweg M (2024) Theory is all you need: AI, human cognition, and decision making. Preprint, submitted April 4, https://doi.org/10.2139/ssrn.4737265.Google Scholar
  • Fleming L, Sorenson O (2001) Technology as a complex adaptive system: Evidence from patent data. Res. Policy 30(7):1019–1039.CrossrefGoogle Scholar
  • Fleming L, Mingo S, Chen D (2007) Collaborative brokerage, generative creativity, and creative success. Admin. Sci. Quart. 52(3):443–475.CrossrefGoogle Scholar
  • Franceschelli G, Musolesi M (2023) On the creativity of large language models. Preprint, submitted March 27, https://arxiv.org/abs/2304.00008.Google Scholar
  • Gavetti G, Levinthal D (2000) Looking forward and looking backward: Cognitive and experiential search. Admin. Sci. Quart. 45(1):113–137.CrossrefGoogle Scholar
  • Gelman A, Hill J (2006) Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Girotra K, Terwiesch C, Ulrich KT (2010) Idea generation and the quality of the best idea. Management Sci. 56(4):591–605.LinkGoogle Scholar
  • Girotra K, Meincke L, Terwiesch C, Ulrich KT (2023) Ideas are dimes a dozen: Large language models for idea generation in innovation. Preprint, submitted August 2, https://doi.org/10.2139/ssrn.4526071.Google Scholar
  • Glaeser EL, Laibson D, Sacerdote B (2002) An economic approach to social capital. Econom. J. 112(483):F437–F458.Google Scholar
  • Gómez-Rodríguez C, Williams P (2023) A confederacy of models: A comprehensive evaluation of LLMs on creative writing. Preprint, submitted October 12, https://arxiv.org/abs/2310.08433.Google Scholar
  • Guzik EE, Byrge C, Gilde C (2023) The originality of machines: AI takes the Torrance Test. J. Creativity 33(3):100065.CrossrefGoogle Scholar
  • Hagendorff T, Fabi S, Kosinski M (2023) Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. Nature Comput. Sci. 3(10):833–838.CrossrefGoogle Scholar
  • Hargadon AB, Bechky BA (2006) When collections of creatives become creative collectives: A field study of problem solving at work. Organ. Sci. 17(4):484–500.LinkGoogle Scholar
  • He VF, Shrestha YR, Puranam P, Miron-Spektor E (2023) Searching together: A theory of human-AI co-creativity. INSEAD Working Paper No. 2023/55/STR/OBH, INSEAD, Fontainebleau, France.Google Scholar
  • Iansiti M, Lakhani KR (2020) Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World (Harvard Business Review Press, Boston).Google Scholar
  • Imbens GW, Rubin DB (2015) Causal Inference for Statistics, Social, and Biomedical Sciences (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Ivcevic Z, Grandinetti M (2024) Artificial intelligence as a tool for creativity. J. Creativity 34(2):10079.CrossrefGoogle Scholar
  • Jang S (2017) Cultural brokerage and quality in multicultural teams. Organ. Sci. 28(6):993–1009.LinkGoogle Scholar
  • Jeppesen LB, Lakhani KR (2010) Marginality and problem-solving effectiveness in broadcast search. Organ. Sci. 21(5):1016–1033.LinkGoogle Scholar
  • Ji Z, Lee N, Frieske R, Yu T, Su D, Xu Y, Ishii E, Bang YJ, Madotto A, Fung P (2023) Survey of hallucination in natural language generation. ACM Comput. Surveys 55(12):1–38.CrossrefGoogle Scholar
  • Jia N, Luo X, Fang Z, Liao C (2023) When and how artificial intelligence augments employee creativity. Acad. Management J. 67(1):5–32.CrossrefGoogle Scholar
  • Kaplan S, Vakili K (2015) The double‐edged sword of recombination in breakthrough innovation. Strategic Management J. 36(10):1435–1457.CrossrefGoogle Scholar
  • Katila R, Ahuja G (2002) Something old, something new: A longitudinal study of search behavior and new product introduction. Acad. Management J. 45(6):1183–1194.CrossrefGoogle Scholar
  • Kenny D, Kashy D, Cook W, Simpson J (2006) Dyadic Data Analysis (The Guildford Press, New York).Google Scholar
  • Kim H, Glaeser EL, Hillis A, Kominers SD, Luca M (2024) Decision authority and the returns to algorithms. Strategic Management J. 45(4):619–648.CrossrefGoogle Scholar
  • Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2018) Human decisions and machine predictions. Quart. J. Econom. 133(1):237–293.CrossrefGoogle Scholar
  • Kneeland MK, Schilling MA, Aharonson BS (2020) Exploring uncharted territory: Knowledge search processes in the origination of outlier innovation. Organ. Sci. 31(3):535–557.LinkGoogle Scholar
  • Koivisto M, Grassini S (2023) Best humans still outperform artificial intelligence in a creative divergent thinking task. Sci. Rep. 13(1):13601.CrossrefGoogle Scholar
  • Kong A, Zhao S, Chen H, Li Q, Qin Y, Sun R, Zhou X (2023) Better zero-shot reasoning with role-play prompting. Preprint, submitted August 15, https://arxiv.org/abs/2308.07702.Google Scholar
  • Kuznetsova A, Brockhoff PB, Christensen RHB (2017) lmerTest package: Tests in linear mixed effects models. J. Statist. Software 82(13):1–26.CrossrefGoogle Scholar
  • Laursen K, Salter A (2006) Open for innovation: The role of openness in explaining innovation performance among UK manufacturing firms. Strategic Management J. 27(2):131–150.CrossrefGoogle Scholar
  • Lebovitz S, Lifshitz-Assaf H, Levina N (2022) To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organ. Sci. 33(1):126–148.LinkGoogle Scholar
  • Leiponen A, Helfat CE (2010) Innovation objectives, knowledge sources, and the benefits of breadth. Strategic Management J. 31(2):224–236.CrossrefGoogle Scholar
  • Lenth R, Love J, Herve M (2018) emmeans: Estimated marginal means, aka least-squares means. Accessed May 20, 2024, https://cran.r-project.org/package=emmeans.Google Scholar
  • Levine SS, Schilke O, Kacperczyk O, Zucker LG (2023) Primer for experimental methods in organization theory. Organ. Sci. 34(6):1997–2025.LinkGoogle Scholar
  • Levinthal DA (1997) Adaptation on rugged landscapes. Management Sci. 43(7):934–950.LinkGoogle Scholar
  • Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, Küttler H, et al. (2020) Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances Neural Inform. Processing Systems 33:9459–9474.Google Scholar
  • Li D, Raymond LR, Bergman P (2020) Hiring as exploration. NBER Working Paper No. 27736, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Lifshitz-Assaf H (2018) Dismantling knowledge boundaries at NASA: The critical role of professional identity in open innovation. Admin. Sci. Quart. 63(4):746–782.CrossrefGoogle Scholar
  • Lindebaum D, Vesa M, Den Hond F (2020) Insights from “the machine stops” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Acad. Management Rev. 45(1):247–263.CrossrefGoogle Scholar
  • Lou B, Wu L (2021) AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms. Management Inform Systems Quart. 45(3):1451–1482.CrossrefGoogle Scholar
  • March JG (1991) Exploration and exploitation in organizational learning. Organ. Sci. 2(1):71–87.LinkGoogle Scholar
  • Meincke L, Mollick ER, Terwiesch C (2024) Prompting diverse ideas: Increasing AI idea variance. Preprint, submitted January 27, https://arxiv.org/abs/2402.01727.Google Scholar
  • Miric M, Jia N, Huang KG (2023) Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents. Strategic Management J. 44(2):491–519.CrossrefGoogle Scholar
  • Mollick E, Nanda R (2016) Wisdom or madness? Comparing crowds with expert evaluation in funding the arts. Management Sci. 62(6):1533–1553.LinkGoogle Scholar
  • Nelson RR (1961) Uncertainty, learning, and the economics of parallel research and development efforts. Rev. Econom. Statist. 43(4):351–364.CrossrefGoogle Scholar
  • Nelson RR, Winter SG (1982) The Schumpeterian tradeoff revisited. Amer. Econom. Rev. 72(1):114–132.Google Scholar
  • Nickerson JA, Zenger TR (2004) A knowledge-based theory of the firm—The problem-solving perspective. Organ. Sci. 15(6):617–632.LinkGoogle Scholar
  • Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Preprint, submitted March 6, https://doi.org/10.2139/ssrn.4375283.Google Scholar
  • Ocasio W (1997) Toward an attention‐based view of the firm. Strategic Management J. 18(S1):187–206.CrossrefGoogle Scholar
  • OpenAI (2024) Strategy: Write clear instructions. Accessed July 11, 2024, https://platform.openai.com/docs/guides/prompt-engineering.Google Scholar
  • Otis N, Clarke RP, Delecourt S, Holtz D, Koning R (2023) The uneven impact of generative AI on entrepreneurial performance. Preprint, submitted January 17, https://doi.org/10.2139/ssrn.4671369.Google Scholar
  • Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A (2022) Training language models to follow instructions with human feedback. Advances Neural Inform. Processing Systems 35:27730–27744.Google Scholar
  • Paik JH, Scholl M, Sergeev R, Randazzo S, Lakhani KR (2020) Innovation contests for high-tech procurement. Res. Tech. Management 63(2):36–45.CrossrefGoogle Scholar
  • Perry-Smith JE (2006) Social yet creative: The role of social relationships in facilitating individual creativity. Acad. Management J. 49(1):85–101.CrossrefGoogle Scholar
  • Perry-Smith JE, Mannucci PV (2017) From creativity to innovation: The social network drivers of the four phases of the idea journey. Acad. Management Rev. 42(1):53–79.CrossrefGoogle Scholar
  • Piezunka H, Dahlander L (2015) Distant search, narrow attention: How crowding alters organizations’ filtering of suggestions in crowdsourcing. Acad. Management J. 58(3):856–880.CrossrefGoogle Scholar
  • Piezunka H, Dahlander L (2019) Idea rejected, tie formed: Organizations’ feedback on crowdsourced ideas. Acad. Management J. 62(2):503–530.CrossrefGoogle Scholar
  • Raisch S, Fomina K (2023) Combining human and artificial intelligence: Hybrid problem-solving in organizations. Acad. Management Rev., ePub ahead of print January 5, https://doi.org/10.5465/amr.2021.0421.Google Scholar
  • Riedl C, Grad T, Lettl C (2024) Competition and collaboration in crowdsourcing communities: What happens when peers evaluate each other? Organ. Sci., ePub ahead of print April 30, https://doi.org/10.1287/orsc.2021.15163.LinkGoogle Scholar
  • Renze M, Guven E (2024) The effect of sampling temperature on problem solving in large language models. Preprint, submitted February 7, https://arxiv.org/abs/2402.05201.Google Scholar
  • Rhee L, Leonardi PM (2018) Which pathway to good ideas? An attention‐based view of innovation in social networks. Strategic Management J. 39(4):1188–1215.CrossrefGoogle Scholar
  • Rindova VP, Petkova AP (2007) When is a new thing a good thing? Technological change, product form design, and perceptions of value for product innovations. Organ. Sci. 18(2):217–232.LinkGoogle Scholar
  • Saravia E (2022) Prompt engineering guide. Accessed June 22, 2024, https://www.promptingguide.ai/.Google Scholar
  • Shanahan M, McDonell K, Reynolds L (2023) Role play with large language models. Nature 623(7987):493–498.CrossrefGoogle Scholar
  • Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489.CrossrefGoogle Scholar
  • Simon HA (1973) The structure of ill structured problems. Artificial Intelligence 4(3–4):181–201.CrossrefGoogle Scholar
  • Stevenson C, Smal I, Baas M, Grasman R, van der Maas H (2022) Putting GPT-3′s creativity to the (alternative uses) test. Preprint, submitted June 10, https://arxiv.org/abs/2206.08932.Google Scholar
  • Taylor CR (1995) Digging for golden carrots: An analysis of research tournaments. Amer. Econom. Rev. 85(4):872–890.Google Scholar
  • Teodoridis F, Bikard M, Vakili K (2019) Creativity at the knowledge frontier: The impact of specialization in fast-and slow-paced domains. Admin. Sci. Quart. 64(4):894–927.CrossrefGoogle Scholar
  • Terwiesch C, Ulrich KT (2009) Innovation Tournaments: Creating and Selecting Exceptional Opportunities (Harvard Business Review Press, Boston).Google Scholar
  • Terwiesch C, Xu Y (2008) Innovation contests, open innovation, and multiagent problem solving. Management Sci. 54(9):1529–1543.LinkGoogle Scholar
  • Tong S, Jia N, Luo X, Fang Z (2021) The Janus face of artificial intelligence feedback: Deployment vs. disclosure effects on employee performance. Strategic Management J. 42(9):1600–1631.CrossrefGoogle Scholar
  • Tripsas M (2009) Technology, identity, and inertia through the lens of “The Digital Photography Company.” Organ. Sci. 20(2):441–460.LinkGoogle Scholar
  • Van de Ven AH (1986) Central problems in the management of innovation. Management Sci. 32(5):590–607.LinkGoogle Scholar
  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances Neural Inform. Processing Systems 28:30.Google Scholar
  • Wang L, Ma C, Feng X, Zhang Z, Yang H, Zhang J, Chen Z, et al. (2024) A survey on large language model based autonomous agents. Front. Comput. Sci. 18(6):186345.Google Scholar
  • Weber M (1978) Economy and Society: An Outline of Interpretive Sociology (University of California Press, Berkeley, CA).Google Scholar
  • Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E, Le Q, Zhou D (2022) Chain-of-thought prompting elicits reasoning in large language models. Preprint, submitted January 28, https://arxiv.org/abs/2201.11903.Google Scholar
  • Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316(5827):1036–1039.CrossrefGoogle Scholar
  • Xi Z, Chen W, Guo X, He W, Ding Y, Hong B, Zhang M, Wang J, Jin S, Zhou E (2023) The rise and potential of large language model based agents: A survey. Preprint, submitted September 14, https://arxiv.org/abs/2309.07864.Google Scholar
  • Yager KG (2023) Domain-specific chatbots for science using embeddings. Digital Discovery 2(6):1850–1861.CrossrefGoogle Scholar
  • Yin Z, Wang H, Horio K, Kawahara D, Sekine S (2024) Should we respect LLMs? A cross-lingual study on the influence of prompt politeness on LLM performance. Preprint, submitted February 22, https://arxiv.org/abs/2402.14531.Google Scholar
  • Yin S, Fu C, Zhao S, Li K, Sun X, Xu T, Chen E (2023) A survey on multimodal large language models. Preprint, submitted June 23, https://arxiv.org/abs/2306.13549.Google Scholar
  • Zamfirescu-Pereira JD, Wong RY, Hartmann B, Yang Q (2023) Why Johnny can’t prompt: How non-AI experts try (and fail) to design LLM prompts. Proc. 2023 CHI Conf. Human Factors Comput. Systems (CHI ‘23) (Association for Computing Machinery, New York).Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.