ORIGINAL ARTICLE
Perception of AI Creativity: Dimensional Exploration and Scale Development
Yongzhong Yang,
Haoran Xu,
Yongzhong Yang
School of Business, Sichuan University, Chendu, China
Search for more papers by this authorCorresponding Author
Haoran Xu
School of Business, Sichuan University, Chendu, China
Correspondence:
Haoran Xu ([email protected])
Search for more papers by this authorYongzhong Yang,
Haoran Xu,
Yongzhong Yang
School of Business, Sichuan University, Chendu, China
Search for more papers by this authorCorresponding Author
Haoran Xu
School of Business, Sichuan University, Chendu, China
Correspondence:
Haoran Xu ([email protected])
Search for more papers by this authorFirst published: 05 May 2025
Funding: This study was supported by the Key Project of the National Social Science Foundation of China: Research on Value Management of Cultural Creativity (18AGL024).
ABSTRACT
With the rapid advancement of artificial intelligence (AI), AI creativity has demonstrated significant potential for application across various fields. This study aims to explore the multidimensional characteristics of AI creativity from the audience's perspective and to develop a corresponding measurement scale. Specifically, Study 1 utilized open-ended interviews with audiences of AI-generated creative products and grounded theory-based data coding to construct a theoretical framework of AI creativity perception. This framework encompasses four core dimensions: originality, depth, credibility, and attractiveness. In Study 2, an exploratory factor analysis and confirmatory factor analysis were conducted to develop a scale with high reliability and validity for measuring AI creativity perception, providing empirical support for the multidimensional framework. To further validate the scale's criterion-related validity, Study 3 examined the effect of AI involvement disclosure on creativity perception. The results reveal that audiences hold biases against AI; although AI is perceived to have a significant advantage in enhancing the originality of creative products, it is viewed as less capable in terms of depth, credibility, and attractiveness. This research offers insights into the future development and iteration of AI creativity.
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
Data are available from the corresponding author upon reasonable request.
References
- Acar, S., C. Burnett, and J. F. Cabra. 2017. “Ingredients of Creativity: Originality and More.” Creativity Research Journal 29, no. 2: 133–144.
- Amankwah-Amoah, J., S. Abdalla, E. Mogaji, A. Elbanna, and Y. K. Dwivedi. 2024. “The Impending Disruption of Creative Industries by Generative AI: Opportunities, Challenges, and Research Agenda.” International Journal of Information Management 79: 102759. https://doi.org/10.1016/j.ijinfomgt.2024.102759.
- Anantrasirichai, N., and D. Bull. 2022. “Artificial Intelligence in the Creative Industries: A Review.” Artificial Intelligence Review 55, no. 1: 589–656.
- Ang, S. H., and S. Y. Low. 2000. “Exploring the Dimensions of AD Creativity.” Psychology & Marketing 17, no. 10: 835–854.
- Baas, M. 2024. “Artificial Intelligence and the Question of Creativity: Art, Data and the Sociocultural Archive of AI-Imaginations.” European Journal of Cultural Studies 27, no. 4: 788–795.
- Babbie, E. 2004. The Practice of Social Research. 10th ed. Thomson/Wadsworth.
- Besemer, S. P. 1998. “Creative Product Analysis Matrix: Testing the Model Structure and a Comparison Among Products—Three Novel Chairs.” Creativity Research Journal 11, no. 4: 333–346. https://doi.org/10.1207/s15326934crj1104_7.
- Besemer, S. P., and K. O'Quin. 1986. “Analyzing Creative Products: Refinement and Test of a Judging Instrument.” Journal of Creative Behavior 20, no. 2: 115–126.
- Besemer, S. P., and K. O'Quin. 1999. “Confirming the Three-Factor Creative Product Analysis Matrix Model in an American Sample.” Creativity Research Journal 12, no. 4: 287–296.
- Besemer, S. P., and D. J. Treffinger. 1981. “Analysis of Creative Products: Review and Synthesis.” Journal of Creative Behavior 15, no. 3: 158–178.
- Boden, M. A. 2004. The Creative Mind: Myths and Mechanisms. Routledge.
10.4324/9780203508527 Google Scholar
- Bollen, K. A. 1989. Structural Equations With Latent Variables. John Wiley & Sons.
10.1002/9781118619179 Google Scholar
- Cheng, H., X. Sun, J. Xie, et al. 2024. “Constructing and Validating the Museum Product Creativity Measurement (MPCM): Dimensions for Creativity Assessment of Souvenir Products in Chinese Urban Historical Museums.” Humanities and Social Sciences Communications 11: 280.
10.1057/s41599-024-02780-5 Google Scholar
- Churchill, G. A., Jr. 1979. “A Paradigm for Developing Better Measures of Marketing Constructs.” Journal of Marketing Research 16, no. 1: 64–73.
- Coca-Cola. 2023. “Coca-Cola Hosts Digital Artists and Tech Enthusiasts for Generative AI Summit.” https://www.coca-colacompany.com/media-center/coca-cola-hosts-digitalartists-tech-enthusiasts-generative-ai-summit.
- Comrey, A. L., and H. B. Lee. 1992. A First Course in Factor Analysis. 2nd ed. Lawrence Erlbaum Associates.
- Corazza, G. E. 2016. “Potential Originality and Effectiveness: The Dynamic Definition of Creativity.” Creativity Research Journal 28, no. 3: 258–267.
- Dastane, O., C. L. Goi, and F. K. Rabbanee. 2023. “The Development and Validation of a Scale to Measure Perceived Value of Mobile Commerce (MVAL-SCALE).” Journal of Retailing and Consumer Services 71: 103222.
- De Garrido, L. 2022. “Conceptual Design of a Creative Artificial Intelligence System Based on the Neurocognitive Bases of Human Creativity in the Brain.” Creativity Research Journal 34, no. 3: 273–294.
- De Vries, K. 2020. “You Never Fake Alone. Creative AI in Action.” Information, Communication & Society 23, no. 14: 2110–2127.
- Dopico-Parada, A., M. J. López-Miguens, and P. Álvarez-González. 2021. “Building Value With Packaging: Development and Validation of a Measurement Scale.” Journal of Retailing and Consumer Services 63: 102685.
- Doshi, A. R., and O. P. Hauser. 2024. “Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content.” Science Advances 10, no. 28: eadn5290. https://doi.org/10.1126/sciadv.adn5290.
- Dumas, D., B. Forthmann, and P. Alexander. 2024. “Using a Model of Domain Learning to Understand the Development of Creativity.” Educational Psychologist 59, no. 3: 143–158.
- Dumas, D., and J. C. Kaufman. 2024. “Evaluation Is Creation: Self and Social Judgments of Creativity Across the Four-C Model.” Educational Psychology Review 36, no. 4: 107.
10.1007/s10648-024-09947-1 Google Scholar
- Fornell, C., and D. F. Larcker. 1981. “Evaluating Structural Equation Models With Unobservable Variables and Measurement Error.” Journal of Marketing Research 18, no. 1: 39–50.
- Garcia, M. B. 2024. “The Paradox of Artificial Creativity: Challenges and Opportunities of Generative AI Artistry.” Creativity Research Journal 8: 1–14.
- Glaser, B. G. 1978. Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.
- Glăveanu, V. P., and R. A. Beghetto. 2021a. Creative Experience: Theory, Research, and Applications. Cambridge University Press.
- Glăveanu, V. P., and R. A. Beghetto. 2021b. “Creative Experience: A Non-Standard Definition of Creativity.” Creativity Research Journal 33, no. 2: 75–80.
- Green, A. E., R. E. Beaty, Y. N. Kenett, and J. C. Kaufman. 2024. “The Process Definition of Creativity.” Creativity Research Journal 36, no. 3: 544–572.
- Guest, G., A. Bunce, and L. Johnson. 2006. “How Many Interviews Are Enough? An Experiment With Data Saturation and Variability.” Field Methods 18, no. 1: 59–82.
- Guilford, J. P. 1950. “Creativity.” American Psychologist 5, no. 9: 444–454.
- Haberland, G. S., and P. A. Dacin. 1992. “The Development of a Measure to Assess Viewers' Judgments of the Creativity of an Advertisement: A Preliminary Study.” Advances in Consumer Research 19, no. 1: 817–825.
- Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2010. Multivariate Data Analysis. 7th ed. Pearson.
- Hao, X., J. Zhang, Z. Lei, and others. 2021. “Platform Leadership: The Multi-Dimensional Construction, Measurement, and Verification of the Impact on Innovation Behavior.” Management World 37, no. 1: 186–199.
- Hennink, M., and B. N. Kaiser. 2022. “Sample Sizes for Saturation in Qualitative Research: A Systematic Review of Empirical Tests.” Social Science & Medicine 292: 114523.
- Herderich, A., H. H. Freudenthaler, and D. Garcia. 2024. “A Computational Method to Reveal Psychological Constructs From Text Data.” Psychological Methods 19: 700.
- Hitsuwari, J., Y. Ueda, W. Yun, and M. Nomura. 2023. “Does Human–AI Collaboration Lead to More Creative Art? Aesthetic Evaluation of Human-Made and AI-Generated Haiku Poetry.” Computers in Human Behavior 139: 107502.
- Horn, D., and G. Salvendy. 2006. “Product Creativity: Conceptual Model, Measurement and Characteristics.” Theoretical Issues in Ergonomics Science 7, no. 4: 395–412.
10.1080/14639220500078195 Google Scholar
- Horn, D., and G. Salvendy. 2009. “Measuring Consumer Perception of Product Creativity: Impact on Satisfaction and Purchasability.” Human Factors and Ergonomics in Manufacturing & Service Industries 19, no. 3: 223–240.
- Hu, L., and P. M. Bentler. 1999. “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives.” Structural Equation Modeling: A Multidisciplinary Journal 6, no. 1: 1–55.
- Huang, L. L., R. P. Chen, and K. W. Chan. 2024. “Pairing Up With Anthropomorphized Artificial Agents: Leveraging Employee Creativity in Service Encounters.” Journal of the Academy of Marketing Science 52: 955–975.
- Hubert, K. F., K. N. Awa, and D. L. Zabelina. 2024. “The Current State of Artificial Intelligence Generative Language Models Is More Creative Than Humans on Divergent Thinking Tasks.” Scientific Reports 14, no. 1: 3440.
- Jackson, P. W., and S. Messick. 1965. “The Person, the Product, and the Response: Conceptual Problems in the Assessment of Creativity.” Journal of Personality 33, no. 3: 309–329.
- Khan, S. N. 2014. “Qualitative Research Method: Grounded Theory.” International Journal of Business and Management 9, no. 11: 224–233. https://doi.org/10.5539/ijbm.v9n11p224.
10.5539/ijbm.v9n11p224 Google Scholar
- Kharkhurin, A. V. 2014. “Creativity. 4in1: Four-Criterion Construct of Creativity.” Creativity Research Journal 26, no. 3: 338–352.
- Larsen, B., and J. Narayan. 2023. “Generative AI: A Game-Changer That Society and Industry Need to Be Ready for World Economic Forum.” https://www.weforum.org/agenda/2023/01/davos23-generative-ai-a-game-changer-industries-and-society-code-developers/.
- Lazovsky, G. S., T. Raz, and Y. N. Kenett. 2024. “The Art of Creative Inquiry—From Question Asking to Prompt Engineering.” Journal of Creative Behavior 59: e671.
- Lee, B. C., and J. Chung. 2024. “An Empirical Investigation of the Impact of ChatGPT on Creativity.” Nature Human Behaviour 8, no. 10: 1906–1914.
- Magni, F., J. Park, and M. M. Chao. 2024. “Humans as Creativity Gatekeepers: Are We Biased Against AI Creativity?” Journal of Business and Psychology 39, no. 3: 643–656.
10.1007/s10869-023-09910-x Google Scholar
- Marrone, R., D. Cropley, and K. Medeiros. 2024. “How Does Narrow AI Impact Human Creativity?” Creativity Research Journal 3: 1–11.
10.1080/10400419.2024.2378264 Google Scholar
- Martin, L., and N. Wilson. 2017. “Defining Creativity With Discovery.” Creativity Research Journal 29, no. 4: 417–425.
- McNeish, D. 2018. “Thanks Coefficient Alpha, We'll Take It From Here.” Psychological Methods 23, no. 3: 412–433.
- McNeish, D., and M. G. Wolf. 2023. “Dynamic Fit Index Cutoffs for Confirmatory Factor Analysis Models.” Psychological Methods 28, no. 1: 61–88.
- Mercanti-Guérin, M. 2008. “Consumers' Perception of the Creativity of Advertisements: Development of a Valid Measurement Scale.” Recherche et Applications en Marketing (English Edition) 23, no. 4: 97–118.
10.1177/205157070802300405 Google Scholar
- Mumford, M. D. 2003. “Where Have We Been, Where Are We Going? Taking Stock in Creativity Research.” Creativity Research Journal 15: 107–120.
- O'Quin, K., and S. P. Besemer. 1989. “The Development, Reliability, and Validity of the Revised Creative Product Semantic Scale.” Creativity Research Journal 2, no. 4: 267–278.
10.1080/10400418909534323 Google Scholar
- Orwig, W., E. R. Edenbaum, J. D. Greene, and D. L. Schacter. 2024. “The Language of Creativity: Evidence From Humans and Large Language Models.” Journal of Creative Behavior 58, no. 1: 128–136.
- Pagani, M., and Y. Wind. 2024. “Unlocking Marketing Creativity Using Artificial Intelligence.” Journal of Interactive Marketing 60, no. 1: 1–24.
- Rafner, J., R. E. Beaty, J. C. Kaufman, T. Lubart, and J. Sherson. 2023. “Creativity in the Age of Generative AI.” Nature Human Behaviour 7, no. 11: 1836–1838.
- Reddy, A. 2022. “Artificial Everyday Creativity: Creative Leaps With AI Through Critical Making.” Digital Creativity 33, no. 4: 295–313.
- Runco, M. A. 2025. “Updating the Standard Definition of Creativity to Account for the Artificial Creativity of AI.” Creativity Research Journal 37, no. 1: 1–5.
- Runco, M. A., and G. J. Jaeger. 2012. “The Standard Definition of Creativity.” Creativity Research Journal 24: 92–96.
- Russell, S., and P. Norvig. 2020. Artificial Intelligence: A Modern Approach. 4th ed. Pearson.
- Stein, M. 1953. “Creativity and Culture.” Journal of Psychology 36, no. 2: 311–322.
- Sternberg, R. J., and T. I. Lubart. 1999. “ The Concept of Creativity: Prospects and Paradigms.” In Handbook of Creativity, edited by R. J. Sternberg, 3–15. Cambridge University Press.
- Strauss, A. L., and J. M. Corbin. 1990. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.
- Tigre Moura, F. 2023. “Artificial Intelligence, Creativity, and Intentionality: The Need for a Paradigm Shift.” Journal of Creative Behavior 57, no. 3: 336–338.
10.1002/jocb.585 Google Scholar
- Tigre Moura, F., C. Castrucci, and C. Hindley. 2023. “Artificial Intelligence Creates Art? An Experimental Investigation of Value and Creativity Perceptions.” Journal of Creative Behavior 57, no. 4: 534–549.
10.1002/jocb.600 Google Scholar
- Vinchon, F., T. Lubart, S. Bartolotta, et al. 2023. “Artificial Intelligence & Creativity: A Manifesto for Collaboration.” Journal of Creative Behavior 57, no. 4: 472–484.
- Walia, C. 2019. “A Dynamic Definition of Creativity.” Creativity Research Journal 31, no. 3: 237–247.
- Wingström, R., J. Hautala, and R. Lundman. 2024. “Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists.” Creativity Research Journal 36, no. 2: 177–193.
10.1080/10400419.2022.2107850 Google Scholar
- Wolf, M. G., and D. McNeish. 2020. “Dynamic Model Fit (R Shiny Application Version 2.0.0).”
- Zhou, E., and D. Lee. 2024. “Generative Artificial Intelligence, Human Creativity, and Art.” PNAS Nexus 3, no. 3: pgae052.
