CACTUS CACTUS J Tour Bus Manag Econ CACTUS – Journal of Tourism Business, Management and Economics 2247-3297 ASE Publishing House Bucharest, Romania 10.24818/CTS/5/2023/1.06 cactus-2023-6 Research Article D80, C80 Business use: is AI surpassing human creativity? Niculae Andrei Daniel andreiniculae30@yahoo.ro Bucharest University of Economic Studies, Bucharest, Romania 15 6 2023 5 1 1562023 Copyright © 2023, The Author(s) 2023 The Author(s) This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). https://creativecommons.org/licenses/by-nc/4.0/

This study aims to assess people’s perceptions regarding AI-generated images compared to those made by humans. This study used quantitative research in the form of a survey to find out how much respondents prefer AI-generated images. The findings indicated that most respondents favoured artificial intelligence (AI)-generated visuals. Age and time spent online were key determinants of this choice. These discoveries have important implications for organisations and people who produce and consume visual content in their jobs. It implies that as people become familiar to them and as technology advances, AI-generated images are likely to gain popularity in the future. It also emphasises how critical it is to be aware of audience preferences and adjust to the evolving state of technology and visual media. In conclusion, this study offers insightful information about the prospective effects of AI-generated images on the creative sectors and the larger society.

 Keywords Artificial Intelligence Artificial Intelligence Tools AI-generated images Human perception Images

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References Chesterman, S. (2020). Artificial intelligence and the limits of legal personality. The International and Comparative Law Quarterly, 69(4), 819–844. https://doi.org/10.1017/S0020589320000366 10.1017/S0020589320000366 Directive 2001/29/EC of the European Parliament and of the Council of 22 June 2001 on the harmonisation of certain aspects of copyright and related rights in the information society. (2001). Official Journal of the European Communities, L 167, 10–19. Egger, J., Pepe, A., Gsaxner, C., Yuan, J., Li, J., & Zorita, E. (2021). Deep learning—A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact. PeerJ Computer Science, 7, Article e773. https://doi.org/10.7717/peerj-cs.773 10.7717/peerj-cs.773 Gardezi, J. S., Elazab, A., Lei, B., & Wang, T. (2019). Breast cancer detection and diagnosis using mammographic data: Systematic review. Journal of Medical Internet Research, 22(6), Article e14464. https://doi.org/10.2196/14464 10.2196/14464 Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2414–2423). IEEE. https://doi.org/10.1109/CVPR.2016.265 10.1109/CVPR.2016.265 ImageNet. (n.d.). In Wikipedia. Retrieved March 29, 2021, from https://en.wikipedia.org/wiki/ImageNet Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2018). Progressive growing of GANs for improved quality, stability, and variation. In International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1710.10196 10.48550/arXiv.1710.10196 Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4396–4405). IEEE. https://doi.org/10.1109/CVPR.2019.00453 10.1109/CVPR.2019.00453 Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology. Retrieved March 29, 2021, from https://psycnet.apa.org/record/1933-01885-001 Nguyen, P. (2019). The monkey selfie, artificial intelligence and authorship in copyright: The limits of human rights. Public Interest Law Journal of New Zealand. Retrieved March 29, 2021, from http://www.nzlii.org/nz/journals/NZPubIntLawJl/2019/7.html Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyene, T. T., Pham, Q. V., & Nguyen, C. M. (2022). Deep learning for deepfakes creation and detection: A survey. https://doi.org/10.2139/ssrn.4030341 10.2139/ssrn.4030341 Singh, A., & Singh, J. (2020). Survey on single image based super-resolution—Implementation challenges and solutions. Multimedia Tools and Applications, 79(3–4), 1641–1672. https://doi.org/10.1007/s11042-019-08254-0 10.1007/s11042-019-08254-0 Singh, P., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of Economics, Commerce and Management, 2(11), 1–22. https://www.researchgate.net/publication/341552596_Sampling_Techniques_and_Determination_of_Sample_Size_in_Applied_Statistics_Research_An_Overview Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7 10.1038/s41591-018-0300-7 Trust & Safety Financial Services & Fintech. (2022). How is AI transforming fraud detection in banks? Retrieved April 4, 2021, from https://www.telusinternational.com/insights/trust-and-safety/article/ai-fraud-detection-in-banks Zhang, D., Mishra, S., Brynjolfsson, E., Etchemendy, J., Ganguli, D., Grosz, B., Lyons, T., Manyika, J., Niebles, J. C., Sellitto, M., Shoham, Y., Clark, J., & Perrault, R. (2021). Artificial intelligence index report 2021. AI Index Steering Committee. https://doi.org/10.48550/arXiv.2103.06312 10.48550/arXiv.2103.06312