Managerial creativity and its development supported by artificial intelligence: a comparative perspective between Europe and Asia and empirical evidence from industrial enterprises

Martina Mandáková; Henrieta Hrablik Chovanová

doi:10.14513/actatechjaur.00903

Authors

Martina Mandáková Institute of Industrial Engineering and Management, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, Ulica Jána Bottu č. 2781/25, 917 24 Trnava, Slovakia https://orcid.org/0009-0004-3755-8240
Henrieta Hrablik Chovanová Institute of Industrial Engineering and Management, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, Ulica Jána Bottu č. 2781/25, 917 24 Trnava, Slovakia https://orcid.org/0000-0001-9459-4193

DOI:

https://doi.org/10.14513/actatechjaur.00903

Keywords:

managerial creativity, empirical analysis, AI in trainings, organizational learning, educational technology, industrial training

Abstract

Managerial creativity is a key competency for ensuring innovation and competitiveness in the context of globalization and rapid technological change. This paper analyses the factors influencing managers' creative thinking, methods for its development and organizational strategies, with a comparative look at European and Asian approaches. Findings suggest that Asian organizations favour collective creativity, structured learning, and technocentric AI integration, while European models emphasize individual autonomy, interdisciplinary collaboration, and a human-centered AI ethic. The study combines a qualitative literature review with a quantitative survey of 109 L&D professionals in industrial enterprises in Slovakia and the Czech Republic. The results showed moderate effectiveness of internal learning processes, with adaptation of new employees, inadequate measurement of outcomes and low motivation to learn being the main barriers. Information flow was often restricted by outdated processes. However, respondents showed high interest in using AI to support personalisation, onboarding and the development of creative thinking. Based on the analysis, a model of AI supporting 'augmented creativity' is proposed, where generative AI acts as a partner to develop divergent thinking and flexibility in problem solving. The study also highlights ethical and organisational challenges, such as automation bias or the risk of limiting creative autonomy, and recommends a hybrid approach combining a European focus on individual initiative and ethics with an Asian collective responsibility and technology focus.

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References

N. Tusquellas, R. Palau, R. Santiago, Analysis of the potential of artificial intelligence for professional development and talent management: A systematic literature review, International Journal of Information Management Data Insights 4 (2) (2024) 100288. https://doi.org/10.1016/j.jjimei.2024.100288

E. Brynjolfsson, A. McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, 1st Edition, W. W. Norton & Company, New York, 2014.

K. Teplická, S. Hurná, S. New approach of costs of quality according their trend during long period in industrial enterprises in SMEs. Management Systems in Production Engineering, 29(1), 20–26. https://doi.org/10.2478/mspe-2021-0003

J. Majerová, Cognitive rationality and sustainable decision based on Maslow’s theorem: A case study in Slovakia, Cognitive Sustainability 1 (1) (2022). https://doi.org/10.55343/CogSust.8

T. M. Amabile, Creativity in Context: Update To The Social Psychology of Creativity, 1st Edition, Routledge, London, 1996. https://doi.org/10.4324/9780429501234

R. Florida, The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life, Basic Books, New York, 2002.

R. J. Sternberg, Wisdom, Intelligence, and Creativity Synthesized, 1st Edition, Cambridge University Press, Cambridge, 2003.

T. M. Amabile, The social psychology of creativity: A componential conceptualization, Journal of Personality and Social Psychology 45 (2) (1983) 357–376. https://doi.org/10.1037/0022-3514.45.2.357

Csikszentmihalyi, M., The Systems Model of Creativity: The Collected Works of Mihaly Csikszentmihalyi, Springer Science + Business Media, Dordrecht–Heidelberg–New York–London, 2014. https://doi.org/10.1007/978-94-017-9085-7

Nonaka, I., Takeuchi, H., The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press, New York–Oxford, 1995. Available online: https://lumsa.it/sites/default/files/UTENTI/u95/LM51_ITA_The%20Knowledge-Creating%20Company.pdf

R. Y. J. Chua, The Costs of Ambient Cultural Disharmony: Indirect Intercultural Conflicts in Social Environment Undermine Creativity, Academy of Management Journal 56 (6) (2013) 1545–1577. https://doi.org/10.5465/amj.2011.0971

C. Li, H. Zhao, T. M. Begley, Transformational leadership dimensions and employee creativity in China: A cross-level analysis, Journal of Business Research 68 (6) (2015) pp. 1149–1156. https://doi.org/10.1016/j.jbusres.2014.11.009

Lee, K., Oh, F. D., Shin, D., & Yoon, H. (2024). Innovation spillovers within business groups: Evidence from Korean chaebols. Emerging Markets Review, 60, 101151. https://doi.org/10.1016/j.ememar.2024.101151

R. B. Ravindran, M. K. Thakur, Foreword: Management education in India: Disciplinary and institutional practices, in: M. Thakur, R. R. Babu (Eds.), Management Education in India: Disciplinary and Institutional Practices, Springer, Singapore, 2016. Available online: https://ssrn.com/abstract=2825752

A. Huseynova, Sustainable Human Resource Management Practices Impacting Employer Branding, Cognitive Sustainability 1 (2) (2022). https://doi.org/10.55343/CogSust.15

D. Marczis, Z. Mihálovits, G. Sebestyén, Sustainability and Climate Risk Data: A New Era for Investment Decision Making in the Age of Climate Change, Cognitive Sustainability 2 (2) (2022). https://doi.org/10.55343/CogSust.64

L. Poškuvienė, K. Čižiūnienė, J. Matijošius, Analysis of Customer Service Quality Models and for their Approbation Opportunities in Aviation, Periodica Polytechnica Transportation Engineering 50 (3) (2022) 285–292. https://doi.org/10.3311/PPtr.15213

V. Gupta, S. Singh, How leaders impact employee creativity: a study of Indian R&D laboratories, Management Research Review 36 (1) (2013) pp. 66–88. https://doi.org/10.1108/01409171311284594

F. Sussan, K. Kim, R. R. Chinta, J. L. Enriquez, Trade-off between creativity and productivity in technology-based SMEs performance: policy implications in South Korea, Journal of the Asia Pacific Economy 22 (3) (2017) pp. 510–524. https://doi.org/10.1080/13547860.2016.1278326

Holubek, D. R. D. Sobrino, M. Matúšová, A new approach for creating and testing safety components integrated into a robotic cell simulation scenario in a virtual reality environment, Journal of Physics: Conference Series 2927 (1) (2024) 012002. https://doi.org/10.1088/1742-6596/2927/1/012002

R. Holubek, M. Kusá, R. Bocák, The case study of new approach to robot programming and layout design by supporting virtual and augmented reality, Journal of Physics: Conference Series 2540 (1) (2023) 012012. https://doi.org/10.1088/1742-6596/2540/1/012012

M. Krynke, M. Mazur, Innovative Work Order Planning with Process Optimization Using Computer Simulation in the Automotive Industry, in the Case of Repair Workshops, Periodica Polytechnica Transportation Engineering 52 (3) (2024) 292–300. https://doi.org/10.3311/PPtr.23546

Sun, S., Li, A. Z., Foo, M. D., Zhou, J., & Lu, J. G. (2025). How and for whom using generative AI affects creativity: A field experiment. Journal of Applied Psychology, 110(12), 1561–1573. https://doi.org/10.1037/apl0001296

Wallas, G. (1926). The Art of Thought. New York, NY, USA: Harcourt, Brace and Company. Available online: https://archive.org/details/theartofthought

Wang, L. (2024). Applying automated machine translation to educational video courses. Education and Information Technologies, 29, 10377–10390. https://doi.org/10.1007/s10639-023-12219-0

B. Shneiderman, Human-Centered Artificial Intelligence: Three Fresh Ideas, AIS Transactions on Human-Computer Interaction 12 (3) (2020) 109–124. https://doi.org/10.17705/1thci.00131

D. Long, B. Magerko, What is AI literacy? Competencies and design considerations, in: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020) pp. 1–16. https://doi.org/10.1145/3313831.3376727

E. Glikson, A. W. Woolley, Human trust in artificial intelligence: Review of empirical research, Academy of Management Annals 14 (2) (2020) pp. 627–660. https://doi.org/10.5465/annals.2018.0057

S. A. D. Popenici, S. Kerr, Exploring the impact of artificial intelligence on teaching and learning in higher education, Research and Practice in Technology Enhanced Learning 12 (2017) 22. https://doi.org/10.1186/s41039-017-0062-8

B. C. Cheong, Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making, Frontiers in Human Dynamics 6 (2024) Article 1421273. https://doi.org/10.3389/fhumd.2024.1421273

European Commission, Ethics Guidelines for Trustworthy AI, Publications Office of the European Union, Brussels, 2019. https://doi.org/10.2759/177365

Y. Ikkatai, Y. Itatsu et al., The relationship between the attitudes of the use of AI and diversity awareness: comparisons between Japan, the US, Germany, and South Korea, AI & Society 40 (4) (2025) pp. 2369–2383. https://doi.org/10.1007/s00146-024-01982-4

D. J. Gunkel, Ars Ex Machina: Rethinking Responsibility in the Age of Creative Machines, in: A. L. Guzman (Ed.), Human–Machine Communication: Rethinking Communication, Technology, and Ourselves, Peter Lang, New York, 2018, pp. 221–236.

C. Yangın Ersanlı, F. Çelik, H. Barjesteh, V. Duran, M. Manoochehrzadeh, A review of global reskilling and upskilling initiatives in the age of AI, AI and Ethics 5 (2025) pp. 5719–5728. https://doi.org/10.1007/s43681-025-00767-9

Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. Hybrid Intelligence. Business & Information Systems Engineering 61 (2019) 637–643. https://doi.org/10.1007/s12599-019-00595-2

Tierney, P., & Farmer, S. M. (2011). Creative self-efficacy development and creative performance over time. Journal of Applied Psychology, 96(2), 277–293. https://doi.org/10.1037/a0020952

Y. Li, J. Chen, S. Li, Collective creativity with AI in organizational innovation: Evidence from East Asia, Technological Forecasting and Social Change 189 (2023) 122319. https://doi.org/10.1016/j.techfore.2023.122319

European Commission, European approach to artificial intelligence, European Commission digital strategy, https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence

Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act), Official Journal of the European Union L 2024/1689 (12.7.2024) Available online: https://eur-lex.europa.eu/eli/reg/2024/1689/oj

N. Benaich and I. Hogarth, State of AI Report 2024, State of AI, 2024. Available online: https://www.stateof.ai

Davenport, T. H., & Mittal, N. (2022, November 14). How Generative AI Is Changing Creative Work. Harvard Business Review. Available online: https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work

European Commission, Key competences for lifelong learning: European Reference Framework, Publications Office of the European Union, Luxembourg, 2019. https://doi.org/10.2766/291008

ENISA (European Union Agency for Cybersecurity), Cybersecurity of AI and Standardisation, ENISA Report, 14 March 2023. Available: https://www.enisa.europa.eu/publications/cybersecurity-of-ai-and-standardisation

Y. Nakayama, Creativity and technology integration in Asian corporate training, Journal of Creative Behavior 56 (4) (2022) 1321–1340. https://doi.org/10.1002/jocb.567

M. Csikszentmihalyi, Implications of a systems perspective for the study of creativity, in: R. J. Sternberg (Ed.), Handbook of Creativity, Cambridge University Press, Cambridge, 1999, pp. 313–335.

K. Teplická, S. Hurná, Model of Performance measurement and Management System in „The Visegrad Group“. TEM journal – technology, education management informatics, 12(3), 2023, 1618-1626. https://doi.org/10.18421/TEM123-43

M. Varbanova, M. Dutra de Barcellos et al., Industry 4.0 implementation factors for agri-food and manufacturing SMEs in Central and Eastern Europe, Serbian Journal of Management 18 (1) (2023) pp. 167–179. https://doi.org/10.5937/sjm18-39939

K. Teplická, Z. Sedláková, Evaluation of the quality of the cement production process in terms of increasing the company´s performance. Processes, 11(3), 2023, https://doi.org/10.3390/pr11030791

K. Teplická, J. Kádárová, S. Hurná, The new model of the engineering education using digitalization and innovative methods. Management System in Production Engineering. 30 (3) 2022, 207-2013. https://doi.org/10.2478/mspe-2022-0026.