References
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press. Ananto, P. K. F., Hsieh, C. C., & Mahendrawathi, E. R. (2021). Competition between online and offline retailer mass customization. Procedia Computer Science, 197(2021), 709–717. https://doi.org/10.1016/j.procs.2021.12.192 Azadeh, A., Moghaddam, M., Nazari, T., & Sheikhalishahi, M. (2016). Optimization of facility layout design with ambiguity by an efficient fuzzy International multivariate Journal of approach. Advanced Manufacturing Technology, 84(1–4), 565–579. https://doi.org/10.1007/s00170-015-7714-x Berisha, B., & Lobov, A. (2021). Overview and Trends for Application of AI Methods for Product Design. IEEE International Conference on Industrial Informatics , 2021-July. https://doi.org/10.1109/INDIN45523.2021.9557531 Bostrom, N. (2014). Superintelligence Paths, Dangers, Strategies. Oxford University Press. Brynjolfsson, E., & McAfee, A. (2017). The Business of Artificial Intelligence. Harvard Business Review. Cantamessa, M., Montagna, F., Altavilla, S., & Casagrande-Seretti, A. (2020). Data-driven design: The new challenges of digitalization on product design and development. Design Science. https://doi.org/10.1017/dsj.2020.25 Chui, M., & Manyika, J. (2018). AI Adoption Advances, but Foundational Barriers Remain. McKinsey Global Institute. Daniyan, I., Muvunzi, R., & Mpofu, K. (2021). Artificial intelligence system for enhancing product’s performance during its life cycle in a railcar industry. Procedia CIRP, 98(2020), 482–487. https://doi.org/10.1016/j.procir.2021.01.138 Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116. Dhar, V. (2020). Should You Trust Artificial Intelligence? Harvard Business Review. Dundas, M., & Chik, S. (2011). Artificial intuition and decision-making in complex systems. Journal of Artificial General Intelligence, 3(2), 1–15. Harfouche, A., Quinio, B., Saba, M., & Bou Saba, P. (2022). The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge. Information Systems Frontiers https://doi.org/10.1007/s10796-022-10352-8 Harrington, L., Daniel, K., Trask, M., & Velmurugan, S. (2015). Comparing the strengths of artificial intelligence and human intuition in managing risks. Global Journal of Innovative Academic Research, 1(2), 1-8. Jennings, C., Wu, D., & Terpenny, J. (2016). Forecasting obsolescence risk and product life cycle with machine learning. IEEE Transactions on Components, Packaging and Manufacturing Technology, 6(9), 1428–1439. https://doi.org/10.1109/TCPMT.2016.2589206 Johanssen, J., & Wang, Q. (2021). Artificial intuition in technology journalism: Between human affect and machine rationality. Convergence: The International Journal of Research into New Media Technologies, 27(5), 1287–1303. https://doi.org/10.1177/1354856520987432 Johnny, A., Trovati, M., & Ray, A. (2020). A computational framework for artificial intuition. Expert Systems with Applications, 159, 113554. https://doi.org/10.1016/j.eswa.2020.113554 Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (49–81). Cambridge University Press. Kaplan, J. (2016). Artificial Intelligence What Everyone Needs to Know. Oxford University Press. Kato, N., Muramatsu, N., Osone, H., Ochiai, Y., & Sato, D. (2018). DeepWear: A case study of collaborative design between human and artificial intelligence. TEI 2018 - Proceedings of the 12th International Conference on Tangible, Embedded, and Embodied Interaction, 529– 536. https://doi.org/10.1145/3173225.3173302 Koricanac, I. (2021). Impact of AI on the Automobile Industry in the U.S. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3841426 Kratschmayr, L., & Ladwig, M. S. (2015). Traditional Product Development Processes and their Limitations: Proposing a Holistic Experience Centered Method. Aachen Colloquium Automobile and Engine Technology, 1561–1570. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. Liu, Z., & Ping, L. (2019). Experience-based learning environments and intuition-driven decision making. Decision Support Systems, 120, 1–11. https://doi.org/10.1016/j.dss.2019.03.005 Mamun, A. Al, Liu, C., Kan, C., & Tian, W. (2022). Securing cyber-physical additive manufacturing systems by in-situ process authentication using streamline video analysis. Journal of Manufacturing Systems, 62(December 2021), 429–440. https://doi.org/10.1016/j.jmsy.2021.12.007 Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative Intelligence How Human and Artificial Intelligence Create Value Along the B2B Sales Funnel. Business Horizons, 63(3), 403-414. Quan, H., Li, S., Zeng, C., Wei, H. & Hu, J. (2023). Big Data and AI-Driven Product Design: A Survey. Appl. Sci., 13, 9433. https://doi.org/10.3390/app13169433 Ray, A., Johnny, A., & Trovati, M. (2018). Alternative path discovery in complex problem-solving systems. Applied Soft Computing, 72, 373–385. https://doi.org/10.1016/j.asoc.2018.07.021 Rogers, C. (2003). Client-centered therapy: Its current practice, implications, and theory. Constable & Robinson. Russell, S., & Norvig, P. (2020). Artificial Intelligence A Modern Approach. Pearson. Shao, Z., Feng, Y., & Hu, Q. (2017). Artificial intuition in adaptive decision-making systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(9), 2504–2515. https://doi.org/10.1109/TSMC.2016.2589264 Shiboldenkov, V., & Nesterova, K. (2020). The smart technologies application for the product life cycle management in modern manufacturing systems. MATEC Web of Conferences, 311, 02020. https://doi.org/10.1051/matecconf/202031102020 Smith, L. (2023). Ethical Considerations in AI Implementation. AI Ethics Review, 9(4), 112-130. Sohn, K., Sung, C. E., Koo, G., & Kwon, O. (2021). Artificial intelligence in the fashion industry: consumer responses to generative adversarial network technology. International Journal of Retail and Distribution Management, 49(1), 61–80. https://doi.org/10.1108/IJRDM-03-20200091 Spirable. (2022). Trends Reshaping the Fashion Industry: Personalisation. Spirable. https://doi.org/https://www.spirable.com/blog/trends-reshaping-the-fashion- industrypersonalisation Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate. Behavioral and Brain Sciences, 23(5), 645–665. https://doi.org/10.1017/S0140525X00003435 Tang, L., Zhao, Y., & Liu, J. (2014). An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Transactions on Evolutionary Computation, 18(2), 209–225. https://doi.org/10.1109/TEVC.2013.2250977 Trovati, M., Johnny, A., & Polatidis, N. (2022). Artificial intuition for context-aware decision- making. IEEE Transactions on Artificial Intelligence, 3(4), 562–575. https://doi.org/10.1109/TAI.2021.3138964 Trovati, M., Teli, K., Polatidis, N., Cullen, U.A., & Bolton, S. (2022). Artificial Intuition for Automated Decision-Making. Applied Artificial Intelligence, 37(1), e2230749. https://doi.org/10.1080/08839514.2023.2230749 Tsang, Y.P., Wong, T.C., Huang, G. Q., & Chun HoWu, Y. H. K. (2020). A Fuzzy-Based Product Life Cycle Prediction for. Energies, 13(3918), 1–23. Wang, L., Liu, Z., Liu, A., & Tao, F. (2021). Artificial intelligence in product lifecycle management. International Journal of Advanced Manufacturing Technology, 114(3–4), 771–796. https://doi.org/10.1007/s00170-021-06882-1 Wang, X. V., Lopez, N. B. N., Ijomah, W., Wang, L., & Li, J. (2015). A smart cloud-based system for the WEE