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References

  1. Abolghasemi, M., Hurley, J., Eshragh, A., & Fahimnia, B. (2020). Demand forecasting in the presence of systematic events: Cases in capturing sales promotions. International Journal of Production Economics, 230. doi: 10.1016/j.ijpe.2020.107892
  2. Achouch, M., Dimitrova, & M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences, 12(16), 8081. doi: 10.3390/app12168081
  3. Adel, A. (2022). Future of industry 5.0 in society: Humancentric solutions, challenges and prospective research areas. Journal of Cloud Computing, 11, 1-15. doi: 10.1186/s13677-022-00314-5
  4. Allahloh, A. S., Sarfraz, M., Ghaleb, A. M., Al-Shamma’a, A. A., Hussein Farh, H. M., & Al-Shaalan, A. M. (2023). Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance. Sustainability 15(11), 8808. doi: 10.3390/su15118808
  5. Arena, F., Collotta, M., Luca L., Ruggieri, M., & Termine, F. G. (2022). Predictive Maintenance in the Automotive Sector: A Literature Review. Mathematical and Computational Applications 27(1), 2. doi: 10.3390/mca27010002
  6. Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., & Denisi, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33, 606-659. doi: 10.1111/1748-8583.12524
  7. Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021). Big data for creating and capturing value in the digitalized environment: unpacking the effects of volume, variety, and veracity on firm performance. Journal of Production and Innovation Management, 38, 49-67. doi: 10.1111/jpim.12545
  8. Cha, J.-H., Jeong, H.-G., Han, S.-W., Kim, D.-C., Oh, J.-H., Hwang, S.-H., & Park, B.-J. (2023). Development of MLOps Platform Based on Power Source Analysis for Considering Manufacturing Environment Changes in Real-Time Processes. In International Conference on Human-Computer Interaction (pp. 224–236). Springer.
  9. Chang, Y.-L., & Ke, J. (2023). Socially Responsible Artificial Intelligence Empowered People Analytics: A Novel Framework Towards Sustainability. Human Resource Development Review, 15344843231200930.
  10. Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research, 281(3), 656-672. doi: 10.1016/j.ejor.2019.06.051
  11. De Mauro, A., Sestino, A., & Bacconi, A. (2022). Machine learning and artificial intelligence use in marketing: a general taxonomy. Italian Journal of Marketing, 439-457. doi: 10.1007/s43039-022-00057-w
  12. Dencheva, V. (2023). Share of marketers using generative artificial intelligence (AI) in their companies in the United States as of March 2023. Retrieved from https://www.statista.com/statistics/1388390/generative-ai-usage-marketing/
  13. Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: a systematic literature review. Journal of Business Research, 121, 283-314. doi: 10.1016/j.jbusres.2020.08.019
  14. Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192. doi: 10.1016/j.techfore.2023.122579
  15. Dworski, B. (2023). C-store retailers weigh in on automation, AI and data challenges. Retrieved from https://www.cstoredive.com/news/c-store-retailers-weighin-on-automation-ai-and-data-challenges/650008/
  16. Ghosh, S. (2022). COVID-19, clean energy stock market, interest rate, oil prices, volatility index, geopolitical risk nexus: evidence from quantile regression. Journal of Economics and Development. doi: 10.1108/jed-04-2022-0073
  17. Global Data. (2023). The impact of artificial intelligence in the consumer goods sector. Retrieved from https://just-drinks.nridigital.com/just_drinks_magazine_aug23/artificial-intelligence-impact-consumergoods-industry
  18. Głodowska, A., Maciejewski, M., & Wach, K. (2023). Navigating the digital landscape: A conceptual framework for understanding digital entrepreneurship and business transformation. International Entrepreneurship Review, 9(4), 7-20. doi: 10.15678/IER.2023.0904.01
  19. Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3, 119-132. doi: 10.1016/j.ijin.2022.08.005
  20. Haponik, A. (2022). How AI improves productivity in manufacturing companies? Retrieved from https://addepto.com/blog/how-ai-improves-productivityin-manufacturing-companies/
  21. Hartung, J., Dold, P. M., Jahn, A., & Heizmann, M. (2022). Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application. Sensors, 22, 6425. doi: 10.3390/s22176425
  22. Heuser, P., Letmathe, P., & Schinner, M. (2022). Workforce planning in production with flexible or budgeted employee training and volatile demand. Journal of Business Economics, 92, 1093-1124. doi: 10.1007/s11573-022-01090-z
  23. Hrnjica, B., & Softic, S. (2020). Explainable AI in Manufacturing: A Predictive Maintenance Case Study. In IFIP International Conference on Advances in Production Management Systems (APMS), (pp. 66–73). Novi Sad, Serbia.
  24. Hu, X., Liu, A., Li, X., Dai, Y., & Nakao, M. (2023). Explainable AI for customer segmentation in product development. CIRP Annals, 72(1), 89-92. doi: 10.1016/j.cirp.2023.03.004
  25. Hull, B. (2011). Manufacturing Best Practices: Optimizing Productivity and Product Quality. Hoboken, New Jersey, USA: John Wiley & Sons.
  26. Hyun Baek, T., & Kim, M. (2023). Ai robo-advisor anthropomorphism: The impact of anthropomorphic appeals and regulatory focus on investment behaviors. Journal of Business Research, 164. doi: 10.1016/j.jbusres.2023.114039
  27. Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of AI. Boston, MA.
  28. Katreddi, S., Kasani, S., & Thiruvengadam, A. (2022). A Review of Applications of Artificial Intelligence in Heavy Duty Trucks. Energies, 15(20), 7457. doi: 10.3390/en15207457
  29. Khang, A., Rani, S., Gujrati, R., Uygun, H., & Gupta, S. K. (2023). Designing Workforce Management Systems for Industry 4.0: Data-Centric and AIEnabled Approaches (1st ed.). CRC Press. doi: 10.1201/9781003357070
  30. Koole, G. M., & Li, S. (2023). A practice-oriented overview of call center workforce planning. Stochastic Systems. doi: 10.1287/stsy.2021.0008
  31. Korzynski, P., Kozminski, A. K., & Baczynska, A. (2023). Navigating leadership challenges with technology: Uncovering the potential of ChatGPT, virtual reality, human capital management systems, robotic process automation, and social media. International Entrepreneurship Review, 9(2), 7-18. doi: 10.15678/IER.2023.0902.01
  32. Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023). Generative artificial intelligence as a new context for management theories: analysis of Chat- GPT. Central European Management Journal, 31(1). doi: 10.1108/CEMJ-02-2023-0091
  33. Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2023). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 102716. doi: 10.1016/j.ijinfomgt.2023.102716
  34. Kumar, A., Gupta, N., & Bapat, G. (2023). Who is making the decisions? How retail managers can use the power of ChatGPT. Journal of Business Strategy. doi:10.1108/jbs-04-2023-0067
  35. Kwong, C. K., Jiang, H., & Luo, X. G. (2016). AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products. Engineering Applications of Artificial Intelligence, 47(10), 49-60. doi: 10.1016/j.engappai.2015.04.001
  36. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21-32.
  37. Lei, Y., Vyas, S., Gupta, S., & Shabaz, M. (2022). AI based study on product development and process design. International Journal of System Assuring Engineering Management, 13(1), 305-311. doi: 10.1007/s13198-021-01404-4
  38. Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65, 279-295. doi: 10.1016/j.jmsy.2022.09.017
  39. Li, X., Pan, L., Zhou, Y., Wu, Z., & Luo, S. (2022). A Temporal– Spatial network embedding model for ICT supply chain market trend forecasting. Applied Soft Computing, 125. doi: 10.1016/j.asoc.2022.109118
  40. Liu, B., Song, C., Liang, X., Lai, M., Yu, Z., & Ji, J. (2023). Regional differences in China’s electric vehicle sales forecasting: Under supply-demand policy scenarios. Energy Policy, 177. doi: 10.1016/j.enpol.2023.113554
  41. Liu, C., Tian, W., & Kan, Ch., (2022). When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development. Journal of Manufacturing Systems, 64, 648-656. doi: 10.1016/j.jmsy.2022.04.010
  42. Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P.-W. (2022). AI-based neural network models for bus passenger demand forecasting using smart card data. Journal of Urban Management, 11(3), 365-380. doi: 10.1016/j.jum.2022.05.002
  43. Ma, S., Fildes, R., & Huang, T. (2016). Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information. European Journal of Operational Research, 249(1), 245-257. doi: 10.1016/j.ejor.2015.08.029
  44. Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. doi: 10.1016/j.technovation.2022.102623
  45. Mathur, S., Kumar, D., Kumar, V., Dantas, A., Verma, R., & Kuca, K. (2023). Xylitol: Production strategies with emphasis on biotechnological approach, scale up, and market trends. Sustainable Chemistry and Pharmacy, 35. doi: 10.1016/j.scp.2023.101203
  46. Mazumdar, T., Raj, S. P., & Sinha, I. (2005). Reference price research: Review and propositions. Journal of Marketing, 69, 84-102. doi: 10.1509/jmkg.2005.69.4.84.
  47. Mazurek, G. (2018). Internet Rzeczy a cyfrowa transformacja – implikacje dla marketingu B2C [The Internet of Things and digital transformation - implications for B2C marketing]. In L. Sułkowski, & D. Kaczorowska-Spychalska (Eds.). Nowy paradygmat rynku [A new market paradigm], (pp. 33–57), Warsaw, Poland: Difin.
  48. Nadira, K. (2023). Implementing AI-Automation in Manufacturing for Product Quality Assurance. Retrieved from https://gleematic.com/implementing-ai-automation-in-manufacturing-for-product-qualityassurance/
  49. Narasimhan, S. (2023). How AI & ML are Revolutionizing Product Quality Control. Retrieved from https://www.hurix.com/how-ai-ml-are-revolutionizingproduct-quality-control/
  50. Njomane, L., & Telukdarie, A. (2022). Impact of COVID-19 food supply chain: Comparing the use of IoT in three South African supermarkets. Technology in Society, 71, 102051. doi: 10.1016/j.techsoc.2022.102051
  51. Nosalska, K., Piatek, Z. M., Mazurek, G., & Rzadca, R. (2018). Industry 4.0: coherent definition framework with technological and organizational interdependencies. Journal of Manufacturing Technology Management, 31(5), 837-862. doi: 10.1108/JMTM-08-2018-0238
  52. Ooi, K. B., Wei-Han Tan, G., Al-Emran, M., Al-Sharafi, M., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kumar Kar, A., Lee, V. H., Loh, X.-M., Micu, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., Teng, Ch., Wamba F. S., & Wong, L.-W. (2023). The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions. Journal of Computer Information Systems. doi: 10.1080/08874417.2023.2261010
  53. Open AI. (2023). Introducing ChatGPT and Whisper APIs. Retrieved from https://openai.com/blog/introducing-chatgpt-and-whisper-apis
  54. Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: Purpose, process, and structure. Journal of the Academy of Marketing Science, 46, 1-5. doi: 10.1007/s11747-017-0563-4
  55. Pandey, R., Uziel, S., Hutschenreuther, T., & Krug, S. (2023) Towards Deploying DNN Models on Edge for Predictive Maintenance Applications. Electronics, 12(3), 639. doi. 10.3390/electronics12030639
  56. Plantec, Q., Deval, M.-A., Hooge, S., & Weil, B. (2023). Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry. Technovation, 124, 102763. doi: 10.1016/j.technovation.2023.102763
  57. Raja, A. (2023). How Generative AI can enhance the Manufacturing Industries? Retrieved from https://www.linkedin.com/pulse/how-generative-ai-canenhance-manufacturing-industries-raja/
  58. Rossini, R., Prato, G., Conzon, D., Pastrone, C., Pereira, E., Reis, J., Gonçalves, G., Henriques, D., Santiago, A. R., & Ferreira, A. (2021). AI environment for predictive maintenance in a manufacturing scenario. In2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), (pp. 1-8). Vasteras, Sweden. doi: 10.1109/ETFA45728.2021.9613359
  59. Rymarczyk, J. (2021). The impact of Industrial Revolution 4.0 on international trade. Entrepreneurial Business and Economics Review, 9(1), 105-117. doi: 10.15678/EBER.2021.090107
  60. Shin, W., Han, J., & Rhee, W. (2021). AI-assistance for predictive maintenance of renewable energy systems. Energy, 221, 119775. doi: 10.1016/j.energy.2021.119775.
  61. Sigov, A., Ratkin, L., Ivanov, L. A., & Xu, L. D. (2022). Emerging Enabling Technologies for Industry 4.0 and Beyond. Information Systems Frontiers. doi: 10.1007/s10796-021-10213-w
  62. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. doi: 10.1016/j.jbusres.2016.08.001
  63. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, Elsevier, 104(C), 333-339. doi: 10.1016/j.jbusres.2019.07.039
  64. Sohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A. (2021). Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 163. doi: 10.1016/j.techfore.2020.120480
  65. Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems, 3, 192-204. doi: 10.1016/j.iotcps.2023.04.006
  66. Srivastava, S. (2023). How AI is Proving as a Game Changer in Manufacturing – Use Cases and Examples. Retrieved from https://appinventiv.com/blog/ai-inmanufacturing/
  67. Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3. doi: 10.1016/j.sca.2023.100026
  68. Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3. doi: 10.1016/j.sca.2023.100026
  69. Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, 107864. doi: 10.1016/j.ress.2021.107864
  70. Vaddi, K., & Khan, M. (2023). A New Era of Quality Assurance – Role of Generative AI in Reshaping Software Testing. Retrieved from https://www.encora.com/insights/a-new-era-of-qa-role-of-generative-ai-inreshaping-software-testing
  71. Vaid, S., Puntoni, S., & Khodr, A. (2023). Artificial intelligence and empirical consumer research: A topic modeling analysis. Journal of Business Research, 166. doi: 10.1016/j.jbusres.2023.114110
  72. Villar, A., Paladini, S., & Buckley, O. (2023). Towards Supply Chain 5.0: Redesigning Supply Chains as Resilient, Sustainable, and Human-Centric Systems in a Post-pandemic World. Operational Research Forum, 4, 60. doi: 10.1007/s43069-023-00234-3
  73. Viverit, L., Heo, C. Y., Pereira, L. N., & Tiana, G. (2023). Application of machine learning to cluster hotel booking curves for hotel demand forecasting. International Journal of Hospitality Management, 111. doi: 10.1016/j.ijhm.2023.103455
  74. Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7-30. doi: 10.15678/EBER.2023.110201
  75. Wang, G., Ledwoch, A., Hasani, R. M., Grosu, R., & Brintrup, A. (2019). A generative neural network model for the quality prediction of work in progress products. Applied Soft Computing, 85, 105683. doi: 10.1016/j.asoc.2019.105683
  76. Wang, T., & Wu, D. (2024). Computer-Aided Traditional Art Design Based on Artificial Intelligence and Human-Computer Interaction. Computer-Aided Design and Applications, 21(S7), 59-73. doi: 10.14733/cadaps.2024.S7.59-73
  77. Wlodarczyk, S. (2023). How Generative AI will transform manufacturing. Retrieved from https://aws.amazon.com/blogs/industries/generative-ai-in-manufacturing/
  78. Xu, Q., Dong, J., Peng, K., & Yang, X. (2024). A novel method of neural network model predictive control integrated process monitoring and applications to hot rolling process. Expert Systems With Applications, 237, 121682. doi: 10.1016/j.eswa.2023.121682
  79. Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18. doi: 10.1016/j.iswa.2023.200235
  80. Zeng, W., Wang, J., Zhang, Y., Han, Y., & Zhao, Q. (2022). DDPG-based continuous thickness and tension coupling control for the unsteady cold rolling process. The International Journal of Advanced Manufacturing Technology, 120(11-12), 7277-7292. doi: 10.1007/s00170-022-09239-4
  81. Zhu, Y., Zhang, J., Wu, J., & Liu, Y. (2022). AI is better when I’m sure: The influence of certainty of needs on consumers’ acceptance of AI chatbots. Journal of Business Research, 150, 642-652. doi: 10.1016/j.jbusres.2022.06.044
DOI: https://doi.org/10.2478/emj-2023-0029 | Journal eISSN: 2543-912X | Journal ISSN: 2543-6597
Language: English
Page range: 76 - 89
Submitted on: Mar 15, 2023
Accepted on: Sep 20, 2023
Published on: Dec 29, 2023
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Doung Cong Doanh, Zdenek Dufek, Joanna Ejdys, Romualdas Ginevičius, Pawel Korzynski, Grzegorz Mazurek, Joanna Paliszkiewicz, Krzysztof Wach, Ewa Ziemba, published by Bialystok University of Technology
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