Digital twin-based cyber-physical manufacturing systems, extended reality metaverse enterprise and production management algorithms, and Internet of Things financial and labor market technologies in generative artificial intelligence economics

Authors

DOI:

https://doi.org/10.24136/oc.3183

Keywords:

generative artificial intelligence economics, fintech, labor market, metaverse enterprise, production management, cyber-physical manufacturing

Abstract

Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes.

Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions.

Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization.

Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.

Downloads

Download data is not yet available.

References

Aguinis, H., Beltran, J. R., & Cope, A. (2024). How to use generative AI as a human resource management assistant. Organizational Dynamics, 53(1), 101029. DOI: https://doi.org/10.1016/j.orgdyn.2024.101029
View in Google Scholar

Amankwah-Amoah, J., Abdalla, S., Mogaji, E., Elbanna, A., & Dwivedi, Y. K. (2024). The impending disruption of creative industries by generative AI: Opportunities, challenges, and research agenda. International Journal of Information Management, 79, 102759. DOI: https://doi.org/10.1016/j.ijinfomgt.2024.102759
View in Google Scholar

Andronie, M., Iatagan, M., Uță, C., Hurloiu, I., Dijmărescu, A., & Dijmărescu, I. (2023a). Big data management algorithms in artificial Internet of Things-based fintech. Oeconomia Copernicana, 14(3), 769–793. DOI: https://doi.org/10.24136/oc.2023.023
View in Google Scholar

Andronie, M., Lăzăroiu, G., Iatagan, M., Hurloiu, I., Ștefănescu, R., Dijmărescu, A., & Dijmărescu, I. (2023b). Big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools in the Internet of Robotic Things. ISPRS International Journal of Geo-Information, 12(2), 35. DOI: https://doi.org/10.3390/ijgi12020035
View in Google Scholar

Andronie, M., Lăzăroiu, G., Iatagan, M., Uță, C., Ștefănescu, R., & Cocoșatu, M. (2021). Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10(20), 2497. DOI: https://doi.org/10.3390/electronics10202497
View in Google Scholar

Andronie, M., Lăzăroiu, G., Karabolevski, O. L., Ștefănescu, R., Hurloiu, I., Dijmărescu, A., & Dijmărescu, I. (2023c). Remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the Internet of Robotic Things. Electronics, 12(1), 22. DOI: https://doi.org/10.3390/electronics12010022
View in Google Scholar

Aysan, A. F., Gozgor, G., & Nanaeva, Z. (2024). Technological perspectives of Metaverse for financial service providers. Technological Forecasting and Social Change, 202, 123323. DOI: https://doi.org/10.1016/j.techfore.2024.123323
View in Google Scholar

Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182. DOI: https://doi.org/10.1002/job.2735
View in Google Scholar

Barbu, C. M., Florea, D. L., Dabija, D. C., & Barbu, M. C. R. (2021). Customer experience in fintech. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1415‒1433. DOI: https://doi.org/10.3390/jtaer16050080
View in Google Scholar

Cao, S. S., Jiang, W., Lei, L. (G.), & Zhou, Q. (C.) (2024). Applied AI for finance and accounting: Alternative data and opportunities. Pacific-Basin Finance Journal, 84, 102307. DOI: https://doi.org/10.1016/j.pacfin.2024.102307
View in Google Scholar

Chen, Y., Wang, G. J., Zhu, Y., Xie, C., & Salah Uddin, G. (2024). Identifying systemic risk drivers of FinTech and traditional financial institutions: machine learning-based prediction and interpretation. European Journal of Finance. DOI: https://doi.org/10.1080/1351847X.2024.2358940
View in Google Scholar

De La Rosa, W., & Bechler, C. J. (2024). Unveiling the adverse effects of artificial intelligence on financial decisions via the AI-IMPACT model. Current Opinion in Psychology, 58, 101843. DOI: https://doi.org/10.1016/j.copsyc.2024.101843
View in Google Scholar

Eisikovits, N., Johnson, W. C., & Markelevich, A. (2024). Should accountants be afraid of AI? Risks and opportunities of incorporating artificial intelligence into accounting and auditing. Accounting Horizons. DOI: https://doi.org/10.2139/ssrn.4748690
View in Google Scholar

Fan, S., Ilk, N., Kumar, A., Xu, R., & Zhao, J. L. (2024). Blockchain as a trust machine: From disillusionment to enlightenment in the era of generative AI. Decision Support Systems, 182, 114251. DOI: https://doi.org/10.1016/j.dss.2024.114251
View in Google Scholar

Giudici, P., Centurelli, M., & Turchetta, S. (2024). Artificial intelligence risk measurement. Expert Systems with Applications, 235, 121220. DOI: https://doi.org/10.1016/j.eswa.2023.121220
View in Google Scholar

Holmström, J., & Carroll, N. (2024). How organizations can innovate with generative AI. Business Horizons. DOI: https://doi.org/10.1016/j.bushor.2024.02.010
View in Google Scholar

Jia, N., Luo, X., Fang, Z., & Liao, C. (2024). When and how artificial intelligence augments employee creativity. Academy of Management Journal, 67(1), 5–32. DOI: https://doi.org/10.5465/amj.2022.0426
View in Google Scholar

Kang, M., Templeton, G. F., Kwak, D.-H., & Um, S. (2024). Development of an AI framework using neural process continuous reinforcement learning to optimize highly volatile financial portfolios. Knowledge-Based Systems, 300, 112017. DOI: https://doi.org/10.1016/j.knosys.2024.112017
View in Google Scholar

Khan, H. H., Kutan, A. M., & Qureshi, F. (2024). Fintech integration: Driving efficiency in banking institutions across the developing nations. Finance Research Letters. DOI: https://doi.org/10.1016/j.frl.2024.105772
View in Google Scholar

Khan, M. S., & Umer, H. (2024). ChatGPT in finance: Applications, challenges, and solutions. Heliyon, 10(2), e24890. DOI: https://doi.org/10.1016/j.heliyon.2024.e24890
View in Google Scholar

Kshetri, N. (2024). Generative artificial intelligence in the financial services industry. Computer, 57(6), 102‒108. DOI: https://doi.org/10.1109/MC.2024.3382452
View in Google Scholar

Kueschnig, M., & Schertler, A. (2024). Fusing futures: Financial institutions’ stock price response to fintech acquisitions. Finance Research Letters, 59, 104779. DOI: https://doi.org/10.1016/j.frl.2023.104779
View in Google Scholar

Lăzăroiu, G., & Rogalska, E. (2023). How generative artificial intelligence technologies shape partial job displacement and labor productivity growth. Oeconomia Copernicana, 14(3), 703–706. DOI: https://doi.org/10.24136/oc.2023.020
View in Google Scholar

Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., Ștefănescu, R., & Dijmărescu, I. (2022). Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the Internet of Manufacturing Things. ISPRS International Journal of Geo-Information, 11(5), 277. DOI: https://doi.org/10.3390/ijgi11050277
View in Google Scholar

Lăzăroiu, G., Bogdan, M., Geamănu, M., Hurloiu, L., Luminița, L., & Ștefănescu, R. (2023). Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management. Oeconomia Copernicana, 14(3), 707–730. DOI: https://doi.org/10.24136/oc.2023.021
View in Google Scholar

Li, J., Chen, W., Liu, Y., Yang, J., Zeng, D., & Zhou, Z. (2024). Neural ordinary differential equation networks for fintech applications using Internet of Things. IEEE Internet of Things Journal, 11(12), 21763–21772. DOI: https://doi.org/10.1109/JIOT.2024.3376748
View in Google Scholar

Lim, T. (2024). Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways. Artificial Intelligence Review, 57, 76. DOI: https://doi.org/10.1007/s10462-024-10708-3
View in Google Scholar

Lin, H., Tian, J., & Cheng, B. (2024). Facilitation or hindrance: The contingent effect of organizational artificial intelligence adoption on proactive career behavior. Computers in Human Behavior, 152, 108092. DOI: https://doi.org/10.1016/j.chb.2023.108092
View in Google Scholar

Nagy, M., & Lăzăroiu G. (2022). Computer vision algorithms, remote sensing data fusion techniques, and mapping and navigation tools in the Industry 4.0-based Slovak automotive sector. Mathematics, 10(19), 3543. DOI: https://doi.org/10.3390/math10193543
View in Google Scholar

Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine intelligence and autonomous robotic technologies in the corporate context of SMEs: Deep learning and virtual simulation algorithms, cyber-physical production networks, and Industry 4.0-based manufacturing systems. Applied Sciences, 13(3), 1681. DOI: https://doi.org/10.3390/app13031681
View in Google Scholar

Oehler, A., & Horn, M. (2024). Does ChatGPT provide better advice than robo-advisors? Finance Research Letters, 60, 104898. DOI: https://doi.org/10.1016/j.frl.2023.104898
View in Google Scholar

Pelău, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics on the acceptance of artificial intelligence in the service industry. Computers in Human Behaviour, 122, 106855. DOI: https://doi.org/10.1016/j.chb.2021.106855
View in Google Scholar

Ramaul, L., Ritala, P., & Ruokonen, M. (2024). Creational and conversational AI affordances: How the new breed of chatbots is revolutionizing knowledge industries. Business Horizons. DOI: https://doi.org/10.1016/j.bushor.2024.05.006
View in Google Scholar

Retkowsky, J., Hafermalz, E., & Huysman, M. (2024). Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects. Business Horizons. DOI: https://doi.org/10.1016/j.bushor.2024.04.009
View in Google Scholar

Sachan, S., Almaghrabi, F., Yang, J.-B., & Xu, D.-L. (2024). Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm. International Review of Financial Analysis, 93, 103149. DOI: https://doi.org/10.1016/j.irfa.2024.103149
View in Google Scholar

Tigges, M., Mestwerdt, S., Tschirner, S., & Mauer, R. (2024). Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data. Technological Forecasting and Social Change, 205, 123491. DOI: https://doi.org/10.1016/j.techfore.2024.123491
View in Google Scholar

Valaskova, K., Gajdosikova, D., & Lăzăroiu, G. (2023). Has the COVID-19 pandemic affected the corporate financial performance? A case study of Slovak enterprises. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 1133–1178. DOI: https://doi.org/10.24136/eq.2023.036
View in Google Scholar

Valaskova, K., Nagy, M., Zabojnik, S., & Lăzăroiu G. (2022). Industry 4.0 wireless networks and cyber-physical smart manufacturing systems as accelerators of value-added growth in Slovak exports. Mathematics, 10(14), 2452. DOI: https://doi.org/10.3390/math10142452
View in Google Scholar

Zhao, C., Sun, X., Wu, M., & Kang, L. (2024). Advancing financial fraud detection: Self-attention generative adversarial networks for precise and effective identification. Finance Research Letters, 60, 104843. DOI: https://doi.org/10.1016/j.frl.2023.104843
View in Google Scholar

Zhu, H., Vigren, O., & Söderberg, I.-L. (2024). Implementing artificial intelligence empowered financial advisory services: A literature review and critical research agenda. Journal of Business Research, 174, 114494. DOI: https://doi.org/10.1016/j.jbusres.2023.114494
View in Google Scholar

Downloads

Published

30-09-2024

Issue

Section

Collective Writing

How to Cite

Lazaroiu, G., Gedeon, T., Rogalska, E., Valaskova, K., Nagy, M., Musa, H., Zvarikova, K., Poliak, M., Horak, J., Crețoiu, R. I., Krulicky, T., Ionescu, L., Popa , C., Hurloiu, L. R., Nistor , F., Avram, L. G., & Braga, V. (2024). Digital twin-based cyber-physical manufacturing systems, extended reality metaverse enterprise and production management algorithms, and Internet of Things financial and labor market technologies in generative artificial intelligence economics. Oeconomia Copernicana, 15(3), 837-870. https://doi.org/10.24136/oc.3183

Similar Articles

121-130 of 318

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 3 > >>