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Cited by 72Digital Transformation in IndustryImpact of AI and Big Data on Business and SocietyEconomic and Industrial Development

Industry 4.0 Wireless Networks and Cyber-Physical Smart Manufacturing Systems as Accelerators of Value-Added Growth in Slovak Exports

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Valaskova, Katarina, Nagy, Marek, Zabojnik, Stanislav, Lăzăroiu, George·2022·Mathematics
DOI 10.3390/math10142452
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Abstract

Industry 4.0 integrates smart and connected production systems that are pivotal in predicting and supporting production in real-time, leading to sustainable organizational performance. In manufacturing, it may increase productivity, sustainability, and energy efficiency, while optimizing competitiveness. The main purpose of this paper is to determine the impact of Industry 4.0 on the Slovak economy through a secondary data analysis in the automotive industry, which is the leading sector in the country. The paper aims to provide a comprehensive analysis of the various opportunities that are available in the value-added growth of car exports in Slovakia. It also explores the case study of PSA Group Slovakia, which highlights the importance of the Industry 4.0 concept in boosting the country’s export growth. The paper proposes a series of recommendations and steps to improve Slovakia’s innovation environment.

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  • The automotive industry is one of the most affected industries by the COVID-19 pandemic.

    “lastly, the automotive industry is one of the most affected industries by the COVID-19 pandemic (Xu et al. 2020; Ishida 2020; Valaskova et al. 2022).”

    From Working Capital Management Impact on Profitability: Pre-Pandemic and Pandemic Evidence from the European Automotive Industry (2022) · cites this paper as Valaskova et al. 2022

    Section 4.3 (page unavailable) reports that an automotive company's entire production was halted because of COVID-19, supporting that the industry was affected. The paper does not establish that automotive was one of the most affected industries compared with other sectors. (LLM, conf=0.83)

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References 84 · Cited by 72

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  1. Hatzigeorgiou, A.; Lodefalk, M. A literature review of the nexus between migration and internationalization. J. Int. Trade Econ. Dev. 2021, 30, 319-340. [CrossRef] · DOI
  2. Chang, B.G.; Wu, K.S. The nonlinear relationship between financial flexibility and enter-prise risk-taking during the COVID-19 pandemic in Taiwan's semiconductor industry. Oeconomia Copernic. 2021, 12, 307-333. [CrossRef] · DOI
  3. Waliszewski, K.; Warchlewska, A. Comparative analysis of Poland and selected countries in terms of household financial behaviour during the COVID-19 pandemic. Equilib. Q. J. Econ. Econ. Policy 2021, 16, 577-615. [CrossRef] · DOI
  4. Minarik, M.; Zabojnik, S.; Pasztorova, J. Sources of Value-Added in V4 automotive GVCs: The Case of Transport and Storage Services and Firm Level Technology Absorption. Cent. Eur. Bus. Rev. 2022, 11. [CrossRef] · DOI
  5. Gereffi, G.; Humphrey, J.; Sturgeon, T. The governance of global value chains. Rev. Int. Political Econ. 2005, 12, 78-104. [CrossRef] · DOI · Paper page
  6. Yao, X.; Shah, W.U.; Yasmeen, R.; Zhang, Y.Z.; Kamal, M.A.; Khan, A. The impact of trade on energy efficiency in the global value chain: A simultaneous equation approach. Sci. Total Environ. 2021, 765, 142759. [CrossRef] · DOI
  7. Nikulin, D.; Wolszczak-Derlacz, J.; Parteka, A. GVC and wage dispersion. Firm-level evi-dence from employee-employer database. Equilib. Q. J. Econ. Econ. Policy 2021, 16, 357-375.
  8. Kordalska, A.; Olczyk, M. New patterns in the position of CEE countries in global value chains: Functional specialisation approach. Oeconomia Copernic. 2021, 12, 35-52. [CrossRef] · DOI
  9. Mugge, D. International economic statistics: Biased arbiters in global affairs? Fudan J. Humanit. Soc. Sci. 2020, 13, 93-112. [CrossRef] · DOI
  10. Ye, C.S.; Ye, Q.; Shi, X.P.; Sun, Y.P. Technology gap, global value chain and carbon intensity: Evidence from global manufacturing industries. Energy Policy 2020, 137, 111094. [CrossRef] · DOI
  11. Hopkins, E.; Siekelova, A. Internet of Things Sensing Networks, Smart Manufacturing Big Data, and Digitized Mass Production in Sustainable Industry 4.0. Econ. Manag. Financ. Mark. 2021, 16, 28-41.
  12. Schoeneman, J.; Zhou, B.L.; Desmarais, B.A. Complex dependence in foreign direct investment: Network theory and empirical analysis. Political Sci. Res. Methods 2022, 12, 243-259. [CrossRef] · DOI
  13. Gray, M.; Kovacova, M. Internet of Things Sensors and Digital Urban Governance in Data-driven Smart Sustainable Cities. Geopolit. Hist. Int. Relat. 2021, 13, 107-120.
  14. Martinez-Noya, A.; Garcia-Canal, E. International evidence on R&D services outsourcing practices by technological firms. Multinatl. Bus. Rev. 2014, 22, 372-393.
  15. Durana, P.; Perkins, N.; Valaskova, K. Artificial Intelligence Data-driven Internet of Things Systems, Real-Time Advanced Analytics, and Cyber-Physical Production Networks in Sustainable Smart Manufacturing. Econ. Manag. Financ. Mark. 2021, 16, 20-30.
  16. Zavadska, Z.; Zavadsky, J. Industry 4.0 and Intelligent Technologies in the Development of the Corporate Operation Management; Belianum: Banska Bystrica, Slovakia, 2020; pp. 130-155.
  17. Kagermann, H.; Wahlster, W. Securing the Future of German Manufacturing Industry: Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0; Federal Ministry of Education and Research: Frankfurt, Germany, 2013; pp. 37-62.
  18. Zabojnik, S. Selected Problems of International Trade and International Business; Econom: Bratislava, Slovakia, 2015; pp. 28-61.
  19. Małkowska, A.; Urbaniec, M.; Kosała, M. The impact of digital transformation on European countries: Insights from a comparative analysis. Equilib. Q. J. Econ. Econ. Policy 2021, 16, 325-355. [CrossRef] · DOI
  20. Clayton, E.; Kral, P. Autonomous Driving Algorithms and Behaviors, Sensing and Computing Technologies, and Connected Vehicle Data in Smart Transportation Networks. Contemp. Read. Law Soc. Justice 2021, 13, 9-22.
  21. Yang, F.; Gu, S. Industry 4.0, a revolution that requires technology and national strategies. Complex Intell. Syst. 2021, 7, 1311-1325. [CrossRef] · DOI
  22. Hermann, M.; Pentek, T.; Otto, B. Design Principles for Industry 4.0 Scenarios: A Literature Review. Technische Universitat Dort- mund. 2016. Available online: http://www.snom.mb.tu-dortmund.de/cms/de/forschung/Arbeitsberichte/Design-Principles- for-Industrie-4_0-Scenarios.pdf (accessed on 2 May 2022).
  23. Klingenberg, C.O.; Borges, M.A.V.; Antunes, J., Jr. Industry 4.0 as a data-driven paradigm: A systematic literature review on technologies. J. Manuf. Technol. Manag. 2019, 32, 570-592. [CrossRef] · DOI
  24. Rogers, S.; Zvarikova, K. Big Data-driven Algorithmic Governance in Sustainable Smart Manufacturing: Robotic Process and Cognitive Automation Technologies. Anal. Metaphys. 2021, 20, 130-144.
  25. Ruttimann, B.G.; Stockli, M.T. Lean and Industry 4.0-Twins, Partners, or Contenders? A Due Clarification Regarding the Supposed Clash of Two Production Systems. J. Sci. Serv. Manag. 2016, 9, 485-500. [CrossRef] · DOI
  26. Kovacova, M.; Lewis, E. Smart Factory Performance, Cognitive Automation, and Industrial Big Data Analytics in Sustainable Manufacturing Internet of Things. J. Self-Gov. Manag. Econ. 2021, 9, 9-21.
  27. Mehmann, J.; Teuteberg, F. The fourth-party logistics service provider approach to support sustainable development goals in transportation-A case study of the German agricultural bulk logistics sector. J. Clean. Prod. 2016, 126, 382-393. [CrossRef] · DOI
  28. Galbraith, A.; Podhorska, I. Artificial Intelligence Data-driven Internet of Things Systems, Robotic Wireless Sensor Networks, and Sustainable Organizational Performance in Cyber-Physical Smart Manufacturing. Econ. Manag. Financ. Mark. 2021, 16, 56-69.
  29. Sony, M. Pros and cons of implementing Industry 4.0 for the organizations: A review and synthesis of evidence. Prod. Manuf. Res. 2020, 8, 244-272. [CrossRef] · DOI
  30. Dalenogare, L.; Benitez, N.; Ayala, N.F.; Frank, A.G. The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 2018, 204, 383-394. [CrossRef] · DOI · Paper page
  31. Stock, T.; Obenaus, M.; Kunz, S.; Kohl, H. Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Saf. Environ. Prot. 2018, 118, 254-267. [CrossRef] · DOI
  32. Hoffmann, M. Smart Agents for the Industry 4.0; Springer: Berlin, Germany, 2019; pp. 226-297.
  33. Said, M.; Shaheen, A.M.; Ginidi, A.R.; El-Sehiemy, R.A.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F. Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer. Processes 2021, 9, 627. [CrossRef] · DOI
  34. Lawrence, J.; Durana, P. Artificial Intelligence-driven Big Data Analytics, Predictive Maintenance Systems, and Internet of Things- based Real-Time Production Logistics in Sustainable Industry 4.0 Wireless Networks. J. Self-Gov. Manag. Econ. 2021, 9, 62-75.
  35. Burke, S.; Zvarikova, K. Urban Internet of Things Systems and Data Monitoring Algorithms in Smart and Environmentally Sustainable Cities. Geopolit. Hist. Int. Relat. 2021, 13, 135-148.
  36. Lăzăroiu, G.; Harrison, A. Internet of Things Sensing Infrastructures and Data-driven Planning Technologies in Smart Sustainable City Governance and Management. Geopolit. Hist. Int. Relat. 2021, 13, 23-36.
  37. Andronie, M.; Lăzăroiu, G.; S , tefănescu, R.; Ionescu, L.; Cocos , atu, M. Neuromanagement decision-making and cognitive algorithmic processes in the technological adoption of mobile commerce apps. Oeconomia Copernic. 2021, 12, 1033-1062. [CrossRef] · DOI
  38. Zhong, R.; Xu, X.; Klotz, E.; Newman, S.T. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 2021, 3, 616-630. [CrossRef] · DOI
  39. Belhadi, A.; Kamble, S.; Jabbour, C.J.C.; Gunasekaran, A.; Ndubisi, N.O.; Venkatesh, M. Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technol. Forecast. Soc. Change 2021, 163, 120-447. [CrossRef] · DOI
  40. Hinds, P.S.; Vogel, R.J.; Clarke-Steffen, L. The possibilities and pitfalls of doing a secondary analysis of a qualitative data set. Qual. Health Res. 1997, 7, 408-424. [CrossRef] · DOI
  41. Svabova, L.; Tesarova, E.N.; Durica, M.; Strakova, L. Evaluation of the impacts of the COVID-19 pandemic on the development of the unemployment rate in Slovakia: Counterfactual be-fore-after comparison. Equilib. Q. J. Econ. Econ. Policy 2021, 16, 261-284. [CrossRef] · DOI
  42. Skare, M.; Gil-Alana, L.A.; Claudio-Quiroga, G.; Pržiklas Družeta, R. Income inequality in China 1952-2017: Persistence and main determinants. Oeconomia Copernic. 2021, 12, 863-888. [CrossRef] · DOI
  43. Bonab, A.-F. The Development of Competitive Advantages of Brand in the Automotive Industry (Case Study: Pars Khodro Co). J. Internet Bank. Commer. 2017, 22, 028.
  44. Pavlinek, P.; Zenka, J. Value creation and value capture in the automotive industry: Empirical evidence from Czechia. Environ. Plan. 2016, 48, 937-959. [CrossRef] · DOI
  45. Pugliese, E.; Napolitano, L.; Zaccaria, A.; Pietronero, L. Coherent diversification in corporate technological portfolios. PLoS ONE 2019, 14, e0223403. [CrossRef] [PubMed] · DOI
  46. Tao, F.; Qi, Q.; Wan, L.; Nee, A.Y.C. Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering 2019, 5, 653-661. [CrossRef] · DOI
  47. Mondejar, M.A.; Avtar, R.; Diaz, H.L.; Dubey, R.K.; Esteban, J.; Gomez-Morales, A.; Hallam, B.; Mbungu, N.T.; Okolo, C.C.; Prasad, K.A.; et al. Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet. Sci. Total Environ. 2021, 794, 148539. [CrossRef] [PubMed] · DOI
  48. Sjodin, D.R.; Parida, V.; Leksell, M.; Petrovic, A. Smart Factory Implementation and Process Innovation. Res.-Technol. Manag. 2018, 61, 22-31. [CrossRef] · DOI
  49. Verhof, P.C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Dong, J.Q.; Fabian, N.; Haenlein, M. Digital transformation: A multidisciplinary reflection and research agenda. J. Bus. Res. 2021, 122, 889-901. [CrossRef] · DOI · Paper page
  50. Jansen, P.; Brey, P. Ethical Analysis of AI and Robotics Technologies; Sienna: Uppsala, NL, USA, 2019; pp. 45-89.
  51. Mergel, I.; Edelmann, N.; Haug, N. Defining digital transformation: Results from expert interviews. Gov. Inf. Q. 2019, 36, 101358. [CrossRef] · DOI · Paper page
  52. Sierra-Perez, J.; Teixeira, J.G.; Romero-Piqueras, C.; Patricio, L. Designing sustainable services with the ECO-Service design method: Bridging user experience with environmental performance. J. Clean. Prod. 2021, 305, 127228. [CrossRef] · DOI
  53. Schot, J.; Steinmueller, W.E. Three frames for innovation policy: R&D, systems of innovation and transformative change. Res. Policy 2018, 47, 1554-1567.
  54. Brioschi, M.; Bonardi, M.; Fabrizio, N.; Fuggetta, A.; Vrga, E.S.; Zuccala, M. Enabling and Promoting Sustainability through Digital API Ecosystems: An example of successful implementation in the smart city domain. Technol. Innov. Manag. Rev. 2021, 11, 4-10. [CrossRef] · DOI
  55. Vuong, T.K.; Mansori, S. An Analysis of the Effects of the Fourth Industrial Revolution on Vietnamese Enterprises. Manag. Dyn. Knowl. Econ. 2021, 9, 447-459.
  56. Krulicky, T.; Horak, J. Business performance and financial health assessment through artificial intelligence. Ekon.-Manaz. Spektrum 2021, 15, 38-51. [CrossRef] · DOI
  57. Krykavskyy, A.; Pokhylchenko, O.; Hayvanovych, N. Supply chain development drivers in industry 4.0 in Ukrainian enterprises. Oeconomia Copernic. 2019, 10, 273-290. [CrossRef] · DOI
  58. Pisar, P.; Bilkova, D. Controlling as a tool for SME management with an emphasis on innovations in the context of Industry 4.0. Equilib. Q. J. Econ. Econ. Policy 2019, 14, 763-785. [CrossRef] · DOI
  59. Kotlebova, J.; Arendas, P.; Chovancova, B. Government expenditures in the support of technological innovations and impact on stock market and real economy: The empirical evidence from the US and Germany. Equilib. Q. J. Econ. Econ. Policy 2020, 15, 717-734. [CrossRef] · DOI
  60. Kubickova, L.; Kormanakova, M.; Vesela, L.; Jelinkova, Z. The Implementation of Industry 4.0 Elements as a Tool Stimulating the Competitiveness of Engineering Enterprises. J. Compet. 2021, 13, 76-94. [CrossRef] · DOI
  61. Lăzăroiu, G.; Kliestik, T.; Novak, A. Internet of Things Smart Devices, Industrial Artificial Intelligence, and Real-Time Sensor Networks in Sustainable Cyber-Physical Production Systems. J. Self-Gov. Manag. Econ. 2021, 9, 20-30. [CrossRef] · DOI
  62. Johnson, E.; Nica, E. Connected Vehicle Technologies, Autonomous Driving Perception Algorithms, and Smart Sustainable Urban Mobility Behaviors in Networked Transport Systems. Contemp. Read. Law Soc. Justice 2021, 13, 37-50. [CrossRef] · DOI
  63. Kliestik, T.; Zvarikova, K.; Lăzăroiu, G. Data-driven Machine Learning and Neural Network Algorithms in the Retailing Environment: Consumer Engagement, Experience, and Purchase Behaviors. Econ. Manag. Financ. Mark. 2022, 17, 57-69. [CrossRef] · DOI
  64. Popescu, G.H.; Valaskova, K.; Horak, J. Augmented Reality Shopping Experiences, Retail Business Analytics, and Machine Vision Algorithms in the Virtual Economy of the Metaverse. J. Self-Gov. Manag. Econ. 2022, 10, 67-81. [CrossRef] · DOI
  65. Lăzăroiu, G.; Andronie, M.; Iatagan, M.; Geamănu, M.; S , tefănescu, R.; Dijmărescu, I. Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things. ISPRS Int. J. Geo-Inf. 2022, 11, 277. [CrossRef] · DOI
  66. Woodward, B.; Kliestik, T. Intelligent Transportation Applications, Autonomous Vehicle Perception Sensor Data, and Decision- Making Self-Driving Car Control Algorithms in Smart Sustainable Urban Mobility Systems. Contemp. Read. Law Soc. Justice 2021, 13, 51-64. [CrossRef] · DOI
  67. Nica, E.; Kliestik, T.; Valaskova, K.; Sabie, O.-M. The Economics of the Metaverse: Immersive Virtual Technologies, Consumer Digital Engagement, and Augmented Reality Shopping Experience. Smart Gov. 2022, 1, 21-34. [CrossRef] · DOI
  68. Andronie, M.; Lăzăroiu, G.; Iatagan, M.; Ut , ă, C.; S , tefănescu, R.; Cocos , atu, M. Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems. Electronics 2021, 10, 2497. [CrossRef] · DOI
  69. Vinerean, S.; Budac, C.; Baltador, L.A.; Dabija, D.-C. Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach. Electronics 2022, 11, 1269. [CrossRef] · DOI
  70. Cooper, H.; Poliak, M.; Konecny, V. Computationally Networked Urbanism and Data-driven Planning Technologies in Smart and Environmentally Sustainable Cities. Geopolit. Hist. Int. Relat. 2021, 13, 20-30. [CrossRef] · DOI
  71. Hamilton, S. Deep Learning Computer Vision Algorithms, Customer Engagement Tools, and Virtual Marketplace Dynamics Data in the Metaverse Economy. J. Self-Gov. Manag. Econ. 2022, 10, 37-51. [CrossRef] · DOI
  72. Konecny, V.; Barnett, C.; Poliak, M. Sensing and Computing Technologies, Intelligent Vehicular Networks, and Big Data-driven Algorithmic Decision-Making in Smart Sustainable Urbanism. Contemp. Read. Law Soc. Justice 2021, 13, 30-39. [CrossRef] · DOI
  73. Lyons, N. Deep Learning-based Computer Vision Algorithms, Immersive Analytics and Simulation Software, and Virtual Reality Modeling Tools in Digital Twin-driven Smart Manufacturing. Econ. Manag. Financ. Mark. 2022, 17, 67-81. [CrossRef] · DOI · Paper page
  74. Andronie, M.; Lăzăroiu, G.; Iatagan, M.; Hurloiu, I.; Dijmărescu, I. Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review. Sustainability 2021, 13, 751. [CrossRef] · DOI
  75. Glogovet , an, A.I.; Dabija, D.-C.; Fiore, M.; Pocol, C.B. Consumer Perception and Understanding of European Union Quality Schemes: A Systematic Literature Review. Sustainability 2022, 14, 1667. [CrossRef] · DOI
  76. Nica, E. Urban Big Data Analytics and Sustainable Governance Networks in Integrated Smart City Planning and Management. Geopolit. Hist. Int. Relat. 2021, 13, 93-106. [CrossRef] · DOI
  77. Poliak, M.; Poliakova, A.; Svabova, L.; Zhuravleva, A.N.; Nica, E. Competitiveness of Price in International Road Freight Transport. J. Compet. 2021, 13, 83-98. [CrossRef] · DOI
  78. Kovacova, M.; Lăzăroiu, G. Sustainable Organizational Performance, Cyber-Physical Production Networks, and Deep Learning- assisted Smart Process Planning in Industry 4.0-based Manufacturing Systems. Econ. Manag. Financ. Mark. 2021, 16, 41-54. [CrossRef] · DOI
  79. Kovacova, M.; Machova, V.; Bennett, D. Immersive Extended Reality Technologies, Data Visualization Tools, and Customer Behavior Analytics in the Metaverse Commerce. J. Self-Gov. Manag. Econ. 2022, 10, 7-21. [CrossRef] · DOI
  80. Popescu, G.H.; Poliak, M.; Manole, C.; Dumitrescu, C.-O. Decentralized Finance, Blockchain Technology, and Digital Assets in Non-Fungible Token (NFT) Markets. Smart Gov. 2022, 1, 64-78. [CrossRef] · DOI
  81. Wallace, S.; Lăzăroiu, G. Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering. Contemp. Read. Law Soc. Justice 2021, 13, 79-92. [CrossRef] · DOI
  82. Poliak, M.; Poliakova, A.; Zhuravleva, N.A.; Nica, E. Identifying the Impact of Parking Policy on Road Transport Economics. Mob. Netw. Appl. 2021. [CrossRef] · DOI
  83. Kliestik, T.; Valaskova, K.; Nica, E.; Kovacova, M.; Lăzăroiu, G. Advanced Methods of Earnings Management: Monotonic Trends and Change-Points under Spotlight in the Visegrad Countries. Oeconomia Copernic. 2020, 11, 371-400. [CrossRef · DOI

Authors
Valaskova, K., Nagy, M., Zabojnik, S., & Lăzăroiu, G.
Affiliations
  • Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
  • University of Žilina (SK)
  • Faculty of Commerce, University of Economics in Bratislava, Dolnozemska Cesta 1, 852 35 Bratislava, Slovakia
  • Spiru Haret University (RO)
Corresponding author
Nagy, Marek
ORCID
Venue
Mathematics
Publisher
MDPI AG
Published
2022-07-14
Volume
10
Issue
14
Pages
2452
ISSN
2227-7390 (ISSN-L)
DOI
10.3390/math10142452
OpenAlex
W4285595011
Cited by
72
References
84
Citations by year
2026: 1 · 2025: 14 · 2024: 19 · 2023: 26 · 2022: 12
FWCI
8.15
Type
journal article
Language
en
Open access
Licence
Locations
  • Mathematics (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Industrial Development
Keywords
Industry 4.0, wireless networks, cyber-physical systems, smart manufacturing, automotive industry, automobile exports, value added growth, export growth, innovation culture, Slovak economy, competitiveness

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