Barriers to digital transformation of high and medium high tech global manufacturing enterprises in Poland
DOI: https://doi.org/10.3846/tede.2026.25217Abstract
The aim of the study is to identify the key barriers to digital transformation and assess the structure of interrelations between these barriers and the selected characteristics of enterprises, their approach to digital transformation, as well as the technological advancement of products in large and very large foreign manufacturing enterprises operating in the high and medium high technology sectors in Poland. The empirical research is based on information from 95 survey respondents. Multidimensional correspondence analysis was applied in the research to simultaneously assess the structure of interrelations between the categories of multiple nominal variables. The conducted research shows that more than 50% of the surveyed enterprises indicated excessive costs of digitalization as the main barrier to its implementation, followed by uncertain market conditions and lack of own financial resources. The most significant difficulties related to the implementation of digitalization were primarily experienced by the enterprises operating in Poland for up to 10 years, free to relocate, not committed to develop innovative products, and presenting negative approach to digital transformation. The research findings provide crucial information useful for businesses, politicians, researchers and technology providers in creating more effective strategies and policies to support the Industry 4.0 development. It can contribute to increased business competitiveness and innovation, thus having a positive impact on the economy in general. The article constitutes an original study based on the authors’ own research.
First published online 28 January 2026
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digitalization, barriers to industrial digitalization, technological advancement of products, high and medium high technology sector enterprises, Poland, multivariate correspondence analysisHow to Cite
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References
Abdi, H., &Valentin, D. (2007). Multiple correspondence analysis. In N. Salkind (Ed.), Encyclopaedia of measurement and statistics (pp. 651–657). SAGE.
Abrell, T., Pihlajamaa, M., Kanto, L., vom Brocke, J., & Uebernickel, F. (2016). The role of users and customers in digital innovation: Insights from B2B manufacturing firms. Information & Management, 53(3), 324–335. https://doi.org/10.1016/j.im.2015.12.005
Aczel, A. (2000). Statystyka w zarządzaniu: Pełny wykład [Statistics in Management: A complete lecture]. PWN.
Adler-Milstein, J. (2021). From digitalization to digital transformation: Policy priorities for closing the gap. JAMA, 325(8), 717–718. https://doi.org/10.1001/jama.2020.27014
Aslanova, I., & Kulichkina, A. (2020). Digital maturity: Definition and model. Proceedings of the 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth, 138, 443–449. https://doi.org/10.2991/aebmr.k.200502.073
Arnarson, H., Yu, H., Olavsbråten, M. M., Bremdal, B. A., & Solvang, B. (2023). Towards smart layout design for a reconfigurable manufacturing system. Journal of Manufacturing Systems, 68, 354–367. https://doi.org/10.1016/j.jmsy.2023.03.012
Barbieri, P., Boffelli, A., Elia, S., Fratocchi, L., & Kalschmidt, M. (2022). How does Industry 4.0 affect international exposure? The interplay between firm innovation and home-country policies in post-offshoring relocation decisions. International Business Review, 31(4), Article 10192. https://doi.org/10.1016/j.ibusrev.2022.101992
Benzécri, J.-P. (1973). Analyse des Données: Tome 2. Analyse de Correspondences [Data anlysis: Vol. 2. Corresponding analysis]. Dunod.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169–196. https://doi.org/10.2307/3250983
Bhatia, V., Sidharth, S., Khare, S. K., Ghorpade, S. C., Kumar, P., Kumar, A., & Agarwal, A. (2024). Intelligent manufacturing in aerospace: Integrating Industry 4.0 technologies for operational excellence and digital transformation. In A. Kumar, P. Kumar, & Y. Liu (Eds.), Industry 4.0 driven manufacturing technologies (pp. 389–434). Springer. https://doi.org/10.1007/978-3-031-68271-1_18
Bogner, E., Voelklein, T., Schroedel, O., & Franke, J. (2016). Study based analysis on the current digitalization degree in the manufacturing industry in Germany. Procedia CIRP, 57, 14–19. https://doi.org/10.1016/j.procir.2016.11.004
Borangiu, T., Trentesaux, D., Thomas, A., Leitão, P., & Barata, J. (2019). Digital transformation of manufacturing through cloud services and resource virtualization. Computers in Industry, 108, 150–162. https://doi.org/10.1016/j.compind.2019.01.006
Burkacky, O., Deichmann, J., Hepp, D., & Mühlreiter, B. (2018). A blueprint for successful digital transformations for automotive suppliers. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-blueprint-for-successful-digital-transformations-for-automotive-suppliers
Calabrese, A., Levialdi Ghiron, N., & Tiburzi, L. (2021). ‘Evolutions’ and ‘revolutions’ in manufacturers’ implementation of industry 4.0: A literature review, a multiple case study, and a conceptual framework. Production Planning & Control, 32(3), 213–227. https://doi.org/10.1080/09537287.2020.1719715
Clausen, S.-E. (1998). Applied correspondence analysis. An introduction. Sage. https://doi.org/10.4135/9781412983426
Chakraborty, A., & Datta, S. (2024, December 15–18). Learning with Sparsely permuted data: A robust Bayesian approach. In Proceedings of the 2024 IEEE International Conference on Big Data (BigData) (pp. 4–13). Washington, DC, USA. IEEE. https://doi.org/10.1109/BigData62323.2024.10825493
Chanias, S., Myers, M. D., & Hess, T. (2019). Digital transformation strategy making in pre-digital organizations: The case of a financial services provider. The Journal of Strategic Information Systems, 28(1), 17–33. https://doi.org/10.1016/j.jsis.2018.11.003
Chen, Z.-S., Liang, C.-Z., Xu, Y.-Q., Pedrycz, W., & Skibniewski, M. J. (2024). Dynamic collective opinion generation framework for digital transformation barrier analysis in the construction industry. Information Fusion, 103, Article 102096. https://doi.org/10.1016/j.inffus.2023.102096
Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley.
Dachs, B., Kinkel, S., & Jäger, A. (2019). Bringing it all back home? Backshoring of manufacturing activities and the adoption of Industry 4.0 technologies. Journal of World Business, 54(6), Article 101017. https://doi.org/10.1016/j.jwb.2019.101017
Dalal, G., Pannu, S., Vyas, P., & Chugh, P. (2024). The pulse of buying: Mapping the conceptual structure and decoding impulse purchase patterns. Global Knowledge, Memory and Communication. https://doi.org/10.1108/GKMC-05-2024-0256
Diener, F., & Špaček, M. (2021). Digital transformation in banking: A managerial perspective on barriers to change. Sustainability, 13(4), Article 2032. https://doi.org/10.3390/su13042032
Etz, S., Marguin, S., & Rabe, H. (2024). Visiting the modern wunderkammer: Social-spatial inequalities in ways of knowing. Museum & Society, 22(1), 67–93. https://doi.org/10.29311/mas.v22i1.4356
European Investment Bank. (2023). Digitalisation in Europe 2022–2023: Evidence from the EIB investment survey. https://www.eib.org/attachments/lucalli/20230112_digitalisation_in_europe_2022_2023_en.pdf
Ferrari, G. N., dos Santos, G. C., Ossani, P. C., Leal, G. C. L., & Galdamez, E. V. C. (2025). Analysis of the climate impact on occupational health and safety using heat stress indexes. International Journal of Environmental Research and Public Health, 22(1), Article 130. https://doi.org/10.3390/ijerph22010130
Fontaine, M., & Neys, O. H. (2024). Classifying, downgrading, reclassifying. The French “women’s” tennis ranking categories facing the test of change? Movement & Sport Sciences – Science & Motricité, (127), 17–28. https://doi.org/10.1051/sm/2024007
Forth, P., Reichert, T., de Laubier, R., & Chakraborty, S. (2020). Flipping the odds of digital transformation success. BCG. https://www.bcg.com/publications/2020/increasing-odds-of-success-in-digital-transformation
Fruchtman, T., Jessel, J., Pan, B., McLeod, S., & Rajaraman, A. (2025). The performance-based IISCA can inform effective and socially meaningful skill-based treatment. Behaviour Analysis in Practice. https://doi.org/10.1007/s40617-024-01036-7
Gkrimpizi, T., Peristeras, V., & Magnisalis, I. (2023). Classification of barriers to digital transformation in higher education institutions: Systematic literature review. Education Sciences, 13(7), Article 746. https://doi.org/10.3390/educsci13070746
Gobble, M. M. (2018). Digitalization, digitization, and innovation. Research-Technology Management, 61(4), 56–59. https://doi.org/10.1080/08956308.2018.1471280
Gong, Ch., & Ribiere, V. (2021). Developing a unified definition of digital transformation. Technovation, 102, Article 102217. https://doi.org/10.1016/j.technovation.2020.102217
Götz, M., & Jankowska, B. (2020). Adoption of Industry 4.0 technologies and company competitiveness: Case studies from a post-transition economy. Foresight and STI Governance 14(4), 61–78. https://doi.org/10.17323/2500-2597.2020.4.61.78
Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple correspondence analysis and related methods. CRC Press. https://doi.org/10.1201/9781420011319
Greenacre, M. J. (2010). Correspondence analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(5), 613–619. https://doi.org/10.1002/wics.114
Greenacre, M. (2017). Correspondence analysis in practice (3rd ed.). CRC press. https://doi.org/10.1201/9781315369983
Greenacre, M., Nenadic, O., & Friendly, M. (2020). ca: Simple, multiple and joint correspondence analysis. CRAN. https://cran.r-project.org/web/packages/ca/index.html
Grzyb, K. (2019). Industry 4.0 market in Poland from the international perspective. Hradec Economic Days, 9(1), 252–262. https://doi.org/10.36689/uhk/hed/2019-01-025
Guo, X., Li, M., Wang, Y., & Mardani, A. (2023). Does digital transformation improve the firm’s performance? From the perspective of digitalisation paradox and managerial myopia. Journal of Business Research, 163, Article 113868. https://doi.org/10.1016/j.jbusres.2023.113868
Glówny Urząd Statystyczny. (2020). Działalność gospodarcza podmiotów z kapitałem zagranicznym w 2019 roku [Business activity of enterprises with foreign capital in 2019]. https://stat.gov.pl/obszary-tematyczne/podmioty-gospodarcze-wyniki-finansowe/przedsiebiorstwa-niefinansowe/dzialalnosc-gospodarcza-przedsiebiorstw-z-kapitalem-zagranicznym-w-2019-roku,26,3.html
Hakhovych, N., Kushnirenko, O., & Venger, V. (2024). Digitalization of industrial production and trade: Technologies, benefits and ways of implementation. In A. Pawlik & K. Shaposhnykov (Eds.), Traditional and innovative approaches in economics: Theory, methodology, practice (pp. 71–90). Baltija Publishing.
Henriette, E., Feki, M., & Boughzala, I. (2015). The shape of digital transformation: A systematic literature review. MCIS 2015 Proceedings, Article 10. https://aisel.aisnet.org/mcis2015/10
Hinings, B., Gegenhuber, T., & Greenwood, R. (2018). Digital innovation and transformation: An institutional perspective. Information and Organization, 28(1), 52–61. https://doi.org/10.1016/j.infoandorg.2018.02.004
Hirschfeld, H. O. (1935). A connection between correlation and contingency. Mathematical Proceedings of the Cambridge Philosophical Society, 31(4), 520–524. https://doi.org/10.1017/S0305004100013517
Hjellbrekke, J. (2018), Multiple correspondence analysis for the social sciences. Routledge. https://doi.org/10.4324/9781315516257
Johannesson, P., Sinha, J., Packmohr, S., & Brink, H. (2023). Comparing barriers to digital transformation between small and medium-sized and large enterprises. In Proceedings of the International Conferences on E-Society 2023 and Mobile Learning 2023 (pp. 74–86). Lisbon, Portugal. IADIS Press.
Johnson, R. A., & Wichern D. W. (2007). Applied multivariate statistical analysis (6th ed.). Pearson.
Johnes, M. D., Hutcheson, S., & Camba, J. D. (2021). Past, present and future barriers to digital transformation in manufacturing: A review. Journal of Manufacturing Systems, 60, 936–948. https://doi.org/10.1016/j.jmsy.2021.03.006
Kalantan, Z. I., Alqarni, R. Z., & Baaqeel, H. (2025). Studying the hidden relationships in mixed data via principal component analysis with application in traumatic brain injury data. Contemporary Mathematics, 6(1), 772–779. https://doi.org/10.37256/cm.6120256017
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–119. https://doi.org/10.1016/j.compind.2018.06.004
Kinkel, S., Dewanti, R. T., Zimmermann, P., & Coates, R. (2017). Measuring reshoring trends in the EU and the US. MAKERS. https://reshoringinstitute.org/wp-content/uploads/2020/11/Measuring-Reshoring-in-the-EU.pdf
Kinkel, S. (2020). Industry 4.0 and reshoring. In L. De Propis & D. Bailey (Eds.), Industry 4.0 and regional transformations (pp. 195–213). Routledge. https://doi.org/10.4324/9780429057984-11
Kiraz, A., Canpolat, O., Özkurt, C., & Taşkın, H. (2020). Analysis of the factors affecting the Industry 4.0 tendency with the structural equation model and an application. Computers & Industrial Engineering, 150, Article 106911. https://doi.org/10.1016/j.cie.2020.106911
Kot, S., Jakubowski, J., & Sokołowski, A. (2007). Statystyka [Statistics]. Difin.
Kumar, P., Bhamu, J., & Sangwan, K. S. (2021). Analysis of barriers to Industry 4.0 adoption in manufacturing organizations: An ISM approach. Procedia CIRP, 98, 85–90. https://doi.org/10.1016/j.procir.2021.01.010
Kutnjak, A. (2021). COVID-19 accelerates digital transformation in industries: Challenges, issues, barriers and problems in transformation. IEEE Access, 9, 79373–79388. https://doi.org/10.1109/ACCESS.2021.3084801
Lammers, T., Tomidei, L., & Trianni, A. (2019). Towards a novel framework of barriers and drivers for digital transformation in industrial supply chains. In Proceedings of the 2019 Portland International Conference on Management of Engineering and Technology (PICMET). Portland, OR, USA. IEEE. https://doi.org/10.23919/PICMET.2019.8893875
Le Roux, B., & Rouanet, H. (2010). Multiple correspondence analysis. Sage. https://doi.org/10.4135/9781412993906
Linderoth, H. C. J., Jacobsson, M., & Elbanna, A. (2018). Barriers for digital transformation: The role of industry. In Australasian Conference on Information Systems 2018. UTS ePRESS. https://doi.org/10.5130/acis2018.az
Lola, I., & Bakeev, M. (2020) Digital transformation in manufacturing: drivers, barriers, and benefits (Higher School of Economics Research Paper No. WP BRP 107/STI/2020). SSRN. https://doi.org/10.2139/ssrn.3570342
Łobejko, S. (2018). Strategie cyfryzacji przedsiębiorstw [Strategies for the digitization of enterprises]. In R. Knosala (Ed.), Innowacje w zarządzaniu i inżynierii produkcji (Vol. 2, pp. 641–652). Oficyna Wydawnicza Polskiego Towarzystwa Zarządzania Produkcją.
Mahmood, F., Khan, A. Z., & Khan, M. B. (2019). Digital organizational transformation issues, challenges and impact: A systematic literature review of a decade. Abasyn Journal of Social Sciences, 12(2), 231–249. https://doi.org/10.34091/AJSS.12.2.03
Meng, X., & Gong, X. (2024). Digital transformation and innovation output of manufacturing companies – an analysis of the mediating role of internal and external transaction costs. PLoS ONE, 19(1), Article e0296876. https://doi.org/10.1371/journal.pone.0296876
Mider, D., & Marcinkowska, A. (2013). Analiza danych ilościowych dla politologów. Praktyczne wprowadzenie z wykorzystaniem programu GNU PSPP [Quantitative data analysis for political scientists: A practical introduction using GNU PSPP]. ACAD.
Nambisan, S., Wright, M., & Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: progress, challenges and key themes. Research Policy, 48(8), Article 103773. https://doi.org/10.1016/j.respol.2019.03.018
Negri, A., Barazzeti, A., & Milesi, S. (2024, November 7–9). The role of clinical experience in changing the epistemological beliefs in psychotherapy trainees and professionals [Conference presentation]. Psicoterapia e ricerca in un mondo che cambia XV congresso nazionale SPR-IAG. Naples, Italy.
Nishisato, S. (2006). Multidimensional nonlinear descriptive analysis. Chapman & Hall/CRC. https://doi.org/10.1201/9781420011203
Omowole, J., Akinwale, A., & Akinwale, O. (2024). Barriers and drivers of digital transformation in SMEs: A conceptual analysis. International Journal of Scientific Research and Technology, 9(1), 1–10.
Packmohr, S., Brink, H., & Paul, F. (2023). Unravelling perceptions of barriers to digital transformation – contrasting small and medium-sized with large enterprises. IADIS International Journal on Computer Science and Information Systems, 18(1), 102–119. https://doi.org/10.33965/ijcsis_2023180107
Pokorska, K. (2023). Rozwój Przemysłu 4.0 a atrakcyjność inwestycyjna Państw Unii Europejskiej ze szczególnym uwzględnieniem Polski [The development of Industry 4.0 and the investment attractiveness of European Union countries, with particular emphasis on Poland]. [Doctoral dissertation, Wroclaw University of Economics and Business].
Raczyk, A., & Dobrowolska-Kaniewska, H. (2009). Kształtowanie struktur przestrzennych sektora przemysłu i usług według poziomów techniki na przykładzie województwa dolnośląskiego [Conditions of spatial diversity of service and industry sectors by level of technology on the example of Lower Silesia]. Studies of the Industrial Geography Commission of the Polish Geographical Society, 13, 42–55. https://doi.org/10.24917/20801653.13.4
Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A.B., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, Article 107546. https://doi.org/10.1016/j.ijpe.2019.107546
Rückert, D., Weiss, C., & Revoltella, D. (2020). Adoption of digital technologies by firms in Europe and the US: Evidence trom the EIB investment survey. https://cepr.org/voxeu/columns/adoption-digital-technologies-firms-europe-and-us-evidence-eib-investment-survey
Saariko, T., Westergren, U. H., & Blomquist, T. (2020). Digital transformation: Five recommendations for the digitally conscious firm. Business Horizons, 63(6), 825–839. https://doi.org/10.1016/j.bushor.2020.07.005
Sarkissian, C., Bahrami, F., & Anoushe, M. (2025). Correspondence or contrast between compound infinitives and missing light verb counterparts: An explanation in the distributed morphology framework. Journal of Researches in Linguistics, 17(1), 1–18.
Senna, P. P., Ferreira, L. M. D., Barros, A. C., Roca, J. B., & Magalhães, V. (2022). Prioritizing barriers for the adoption of Industry 4.0 technologies. Computers & Industrial Engineering, 171, Article 108428. https://doi.org/10.1016/j.cie.2022.108428
Senna, P. P., Roca, J., & Barros, A. (2023). Overcoming barriers to manufacturing digitalization: Policies across EU countries. Technological Forecasting and Social Change, 196, Article 122822. https://doi.org/10.1016/j.techfore.2023.122822
Severeyn, E., La Cruz, A., Puente, C., Velásquez, J., & Huerta, M. (2024, August, 21–23). Unveiling the barriers to e-business adoption in Venezuelan public enterprises: A multiple correspondence analysis approach. In Proceedings of the 2024 IEEE Colombian Conference on Communications and Computing (COLCOM). Barranquilla, Colombia. IEEE. https://doi.org/10.1109/COLCOM62950.2024.10720252
Sivertsen, M. F. (2025). Taste on Facebook: Revisiting the omnivore-univore hypothesis using digital trace data. Poetics, 109, Article 101968. https://doi.org/10.1016/j.poetic.2024.101968
Stentoft, J., Wickstrom, K. A., Philipsen, K., & Haug, A. (2021). Drivers and barriers for Industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers. Production & Planning Control, 32(10), 811–828. https://doi.org/10.1080/09537287.2020.1768318
Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP, 52, 161–166. https://doi.org/10.1016/j.procir.2016.07.040
Solow, R. M. (1987). We’d better watch out. New York Times Book Review, 36.
Snieška, V., Navickas, V., Havierniková, K., Okręglicka, M., & Gajda, W. (2020). Technical, information and innovation risks of industry 4.0 in small and medium-sized enterprises – case of Slovakia and Poland. Journal of Business Economics and Management, 21(5), 1269–1284. https://doi.org/10.3846/jbem.2020.12279
Sudolska, A., & Łapińska J. (2020). Exploring determinants of innovation capability in manufacturing companies operating in Poland. Sustainability, 12(17), Article 7101. https://doi.org/10.3390/su12177101
Śledziewska, K., & Włoch, R. (2020). Gospodarka cyfrowa. Jak nowe technologie zmieniają świat [The digital economy. How new technologies are changing the world]. Wydawnictwa Uniwersytetu Warszawskiego. https://doi.org/10.31338/uw.9788323541943
Śledziewska, K., & Włoch, R. (2024). Polish SMEs on the road to Industry 4.0 – opportunities, challenges and barriers to innovation. International Business and Global Economy, (43), 51–75. https://doi.org/10.26881/ibage.2024.43.04
Tangi, L., Janssen, M., Benedetti, M., & Noci, G. (2020). Barriers and drivers of digital transformation in public organizations: Results from a survey in the Netherlands. In G. V. Pereira, M. Janssen, H. Lee, I. Lindgren, M. P. Rodríguez Bolívar, H. J. Scholl, & A. Zuiderwijk (Eds.), Lecture notes in computer science: Vol. 12219. Electronic Government (pp. 42–56). Springer. https://doi.org/10.1007/978-3-030-57599-1_4
Tripathi, S., & Gupta, M. (2019, December 12–14). Impact of barriers on industry 4.0 transformation dimensions. In Proceedings of the International Conference on Precision, Meso, Micro and Nano Engineering (COPEN 2019). Indian Institute of Technology Indore, India.
Turovets, Y., & Vishnevskiy, K. (2019). Patterns of digitalisation in machinery-building industries: Evidence from Russia. Engineering Management in Production and Services, 11(4), 7–22. https://doi.org/10.2478/emj-2019-0029
Trittin-Ulbrich, H., Scherer, A. G., Munro, I., & Whelan, G. (2021). Exploring the dark and unexpected sides of digitalization: Toward a critical agenda. Organization, 28(1), 8–25. https://doi.org/10.1177/1350508420968184
Uzule, K., & Verina, N. (2023). Digital barriers in digital transformation: Literature review. Economics and Culture, 20(1), 125–143. https://doi.org/10.2478/jec-2023-0011
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Vogelsang, K., Liere-Netheler, K., Packmohr, S., & Hoppe, U. (2019). Barriers to digital transformation in manufacturing: Development of a research agenda. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 4937–4946). University of Hawaii at Manoa. https://doi.org/10.24251/HICSS.2019.594
Wang, X., Kumar, V., Kumari, A., & Kuzmin, E. (2022). Impact of digital technology on supply chain efficiency in manufacturing industry. In V. Kumar, J. Leng, V. Akberdina & E. Kuzmin (Eds.), Lecture notes in information systems and organisation: Vol. 54. Digital transformation in industry (pp. 347–371). Springer. https://doi.org/10.1007/978-3-030-94617-3_25
Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
Zangiacomi, A., Pessot, E., Fornasiero, R., Bertetti, M., & Sacco, M. (2019). Moving towards digitalization: A multiple case study in manufacturing. Production Planning & Control, 31(2–3), 143–157. https://doi.org/10.1080/09537287.2019.1631468
Zhao, L., Liu, C., Qi, E., & Shi, S. (2024). Multi-objective optimization in order to allocate computing and telecommunication resources based on non-orthogonal access, participation of cloud server and edge server in 5G networks. Journal of King Saud University-Computer and Information Sciences, 36(8), Article 102187. https://doi.org/10.1016/j.jksuci.2024.102187
Zhang, G., Xu, T., Yin, W., Bateni, S. M., Jun, C., Kim, D., Liu, S., Xu, Z., Ming, W., & Wang, J. (2024). A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly. Remote Sensing of Environment, 313, Article 114359. https://doi.org/10.1016/j.rse.2024.114359
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