İşletmelerde Endüstri 4.0 ile İlgili Dijital Bölünme: Avrupa Birliği-28 Ülkeleri Üzerine Bir Analiz
Year 2020,
, 225 - 244, 30.07.2020
Hayriye Atik
,
Fatma Ünlü
Abstract
Dijital bölünme literatürde genellikle farklı göstergeler kullanılarak ölçülmektedir. Literatürden farklı olarak, bu çalışma Endüstri 4.0 göstergelerine göre Avrupa Birliği’ne üye devletler arasındaki dijital bölünmeye işaret etmektedir. Finlandiya, İngiltere ve Almanya gibi AB ortalamasının üstünde ekonomik gelişmişlik performansına sahip olan ülkeler Endüstri 4.0 açısından yüksek performansa sahipken; G. Kıbrıs, Bulgaristan ve Romanya gibi ülkeler AB ortalamasının altında performans sergilemiştir. Bu durum AB üyesi ülkelerde dijital bölünmenin açık bir şekilde gözlemlendiğini göstermektedir. AB ülkeleri özellikle ortalamanın altında değerlere sahip olan üye ülkeler için özel politikalar geliştirerek ve bu politikaları destekleyerek üye ülkeler arasındaki dijital açığı kapatmalıdır. Bu çalışmanın sonuçları AB’deki politikacılara ve üye ülkelere yol gösterecektir.
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- DeCoster, J. (1998), Overview of Factor Analysis. <http://www.stat-help.com/notes.html>, 26.06 2018.
- Dewan, S.; D. Ganley & K. L. Kraemer (2005), “Across the Digital Divide: a Cross-Country Analysis of the Determinants of IT Penetration”, Journal of the Association of Information Systems, 6(12), 298-337.
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- Jacoby, W.G. & D. J. Ciuk (2014), Multidimensional Scaling: An Introduction, <http://polisci.msu.edu/jacoby/research/scaling/intromds/JacobyCiuk,%20MDS,%20V2,2010-29-14.pdf>, 17.06.2018.
- Jaworska, N. & A. C. Anastasova (2009), “A Review of Multidimensional Scaling (MDS) and its Utility in Various Psychological Domains”, Tutorials in Quantitative Methods for Psychology, 5 (1), 1‐10.
- Jia, X.; Q. Feng; T. Fan & Q. Lei (2012), “RFID Technology and Its applications in Internet of Things (IoT)”, 2nd International Conference on Consumer Electronics, Communications and Networks, 21-23 April, 2012.
- Jin X.; B. W. Wah; X. Cheng & Y. Wang (2015), “Significance and Challenges of Big Data Research”, Big Data Research, 2, 59-64.
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- Kruskal, J. B. (1964), “Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis. Psychometrika”, 29, 1–27
- Kuruczleki, E.; A. Pelle; R. Laczi & B. Fekete (2016), “The Readiness of the European Union to Embrace the Fourth Industrial Revolution”, Management, 11 (4), 327-347. Lee, I. & K. Lee (2015), “The Internet of Things (IoT): Applications, Investments, and Challenges for Enterprises”, Business Horizons, 58 (4), 431-440.
- Lom, M.; O. Pribly & M. Svitek (2016), “Industry 4.0 as a Part of Smart Cities”, Smart Cities Symposium Prague.
- Manly, B. F. J. (1986), Multivariate Statistical Methods: A Primer. Arrowsmith: Bristol.
- Marston, S.; Z. Li; S. Bandyopadhyay; J. Zhang & A. Ghalsasi (2011), “Cloud Computing- The Business Perspective”, Decision Suppoer Ssytems, 51, 176-189.
- McKinsey (2011), Big Data: The Next Frontier for Innovation, Competition, Productivity, In McKinsey Global Institute Report.
- Nakip, M. (2006), Pazarlama Araştırmaları Teknikler ve (SPSS Destekli) Uygulamalar, 2st ed.,Seçkin Yayıncılık: Ankara.Nick, G. & F. Pongrácz (2016), “How to Measure Industry 4.0 Readiness of Cities”, International Scientific Journal, 1(2), 136-141. OECD (2001), Understanding the Digital Divide. 1st ed.; OECD: Paris.
- Platform Industrie 4.0. (2013), Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0.
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- Schlechtendahl, J.; M. Keinert, M.; F. Kretschmer; A. Lechler & A. Verl (2015), “Making Existing Production System Industry 4.0 –Ready”, Production Engineering, 9 (1), 143-148.
- Sharma, S. (1996), Applied Multivariate Techniques. John Wiley & Sons, Inc.: New York.
- Stock, T. & G. Seliger (2016), “Opportunities of Sustainable Manufacturing in Industry 4.0, 13th Global Conference on Sustainable Manufacturing - Decoupling Growth from Resource Use”, Procedia CIRP 40, 536–541.
- Stojkić, Z.; I. Veža, I. & I. Bošnjak (2016), “A Concept of Information System Implementation (CRM and ERP) within Industry 4.0”, Proceedings of the 26th DAAAM International Symposium, 912-919.
- Tabachnick, B. G. & L. S. Fidell (2015), Using Multivariate Statistics, Pearson: UK.
- Taherdoost, H.; S. Sahibuddin & N. Jalaliyoon (2014), “Exploratory Factor Analysis; Concepts and Theory”, Advances in Applied and Pure Mathematics, 375-382.
- Tavakol, M. & R. Dennick (2011), “Making Sense of Cronbach’s Alpha”, International Journal of Medical Education, 53-55.
- Tryfos, P. (1997), Methods for Business Analysis and Forecasting: Text & Cases, Wiley.
- Tucker, L.R. & R. C. MacCallum (1997), Exploratory Factor Analysis, Unpublished Manuscript, Ohio State University, Columbus.
- Verma, J. P. (2013), Data Analysis in Management with SPSS Software, Springer: India.
- Vicente, M. R. & A. J. Lopez (2011), “Assessing the Regional Digital Divide across the European Union-27. Telecommunications Policy”, 35, 220–237.
- Weinberg, S. L. (1991), “An Introduction to Multidimensional Scaling”, Measurement and Evaluation in Counselling and Development, 24, 12-36.
- Wickelmaier, F. (2003), An Introduction to MDS, Unpublished manuscript, Sound Quality Research Unit at Aalborg University
- Xu, L. D. & L. Duan (2019), “Big Data for Cyber Physical Systems in Industry 4.0: A Survey”, Enterprise Information Systems, 13(2), 148-169.
- Yong, A.G. & S. Pearce (2013), “A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis”, Tutorials in Quantitative Methods for Psychology, 9(2), 79-94.
Industry 4.0-Related Digital Divide in Enterprises: An Analysis for The European Union-28
Year 2020,
, 225 - 244, 30.07.2020
Hayriye Atik
,
Fatma Ünlü
Abstract
Digital divide has been measured using various indicators in the literature so far. In contrast from the literature, this paper addresses the digital divide within European Union member states according to Industry 4.0-related indicators that have been used for the first time in empirical literature. While Finland, the UK and Germany are among the countries with economic development levels above the EU average, Cyprus, Bulgaria, and Romania are among the countries that are below the Union average in terms of Industry 4.0-related development. It is clear that an Industry 4.0-related digital divide is observable within EU member countries. The European Union should try to decrease the digital gap between member countries by developing and supporting special Industry 4.0 policies, especially for members below the EU average. Therefore, the results of this article will help policy makers in the EU and in member countries.
References
- Almada-Lobo, F. (2015), “The Industry 4.0 Revolution and the Future of Manufacturing Execution Systems (MES)”, Journal of Innovation Managemet, 3 (4), 16-21.
- Atik, H. & F. Ünlü (2018), “Industry 4.0 and the European Union: An Empirical Analysis”, H. Arapgiroğlu et al. (eds), The Most Recent Studies in Science and Art içinde, Ankara: Gece Publishing, 928-938.
- Atik, H. & F. Ünlü (2017), “Science Performance of Turkey in 21st Century: A Multivariate Statistical Comparison with the OECD Countries”, H. Arapgiroğlu et al. (eds), Researches on Science and Art in 21st Century Turkey, içinde, Ankara: Gece Publishing, 1030-1038.
- Basl, J. Pilot (2017), “Study of Readiness of Czech Companies to Implement the Principles of Industry 4.0.”, Management and Production Engineering Review, 8(2), 3-8.
- Basl, J. (2016), “The Pilot Survey of Industry 4.0 Principles penetration in the Selected Czech and Polish Companies”, Journal of Systems Integration, 7(4), 3-8.
- Baunsgaard, V. V. & S. R. Clegg (2015), “Innovation: A Critical Assessment of the Concept and Scope of Literature”, Selen et al. (eds), The Handbook of Service Innovation içinde, Springer: London, 5-25.
- Bello-Orgaz, G.; J. J. Jung & D. Camacho (2016), “Societal Big Data: Recent Achievements and New Challenges”, Information Fusion, 28, 45-59.
- Bittighofer, D.; M. Dust; A. Irslinge.; M. Liebich & L. Martin (2018), “State of Industry 4.0 across German Companies: A Pilot Study”, 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC).
- Borg, I. & P.J.F. Groenen (2005), Modern Multidimensional Scaling: Theory and Applications, 2st ed., Springer: Germany.
- Brettel, M.; N. Friederichsen; M. Keller & M. Rosenberg (2014), “How Virtualisation, Decentralisation and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective”, International Journal of Information and Communication Engineering, 8 (1), 37-44.
- Cox, T. F. & M. A. A. Cox (2001), Multidimensional Scaling. Chapman and Hall: New York.
- Cruz-Jesus, F.; T. Oliveira & F. Bacao (2012), “Digital Divide across the European Union”, Information & Management, 49 (6), 278-291.
- Cruz-Jesus, F.; M. R. Vicente; F. Bacao & T. Oliveira (2016), “The Education Related Digital Divide: An Analysis for the EU-28”, Computers in Human Behaviour, 56, 72-82.
- Cuervo, M. R. V. & A. J. L. Menéndez (2006), “A Multivariate Framework for the Analysis of the Digital Divide: Evidence for the European Union-15”, Information& Management, 43 (6), 756-766.
- De Leeuw, J. and W. Heiser (1982), “Theory of Multidimensional Scaling”, P. R. Krishnaiah & L. N. Kanal (eds), Handbook of Statistics içinde, North-Holland Publishing Company: Netherlands, 285-316.
- DeCoster, J. (1998), Overview of Factor Analysis. <http://www.stat-help.com/notes.html>, 26.06 2018.
- Dewan, S.; D. Ganley & K. L. Kraemer (2005), “Across the Digital Divide: a Cross-Country Analysis of the Determinants of IT Penetration”, Journal of the Association of Information Systems, 6(12), 298-337.
- Dukić, B.; S. Dugandžić & S. Dukić (2017), “Conceptual CRM Application Database Model in the Function of Physical Products Distribution for Known Customer”, 17 th International Scientific Conference Business Logistics in Modern Management, October 12-13 Osijek, Croatia.
- European Parliament (2016), Industry 4.0. Brussels: European Union, Policy Department A, Economic and Scientific Policies, <http://www.europarl.europa.eu/studies>, 25.07.2018.
- European Union (2016), Smarter, Greener, More inclusive? Indicators to Support the Europe 2020 Strategy. 1st ed.; European Union: Luxembourg.
- Eurostat (2018), Digital Economy and Society Statistics, https://ec.europa.eu/eurostat/web/digital-economy-and-society/data/database, 12.11.2018.
- Giguere, G. (2006), “Collecting and Analyzing data in Multidimensional Scaling Experiments: A Guide for Psychologists using SPSS”, Tutorials in Quantitative Methods for Psychology, 2(1), 26‐37.
- Gilchrist, A. (2016), Industry 4.0: The Industrial Internet of Things, Apress, Thailand.
- Greengard, S. (2015), The Internet of Things. The MIT Press Essential Knowledge Series. MIT Press: USA.
- Haddara, M. & A. Elregal (2015), “The Readiness of ERP Systems for the Factory of the Future”, Procedia Computer Science, 64, 721-728.
- Hair, J.; R. Anderson; R. Tatham & W. Black (1995), Multivariate Data Analysis: With Readings. Prentice Hall International: London, UK.
- Hashem, A.B.T.; I. Yaqoop; N. B. Anvar; S. Mokhtar; A. Gani & U.S. Khan (2015), “The Rise of “Big Data” on Cloud Computing: Review and Open Research Issues”, Information Systems, 47, 98-115.
- INFOSYS (2015), Industry 4.0: The State of the Nations, <http://www.experienceinfosys.com/industry-4-0>, 15.06.2018.
- Jacoby, W.G. & D. J. Ciuk (2014), Multidimensional Scaling: An Introduction, <http://polisci.msu.edu/jacoby/research/scaling/intromds/JacobyCiuk,%20MDS,%20V2,2010-29-14.pdf>, 17.06.2018.
- Jaworska, N. & A. C. Anastasova (2009), “A Review of Multidimensional Scaling (MDS) and its Utility in Various Psychological Domains”, Tutorials in Quantitative Methods for Psychology, 5 (1), 1‐10.
- Jia, X.; Q. Feng; T. Fan & Q. Lei (2012), “RFID Technology and Its applications in Internet of Things (IoT)”, 2nd International Conference on Consumer Electronics, Communications and Networks, 21-23 April, 2012.
- Jin X.; B. W. Wah; X. Cheng & Y. Wang (2015), “Significance and Challenges of Big Data Research”, Big Data Research, 2, 59-64.
- Kagermann, H. & W. Wahlster (2013), Securing the Future of German manufacturing Industry: Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0, Working Group, Acatech- National Academy of Science and Engineering, Germany, Final Report of the Industrie 4.0.
- Kruskal, J. B. (1964), “Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis. Psychometrika”, 29, 1–27
- Kuruczleki, E.; A. Pelle; R. Laczi & B. Fekete (2016), “The Readiness of the European Union to Embrace the Fourth Industrial Revolution”, Management, 11 (4), 327-347. Lee, I. & K. Lee (2015), “The Internet of Things (IoT): Applications, Investments, and Challenges for Enterprises”, Business Horizons, 58 (4), 431-440.
- Lom, M.; O. Pribly & M. Svitek (2016), “Industry 4.0 as a Part of Smart Cities”, Smart Cities Symposium Prague.
- Manly, B. F. J. (1986), Multivariate Statistical Methods: A Primer. Arrowsmith: Bristol.
- Marston, S.; Z. Li; S. Bandyopadhyay; J. Zhang & A. Ghalsasi (2011), “Cloud Computing- The Business Perspective”, Decision Suppoer Ssytems, 51, 176-189.
- McKinsey (2011), Big Data: The Next Frontier for Innovation, Competition, Productivity, In McKinsey Global Institute Report.
- Nakip, M. (2006), Pazarlama Araştırmaları Teknikler ve (SPSS Destekli) Uygulamalar, 2st ed.,Seçkin Yayıncılık: Ankara.Nick, G. & F. Pongrácz (2016), “How to Measure Industry 4.0 Readiness of Cities”, International Scientific Journal, 1(2), 136-141. OECD (2001), Understanding the Digital Divide. 1st ed.; OECD: Paris.
- Platform Industrie 4.0. (2013), Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0.
- Roblek, V.; M. Meško, M. & A. Krapež (2016), “A Complex View of Industry 4.0”, Sagepub, April-June, 1-11.
- Roland Berger (2015), Industry 4.0: The Role of Switzerland within a European Manufacturing Revolution, <https://www.rolandberger.com/en/Publications/Industry-4.0-The-role-ofSwitzerland.html>, 17.06.2018.
- Roland Berger (2014), Think Act: Coo Insights- Industry 4.0, http://www.rolandberger.com/media/pdf/Roland_Berger_TAM_COOInsights_E_201501/3.PDF, 28.06.2015.
- Saeed, N.; H. Nam; M.I.U Hag & D.M.S. Bahtti (2018), “A Survey on Multidimensional Scaling”, ACM Computing Surveys, 1(1), 1-26.
- Schlechtendahl, J.; M. Keinert, M.; F. Kretschmer; A. Lechler & A. Verl (2015), “Making Existing Production System Industry 4.0 –Ready”, Production Engineering, 9 (1), 143-148.
- Sharma, S. (1996), Applied Multivariate Techniques. John Wiley & Sons, Inc.: New York.
- Stock, T. & G. Seliger (2016), “Opportunities of Sustainable Manufacturing in Industry 4.0, 13th Global Conference on Sustainable Manufacturing - Decoupling Growth from Resource Use”, Procedia CIRP 40, 536–541.
- Stojkić, Z.; I. Veža, I. & I. Bošnjak (2016), “A Concept of Information System Implementation (CRM and ERP) within Industry 4.0”, Proceedings of the 26th DAAAM International Symposium, 912-919.
- Tabachnick, B. G. & L. S. Fidell (2015), Using Multivariate Statistics, Pearson: UK.
- Taherdoost, H.; S. Sahibuddin & N. Jalaliyoon (2014), “Exploratory Factor Analysis; Concepts and Theory”, Advances in Applied and Pure Mathematics, 375-382.
- Tavakol, M. & R. Dennick (2011), “Making Sense of Cronbach’s Alpha”, International Journal of Medical Education, 53-55.
- Tryfos, P. (1997), Methods for Business Analysis and Forecasting: Text & Cases, Wiley.
- Tucker, L.R. & R. C. MacCallum (1997), Exploratory Factor Analysis, Unpublished Manuscript, Ohio State University, Columbus.
- Verma, J. P. (2013), Data Analysis in Management with SPSS Software, Springer: India.
- Vicente, M. R. & A. J. Lopez (2011), “Assessing the Regional Digital Divide across the European Union-27. Telecommunications Policy”, 35, 220–237.
- Weinberg, S. L. (1991), “An Introduction to Multidimensional Scaling”, Measurement and Evaluation in Counselling and Development, 24, 12-36.
- Wickelmaier, F. (2003), An Introduction to MDS, Unpublished manuscript, Sound Quality Research Unit at Aalborg University
- Xu, L. D. & L. Duan (2019), “Big Data for Cyber Physical Systems in Industry 4.0: A Survey”, Enterprise Information Systems, 13(2), 148-169.
- Yong, A.G. & S. Pearce (2013), “A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis”, Tutorials in Quantitative Methods for Psychology, 9(2), 79-94.