Development and validation of an Industry 4.0 adaptation potential scale (4IRAPS)
Abstract
The aim of this study was to develop a scale that can measure the potential of adapting to Industry 4.0, which refers to the fourth industrial revolution described as a combination of the innovation of various digital technologies rapidly developed in recent years. In addition, the reliability and validity of the Industry 4.0 Adaptation Potential (4IRAPS) is demonstrated. This research was conducted in two stages of a pilot and a main study. The data was collected from 174 participants enrolled in technical and management departments at the graduate and associate degree levels of two different universities. A 50-item questionnaire concerning Industry 4.0 prepared by experts experienced in this field was applied to the participants. As a result of a factor analysis, 30 items and 11 subscales with low a factor load and reliability level were removed from the questionnaire. The reliability and validity of 4IRAPS were verified by” the analyses via PLS-SEM. Finally, the remaining four sub-dimensions referring to Industry 4.0 were labelled as interested, effort for adaptation, readiness, and pessimism. This study developed the first scale of the industry 4.0 adaptation potential. The scale consists of four sub-dimensions and 17 items. It was determined that this scale was statistically reliable and valid.
First published online 23 March 2021
Keyword : industry 4.0, adaptation potential, scale development, effort, pessimism about industry 4.0
How to Cite
Sözbilir, F. (2021). Development and validation of an Industry 4.0 adaptation potential scale (4IRAPS). Technological and Economic Development of Economy, 27(3), 704-721. https://doi.org/10.3846/tede.2021.14513
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Bauer, W., Schlund, S., Hornung, T., & Schuler, S. (2018). Digitalization of industrial value chains – A review and evaluation of existing use cases of Industry 4.0 in Germany. Scientific Journal of Logistics, 14(3), 331–340. https://doi.org/10.17270/J.LOG.2018.288
Charlton, E. (2019, January 14). These are the 10 most in-demand skills of 2019, according to LinkedIn. World Economic Forum. https://www.weforum.org/agenda/2019/01/the-hard-and-soft-skills-tofutureproof-your-career-according-to-linkedin/
Chin, W. (1998). Issues and opinions on structural equation modelling. MIS Quarterly, 22(1), 7–16. www.jstor.org/stable/249674
Crocker, L., & Algina, J. (1986). Classical and modern test theory. Holt, Rinehart & Winston.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555
Demirci, K., Orhan, H., Demirdas, A., Akpinar, A., & Sert, H. (2014). Validity and reliability of the Turkish Version of the smartphone addiction scale in a younger population. Bulletin of Clinical Psychopharmacology, 24(3), 226–234. https://doi.org/10.5455/bcp.20140710040824
Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: Tinyyeteffective measures of the Big Five factors of personality. Psychological Assessment, 18(2), 192–203. https://doi.org/10.1037/1040-3590.18.2.192
Eberhard, B., Podio, M., Alonso, A. P., Radovica, E., Avotina, L., Peiseniece, L., Sendon, M. C., Gonzales Lozano, A., & Solé-Pla, J. (2017). Smart work: The transformation of the labour market due to the fourth industrial revolution (I4.0). International Journal of Business and Economic Sciences Applied Research (IJBESAR), 10(3), 47–66.
Field, A. P. (2009). Discovering statistics using SPSS. SAGE Publications.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Forsell, T., Tower, J., & Polman, R. (2020). Development of a scale to measure social capital in recreation and Sport Clubs. Leisure Sciences, 42(1), 106–122. https://doi.org/10.1080/01490400.2018.1442268
Gorsuch, R. L. (1983). Factor analysis. Lawrence Erlbaum Associates.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014a). Multivariate data analysis: Pearson new international edition. Pearson Education Limited.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014b). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publication.
Hamada, T. (2019). Determinants of decision-makers’ attitudes toward Industry 4.0 adaptation. Social Sciences, 8(5), 1–18. https://doi.org/10.3390/socsci8050140
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New Challenges to international marketing: Vol. 20. Advances in international marketing (pp. 277–320). Emerald Group Publishing Limited, Bingley. https://doi.org/10.1108/S1474-7979(2009)0000020014
Hertzog, M. A. (2008). Considerations in determining sample size for pilot studies. Research in Nursing & Health, 31(2), 180–191. https://doi.org/10.1002/nur.20247
Isaac, S., & Michael, W. B. (1995). Handbook in research and evaluation: A collection of principles, methods, and strategies useful in the planning, design, and evaluation of studies in education and the behavioral sciences (3 ed.). EdITS Publishers.
Johanson, G. A., & Brooks, G. P. (2010). Initial scale development: Sample size for pilot studies. Educational and Psychological Measurement, 70(3), 394–400. https://doi.org/10.1177/0013164409355692
Koca, D. (2020). Sanayi devrimlerinin tarihsel arka planı ve işgücü becerileri üzerindeki yansımaları. OPUS International Journal of Society Researches, 16(31), 4531–4558. https://doi.org/10.26466/opus.704841
Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). SPSS for intermediate statistics: Use and interpretation (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410611420
Magson, N. R., Craven, R. G., & Bodkin-Andrews, G. H. (2014). Measuring social capital: The development of the social capital and cohesion scale and the associations between social capital and mental health. Australian Journal of Educational & Developmental Psychology, 14, 202–216.
Pereira, A., & Romero, F. A. (2017). Review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 13, 1206–1214. https://doi.org/10.1016/j.promfg.2017.09.032
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. http://www.smartpls.com
Robinson, J. P., Shaver, P. R., & Wrightsman, L. S. (1991). Criteria for scale selection and evaluation. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes: Measures of social psychological attitudes (pp. 1–16). Academic Press. https://doi.org/10.1016/B978-0-12-590241-0.50005-8
Ruppert, T., Jaskó, S., Holczinger, T., & Abonyi, J. (2018). Enabling technologies for Operator 4.0: A survey. Applied Sciences, 8(9), 1650. https://doi.org/10.3390/app8091650
Sanders, A., Elangeswaran, C., & Wulfsberg, J. (2016). Industry 4.0 implies lean manufacturing: Research activities in Industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management, 9(3), 811–833. https://doi.org/10.3926/jiem.1940
Schmidt, R., Möhring, M., Härting, R. C., Reichstein, C., Neumaier, P., & Jozinovic’, P. (2015). Industry 4.0 – potentials for creating smart products: Empirical research results. In W. Abramowicz (Ed.), Lecture notes in business information processing: Vol. 208. Business information systems (pp. 16–27). Springer International Publishing. https://doi.org/10.1007/978-3-319-19027-3_2
Slavec, A., & Drnovsek, M. (2012). A perspective on scale development in entrepreneurship research. Economic and Business Review for Central and South-Eastern Europe, 14(1), 39–62.
Solís, M., & Mora-Esquivel, R. (2019). Development and validation of a measurement scale of the innovative culture in work teams. International Journal of Innovation Science, 11(2), 299–322. https://doi.org/10.1108/IJIS-07-2018-0073
Stein, C. M., Morris, N. J., & Nock, N. L. (2012). Structural equation modelling. In R. Elston, J. Satagopan, & S. Sun (Eds.), Statistical human genetics: Methods and protocols. Methods in molecular biology (pp. 495–512). https://doi.org/10.1007/978-1-61779-555-8_27
Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0-A glimpse. Procedia Manufacturing, 20(1), 233–238. https://doi.org/10.1016/j.promfg.2018.02.034
World Economic Forum. (2016). The Future of jobs: Employment, skills and workforce strategy for the fourth industrial revolution (Global Challenge Insight Report). http://www3.weforum.org/docs/ WEF_Future_of_Jobs.pdf
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806
Yaşlıoğlu, M. M. (2017). Factor analysis and validity in social sciences: Application of exploratory and confirmatory factor analyses. Istanbul University Journal of the School of Business, 46(Special Issue), 74–85. https://dergipark.org.tr/tr/pub/iuisletme/issue/32177/357061
Zhong, R., Xu, X., Klotz, E., & Newman, S. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616–630. https://doi.org/10.1016/J.ENG.2017.05.015