The Relationship of Industry 4.0 and Business Performance in Malaysian Manufacturing Firms: A PLS-SEM Model

  • Ooi Yenn Harn
  • Ng Tan Ching
  • Cheong Wen Chiet
  • Liang Meng Suan
  • Yew Ming Chian

Abstract

Since the last decade, the industrial sector had rapidly evolved with the presence of state-of-the-art technologies that had transformed industries, society, as well as processes due to the novel opportunities stipulated by Industry 4.0 Digital Technologies. Thus, manufacturing industry internationally have been pursuing the paradigm shift and attempting to implement Industry 4.0 Digital Technologies into their respective organizations. However, albeit the fact that Industry 4.0 had promised substantial improvement to manufacturing system and processes, the correlation and influence of Industry 4.0 Digital Technologies upon Business Performance are still ambivalent as there are insufficient empirical data and studies to support the implementation and lots of challenges were met during the process of implementation. Therefore, the paper aims to provide empirical evidence collected from manufacturing firms in Malaysia via questionnaire to bridge the literature gap by identifying the correlation concerning Industry 4.0 Digital Technologies and Business Performance with Partial Least Squares Structural Equation Modelling (PLS-SEM) method to model their correlation. Accordingly, 124 responses were gathered, and results implied that Industry 4.0 Digital Technologies are directly positive correlated with Business Performance. In other words, manufacturing firms intending on achieving high excellence in Business Performance, Industry 4.0 Digital Technologies will be the impeccable solution.

References

Ali Memon, M., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Huei Cham, T. (2020). Journal of Applied Structural Equation Modeling SAMPLE SIZE FOR SURVEY RESEARCH: REVIEW AND RECOMMENDATIONS. In Journal of Applied Structural Equation Modeling (Vol. 4, Issue 2).
Bagozzi, R. P., & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1177/009207038801600107
Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26, 643–659.
Bortolotti, T., Romano, P., & Nicoletti, B. (2010). Lean first, then automate: An integrated model for process improvement in pure service-providing companies. IFIP Advances in Information and Communication Technology, 338 AICT, 579–586. https://doi.org/10.1007/978-3-642-16358-6_72
Brettel, Malte, Niklas Friederichsen, Michael Keller, & Marius Rosenberg. (2014). How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Industrial Science and Engineering, 8, 37–44.
Buer, S. V., Strandhagen, J. O., & Chan, F. T. S. (2018). The link between industry 4.0 and lean manufacturing: Mapping current research and establishing a research agenda. International Journal of Production Research, 56(8), 2924–2940. https://doi.org/10.1080/00207543.2018.1442945
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences Second Edition.
Deloitte AG. (2015). Industry 4.0 Challenges and solutions for the digital transformation and use of exponential technologies. In Deloitte, Zurich. https://doi.org/10.1057/9780230514027_2
Ejsmont, K., & Gładysz, B. (2020). Lean Industry 4.0—Wastes Versus Technology Framework. Lecture Notes in Mechanical Engineering, 537–546. https://doi.org/10.1007/978-981-15-1910-9_44
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. In Source: Journal of Marketing Research (Vol. 18, Issue 1). https://www.jstor.org/stable/3151312
Geisser, S. (1974). A Predictive Approach to the Random Effects Model. Biometrika, 61(1), 101–107.
Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910–936. https://doi.org/10.1108/JMTM-02-2018-0057
Ghobakhloo, M., & Fathi, M. (2020). Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management, 31(1), 1–30. https://doi.org/10.1108/JMTM-11-2018-0417
Ghobakhloo, M., & Ng, T. C. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107. https://doi.org/10.1016/j.jii.2019.100107
Hair, J., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis. Cengage Learning.
Hair, J. F., Black, W. C., Babin, B., Anderson, R., & Tatham, R. L. (2010). SEM: An introduction. Multivariate data analysis: A global perspective. Multivariate Data Analysis: A Global Perspective, 629–686.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). In Practical Assessment, Research and Evaluation (2nd Edition, Vol. 21, Issue 1). SAGE Publications, Inc.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. In European Business Review (Vol. 31, Issue 1, pp. 2–24). Emerald Group Publishing Ltd. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/EJM-10-2018-0665
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. SAGE Publications, Inc.
Haseeb, M., Hussain, H. I., Ślusarczyk, B., & Jermsittiparsert, K. (2019). Industry 4.0: A solution towards technology challenges of sustainable business performance. Social Sciences, 8(5). https://doi.org/10.3390/socsci8050154
Imran, M., Hameed, W. ul, & Haque, A. ul. (2018). Influence of Industry 4.0 on the production and service sectors in Pakistan: Evidence from textile and logistics industries. Social Sciences, 7(12), 0–21. https://doi.org/10.3390/socsci7120246
Kamble, S., Gunasekaran, A., & Dhone, N. C. (2020). Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. International Journal of Production Research, 58(5), 1319–1337. https://doi.org/10.1080/00207543.2019.1630772
Kaur, B. (2019). Malaysia falls behind in IR4.0 migration. New Straits Times. https://www.nst.com.my/news/nation/2019/08/514242/malaysia-falls-behind-ir40-migration
Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd Edition). Guilford Publications.
Kock, N., & Lynn, G. S. (2012). Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations. In Journal of the Association for Information Systems (Vol. 13, Issue 7).
Kolberg, D., & Zühlke, D. (2015). Lean Automation enabled by Industry 4.0 Technologies. IFAC-PapersOnLine, 28(3), 1870–1875. https://doi.org/10.1016/j.ifacol.2015.06.359
Kusiak, A. (2017). Smart Manufacturing. International Journal of Production Research, 7543(October), 1–10. https://doi.org/10.1080/00207543.2017.1351644
Kwong-Kay Wong, K. (2019). Mastering Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS in 38 Hours. iUniverse.
Lee, M., Lee, Y., & Chou, C. J. (2017). Essential Implications of the Digital Transformation in Industry 4.0. Essential Implications of the Digital Transformation in Industry 4.0, 76(August), 465–467.
Memon, M. A., Ting, H., Ramayah, T., Chuah, F., & Cheah, J. H. (2017). A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A malaysian scenario. In Journal of Applied Structural Equation Modeling (Vol. 1, Issue 1, pp. i–xiii). Sarawak Research Society. https://doi.org/10.47263/jasem.1(1)01
Ministry of International Trade and Industry (MITI). (2018). Industry 4WRD : NATIONAL POLICY ON INDUSTRY 4.0. Ministry of International Trade and Industry.
Nawanir, G. (2016). The Effect Of Lean Manufacturing On Operations Performance And Business Performance In Manufacturing Companies In Indonesia. School of Technology Management and Logistics, College of Business, Universiti Utara Malaysia. http://etd.uum.edu.my/6710/2/s93557_01.pdf
Ng, T. C., & Ghobakhloo, M. (2018). What Determines Lean Manufacturing Implementation? A CB-SEM Model. Economies, 6(1), 9. https://doi.org/10.3390/economies6010009
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Pereira, A. C., Dinis-Carvalho, J., Alves, A. C., & Arezes, P. (2019). How Industry 4.0 can enhance lean practices. FME Transactions, 47(4), 810–822. https://doi.org/10.5937/fmet1904810P
Pierre-Olivier, B.-M. (2017). Industry 4.0: The New Industrial Revolution Are Canadian manufacturers ready? (Issue May).
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. In Journal of Applied Psychology (Vol. 88, Issue 5, pp. 879–903). https://doi.org/10.1037/0021-9010.88.5.879
Prinz, C., Kreggenfeld, N., & Kuhlenkötter, B. (2018). Lean meets Industrie 4.0 - A practical approach to interlink the method world and cyber-physical world. Procedia Manufacturing, 23(2017), 21–26. https://doi.org/10.1016/j.promfg.2018.03.155
Ramayah, T., Cheah, J. H., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0: An Updated and Practical Guide to Statistical Analysis (2nd ed.). Pearson Singapore.
Ringle, C. M., Wende, S., & Becker, J.-Michael. (2015). SmartPLS 3 (No. 3). SmartPLS. https://www.smartpls.com/
Rossini, M., Costa, F., Staudacher, A. P., & Tortorella, G. (2019). Industry 4.0 and lean production: An empirical study. IFAC-PapersOnLine, 52(13), 42–47. https://doi.org/10.1016/j.ifacol.2019.11.122
Rüttimann, B. G., & Stöckli, M. T. (2016). Lean and Industry 4.0—Twins, Partners, or Contenders? A Due Clarification Regarding the Supposed Clash of Two Production Systems. Journal of Service Science and Management, 09(06), 485–500. https://doi.org/10.4236/jssm.2016.96051
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial Least Squares Structural Equation Modeling. In Handbook of Market Research (pp. 1–40). Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-1
Schmidt, R., Möhring, M., Härting, R. C., Reichstein, C., Neumaier, P., & Jozinović, P. (2015). Industry 4.0 - Potentials for creating smart products: Empirical research results. Lecture Notes in Business Information Processing, 208(June), 16–27. https://doi.org/10.1007/978-3-319-19027-3_2
Schwab, K. (2016). The Fourth Industrial Revolution: what it means, how to respond. World Economic Forum.
Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564. https://doi.org/10.1016/j.jbusres.2016.03.049
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Sommer, L. (2015). Industrial revolution - Industry 4.0: Are German manufacturing SMEs the first victims of this revolution? Journal of Industrial Engineering and Management, 8(5), 1512–1532. https://doi.org/10.3926/jiem.1470
Sony, M. (2018). Industry 4.0 and lean management: a proposed integration model and research propositions. Production and Manufacturing Research, 6(1), 416–432. https://doi.org/10.1080/21693277.2018.1540949
Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society, 36(2), 111–147.
Strandhagen, J. W., Alfnes, E., Strandhagen, J. O., & Vallandingham, L. R. (2017). The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study. Advances in Manufacturing, 5(4), 344–358. https://doi.org/10.1007/s40436-017-0200-y
Szász, L., Demeter, K., Rácz, B. G., & Losonci, D. (2021). Industry 4.0: a review and analysis of contingency and performance effects. Journal of Manufacturing Technology Management, 32(3), 667–694. https://doi.org/10.1108/JMTM-10-2019-0371
Tortorella, G. L., & Fettermann, D. (2018). Implementation of industry 4.0 and lean production in brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975–2987. https://doi.org/10.1080/00207543.2017.1391420
Tortorella, G. L., Giglio, R., & van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations and Production Management, 39, 860–886. https://doi.org/10.1108/IJOPM-01-2019-0005
Vogel-Heuser, B., & Hess, D. (2016). Guest Editorial Industry 4.0-Prerequisites and Visions. IEEE Transactions on Automation Science and Engineering, 13(2), 411–413. https://doi.org/10.1109/TASE.2016.2523639
Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D. (2015). Towards Industry 4.0-Standardization as the Crucial Challenge for Highly Modular, Multi-Vendor Production Systems. IFAC - Papers On Line, 48, 579–584.
Zawadzki, P., & Żywicki, K. (2016). Smart Product Design and Production Control for Effective Mass Customization in the Industry 4.0 Concept. Management and Production Engineering Review, 7, 105–112.
Published
2023-03-31
How to Cite
YENN HARN, Ooi et al. The Relationship of Industry 4.0 and Business Performance in Malaysian Manufacturing Firms: A PLS-SEM Model. International Journal of Business and Technology Management, [S.l.], v. 5, n. 1, p. 431-445, mar. 2023. ISSN 2682-7646. Available at: <https://myjms.mohe.gov.my/index.php/ijbtm/article/view/21963>. Date accessed: 21 july 2024.
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Articles