Abstract
IT-driven transformation processes in manufacturing result in major changes for employees’ daily work and they are ever-present in media and research, called inter alia digitalization, computerization or Industry 4.0. A variety of research focuses on these new hardware and software technologies. Research dealing with motivational processes and attitudes of employees in manufacturing, however, is lacking. How do they face these changes?
Data were collected using a standardized questionnaire (KFZA) that was returned by n = 109 apprentices working in manufacturing. The questionnaire queried the actual state of the work situation, as well as the target state. Additionally, fears, opportunities, and general aspects participants associate with a highly digitalized work environment, were also investigated.
Results are divided. Firstly, they show that young employees are negatively opposed to the changes of Industry 4.0 and digitalization in manufacturing as they fear massive job losses. Then again, they favour the idea of learning something new, having a greater degree of self-determination, and versatility, which is also connected to future work tasks.
Thus, early clarification of realistic risks and chances connected to digitalization in manufacturing is necessary in order to prevent young employees from being resigned and disillusioned before they even start their professional career.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adolph S, Tisch M, Metternich J (2014) Challenges and approaches to competency development for future production. J Int Sci Publ Educ Altern 12:1001–1010
Prinz C, Morlock F, Freith S, Kreggenfeld N, Kreimeier D, Kuhlenkötter B (2016) Learning Factory modules for smart factories in Industrie 4.0. Proc CIRP 54:113–118
Kagermann H, Helbig J, Hellinger A, Wahlster W (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0: securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion
Spöttl G, Gorldt C, Windelband L, Grantz T, Richter T (2016) Industrie 4.0–Auswirkungen auf Aus-und Weiterbildung in der M + E Industrie. bayme vbm Studie
Wolter MI, Mönnig A, Hummel M, Schneemann C, Weber E, Zika G, Helmrich R, Maier T, Neuber-Pohl C (2015) Industrie 4.0 und die Folgen für Arbeitsmarkt und Wirtschaft: Szenario-Rechnungen im Rahmen der BIBB-IAB-Qualifikations-und Berufsfeldprojektionen
Wang S, Wan J, Li D, Zhang C (2016) Implementing smart factory of Industrie 4.0: an outlook. Int J Distrib Sens Netw 12(1):3159805
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660
Zuehlke D (2008) Smartfactory–from vision to reality in factory technologies. IFAC Proc Vol 41(2):14101–14108
Valdez AC, Brauner P, Schaar AK, Holzinger A, Ziefle M (2015) Reducing complexity with simplicity-usability methods for Industry 4.0
Paelke V (2014) Augmented reality in the smart factory: supporting workers in an Industry 4.0. environment. In: 2014 IEEE Emerging technology and factory automation (ETFA). IEEE, pp 1–4
Gorecky D, Schmitt M, Loskyll M, Zühlke D (2014) Human-machine-interaction in the Industry 4.0 era. In: 2014 12th IEEE international conference on industrial informatics (INDIN). IEEE, pp 289–294
Guo Q (2015) Learning in a mixed reality system in the context of ‘Industrie 4.0’. J Tech Educ (JOTED) 3(2)
Reinhart G, Bengler K, Dollinger C, Intra C, Lock C, Popova-Dlugosch S, Rimpau C, Schmidtler J, Teubner S, Vernim S (2017) Der Mensch in der Produktion von Morgen. In: Handbuch Industrie 4.0: Geschäftsmodelle, Prozesse, Technik. Carl Hanser Verlag GmbH Co KG, pp 51–88
Prümper J, Hartmannsgruber K, Frese M (1995) KFZA. Kurz-Fragebogen zur Arbeitsanalyse. Zeitschrift für Arbeits-und Organisations psychologie A&O 39(3):125–131
Mir E, Kada O, Brunkel H, Kohlmann E, Kohlmann CW (2016) Wie nehmen Auszubildende der Altenpflege die Arbeits-und Organisationsstrukturen in der Praxis wahr? HeilberufeScience 7(2):83–87
Lumley T, Diehr P, Emerson S, Chen L (2002) The importance of the normality assumption in large public health data sets. Ann Rev Pub Health 23(1):151–169
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Adam, C., Aringer-Walch, C., Bengler, K. (2019). Digitalization in Manufacturing – Employees, Do You Want to Work There?. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 825. Springer, Cham. https://doi.org/10.1007/978-3-319-96068-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-96068-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96067-8
Online ISBN: 978-3-319-96068-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)