Abstract
AbstractIntelligent assistance systems (IAS) are designed to counteract rising cognitive demands caused by increasingly individualized manufacturing processes in assembly. How IAS affect work characteristics which are crucial for promoting work motivation of employees is yet unclear. Based on the cyber-physical systems transformation framework, the model of routine-biased technological change, and a comprehensive model of work design, we expected in- and decreases in motivational work characteristics (MWC) when working with IAS. Furthermore, we posited a buffering effect of the option of voluntary use on decreasing knowledge characteristics. Applying an online case study with experimental vignette methodology (EVM) allowed us to identify effects of the IAS on MWC before it is widely implemented. 203 German and British blue-collar workers evaluated an assembly workplace according to three experimental conditions (work without IAS, work with IAS, work with voluntary use of IAS). We identified enhanced feedback from the job and information processing in work with IAS in contrast to a traditional assembly workplace but found no restrictions (or elevations) in terms of other task (i.e., autonomy) or knowledge characteristics (i.e., job complexity, problem solving, specialization, skill variety). Thus, our results indicate that the IAS improves some motivational work characteristics of the assembly workplace, although it misses the primary goal of cognitive relief. Our study highlights the need for work design theories that specify the effect of IAS on motivational work characteristics and the potential benefit of IAS in assembly of the future.
Funder
Bundesministerium für Bildung und Forschung
Ruprecht-Karls-Universität Heidelberg
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Reference45 articles.
1. Aguinis, H., & Bradley, K. J. (2014). Best practice recommendations for designing and implementing experimental vignette methodology studies. Organizational Research Methods, 17(4), 351–371. https://doi.org/10.1177/1094428114547952
2. Apt, W., Bovenschulte, M., Priesack, K., & Hartmann, E. A. (2018). Einsatz von digitalen Assistenzsystemen im Betrieb [Use of digital assistance systems in practice]. Bundesministerium für Arbeit und Soziales. Retrieved from https://www.iit-berlin.de/iit-docs/0b0ab71d0ed949269fa39e2b38555fde_Einsatz-von-digitalen-Assistenzsystemen-im-Betrieb.pdf
3. Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801
4. Baethge-Kinsky, V. (2020). Digitized industrial work: Requirements, opportunities, and problems of competence development. Frontiers in Sociology, 5, 33. https://doi.org/10.3389/fsoc.2020.00033
5. Berkers, H. A., Rispens, S., & Le Blanc, P. M. (2022). The role of robotization in work design: A comparative case study among logistic warehouses. The International Journal of Human Resource Management, 1–24. https://doi.org/10.1080/09585192.2022.2043925
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献