Impact of technology investment on firm’s production efficiency factor in manufacturing
Abstract
The goal of this paper is to investigate the impact of technology investments on production efficiency in manufacturing companies and how different these relationships are for low-technology and high-technology companies. The empirical part was based on the analysis of 2,848 large, small and medium-sized Czech companies by using Bayesian networks (BNs). The results show that technological investments have the greatest positive impact on the growth of labour productivity and on a decline in labour intensity in low technology enterprises. The technological investments have a positive impact on labour productivity growth in high-technology enterprises, but at the same time, the technological investments have an impact on the increase of labour intensity. On the contrary, the influence of investment growth was insignificant on the indicators of material and services intensity. Technologically intensive investments have a different impact on small, mediumsized and on large enterprises. The reaction of large companies depends on the category of technology intensity in contrast to small and medium-size enterprises.
First published online 17 November 2020
Keyword : performance, technology investment, Bayesian networks, manufacturing, technological intensity, labour productivity
How to Cite
Novotná, M., Volek, T., Rost, M., & Vrchota, J. (2021). Impact of technology investment on firm’s production efficiency factor in manufacturing. Journal of Business Economics and Management, 22(1), 135-155. https://doi.org/10.3846/jbem.2020.13635
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hawash, R., & Lang, G. (2020). Does the digital gap matter? Estimating the impact of ICT on productivity in developing countries. Eurasian Economic Review, 10(2), 189–209. https://doi.org/10.1007/s40822-019-00133-1
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Kijek, T., & Kijek, A. (2019). Is innovation the key to solving the productivity paradox? Journal of Innovation & Knowledge, 4(4), 219–225. https://doi.org/10.1016/j.jik.2017.12.010
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT Press.
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