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Impact of technology investment on firm’s production efficiency factor in manufacturing

    Martina Novotná   Affiliation
    ; Tomáš Volek   Affiliation
    ; Michael Rost Affiliation
    ; Jaroslav Vrchota Affiliation

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
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Jan 27, 2021
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References

Aldieri, L., Cincera, M., Garofalo, A., & Vinci, C. P. (2008). Micro evidence of the effects of R&D on labour productivity for large international R&D firms. International Journal of Manpower, 29(3), 198–215. https://doi.org/10.1108/01437720810878888

Badescu, M., & Garcés-Ayerbe, C. (2009). The impact of information technologies on firm productivity: Empirical evidence from Spain. Technovation, 29(2), 122–129. https://doi.org/10.1016/j.technovation.2008.07.005

Bahrin, M. A. K., Othman, M. F., Azli, N. N., & Talib, M. F. (2016). Industry 4.0: A review on industrial automation and robotic. Jurnal Teknologi, 78(6–13), 137–143. https://doi.org/10.11113/jt.v78.9285

Barro, R. J., & Sala-i-Martin, X. (2004). Economic growth (2nd ed.). MIT Press.

Bartodziej, C. J. (2017). The concept industry 4.0. An empirical analysis of technologies and applications in production logistics (pp. 27–50). Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-16502-4

Basile, R., & De Benedictis, L. (2008). Regional unemployment and productivity in Europe. Papers in Regional Science, 87(2), 173–192. https://doi.org/10.1111/j.1435-5957.2007.00152.x

Baumann, J., & Kritikos, A. S. (2016). The link between R&D, innovation and productivity: Are micro firms different? Research Policy, 45(6), 1263–1274. https://doi.org/10.1016/j.respol.2016.03.008

Becchetti, L., Bedoya, D. a. l., & Paganetto, L. (2003). ICT investment, productivity and efficiency: Evidence at firm level using a stochastic frontier approach. Journal of Productivity Analysis, 20(2), 143–167. https://doi.org/10.1023/A:1025128121853

Beugelsdijk, S., Klasing, M. J., & Milionis, P. (2018). Regional economic development in Europe: The role of total factor productivity. Regional Studies, 52(4), 461–476. https://doi.org/10.1080/00343404.2017.1334118

Birkel, H. S., Veile, J. W., Mueller, J. M., Hartmann, E., & Voigt, K.-I. (2019). Development of a risk framework for industry 4.0 in the context of sustainability for established manufacturers. Sustainability, 11(2), 384. https://doi.org/10.3390/su11020384

Blažková, I., & Dvouletý, O. (2019). Investigating the differences in entrepreneurial success through the firm-specific factors. Journal of Entrepreneurship in Emerging Economies, 11(2), 154–76. https://doi.org/10.1108/JEEE-11-2017-0093

Borin, A., & Mancini, M. (2016). Foreign direct investment and firm performance: An empirical analysis of Italian firms. Review of World Economics, 152, 705–732. https://doi.org/10.1007/s10290-016-0255-z

Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, N. (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(1), 37–44. https://waset.org/publications/9997144/how-virtualization-decentralization-and-network-building-change-themanufacturing-landscape-an-industry-4.0-perspective

Büchi, G., Cugno, M., & Castagnoli, R. (2020). Smart factory performance and Industry 4.0. Technological Forecasting and Social Change, 150, 119790. https://doi.org/10.1016/j.techfore.2019.119790

Chung, H. (2018). ICT investment-specific technological change and productivity growth in Korea: Comparison of 1996–2005 and 2006–2015. Telecommunications Policy, 42(1), 78–90. https://doi.org/10.1016/j.telpol.2017.08.005

Dosi, G., Grazzi, M., & Moschella, D. (2015). Technology and costs in international competitiveness: From countries and sectors to firms. Research Policy, 44(10), 1795–1814. https://doi.org/10.1016/j.respol.2015.05.012

Driffield, N., & Temouri, Y. (2014). Inward investment and the drivers of post recession recovery in Germany. Jahrbucher Fur Nationalokonomie Und Statistik, 234(6), 775–799. https://doi.org/10.1515/9783110511161-006

Egger, P., & Pfaffermayr, M. (2001). A note on labour productivity and foreign inward direct investment. Applied Economics Letters, 8(4), 229–232. https://doi.org/10.1080/135048501750103917

Eichhorst, W., Hinte, H., Rinne, U., & Tobsch, V. (2017). How big is the gig? Assessing the preliminary evidence on the effects of digitalization on the labor market. Mrev Management Revue, 28(3), 298–318. https://doi.org/10.5771/0935-9915-2017-3-298

EUR-Lex. (2003). Commission recommendation. Official Journal of the European Union L, 124, 36–41. http://eur-lex.europa.eu/eli/reco/2003/361/oj

Eurostat (n.d.). Aggregations of manufacturing based on NACE Rev. 2. Eurostat indicators on High-tech industry and Knowledge – intensive services. Annex 3 – High-tech aggregation by NACE Rev.2. http://ec.europa.eu/eurostat/cache/metadata/Annexes/htec_esms_an3.pdf

Eurostat. (2019). National accounts aggregates by industry. https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10_a64&lang=en

Filippetti, A., & Peyrache, A. (2015). Labour productivity and technology gap in European regions: A conditional frontier approach. Regional Studies, 49(4), 532–554. https://doi.org/10.1111/jcms.12864

Freeman, C., & Perez, C. (1988). Structural crisis of adjustment, business cycles and investment behaviour. G. Dosi, C. Freeman, R. Nelson, G. Silverberg, & L. Soete (Eds.), Technical change and economic theory (pp. 38–66). Pinter.

Geissbauer, R., Vedso, J., & Schrauf, S. (2016). Industry 4.0: Building the digital enterprise: 2016 global industry 4.0 survey. PwC, Munich. https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016.pdf

Gibson, D. V., & Naquin, H. (2011). Investing in innovation to enable global competitiveness: The case of Portugal. Technological Forecasting and Social Change, 78(8), 1299–1309. https://doi.org/10.1016/j.techfore.2011.04.004

Girma, S., Greenaway, D., & Wakelin, K. (2001). Who benefits from foreign direct investment in the UK? Scottish Journal of Political Economy, 48(2), 119–133. https://doi.org/10.1111/1467-9485.00189

Grubicka, J., & Matuska, E. (2015). Sustainable entrepreneurship in conditions of un (safety) and technological convergence. Entrepreneurship and Sustainability Issues, 2(4), 188–197. https://doi.org/10.9770/jesi.2015.2.4(2)

Guest, R. (2011). Population ageing, capital intensity and labour productivity. Pacific Economic Review, 16(3), 371–388. https://doi.org/10.1111/j.1468-0106.2011.00553.x

Haller, S. A. (2014). Do domestic firms benefit from foreign presence and import competition in Irish services sectors? The World Economy, 37(2), 219–243. https://doi.org/10.1111/twec.12120

Hartemink, A. J. (2001). Principled computational methods for the validation and discovery of genetic regulatory networks [PhD thesis, School of Electrical Engineering and Computer Science]. Massachusetts Institute of Technology.

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

Heidenreich, M. (2009). Innovation patterns and location of European low- and medium-technology industries. Research Policy, 38(3), 483–494. https://doi.org/10.1016/j.respol.2008.10.005

Jardim-Goncalves, R., Romero, D., & Grilo, A. (2017). Factories of the future: challenges and leading innovations in intelligent manufacturing. International Journal of Computer Integrated Manufacturing, 30(1), 4–14.

Jiang, J., Su, P., & Ge, Z. (2020). The high- and new-technology enterprise identification, marketization process and the total factor productivity of enterprise. Kybernetes. https://doi.org/10.1108/K-11-2019-0743

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.

Kraft, J., & Kraftova, I. (2012). Innovation – globalization – growth (selected relations). Inzinerine Ekonomika-Engineering Economics, 23(4), 395–405. https://doi.org/10.5755/j01.ee.23.4.2568

Kraftova, I., Mateja, Z., & Prasilova, P. (2011). Economic performance: variability of businesses within each industry and among industries. Inzinerine Ekonomika-Engineering Economics, 22(5), 459–467. https://doi.org/10.5755/j01.ee.22.5.964

Leitmanova, I. F., & Krutina, V. (2009). Monitoring the efficiency of regions – value added utilization (with a View to South Bohemia Region). Ekonomicky Casopis, 57(10), 1018–1037.

Leung, D., Meh, C., & Terajima, Y. (2008). Firm size and productivity (Working Paper No. 2008, 45). Bank of Canada.

MacDougall, W. (2014). Industrie 4.0: smart manufacturing for the future. Trade & Invest, Germany. https://www.manufacturing-policy.eng.cam.ac.uk/policies-documents-folder/germany-industrie-4-0-smart-manufacturing-for-the-future-gtai/view

Machek, O., & Hnilica, J. (2012). Total factor productivity approach in competitive and regulated world. Procedia – Social and Behavioral Sciences, 57, 223–230. https://doi.org/10.1016/j.sbspro.2012.09.1178

Mařík, V., Beran, H., Bízková, R., Bunček, M., Burčík, J., Burget, P., & Burian, J. (2016). Industry 4.0 – the initiative for the Czech Republic. The Report Approved by the Government of the Czech Republic on August, 23, 2016.

Müller, J. M., Buliga, O., & Voigt, K.-I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17. https://doi.org/10.1016/j.techfore.2017.12.019

Mura, L., Ključnikov, A., Tvaronavičienė, M., & Androniceanu, A. (2017). Development trends in human resource management in small and medium enterprises in the Visegrad Group. Acta Polytechnica Hungarica, 14(7), 105–122. https://doi.org/10.12700/APH.14.7.2017.7.7

Nagarajan, R., Scutari, M., & Lebre, S. (2013). Bayesian networks in r with applications in systems biology. Springer-Verlag, New York. https://doi.org/10.1007/978-1-4614-6446-4

OECD stat. (2019). Gross domestic product (GDP). https://stats.oecd.org/viewhtml.aspx?datasetcode=SNA_TABLE1&lang=en

OECD/Eurostat. (2005). Oslo manual: guidelines for collecting and interpreting innovation data (3rd ed.) OECD Publishing, Paris. http://www.oecd.org/innovation/inno/oslo-manual-guidelines-for-collecting-and-interpreting-innovation-data.htm

Ortega-Argilés, R., Piva, M., & Vivarelli, M. (2015). The productivity impact of R&D investment: Are high-tech sectors still ahead? Economics of Innovation and New Technology, 24(3), 204–222. https://doi.org/10.1080/10438599.2014.918440

Parisi, M. L., Schiantarelli, F., & Sembenelli, A. (2006). Productivity, innovation and R&D: Micro evidence for Italy. European Economic Review, 50(8), 2037–2061. https://doi.org/10.1016/j.euroecorev.2005.08.002

Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, Los Angeles. https://doi.org/10.1016/B978-0-08-051489-5.50008-4

Poor, P., & Basl, J. (2019, February 5–6). Readiness of companies in relation to industry 4.0 implementation. In P. Jedlicka, P. Maresova, & I. Soukal (Eds.), International Scientific Conference “Hradec Economic Days” (Vol. 9, pp. 236–248). Univ Hradec Kralove. https://doi.org/10.36689/uhk/hed/2019-02-024

Preenen, P., Vergeer, R., Kraan, K., & Dhondt, S. (2015). Labour productivity and innovation performance: The importance of internal labour flexibility practices. Economic and Industrial Democracy, 38, 1–23. https://doi.org/10.1177/0143831X15572836

Ramírez, S., Gallego, J., & Tamayo, M. (2019). Human capital, innovation and productivity in Colombian enterprises: A structural approach using instrumental variables. Economics of Innovation and New Technology, 29(6), 1–18. https://doi.org/10.1080/10438599.2019.1664700

Río, F. D., & Lores, F. X. (2019). The decline in capital efficiency and labour share. Economica, 86(344), 635–662. https://doi.org/10.1111/ecca.12279

Romer, P. (1990). Endogenous technological change. Journal of Political Economy, 98(5), 71–102. https://doi.org/10.1086/261725

Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Consulting Group, Boston.

Scutari, M. (2010). Learning bayesian networks with the bnlearn r package. Journal of Statistical Software, 35(3), 1–22. https://doi.org/10.18637/jss.v035.i03

Scutari, M. (2017). Bayesian network constraint-based structure learning algorithms: Parallel and optimized implementations in the bnlearn R package. Journal of Statistical Software, 77(2), 1–20. https://doi.org/10.18637/jss.v077.i02

Scutari, M., & Denis, J. B. (2014). Bayesian networks with examples in r (1st ed.). CRC Press, Taylor & Francis Group, Boca Raton. https://doi.org/10.1201/b17065

Seo, H. S., & Kim, Y. (2020). Intangible assets investment and firms’ performance: Evidence from small and medium-sized enterprises in Korea. Journal of Business Economics and Management 20(2), 421–445. https://doi.org/10.3846/jbem.2020.12022

Spiezia, V. (2012). ICT investments and productivity: Measuring the contribution of ICTS to growth. OECD Journal: Economic Studies, 2012(1), 199–211. https://doi.org/10.1787/eco_studies-2012-5k8xdhj4tv0t

Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. In G. Seliger, H. Kohl, & J. Mallon (Eds.), 13th Global Conference on Sustainable Manufacturing – Decoupling Growth from Resource Use. Procedia CIRP, 40, 536–541. https://doi.org/10.1016/j.procir.2016.01.129

Stundziene, A., & Saboniene, A. (2019). Tangible investment and labour productivity: Evidence from European manufacturing. Economic Research-Ekonomska Istraživanja, 32(1), 3519–3537. https://doi.org/10.1080/1331677X.2019.1666024

Syverson, C. (2011). What determines productivity? Journal of Economic Literature, 49(2), 326–365. https://doi.org/10.1257/jel.49.2.326

Takahashi, T. (2012). Capital growth paths of the neoclassical growth model. PLOS ONE, 7(11), e49484. https://doi.org/10.1371/journal.pone.0049484

Tambe, P., & Hitt, L. (2011). The productivity of information technology investments: New evidence from IT labor data. Information Systems Research, 23(3), 599–848. https://doi.org/10.1287/isre.1110.0398

Tello, M. (2015). Firms’ innovation, public financial support, and total factor productivity: The case of manufactures in Peru. Review of Development Economics, 19.

Tello, M. (2017). Innovation and productivity in services and manufacturing firms: The case of Peru: Mario D. Tello. CEPAL Review, 121, 69–86. https://doi.org/10.18356/a4c7eea5-en

Thatcher, M. E., & Oliver, J. R. (2001). The impact of technology investments on a firm’s production efficiency, product quality, and productivity. Journal of Management Information Systems, 18(2), 17–45. https://doi.org/10.1080/07421222.2001.11045685

Trexima. (2018). The role and level of labour productivity in the Czech Republic. https://ipodpora.odbory.info/soubory/dms/ukony/22311/6/Studie%20-%20Role%20a%20%c3%barovn%c4%9b%20produktivity%20pr%c3%a1ce%20v%20%c4%8cR.pdf

Ulku, H. (2007). R&D, innovation, and growth: Evidence from four manufacturing sectors in OECD countries. Oxford Economic Papers, 59(3), 513–535. https://doi.org/10.1093/oep/gpl022

Van Ark, B. (2016). The productivity paradox of the new digital economy. International Productivity Monitor, 31, 3–18.

Van Beveren, I. (2012). Total factor productivity estimation: A practical review. Journal of Economic Surveys, 26(1), 98–128. https://doi.org/10.1111/j.1467-6419.2010.00631.x

Vokoun, M. (2016) Innovation behaviour of firms in a small open economy: The case of the Czech manufacturing industry. Empirica, 43(1), 111–139. https://doi.org/10.1007/s10663-015-9296-0

Wakelin, K. (2001) Productivity growth and R&D expenditure in UK manufacturing firms. Research Policy, 30(7), 1079–1090. https://doi.org/10.1016/s0048-7333(00)00136-0

Wolter, M. I., Mönnig, A., Hummel, M., Schneemann, C., Weber, E., Zika, G., & Neuber-Pohl, C. (2015). Industry 4.0 and the consequences for labour market and economy: Scenario calculations in line with the BIBB-IAB qualifications and occupational field projections. Institute for Employment Research, Nuremberg, Germany.

Zawislak, P. A., Fracasso, E. M., & Tello-Gamarra, J. (2018). Technological intensity and innovation capability in industrial firms. Innovation & Management Review, 15(2), 189–207. https://doi.org/10.1108/INMR-04-2018-012