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On information and communication technology and production cost in construction industry: evidence from the Netherlands

Author

Listed:
  • Arno J. Van der Vlist
  • Marien H. Vrolijk
  • Geert P.M.R. Dewulf

Abstract

The interplay between information and communication technology (ICT) and the competitiveness of construction firms is considered. More specifically, the question is whether firms that invest in information and communication technology have a production cost advantage. The economics literature hypothesizes that ICT brings about a production cost advantage, as ICT brings flexibility and improves the planning, organization and control of work. To test this proposition for the construction industry, a production cost function allowing for the inclusion of ICT is formulated. Using statistical nearest-neighbour matching methods it is possible to identify the effect of ICT on production costs thereby controlling for economic moderators. Data from a sample of Dutch construction firms reveal that those firms that installed ICT capital do have a production cost advantage. The results indicate further that firms need a minimum level of ICT capital to fully benefit from its production cost advantage.

Suggested Citation

  • Arno J. Van der Vlist & Marien H. Vrolijk & Geert P.M.R. Dewulf, 2014. "On information and communication technology and production cost in construction industry: evidence from the Netherlands," Construction Management and Economics, Taylor & Francis Journals, vol. 32(6), pages 641-651, June.
    • Handle: RePEc:taf:conmgt:v:32:y:2014:i:6:p:641-651
    DOI: 10.1080/01446193.2014.911932
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    1. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    2. Mattias Jacobsson & Henrik C.J. Linderoth, 2012. "User perceptions of ICT impacts in Swedish construction companies: ‘it’s fine, just as it is’," Construction Management and Economics, Taylor & Francis Journals, vol. 30(5), pages 339-357, February.
    3. Paola Giuri & Salvatore Torrisi & Natalia Zinovyeva, 2008. "ICT, skills, and organizational change: evidence from Italian manufacturing firms," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 17(1), pages 29-64, February.
    4. Mattias Jacobsson & Henrik Linderoth, 2010. "The influence of contextual elements, actors' frames of reference, and technology on the adoption and use of ICT in construction projects: a Swedish case study," Construction Management and Economics, Taylor & Francis Journals, vol. 28(1), pages 13-23.
    5. Simon Commander & Rupert Harrison & Naercio Menezes-Filho, 2011. "ICT and Productivity in Developing Countries: New Firm-Level Evidence from Brazil and India," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 528-541, May.
    6. Jiukun Dai & Paul Goodrum & William Maloney, 2007. "Analysis of craft workers' and foremen's perceptions of the factors affecting construction labour productivity," Construction Management and Economics, Taylor & Francis Journals, vol. 25(11), pages 1139-1152.
    7. Gary S. Hansen & Birger Wernerfelt, 1989. "Determinants of firm performance: The relative importance of economic and organizational factors," Strategic Management Journal, Wiley Blackwell, vol. 10(5), pages 399-411, September.
    8. Les Ruddock & Steven Ruddock, 2011. "Evaluation of trends in the UK construction industry using growth and productivity accounts," Construction Management and Economics, Taylor & Francis Journals, vol. 29(12), pages 1229-1239.
    9. Vachara Peansupap & Derek Walker, 2006. "Innovation diffusion at the implementation stage of a construction project: a case study of information communication technology," Construction Management and Economics, Taylor & Francis Journals, vol. 24(3), pages 321-332.
    10. Emrah Acar & Ismail Kocak & Yildiz Sey & David Arditi, 2005. "Use of information and communication technologies by small and medium-sized enterprises (SMEs) in building construction," Construction Management and Economics, Taylor & Francis Journals, vol. 23(7), pages 713-722.
    11. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    12. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    13. Mark Koetse & Arno Vlist & Henri Groot, 2006. "The Impact of Perceived Expectations and Uncertainty on Firm Investment," Small Business Economics, Springer, vol. 26(4), pages 365-376, May.
    14. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
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    Cited by:

    1. Hui Zhang & Yuyao Qian & Liying Yu & Zheng Wang, 2020. "Integrated Development of Information Technology and the Real Economy in China Based on Provincial Panel Data," Sustainability, MDPI, vol. 12(17), pages 1-17, August.

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