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External Technology Supply and Client-Side Innovation

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Book cover Technology Transfer in a Global Economy

Part of the book series: International Studies in Entrepreneurship ((ISEN,volume 28))

Abstract

Flexibility in response to competitive pressure from globalized markets and increasingly individualized costumer desires has become vital for firms. A common strategy to address this challenge is to employ a dynamic concept of organization and reach beyond the boundaries of the firm. Accordingly, technology transfer from providers of knowledge-intensive business services attracts more and more attention.

In this context, we focus on external supply of information technology and client-side innovation. The aim of this chapter is to contribute to resolving an empirical puzzle arising from the prior literature. Some authors find beneficial effects of IT outsourcing, others underline that firms often fail to achieve expected strategic goals.

Our stylized theoretical model combines a knowledge production function framework and transaction cost economics. We hypothesize that the right balance between internal and external knowledge is critical for innovation.

The empirical application is German firm-level data covering a wide range of industries, 2003–2006. Our results largely support the theoretical arguments and suggest a positive linear relationship between the level of outsourcing and process innovation. For product innovation, we find a hump shape.

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Notes

  1. 1.

    KIBS are defined as firms from the NACE 72–74 sectors, that is, computer and related activities, research and development, and other business services such as legal services, accounting, and advertising (Muller and Doloreux 2009).

  2. 2.

    Note that we are not considering a cost of implementation, such as liquidity constraints or advertising. That is, we implicitly assume this cost C to be zero in Prob(K  –  C  >  τ).

  3. 3.

    For simplicity, we set K 0  =  0 and t  =  E  =  1, such that \( K=1-{c}_{w,s}\forall \omega \rm{ }\in \{\theta =0,\theta =0.5,\theta =1\}\).

  4. 4.

    See Fig. 10.7 for a plot of the three dimensions: K, s, and \( \theta \).

  5. 5.

    Those IT services are installation of new hard- and software, system support and maintenance, support help desk, software development, Internet/web maintenance and design, IT training, IT security and on-demand computing.

  6. 6.

    Descriptive statistics on IT intensity can be found in Table 10.1.

  7. 7.

    See Acs and Audretsch (2003) for a summary of key issues in the empirical literature in favor of a firm size effect on innovation.

  8. 8.

    See Table 10.4 for an industry classification.

  9. 9.

    In some sense, this is in line with the literature on productivity gaps between East and West Germany. See, for example, Smolny (2012).

  10. 10.

    A test with the null of a U shape (negative slope at the lower bound and positive slope at the upper bound) or monotone function (sign of the slope is equal at both bounds) can be rejected for γ  >  0.4. See Lind and Mehlum (2010) for a description of the test.

  11. 11.

    The fact that the coefficients are insignificant in the respective other specification can be explained by looking at the descriptive statistics in Table 10.1. First, the proportion of firms that report product innovation at the lower end of the outsourcing level does not largely differ from those at the upper end. Second, although there is a kink in the proportion of firms reporting process innovation for a medium level of outsourcing, differences between the lower and the upper end are rather substantial. Figures 10.8–10.11 further illustrate our findings.

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Acknowledgements

I would like to thank Centre for European Economic Research, Mannheim, Germany (ZEW), for providing access to the data during my visits in 2009 and 2010. This chapter benefited from presentations at 4th International Conference on Industrial Dynamics, Innovation Policy, and Growth (Izmir 2010), 12th EUNIP Conference (Reus 2010), 9th ZEW Conference on the Economics of Information and Communication Technologies (Mannheim 2011), Technology Transfer Society Annual Conference (Augsburg 2011), INFORMS Annual Meeting (Charlotte 2011) and seminars held at ZEW Mannheim, Ulm University, LMU Munich, and IfW Kiel. I am particularly grateful to Werner Smolny, Tobias Kretschmer, Daniel Cerquera, Benjamin Engelstätter, Daniel Siepe, and Daniela Maccari-Peukert for their very helpful comments. Research assistance by Veronika Kneissl and Marvin Fleischmann is greatly acknowledged. All errors are mine.

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Authors and Affiliations

  1. Institute for Strategy, Technology and Organization, Munich School of Management, Ludwig-Maximilians-Universität München, Schackstr. 4, 80539, Munich, Germany

    Christian Peukert

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  1. Christian Peukert
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Correspondence to Christian Peukert .

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Editors and Affiliations

  1. , Institute of Development Strategies, Indiana University, East 10th Street 1315, Bloomington, 47405, Indiana, USA

    David B. Audretsch

  2. , Department of Management & Organization, University of Augsburg, Universitaetsstr. 16, Augsburg, 86159, Germany

    Erik E. Lehmann

  3. , Department of Economics, University of North Carolina Greensboro, Greensboro, 27402, North Carolina, USA

    Albert N. Link

  4. , Department of Business Administration, University of Augsburg, Universitaetsstr. 16, Augsburg, 86159, Germany

    Alexander Starnecker

Appendix

Appendix

Fig. 10.6
figure 00106

IT outsourcing in the EU15 countries in 2007. Percentage share of all enterprises with at least ten persons employed, grouped by sector where external suppliers performed (fully or partly) ICT functions requiring ICT specialists (IT outsourcing) during 2007. *Due to data restrictions without financial sector, data for the United Kingdom is not available (Source: Eurostat, information society statistics on enterprises 2007)

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Fig. 10.7
figure 00107

Accumulated knowledge as a function of the outsourcing level and specificity

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Table 10.4 Industry classification
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Fig. 10.8
figure 00108

Predicted probability of product innovation: γ  =  0.3, γ  =  0.6, and γ  =  0.9 (A). Predicted probability of product innovation as a function of θ and 90% confidence interval, according to specification A without a squared term. All covariates fixed at the mean

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Fig. 10.9
figure 00109

Predicted probability of process innovation: γ  =  0.3, γ  =  0.6, and γ  =  0.9 (A). Predicted probability of process innovation as a function of θ and 90% confidence interval, according to specification A without a squared term. All covariates fixed at the mean

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Fig. 10.10
figure 001010

Predicted probability of product innovation: γ  =  0.3, γ  =  0.6, and γ  =  0.9 (B). Predicted probability of product innovation as a function of θ and 90% confidence interval, according to specification B with a squared term. All covariates fixed at the mean

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Fig. 10.11
figure 001011

Predicted probability of process innovation: γ  =  0.3, γ  =  0.6, and γ  =  0.9 (B). Predicted probability of process innovation as a function of θ and 90% confidence interval, according to specification B with a squared term. All covariates fixed at the mean

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Peukert, C. (2012). External Technology Supply and Client-Side Innovation. In: Audretsch, D., Lehmann, E., Link, A., Starnecker, A. (eds) Technology Transfer in a Global Economy. International Studies in Entrepreneurship, vol 28. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-6102-9_10

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