Skip to main content

Advertisement

SpringerLink
Log in

Industry diversity and its impact on the innovation performance of firms

An empirical analysis based on panel data (firm-level)

  • Regular Article
  • Published:
Journal of Evolutionary Economics Aims and scope Submit manuscript

Abstract

This paper investigates empirically the impact of diversity on the innovation performance of a firm. We created a measure for diversity that mirrors differences in the resource base of firms within an industry and tested its impact in addition to more traditional factors such as technology-push, demand-pull, and firm-size, based on panel data stemming from three representative cross sectional surveys carried out in the years 1996, 1999, and 2002, respectively. In fact, diversity has a significant positive impact on the innovation intensity of firms and thus supports more theoretical findings in this area. We also find empirical evidence for the technology push and the demand pull hypotheses, as well as the importance of competition for innovation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. The SIS is very similar to the CIS.

  2. The term “diversity” or “diverse” is motivated by the definition given in Section 3. Heterogeneous is used synonymously with diversity.

  3. Nelson (2006) comments on Winter (2006).

  4. As to challenges to implement or modify working routines in an organization see e.g. Lazaric and Denis (2005) or Pentland and Feldman (2005).

  5. Utterback (1996) describes the history of companies that were unable to change their innovation behavior, since they stuck to their “sunk” investments and technologies, although newer (better) technologies were already on the market.

  6. Solow et al. (1993) and Weitzman (1992; Weitzman (1993) measured diversity based on genetic distances. They defined the value of a species for the diversity of a subsample S, according to the “genetic” distance to other species element of S. The genetic distances are measured, e.g. based on a taxonomic tree. Such a tree indicates the ancestors of a specific species and the time passed from its separation from the species. Thus the longer ago a species separated from another one, the greater is its “genetic” distance. This approach can be seen as a conceptual starting point for the Nehring and Puppe (2002) measure.

  7. X i = value in line i of firm vector \(\boldsymbol{X}\); Y i = value in line i of firm vector \(\boldsymbol{Y}\).

  8. If we would change to a three digit level, we would have 191 different markets. In 118 markets we would have fewer than 20 observations. In 88 markets we would have fewer than ten firms and still, in 40 markets, there would be fewer than four observations.

  9. The importance of size for innovation performance on an industry level was also shown by Majumdar (1995) for the telecommunication sector. Tsai (2001) showed that even the size of business units impacts innovation behavior. The share of exports and R&D expenditures also impact the way firms organize their R&D. This was found in additional calculations based on the empirical models applied in Woerter (2007). The empirical data do not enable us to dig deeper into the driving forces for organizational routines, as investigated in Feldman (2000), or Becker et al. (2005).

  10. Standardization was conducted using SAS software according to the following formula: \(x_i =\frac{S\times \left( {{x}'_i -\bar {X}} \right)}{S_x }+M\); while x i is the new standardized value, S is the chosen standard deviation value, M is the mean value, S x is the variables standard deviation, x′ i is the observation’s value, and \(\;\overline{{\kern-2pt}X}\) is the variables mean.

  11. This figure represents the response rate for the manufacturing sector. The response rate for the service sector and the construction sector amounts to 31.6%. The respective figures for 1999 and 2002 cover all three sectors.

  12. In order to carry out this calculation, we used the STATA software.

  13. The Heckman estimation, the test for heteroscedasticity and autocorrelation are not presented in the paper.

References

  • Arvanitis S, Hollenstein H (1996) Industrial innovation in Switzerland: a model-based analysis with survey data. In: Kleinknecht A (ed) Determinants of innovation, the message from new indicators. Macmillan, London, pp 13–62

    Google Scholar 

  • Becker MC, Lazaric N, Nelson RR, Winter SG (2005) Applying organizational routines in understanding organizational change. Ind Corp Change 14(5):775–791. doi:10.1093/icc/dth071

    Article  Google Scholar 

  • Brouwer E, Kleinknecht A (1996) Determinants of innovation: a micro econometric analysis of three alternative innovative output indicators. In: Kleinknecht A (ed) Determinants of innovation, the message from new indicators. Macmillan, London, pp 99–124

    Google Scholar 

  • Chiaromonte F, Dosi G (1993) The micro foundations of competitiveness and their macroeconomic implications. In: Foray D, Freeman Ch (eds) Technology and the wealth of nations. Pinter, London, pp 107–134

    Google Scholar 

  • Cohen WM (1995) Empirical studies of innovative activity and performance. In: Stoneman P (ed) Handbook of the economics of innovation and technological change. Blackwell, Oxford

    Google Scholar 

  • Cohen WM, Levin RC (1989) Empirical studies of innovation of market structure. In: Schmalensee R, Willig R (eds) Handbook of industrial organization. North-Holland, Amsterdam

    Google Scholar 

  • Crépon B, Duguet E, Mairesse J (1998) Research and development, innovation and productivity: an econometric analysis at the firm level. Econ Innov New Technol 7(2):115–158

    Article  Google Scholar 

  • Davis R, Thomas LG (1993) Direct estimation of synergy: a new approach to the diversity–performance debate. Manag Sci 39(11):1334–1346

    Article  Google Scholar 

  • Dosi G (1988) Sources, procedures, and microeconomic effects of innovation. J Econ Lit 26:1120–1171 (September)

    Google Scholar 

  • Dosi G (2005) Statistical regularities in the evolution of industries. A Guide through some evidence and challenges for the theory. LEM working-paper series 2005/17, Pisa

  • Feldman MS (2000) Organizational routines as a source of continuous change. Organ Science 11(6):611–629 (November–December)

    Article  Google Scholar 

  • Feldman MP, Audretsch DB (1999) Innovation in cities: science-based diversity, specialisation and localized competition. Eur Econ Rev 43:409–429

    Article  Google Scholar 

  • Glaeser E, Kallal H, Scheinkam J, Shleifer A (1992) Growth in cities. J Polit Econ 100:1126–1152

    Article  Google Scholar 

  • Greunz L (2004) Industrial structure and innovation—evidence from European regions. J Evol Econ 14:563–592. doi:10.1007/s00191-004-0234-8

    Article  Google Scholar 

  • Griliches Z, Mairesse J (1997) Production function: the search for identification. In: Strøm S (ed) econometrics and econmic theory in the Twentieth Century: the Ragner Frisch Centennial Symposium. Cambridge University Press, Cambridge, MA

    Google Scholar 

  • Heckman JJ (1976) The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. Ann Econ Soc Meas 5:475–492

    Google Scholar 

  • Henderson V, Kuncoro A, Turner M (1995) Industrial development in cities. J Polit Econ 103(5):1067–1090

    Article  Google Scholar 

  • Holland JH, Holyoak KJ, Nisbett RR, Thagard PE (1986) Induction—processes of inference, learning, and discovery. MIT, Boston, MA

    Google Scholar 

  • Jacobs J (1969) The economy of cities. Random House, New York

    Google Scholar 

  • Jacquemin AP, Berry CH (1979) Entropy measure of diversification and corporate growth. J Ind Econ 27(4):359–369

    Article  Google Scholar 

  • Janz N, Lööf H, Peters B (2003) Firm level innovation and productivity—is there a common story across countries? Probl Perspect Manag 2:1–22

    Google Scholar 

  • Lazaric N, Denis B (2005) Routinization and memorization of tasks in a workshop: the case of the introduction of ISO norms. Ind Corp Change 14(5):873–896

    Article  Google Scholar 

  • Llerena P, Oltra V (2002) Diversity of innovative strategy as a source of technological performance. Struct Change Econ Growth 13:179–201

    Google Scholar 

  • Mairesse J, Mohnen P (2001) To be or not to be innovative: an exercise in measurement. MERIT-Infonomics Research Memorandum series Nr. 2001-039, MERIT, Maastricht

  • Majumdar SK (1995) The determinants of investment in new technology: an examination of alternative hypotheses. Technol Forecast Soc Change 50:235–247

    Article  Google Scholar 

  • Mansfield E (1968) The economics of technical change. Norton, New York

    Google Scholar 

  • March JG (1994) Three lectures on efficiency and adaptiveness in organization. First Goran and Louise Ehrnrooth Lectures, Helsinki

  • Metcalfe JS, Miles I (1994) Standards, selection and variety: an evolutionary approach. Inform Econ Policy 6:243–268

    Article  Google Scholar 

  • Mohnen P, Dagenais M (2001) Towards an innovation intensity index. The case of CIS 1 in Denmark and Ireland. In: Kleinknecht A, Mohnen P (eds) Innovation and Firm Performance. Econometric Explorations of Survey Data. Palgrave, London

  • Nehring K, Puppe C (2002) A theory of diversity. Econometrica 70(3):1155–1198

    Article  Google Scholar 

  • Nelson RR (1959) The simple economics of basic scientific research. J Polit Econ 67:297–306

    Article  Google Scholar 

  • Nelson RR (1995) Recent evolutionary theorizing about economic change. J Econ Lit 33:48–90 (March)

    Google Scholar 

  • Nelson RR (2006) Commentary on Sidney Winter’s “Toward a neo-Schumpeterian theory of the firm”. Ind Corp Change 15(1):145–149

    Article  Google Scholar 

  • Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Belknap of Harvard University Press, Cambridge, MA

    Google Scholar 

  • Nguyen P, Saviotti PP, Trommetter M, Bourgeois B (2005) Variety and the evolution of refinery processing. Ind Corp Change 14(3):469–500

    Article  Google Scholar 

  • Pentland BT, Feldman MS (2005) Organizational routines as a unit of analysis. Ind Corp Change 14(5):793–815

    Article  Google Scholar 

  • Phillips A (1966) Patents, competition, and technical progress. Am Econ Rev 56:301–310

    Google Scholar 

  • Raymond W, Mohnen P, Palm F, Van der Loeff SS (2004) An empirically-based taxonomy of Dutch manufacturing: innovation policy implications. MERIT-Infonomics Research Memorandum series 2004-011, MERIT, Maastricht

  • Robins J, Wiersema MF (1995) A resource-based approach to the multibusiness firm: empirical analysis of portfolio interrelationships and corporate financial performance. Strateg Manage J 16(4):277–299

    Article  Google Scholar 

  • Rosenberg N (1976) Perspectives on technology. Cambridge University Press, Cambridge, MA

    Google Scholar 

  • Saviotti PP (1996) Technological evolution, variety and the economy. Edward Elgar, Cheltenham

    Google Scholar 

  • Saviotti PP, Mani GS (1995) Competition, variety and technological evolution: a replicator dynamics model. J Evol Econ 5:369–392

    Article  Google Scholar 

  • Schmookler J (1966) Invention and growth, Schumpeterian perspectives. MIT, Cambridge, Mass

    Google Scholar 

  • Schumpeter AJ (1912) Theorie der wirtschaftlichen entwicklung. Duncker und Humblot, Leipzig

    Google Scholar 

  • Schumpeter AJ (1975) Capitalism, socialism and democracy. Harper Perennial, New York

    Google Scholar 

  • Silverberg G, Dosi G, Orsenigo L (1988) Innovation, diversity and diffusion: a self-organisation model. Econ J 98:1032–1054 (December)

    Article  Google Scholar 

  • Simon HA (1956) Rational choice and the structure of the environment. In: Egidi M, Marris R (eds) Economics, bounded rationality and the cognitive revolution, Herbert Simon. Edward Elgar, Aldershot, Brookfield (rev. 1992)

    Google Scholar 

  • Simon HA (1981) Entscheidungsverhalten in Organisationen. Verlag Moderne Industrie, Munich

    Google Scholar 

  • Solow A, Polasky St, Broadus J (1993) On the measurement of biological diversity. J Environ Econ Manage 24:60–68

    Article  Google Scholar 

  • Stirling A (1998) On the economics and analysis of diversity. SPRU electronic working paper series, paper no. 28, October

  • Stirling A (2004) Diverse design: fostering technological diversity in innovation for sustainability. Paper submitted to Colloquium DEA, Durham Business School and Cranfield School of Management on: the role of diversity in social systems and its relation to innovation, Bologna, 12–13 July

  • Stirling A (2007) A general framework for analysing diversity in science, technology and society. J R Soc Interface 4:707–719

    Article  Google Scholar 

  • Tsai W (2001) Knowledge transfer in intraorganizational networks: effects of network position and absorptive capacity on business unit innovation and performance. Acad Manage J 44(5):996–1004

    Article  Google Scholar 

  • Utterback JM (1996) Mastering the dynamics of innovation. Harvard Business School, Boston, MA

    Google Scholar 

  • Weitzman ML (1992) On diversity. Q J Econ 107(2):363–405 (May)

    Article  Google Scholar 

  • Weitzman ML (1993) What to preserve? An application of diversity theory to crane conservation. Q J Econ 108(1):157–183 (February)

    Article  Google Scholar 

  • Wernerfelt B (1984) A resource-based view of the firm. Strateg Manage J 5:171–180

    Article  Google Scholar 

  • Winter SG (2006) Toward a neo-Schumpeterian theory of the firm. Ind Corp Change 15(1):125–141

    Article  Google Scholar 

  • Woerter M (2007) Driving forces for research and development strategies. KOF working paper no. 184, Zurich. www.kof.ethz.ch

  • Wooldridge JM (2002) Econometric analysis of cross section and panel data. MIT, Cambridge, MA

    Google Scholar 

Download references

Acknowledgements

I would like to thank N. Sydow for programming SAS macros in order to apply the measure of diversity. Many thanks also to N. Lazaric and Th. Brenner (discussants at the Druid Conference 2007) and S. Arvanitis, A. Mueller, and the participants of the KOF research seminar for their valuable comments on the paper.

Author information

Authors and Affiliations

  1. ETH Zurich, KOF Swiss Economic Institute, 8092, Zurich, Switzerland

    Martin Woerter

Authors
  1. Martin Woerter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Woerter.

Appendix

Appendix

Table 6

Table 6 Correlations between determinants
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Woerter, M. Industry diversity and its impact on the innovation performance of firms. J Evol Econ 19, 675–700 (2009). https://doi.org/10.1007/s00191-008-0118-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00191-008-0118-4

Keywords

JEL Classification

Search

Navigation