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.
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Notes
The SIS is very similar to the CIS.
The term “diversity” or “diverse” is motivated by the definition given in Section 3. Heterogeneous is used synonymously with diversity.
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.
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.
X i = value in line i of firm vector \(\boldsymbol{X}\); Y i = value in line i of firm vector \(\boldsymbol{Y}\).
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.
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).
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.
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.
In order to carry out this calculation, we used the STATA software.
The Heckman estimation, the test for heteroscedasticity and autocorrelation are not presented in the paper.
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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.
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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
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DOI: https://doi.org/10.1007/s00191-008-0118-4