Abstract
In this paper we empirically investigate the impact of two external knowledge acquisition strategies—‘buy’ and ‘cooperate’—on firm’s innovation performance. Taking a direct (productivity) approach, we test for complementarity effects in the simultaneous use of the two strategies, and in the intensity of their use. Our results—based on panels of Dutch and Swiss innovating firms—suggest that while both ‘buy’ and ‘cooperate’ have a positive effect on innovation, there is little statistical evidence that using them simultaneously leads to higher innovation performance. Results from the Dutch sample provide some indication that there are positive economies of scope in doing external and cooperative R&D simultaneously.
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Notes
The full argument is: Outsourcing can be profitable only if the expected cost advantage is higher than the sum of costs of search efforts to find a suitable supplier, the costs of related asset-specific investments and the costs of contract imperfectness.
We assume that most of R&D investment is targeted to product innovations (see the discussion in Scherer 1991). Fritsch and Meschede (2001) estimated the share of product R&D expenditure in all R&D in a sample of 1,800 German enterprises in the 1990s to be about 61 %. For US firms in the 1970s the share of product-oriented R&D expenditure was estimated at 68 % (Scherer 1984). Based on information about 4,000 major innovations in UK manufacturing from the Second World War until 1983 reported in Pavitt et al. (1987), Scherer (1991) estimated the share of product-focused R&D expenditure of about 70 %. A product-R&D expenditure share of about two thirds appears to be a structural characteristic of R&D expenditure in most technologically advanced countries. As a consequence, we assume that the potential error in ignoring process innovation is small. In any case, at least for the Netherlands we have no quantitative measure of process innovation, and therefore we have preferred not to include this part of innovation in our analysis.
This variable for the sales share of innovative products contains zeroes because some R&D performers are non-product innovators. They are about 15 % in the Netherlands, hence we run two separate probit regressions (one for the occurrence of R&D, as already discussed in text above, and, conditional on that, one on the occurrence of product innovation; excluded variable: “firm being part of a group”) and we subsequently use the two inverse Mills ratios from these probit regressions to control for truncated distributions of the error terms in the share of innovative sales regression (see Tables 4, 5 and 6). In Switzerland there are only 108 manufacturing firms and 55 service firms that have zero value for innovation output; in sum only 6 % of our 2,739 observations. Hence, it is not necessary to apply additional Mills ratios for Switzerland.
There are two differences with respect to the definition of the variables for external R&D and cooperation between the Swiss and the Dutch survey questionnaire. First, in the Swiss innovation survey questionnaire external R&D is explicitly excluded in the definition of cooperation, but this is only implicitly the case for the Dutch questionnaire. So a potential overlap between external R&D and cooperation in the Dutch data cannot be ruled out. We assume that there are few such cases of overlap. The alternative estimates that use quantitative variables for external R&D and collaboration serve as a robustness test for the estimates that are based on the qualitative data. Second, the Swiss data refer only to R&D cooperation, whereas the Dutch data cover also cooperation in other innovation activities (e.g., construction, design). By concentrating on firms that do internal R&D in the Dutch survey, we minimize this difference in the definition of cooperation between the two countries because R&D-active firms are more likely to engage in R&D cooperation than in other innovation activities. There are further measurement differences regarding the number of cooperation partners and incoming knowledge spillovers (see Table 2).
We run regressions separately for small / medium-sized firms and large firms; high-tech and low-tech firms; and high and low R&D intensity firms. For the Netherlands no relevant differences could be found as compared with the results for the whole sample. For Switzerland there are some additional insights: the overall positive effect for ‘Make&Buy&Coop’ (‘mixed’ form)can be traced back to ‘large’ firms; the overall effects for ‘Make’, ‘Make squared’ and ‘Coop squared’ can be traced back to low-tech manufacturing; finally, the overall effect of ‘Buy&Coop’ can be traced back to firms with low R&D intensity. These additional estimates are available upon request.
F-tests for the joint significance of industry and time dummy variables reject, with the exception of the Swiss manufacturing sector, the null hypothesis that the industry dummy variables and time dummy variables are jointly equal to zero. Moreover, we find that the coefficients of the industry dummy variables strongly correlate with the average sales share of the industries. The correlation coefficients for the manufacturing and the service industries for Switzerland are 0.95 and 0.84 respectively. For the Netherlands the respective correlation coefficients are 0.92 (manufacturing) and 0.90 (services). See also footnote 9.
We estimated the IV models without individual effects and then with the individual effects: The former allowing us to do the tests with regard to the quality of the instruments; the latter gives us estimates of the individual effects (the estimates without individual effects are found in Tables 11 and 12, respectively in the “Appendix”).
The industry dummy variables capture the effects of industry-specific variables that we do not control for. The coefficients of those industry dummy variables reproduce the ranking of the industries in terms of the logarithms of the shares of innovative sales. In Switzerland, the shares of sales that are due to new products are particularly high in electronic and optical products, vehicles and electrical equipment (around 30 %) for manufacturing and in information technology/R&D (29 %) and telecommunications (21 %). In the Netherlands, the sectors with the highest share of sales due to new products are machinery and equipment, office accounting and computing machinery, and motor vehicles (around 27 %) in manufacturing and environmental services (17 %) in services.
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The fruitful comments and suggestions of two anonymous referees and the General Editor of this journal are gratefully acknowledged.
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Appendix
Appendix
See Tables 8, 9, 10, 11 and 12.
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Arvanitis, S., Lokshin, B., Mohnen, P. et al. Impact of External Knowledge Acquisition Strategies on Innovation: A Comparative Study Based on Dutch and Swiss Panel Data. Rev Ind Organ 46, 359–382 (2015). https://doi.org/10.1007/s11151-015-9450-7
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DOI: https://doi.org/10.1007/s11151-015-9450-7