Cooperative R&D and Firm Performance

We analyse the impact of R&D cooperation on firm performance differentiating between four types of R&D partners (competitors, suppliers, customers, and universities & research institutes), and considering two performance measures: labour productivity and productivity in innovative (new to the market) sales. Using data on a large sample of Dutch innovating firms in two waves of the Community Innovation Survey (1996, 1998), we examine the impact of R&D (collaboration) in 1996 on subsequent productivity growth in 1996-1998. We find that supplier and competitor cooperation have a significant impact on labour productivity growth, while competitor cooperation and collaboration with universities & research institutes positively affects growth in innovative sales per employee. Innovative sales are furthermore stimulated by incoming spillovers (not due to collaboration) from customers and universities. The results confirm a major heterogeneity in the rationales and goals of R&D cooperation, with competitor and supplier cooperation focused on incremental innovations improving the productivity performance of firms, while university cooperation and again competitor cooperation are instrumental in creating and bringing to market radical innovations generating sales or products that are novel to the market, improving the growth performance of firms.research and development ;

Testing for complementarity and substitutability in case of multiple practices

A number of recent empirical studies of firm-level productivity (growth) have been concerned with establishing potential complementarity between multiple organizational design practices. These papers have drawn conclusions on basis of the effect of the interaction term between each possible pair of practices. In this paper we show that this approach may lead to misleading results in case more than two practices are considered. We develop a proper testing procedure for complementarity and substitutability in case there are multiple organizational practices that affect output. The testing methodology is illustrated by empirical examples of three and four innovation practices affecting productivity. The testing framework can easily be applied to test for supermodularity.industrial organization ;

Complementarity in R&D cooperation strategies

This paper assesses the performance effects of simultaneous engagement in R&D cooperation with different partners (competitors, clients, suppliers, and universities and research institutes). We test whether these different types of R&D cooperation are complements in improving productivity. The results suggest that the joint adoption of cooperation strategies could be either beneficial or detrimental to firm performance, depending on firm size and specific strategy combinations. Customer cooperation helps to increase market acceptance and diffusion of product innovations and enhances the impact ofcompetitor and university cooperation. On the other hand, smaller firms also face diseconomies in pursuing multiple R&D cooperation strategies, which may stem from higher costs and complexity of simultaneously managing multiple partnerships with different innovation objectives.management and organization theory ;

Detecting independence of random vectors: generalized distance covariance and Gaussian covariance

Distance covariance is a quantity to measure the dependence of two random vectors. We show that the original concept introduced and developed by Sz\'{e}kely, Rizzo and Bakirov can be embedded into a more general framework based on symmetric L\'{e}vy measures and the corresponding real-valued continuous negative definite functions. The L\'{e}vy measures replace the weight functions used in the original definition of distance covariance. All essential properties of distance covariance are preserved in this new framework. From a practical point of view this allows less restrictive moment conditions on the underlying random variables and one can use other distance functions than Euclidean distance, e.g. Minkowski distance. Most importantly, it serves as the basic building block for distance multivariance, a quantity to measure and estimate dependence of multiple random vectors, which is introduced in a follow-up paper [Distance Multivariance: New dependence measures for random vectors (submitted). Revised version of arXiv: 1711.07775v1] to the present article.Comment: Published at https://doi.org/10.15559/18-VMSTA116 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/