Category: Open Innovation

How Robust are the Results?

Today I have the pleasure to present some of our (Galia, Laursen, Salter and my) recent research. Here is briefly what it is about:

Introduction.

As stated by Hubbard, Vetter and Little (1998: 251): “The goal of science is empirical generalization, or knowledge development leading to some degree of understanding.” However, in many fields of science, the integrity of the pertinent empirical literatures are open to question because of what Rosenthal (1979) dubbed the “file drawer problem,” which implies that journals may be dominated by papers reporting results that are Type I errors (erroneous rejections of the null hypothesis), while the null outcome remain in the file drawers of researchers. In the top five journals of strategic management research, Goldfarb and King (2016) report that between 24 to 40 percent of the findings are likely the result of chance rather than a reflection of true relationships.

Replication studies can help reduce this problem by establishing a set of robust empirical results (Bettis, 2012). In addition, even if we assume away the “file drawer problem”, statistical tests by nature produce Type I errors. The result is that in strategic management general and in open innovation research in particular, we know too little about which results are empirically generalizable, and hence whether they potentially add to our understanding. In many cases, however, researchers work on similar data sets and use similar or identical dependent variables, so that in principle, the robust (and not so robust) results could be extracted, while controlling for a host of other factors. When such general datasets are available, large scale replication studies can be conducted. By large-scale replication studies, we mean studies where different independent variables are included in a single empirical model with the same dependent variable. However, in these large scale replications as in most empirical applications, the “true model”, and therefore, the appropriate selection of explanatory variables, is essentially unknown, which leads to a phenomenon described as “model uncertainty” (Chatfield, 1995). Disregarding model uncertainty results in too small standard errors and too strong confidence in the statistical findings (Raftery, 1995). Additionally, it is model uncertainty that fundamentally facilitates the “file drawer problem”.

Continue Reading..

Foundations of Economic Change – Behavior, Interaction and Aggregate Outcome

Currently I am attending the 15th conference of the International Joseph Alois Schumpeter Society.

It is a marvelous event with a line up of speakers that is really breath-taking for all of the participants. Brian Arthur, David Audretsch, Giulio Bottazzi, Guido Buenstorf, Wesley Cohen, Herbert Dawid, Giovanni Dosi, Magda Fontana, Dominique Foray, Koen Frenken, DAniella Laureiro-Martinez, Mariana Mazzucato, John S. Metcalfe, Pierre Mohnen, Richard Nelson, Carlota Perez, Mario Pianta, Ulrich Witt, and of course the host Uwe Cantner.

I am presenting Sverre’s and my paper “KIS, Urban Location & Innovation”. Also I co-authored a poster with Christina Koller about the effect the experience of patent attorneys has on the quality of patents.

 

 

 

Urban agglomerations, knowledge intensive services and innovation: Establishing the core connections

It took us a while to go from an idea to an accepted paper. But now Sverre Herstad and I have received the news that our paper “Urban agglomerations, knowledge intensive services and innovation: Establishing the core connections” is accepted for publication in Entrepreneurship and Regional Development.

In this paper we investigate how resources available in urban agglomerations influence the (1) organizational form, (2) innovation activity and (3) collaborative linkages of knowledge intensive business services firms (KIBS). We use rather comprehensive Norwegian data: We use the Norwegian employer-employee (LEED) registers for the years 2000 -2008 to connect the organizational forms and labour market positions of individual KIBS to their physical locations. For the decision to engage and for subsequent collaborative ties we utilize unique establishment-level information available from the Norwegian Community Innovation Survey of 2008.

We find that compared to their counterparts elsewhere, KIBS located in Norwegian large-city labour market regions are more likely to be independent from multi-establishment business organizations and thus reliant on resources available externally, in their locations. This is most pronounced in the central and western business districts of the capital, wherein independent KIBS exhibit high turnover of professionals and are less inclined to engage actively in innovation. Yet, those that do engage use the capital region economy as a platform for engaging with both domestic and international collaboration partners. Only by consecutively analysing these aspects and accounting for the selection processes involved is the empirical analysis able to uncover contrasting firm-level responses to the same urban economy resource base.

Best Paper Award

Recently we received the news that our paper “Open innovation practices and their effect on innovation performance” published by the International Journal of Innovation and Technology Management has received the Best Paper Award 2012 by the journal. To the best of our knowledge this paper is the first to develop a complete indicator framework for examining open innovation practices and their impact on innovativeness and commercial innovation performance. The analysis  yields a number of results which are relevant for innovation management and policy. Visit IJITM’s site here. Implications for innovation policy are discussed more thoroughly here.

One of the most downloaded articles in Applied Economic Letters

We have just received the notice that our paper “The relationship between international innovation collaboration, intramural R&D and SMEs innovation performance: a quantile regression approach” is among the Top 10 of the most downloaded papers in Applied Economics Letters in 2013. Currently it is free for download.

Regional Knowledge Bases & Extra-Regional Collaboration

Recently we, that is Christina Koller, Sverre J. Herstad and I, received the news that our joint paper “Does the composition of regional knowledge bases influence extra-regional collaboration for innovation?” is published by Applied Economics Letters today.

The paper emphasizes that ther is a growing research interest in the relationship between the composition of regional knowledge bases, and the extra-regional collaborative ties  by actors during their development work. In order to investigate this relationship, we use patent data to characterize European NUTS 3 regions by their i) comparative Technological Specializations; and ii) Related Technological Variety. We find domestic, extra-regional collaboration to be negatively associated with regional Technological Specialization and Related Technological Variety. At the same time, we find Related Technological Variety to serve in support of international innovation collaboration.

Having the data ready it was technically a bit demanding to have a balanced panel and a fractional response variable. First that reminded us very much of the situation in Papke and Wooldridge (1996), where they introduce a regression model for fractional response in cross section data. This suggested regression model resembles the traditional logit or probit for binary responses. Papke and Wooldridge recently extended the model for balanced panels (Papke and Wooldridge, 2008; Wooldridge, 2011) and suggest estimating population-averaged panel-data models by using a Generalized Estimating Equation (GEE; Liang and Zenger, 1986). Therefore, we estimate a generalized linear model with our fractional dependent variable  and the independent variables. In our particular case we use the logit function as the link function  and the binomial distribution as the distribution. Additionally, the GEE estimation requires the specification of a working correlation matrix , which postulates that the correlations are not a function of the independent variables. We employ a so-called ‘exchangeable’ correlation matrix that is particularly suitable here, as our panel contains a rather small time dimension (Papke and Wooldridge, 2008). To control for fixed technology and country effects, we follow Papke and Wooldridge (2008).

References.

Liang, K.-Y. and Zeger, S. L. (1986) Longitudinal data analysis using generalized linear models, Biometrika, 7, 13–22.

Papke, L. E. and Wooldridge, J. M. (1996) Econometric methods for fractional response variables with an application to 401 (K) plan participation rates, Journal of Applied Econometrics, 11, 619–32.

Papke, L. E. and Wooldridge, J. M. (2008) Panel data methods for fractional response variables with an application to test pass rates, Journal of Econometrics, 145, 121–33.

Open innovation practices and their effect on innovation performance

We all are relieved. A paper that has been accepted for publication about 2.5 years ago, has finally been published. “Open innovation practices and their effect on innovation performance” by Carter Bloch, Sverre Herstad, Els van de Velde and myself has been published last night by International Journal of Innovation and Technology Management (IJITM).

The findings in the paper date back to a project under the Vision EraNet umbrella, which is also reported here.

This paper develops a novel indicator framework for examining open innovation practices and their impact on performance. The analysis, which is based on Community Innovation Survey (CIS) data for Austria, Belgium, Denmark and Norway, yields a number of interesting results. First, we find that open innovation practices have a strong impact on innovation performance. Second, results suggest that broad-based approaches yield the strongest impacts, and that the collective of open innovation strategies appear more important than individual practices. Third, intramural investments are still important for innovative performance, stressing that open innovation is not a substitute for internal knowledge building.

Sverre Herstad and myself, together with others, extended the analysis reported in this paper in a project documented here.