On the Relevance of Decreasing Returns on Innovation Performance

On the relevance of decreasing returns on innovation performance

Authors: Javier Barbero1, Jon Mikel Zabala-Iturriagagoitia2 & Jose Luis Zofio3.

1Oviedo Efficiency Group (OEG), Universidad de Oviedo.

2Deusto Business School.

3Department of Economics, Universidad Autónoma de Madrid.

Innovations (the ‘residual’ in growth accounting) are the most important source of productivity growth and thereby of increased welfare. Due to the impact that innovation is having in most economies, and also as a consequence of increasing interests from policy-makers concerning public accountability (Lovell, 2002; Batterbury, 2006), there has been an increasing development, use and exploitation of indicators to improve the measurement of innovation systems (Castro-Martínez et al., 2009; Dziallas and Blind, 2019). Several concepts have been introduced in the literature to assess and characterize innovation systems, such as innovation capacity, innovation potential, propensity to innovate, innovativeness or innovation performance to mention a few (Carayannis et al., 2015; Furman et al., 2002; Hagedoorn and Cloodt, 2003; Jordan, 2010; Mairesse and Mohnen, 2002; Prajogo and Ahmed, 2006; Zabala-Iturriagagoitia et al., 2007a). However, few have tackled the actual meaning behind these concepts, neither offering robust definitions that allow distinguishing them, nor discussing their potential complementary effects (Carayannis and Grigoroudis, 2014; Lee, 2015).

In this article we relate the volume of innovation inputs available to an innovation system and its performance. Innovation inputs is here used as a measure of the amount of resources that are invested in the innovation system. In turn, innovation performance is defined as the relationship between these resources (i.e. innovation inputs) and the results (i.e. innovation outputs) produced. Hence, innovation performance is defined as a measure of the efficiency levels achieved by a particular innovation system, or, equivalently, its relative productivity (see Edquist et al., 2018). Both concepts are rendered operational in the paper through corresponding scalar measures of input size and efficiency. With this we aim to contribute to the identification of the main problems of every innovation system over time and space, so innovation policies can act on actual existing systemic failures (Edquist, 2011).

The European Commission has been one of the most active agents as to the measurement of innovation with the development of the European Innovation Scoreboard (between 2010 and 2015, Innovation Union Scoreboard) and the implementation of the Community Innovation Surveys (CIS). Other scoreboards also include the UK Competitiveness Index, the index of the Massachusetts Innovation Economy, the Global Innovation Index, the Nordic Innovation Monitor or the Bloomberg Innovation Index to mention a few. What these approaches share is that they all are based on the use of a synthetic scalar measure that, through composite indicators, provides a ranking of the countries under study, with consequent political implications.

However, the simplistic use of synthetic composite indicators may be dangerous because the rankings derived from them are often taken for granted, without any in-depth discussion of their validity (Grupp and Mogee, 2004; Grupp and Schubert, 2010: 69). They can have a communication function raising awareness about innovation policy, but they should not be instrumentally used to make policy decisions without relevant qualifications (see Edquist et al., 2018). However, if innovation scoreboards are expected to have a real impact on the definition of innovation policies, it is essential that they set the ground for an exhaustive characterization of innovation systems. We believe that such characterization can only be achieved when it is based on sound scientific concepts and methodologies, which are able to identify the strengths and weaknesses of every innovation system, so the policy design can take this diagnosis as a point of departure. We also contend that these indicators should be clearly defined so as to ease their interpretation and serve for evaluation purposes.

In this paper we rely on the data provided by the European Innovation Scoreboard (EIS). The EIS aims to “provide a comparative assessment of the research and innovation performance of the EU Member States and the relative strengths and weaknesses of their research and innovation systems” (European Union, 2017: 8). The EIS is the main instrument used by the European Commission to monitor the results achieved by the Innovation Union, which is one of the flagship initiatives defined by the European Union within its Europe 2020 Strategy to create an innovation-friendly environment that supports the generation, emergence and diffusion of innovations. The EIS calculates a Summary Innovation Index (SII) that synthetizes all the indicators included in the EIS, regardless of their character (i.e. inputs, outputs, determinants, outcomes, impacts), by calculating their arithmetic mean (i.e. the basic aggregating function one may rely on to obtain a single scalar measure). The SII ranks all EU countries according to what is explicitly called “EU Member States’ Innovation Performance” (European Union, 2017), so the underlying logic is that the bigger the SII (‘size’ in terms of all indicators), the better the innovation performance.

As we show in what follows, we rely on the simplicity of the SII to calculate a measure of the innovation inputs available to the innovation system, based on the concept of ‘scale’, as defined in production theory. Production theory studies the relation between the amount of inputs used within a system (i.e., the scale of the system) and the amount of outputs that such system is capable of producing (Shephard, 1970). From this relation a natural measure of relative performance emerges by comparing the two through the concept of efficiency or productivity (i.e., the ratio of an aggregate output index to an aggregate input index). As this analytical framework requires the categorization of (suitable) indicators as inputs or outputs, we follow Edquist et al. (2018), who take a step forward from the EIS and identify them as belonging to one of these two categories. This enables us to identify the underlying characteristics of the production process when evaluating innovation performance; most particularly the nature of returns to scale determining whether a given rate of growth in inputs brings a larger, equal, or smaller rate of growth in outputs (i.e., increasing, constant, or decreasing returns to scale).

A key feature of the concept of innovation performance used in this paper is that it is defined in relative terms. For this purpose we rely on the literature on efficiency and productivity (Fried et al., 2008) to assess the innovation performance of national innovation systems in Europe. This is interactively determined by multi-lateral comparisons of multiple input-output combinations (Guan and Chen, 2012). To measure innovation performance in a robust manner we introduce to the field of innovation the advanced Data Envelopment Analysis techniques related to the Technique for Order of Preference by Similarity to Ideal Solutions (DEA-TOPSIS). The rationale for using the DEA- TOPSIS methods lies in that it helps overcome the severe limitations of the standard DEA approach, which has already been applied to assess innovation performance in previous research efforts (Edquist et al., 2018). Hence, through the application of DEA-TOPSIS methods we aim for a more robust assessment of innovation performance through the analysis of the returns to scale in which national innovation systems operate in Europe.

Our results indicate that in many cases, countries often regarded as ‘innovation leaders’, using the labeling of the EIS, count with an innovation performance far from satisfactory. This suggests that innovation leading countries, while dedicating vast resources (i.e., high innovation inputs), often do not manage to produce as much output as it could be expected (i.e., low innovation performance), pointing to the existence of decreasing returns to scale in innovation. We confirm such hypothesis at the aggregate country level. This shows that the relative allocation of resources currently made in many countries does not yield the expected returns, which calls for a reconsideration of how innovation decisions are made in the policy field. Despite decreasing returns are also observed in other activities (see Madsen, 2007; Lang, 2009), innovation, and hence innovation policy, is still being regarded as a field in which the more resources invested, the better the relative performance of the innovation system is likely to be (Rodríguez Pose and Crescenzi, 2008). The evidence provided in this study shows otherwise; i.e. that the bigger is not always the better. Fragkandreas (2013) also discusses the existence of this paradox, in the sense that innovation seems not to pay-off for some territories in Europe, even highly innovative ones, as these grow at a slower pace than other peers. It is true that innovation may continue being the engine that provides the solutions to the grand challenges we are currently facing. However, the evidence provided by Strumsky et al. (2010) also raises some doubts as to whether innovation activities in developed economies will continue being as productive as they were in the past (see also Crafts, 2018). R&D networks and their effects on heterogeneous modes of knowledge creation: Evidence from a spatial econometric perspective

Authors: Martina Neuländtner1 & Thomas Scherngell1.

1AIT Austrian Institute of Technology.

Focus and conceptual framework

The complex nature of the knowledge creation process demands access to diverse knowledge, its adaption, application and diffusion. This spurs the research interest in networks of research and development (R&D), as they serve as channels for transmitting knowledge – also over larger geographical distances (e.g. Autant‐Bernard et al. 2007). By this, they are considered to help to overcome geographical barriers for accessing external knowledge (Neuländtner and Scherngell 2020), and to reduce regional disparities in the distribution of knowledge, by moderating in some way the strong geographical localisation of knowledge flows due to the sticky nature of knowledge (Asheim and Isaksen 2002). Especially, in a knowledge-intensive economy, such R&D networks can provide timely access to knowledge and resources that are otherwise unavailable but are necessary to stay competitive and up-to-date in a rapidly changing field (Powell et al. 1996). From a regional perspective, cross-regional collaborations can avoid a possible lock-in to an increasingly obsolete technological trajectory (Cantwell and Iammarino 2005).

While these considerations may be valid on average – based on empirical insights, e.g. from Wanzenböck and Piribauer (2018) – there are at the same time strong arguments that such network effects are not uniform and homogeneous across several dimensions. On the one hand, effects are considered to differ concerning the underlying type of knowledge creation. On the other hand, different network structural characteristics, e.g. a specific centrality in a network, are assumed to produce different effects on knowledge creation. In this study, we specifically argue that various network effects are at stake given the differing nature and extent of knowledge creation processes across regions since they rely on different resources and are shaped and coordinated by the region- specific institutional system of innovation (Tödtling and Trippl 2005).

These arguments are also underlined when taking a network science perspective, stressing that the creation of new knowledge results from collective actions of different actors that are connected by various linkages ranging from informal to formalised network relationships (Acs et al. 2002). Different knowledge bases and technological competences shape these interactions, research and collaboration strategies and rationales, and different spatial scales of knowledge creation (Tödtling et al. 2006). Scholars have conceptualised them as different modes or regimes of knowledge creation, referring to specific characteristics of the knowledge creation processes (e.g. March 1991, Gibbons et al. 1994, Nonaka 1994, Nowotny et al. 2003, Moodysson et al. 2008). In this study, we are inspired by the conception of March (1991) distinguishing between knowledge exploitation and exploration, where we understand exploitation-oriented knowledge creation as technological knowledge and application-oriented, mostly occurring in an industrial setting, and specify exploration-oriented knowledge creation as scientific knowledge, mostly occurring in an academic setting.

While there are a number of previous works exploring network effects on regional knowledge creation that acknowledge the critical role of networks in the process of knowledge creation (e.g. Sebestyén and Varga 2013, Wanzenböck and Piribauer 2018, Hazır et al. 2018, Wanzenböck et al. 2020), potential idiosyncrasies of network effects across different modes of knowledge creation are neglected so far. To fill this research gap, this study aims to analyse the effects of R&D networks on regional knowledge creation of different forms, namely knowledge exploitation and knowledge exploration. Moreover, we consider a distinction in the effect estimates on the quantity versus the quality of regional knowledge creation. The joint focus on different modes and outputs of knowledge creation at the same time allows for a comparison of network effects across technological and scientific output and the technological importance (quality) of knowledge created. Empirically, the R&D networks are constructed based on inter-regional R&D projects of the EU Framework Programmes (FPs) observed for the European NUTS-2 regions. To proxy exploitative and explorative knowledge creation, systematic and comprehensive information on regional patent applications and scientific publications, respectively, are used.

We employ a set of spatial Durbin models (SDM) in an empirically augmented Knowledge Production Function (KPF) framework to explore the differences between exploitative and explorative knowledge creation across European regions. The models differ with respect to the dependent variable – patent applications or scientific publications. Specifically, to analyse the impact of different network effects, we include a region’s (i) degree centrality (the number of partner regions), and (ii) authority score (expresses how well-connected the region is to other regions that are themselves well-connected) as explanatory variables in the basic model. Using spatial econometric techniques allows us to explicitly model the spatial dependence structure of knowledge creation, leading to conclusions of potential agglomeration effects driven by spatial proximity. In doing so, we are able (i) to examine if the effects of R&D networks (degree and authority) vary across different modes of knowledge creation, but also (ii) to analyse the impact of neighbouring regions in terms of their network embeddedness and connectivity on a region’s knowledge creation, and (iii) evaluate individual region-specific effects concerning their relative (direct and indirect, i.e. spillover) gains from R&D networks to identify regions with specific development potentials in this respect.

Main results and discussion

The results of the model estimations show clearly that EU funded networks are a significant driver for both exploitative and explorative knowledge creation. Specifically, the results point to a higher positive impact of networks on explorative than on exploitative knowledge creation for the quantity of knowledge output; the opposite is true for knowledge quality. This indicates higher importance of cross-region network embeddedness and connectivity within a scientific and academic setting in terms of increasing knowledge quantity and emphasises the role of networks in an application and technology-oriented environment for enhancing the quality of knowledge creation. In other words, cross-regional interactions – thus, integrating knowledge from various other regions – are more important to create a high publication output rather than patenting output. Nevertheless, creating knowledge of economic and technological importance (high-quality patents) indeed benefits from a large and diverse set of network partners, even to a more considerable extent than for advancing scientific excellence. Additional positive indirect network effects for exploration- and quantity-driven knowledge creation highlight the importance of being co-located to central and authoritative regions to benefit from respective regional spillovers. Thus, being geographically close to strongly connected regions, may especially accelerate the regions’ output in terms of explorative knowledge creation.

Moreover, examining the region-specific effect estimates reveals economic, technological or scientific regional potentials, especially for less developed regions that are amongst the most benefiting regions regarding direct and spillover network effects from neighbouring regions (e.g. surrounding regions of Bratislava and Bucharest). Strikingly, we find a large number of UK regions exhibiting relatively high positive impact from EU-funded networks, pointing to a pessimistic finding in the context of ‘Brexit’ and its consequences for the UK innovation system.

Policy-wise, these results allow deriving a set of conclusions regarding the role of EU funded joint projects as drivers for regional knowledge creation: fostering the access to such R&D networks may be beneficial to (i) accelerate knowledge quantity, especially in terms of the region’s explorative knowledge creation, and (ii) enhance a region’s exploitation-oriented and quality-driven knowledge output, i.e. represented by the high-quality patents. However, exploration-oriented knowledge quality, high-quality and high-impact scientific publications (as proxied by the Mean Normalized Citation Score - MNCS) is not affected by network connectedness and hence requires alternative policy measures, such as providing fruitful research environments to attract star-scientists and high- level research experts. Emphasising the role of spatial spillover effects of R&D networks, funding of EU funded joint projects may facilitate a catch-up process by less developed regions regardless of their initial region-internal knowledge-stock and network capabilities. In particular, the results show strong positive spatial externalities regarding the regions’ output in terms of explorative knowledge creation, thus possibly enabling lagging regions to also take part in more explorative and scientific orientated knowledge communities and debates.

References

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Autant‐Bernard, C., J. Mairesse and N. Massard (2007). "Spatial knowledge diffusion through collaborative networks." Papers in Regional Science 86(3): 341-350.

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Tödtling, F., P. Lehner and M. Trippl (2006). "Innovation in knowledge intensive industries: The nature and geography of knowledge links." European planning studies 14(8): 1035-1058.

Wanzenböck, I. and P. Piribauer (2018). "R&D networks and regional knowledge production in Europe: Evidence from a space‐time model." Papers in Regional Science 97: S1-S24.

Wanzenböck, I., M. Neuländtner and T. Scherngell (2020). "Impacts of EU funded R&D networks on the generation of key enabling technologies: Empirical evidence from a regional perspective." Papers in Regional Science 99(1): 3-24. DOES HOLDING MULTIPLE AFFILIATIONS AFFECT IMPACT? ANALYSING PUBLICATIONS, PATENTS AND MEDIA APPEARANCES OF DUTCH PROFESSORS

Paula Schipper*, Jarno Hoekman*, Alfredo Yegros-Yegros**, Koen Frenken*

* Copernicus Institute of Sustainable Development, Utrecht University

** Centre for Science and Technology Studies (CWTS), Leiden University

Abstract prepared for Eu-SPRI conference 2021

A key pillar of research policy concerns the nurturing of research collaboration, to foster – amongst others – transdisciplinary research. With the most emphasis being on the establishment of collaborative research teams within science, a neglected aspect of collaboration has been researchers employed simultaneously by various organisations. Multiple affiliated researchers formally connect two academic organizations or one academic and one non-academic organization for a sustained period of time. They can service multiple aims and work according to different logics by combining affiliations (Rathenau Institute, 2020). For organizations, they are attractive as they bring a network and associated funding opportunities. At the same time, for the researcher, multiple affiliations may support their own research, their specialisation in certain subjects, their personal network and ultimately their career prospects.

In bibliometrics, the phenomenon of multiple-affiliation authors has only recently been subject of empirical research, notably by Hottenrott and colleagues. A first study was carried out by Hottenrott and Lawson (2015), who explored this phenomenon in Germany, Japan and the UK, looking at the fields of biology, chemistry, and engineering during the period 2008-2014. They found that the share of authors with multiple affiliations rose from five percent in 2008 to 10 percent in 2014, indicating a doubling in just six years. They also found that authors with multiple affiliations are more often found in highly-cited papers, particularly in the case of authors from Japan and Germany in the fields of biology and chemistry. In a more comprehensive study based on 20.5 million articles listed in Scopus, Hottenrott et al. (2019) found that by 2019 almost one in three articles was (co-)authored by authors with multiple affiliations. This growth of multiple affiliations is prevalent in all disciplines and strongest in high impact journals.

These studies, and similar ones (Huang and Chang 2018; Sanfilippo et al. 2018; Tong et al. 2020), point to pervasive patterns in the rise of authors with multiple affiliations and the association between multiple affiliations and citation impact. However, the studies are limited in focusing solely on citation impact in journal articles. Other impact measures may be equally relevant, especially for researchers who connect to non-academic organizations. Furthermore, methodological questions remain, as anyone who

1 lists multiple affiliations in a publication is considered a co-affiliated author. As a measure of formal co-affiliation in terms of holding multiple employment contracts, this measurement may suffer both from many false positives as authors may list multiple affiliations without holding multiple employment contracts (for example, when changing jobs or having been an unpaid guest researcher) and from many false negative as researchers holding multiple employment contracts may not be bounded to report these on (all) their publications.

This study takes a different approach to the analysis of multiple affiliations. First, as impact measures, we look not just at citations to scientific articles but also to citations to patents and mentioning in newspaper media. In this way, we can assess the output and various roles of researchers with multiple affiliations, compared to researchers with a single affiliation. Second, we identify researchers with multiple affiliations by starting from a personnel list of professors employed by (Dutch) universities and then checking their CVs for multiple affiliations. rather than relying on their reporting in scientific articles. As we are interested in the different roles and impacts that multiple-affiliation professors can have, we limit the analysis to professors with at least one university affiliation (in The Netherlands) and one affiliation with a non-academic organization (e.g., at a firm, a governmental organization, or a public research organization).

The specific hypotheses we want to explore in the study revolve around the impact that professors with multiple affiliations have on ‘science’ (citations to academic output), industry (citations to patents), and society (media appearances in newspapers). The first hypothesis, in line with previous research on citation impact, holds that professors with multiple affiliations have, on average, a higher impact in terms of scientific citations, patents citations and media appearances, compared to professors with a single affiliation. This hypothesis expands the notion of “star” scientists” (Zucker & Darby, 1996) to societal impact and other scientific disciplines.

The second hypothesis holds that professors’ scientific citation impact, patent citation impact and media citation impact are positively correlated (independent of the number of affiliations). This hypothesis follows from institutional theory that states that one field's reputation carries over, albeit imperfectly, to other fields.

The third hypothesis holds that the correlation between scientific citation impact, patent citation impact and media citation impact is lower for multiple-affiliation professors than for single-affiliation professors. This expectation follows again from the institutionalized ‘archetypes’ of multiple-affiliation professors we expect to find: those specializing on research outside academia with typically a one-day- a-week appointment at the university (with relatively high number of patent citations and a relatively low number of scientific citations and media appearances) and those specializing on university-based research with typically a one-day-a-week appointment at an organization other than a university (publishing with relatively low number of patent citations and a relatively high number of scientific

2 citations and media appearances). A further stratification is made between full professors (scientific focus) and extraordinary professors (business focus).

The fourth hypothesis holds that, regarding citations, the impact distribution of multiple-affiliation professors is more spread than the impact distribution of single-affiliation professors. This expectation follows again from the ‘archetypes’ of multiple-affiliation professors we expect to find.

Data collection will start from taking a random, representative sample of Dutch professors from the university websites in 2015. We collect CV data to assess whether they held multiple affiliations in 2015 and also collect information on age, gender, experience and scientific discipline (university faculty). We then collect information on the scientific, patent and media citation impact of the cohort for the period 2016-2020. We compare impact, impact distribution and correlation between impact measures between single-affiliation and multiple-affiliation professors. We also aim to build regression models to test for the effect of multiple-affiliation professors on impact after controlling for age, gender, experience and university dummies.

Our results will inform ongoing debates about the relationships between scientific excellence and societal impact and a better understanding of whether and how multiple affiliations of researchers contribute to strengthening this relationship.

References

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Tong S, Yue T, Shen Z, Yang L. 2020. The effect of national and international multiple affiliations on citation impact. arXiv.

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