Fig 3 - uploaded by Andrea Fronzetti Colladon
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Innovation response to technological shocks: Neighbor innovativeness vs network structure. Notes: The graph reports the forward effect coefficients associated with the local projection estimation in Eq. (11). All estimates use fixed effects, time dummies, and standard errors robust to heteroskedasticity. The graphs report the confidence bands at 90 and 95%. The event is identified with the year of peak increase in the variable under assessment. The graph on the left-hand (right-hand) side considers a shock to neighbor innovativeness (network position) while keeping the other measure of technological interdependence as control. The degree of neighbor innovativeness is measured with the proximity-weighted number of quality-adjusted patents of the other sectors. The structure of the network linkages is summarized by the latent factor extracted from the indicators of network degree, betweenness, closeness and distinctiveness centrality.
Source publication
How does technological interdependence affect innovation? We address this question by examining the influence of neighbors' innovativeness and the structure of the innovators' network on a sector's capacity to develop new technologies. We study these two dimensions of technological interdependence by applying novel methods of text mining and networ...
Context in source publication
Context 1
... in understanding whether the propagation of technological shocks has changed recently, we distinguish the responses by the time interval of the data. Accordingly, we first consider the entire period between 1976 and 2021 and then look at the interval since 1995. 14 The results of the event analysis offer particularly valuable insights (see Fig. 3). Considering the overall time span, there is no systematic response of sector innovation to positive shocks affecting both dimensions of technological interdependence (neighbor innovativeness and network structure). However, the pattern of results changes remarkably from 1995 onward, when the effect of an unanticipated change in ...
Citations
... Patent data provide a reliable means to measure different aspects of both individual and collective economic behaviors (e.g., Griliches 1990, Nagaoka et al. 2010, Kogan et al. 2017. Patent citation data specifically has been shown to be informative about the importance of individual patents, future corporate R&D activity and stock market valuation (Trajtenberg 1990, Hall et al. 2005, Fronzetti Colladon et al. 2025, as well as highly associated to domain level technological improvement rates (Benson and Magee 2015, Magee et al. 2016, Triulzi et al. 2020, Singh et al. 2021). ...
... The structure of patent citation networks has a self-referencing effect on the citations a patent receives (forward citations). For example, an inclination towards transitivity (citing references of references) has been found (An andDing 2018, Chakraborty et al. 2020), while some patents are more important than other patents in the development of a domain (Fronzetti Colladon et al. 2025). These effects have been found with the usage of exponential random graph models (ERGM), which where specifically developed to estimates autocorrelation effects of network structures (Holland where the drop probability is largest, and most impact full. ...
... Importance of patents as measured by average degree centrality affects technology improvement rate (Benson and Magee 2015). In (Fronzetti Colladon et al. 2025) the authors include a composite of centrality measures in mapping technological independence of patents, including Katz centrality (Katz 1953), degree centrality, betweennness centrality, closeness centrality (Freeman 1977) and distinctiveness (Fronzetti Colladon and Naldi 2020). We do see value in that approach when assessing technological independence, yet in our model it would introduce ambiguity. ...
We explore a dynamic patent citation network model to explain the established link between network structure and technological improvement rate. This model, a type of survival model, posits that the *dynamic* network structure determines the *constant* improvement rate, requiring consistent structural reproduction over time. The model's hazard rate, the probability of a patent being cited, represents "knowledge production," reflecting the output of new patents given existing ones. Analyzing hydrogen technology patents, we find distinct subdomain knowledge production rates, but consistent development across subdomains. "Distribution" patents show the lowest production rate, suggesting dominant "distribution" costs in pricing. Further modeling shows Katz-centrality predicts knowledge production, outperforming subdomain classification. Lower Katz centrality in "distribution" suggests inherent organizational differences in invention. Exploitative learning (within-subdomain citations) correlates with higher patenting opportunity costs, potentially explaining slower "distribution" development, as high investment needs may incentivize monopolization over knowledge sharing.
This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five Distinctiveness measures are considered, as well as a more meaningful range of the α parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.
This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five distinctiveness measures are considered, as well as a more meaningful range of the {\alpha} parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.
