Familiar but also radical? The moderating role of regional clusters for family firms in the emergence of radical innovation

Grashof, Nils Article — Published Version Familiar but also radical? The moderating role of regional clusters for family firms in the emergence of radical innovation Review of Regional Research Provided in Cooperation with: Springer Nature Suggested Citation: Grashof, Nils (2024) : Familiar but also radical? The moderating role of regional clusters for family firms in the emergence of radical innovation, Review of Regional Research, ISSN 1613-9836, Springer, Berlin, Heidelberg, Vol. 45, Iss. 1, pp. 17-49, https://doi.org/10.1007/s10037-023-00199-0 This Version is available at: https://hdl.handle.net/10419/323282 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ ORIGINAL PAPER https://doi.org/10.1007/s10037-023-00199-0 Review of Regional Research (2025) 45:17–49 Familiar but also radical? The moderating role of regional clusters for family firms in the emergence of radical innovation Nils Grashof1 Accepted: 8 December 2023 / Published online: 19 January 2024 © The Author(s) 2024 Abstract Family firms are widely acknowledged to be the most predominant form of organization and hold a great relevance in most economies. Nevertheless, despite their popularity, research has thus far yielded inconsistent findings with regard to their innovative performance. This paper aims to address this research gap by focussing on a specific form of innovation: radical innovation. It seeks to determine the propensity of family firms to generate such innovations. Furthermore, by considering the heterogeneity between regions and firms, this paper also investigates the potential moderating effects of being located in a regional cluster and firm size. Based on various data sources, it is empirically shown that family firms are on average less capable of producing radical innovation than non-family firms. However, the corresponding regional context matters in this regard. By being located within regional clusters, family firms can reap the benefits of localization externalities, leading to produce more radical innovations than being located outside regional clusters. Keywords Radical innovation · Recombinant novelty · Family firms · Regional clusters · Agglomeration · Firm size 1 Introduction It is widely acknowledged that family firms are the most predominant form of organization and that they have a great relevance in most economies (Basco and Nils Grashof nils.gras[email protected] 1Faculty of Economics and Business Administration, Friedrich Schiller University Jena, Carl-Zeiss-Str. 3, 07743 Jena, Germany K 18 N. Grashof Bartkeviˇ ci¯ ut˙ e2016; Bjuggren et al. 2011; Cappelli et al. 2021). They account for the dominant proportion of companies (between 65 to 80% of all European companies) and a large proportion (on average between 40 and 50% of all jobs) of European private employment (European Family Businesses 2021). As a result, the phenomenon of family firm has not only captured the attention of researchers, but also emerged as a subject of considerable interest among policymakers. For instance, in her initial speech as the President-elect Ursula von der Leyen highlighted that: “We should never forget that competitive sustainability has always been at the heart of our social market economy. We just called it differently. Think of the familyowned businesses all across our Union. They were not built solely on shareholder value or the next bonuses. They were built to last, to pass down generations, to provide a fair living to employees. They were built on passion for quality, tradition and innovation.” (Speech by President-elect von der Leyen in the European Parliament Plenary, 2019).1 Despite their popularity and economic relevance, when it comes to innovation, one of the key factors for economic development (e.g. Verspagen, 2005), research on family firms has so far only found rather inconsistent results (Calabrò et al., 2019). To further resolve the inconsistencies in the results, recent research has highlighted the need to distinguish between different types of innovation and to study radical innovation in particular (Calabrò et al. 2019; Hu and Hughes 2020). In contrast to incremental innovations, radical innovations arise from the synthesis of previously unconnected knowledge pieces (Fleming 2001;Nerkar2003; Weitzman 1998).2The atypical combination processes also make radical innovations more expensive and more likely to fail than incremental innovations (Ayres 1988;Fleming 2007). Nonetheless, in the event of success, they can establish a completely new technological approach that leads to further incremental follow-up innovations and thereby provide enormous economic benefits (Ahuja and Lampert 2001; Arthur 2007). For this reason, radical innovations have attracted increasing interest from policy makers (e.g. SprinD3) and academics (e.g. Shkolnykova and Kudic 2021), who, given the distinctive characteristics of radical innovations, have recently highlighted differences between different types of firms, such as SMEs and large firms, in their ability to generate these innovations (e.g. Grashof and Kopka 2023). However, despite recent calls (e.g. Hu and Hughes 2020) and some important exceptions (e.g. Nieto et al. 2015; Schäfer et al. 2017), there has been limited research on radical innovation in the specific firm type of family firms—especially from a quantitative empirical perspective. In a first step, this paper therefore aims to contribute to the ongoing discussion about the relationship between family firms and innovation by 1The speech is also accessible under: https://multimedia.europarl.europa.eu/en/presentation-by-thecommission-president-elect-of-the-college-of-commissioners-and-their-programme-statement-by-ursulavon-der-leyen-president-elect-of-the-ec_I180740-V_v. 2These combinations are sometimes also called ‘atypical combinations’ (e.g. Uzzi et al. 2013). 3In 2019, the German government founded the national agency “Agentur für Sprunginnovationen” (SprinD). For more information, please see BMBF (2020). K Familiar but also radical? The moderating role of regional clusters for family firms in the... 19 empirically investigating the extent to which family firms are more likely to create radical innovations4than non-family firms. Beyond examining firm-specific differences (family vs. non-family firms) in the emergence of radical innovation, the specific context might also play a role. Following Basco et al. (2021a), it is argued that contextual factors of heterogeneity are often overlooked when examining the innovative performance of family firms. Not considering these contextual influences, however, can lead to potential misinterpretations (De Massis et al. 2012). In a second step, it is therefore empirically investigated under which conditions family firms can actually generate radical innovation. Based on recent efforts to link family businesses with the regional context (e.g. Basco 2015; Basco et al. 2021b) and the current discussion about the role of regional clusters for the emergence of radical innovation (e.g. Grashof et al. 2019), the potential moderating role of regional clusters is considered. In addition to the regional context, following the concept of the resource-based view (e.g. Barney 1991) and the suggestions of De Massis et al. (2012), differences in terms of firm size are also examined as a potential moderating variable. To empirically investigate these two research gaps, several data sources are combined, particularly firm-level information from the ORBIS database and information on inventions from the PATSTAT database. The resulting database includes 10,596 patent-active companies in Germany between 2012 and 2020. Due to data limitations (regarding the identification of clusters and family firms), the final data set is pooled and a cross-sectional analysis is performed. Since the corresponding dependent variable is a count variable, suffering from over-dispersion, a zero-inflated negative binomial regression approach with robust standard errors is applied. By investigating the two underlying research questions in a quantitative way, this article extends previous research in regional and innovation studies with respect to a better understanding of heterogenous economic actors (in this case family firms) in the context of regional clusters and radical innovation, as well as in family business studies with regard to the relevance of the regional context in studying the (innovative) performance of family firms. Besides these scientific contributions, this paper also offers practical insights for (regional) policy makers to fully understand the heterogeneity of family firms and thus harness the potential of family firms in creating radical innovations. The remainder of this paper is structured as follows: The subsequent section introduces the theoretical background on family firms, radical innovations, regional clusters and firm size, thereby deducting three hypotheses. In the third section, the methodological approach, the database and the corresponding variables are described in detail. Thereafter, in the fourth section, the main findings are presented and discussed. The paper will end with concluding remarks, including limitations and promising future research endeavours. 4Similar to Castaldi et al. (2015), the terms “innovation” and “invention” are used interchangeably here, because the theoretical framework of recombinant innovation also uses the term “innovation”. But, it is highlighted that this study focuses on technological achievements rather than successful commercialization. K 20 N. Grashof 2 Theoretical background 2.1 Family firms and radical innovations Innovation is generally understood to be the result of (re)combining existing knowledge in a unique way to create something new (Arthur 2007; Basalla 1988, Castaldi et al. 2015). This common understanding of innovation has its roots in Schumpeter’s idea of “Neue Kombinationen” (Schumpeter 1934) and the related work by Weitzman (1998) introducing the concept of “recombinant innovation”, which is defined as “(...) the way that old ideas can be reconfigured in new ways to make new ideas.” (Weitzman 1998, p. 333). Nevertheless, the corresponding degree of novelty can thereby be quite different (e.g. Suwala 2017). On the one hand, incremental innovations can be characterized by a reuse and refinement of existing combinations, referring to exploitative search processes (March 1991;Mewes2019). They develop along well-defined trajectories and are therefore the norm (Dosi 1982; Schoenmakers and Duysters 2010; Verhoeven et al. 2016). On the other hand, radical innovations rely on an explorative search for and development of completely new combinations of knowledge pieces that have not been put together before (Fleming 2001; March 1991;Mewes2019). Since the exploration of these new and previously unknown combinations is accompanied by higher costs and higher risks for failure (in technological as well as commercial terms) than incremental innovations (Ayres 1988;Fleming2007), they are relatively rare (Fleming 2001; Hesse and Fornahl 2020). Nevertheless, if successful, radical innovations can establish a completely new technological approach (Arthur 2007; Verhoeven et al. 2016) leading to strong competitive advantages (e.g. Castaldi et al. 2015) as well as to the creation of entire new markets and industries (e.g. Grillitsch et al. 2018; Henderson and Clark 1990; Tushman and Anderson 1986). However, the ability to generate radical innovation may differ between different types of firms (e.g. Grashof and Kopka 2023). While research on the relationship between firm size and innovation in general has a long history (e.g. Cohen 2010), particularly in recent years there has been a growing interest in the specific case of family firms and their innovativeness (Calabrò et al. 2019;Urbinatietal.2017). The economic relevance of family firms has led researchers to pay attention to the ways in which family firms behave differently from those of non-family firms, and to the way in which family-specific attributes contribute to family firms’ greater profitability and productivity (Cappelli et al. 2021; Sraer and Thesmar 2007)aswell as their innovativeness (Nieto et al. 2015; Zybura et al. 2021). In fact, scholars have ascribed some characteristics to family firms that positively affect innovation, such as their long-term orientation (particularly because their family’s fortune, reputation and future are at stake), the rather informal knowledge sharing as well as stewardship behaviour (Miller et al. 2008;Zahra2012, Zellweger 2007).5But, at the same time, scholars have also ascribed some characteristics to family firms that negatively affect innovation (Aiello et al. 2020; Hu and Hughes 2020). For example, these include 5Although, these characteristics may contribute in different degrees to the emergence of incremental and radical innovations (Nieto et al., 2015). K Familiar but also radical? The moderating role of regional clusters for family firms in the... 21 the frequently observed risk aversion of family firms due to concerns about wealth preservation, parental altruism, which may favour the hiring of family members resulting potentially in a shortage of qualified managers, and the overall rather low innovation capabilities (De Massis et al. 2014; Gómez-Mejía et al. 2007; Sciascia et al. 2015; Sirmon and Hitt 2003). Despite this variety of arguments (going in both directions), in the case of radical innovations, family firms are here assumed to be less likely to achieve them. In line with the argumentation by Nieto et al. (2015), it is reasonable to assume that particularly the risk aversion and the desire to preserve socio-emotional wealth hinder the engagement in explorative search processes. Regarding the latter aspect, contrary to non-family firms, family firms are confronted with the tension between economic and non-economic goals. These non-economic goals encompass among other aspects the wealth preservation for future generations, constant control over the company, a strong family identity and intrafamily succession, as well as the preservation of binding social ties to clients and suppliers (Filser et al. 2018; Gómez-Mejía et al. 2007; Nieto et al. 2015). Since radical innovations rely on the pioneering recombination of former unconnected knowledge pieces, which is accompanied by uncertainty and risk (Fleming 2001;Nerkar2003) as well as potential disruption to previous social ties (Nieto et al. 2015), it is likely that family firms are relatively reluctant to introducing radical innovations. Thus, the following hypothesis is proposed: Hypothesis 1: Family firms are less likely to develop radical innovations than nonfamily firms. 2.2 Regional clusters and radical innovations in family firms While the geographical concentration of economic activities and the possible economic effects have fascinated researchers from multiples disciplines (Grashof 2020), at least since Marshall (1920), in the case of family business research the regional context has often been neglected (Basco 2015). Recently, however, there have been efforts to link family businesses with the regional context (e.g. Basco et al. 2021b; Basco and Suwala 2020). In this context, based on the notion of the regional familiness approach6(Basco 2015), it has been argued that the relatively high local embeddedness of family firms allows them to better exploit the proximity dimensions of the corresponding regional context (Basco et al. 2021a, Boschma 2005).7 This goes in line with previous research showing that the sole location in regional clusters is not sufficient to actually benefit from potential localization externalities, 6Regional familiness is originally definied as (...) the embeddedness of family businesses in social, economic, and productive structures within the spatial context and the type of connections that emerge and interact with regional factors (i.e., tangible and intangible factors) and regional processes (e.g., spillovers, information exchange, learning processes, social interactions, competition dynamics, and institutional dynamics) through regional proximity dimensions (i.e., relational, institutional, organizational, social, and cognitive proximity) (Basco 2015, p. 260). 7As described by Basco and Suwala (2021), there is a research tradition to address family firms in regional studies in the case of Industrial districts, which has only recently been taking up again (e.g Cucculelli and Storai 2015; Pittino et al. 2021). K 22 N. Grashof such as knowledge spillovers (Grashof 2021; Hervas-Oliver et al. 2018). Through their strong regional ties and engagement, family firms are more strongly embedded in the corresponding regional innovation system than non-family firms (Basco et al. 2021a; Block and Spiegel 2013; Déniz and Suárez 2005), making them more likely to benefit from localization externalities, particularly from the supply-side ones (McCann and Folta 2008). As a result of their long-standing regional presence and their social relationships (Basco et al. 2021a), family firms can create a certain (regional) reputation in terms of trustworthiness, security and stability making them more likely to attract local talents from the specialised labour pool (Hauswald et al. 2016). Having such an access to the specialized labour pool can potentially lead to radically new ideas, as the expertise of local human resources can challenge conventional processes and mindsets (Bekkers and Freitas 2008; Grashof et al. 2019; Zucker et al. 2002).8Moreover, the high degree of regional embeddedness and longterm orientation have both the potential to reduce the transaction and coordination costs for cooperation, e.g. with (local) suppliers, leading ultimately to more (trustful) relationships and more knowledge exchange (Block and Spiegel 2013). The access to these knowledge spillovers can be used to enrich in-house knowledge and thereby create rather radical new knowledge (Dong et al. 2017; Faems et al. 2005). This holds particularly true, since the pronounced social proximity may additionally allow to overcome the challenges associated with cooperating with cognitive distant partners (Adjei et al. 2019;Boschma2005). Furthermore, due to the rather close, trustful and long-term oriented relationships of family firms (Block and Spiegel 2013; Miller et al. 2008) it is also likely that particular tacit knowledge is exchanged (Adjei et al. 2019), which is relevant for the creation of radical innovations (Audretsch 1998; Mascitelli 2000). However, it has also been suggested that, over time, firms located within clusters may face (cognitive) inertia with respect to market and technological change, which hampers radical innovation (Hassink 2007; Pouder and St John 1996; Schamp 2005). Furthermore, when local networks heavily depend on local face-to-face contacts and tacit knowledge, they become more susceptible to lock-in situations, thereby perpetuating the inertia of firms situated within clusters (Boschma 2005; Martin and Sunley 2003). Moreover, in the case of industrial districts it has recently been shown that the potential locational advantages do not necessarily match well with the firm-specific advantages of family firms due to redundancies, but instead may even decrease the financial performance of family firms (Cucculelli and Storai 2015; Pittino et al. 2021). Nevertheless, it is important to note that industrial districts represent a special form of a regional cluster, with a particular emphasis on the social dimension, thereby making redundancies with the firm-specific advantages of family firms more likely (Grashof and Fornahl 2021; Pittino et al. 2021). Additionally, this study focuses on innovation rather than financial performance, unlike Cucculelli and Storai (2015) as well as Pittino et al. (2021), which are not necessarily intertwined (Grashof 8However, it has also been highlighted that family firms tend to hire employees with a similar cognitive background, which would of course limit or even offset the benefits from the incorporated knowledge of new employees (Brinkerink 2018). K Familiar but also radical? The moderating role of regional clusters for family firms in the... 23 and Fornahl 2021). For the concrete research context of this study, it is therefore reasonable to assume that family firms, which are more locally embedded than non-family firms, are better capable to exploit the localization externalities within regional clusters (Basco 2015; Basco et al. 2021a), which have been shown to rather promote the emergence of radical innovations (e.g. Grashof et al. 2019). Thus, the following hypothesis is proposed: Hypothesis 2: Being located in a regional cluster increases the likelihood for family firms to create radical innovations (compared to family firms located outside regional clusters). 2.3 Firm size and radical innovations in family firms However, family firms are of course not a homogenous group (Corbetta and Salvato 2004; Filser et al. 2018). In line with the resource-based view9(RBV), they differ in terms of their resources10 and capabilities (Barney 1991;Werneretal.2018). One prominent aspect that has been frequently considered in this context is the size of firms (Nieto et al., 2015). Prior research has shown that firm size is a crucial factor in driving overall innovation in firms (e.g. Cohen and Klepper 1996). Nevertheless, when it comes to radical innovations, there are rather few findings and these are generally inconsistent (Chandy and Tellis 2000). For example, in their recent study about the influence of AI knowledge on the emergence of radical innovations in firms, Grashof and Kopka (2023) found different results with respect to the role of firm size depending on the underlying features of AI technologies (AI applications vs. AI techniques). In general, there exist arguments in favour and against a positive influence of firm size on the emergence of radical innovations. While large firms have, on the one hand, more (financial) resources and (technical) capabilities to actually develop this type of innovation, on the other hand, they are more likely to face inertia due their complex internal structure, making them rather inflexible to new (rather radical) ideas (Chandy and Tellis 2000; Colombo et al. 2015). In the case of family firms, firm size has also been shown to matter for firm performance in general (e.g. Cucculelli and Storai 2015) and firm innovativeness in particular (e.g. Werner et al. 2018). However, as described in section 2.1., family firms have some unique characteristics that could enhance or mitigate the firm size effect compared to non-family firms. Recent empirical studies for instance show that small family firms are more innovative than small non-family firms, while 9The Resource-Based View (RBV) assumes that resources are unequally distributed among firms and are immobile, resulting in varying resource endowments and their persistence over time. As a result of this imbalance, firms can potentially achieve a resource-based competitive advantage by leveraging their internal resource base. Therefore, the central concept of the RBV focuses on how firms can utilize their resources to gain a competitive advantage (Barney 1991; Grashof and Kopka 2023;Newbert2007). 10 Following the widely used definition by Barney (1991), resources are here defined as “(...) all assets, capabilities, organizational processes, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness.” (Barney 1991, p. 101). K 24 N. Grashof the opposite holds true for larger firms (Werner et al. 2018). Whether this also applies to radical innovations is still unclear. In general, it has been argued that the tendency of family firms to keep the control of the business in family hands (Aiello et al. 2020; Sirmon and Hitt 2003), might imply relatively high agency costs (e.g. implementation of an incentive system) when they are large. Contrary, smaller family firms do not face these agency costs, as influential management positions can be filled with family members, thereby providing them with more financial resources that can be invested in innovative activities (Werner et al. 2018). Moreover, in light of the relatively high level of ownership concentration within family firms it is also likely that a relatively large and complex internal structure will slow down decision making processes, thereby enforcing the inertia and rigidity of large family firms (Aiello et al. 2020;Werneretal.2018). In addition, as family firms are less likely to use risky financial capital to avoid losing control, they may have more problems than (large) non-family firms in financing their growth and related innovation activities (Aiello et al. 2022; Gómez-Mejía et al. 2007; Kets de Vries 1993). Despite the potential advantages in terms of more (financial) resources and (technical) capabilities, in the specific case of family firms it is therefore reasonable to assume that the potential disadvantages in terms of inflexibility outweigh, so that smaller family firms are better able to introduce radical innovations than non-family firms, while large family firms are less able to do so. Thus, the following hypothesis is proposed: Hypothesis 3: Firm size inhibits the creation of radical innovations in family firms more than in the case of non-family firms. 3 Empirical background 3.1 Data The sample for the empirical analysis is constructed by using several data sources. In particular, similar to previous approaches (e.g. Grashof et al. 2020), this study combines firm-level information (e.g. ownership information) from the ORBIS database, offered by Bureau van Dijk (BvD), and information on inventive activities (e.g. technology classes) from the PATSTAT database.11 The resulting data set contains detailed information about 10,596 actively patenting (i.e. at least one patent filed) organisations in Germany between 2012 and 2020, of which 8.75% actually filed radical patents.12 In particular, non-family firms are relatively well represented, with almost twice as many non-family firms (604) filing radical patents as family firms (323). 11 To match the patent data with the firm-level data from ORBIS, a unique patent identifier is created based on information from PATSTAT. 12 Table 3in the appendix reports the distribution across industries. K Familiar but also radical? The moderating role of regional clusters for family firms in the... 31 Table 2 Regression results: Family firms and radical innovation Dependent variable rad_count rad_count rad_count rad_count (1.) (2.) (3.) (4.) Family_dummy – –0.521*** –0.746*** 0.264 – (0.158) (0.134) (0.358) Cluster_dummy – – 0.535** – – – (0.254) – Family_dummy* cluster_dummy – – 0.757* – – – (0.436) – Log_size_mean –––0.392*** –––(0.044) Family_dummy* log_size_mean – – – –0.066 –––(0.057) Age_mean 0.012*** 0.012*** 0.009*** 0.001 (0.002) (0.002) (0.002) (0.001) Independence_dummy 1.075*** 0.923*** 0.986*** 0.437** (0.295) (0.298) (0.305) (0.209) researchintensiveindustry 0.282* 0.251 0.181 –0.034 (0.151) (0.154) (0.130) (0.099) Popdens_mean 0.0002** 0.0001* 0.0001 0.00004 (0.0001) (0.0001) (0.0001) (0.0001) Share_academics_mean 0.054** 0.048** 0.055*** 0.022 (0.022) (0.020) (0.019) (0.016) Constant –2.536*** –2.113*** –2.272*** –2.983*** (0.811) (0.741) (0.723) (0.848) Inflate: pat_count –0.249*** –0.024*** –0.231*** –0.212*** (0.032) (0.033) (0.032) (0.039) Constant 2.864*** 2.779*** 2.748*** 2.540*** (0.094) (0.098) (0.096) (0.105) N 10,431 10,431 10,431 10,293 McFadden’s Adj R2 0.184 0.186 0.191 0.225 Log-likelihood –3854.557 –3840.852 –3813.604 –3621.056 Akaike Inf. Crit.*N7727 7702 7661 7270 Robust standard errors in parentheses *p< 0.10, ** p< 0.05, *** p<0.01 locating in highly urbanized regions (at least in Model 1 and 2) is advantageous for the creation of radical innovation by companies. This can eventually be explained by the large diversity of different actors and knowledge within these regions which offers a rather large potential for knowledge recombination (Hesse and Fornahl 2020). In addition, the proportion of average share of persons with tertiary education and/or with S&T occupation in the region, proxying the regional absorptive capacity, seem to matter for the emergence of radical innovation, which is line with previous studies (e.g. Hesse 2020). K 32 N. Grashof Model 2 introduces the family firm dummy variable (family_dummy). As assumed (see Hypothesis 1), a significant negative influence is found. Family firms are therefore on average less capable of creating radical innovations than non-family firms. This can be explained by the risk aversion and the desire to preserve socio-emotional wealth which both hinder the engagement in explorative search processes and thereby the creation of radical innovations (Nieto et al. 2015). Hence, Hypothesis 1 cannot be rejected. By following an “interactionist approach” (Beugelsdijk 2007), the potential moderating role of the regional context is additionally investigated in Model 3, where a dummy variable for firms’ location in a regional cluster is introduced. As indicated by the significant positive interaction term (β= 0.757; p= 0.082), being located in a cluster increases the likelihood for family firms to create radical innovations.25 Due to their active participation and close connections within their region, family firms are more strongly embedded in the regional innovation system compared to non-family firms (Basco et al. 2021a; Block and Spiegel 2013; Déniz and Suárez 2005). As a result, they are more likely to reap the benefits of localization externalities within regional clusters (Basco 2015; Basco et al. 2021a), which have been shown to foster the emergence of radical innovation (e.g. Grashof et al. 2019). Consequently, it can be resumed that Hypothesis 2 cannot be rejected. Since not all family firms belong to a homogenous group (e.g. Filser et al. 2018), in Model 4, the moderating influence of firm size is tested. While the influence of firm size takes the assumed negative direction, it is however insignificant. Firm size therefore does not significantly inhibit the creation of radical innovations in family firms more than in non-family firms.26 Instead, in the case of non-family firms, we even find evidence for a significant positive influence of firm size, meaning that larger non-family firms are better capable of generating radical innovation. Indeed, in both cases a significant positive coefficient is found for firm size (see Fig. 3in the Appendix). For non-family firms the average marginal effect (AME) is 0.107, while for family firms it is slightly lower (AME is 0.073), although still highly significant. Consequently, Hypothesis 3 has to be rejected. Overall, the results show that, on average, family businesses are less likely to produce radical innovation than non-family businesses. However, the corresponding regional context matters in this context. Family firms that are located in regional clusters can create more radical innovation, since they can exploit the advantages of localization externalities through their strong regional embeddedness. 5Conclusion Despite the relatively extensive research on innovation in family firms (e.g. Calabrò et al. 2019), it remains unclear whether family firms have a greater likelihood to 25 In general, 9.77% of all family firms in the sample (corresponding to 568 firms) are located in regional clusters. Of these family firms, 9.33% (corresponding to 53 family firms) created radical innovations, compared to 5.15% (corresponding to 270 family firms) in the case of non-clustered family firms. 26 The corresponding predicted margins are illustrated in Fig. 2in the Appendix. K Familiar but also radical? The moderating role of regional clusters for family firms in the... 33 generate radical innovation and to what extent contextual variables moderate this relationship. This paper addressed these two research gaps by combining several data sources to empirically examine the relationship between family firms and radical innovation as well as the moderating effects of regional clusters and firm size. In summary, the study provides three main results. First, it shows that, on average, family firms create less radical innovation than non-family firms. Their risk aversion and desire to preserve socio-emotional wealth may act in this context as obstacles to engaging in exploratory search processes, which ultimately hinders the creation of radical innovation (Nieto et al. 2015). Second, the study shows that the specific context in regional clusters can moderate this relationship. On average, being located in a cluster favors the emergence of radical innovations in family businesses. Clusters therefore constitute a beneficial environment (e.g. through their localization externalities) for family firms. The potential substitution effect between the rather redundant conditions enhancing the advantages of industrial districts and family firms shown in recent studies (e.g. Cucculelli and Storai 2015; Pittino et al. 2021) can therefore not be confirmed for the more general context of regional clusters. Unlike in the case of industrial districts, regional clusters do not necessarily build on the social and cultural dimension (Grashof and Fornahl 2021). Hence, it seems promising for future research to further differentiate regional clusters according to their characteristics (e.g. size, share of SMEs, strength of social and cultural background). Third, the study shows that, unlike innovation in general (e.g. Werner et al. 2018), the size of firms does not significantly hinder the creation of radical innovations in family firms more than in non-family firms. Instead, it is revealed that in both cases larger firms are more likely to file new radical patents, which is consistent with previous research (e.g. Grashof and Kopka 2023) that explains this by the fact that larger firms can benefit from more internal R&D resources (OrtegaArgilés et al. 2009; Rammer and Schubert 2016). Nevertheless, this study does not come without limitations, thereby offering additional opportunities for further research. First of all, the underlying data base for determining radical innovations and knowledge variety are patents, which have some drawbacks (e.g. Griliches 1990). Future research could therefore use alternative, non-patent-based data (e.g. Hervas-Oliver et al. 2019). Additionally, future research could also investigate alternative patent-based measures for the emergence of radical innovation, e.g. backward citations, and its impact and diffusion, e.g. forward citations (Dahlin and Behrens 2005; Trajtenberg et al. 1997). Moreover, due to data constraints (with respect to the identification of clusters and family firms), the corresponding empirical analysis is only based on pooled cross-sectional data, which raises potential concerns of endogeneity (e.g. Block and Spiegel 2013; Grashof et al. 2019). Future research may therefore use panel data in order to determine dynamic effects. In this context, it is also useful for future studies to additionally examine other types of regions (e.g. rural vs. urban regions) in order to further disentangle the cluster effect. Furthermore, similar to previous studies (e.g. Block and Spiegel 2013), due to data availability only information on family ownership is used to determine family firms. Future studies may consider the direct influence of family members in terms of firm management or supervisory board. In this context, it also seems interesting to additionally consider the influence of the individual characterisK 34 N. Grashof tics of the owners, especially between different generations in leadership (De Massis et al. 2012). Lastly, the analysis is limited to the high-tech and polycentric country Germany. The consideration of further countries with different levels of economic development and regional structure could be taken up by future studies to control for potential country effects. Nevertheless, despite these limitations all in all it can be resumed that the results about the relationship between family firms and radical innovations contribute to the family firm, regional and innovation economics literature. The article provides insights about the emergence of radical innovation on the firm-level by examining the corresponding role of family firms in Germany and the moderating role of regional clusters and firm size. In doing so, it extends recent studies in innovation economics that have examined radical innovation across different types of firms (e.g. Grashof and Kopka 2023). Furthermore, it contributes to the regional studies literature by showing that the cluster-specific benefits do not accrue to all types of firms, but rather to specific ones (such as family firms), which adds to previous studies that attempt to explain the heterogenous firm-specific performance effects of being located in regional clusters (e.g. Hervas-Oliver et al. 2018). At the same time, it also contributes to the family business studies literature by stressing the relevance of the regional context when examining the (innovative) performance of family firms, which supports recent efforts to consider contextual heterogeneity in family business research (e.g. Basco et al. 2021a). In addition to the scientific contribution, the results also offer relevant policy implications. The results on the lower average radical patent activities in family businesses require special attention in the form of policy measures that should primarily address the risk avoidance of family businesses, such as technical services and advice that can provide an external perspective and reduce the risk of potentially misleading investments (Jones and Grimshaw 2016; Shapira and Youtie 2016). Furthermore, by finding evidence for a moderating influence of the location of firms in a regional cluster, this study additionally shows that the relationship between family firms and radical innovation is far more complex than often assumed. Hence, there is a need for a more differentiated view on family firms that also reflects this heterogeneity (De Massis et al. 2012). By following such an approach, family firms can also successfully become more radical in their innovation process. K Familiar but also radical? The moderating role of regional clusters for family firms in the... 35 6 Appendix Table 3 Industrial distribution of sample and share of dependent variable NACE Activity No. of firms Share of radical patents (in %) 1 Crop and animal production, hunting and related service activities 10 2.27 3 Fishing and aquaculture 1 0.00 5 Mining of coal and lignite 1 0.00 6 Extraction of crude petroleum and natural gas 1 0.00 7 Mining of metal ores 1 0.00 8 Other mining and quarrying 16 4.61 10 Manufacture of food products 92 3.48 11 Manufacture of beverages 6 1.49 12 Manufacture of tobacco products 1 12.50 13 Manufacture of textiles 102 2.58 14 Manufacture of wearing apparel 17 11.08 15 Manufacture of leather and related products 20 3.64 16 Manufacture of wood and of products of wood and cork. except furniture; manufacture of articles of straw and plaiting materials 77 2.86 17 Manufacture of paper and paper products 76 2.47 18 Printing and reproduction of recorded media 34 0.90 19 Manufacture of coke and refined petroleum products 8 8.62 20 Manufacture of chemicals and chemical products 248 2.01 21 Manufacture of basic pharmaceutical products and pharmaceutical preparations 107 1.35 22 Manufacture of rubber and plastic products 391 1.05 23 Manufacture of other non-metallic mineral products 157 3.66 24 Manufacture of basic metals 113 2.90 25 Manufacture of fabricated metal products. except machinery and equipment 741 2.61 26 Manufacture of computer. electronic and optical products 707 2.23 27 Manufacture of electrical equipment 389 1.66 28 Manufacture of machinery and equipment n.e.c. 1339 2.34 29 Manufacture of motor vehicles. trailers and semi-trailers 152 1.64 30 Manufacture of other transport equipment 79 4.60 31 Manufacture of furniture 83 1.01 32 Other manufacturing 385 2.10 33 Repair and installation of machinery and equipment 58 2.26 35 Electricity. gas. steam and air conditioning supply 59 5.43 K 36 N. Grashof Table 3 (Continued) NACE Activity No. of firms Share of radical patents (in %) 36 Water collection. treatment and supply 4 0.00 37 Sewerage 7 8.33 38 Waste collection. treatment and disposal activities; materials recovery 35 2.08 39 Remediation activities and other waste management services 3 0.00 41 Construction of buildings 44 1.52 42 Civil engineering 37 1.02 43 Specialised construction activities 278 2.22 45 Wholesale and retail trade and repair of motor vehicles and motorcycles 82 1.99 46 Wholesale trade. except of motor vehicles and motorcycles 1269 1.57 47 Retail trade. except of motor vehicles and motorcycles 275 2.40 49 Land transport and transport via pipelines 29 3.92 50 Water transport 3 16.67 51 Air transport 1 0.00 52 Warehousing and support activities for transportation 37 4.08 53 Postal and courier activities 3 3.29 55 Accommodation 1 0.00 56 Food and beverage service activities 11 0.00 58 Publishing activities 7 0.00 59 Motion picture. video and television programme production. sound recording and music publishing activities 67.69 60 Programming and broadcasting activities 1 0.00 61 Telecommunications 14 0.27 62 Computer programming. consultancy and related activities 408 1.78 63 Information service activities 23 0.00 64 Financial service activities. except insurance and pension funding 232 2.05 66 Activities auxiliary to financial services and insurance activities 39 1.91 68 Real estate activities 157 2.47 69 Legal and accounting activities 9 1.64 70 Activities of head offices; management consultancy activities 481 2.16 71 Architectural and engineering activities; technical testing and analysis 583 3.03 72 Scientific research and development 420 2.08 73 Advertising and market research 26 0.00 74 Other professional. scientific and technical activities 94 3.13 75 Veterinary activities 2 0.00 77 Rental and leasing activities 48 3.73 78 Employment activities 8 0.00 79 Travel agency. tour operator and other reservation service and related activities 70.00 K Familiar but also radical? The moderating role of regional clusters for family firms in the... 37 Table 3 (Continued) NACE Activity No. of firms Share of radical patents (in %) 80 Security and investigation activities 1 0.00 81 Services to buildings and landscape activities 34 3.33 82 Office administrative. office support and other business support activities 185 4.04 84 Public administration and defence; compulsory social security 4 0.00 85 Education 24 1.30 86 Human health activities 42 0.64 87 Residential care activities 3 0.00 88 Social work activities without accommodation 7 18.18 90 Creative. arts and entertainment activities 3 0.00 92 Gambling and betting activities 4 0.00 93 Sports activities and amusement and recreation activities 9 0.00 94 Activities of membership organisations 13 1.79 95 Repair of computers and personal and household goods 1 0.00 96 Other personal service activities 111 1.99 K 38 N. Grashof Table 4 Pairwise correlation matrix Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) rad_count 1.000 –––––––– (2) family_dummy –0.046*** 1.000 – – – – – – – (3) cluster_dummy 0.050*** –0.035*** 1.000 – – – – – – (4) age_mean 0.090*** –0.098*** 0.153*** 1.000 – – – – – (5) independence_dummy 0.099*** –0.171*** 0.022** 0.150*** 1.000 – – – – (6) researchintensiveindustry 0.045*** –0.096*** 0.061*** 0.071*** 0.031*** 1.000 – – – (7) popdens_mean 0.018* –0.095*** –0.098*** –0.045*** 0.035*** –0.032*** 1.000 – – (8) share_academics_mean 0.030*** –0.046*** –0.058*** –0.107*** 0.020** 0.010 0.307*** 1.000 – (9) log_size_mean 0.169*** –0.381*** 0.164*** 0.383*** 0.148*** 0.181*** –0.019* –0.068*** 1.000 *** p < 0.01, ** p< 0.05, * p< 0.1 K Familiar but also radical? The moderating role of regional clusters for family firms in the... 39 Table 5 Regression results (with mean number of radical and non-radical patents) Dependent variable rad_mean rad_mean rad_mean rad_mean (1.) (2.) (3.) (4.) Family_dummy – –0.988*** –1.278*** –0.088 – (0.205) (0.147) (0.383) Cluster_dummy – – 0.858*** – – – (0.285) – Family_dummy* cluster_dummy – – 0.894** – – – (0.447) – Log_size_mean –––0.685*** –––(0.042) Family_dummy* log_size_mean – – – –0.006 –––(0.068) Age_mean 0.015*** 0.014*** 0.012*** 0.001 (0.003) (0.003) (0.002) (0.001) Independence_dummy 1.511*** 1.165*** 1.244*** 0.363 (0.336) (0.339) (0.002) (0.225) Researchintensiveindustry 0.886*** 0.820*** 0.743*** 0.435*** (0.182) (0.179) (0.166) (0.141) Popdens_mean 0.0002* 0.0001 0.0001* 0.00003 (0.0001) (0.0001) (0.0001) (0.0001) Share_academics_mean 0.075*** 0.074*** 0.081*** 0.049*** (0.026) (0.026) (0.027) (0.018) Constant –7.444*** –6.963*** –7.277*** –9.336*** (0.966) (0.977) (1.005) (0.773) Inflate: pat_mean 0.0001 0.0002 0.0007 0.001** (0.001) (0.001) (0.001) (0.001) Constant –24.907*** –23.219*** –23.475*** –26.534*** (0.274) (0.153) (0.077) (0.058) N 10,431 10,431 10,431 10,293 McFadden’s Adj R2 0.157 0.174 0.193 0.378 Log-likelihood –1367.406 –1338.237 –1300.830 –992.307 Akaike Inf. Crit.*N2753 2697 2636 2013 Robust standard errors in parentheses *p< 0.10, ** p< 0.05, *** p<0.01 K 40 N. Grashof Table 6 Regression results (for t1: 2012–2016; t2: 2016–2020) (a) Time period t1: 2012–2016 Dependent variable rad_count rad_count rad_count rad_count (1.) (2.) (3.) (4.) Family_dummy – –0.486*** –0.719*** 0.097 – (0.167) (0.138) (0.513) Cluster_dummy – – 0.520* – – – (0.270) – Family_dummy* cluster_dummy – – 0.739* – – – (0.442) – Log_size_mean – – – 0.375*** – – – (0.063) Family_dummy* log_size_mean – – – –0.035 – – – (0.074) Age_mean 0.011*** 0.012*** 0.009*** –0.0002 (0.002) (0.002) (0.002) (0.001) Independence_dummy 0.100*** 0.859*** 0.937*** 0.408 (0.300) (0.304) (0.320) (0.262) Researchintensiveindustry 0.245 0.216 0.148 –0.054 (0.156) (0.156) (0.132) (0.116) Popdens_mean 0.0002** 0.0002* 0.0001 0.00004 (0.0001) (0.0001) (0.0001) (0.0001) Share_academics_mean 0.062*** 0.056*** 0.064*** 0.035* (0.022) (0.021) (0.020) (0.020) Constant –2.978*** –2.609*** –2.796*** –3.489*** (0.836) (0.783) (0.767) (1.147) Inflate: pat_count –0.384*** –0.375*** –0.367*** –0.253*** (0.059) (0.060) (0.061) (0.069) Constant 3.287*** 3.204*** 3.167*** 2.809*** (0.119) (0.124) (0.122) (0.151) N 10,431 10,431 10,431 6,812 McFadden’s Adj R2 0.212 0.214 0.218 0.242 Log-likelihood –2926.274 –2917.165 –2896.779 –2234.260 Akaike Inf. Crit.*N5871 5854 5828 4497 K Familiar but also radical? The moderating role of regional clusters for family firms in the... 47 Ebert T, Gebauer JE, Brenner T, Bleidorn W, Gosling SD, Potter J, Rentfrow PJ (2019) Are regional differences in personality and their correlates robust? Applying spatial analysis techniques to examine regional variation in personality across the US and Germany. Working Papers on Innovation and Space, No. 05.19. Philipps University, Marburg European Cluster Observatory (2018) Methodology—indicators. http://www.clusterobservatory.eu/index. html#!view=aboutobservatory;url=/aboutobservatory/methodology/indicators/. Accessed 16 July 2023 European Commission (2008) The concept of clusters and cluster policies and their role for competitiveness and innovation: Main statistical results and lessons learned. Europe INNOVA / PROINNO Europe paper N° 9. European Family Businesses (2021) About European family businesses. https://europeanfamilybusinesses .eu/about-european-family-businesses/. Accessed 15 Nov 2021 Faems D, Van Looy B, Debackere K (2005) Interorganizational collaboration and innovation: toward a portfolio approach. J Prod Innov Manag 22(3):238–250 Filser M, De Massis A, Gast J, Kraus S, Niemand T (2018) Tracing the roots of innovativeness in family SMEs: the effect of family functionality and socioemotional wealth. J Prod Innov Manag 35(4):609–628 Fleming L (2001) Recombinant uncertainty in technological search. Manage Sci 47(1):117–132 Fleming L (2007) Breakthroughs and the long tail of innovations. MIT Sloan Manage Rev 49(1):1–11 Gehrke B, Frietsch R, Neuhäusler P, Rammer C, Leidmann M (2013) Neuabgrenzung forschungsintensiver Industrien und Güter: NIW/ISI/ZEW-Listen 2012 (No. 8-2013) (Studien zum deutschen Innovationssystem) Gómez-Mejía LR, Haynes KT, Núñez-Nickel M, Jacobson KJL, Moyano-Fuentes J (2007) Socioemotional wealth and business risks in family-controlled firms: evidence from Spanish olive oil mills. Adm Sci Q 52(1):106–137 Grashof N (2020) Firm-specific cluster effects: a meta-analysis. Pap Reg Sci 99(5):1237–1260 Grashof N (2021) Spill over or Spill out?—A multilevel analysis of the cluster and firm performance relationship. Ind Innov 28(10):1298–1331 Grashof N, Brenner T (2021) Radical innovations in clusters: the role of cluster internal and external relationships. In: Fornahl D, Grashof N (eds) The globalization of regional clusters: between localization and internationalization. Edward Elgar Publishing, Cheltenham, pp 189–209 Grashof N, Fornahl D (2021) “To be or not to be” located in a cluster?—A descriptive meta-analysis of the firm-specific cluster effect. Ann Reg Sci 67(3):541–591 Grashof N, Kopka A (2023) Artificial intelligence and radical innovation: an opportunity for all companies? Small Bus Econ 61(2):771–797 Grashof N, Hesse K, Fornahl D (2019) Radical or not? The role of clusters in the emergence of radical innovations. Eur Plan Stud 27(10):1904–1923 Grashof N, Kopka A, Wessendorf C, Fornahl D (2020) Industry 4.0 and clusters: complementaries or substitutes in firm’s knowledge creation? Compet Rev 31(1):83–105 Greene WH (1994) Accounting for excess zeros and sample selection in poisson and negative binomial regression models. Stern School of Business working paper no. 94–10 Griliches Z (1990) Patent statistics as economic indicators: a survey. J Econ Lit 28(4):1661–1707 Grillitsch M, Asheim B, Trippl M (2018) Unrelated knowledge combinations: the unexplored potential for regional industrial path development. Camb J Reg Econ Soc 11(2):257–274 Hassink R (2007) The strength of weak lock-ins: the renewal of the Westmünsterland textile industry. Environ Plan A 39(5):1147–1165 Hauswald H, Hack A, Kellermanns FW, Patzelt H (2016) Attracting new talent to family firms: who is attracted and under what conditions? Entrepreneursh Theory Pract 40(5):963–989 Henderson RM, Clark KB (1990) Architectural innovation: the reconfiguration of existing product technologies and the failure of established firms. Adm Sci Q 35(1):9–30 Herriott SR, Levinthal D, March JG (1985) Learning from experience in organizations. Am Econ Rev 75(2):298–302 Hervas-Oliver JL, Sempere-Ripoll F, Rojas Alvarado R, Estelles-Miguel S (2018) Agglomerations and firm performance: who benefits and how much? Reg Stud 52(3):338–349 Hervas-Oliver JL, Sempere-Ripoll F, Estelles-Miguel S, Rojas-Alvarado R (2019) Radical vs incremental innovation in Marshallian Industrial Districts in the Valencian Region: what prevails? Eur Plan Stud 27(10):1924–1939 Hesse K (2020) Related to whom? The impact of relatedness to regional frontier firms on radical innovations. Bremen Papers on Economics & Innovation. Universität Bremen K 48 N. Grashof Hesse K, Fornahl D (2020) Essential ingredients for radical innovations? The role of (un-)related variety and external linkages in Germany. Pap Reg Sci 99(5):1165–1183 Hu Q, Hughes M (2020) Radical innovation in family firms: a systematic analysis and research agenda. Int J Entrepreneurial Behav Res 26(6):1199–1234 Jones BF (2009) The burden of knowledge and the “death of the renaissance man”: Is innovation getting harder? Review of Economic Studies 76(1):283–317 Jones B, Grimshaw D (2016) The impact of skill formation policies on innovation. In: Edler J, Cunningham P, Gök A, Shapira P (eds) Handbook of innovation policy impact. Edward Elgar Publishing, pp 108–128 Kets de Vries MF (1993) The dynamics of family controlled firms: The good and the bad news. Organ Dyn 21(3):59–71 Lee C, Lee K, Pennings JM (2001) Internal capabilities, external networks, and performance: a study on technology-based ventures. Strateg Manag J 22(6-7):615–640 Levitt B, March JG (1988) Organizational learning. Annu Rev Sociol 14(1):319–338 Majumdar SK (1997) The impact of size and age on firm-level performance: some evidence from India. Rev Ind Organ 12:231–241 March JG (1991) Exploration and exploitation in organizational learning. Organ Sci 2(1):71–87 Marshall A (1920) Principles of economics, 8th edn. Macmillan, London Martin R, Sunley P (2003) Deconstructing clusters: chaotic concept or policy panacea? J Econ Geogr 3(1):5–35 Mascitelli R (2000) From experience: Harnessing tacit knowledge to achieve breakthrough innovation. J Prod Innov Manag 17(3):179–193 McCann BT, Folta TB (2008) Location matters: where we have been and where we might go in agglomeration research. J Manage 34(3):532–565 Mewes L (2019) Scaling of atypical knowledge combinations in American metropolitan areas from 1836 to 2010. Econ Geog 95(4):341–361 Mewes L, Broekel T (2020) Subsidized to change? The impact of R&D policy on regional technological diversification. Ann Reg Sci 65:221–252 Miller D, Le Breton-Miller I, Scholnick B (2008) Stewardship vs. stagnation: an empirical comparison of small family and non-family businesses. J Management Studies 45(1):50–78 Nerkar A (2003) Old is gold? The value of temporal exploration in the creation of new knowledge. Manage Sci 49(2):211–229 Newbert SL (2007) Empirical research on the resource-based view of the firm: an assessment and suggestions for future research. Strateg Manag J 28(2):121–146 Nieto MJ, Santamaria L, Fernandez Z (2015) Understanding the innovation behavior of family firms. Small Bus Econ 53(2):382–399 Ortega-Argilés R, Vivarelli M, Voigt P (2009) R&D in SMEs: a paradox? Small Bus Econ 33(1):3–11. https://doi.org/10.1007/s11187-009-9187-5 Park M, Leahey E, Funk RJ (2023) Papers and patents are becoming less disruptive over time. Nature 613(7942):138–144 Pittino D, Visintin F, Minichilli A, Compagno C (2021) Family involvement in governance and firm performance in industrial districts. The moderating role of the industry’s technological paradigm. Entrepreneursh Reg Dev 33(7-8):514–531 Pouder R, St John CH (1996) Hot spots and blind spots: Geographical clusters of firms and innovation. Acad Manage Rev 21(4):1192–1225 Rammer C, Schubert T (2016) Concentration on the few? R&D and innovation in German firms between 2001 and 2013. Fraunhofer ISI Discussion Papers-Innovation Systems and Policy Analysis (No. 54) Rigby DL, Brown WM (2015) Who benefits from agglomeration? Reg Stud 49(1):28–43 Schäfer D, Stephan A, Mosquera JS (2017) Family ownership: does it matter for funding and success of corporate innovations? Small Bus Econ 48:931–951 Schaffer ME, Stillman S (2010) xtoverid: stata module to calculate tests of overidentifying restrictions after xtreg, xtivreg, xtivreg2 and xthtaylor. http://ideas.repec.org/c/boc/bocode/s456779.html. Accessed 11 Jan 2021 Schamp EW (2005) Decline of the district, renewal of firms: an evolutionary approach to footwear production in the Pirmasens area, Germany. Environ Plan A 37(4):617–634 Schoenmakers W, Duysters G (2010) The technological origins of radical inventions. Res Policy 39(8):1051–1059 Scholl T, Brenner T (2016) Detecting spatial clustering using a firm-level cluster index. Reg Stud 50(6):1054–1068 K Familiar but also radical? The moderating role of regional clusters for family firms in the... 49 Schumpeter JA (1934) The theory of economic development. Harvard University Press, Cambridge Sciascia S, Nordqvist M, Mazzola P, De Massis A (2015) Family ownership and R&D intensity in smalland medium-sized firms. J Prod Innov Manag 32(3):349–360 Shapira P, Youtie J (2016) The impact of technology and innovation advisory services. In: Edler J, Cunningham P, Gök A, Shapira P (eds) Handbook of Innovation Policy Impact. Edward Elgar Publishing, pp 161–195 Shkolnykova M, Kudic M (2021) Who benefits from SMEs’ radical innovations?—empirical evidence from German biotechnology. Small Bus Econ. https://doi.org/10.1007/s11187-021-00464-x Sirmon DG, Hitt MA (2003) Managing resources: linking unique resources, management, and wealth creation in family firms. Entrepreneursh Theory Pract 27(4):339–358 Sraer D, Thesmar D (2007) Performance and behavior of family firms: evidence from the French stock market. J Eur Econ Assoc 5(4):709–751 Suwala L (2017) On creativity: from conceptual ideas towards a systemic understanding. In: Brydges T, Hracs BJ, Jansson J, Pasquinelli C, Power D, Sjöholm J (eds) European Colloquium on Culture, Creativity and Economy (CCE). Working Paper Compendium. Kunnskapsverket, Lillehammer, pp 82–111 Trajtenberg M, Henderson R, Jaffe A (1997) University versus corporate patents: A window on the basicness of invention. Econ Innov New Technol 5(1):19–50 Tushman ML, Anderson P (1986) Technological discontinuities and organizational environments. Adm Sci Q 31(3):439–465 Urbinati A, Franzò S, De Massis A, Frattini F (2017) Innovation in family firms: a review of prior studies and a framework for future research. In: Brem A, Viardot E (eds) Revolution of innovation management. Palgrave Macmillan, London, pp 213–246 Uzzi B, Mukherjee S, Stringer M, Jones B (2013) Atypical combinations and scientific impact. Science 342(6157):468–472 Verhoeven D, Bakker J, Veugelers R (2016) Measuring technological novelty with patent-based indicators. Res Policy 45(3):707–723 Verspagen B (2005) Innovation and economic growth. In: Fagerberg J, Mowery D, Nelson R (eds) The Oxford handbook of innovation. Oxford University Press, Oxford Weitzman ML (1998) Recombinant growth. Q J Econ 113(2):331–360 Werner A, Schröder C, Chlosta S (2018) Driving factors of innovation in family and non-family SMEs. Small Bus Econ 50(1):201–218 Wooldridge JM (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge Zahra SA (2012) Organizational learning and entrepreneurship in family firms: exploring the moderating effect of ownership and cohesion. Small Bus Econ 38(1):51–65 Zellweger TM (2007) Time horizon, costs of equity capital, and generic investment strategies of firms. Fam Bus Rev 20(1):1–15 Zucker LG, Darby MR, Torero M (2002) Labor mobility from academe to commerce. J Labor Econ 20(3):629–660 Zybura J, Zybura N, Ahrens JP, Woywode M (2021) Innovation in the post-succession phase of family firms: Family CEO successors and leadership constellations as resources. J Fam Bus Strateg 12(2):100336 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. K