New challenges in European innovation partnerships: SOEs, POEs and foreign MNEs during crises

Vol.:(0123456789) Eurasian Business Review (2024) 14:1053–1092 https://doi.org/10.1007/s40821-024-00279-2 REGULAR ARTICLE New challenges inEuropean innovation partnerships: SOEs, POEs andforeign MNEs duringcrises AntonioGarcía‑Sánchez1,3 · RuthRama2,3 Received: 25 November 2023 / Revised: 23 June 2024 / Accepted: 5 September 2024 / Published online: 3 November 2024 © The Author(s) 2024 Abstract Cooperation for innovation, not only enhances the innovative and economic performance of companies but also fosters growth and strengthens the resilience of firms in the realm of innovation activities. In this study, we delve into the cooperative activities of innovative Spanish companies with their European counterparts. We employ six logit models using panel data to scrutinize the impact of ownership on innovation cooperation and the determinants of collaboration across different phases of the business cycle, spanning from 2004 to 2016, which we divide into three sub-periods: the pre-crisis (2004–2007), the crisis (2008–2013), and the recovery (2014–2016). Throughout the businesses cycle, state-owned enterprises are the most prone to engage in cooperative innovation with European partners, while unaffiliated domestic firms are the least prone. Foreign subsidiaries clearly outperform unaffiliated domestic firms and slightly outperform domestic business groups in prosperous times, but do not outperform state-owned enterprises even during crises. The drivers of cooperation for innovation with European partners evolve over time, becoming particularly challenging during crises. The factors influencing cooperation with European universities exhibit distinct characteristics. The results contain policy and management implications. Keywords Cooperation for innovation· Industry-university cooperation· Internationalisation of R&D· Business cycle· Crisis· Foreign subsidiaries· Stateowned enterprises 1 Introduction Firms are increasingly engaging in cooperation for innovation1 with a variety of partners in order to access new technology, share expenditures, and reduce time to market. This strategy consists of the active participation of a firm in innovative activities Extended author information available on the last page of the article 1 Also termed in the literature and in this article as collaboration for innovation. 1054 Eurasian Business Review (2024) 14:1053–1092 carried out either with other companies or with institutions, such as universities. The literature suggests that this is a worthy strategy. Firms that engage in cooperation for innovation are more likely than others to be innovative, and even to perform better and grow faster (Archibugi etal., 2013; Arvanitis & Bolli, 2013; Cantabene & Grassi, 2022; Fernández-Sastre, 2012; Freire & Gonçalves, 2022; Radicic etal., 2019; Srholec, 2014; Tether, 2002; Trigo & Vence, 2012; Vega-Jurado etal., 2009). According to certain authors, collaborations positively impact the internal R&D of the cooperative2 firm, and vice versa (Colombo & Garrone, 1996; Veugelers, 1997). Most importantly, firms that are able to cooperate fare better than others during crises (D’Agostino & Moreno, 2018; Xie etal., 2022; Zouaghi etal., 2018). Even in the midst of the 2008 crisis, certain firms managed to boost their R&D investment for several reasons, one of which was their active participation in cooperation for innovation (Archibugi etal., 2013; Hansen & Nybakk, 2018; Holl & Rama, 2016; Paunov, 2012). Beyond its importance at the microeconomic level, cooperative innovation also contributes towards the development of the National Innovation System (NIS) and may be a tool of industrial policy (Freire & Gonçalves, 2022; Liu etal., 2017). International cooperation is often crucial for firms located in countries that are not at the forefront of science and technology since it enables them to access resources, knowledge, markets, and opportunities that may not be readily available within their own borders (Freire & Gonçalves, 2022). Collaboration with foreign universities may specifically help firms to transfer cutting-edge skills from academia into the industry. This scenario is particularly relevant for many peripheral European countries and emerging economies. Mostly based on empirical evidence provided by the Community Innovation Survey (CIS) of the European Union (EU), the rich literature has now substantially increased our understanding of cooperation for innovation. Nonetheless, at least two aspects deserve further consideration. Despite the potential benefits of cooperation, it remains uncertain whether firms can successfully partake in collaborative endeavours, and what distinguishes those that can surmount such challenges during times of crisis. The existing literature seldom addresses these questions, with only a few exceptions (García-Sánchez & Rama, 2020, 2022; Wang, 2021), as most of the available evidence on cooperation primarily pertains to “normal” phases of the business cycle. Srholec (2015) notes that the majority of studies focused on cooperation for innovation lean on cross-sectional evidence, thus obscuring our understanding of the dynamics involved. This concern is also echoed by other scholars in the field of cooperation (Bianchi etal., 2019; Friedberg & Neuville, 1999). The creation of innovative networks across Europe stands as a crucial strategy for the EU in the realms of innovation, reindustrialization, and reducing global dependence. This is especially pertinent in critical sectors like defence, semiconductors, and sustainable energy.3 However, there remains a notable lack of understanding concerning the dynamics of this cooperation. Therefore, there is a need for more longitudinal 2 In this article, the terms “cooperation” and “cooperative” refer to cooperation for innovation. 3 https:// commi ssion. europa. eu/ strat egy- and- policy/ prior ities- 2019- 2024/ europefit- digit alage/ europ eanchips- act_ April 2023. 1055 Eurasian Business Review (2024) 14:1053–1092 research in this area to address this limitation and gain a deeper understanding of the subject. Secondly, the association of ownership and cooperation in the context of innovation has garnered significant attention from researchers (Arvanitis & Bolli, 2013; Cozza etal., 2018; Dachs etal., 2008; Ebersberger etal., 2011; Holl & Rama, 2014; Srholec, 2009; Veugelers & Cassiman, 2004). However, it remains incomplete as it seldom incorporates state-ownership into its analysis (García-Sánchez & Rama, 2022; Roud & Vlasova, 2020; Wang, 2021). Addressing this gap in the literature is crucial since stated-owned enterprises (SOEs) have traditionally played a major role in the economy of many European countries and emerging countries (Böwer, 2017; Bruton etal., 2015). Their numbers and significance have increased worldwide, since they are considered as a policy tool in times of crises (Gasperin, 2022; He etal., 2016; Nurgozhayeva, 2022; UNCTAD, 2021). However, their innovation strategies and, specifically, their cooperative behaviour have largely remained overlooked (Castelnovo, 2022; Gershman etal., 2016; González Álvarez & Argothy, 2019). Finally, the EU has in place several programmes4to encourage collaboration between universities, business and other stakeholders of different EU member countries in order to bring to market new products and technologies. The promotion of collaborations between firms, especially multinational enterprises (MNEs), and universities was especially noticeable during the 2008 financial crisis (Gómez-Aguayo etal., 2024). However, dynamic analyses of partnerships with European universities are scarce or, in some cases, non-existent. We strive to contribute towards filling the aforementioned gaps in the literature and, in doing so, the following questions are addressed. Which types of innovative firms are more likely to successfully navigate challenges during times of crisis and participate in cooperative efforts? Do drivers of cooperation change when a crisis erupts? Does ownership influence the probability that a firm participates in such partnerships during downturns? Are there specific drivers of innovation cooperation with European universities? Considering the aforementioned scarcity of analyses, our particular interest lies in comparing the behaviour of SOEs with that of Private- Owned Enterprises (POEs), national and multinational. The idea behind this inquiry is that SOEs are subject to unique institutional arrangements, constraints and stimuli that positively influence their cooperative behaviour. In this article, panel data is employed to analyse a sample of firms located in Spain and their collaboration with European partners5 (outside Spain) in 2004–2016, as well as the specific case of their partnerships with European universities. The period is divided into three sub-periods: a pre-crisis phase (2004–2007), a crisis phase (2008–2013), and a recovery phase (2014–2016). The first was characterised by high rates of growth, but Spain was subsequently badly hit by the 2008 financial 4 See, among others, https:// eit. europa. eu/ ourcommu nities/ eitmanuf actur ing, https:// eit. europa. eu/ ourcommu nities/ eitdigit al, https:// eic. ec. europa. eu/ eicfundi ngoppor tunit ies/ busin essaccel erati onservi ces_ en October 2023. 5 Includes EU-28 and EFTA countries. 1056 Eurasian Business Review (2024) 14:1053–1092 crisis, and its recovery period started later than in other EU countries. The European Innovation Scoreboard labels Spain as a “moderate innovator”.6 During the crisis, Spanish enterprises often abandoned innovation (Brzozowski & Cucculelli, 2016; Holl & Rama, 2016; Zouaghi etal., 2018), and public finance devoted to innovation was substantially reduced (Cruz-Castro etal., 2018). The significant impact of the 2008 crisis on Spain’s NIS makes it an intriguing case for the analysis of cooperation for innovation with European partners (CIEP) throughout the business cycle. On the other hand, the analysis of cooperation during the 2008 crisis may be of interest since this arrangement constitutes a coping strategy that can be applied to deal with other uncertain environments (Srholec, 2015; Wang, 2021). While different types of crises affect firms in varying ways, the pro-cyclical nature of innovation remains a consistent characteristic across diverse crisis scenarios (Archibugi etal., 2013; Brzozowski & Cucculelli, 2016; Busom & Vélez-Ospina, 2021; Friz & Günther, 2021; Geroski & Walters, 1995). This means that investment in innovative activities tends to decline during economic downturns and, conversely, increases during economic upturns. Given the recent succession of crises (e.g., the 2008 crisis, the COVID-19 pandemic, and war), understanding the cooperative behaviour of firms during downturns has become an urgent task. In this article, two contributions are made to the literature. Being a pioneering study in dynamic analysis of intra-European cooperation across the business cycle, this article sheds light on how the determinants of cooperation undergo transformations under harsh economic conditions. Additionally, our findings underscore the significance of accounting for state ownership as a crucial factor in examining the interplay between ownership structures and cooperation dynamics. In Sect.2, we examine the relevant literature that forms the theoretical foundation and the context setting of our investigation, and articulate our hypotheses. Section3 presents the methodology, and Sect.4 the results and the discussion. Section5 concludes. 2 Review oftheliterature andhypotheses 2.1 Defining international cooperation International cooperation refers to partners located in different countries working together to develop new ideas, technologies, and products. Although there are exceptions (Arvanitis & Bolli, 2013; Cozza etal., 2018; De Faria & Schmidt, 2012; Ebersberger et al., 2011; Fernández-Sastre, 2012; Holl & Rama, 2014; Srholec, 2014), the majority of the available studies fail to distinguish between domestic and international cooperation for innovation. The question is important since cultural and institutional distance may raise specific impediments to international cooperation (Edwards-Schachter etal., 2013; Gershman, 2012; Posselt & Rauch, 2011; 6 https:// resea rch- and- innov ation. ec. europa. eu/ stati stics/ perfo r manceindic ators/ europ eaninnov ationscore board_ en October 2023. 1057 Eurasian Business Review (2024) 14:1053–1092 Schmiele, 2012). Herein, the focus is on the collaboration of firms located in Spain with partners located in other European countries since, in this case, the institutional, regulatory, and business environment of such collaborations is likely to be relatively homogeneous. Accessing new markets constitutes one of the goals of firms that engage in international cooperation (Arranz & Fernández de Arroyabe, 2008; Calvo, 2023; Edwards-Schachter etal., 2013), but not necessarily the most important. Analyses of EU firms signal size, absorptive capacity, appropriability, export experience, incoming spillovers, risk-sharing, and R&D cost-sharing as the major drivers of international cooperation (Arvanitis & Bolli, 2013; Barajas & Huergo, 2010; De Faria & Schmidt, 2012). Certain authors note that cooperation with different types of partners, such as suppliers or universities, involves different drivers (Arranz & Fernández de Arroyabe, 2008; Badillo etal., 2017; Segarra-Blasco & Arauzo-Carod, 2008). For instance, in the specific case of partnerships with universities, it is crucial to consider the motivations of such institutions in collaborations. Perri etal. (2017) argue that partnerships initiated within research institutions, as commonly seen in industry-university collaborations, differ in motivations from those initiated within companies. The former are based on social ties and respond to “open science” incentives. With some exceptions (Rõigas etal., 2018; Segarra-Blasco & Arauzo-Carod, 2008), previous analyses of industry-university collaborations often fail to distinguish between local universities and those located abroad, and they also lack a dynamic perspective on these relationships. 2.2 Explaining cooperation Our topic stands at the cross-roads of several lines of research. Three main theoretical approaches have sought to elucidate cooperation, each emphasizing distinct aspects: motives for cooperation, opportunities for cooperation, and conducive environments for establishing such arrangements. The resources-based-view (RBV) of the firm focuses on drivers of cooperation: firms cooperate in order to reduce the risks and costs of R&D, shorten the product life cycle, expand their product range, access new knowledge and new markets, and solve technical difficulties (Arranz & Fernández de Arroyabe, 2008; Bayona etal., 2001; De Faria & Schmidt, 2012; Edwards-Schachter etal., 2013; Miotti & Sachwald, 2003). This theoretical proposition has garnered empirical support. For example, in a study on a French sample, it was found that firms encountering impediments to innovation, particularly those related to financial challenges, tend to participate in cooperation for innovation (Antonioli etal., 2017). Similarly, the majority of prior studies have noted that knowledge-related challenges frequently motivate firms to embark on collaborative initiatives (Salazar-Elena etal., 2023). However, other authors have argued that opportunities to collaborate should also be considered since possession of technical or commercial capital determines the attractiveness of a firm to potential partners (Ahuja, 2000). In choosing partners, companies appraise both their technical skills and their market potential (Bianchi etal., 2019; 1058 Eurasian Business Review (2024) 14:1053–1092 García-Sánchez et al., 2017). When analysing industrial partnerships, Friedberg & Neuville (1999) observe that decisions are contingent upon the perceived quality of the firms involved. In the “market” for partnerships, they claim, organizations and their reputations are in competition. Each of these theories would yield distinct predictions regarding the role of crises as drivers of cooperation. The RBV suggests that firms confronting financial constraints for R&D funding or encountering market difficulties during crises are more inclined to engage in cooperation compared to their counterparts. Conversely, the argument focusing on cooperative opportunities suggests that companies facing fewer difficulties are more likely to collaborate than their counterparts, owing to their appeal to potential partners amidst challenging economic conditions. Finally, the social capital theory identifies the environmental conditions that facilitate cooperation. Social networks provide an effective tool for the prevention of and punishment for opportunistic economic behaviour, and instead create trust between partners (Granovetter, 2005). In the context of partnerships, trust holds significant importance when it comes to managing uncertainty, as it signifies the ability to anticipate a partner’s future actions (Vahlne & Johanson, 2021). Initially, the concept of social capital was linked to the idea of geographic proximity. However, in more recent literature, it has evolved to encompass the concept of international social capital. In the international scene, the concept incorporates a wide range of assets, such as an understanding of foreign-market institutions and of decision-making processes. A variety of relationships and agreements provide opportunities to increase the social capital of a company and, consequently, its ability to cooperate, such as production subcontracting, common membership of associations, and professional connections (Granovetter, 2005). Interlocking directorates, which are formed when an individual participates in two or more boards of directors, also contribute towards creating social structures and trust (Aguilera, 1998; Cao etal., 2023; Wang, 2021). There are also transnational interlocking ties between firms, as demonstrated by Valeeva (2022) in her study of global cities connected through the exchange of transnational board members. She maintains that this corporate elite community is built upon well-estab- lished national networks of relationships. Structural holes may also create opportunities for actors who are able to bridge such holes. These are gaps between unconnected groups of economic players (Saglietto etal., 2020). A third player may obtain intermediation benefits by linking those unconnected groups, and the arrangement may create new opportunities since complex networks may generate a greater variety of ideas and resources. Therefore, extended networks might display both direct ties and indirect ties between players through an intermediary (Saglietto etal., 2020). The obtaining of a subsidy may signal the receiver as a valuable potential partner and, consequently, may also mitigate uncertainty and facilitate collaboration (Bianchi etal., 2019). 2.3 Cooperation andcrises Even though cooperation appears to be a resilience-enhancing factor, there has been a noticeable lack of comprehensive analysis concerning the collaborative behavior of firms during periods of crisis (D’Agostino & Moreno, 2018). The few available empirical studies on this subject reveal that firms tend to exhibit either 1059 Eurasian Business Review (2024) 14:1053–1092 no countercyclical inclinations or, in some cases, a reduction in their cooperative initiatives during crises (Azagra-Caro etal., 2019; Hoffmann etal., 2017; Lincoln etal., 2017). Cooperation comes with associated costs, risks, agency problems and the challenge of identifying reliable partners, which can become even more daunting in times of crisis (Edwards-Schachter etal., 2013; Friedberg & Neuville, 1999; Williams & Ecker, 2014). Based on the limited available evidence, it appears that fostering domestic cooperation poses greater challenges in times of economic hardship; nevertheless, firms with cooperative experience demonstrate a resilience that enables them to maintain a cooperative stance (García-Sánchez & Rama, 2022). Persisting in cooperative activities has the potential to enable a company to expand its international social capital and enhance its managerial capabilities for collaboration, thus enabling it to overcome challenges. However, to the best of our knowledge, this particular question has not yet been explored in an international context. The primary obstacle seems to be the scarcity of available panel data. Previous research suggests that cooperation with universities specifically may exhibit a different logic throughout the business cycle. For instance, a bibliometric study involving Spanish organizations, including both firms and universities, analysed data from 2000 to 2016 and found that university-industry co-authorships clearly exhibited countercyclical characteristics (Gómez-Aguayo et al., 2024). In our view, this suggests that collaborations between Spanish firms and universities, whether domestic or international, may also be countercyclical, as co-publications, to some extent, reflect the presence of industry-university collaborations. However, not all collaborations result in publications, and to the best of our knowledge, the specific evolution of industry-university cooperation throughout the business cycle has not been explored. 2.4 Cooperation andownership This subsection explores whether various types of ownership act as catalysts for international cooperation. 2.4.1 Group ownership A business group consists of two or more legally defined enterprises under common ownership. Groups may be national or multinational. Group membership facilitates the engagement of a firm in cooperative innovation since groups provide access to greater resources, such as finance, equipment, and facilities (Arranz & Fernández de Arroyabe, 2008; Arvanitis & Bolli, 2013; Molero & Heijs, 2002). In contrast, unaffiliated firms are, in most cases, SMEs that experience difficulties in establishing cooperative relationships (Belderbos etal., 2006; Ebersberger etal., 2011; Radicic etal., 2019; Segarra-Blasco & Arauzo-Carod, 2008). Therefore, we expect that business groups are more likely than unaffiliated firms to sustain successful CIEP during downturns. 1060 Eurasian Business Review (2024) 14:1053–1092 2.4.2 Foreign ownership The concept of international cooperation in the context of foreign ownership has evolved through the convergence of studies on cooperation and the internationalization of R&D (Barajas & Huergo, 2010). Recent International Business (IB) literature highlights the network-like nature of international R&D activities (Papanastassiou etal., 2020), emphasizing that international innovation activities can be viewed as a distinct form of Foreign Direct Investment (FDI) (Schmiele, 2012). Calvo (2023) adds that firms can internationalize through cooperation, especially in knowledge and capital-intensive services, without necessarily engaging in FDI. The impact of foreign ownership on cooperation has been extensively studied, often comparing foreign subsidiaries (FS) with domestic business groups (DBG) rather than unaffiliated domestic firms, to account for their inherent business group structures. Studies generally find that foreign ownership tends to increase international cooperation but can reduce domestic collaboration (Ebersberger etal., 2011). This pattern holds across various European countries including Belgium (Veugelers & Cassiman, 2004), Italy (Cozza etal., 2018), and several other EU nations (Srholec, 2009). However, FS in Spain show a tendency toward local cooperation rather than international, especially in comparison to DBG (Fernández-Sastre, 2012; Holl & Rama, 2014; Álvaro – Moya etal. 2022). This may be influenced by the characteristics of the host country and the MNEs’ objectives. FS in less developed countries, such as Portugal (De Faria & Schmidt, 2012), often use these locations as bases for broader European innovation activities due to local partnering challenges (Ebersberger etal., 2011; Srholec, 2009). During crises, the relationship between foreign ownership and cooperation becomes complex. Studies suggest that during the 2008 financial crisis, FS in Italy and Latin America reduced collaboration with domestic partners (Brancati etal., 2017; Paunov, 2012). In Spain, FS in Information and Communication Technologies performed better than domestic POEs due to easier access to international finance (García-Sánchez & Rama, 2020). However, FS in Spanish manufacturing and services maintained higher levels of local cooperation than unaffiliated domestic firms, but not more than DBG; SOEs proved more capable than POEs, domestic or foreign, in sustaining domestic collaboration (García-Sánchez & Rama, 2022). We expect that foreign subsidiaries’ ability to secure international credit facilitated international cooperation during the crisis, though their focus on local partnerships sometimes counterbalanced this effect. In the realm of institutional theory, a parallel domain to the aforementioned literature has emerged, investigating the relationship between state ownership and economic efficiency. This particular strand of research compares SOEs and POEs, but, as noted, investigations in terms of innovation and cooperation have frequently been overlooked (Introduction). We contend that to attain a comprehensive understanding of the ownership structures influencing choices in innovation collaboration, it is crucial to integrate institutional theory. This is particularly relevant due to the substantial presence of state-ownership as a significant ownership structure in many economies. We address this question below. 1061 Eurasian Business Review (2024) 14:1053–1092 2.4.3 State‑ownership According to the Organisation for Co-operation and Development (OECD), SOEs are enterprises where the state has substantial control through full, majority, or significant minority ownership (Medina etal., 2022). Within Europe, their presence is significant in countries such as France, Italy, Sweden, and in new-member countries of the EU. The goals of SOEs and POEs differ. Those of SOEs go beyond mere profit maximisation to also include societal goals, diversification of the economy, industrial policy, monopoly control, support to new technologies, knowledge diffusion, green transition, and defence (Antonelli etal., 2014; Archibugi & Mariella, 2021; Benassi & Landoni, 2018; Gershman etal., 2019; Palmberg, 2002; Steffen etal., 2022; Tönurist & Karo, 2016). In Europe, most SOEs have evolved towards more efficient forms of corporate organisation (He etal., 2016). According to the aforementioned authors, reform often entailed governance structures of a more complex character with new shareholders having a role to play in SOEs internationalization. Are SOEs innovative? Landoni (2020) contends that the role of SOEs in innovation has been largely underestimated and certain empirical studies support this view. Italian business history (Antonelli etal., 2014; Gasperin, 2022) and case studies on Russian and Western European SOEs suggest that these firms are able to innovate (Archibugi & Mariella, 2021; Benassi & Landoni, 2018; Gershman etal., 2019; Palmberg, 2002; Rama & Ferguson, 2007). Furthermore, quantitative studies that focus on EU firms support the view that SOEs are more prone to innovating than are POEs, at least in certain sectors (Castelnovo, 2022; Steffen etal., 2022). Moreover, the institutional literature suggests that R&D spending tends to decrease when SOEs are privatised due to the reorientation of these firms to short-term benefits and the new managers’ lack of interest in basic research (Sánchez Carreira & Vence Deza, 2009). The literature suggests several explanations behind the innovativeness of SOEs. Due to their long-term perspective on profit-making (“patient capital”) and their easier access to R&D funding, these companies are more prone than POEs to invest in basic research and in technological fields that are risky and/or slow to produce results (Antonelli etal., 2014; Landoni, 2020; Ortega, 2016; Li etal., 2022). Furthermore, coordination with governments provides SOES with strategic advantages in assessing linkages between different industries and knowledge fields (Benassi & Landoni, 2018). Empirical evidence on their cooperative activities is still scarce. However, according to Benassi & Landoni (2018), SOEs frequently network with other organisations and, in doing so, constitute vehicles of possible recombination of knowledge. Case studies suggest that Russian and Western European SOEs participate in domestic cooperation for innovation with both domestic POEs and universities; and, in the West, also with FS (Alonso-Gil & Vázquez-Barquero, 2010; Antonelli etal., 2014; Calvo, 2019; Gershman etal., 2019; López etal., 2002; Rama & Ferguson, 2007). Recently, a few quantitative studies establish that compared to POEs, domestic or foreign, SOEs are more predisposed to cooperate locally for innovation (García-Sánchez & Rama, 2022; Roud & Vlasova, 2020; Wang, 2021). Within the EU, certain institutional mechanisms are at work to promote the engagement of 1068 Eurasian Business Review (2024) 14:1053–1092 4 Results anddiscussion 4.1 Descriptive statistics Our sample comprises 31,031 observations. Throughout the 2004–2016 period, SOEs were the companies most prone to cooperating with European partners (29%), and unaffiliated firms the least prone (6%). Cooperative firms accounted for approximately 20% each of DBG and FS. The data reveal a notable trend in the involvement of firms in CIEP and demonstrates that the percentage of firms engaged in such partnerships increased from 12% before the crisis to 13% during the crisis and further to 16% during the recovery (Table1). The Pearson’s χ2 and Cramer’s V indicate an association between cooperation engagement and the business cycle phase. This suggests that even in the context of a “moderate innovator” like Spain, there is a growing inclination among firms to participate in such cooperative arrangements. The key question that arises from this observation is whether this surge in the percentage of cooperative firms was a reactive response by companies, adopting cooperation as a resilience strategy during the crisis. This question is addressed below. Next, we examine the progression of the average number of diverse European partners (e.g., clients, suppliers, universities) per firm. The number of partner types increased during the crisis, followed by a decrease during the recovery period (Table2). Pearson’s χ2 and Cramer’s V indicate a significant association between Table 2 Average number of European partnership types, by phase of the business cycle (2004–2016). Source: Authors’ own based on PITEC data Pearson Chi2(12) = 70.2377 Pr = 0.000 Cramér’s V = 0.0396 Number of different types of partners Business cycle phase Total Boom Crisis Recovery None: freq 6123 9141 4147 19,411 % 88.0 86.7 84.4 86.6 1: freq 443 671 392 1506 % 6.4 6.4 8.0 6.7 2: freq 210 357 154 721 % 3.0 3.4 3.1 3.2 3: freq 104 165 93 362 % 1.5 1.57 1.9 1.6 4: freq 49 100 49 198 % 0.7 0.9 1.0 0.9 5: freq 24 78 48 150 % 0.3 0.7 1.0 0.7 6: freq 6 30 28 64 % 0.1 0.3 0.6 0.3 Total: freq 6959 10,542 4,911 22,412 % 100.0 100.0 100.0 100.0 1069 Eurasian Business Review (2024) 14:1053–1092 the average number of partner types and the phase of the business cycle. Furthermore, a Bonferroni test (not displayed) reveals statistically significant differences between the crisis and the boom, as well as between the crisis and the recovery phases. There appears to be a phenomenon of deepening cooperative arrangements, wherein firms with prior experience in cooperation extended their engagement to include new types of partners when the crisis emerged. 4.2 Results anddiscussion 4.2.1 Effects ofthecrisis We begin by examining whether the sample firms turned to CIEP in response to the crisis. In the analysis spanning the entire period from 2004 to 2016, the variable representing the in-crisis period in Spain (crisis 2008–2013) exhibits a positive coefficient significant at the 5% level, suggesting that the downturn triggered CIEP (Table3, column 2). However, when introducing Eu_persistence and variables indicating the persistence of cooperation in other geographic areas (Local_persistence and US_persistence) into the model (Table3, column 3), the coefficient of crisis 2008–2013 becomes negative and statistically significant at 5%. This indicates that extraordinarily critical circumstances in the home country did not act as a trigger for CIEP. In fact, the crisis itself had a detrimental impact on firms’ likelihood to collaborate with European partners when the model controls for prior experience. This is in accordance with previous studies on the negative effect of crises on cooperation (subsection2.1). Instead, the growing engagement of the sample companies in CIEP can be attributed to their accumulated cooperative expertise over the entire period. In their review of the literature, Freire & Gonçalves (2022) find that, while initially firms may find it difficult to cooperate with foreign partners, such a difficulty dissipates with time. On the other hand, it is plausible that this experience leads to a deepening of cooperation and an increase in network complexity during the crisis (as discussed in the previous subsection). Nevertheless, due to space constraints, we are unable to explore this concept further in this section. Persistence has a particularly significant impact on CIEP for firms with prior experience in, specifically, the European area. Throughout the entire period, these firms had, on average, an 11% higher likelihood of participating in CIEP (Table3, column 4). Hypothesis 1 is supported. 4.2.2 The role ofownership Table4 shows the dynamics of cooperation with all types of European partners (columns 1, 3 and 5) and, specifically, with European universities (columns 2, 4 and 6). The results of estimations shown in columns report marginal effects (dy/dx). Results for the pre-crisis or boom period are in column 1 for all types of partnerships and on column 2 for, specifically, partnerships with European universities. Those for the period of crisis are in columns 3 and 4, respectively, for all partnerships and for partnerships with universities. Finally, results for the recovery period are in columns 1070 Eurasian Business Review (2024) 14:1053–1092 Table 3 Determinants of cooperation for innovation with European partners (outside business group), 2004–2016. Source: Authors’ own from PITEC data Apparent Persistence—adjusted Coef. (se) Sig. (1) Margins (se) Sig. (1) Coef. (se) Sig. (1) Margins (se) Sig. (1) r_turn 0.00048 *** 0.00003 *** 0.00046 *** 0.00003 *** (0.000) (0.000) i_newmar 0.34763 *** 0.01988 *** 0.39525 *** 0.02319 *** (0.077) (0.083) mk_EU 0.62281 *** 0.03091 *** 0.70999 *** 0.03566 *** (0.168) (0.175) mk_other_intrnational (outside Europe) 0.91536 *** 0.04803 *** 0.92336 *** 0.04860 *** (0.160) (0.163) i_intRDexp 0.16246 + 0.00929 + 0.26636 ** 0.01563 ** (0.094) (0.100) i_extRDexp 0.42276 *** 0.02417 *** 0.41191 *** 0.02417 *** (0.082) (0.086) i_other_innov_exp 0.33701 *** 0.01927 *** 0.42494 *** 0.02494 *** (0.072) (0.077) i_RDemployees 0.42656 *** 0.02439 *** 0.26847 ** 0.01576 ** (0.096) (0.102) spill_int 0.44428 *** 0.02540 *** 0.43586 *** 0.02558 *** (0.079) (0.084) spill_tech 0.74485 *** 0.04259 *** 0.70052 *** 0.04111 *** (0.143) (0.150) spill_dissem 0.23671 + 0.01353 + 0.23548 0.01382 (0.137) (0.145) spill_valuechcompet 0.48160 *** 0.02754 *** 0.44719 *** 0.02624 *** (0.089) (0.094) 1071 Eurasian Business Review (2024) 14:1053–1092 Table 3 (continued) Apparent Persistence—adjusted Coef. (se) Sig. (1) Margins (se) Sig. (1) Coef. (se) Sig. (1) Margins (se) Sig. (1) h_knowl_diff 0.37071 * 0.02120 * 0.38022 * 0.02231 * (0.160) (0.168) h_comp_diff 0.15708 + 0.00898 + 0.09211 0.00541 (0.086) (0.091) i_fund_EU 1.93314 *** 0.11054 *** 1.91691 *** 0.11249 *** (0.149) (0.156) Crisis 2008–2013 0.15626 * 0.00866 * – 0.16280 * – 0.00949 * (0.068) (0.078) Recovery 2014–2016 0.42498 *** 0.02468 *** 0.07088 0.00432 (0.081) (0.091) State-owned 1.82723 *** 0.17260 *** 1.17802 ** 0.10744 ** (0.477) (0.382) Unaffil – 1.80893 *** – 0.10132 *** – 1.32668 *** – 0.07635 *** (0.180) (0.145) Dynamic (dynamic and RTA) – 0.33942 – 0.02057 – 0.31771 + – 0.01937 + (0.224) (0.178) Withdrawal (non-dynamic and non-RTA) – 0.76834 + – 0.04330 + – 0.48276 – 0.02850 (0.416) (0.327) other_services – 1.58735 *** – 0.07740 *** – 0.94321 *** – 0.05088 *** (0.273) (0.220) local_persistence 0.75339 *** 0.04421 *** (0.084) 1072 Eurasian Business Review (2024) 14:1053–1092 Table 3 (continued) Apparent Persistence—adjusted Coef. (se) Sig. (1) Margins (se) Sig. (1) Coef. (se) Sig. (1) Margins (se) Sig. (1) Eu_persistence 1.83178 *** 0.10750 *** (0.106) US_persistence 0.49382 * 0.02898 * (0.229) Constant – 4.78866 *** – 4.84740 *** (0.249) (0.235) lnsig2u 1.84209 *** 1.16138 *** (0.072) (0.092) Prob > chi20.000 0.000 N. of cases 22411 20740 sigma_u 2.51192 1.78727 Rho 0.65729 0.49263 Variables i_newfirm, mk_local/regional, i_own_fundRD, h_eco_diff, h_mark_diff, multinational, and challenge sector type as well as KIBS do not exhibit statistically significant effects. Marginal effects (Apparent and persistence-adjusted) (1) + p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001 1073 Eurasian Business Review (2024) 14:1053–1092 Table 4 Drivers of cooperation for innovation with European partners throughout the business cycle (marginal effects): comparative analysis all-partners vs. universities. Source: Authors’ own from PITEC data Boom (2004–2007) Crisis (2008–2013) Recovery (2014–2016) All partners Universities All partners Universities All partners Universities Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) r_turn 0.00003 *** 0.00001 * 0.00003 *** 0.00001 *** 0.00004 *** 0.00002 ** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) i_newmar 0.02270 * 0.00445 0.02520 *** 0.00553 0.01613 – 0.00841 (0.202) (0.439) (0.127) (0.246) (0.186) (0.393) i_newent 0.01115 0.00260 0.01189 + 0.00117 0.00900 0.00836 (0.193) (0.410) (0.121) (0.237) (0.170) (0.341) mk_Eu 0.02834 + 0.00835 0.04736 *** 0.01597 ** 0.01286 0.00982 (0.416) (1.004) (0.262) (0.510) (0.382) (0.690) mk_other_international (outside Europe) 0.03956 * 0.01762 * 0.05075 *** 0.01752 ** 0.05027 ** 0.00791 (0.364) (0.872) (0.247) (0.486) (0.331) (0.630) local_persistence 0.04715 *** 0.00615 0.05755 *** 0.00955 * 0.05623 *** 0.01008 (0.217) (0.484) (0.132) (0.291) (0.183) (0.405) Eu_persistence 0.04988 ** 0.01728 ** 0.12106 *** 0.02865 *** 0.14708 *** 0.03456 *** (0.379) (0.607) (0.177) (0.293) (0.234) (0.406) US_persistence – 0.02480 0.02269 * 0.04608 * 0.01643 ** 0.06810 * 0.02108 * (0.589) (0.902) (0.343) (0.414) (0.540) (0.657) i_intRDexp 0.00821 – 0.00097 0.00619 0.01022 * 0.03419 ** 0.00651 (0.235) (0.496) (0.150) (0.296) (0.209) (0.422) i_extRDexp 0.02119 * 0.01118 * 0.02592 *** 0.00380 0.03367 ** 0.00660 (0.207) (0.432) (0.129) (0.244) (0.189) (0.365) i_otherinnovexp 0.03847 *** 0.00874 * 0.02126 *** 0.00464 0.03348 *** 0.00756 (0.186) (0.391) (0.116) (0.220) (0.170) (0.329) 1074 Eurasian Business Review (2024) 14:1053–1092 Table 4 (continued) Boom (2004–2007) Crisis (2008–2013) Recovery (2014–2016) All partners Universities All partners Universities All partners Universities Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) i_RDemployees 0.01597 0.00247 0.03025 *** 0.00625 – 0.01051 0.00850 (0.242) (0.497) (0.159) (0.314) (0.212) (0.437) spill_int 0.02448 ** 0.01213 * 0.02020 ** – 0.00111 0.04537 *** 0.00122 (0.204) (0.491) (0.130) (0.267) (0.187) (0.383) spill_tech 0.07513 *** 0.02924 *** 0.03256 ** 0.02465 *** 0.06271 *** 0.04446 *** (0.409) (0.681) (0.228) (0.333) (0.297) (0.469) spill_dissem 0.00954 0.01421 * 0.01257 0.00904 + 0.00364 0.01212 (0.345) (0.595) (0.228) (0.395) (0.307) (0.471) spill_valuechcompet 0.04243 *** 0.00731 0.02869 *** – 0.00070 0.03456 ** 0.00289 (0.236) (0.467) (0.144) (0.292) (0.194) (0.379) h_comp_diff – 0.00212 – 0.00327 0.01622 * 0.00655 + 0.00663 0.00339 (0.229) (0.482) (0.135) (0.255) (0.188) (0.377) h_mark_diff – 0.01688 0.00188 – 0.05380 + 0.00183 – 0.07674 0.00000 (0.613) (1.655) (0.552) (0.849) (0.899) (.) i_fund_EU 0.16999 *** 0.04029 *** 0.09963 *** 0.02787 *** 0.14329 *** 0.05378 *** (0.443) (0.598) (0.249) (0.331) (0.313) (0.473) State-owned 0.13845 ** 0.04822 + 0.10729 ** 0.03640 * 0.07048 0.03395 + (0.728) (1.261) (0.456) (0.787) (0.581) (0.863) Unaffil – 0.06718 *** – 0.00861 – 0.07663 *** – 0.01358 ** – 0.06050 *** – 0.01199 + (0.287) (0.558) (0.186) (0.382) (0.215) (0.454) 1075 Eurasian Business Review (2024) 14:1053–1092 Table 4 (continued) Boom (2004–2007) Crisis (2008–2013) Recovery (2014–2016) All partners Universities All partners Universities All partners Universities Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Margins (se) Sig. (1) Multinational 0.03738 + 0.00045 0.01955 – 0.00222 0.02965 + – 0.00370 (0.319) (0.676) (0.208) (0.453) (0.242) (0.561) Dynamic (dynamic and RTA) 0.03318 * – 0.00161 – 0.03064 * – 0.00818 – 0.04660 ** – 0.01758 * (0.321) (0.679) (0.218) (0.478) (0.263) (0.676) Withdrawal (non-dynamic & non-RTA) 0.04762 – 0.00367 – 0.04047 + – 0.01952 + – 0.04646 + – 0.02263 (0.566) (1.414) (0.403) (1.359) (0.496) (1.665) KIBS 0.00317 0.02621 * – 0.01364 0.02716 ** – 0.00515 0.03454 ** (0.394) (0.730) (0.250) (0.451) (0.284) (0.525) other_services – 0.04788 ** – 0.00661 – 0.04753 ** 0.00101 – 0.03575 + 0.00558 (0.468) (0.985) (0.281) (0.566) (0.333) (0.675) lnsig2u 1.99442 *** 2.43428 *** 1.20657 *** 2.00134 *** 1.11588 *** 1.99666 *** (0.190) (0.311) (0.138) (0.172) (0.190) (0.223) Prob > chi20.000 0.000 0.000 0.000 0.000 0.000 N. of cases 5287 5287 10542 10542 4911 4796 sigma_u 2.71071 3.37751 1.82811 2.72010 1.74707 2.71374 Rho 0.69074 0.77616 0.50393 0.69221 0.48127 0.69122 Variables mk_local/regional, i_owm_fundRD, h_knowl_diff, h_eco_diff, challenge (dynamic but non-RTA) do not have statistically significant effect (1) + p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001 1076 Eurasian Business Review (2024) 14:1053–1092 5 and 6, respectively, for all partnerships and for partnerships with universities. The econometric models are robust and possess significant explanatory power. We start by analysing all types of partnerships. During the crisis and the entire period, unaffiliated domestic firms (unanaffil) were systematically less likely to participate in CIEP than DBG (Table4, columns 1, 3 and 5). Being an unaffiliated domestic firm reduced the probability of cooperating with European partners by 7% during the boom, by 8% during the crisis, and by 6% during the recovery period. This result is in line with Arvanitis & Bolli (2013) who discovered compelling evidence of the positive impact of group membership on a company’s likelihood of engaging in international cooperation across all five Western European countries examined in their study. Our results may be explained by two reasons that do not contradict each other. International cooperation necessitates substantial search expenditures; which unaffiliated firms may struggle to afford, especially during a downturn. On the other hand, our unaffiliated firms may be part of extensive networks and may establish indirect connections with European partners (Saglietto etal., 2020). In the context of Spain, unaffiliated firms are able to engage in local cooperation for innovation, particularly during periods of economic prosperity (García-Sánchez & Rama, 2022). As a result, the larger firms with which they collaborate locally, including DBG, SOEs, and FS, could potentially engage in European partnerships and reap the benefits of bridging structural holes between unaffiliated Spanish firms and their European counterparts. This view is consistent with evidence provided by case studies on Spanish industries with a national pyramidal structure and substantial international projection, such as automobiles, telecommunications, and aeronautical engineering (Badillo etal., 2017; Rama & Ferguson, 2007; Sanz Menéndez etal., 1999). An organisation consisting of extended national/supranational networks of innovators is also prevalent in the Spanish defence industry in the framework of integrated EU projects (Callado-Muñoz etal., 2022; Ortega, 2016). Nevertheless, this speculative interpretation cannot be tested with the available data. The coefficient for multinational is marginally significant at the 10% level during both the boom and recovery phases. This suggests that FS have a slightly higher likelihood of engaging in CIEP during prosperous times. However, during crises, their behaviour aligns more closely with that of DBG. In our perspective, the resemblance of their behaviour to that of DBG does not stem from an imitative strategy. Instead, it results from a complex interplay of different strategies within FS and their conflicting impacts on CIEP. On one hand, their significant interest in local cooperation (Fernández-Sastre, 2012; Holl & Rama, 2014), might suggest a reduced inclination toward CIEP. Undisclosed estimations suggest that in dynamic sectors where Spain holds technological advantages (dynamic), FS demonstrate reluctance to engage in CIEP, often preferring to collaborate with efficient local partners (i.e., Spanish partners or other FS located in Spain) (available upon request). Our interpretation appears to be validated by García-Sánchez etal. (2017), who note that FS active in Spain indeed form stronger cooperative connections with local counterparts in those sectors. On the other hand, easier access of FS to international credit suggests a potential facilitation for engaging in CIEP, even during a crisis. Additionally, their strong involvement in exports implies a propensity for participating in 1077 Eurasian Business Review (2024) 14:1053–1092 international cooperation to adapt their products to the needs of foreign customers (subsection2.1). Álvarez etal. (2011) observed that by 2008, approximately 30% of FS operating in Spain directed over 50% of their sales toward international markets. The effects of these two strategies may potentially offset each other. State-ownership (state-owned) increased the probability that a company was engaged in CIEP by 14% during the boom and by 11% during the crisis (Table4, columns 1 and 3). Our results contradict those of Clò etal. (2023), who argue that reformed SOEs align with POEs in their internationalization patterns and strategies. However, we do not find such similarity, at least in terms of the internationalization formula involving international cooperation. In our sample, SOEs are more inclined than POEs, whether national or multinational, to participate in CIEP during times of crisis. After Eu_persistence, state-owned constitutes the strongest driver of CIEP during that period and displays an effect as robust as that of obtaining above-average EU funding for innovation. Several reasons may explain this result. Over the years, Spanish SOEs have accumulated substantial social capital through exports, FDI, and inter-firm linkages. They have been exporters since the 1950s, when barely 12% of Spanish companies had any export activity (Binda, 2012). When the crisis erupted, SOEs were particularly well-positioned to capitalize on these early linkages to counteract the fall of the domestic demand. For instance, Indra, a large high-tech company that returned to state ownership during the 2008 crisis, increased its exports from 30 to 40% of its sales before the 2008 crisis to 60% after the crisis inception (Álvarez Cuiñas, 2015). Moreover, numerous Spanish SOEs internationalized through FDI (Cuervo-Cazurra, 2018). These circumstances may have played a role in fostering international linkages and nurturing the growth of their social capital over the years. Moreover, their ability to engage with foreign partners has probably expanded through reform, owing to the significant participation of international institutional investors, including pension funds, among their new stakeholders (Etchemendy, 2004). Such investors can aid reformed SOEs in securing international financing and tapping into global knowledge networks (He etal., 2016; Panicker etal., 2022). Interlocking directorates may have indirectly facilitated SOEs in leveraging the international experience of other companies (Yildiz etal., 2023). Spanish SOEs, in particular, have frequently engaged in such practices (Aguilera, 1998; Calvo, 2019). Furthermore, it is plausible that SOEs are viewed as particularly suitable partners by specific foreign organizations. For instance, German firms engaged in cooperation with competitors in Europe tend to favour partnerships with public sector clients, governments, and research institutions, rather than partnerships with private firms (Navío-Marco etal., 2019). The collaboration with competitors is perceived as a risky strategy due to the potential for involuntary spillovers. In their review of the cooperation literature, Freire & Gonçalves (2022, p. 3395) find that “partnerships with government-funded or related organizations usually have a positive effect” on innovative performance. Finally, SOEs may also elicit more trust from prospective partners since they are financially backed by the state, a crucial consideration during a financial crisis. In this scenario, being an “institutional exception” probably worked to the advantage of SOEs. 1084 Eurasian Business Review (2024) 14:1053–1092 Table 5 (continued) Variables Descriptive statistics Name Type Description (Relative frequencies in parenthesis for dummy and categorical variables) i_intRDexp Dummy Internal r&d expenditures above the two-digit industry average 1 = yes (36.3%) 0 = no N = 29,092; n = 2238 i_extRDexp Dummy External r&d expenditures above the two-digit industry average 1 = yes (16.9%) 0 = no N = 29,092; n = 2238 i_other_innov_exp Dummy Expenditures in other innovative activities above the two-digit industry average 1 = yes (19.4%) 0 = no N = 29,092; n = 2238 i_RDemployees Dummy Number of r&d employees per 1,000 employees above the two-digit industry average 1 = yes (39.3%) 0 = no N = 29,094; n = 2238 i_owm_fund_RD Dummy Share of own funds used for internal r&d above the two-digit industry average 1 = yes (41.7%) 0 = no N = 29,094; n = 2238 i_fund_EU Dummy Share of eu funds used for internal r&d above the two-digit industry average 1 = yes (2.7%) 0 = no N = 29,094; n = 2238 spill_int Dummy High importance of internal information for innovation 1 = yes (61.8%) 0 = no N = 22,411; n = 2044 spill_tech Dummy High importance of knowledge institutions information for innovation 1 = yes (4.8%) 0 = no N = 22,411; n = 2044 1085 Eurasian Business Review (2024) 14:1053–1092 Table 5 (continued) Variables Descriptive statistics Name Type Description (Relative frequencies in parenthesis for dummy and categorical variables) spill_dissem Dummy High importance of dissemination channels for innovation 1 = yes (5.9%) 0 = no N = 22,411; n = 2044 spill_valuechcompet Dummy High importance of client, supplier, and competitor information for innovation 1 = yes (15.6%) 0 = no N = 22,411; n = 2044 h_knowl_diff Dummy High hindrance from knowledge access barriers in innovation 1 = yes (5.4%) 0 = no N = 29,094; n = 2238 h_eco_diff Dummy Economic difficulties hinder innovative activities 1 = yes (23.9%) 0 = no N = 29,094; n = 2238 h_comp_diff Dummy Competitive difficulties hinder innovative activities 1 = yes (21.8%) 0 = no N = 29,094; n = 2238 h_mark_diff Dummy Market access barriers hinder innovative activities 1 = yes (4%) 0 = no N = 29,094; n = 2238 mksize Categorical Declared market breadth by firm (local–regional/national/europe (eu, efta and associated countries)/rest of the world) 1 = local/regional: 8.56% 2 = national: 20.37% 3 = Europe: 14.57% 4 = other international (outside Europe): 56.5% N = 29,094; n = 2238 1086 Eurasian Business Review (2024) 14:1053–1092 Table 5 (continued) Variables Descriptive statistics Name Type Description (Relative frequencies in parenthesis for dummy and categorical variables) Sectors Categorical Molero and García (2008) taxonomy for manufacturing, and eurostat for services 3 = dynamic (Fast growth in worldwide patenting and RTA * in Spain): 15.1% 4 = stationary (Slow growth in worldwide patenting and RTA * in Spain): 25.4% 5 = withdrawal (Slow growth in worldwide patenting and non-RTA*): 3.7% 6 = challenge (Fast growth in worldwide patenting and non-RTA* in Spain): 16.6% 9 = KIBS Knowledge Intensive Business Services: 11.5% 10 = Other services: 27.7% N = 29,094; n = 2238 Crisis Categorical Business cycle phase based on the spanish gdp path 0 = boom (2004–2007): 8.56% 1 = crisis (2008–2013): 20.37% 2 = recovery (2014–2017): 14.57% N = 29,094; n = 2238 r_turn Continuous Base 100 index of firm’s turnover vs. Two-digit industry average Mean: 167.75 || min: 0 || Max. 8363.32 Std. dev.: 466.52 (overall) 440.75 (between) 153.15 (within) N = 29,094; n = 2238 *The abbreviation RTA stands for Revealed Technological Advantages RTA revealed technological advantages 1087 Eurasian Business Review (2024) 14:1053–1092 Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by project [AICO/2021/021], INGENIO (CSIC)-UPV Generalitat Valenciana. The funding source had no involvement in the preparation of the article. Declarations Conflict of interest None. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 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Authors and Affiliations AntonioGarcía‑Sánchez1,3 · RuthRama2,3 * Ruth Rama r[email protected] Antonio García-Sánchez [email protected] 1 Department ofEconomics andEconomic History, Research Group ofEconomic Analysis andPolitical Economy, Universidad de Sevilla, Seville, Spain 2 IEGD, CSIC, National Research Council ofSpain, Albasanz, 26-28; room 3E-23, 28037Madrid, Spain 3 GRINEI (Research Group onEconomics andPolitics ofInnovation), ICEI, Universidad Complutense de Madrid, Madrid, Spain