The modern economic development paradigm relies heavily on the application and commercialisation of innovations. From leading multinational corporations (MNCs) to small and medium enterprises (SMEs), economic agents engage in innovation activities to sustain their competitiveness. Therefore, harnessing innovation has become a focal interest for many actors. European Union (EU) sees it as a primary tool for reaching strategic goals and achieving sustainable social development (European Union n.d.).
However, the situation is not as straightforward. Among the EU members, the developmental gap is acknowledged as an essential issue and obstacle to development. It is especially true when comparing Central and Eastern Europe (CEE) with Western and Northern members. Among the CEE countries, only two regions can be considered innovative leaders (European Commission, Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs 2021a). And even among these, the development is uneven. Given various transition paths, institutional performance, sectoral differences and internal regional interactions (Isaksen 2001; Tödtling and Trippl 2005; Radosevic 2002), the sub-national regions of CEE represent a vast pool of innovation systems.
Emerging as new market economies after the 1990s, many of these regions considered fast integration into the global economy as a strategy for development (Gereffi 2014; Ernst 2002; Ernst and Kim 2001). This factor created additional opportunities associated with technology and knowledge transfer, learning and adoption skills (Giuliani, Pietrobelli, and Rabellotti 2005; Pietrobelli and Rabellotti 2011) and required constant bargaining and negotiations (Gereffi 2001; Dicken 2005). Therefore, it is to acknowledge another set of parameters in the CEE regional innovation.
Combining internal particularities and external conditions creates an avalanche of developmental possibilities. Therefore, the question emerges of what practices were the most useful. And among all the cases, what were the best strategies to follow? What constitutes an efficient innovation process in CEE, and what parameters can be universally applied? To answer these questions, the research will follow the first Research Question (RQ):
RQ1:
However, observing the impact of separate forces might not be conclusive. Sometimes accumulation of several factors is crucial in creating an adequate environment for innovation (e.g., Pietrobelli and Rabellotti 2011). It is especially true in the context of CEE, with its vast pool of regions and unique developmental paths. Various combinations of regional and extra-regional phenomena can have parallel yet similar effects. Therefore, the article's second aim is to understand what forces complement each other and empower innovation (RQ2).
RQ2:
Identifying the paths to innovation is imperative to solving the developmental gap in the EU. As such, it will reveal the practices to focus on in formulating new public policy and strengthening existing strategies. Moreover, it can help understand the CEE dynamics exclusively. Researching innovation conditions specific to the area should discourage local governments from copy-pasting ‘western practices’ and focusing more on the solutions that fit their context.
Concerning the sub-national and regional levels of analysis, the innovation process is thought to result from local cooperation and engagement. The Regional Innovation System (RIS) approach highlights the importance of voluntary interactions (social and economic), joint ventures and support forces as its main mechanisms (Asheim, Smith, and Oughton 2011; Doloreux and Porto Gomez 2017). When actors engage in a collaborative activity, it creates conditions for ‘learning by interaction’ (Cooke 1992). Learning builds up competencies that companies can further utilise in their activities. These are means to strengthen their absorptive capacity, contributing to the implementation of innovation (Kallio, Harmaakorpi, and Pihkala 2010; Tödtling and Trippl 2018). Knowledge and information are critical in this matter (Cooke 2003; Asheim and Coenen 2006; Asheim et al. 2005). Through their diffusion, enterprises gain innovative possibilities and replace old technology (Arieli et al. 2020). Moreover, depending on knowledge's practical and scientific utilisation, it can contribute to various learning and innovation patterns (Asheim et al. 2005).
Knowledge and learning are the backbones of innovation activities, so a network is the ‘locus for innovation’ (Spender et al. 2017; Radosevic 2002). It acts as a platform for knowledge and information sharing. Depending on its particularities (structure and links), shared knowledge's speed, degree and volume vary (Burt 2000; Wani and Ali 2015).
The importance of network characteristics and strength of ties has long been a parameter in the socio-economic field (Granovetter 1973, 1985). Therefore, dense connections and strong ties enable deeper embeddedness. With it, a firm's boundaries become ‘porous’ (Chesbrough 2003), leading to more interaction, learning and distribution of knowledge into the network. Therefore, the structure and connectivity in a network can shape (and are shaped) by the processual innovation activities. Stuck, Broekel and Revilla Diez (2014) considered network aspects in articulating RIS typology. According to them, the innovation practices are related to collaboration architecture and partnership preferences. Therefore, it allows us to formulate the first hypothesis:
H1:
Besides the interaction dimension, the RIS paradigm considers the available assistance and innovation-facilitating institutions. The theory differentiates them as the Support Subsystem (Tödtling and Trippl 2005; Coenen, Moodysson, and Asheim 2004; Asheim and Isaksen 1997), whose main purpose is to create an efficient environment for innovation (Isaksen 2001). Their activity
These are defined by the functionality and services provided by specialized institutions and organisations. H2:
When discussing the network externalities and support factors, many academics touch on the issue of cultural and moral norms. It can take the form of social capital in a network structure, as means to generate entrepreneurial benefits (e.g., Burt 2000), or as a parameter in the institutional framework (e.g., Zukauskaite 2018). This attention comes from the role that regional mindset plays regarding the other two aspects. Shared norms, visions, problems and attitudes are important factors for developing collaboration (Boschma 2005; Bland et al. 2010). Social, cultural and institutional proximities contribute to easier contact and reduce collaboration costs. By ‘smoothing’ the interaction, it empowers the network learning abilities and therefore contributes to the innovation process (Lin and McDonough 2014).
The second argument to consider is an agent's perception and individual abilities reflected as mental potential. This approach can be traced back to Schumpeter's vision of entrepreneurship (Schumpeter 1983 [1934]; Śledzik 2013). In his first work on innovation, he described the entrepreneur as the actor of development. Through personal traits, he implements innovation and causes Creative Destruction. This is now known as the Mark I model of innovation (Croitoru 2012), which implies introducing new technology through establishing start-ups and continuous new entries.
Given its importance, some scientific works capture cognitive frames as a separate parameter to explain innovation (Modic and Rončević 2018; Cepoi and Golob 2017). Acting as both network and agent characteristics, the mindset contributes to implementing different innovation practices and enables innovation ambidexterity
The ability to engage in knowledge and technology exploration and exploitation simultaneously. H3:
With the liberalisation and globalisation of the economy, endogenous factors explain only a part of the developmental opportunities available to regions. The integration into modular production chains, and their embeddedness in a regional context, brings additional possibilities for value creation, capture and enhancement (Henderson et al. 2002; Yeung and Coe 2015; Coe, Dicken, and Hess 2008). These Transnational Value Chains (TVCs) permeate multiple cultural and institutional frameworks, linking various RIS on a global scale (Dicken 2005). Maintaining such connections is important for avoiding ‘locked-in’ innovation systems (Tödtling and Trippl 2005). The cooperation with TVC ambassadors in regions diversifies the knowledge pool (Bathelt, Malmberg, and Maskell 2004; Martin and Moodysson 2013). In some cases, the TVC representatives become leading actors in the regional network, manifesting control over innovation cooperation and practices (Stuck, Broekel, and Revilla Diez 2014). Therefore, they dictate the innovation vector and the network composition (e.g., local sector and group) to benefit from their information.
Integrating into modular production triggers technology, knowledge and skills transfer, enhancing local learning (Pietrobelli and Rabellotti 2011; Giuliani, Pietrobelli, and Rabellotti 2005). As the relationship progresses, the volume, sophistication and quality of knowledge passing to the region increases, offering further development paths for local RIS (Ernst 2002, 2009). Therefore, TVCs’ adaptation into a regional framework contributes not only as a source of new knowledge but also as pressure and motivation to gradually improve absorptive capacity. Depending on the regional production competencies, the ability of regions to satisfy the demand of production networks, and the governance system within the TVC, regions can develop different models of ‘upgrading’ and value capture (Gereffi 2014; Ernst 2002). This process is associated with continuous bargaining between the TVC leaders and regional stakeholders. As such, the embeddedness conditions and the position in the production process will create different innovation possibilities. Hence, the fourth hypothesis:
H4:
Endogenous and exogenous factors amplify regional conditions contributing to innovation. Yet, many of these parameters act simultaneously, making it difficult to observe any individual impact. Different contextual combinations create distinct parameters and offer divergent opportunities to regional stakeholders. In such a case, the analysis cannot end with testing the aforementioned hypotheses, as it has to consider an inclusive model where the same outcome can be achieved through different paths.
To analyse the impact of endogenous and exogenous forces on innovation performance in CEE, the research will consider eight regions. The sample includes Continental Croatia (Croatia), Goriska (Slovenia), Moravia-Silesia (Czech Republic), Nord (Republic of Moldova), Sofia (Bulgaria), Zakarpattya (Ukraine), Cluj and Timis (Romania). As the goal is to see general patterns of the innovation process, this sample offers the necessary diversity. It represents a wide spectre from peripheric regions (e.g., Zakarpattya, Nord), semi-periphery (e.g., Timis) to central, capital administrative units (e.g., Sofia). Moreover, most of these regions have dissimilar national contexts, which excludes the observation bias on this level.
Given the small number of cases and the intent to answer the second research question (RQ2), building a statistical model is unsuitable for the study. Therefore, the research design considers Qualitative Comparative Analysis (QCA) (Ragin 2008, 2014). QCA allows the evaluation of the association between variables (the outcome and conditions leading to it) based on a set relationship. It is not as competent to offer generalisations as statistic regression (Seawright 2005), but the asymmetric approach offers the possibility to establish equifinality of the outcome (Wagemann and Schneider 2010) based on Boolean logic (AND, OR, NOT). The QCA operates on identifying Sufficient (when a condition is a subset of the outcome) and Necessary (when the outcome is a subset of a condition) associations with the outcome.
The data for the analysis was collected through the Jean Monnet Centre of Excellence, entitled ‘Technology and Innovations in Regional Development for Europe 2020 (TIR2020)
Jean Monnet Center of Excellence, Technology and Innovations in Regional Development for Europe 2020 (TIR2020), Co-funded by the Erasmus+ programme of the European Union, Key Action: Cooperation for innovation and the exchange of good practices, Action Type: Knowledge Alliances for higher education, Project Reference: 587540-EPP-1-2017-1-SI-EPPJMO-CoE. More information available at
Questionnarie variables
Innovation process (control conditions) | Information quality and disseminations means (Q2) | |
Engagement of the regional private sector in R&D activities (Q3) | ||
Open Innovation practices (Q4) | ||
Institutional parameters | Quality of Institutional support (Q5) | |
Capacity to attract talented people (Q6) | ||
Capacity to retain talented people (Q7) | ||
Impact of Innovation Policy (Q8) | ||
Network parameters | Network structure/connectivity (Q9) | |
Degree of cooperation between regional stakeholders (Q10) | ||
Degree of cooperation between regional and stakeholders outside the region (Q11) | ||
Manifestation of regional trust (Q12) | ||
Cognitive parameters | Openness to entrepreneurship (Q13) | |
Practices of lifelong learning (Q14) | ||
Attitude towards competition (Q15) | ||
Attitude towards globalisation (Q16) | ||
Value chains parameters | Degree of embeddedness of TVC partners in regional processes (Q17) | |
The role in modular global production (Q18) | ||
TVC's engagement in the local innovation process and R&D (Q19) | ||
Dependence on economic exchange with TVC partners (Q20) |
R&D, Research and Development; TVC, transnational value chain.
Each question offered four labels describing a possible regional scenario (Annex 1). Respondents had to unanimously agree on one score that best describes the region. Besides offering additional validity,
When all the respondents agreed on a score easily, it indicated that the label describes the regional situation adequately. On the contrary, when a debate emerged, it put respondents’ opinions against each other, which decreased the impact of personal or professional bias.
The data collection ended with a database of ordinal scores (1–4). Therefore, the research will rely on the fuzzy-set QCA (fsQCA) that can accommodate a non-binary variation. The fsQCA assesses set relationship based on threshold comparison (Seawright 2005) after calibration – i.e., determination of set membership (Ragin 2008). The lowest score is associated to non-membership (calibration value 0), and a score of 4 represents full membership (calibration value 1). The mid scores indicate a position of ‘more out than in’ (score 2, calibration value 0.33) and ‘more in than out’ (score 3, calibration value 0.67).
To determine sufficient and necessary conditions, the QCA methodology relies on the indicators of Consistency/Inclusion (strength of a relationship) and Coverage (importance of a relationship). In fsQCA, both operate on the premise of calibration variation between the outcome and the condition (Table 2). Auxiliary parameters include Relevance of Necessity (RoN) as indicators of the irrelevance of a condition or combination of conditions. For example, it has low values when a condition is constant (or close to it) and thus unimportant in the analysis (Ragin 2008; Mattke et al. 2022). Further, Proportional Reduction of Inconsistency (PRI) indicates the degree to which a condition is associated with the outcome and its negation (Mattke et al. 2022). This is important to consider by comparing the PRI between cases to understand which has a better (meaningful) relationship.
fsQCA indicators
Sufficiency |
|
|
Necessity |
|
|
fsQCA, fuzzy-set QCA.
Given the low number of observations and a vast number of variables in the analysis, the research will deviate from the suggested thresholds of sufficiency and necessity (Mattke et al. 2022). The benchmark is selected based on the sample context. For necessity, the validation criteria are set to accommodate one divergent case out of eight possible (inclusion, consistency and RoN, at 0.875). For sufficiency, the consistency threshold (that validates the empirical evidence of a condition) is set at the same value. However, sufficient conditions require less coverage, based on the possibility of having multiple conditions leading to the outcome. Consequently, the analysis will consider 0.5 as a validation point (4 out of 8 cases). This can be enough to suggest that a condition has a meaningful impact on CEE and should be considered in future policy.
The fsQCA methodology operates on the premise of the association of variables and the alignment of calibrated values. It neither provides proof of a relationship nor indicates the vector of interdependency. The strength of QCA comes through the possibility of validating certain individual conditions and combinations of conditions through empirical examples and theoretical models. Therefore, the associations (especially cases that include conjunction and disjunction of different conditions) will be critically examined through a logical and theoretical lens.
The analysis revealed several sufficient conditions that met the necessary thresholds (Table 3). Among control conditions,
Individual sufficient and necessary conditions
0.813 | 0.929 | 0.625 | 0.929 | 0.813 | 0.727 | |
0.917 | 0.786 | 0.800 | 0.786 | 0.917 | 0.923 | |
1.000 | 0.643 | 1.000 | 0.643 | 1.000 | 1.000 | |
0.929 | 0.929 | 0.833 | 0.929 | 0.929 | 0.909 | |
0.800 | 0.857 | 0.571 | 0.857 | 0.800 | 0.750 | |
1.000 | 0.786 | 1.000 | 0.786 | 1.000 | 1.000 | |
1.000 | 0.857 | 1.000 | 0.857 | 1.000 | 1.000 | |
0.846 | 0.786 | 0.600 | 0.786 | 0.846 | 0.846 | |
1.000 | 0.929 | 1.000 | 0.929 | 1.000 | 1.000 | |
0.800 | 0.857 | 0.571 | 0.857 | 0.800 | 0.750 | |
0.867 | 0.929 | 0.714 | 0.929 | 0.867 | 0.818 | |
0.800 | 0.857 | 0.571 | 0.857 | 0.800 | 0.750 | |
0.632 | 0.857 | 0.364 | 0.857 | 0.632 | 0.417 | |
1.000 | 0.786 | 1.000 | 0.786 | 1.000 | 1.000 | |
0.765 | 0.929 | 0.556 | 0.929 | 0.765 | 0.636 | |
0.813 | 0.929 | 0.625 | 0.929 | 0.813 | 0.727 | |
0.867 | 0.929 | 0.714 | 0.929 | 0.867 | 0.818 | |
0.909 | 0.714 | 0.750 | 0.714 | 0.909 | 0.929 | |
0.765 | 0.929 | 0.556 | 0.929 | 0.765 | 0.636 |
PRI, proportional reduction of inconsistency; RoN, relevance of necessity; TVC, transnational value chain.
Regional cooperation (
In the case of necessity, the list is significantly shorter. Only two conditions met the qualification parameters. Regional cooperation (
To assess the equifinality of multiple conditions, QCA methodology consists of three possible combinations: ‘AND/*’ (conjunction of two sets), ‘OR/+’ (disjunction of two sets) and ‘NOT/~’ (inversion of the set value) (Ragin 2008; Dușa n.d.). Each indicates a Boolean algebra operation to set merging and combinations. In fs QCA, AND selects the minimal value out of the presented conditions, OR considers the highest value, and NOT indicates a reversed score (1-condition). In this context, the number of possible outcomes rises gradually. To avoid redundancy, QCA performs a minimisation process, selecting only the conditions that have a meaningful (logical) impact and omitting redundant conditions. However, the possibility of combining multiple conditions creates new boundaries for error. The distribution of values for analysing and selecting particular scores among a group of conditions creates a premise to see false associations. These cases will be considered a calculation excess and explained in the context of the analysis. The results for combinations that met the validation threshold are presented in Table 4. Some of them replicate the findings of previous analyses (Table 3), such as the importance of
Combination of conditions. Sufficiency
1 | ~ |
1.000 | 0.571 | 1.000 |
2 | 0.917 | 0.786 | 0.800 | |
3 | 1.000 | 0.643 | 1.000 | |
4 | 0.929 | 0.929 | 0.833 | |
5 | 1.000 | 0.786 | 1.000 | |
6 | 1.000 | 0.857 | 1.000 | |
7 | 1.000 | 0.929 | 1.000 | |
8 | ~ |
1.000 | 0.643 | 1.000 |
9 | 1.000 | 0.786 | 1.000 | |
10 | ~ |
1.000 | 0.500 | 1.000 |
11 | ~ |
1.000 | 0.571 | 1.000 |
12 | 0.923 | 0.857 | 0.800 | |
13 | 0.923 | 0.857 | 0.800 | |
14 | ~ |
1.000 | 0.571 | 1.000 |
15 | ~ |
1.000 | 0.786 | 1.000 |
16 | 1.000 | 0.714 | 1.000 | |
17 | 1.000 | 0.857 | 1.000 | |
18 | ~ |
1.000 | 0.714 | 1.000 |
19 | ~ |
1.000 | 0.714 | 1.000 |
20 | 1.000 | 0.714 | 1.000 | |
21 | ~ |
1.000 | 0.643 | 1.000 |
22 | 1.000 | 0.786 | 1.000 | |
23 | 1.000 | 0.929 | 1.000 | |
24 | 0.929 | 0.929 | 0.833 | |
25 | 1.000 | 0.643 | 1.000 | |
26 | 0.929 | 0.929 | 0.833 | |
27 | 0.923 | 0.857 | 0.800 | |
28 | 1.000 | 0.786 | 1.000 | |
29 | 0.929 | 0.929 | 0.833 | |
30 | 0.929 | 0.929 | 0.833 |
PRI, proportional reduction of inconsistency; TVC, transnational value chain.
Some results are to be considered a calculation excess based on the reversion of scores. When a score is low, after the reversion and conjunction, it can meet the inclusion and coverage marks. Cases 1, 8, 10, 11, 15, 18, 19, 21 and 22 (Table 4) pose doubts about their logical and theoretical relevance. For example, a shortage of information flow in the region (
Some cases can consider a negation. For example,
The entrepreneurial mindset seems to thrive when coupled with exogenous conditions (17, 27, 28). When R&D is unavailable, local clustering and reliance on the Mark I innovation model (Croitoru 2012) lead to innovation. A possible conclusion in this scenario is that pushing regional start-ups to supply global demand opens additional perspectives for them and gives them ‘space’ for growth. The TVCs consider the importance of regional innovation, and new entries capitalise on regional knowledge (Audretsch, Grilo, and Thurik 2011). Therefore, the innovation system benefits TVCs, and, in return, the local stakeholders gain more value capture.
The attraction of talented people covariates with innovativeness in regions with a high entrepreneurial spirit, a good network and information-sharing conditions. By opening new businesses, sharing information that was not in the region and integrating into the local network, they contribute to innovation. Similar to
The combination of exogenous factors among themselves (
Concerning the necessary conditions, 86 combinations passed the validation threshold (Table 5). Many of these conditions can be considered a ‘calculation relationship’ rather than real associations. Multiple observations contain negated outcomes which raise doubt about their validity. Especially disjunction of sets acts as means to have numerous combinations that fit the threshold but are not relevant empirically. Therefore, the analysis will consider a joint interpretation of conditions indicating the same conclusions.
Combination of conditions. Necessity
1 | 0.929 | 0.929 | 0.909 | 44 | 0.929 | 0.929 | 0.909 | ||
2 | 0.929 | 1.000 | 1.000 | 45 | 0.929 | 1.000 | 1.000 | ||
3 | 0.929 | 1.000 | 1.000 | 46 | 1.000 | 0.933 | 0.900 | ||
4 | 0.929 | 0.929 | 0.909 | 47 | 0.929 | 1.000 | 1.000 | ||
5 | 0.929 | 1.000 | 1.000 | 48 | 0.929 | 1.000 | 1.000 | ||
6 | 0.929 | 1.000 | 1.000 | 49 | 0.929 | 0.929 | 0.909 | ||
7 | 0.929 | 1.000 | 1.000 | 50 | 1.000 | 0.933 | 0.900 | ||
8 | 0.929 | 1.000 | 1.000 | 51 | 0.929 | 1.000 | 1.000 | ||
9 | 0.929 | 0.929 | 0.909 | 52 | 0.929 | 0.929 | 0.909 | ||
10 | 0.929 | 0.929 | 0.909 | 53 | 0.929 | 1.000 | 1.000 | ||
11 | 0.929 | 0.929 | 0.909 | 54 | 0.929 | 0.929 | 0.909 | ||
12 | 0.929 | 0.929 | 0.909 | 55 | 0.929 | 0.929 | 0.909 | ||
13 | 0.929 | 1.000 | 1.000 | 56 | ~ |
0.929 | 1.000 | 1.000 | |
14 | 0.929 | 1.000 | 1.000 | 57 | 0.929 | 0.929 | 0.909 | ||
15 | 0.929 | 1.000 | 1.000 | 58 | 0.929 | 0.929 | 0.909 | ||
16 | 0.929 | 1.000 | 1.000 | 59 | 0.929 | 1.000 | 1.000 | ||
17 | 0.929 | 1.000 | 1.000 | 60 | 0.929 | 0.929 | 0.909 | ||
18 | 0.929 | 1.000 | 1.000 | 61 | 0.929 | 0.929 | 0.909 | ||
19 | 0.929 | 1.000 | 1.000 | 62 | 0.929 | 0.929 | 0.909 | ||
20 | 0.929 | 1.000 | 1.000 | 63 | 0.929 | 0.929 | 0.909 | ||
21 | 0.929 | 0.929 | 0.909 | 64 | 0.929 | 0.929 | 0.909 | ||
22 | 0.929 | 1.000 | 1.000 | 65 | 0.929 | 0.929 | 0.909 | ||
23 | 0.929 | 1.000 | 1.000 | 66 | 0.929 | 1.000 | 1.000 | ||
24 | 0.929 | 1.000 | 1.000 | 67 | 0.929 | 0.929 | 0.909 | ||
25 | 0.929 | 1.000 | 1.000 | 68 | 0.929 | 0.929 | 0.909 | ||
26 | 0.929 | 1.000 | 1.000 | 69 | 0.929 | 0.929 | 0.909 | ||
27 | 0.929 | 1.000 | 1.000 | 70 | 0.929 | 0.929 | 0.909 | ||
28 | 0.929 | 1.000 | 1.000 | 71 | 0.929 | 0.929 | 0.909 | ||
29 | 0.929 | 1.000 | 1.000 | 72 | 0.929 | 0.929 | 0.909 | ||
30 | 0.929 | 1.000 | 1.000 | 73 | 0.929 | 0.929 | 0.909 | ||
31 | 0.929 | 0.929 | 0.909 | 74 | 0.929 | 0.929 | 0.909 | ||
32 | 1.000 | 0.933 | 0.900 | 75 | 0.929 | 0.929 | 0.909 | ||
33 | 0.929 | 0.929 | 0.909 | 76 | 0.929 | 0.929 | 0.909 | ||
34 | 0.929 | 0.929 | 0.909 | 77 | 0.929 | 0.929 | 0.909 | ||
35 | 0.929 | 0.929 | 0.909 | 78 | 0.929 | 0.929 | 0.909 | ||
36 | 0.929 | 0.929 | 0.909 | 79 | 0.929 | 0.929 | 0.909 | ||
37 | 0.929 | 1.000 | 1.000 | 80 | 0.929 | 0.929 | 0.909 | ||
38 | 0.929 | 1.000 | 1.000 | 81 | 0.929 | 0.929 | 0.909 | ||
39 | 0.929 | 1.000 | 1.000 | 82 | 0.929 | 1.000 | 1.000 | ||
40 | 1.000 | 0.933 | 0.900 | 83 | 0.929 | 0.929 | 0.909 | ||
41 | 0.929 | 0.929 | 0.909 | 84 | 0.929 | 0.929 | 0.909 | ||
42 | 0.929 | 0.929 | 0.909 | 85 | ~ |
0.929 | 0.929 | 0.909 | |
43 | 0.929 | 1.000 | 1.000 | 86 | 0.929 | 0.929 | 0.909 |
RoN, relevance of necessity; TVC, transnational value chain.
Some important observations can be drawn for the RIS approach in CEE. First, the combination
The necessity configurations also confirm some previous conclusions. This is related to the importance of R&D in the CEE area. The
Several groups of combinations (5–10, 13–28) represent a conjunction of endogenous and exogenous conditions.
The disjunction (OR) configurations will not be considered, given the risk of redundant association. These cases resemble a mathematical excess of the fsQCA, which was triggered by a small number of observed regions and a big number of conditions (Mattke et al. 2022).
Regional innovation represents a very complex concept that shows distinctive parameters in different types of regions based on their internal and historical background (Radosevic 2002; Tödtling and Trippl 2005). The research sought to see developmental patterns in CEE by testing the impact of network configuration (H1), institutional framework (H2), regional mindset (H3) and influence for integration into global value chains (H4) on innovation performance. Additionally, the study identified various combinations of parameters that are associated with innovation in the European macro-region.
The fsQCA analysis partially confirmed all the initial assumptions. Regional cooperation, the potential of support institutions and the efficiency of innovation policy create the basis to consider the first two hypotheses and act in defence of the RIS approach. The results also confirmed that enabling mindset, associated with a positive attitude to local competition and a trustworthy environment, contributes to developing innovative practices. Similarly, the implication of TVC partners in local R&D and innovation can help understand the regional potential.
The study also found that it might be wrong to consider endogenous and exogenous conditions separately. These are compatible and circumstantial to local predetermined factors. Their impact is conjunctive, and the (unique) combinations that emerge in regional contexts create (unique) regional innovation pressures, conditions and opportunities.
In theoretical terms, research provides additional validation of RIS and GPN theories. The regional cooperation and institutional framework are the only sufficient and necessary conditions in the analysis. Their strong association with innovation suggests that regional stakeholders in CEE consider this model. The range of configuration with regional and global factors should urge local authorities to also analyse the global context and enhance their bargaining potential (Coe et al. 2004). Another practical implication is that regions should search to increase the degree of general and institutional trust. It was part of multiple configurations, including regional and global factors leading to innovation. Trust offers necessary network flexibility (the ability to easily switch to the right partner at the right time), which boosts the efficiency of regional cooperation, institutional impact and collaboration/integration into TVCs.
The research offers an extensive range of conclusions and, through fsQCA, a vast list of combinations relevant to innovation. However, it does not offer the possibility to generalise these findings. Many of the configurations reconstruct previously researched models, but others must be tested and proved empirically. The research on innovation is not easy and therefore requires continuous confirmations. Further analysis should verify the validity of these configurations.