The world we live in is changing at a fast pace and facing various environmental and social challenges. This applies also to European societies that are facing numerous developmental and institutional challenges. New solutions are needed within organisational and systemic environments, which are oriented towards improving the welfare of society and the wellbeing of each individual. There is a strong demand for social innovations, which imply new concepts and concrete actions in that regard.
In recent decades, the concept of social innovation has gained prominence within social science research. There are various definitions of the concepts, which emphasise the approaches, actions and civil cooperation to provide a sustainable and prosperous society for all. Herein, we take into account the understanding of the concept, which considers social innovation as the process and also as the product. In this regard, the focus is not only on the organisational and social processes but also on their outputs (Baker and Mehmood 2015, 3), bringing forth the solutions to current social demands.
On the one hand, social innovations are supposed to embody the spirit of innovation and entrepreneurship – especially when related to social enterprises. This brings it very close to the modern ideas of development – as progress, based on innovations. On the other hand, it embodies the dimension of ‘the social’, the public good, the ideas of responsibility and solidarity. As such, it is closely related to the ideas of sustainability.
Rapid technological change, as new information and communication technologies play an increasingly important role in all major areas of society, is also affecting the nature of social innovation. These are increasingly based on the use of digital resources to tackle the main challenges and solve the key problems modern societies are facing. In this regard, we can talk about
Despite the popularity of the concept, the indicators of social innovation are rare. The few existing ones are rather complex and – also due to this fact – only tend to provide the data for a very small number of countries. Relatively comprehensive indicators of social innovation applicable at the macro level include The Social Innovation Index applied by The Economist Intelligence Unit (Economist Intelligence Unit n.d.) and the European Digital Social Innovation Index (EDSII) (Nesta 2019). Although the latter focuses on digital social innovation and is collected at the levels of cities, it is the only one that provides social innovation data for all of the European Union (EU) member states. It is thus also applied and tested in our research.
While considering social innovations as an important aspect of a sustainable society, there is a need to explore the broader context conditioning social innovations to emerge and to flourish in society.
Using the metaphor coined by Giddens (1991), we are in a juggernaut threatening to run out of control. There is a need for steering, which supports and provides new ideas, actions and cooperation to meet all the emerging social needs. Governance plays a crucial role in providing the proper institutional and legislative environment for social innovation to flourish (Galego et al. 2021), engaging collaboration environments of public actors with those from the economy and civil society (Galego et al. 2021; Swyngedouw 2005). The important role is played by participatory and network governance enabling to steer across multi-level scales (Rauschmayer, Paavola, and Wittmer 2009); (Baker and Mehmood 2015, 10). In our research, we understand the concept of active society in terms of quality of governance that combines its effectiveness (as a proxy for steering capacity) with the inclusiveness in terms of democratic political participation, freedom of expression, association and media (as a proxy for polyarchy as ‘real existing democracy as perceived by Robert Dahl [Dahl 1971]). The hypothetical model that serves as a basis of the research is presented in Figure 1.
The other important factor which we pay attention to in this paper refers to cultural attributes that people share in certain national environments. It has been argued that it is essential to explore shared values among people conditioning similar perceptions, attachments, thinking and behaviour, which potentially lead to social innovation ranging from bottom-up initiatives to general macro-level actions (Gaye Karacay 2021). In this regard, we lean on Hofstede's definition, saying that culture is ‘the collective programming of the mind distinguishing the members of one group or category of people from others’ (‘Hofstede Insights’ n.d.). The Hofstede model of national culture consists of six dimensions. On that basis, we can differentiate between national cultures, which can differently influence social innovations depending on certain cultural orientations and meanings.
In addition to culture, there are also structural settings referring to overall social development, which can influence social innovation to enhance. In this regard, we consider the Human development index (HDI) to play a certain role as well. While usually social innovations are seen as generators for human development (Ziegler, n.d.), we intend to test how the general developmental conditions influence social innovation in the first place.
Accordingly, the purpose of this paper is to determine whether there is a combination of democratic and effective governance conditions in digital social innovation, and the role of human development and culture in that regard.
Based on the theoretical premises and existing empirical research, we thus intend the following:
Present and adapt the indicators of digital social innovation for cross-national comparative analysis of the European countries; Transform the indicators of social innovation, human development, culture and active society in terms of effective and democratic governance into the set memberships to make them suitable for the application of the fuzzy sets analysis; Provide the fuzzy sets analyses to test whether governance, cultural and developmental factors are sufficient and necessary conditions for the flourishing of social innovations.
In methodological terms, our analysis is based on the fuzzy sets approach (C. Ragin 2000; C. C. Ragin 2008). While classical statistical methods typically present a feature of the analysed cases in terms of variables, fuzzy sets analysis presents the same feature in terms of memberships of the cases in a set. Having a certain property thus implies belonging to a certain set that refers to this property. However, the features in social science are not just dichotomous in terms of having this feature or not having it – but rather continuous in terms of a given feature being more or less present. This implies that most features are not presented in terms of classical (crisp) sets (where a unit either belongs to it or not) but in terms of fuzzy sets, where a unit belongs to the set to a certain extent (cf. [C. Ragin 2000; C. C. Ragin 2008]).
For our research, we have applied the EDSII (Nesta 2019) as it is the only indicator of digital social innovation that has been collected systematically within the European framework. Their database refers to 60 European cities (including those of all EU member states plus the United Kingdom but excluding Luxemburg) and combined 32 indicators for six different categories: funding, skills, civil society, collaboration, infrastructure and diversity with inclusion (Nesta 2019).
Since our analysis is focused on the cross-national perspective, we need to transform these data to the national level. For this purpose, we took the two most digitally socially innovative cities for each country and used their mean value as the proxy for the national level digital social innovation. Some – typically smaller – countries are only represented by one city, whose value, in that case, has been taken as the only indicator for the country.
To provide some additional checks on the validity of using the city-level indicators as an approximation of the national-level social innovation, we have compared the data extracted from the EDSII database with the national-level social innovation indicator developed by The Economist Intelligence Unit (Economist Intelligence Unit n.d.). The data are presented in Table 1.
EDSII and the corresponding set memberships.
Denmark | 71.2 | 73.8 | Fully highly digitally socially innovative | 1.00 | 1.000 |
United Kingdom | 77.3 | 72.2 | 1.00 | 0.999999 | |
Netherlands | 57.7 | 68.7 | 1.00 | 0.999992 | |
Spain | 44.8 | 63.6 | 1.00 | 0.999885 | |
Sweden | 65.7 | 63.3 | 1.00 | 0.999867 | |
Finland | 59.2 | 58.2 | 1.00 | 0998647 | |
France | 66.4 | 56.9 | 1.00 | 0.997699 | |
Belgium | 69.2 | 55.9 | 1.00 | 0.996591 | |
Ireland | 56.5 | 53.1 | 1.00 | 0.99047 | |
Austria | n.a. | 51.5 | 1.00 | 0.983639 | |
Germany | 66 | 50.3 | 1.00 | 0.976011 | |
Slovenia | n.a. | 41.1 | Mostly highly digitally socially innovative | 0.67 | 0.772396 |
Latvia | n.a. | 39.2 | 0.67 | 0691402 | |
Czechia | n.a. | 38.1 | 0.67 | 0.640847 | |
Portugal | 52 | 37.0 | 0.67 | 0.589042 | |
Poland | 52.6 | 33.8 | Mostly not highly digitally socially innovative | 0.33 | 0.441652 |
Lithuania | n.a. | 32.9 | 0.33 | 0.403235 | |
Cyprus | n.a. | 32.8 | 0.33 | 0.399082 | |
Slovakia | n.a. | 32.3 | 0.33 | 0.378686 | |
Estonia | n.a. | 30.0 | 0.33 | 0.29327 | |
Hungary | n.a. | 28.1 | 0.33 | 0.233298 | |
Italy | 57.5 | 24.7 | 0.33 | 0.147394 | |
Romania | n.a. | 17.9 | Not highly digitally socially innovative | 0.00 | 0.037805 |
Bulgaria | n.a. | 14.8 | 0.00 | 0.011962 | |
Greece | n.a. | 13.7 | 0.00 | 0.006631 | |
Croatia | n.a. | 10.7 | 0.00 | 0.000499 |
EDSII, European Digital Social Innovation Index.
While both indices are not measuring precisely the same phenomenon, some differences between them are expected and do not question the data validity. The correlation between the two indicators turns out rather high, with the Pearson correlation coefficient of 0.47 for the countries included in both databases. However, when we drop Spain as an outlier (where the key cities of Madrid and Barcelona are less representative for the rest of the country), this coefficient increases even to 0.69. While we cannot see these correlations as the proof of validity of our data, a good correspondence between the indices at least demonstrates that such validity cannot be disproved on this basis.
For our analysis, we need to transform the index values into set memberships. Consequently, we construct a set of highly digitally socially innovative countries. As it is based on a ratio scale variable, this set should not be understood in classical terms, as a crisp set, to which an element belongs or not belongs, but rather as a fuzzy set – implying that an individual country may belong to this set to a certain extent. While crisp set memberships are expressed in dichotomous terms being either 1 (member) or 0 (not a member), fuzzy set membership may take any value between 1 (fully in the set) and 0 (fully out of the set).
A calibration procedure is required to translate an original indicator value into the corresponding fuzzy set membership (C. Ragin 2000; C. C. Ragin 2008). On the one hand, we need to be as precise as possible while setting the membership. On the other hand, we need to distinguish between irrelevant differences. This distinguishes the construction of fuzzy set memberships from the simple dimension indices that just transform the variable scales to range from 0 to 1.
The fuzzy set membership in our case is calibrated around four anchors corresponding to a certain level of membership:
fully highly innovative (membership 1.0); mostly highly innovative (0.67); mostly not highly innovative (0.33); not highly innovative (0.0).
Based on our knowledge of the European countries in question, we have assigned the closest anchor to each country, thus classifying them into four distinct categories. The calibrated fuzzy set membership is then calculated based on the indirect calibration procedure using the Stata software (
The behaviour of individuals in all areas of human life is largely culturally conditioned. In this regard, the culture, in the form of values, ideas, cognitive and behavioural patterns, exerts a strong impact on societal development. The past decades brought increased awareness of the importance of so-called intangible resources for success in different realms of society, including a variety of cultural factors. This is eloquently evidenced by the titles of books such as
Culture as ‘mental programming’ or ‘software of the mind’ (Hofstede, Hofstede, and Minkov 2010), shapes everything, although it does not determine everything (Hickson and Pugh 2002). It can be defined as a complex whole, consisting of many elements, which permeates all areas of the society and its subsystems: politics, economics, social relations, habits, traditions and so on (Jelovac and Rek 2010). Culture is a multifaceted, rather ambiguous and multidimensional phenomenon, understood as a ‘living system’ that allows individuals and groups to cooperate with the outside world.
There are significant differences between countries and regions in terms of the prevailing cultural patterns.
Based on data from the
Our approach relies on Hofstede's multidimensional model of cultural patterns (Hofstede 2001; 2011; Minkov and Hofstede 2011) as it encompasses the complex nature of this phenomenon. In our empirical analysis, we applied six dimensions on the basis of which, according to Hofstede, different national cultures can be classified: Power distance (the degree to which the less powerful members of a society accept and expect that power is distributed unequally), Individualism vs. collectivism (preference for a loosely knit social framework vs. preference for a tightly knit framework in society), Femininity vs. masculinity (preference in society for achievement, heroism, assertiveness, and material rewards for success vs. preference for cooperation, modesty, caring for the weak and quality of life), Uncertainty avoidance (the degree to which the members of a society feel uncomfortable with uncertainty and ambiguity), Long term vs. short term orientations (preference to maintain time-honoured traditions and norm vs. encouraging thrift and efforts in modern education as a way to prepare for the future), and Indulgence vs. restraint (free gratification of basic and natural human drives vs. suppression of gratification of needs). Data for different countries can be found at Hofstede Insights (‘Hofstede Insights’ n.d.).
The data and their transformations into fuzzy set memberships through indirect calibration are presented in Table 2.
Aspects of culture and the corresponding set memberships
Austria | 11 | 0 | 0.00070 | 55 | 0.33 | 0.32145 | 79 | 1.00 | 0.89129 |
Belgium | 65 | 0.67 | 0.65506 | 75 | 0.67 | 0.70823 | 54 | 0.67 | 0.55857 |
Bulgaria | 70 | 0.67 | 0.72202 | 30 | 0.00 | 0.00166 | 40 | 0.33 | 0.37055 |
Croatia | 73 | 0.67 | 0.76144 | 33 | 0.00 | 0.00629 | 40 | 0.33 | 0.37055 |
Cyprus | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
Czechia | 57 | 0.67 | 0.55178 | 58 | 0.33 | 0.38860 | 57 | 0.67 | 0.60133 |
Denmark | 18 | 0 | 0.05309 | 74 | 0.67 | 0.69324 | 16 | 0.00 | 0.01334 |
Estonia | 40 | 0.33 | 0.36226 | 60 | 0.33 | 0.43245 | 30 | 0.33 | 0.22630 |
Finland | 33 | 0.33 | 0.28297 | 63 | 0.67 | 0.49580 | 26 | 0.33 | 0.15893 |
France | 68 | 0.67 | 0.69527 | 71 | 0.67 | 0.64528 | 43 | 0.33 | 0.41015 |
Germany | 35 | 0.33 | 0.30684 | 67 | 0.67 | 0.57437 | 66 | 0.67 | 0.73194 |
Greece | 60 | 0.67 | 0.58948 | 35 | 0.00 | 0.01275 | 57 | 0.67 | 0.60133 |
Hungary | 46 | 0.33 | 0.42606 | 80 | 0.67 | 0.77593 | 88 | 1.00 | 0.95596 |
Ireland | 28 | 0.33 | 0.21602 | 70 | 0.67 | 0.62829 | 68 | 0.67 | 0.76007 |
Italy | 50 | 0.33 | 0.46972 | 76 | 0.67 | 0.72273 | 70 | 0.67 | 0.78727 |
Latvia | 44 | 0.33 | 0.40475 | 70 | 0.67 | 0.62829 | 9 | 0.00 | 0.00000 |
Lithuania | 42 | 0.33 | 0.38355 | 60 | 0.33 | 0.43245 | 19 | 0.00 | 0.04367 |
Netherlands | 38 | 0.33 | 0.34061 | 80 | 0.67 | 0.77593 | 14 | 0.00 | 0.00377 |
Poland | 68 | 0.67 | 0.69527 | 60 | 0.33 | 0.43245 | 64 | 0.67 | 0.70319 |
Portugal | 63 | 0.67 | 0.62851 | 27 | 0.00 | 0.00028 | 31 | 0.33 | 0.24219 |
Romania | 90 | 1 | 0.93308 | 30 | 0.00 | 0.00166 | 42 | 0.33 | 0.39700 |
Slovakia | n.a. | n.a. | n.a. | 52 | 0.33 | 0.25452 | n.a. | n.a. | n.a. |
Slovenia | 71 | 0.67 | 0.73528 | 27 | 0.00 | 0.00028 | 19 | 0.00 | 0.04367 |
Spain | 57 | 0.67 | 0.55178 | 51 | 0.33 | 0.23271 | 42 | 0.33 | 0.39700 |
Sweden | 31 | 0.33 | 0.25761 | 71 | 0.67 | 0.64528 | 5 | 0.00 | 0.00000 |
U.K. | 35 | 0.33 | 0.30684 | 89 | 1.00 | 0.86909 | 66 | 0.67 | 0.73194 |
Austria | 70 | 0.33 | 0.49281 | 60 | 0.67 | 0.60105 | 63 | 1.00 | 0.97891 |
Belgium | 94 | 1.00 | 0.93335 | 82 | 1.00 | 0.92309 | 57 | 1.00 | 0.93711 |
Bulgaria | 85 | 0.67 | 0.81777 | 69 | 0.67 | 0.78117 | 16 | 0.00 | 0.00795 |
Croatia | 80 | 0.67 | 0.72034 | 58 | 0.67 | 0.55048 | 33 | 0.33 | 0.37210 |
Cyprus | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. | n.a. |
Czechia | 74 | 0.67 | 0.58469 | 70 | 0.67 | 0.79653 | 29 | 0.33 | 0.25817 |
Denmark | 23 | 0.00 | 0.00108 | 35 | 0.00 | 0.02032 | 70 | 1.00 | 0.99562 |
Estonia | 60 | 0.33 | 0.29371 | 82 | 1.00 | 0.92309 | 16 | 0.00 | 0.00795 |
Finland | 59 | 0.33 | 0.27714 | 38 | 0.00 | 0.04789 | 57 | 1.00 | 0.93711 |
France | 86 | 0.67 | 0.83460 | 63 | 0.67 | 0.66978 | 48 | 0.67 | 0.78851 |
Germany | 65 | 0.33 | 0.38605 | 83 | 1.00 | 0.92970 | 40 | 0.67 | 0.57661 |
Greece | 100 | 1.00 | 0.97096 | 45 | 0.33 | 0.18137 | 50 | 0.67 | 0.83143 |
Hungary | 82 | 0.67 | 0.76173 | 58 | 0.67 | 0.55048 | 31 | 0.33 | 0.31456 |
Ireland | 35 | 0.00 | 0.03290 | 24 | 0.00 | 0.00004 | 65 | 1.00 | 0.98608 |
Italy | 75 | 0.67 | 0.60790 | 61 | 0.67 | 0.62493 | 30 | 0.33 | 0.28618 |
Latvia | 63 | 0.33 | 0.34722 | 69 | 0.67 | 0.78117 | 13 | 0.00 | 0.00061 |
Lithuania | 65 | 0.33 | 0.38605 | 82 | 1.00 | 0.92309 | 16 | 0.00 | 0.00795 |
Netherlands | 53 | 0.33 | 0.19051 | 67 | 0.67 | 0.74779 | 68 | 1.00 | 0.99290 |
Poland | 93 | 1.00 | 0.92437 | 38 | 0.00 | 0.04789 | 29 | 0.33 | 0.25817 |
Portugal | 99 | 1.00 | 0.96636 | 28 | 0.00 | 0.00084 | 33 | 0.33 | 0.37210 |
Romania | 90 | 1.00 | 0.89187 | 52 | 0.33 | 0.38084 | 20 | 0.00 | 0.04819 |
Slovakia | 51 | 0.33 | 0.16607 | 77 | 0.67 | 0.88194 | 28 | 0.33 | 0.23060 |
Slovenia | 88 | 1.00 | 0.86527 | 49 | 0.33 | 0.29199 | 48 | 0.67 | 0.78851 |
Spain | 86 | 0.67 | 0.83460 | 48 | 0.33 | 0.26302 | 44 | 0.67 | 0.68873 |
Sweden | 29 | 0.00 | 0.00978 | 53 | 0.33 | 0.41039 | 78 | 1.00 | 0.99952 |
U.K. | 35 | 0.00 | 0.03290 | 51 | 0.33 | 0.35112 | 69 | 1.00 | 0.99440 |
In general terms, governance refers to the frameworks within which people act and politics occur and thus refers to the system of measures that involve setting the rules for the exercise of power. Settling conflicts over such rules (Hyden 1999, 85) is one of the most widely used political concepts and takes a central place in contemporary debates in social sciences (Pierre and Peters 2000). Our concept of democratic governance refers to governance as politics (expression of different interests, political competition, and relations between political actors) as well as to governance as policy (creation and implementation of different policies concerning different social spheres). In the language of systems theory, it refers to the ‘input’ (impulses from the environment, like political demand and support) and ‘output’ dimension (‘products’ of the political system, e.g. legislation and various social arrangements) of the political system (Almond 1966; Blondel 1972). As stated by March and Olsen (March and Olsen 1995, 44), ‘democratic governance is more than the management of efficient political coalition building and exchange within prior constraints … It involves molding social and political life-shaping history, an understanding of it, and an ability to learn from it’.
The concept consists of the following two dimensions: inclusiveness and effectiveness.
Besides electoral participation, there are also other important forms of political participation (like engagement in political parties and civic associations), which are of a more complex nature and demand more commitment from citizens. Participation has some meaning only in the context of political pluralism, whose main characteristic is a competition of different actors for political support that leads to the existence of political alternatives. For real competition to take place, institutional requisites are necessary that, first of all, enable autonomous organisational of social groups, and, second, assure their equal treatment in the political and public spheres. We refer to Etzioni's distinction between ‘effectiveness’, that is, the extent to which goals (of organisation) have been fulfilled, and ‘efficiency’, i.e. costs of certain results (Etzioni 1964). In our case, ‘effectiveness’ is perceived in wider terms (it refers to general developmental goals), while ‘efficiency’ is seen in more narrow sense (it refers to developmental costs). For example, reaching a high level of material welfare is a matter of effectiveness, while global warming is a matter of efficiency.
An increasing body of scholarly research claims the strong relationship between governance and social innovations, writing about the role of citizen movements, community collaboration, civil society organisations, collective actions, social relations, socio-political transformations and new governance arrangements (Galego et al. 2021, 19). This relationship is mutual. On one hand, inclusive and effective political institutions are necessary for utilisation of the creative potentials of different social groups. On the other hand, these creative potentials in the form of civic engagement can lead to systemic reforms, making institutions more responsive and accountable to the citizenry. Interactions between different actors contribute to improving the quality of public services, creating intermediate structures, and encouraging self-reflexivity in governmental practice and institutions (Massey and Johnston-Miller 2016).
In general, regarding the assessment of the relevance of political indicators, we can say that indices of different dimensions of quality of governance, based on a large and diverse set of databases, most credibly reflect the actual situation in individual areas in a comparative perspective. This can be said for the Worldwide Governance Indicators of the World Bank (World Bank n.d.) that we choose for our analysis. The research survey The survey covers more than 200 countries and territories. Its estimates and indexes are constructed on the basis of very wide spectrum of more than 300 indicators from numerous data sources. For more information on the survey, its methodology and results, see (World Bank n.d.). The countries are assessed with values on the scale from −2.5 to 2.5 (lower value is worse).
As with digital social innovation and sustainable development, we construct the set of countries with highly inclusive governance and the set of countries with highly effective governance. The memberships in these fuzzy sets are calibrated based on the World Bank data described above and presented in Table 3.
Inclusiveness and effectiveness of governance and the corresponding set memberships.
Austria | 1.38 | 1.00 | 0.9366 | 1.45 | 1.00 | 0.9557 |
Belgium | 1.40 | 1.00 | 0.9433 | 1.17 | 0.67 | 0.8033 |
Bulgaria | 0.32 | 0.00 | 0.0463 | 0.27 | 0.00 | 0.8515 |
Croatia | 0.50 | 0.33 | 0.2763 | 0.46 | 0.00 | 0.0259 |
Cyprus | 1.04 | 0.67 | 0.7454 | 0.92 | 0.67 | 0.5471 |
Czechia | 0.93 | 0.67 | 0.6636 | 0.92 | 0.67 | 0.5471 |
Denmark | 1.61 | 1.00 | 0.9861 | 1.87 | 1.00 | 0.9988 |
Estonia | 1.21 | 0.67 | 0.8574 | 1.19 | 0.67 | 0.8193 |
Finland | 1.61 | 1.00 | 0.9861 | 1.98 | 1.00 | 0.9996 |
France | 1.18 | 0.67 | 0.8396 | 1.48 | 1.00 | 0.9638 |
Germany | 1.42 | 1.00 | 0.9495 | 1.62 | 1.00 | 0.9873 |
Greece | 0.86 | 0.67 | 0.6098 | 0.34 | 0.00 | 0.0008 |
Hungary | 0.32 | 0.00 | 0.0463 | 0.49 | 0.00 | 0.0426 |
Ireland | 1.32 | 1.00 | 0.9131 | 1.42 | 1.00 | 0.9463 |
Italy | 1.05 | 0.67 | 0.7526 | 0.41 | 0.00 | 0.0087 |
Latvia | 0.81 | 0.67 | 0.5706 | 1.04 | 0.67 | 0.6816 |
Lithuania | 0.92 | 0.67 | 0.6559 | 1.07 | 0.67 | 0.7122 |
Netherlands | 1.60 | 1.00 | 0.9850 | 1.85 | 1.00 | 0.9985 |
Poland | 0.72 | 0.67 | 0.4975 | 0.66 | 0.33 | 0.2164 |
Portugal | 1.21 | 0.67 | 0.8574 | 1.21 | 0.67 | 0.8345 |
Romania | 0.46 | 0.33 | 0.2247 | 0.25 | 0.00 | 0.1122 |
Slovakia | 0.88 | 0.67 | 0.6253 | 0.71 | 0.33 | 0.2809 |
Slovenia | 0.99 | 0.67 | 0.7087 | 1.13 | 0.67 | 0.7690 |
Spain | 1.07 | 0.67 | 0.7668 | 1.00 | 0.67 | 0.6388 |
Sweden | 1.61 | 1.00 | 0.9861 | 1.83 | 1.00 | 0.9982 |
United Kingdom | 1.39 | 1.00 | 0.9400 | 1.34 | 1.00 | 0.9138 |
The development of society is a multidimensional phenomenon that encompasses both economic and non-economic aspects. It encompasses material well-being, social inclusion, level of healthiness, intellectual capital, and so on. It refers to society's endowment with different types of resources. It can be understood as a manifestation of developmental performance. Developmentally successful are those societies that, based on the efficient use of their internal resources, most successfully manage the challenges that come from their environment. In this respect, Parsons’ concept of adaptability is useful, which, in contrast to passive adaptation to a relatively static environment, means the permanent ability to successfully cope with a dynamic and thus highly variable environment (Parsons 1951, 1967). Developmental performance is largely related to systemic competitiveness, that is, the capability of an individual social system to participate equally in the processes of international exchange (this is not about the economic exchange, but also about the exchange in other important areas, such as science, culture, etc.).
Societies with a high level of developmental performance are better equipped with material, technological and intellectual resources. Possession of these resources boosts the creativity of individuals. As such, it is important for digital social innovations. In this regard, we can presume that the level of socio-economic development is significantly connected to the innovative potential of society.
As an empirical measure of the level of development, we apply the United Nations Development Programme's HDI. It covers three dimensions: Decent standard of living, Knowledge, and Long and healthy life. It consists of three indexes: GNI index (GNI per capita – PPP), Education index (expected and mean year of schooling), and Life expectancy index (life expectancy at birth) (United Nations Development Programme n.d.).
In the fuzzy set analysis, the issue of causality is presented in terms of combinations of potential causes that may or may not lead to a certain effect. If the cause is always present whenever the effect is present, this cause is considered
The appropriateness of potential causal combinations – besides their theoretical soundness of course – are assessed in terms of consistency and coverage. Consistency implies the absence of empirical exceptions to the causal rule established by the model. It can be seen as analogous to statistical significance. When the values for consistency are low, especially below 0.8, which is a well-established threshold for raw consistency (C. C. Ragin 2008; Greckhamer et al. 2018, 489), one cannot reject the null hypothesis that there is no causal connection. Coverage, on the other hand, refers to the explanatory strength of the causal model, in terms of how much the effect is explained by the combination of causes provided by the model. Its function is thus somewhat comparable to the determination coefficient in regression analysis (C. C. Ragin 2008).
All of the possible combinations of potential conditions for digital social innovation can be expressed in terms of truth tables. The full complexity of all existing paths towards very high levels of digital social innovations is presented in Table 4. Eight existing causal options have been identified in this regard, which means that they can be seen as alternatives (logical ‘or’) to each other. However, they all share both aspects of governance included in our model, namely inclusiveness (Voice) and effectiveness. This implies that both aspects of governance are necessary for high performance in digital social innovations.
Human development index scores and the corresponding set memberships.
Austria | 0.922 | 1 | 0.9028 |
Belgium | 0.931 | 1 | 0.9561 |
Bulgaria | 0.816 | 0 | 0.1206 |
Croatia | 0.851 | 0.33 | 0.1567 |
Cyprus | 0.887 | 0.33 | 0.4269 |
Czechia | 0.9 | 0.67 | 0.6227 |
Denmark | 0.94 | 1 | 0.9823 |
Estonia | 0.892 | 0.33 | 0.4980 |
Finland | 0.938 | 1 | 0.9782 |
France | 0.901 | 0.67 | 0.6385 |
Germany | 0.947 | 1 | 0.9919 |
Greece | 0.888 | 0.33 | 0.4405 |
Hungary | 0.854 | 0.33 | 0.1664 |
Ireland | 0.955 | 1 | 0.9969 |
Italy | 0.892 | 0.33 | 0.4980 |
Latvia | 0.866 | 0.33 | 0.2236 |
Lithuania | 0.882 | 0.33 | 0.3641 |
Netherlands | 0.944 | 1 | 0.9886 |
Poland | 0.88 | 0.33 | 0.3416 |
Portugal | 0.864 | 0.33 | 0.2117 |
Romania | 0.828 | 0 | 0.1201 |
Slovakia | 0.86 | 0.33 | 0.1910 |
Slovenia | 0.917 | 0.67 | 0.8573 |
Spain | 0.904 | 0.67 | 0.6854 |
Sweden | 0.945 | 1 | 0.9898 |
United Kingdom | 0.932 | 1 | 0.9601 |
HDI, Human Development Index.
Consistency and coverage of the consistent causal paths to digital social innovation. The complex solution.
1 | ~Power Distance * Individualism * ~Uncertainty Avoidance * Indulgence * HDI | 0.078 | 0.999 |
2 | ~Power Distance * ~Masculinity * ~Uncertainty Avoidance * ~Long Term Orientation * Indulgence * HDI | 0.031 | 0.991 |
3 | ~Power Distance * Masculinity * ~Uncertainty Avoidance * Long Term Orientation * Indulgence * HDI | 0.012 | 0.985 |
4 | Power Distance * Individualism * Uncertainty Avoidance * Long Term Orientation * Indulgence * HDI | 0.058 | 0.998 |
5 | Power Distance * ~Individualism * ~Masculinity * Uncertainty Avoidance * ~Long Term Orientation * Indulgence * HDI | 0.053 | 0.988 |
6 | Power Distance * ~Individualism * Masculinity * Uncertainty Avoidance * Long Term Orientation * ~Indulgence * HDI | 0.011 | 0.935 |
7 | ~Power Distance * ~Masculinity * ~Uncertainty Avoidance * Long Term Orientation * ~Indulgence * ~HDI | 0.049 | 0.824 |
8 | Power Distance * ~Individualism * ~Masculinity * Uncertainty Avoidance * ~Long Term Orientation * ~Indulgence * ~HDI | 0.025 | 0.928 |
COMPLETE MODEL – Overall coverage and consistency: | 0.700 | 0.947 |
HDI, Human Development Index.
On the other hand, very high levels of digital social innovation are achievable even without very high levels of socio-economic development: while causal paths 1–6 presuppose very high HDI levels as a condition for high social innovativeness, this condition is absent from paths 7 and 8.
The two countries achieving high social innovativeness without very high HDI are Lithuania (path 7) and Portugal (path 8). The former is characterised, in cultural terms, by the lack of power distance and uncertainty avoidance, combined with a long-term orientation. The latter is characterised, on the other hand, by high power distance, uncertainty avoidance, and short-term orientation, combined with the lack of individualism. Both paths share the lack of both indulgence and masculinity. The six paths that involve very high levels of HDI are also quite diverse in cultural terms. Paths 1–3 share high levels of indulgence and the lack of power distance, as well as the lack of uncertainty avoidance. Path 1 includes Denmark, the United Kingdom, the Netherlands and Sweden, combined with high individualism and the lack of uncertainty avoidance. In Denmark (path 2, which also includes Finland and Sweden), this is also combined with low masculinity and low long-term orientation. On the other hand, Germany and Austria are examples of path 3, characterised by high long-term orientation and masculinity.
Paths 4–6 all involve high-power distance and uncertainty avoidance. France and Belgium (path 4) combine that with individualism, long-term orientation, and indulgence. Paths 5 (Slovenia) and 6 (Czechia) are more specific. While they both share the lack of individualism, Slovenia has lower masculinity and more short-term orientation when compared with Czechia.
While this diversity of paths may be quite interesting, it also indicates the major drawbacks of this complex explanatory model. We can note eight different paths, but most of them consist of a single case. This may imply some particular (national) specifics that could hardly be generalised in any persuasive way. In addition, the small size of our sample combined with a great number of sets (i.e. two for governance, six for culture and one for development) inevitably produces a high number of empirically non-existent combinations that cannot be tested. In the language of fuzzy sets analysis, such cases are called counterfactuals, as they cannot be found among our actual cases. As such, combinations would be logically possible – at least based on Hofstede's theory, which sees the cultural dimensions as mutually independent and thus potentially compatible with each other and with different levels of socio-economic development, one cannot exclude the possibilities that also several other combinations could lead to high levels of digital social innovation.
In addition, the numerical parameters of the complex model may also question it. While its overall consistency of 0.947 is beyond any doubt, its overall coverage of 0.700 is far from impressive. While the model is highly consistent, its overall explanatory power is rather weak.
Consequently, the claim that high levels of particular cultural features and/or very high levels of socio-economic development are
On the other hand, the two conditions related to governance seem to be necessary, since they appear in all eight paths identified by our complex model.
All of this calls for a more parsimonious explanation that may drop the unnecessary elements from the explanatory model. Such a model is presented in Table 6. It is a combination of inclusive (Voice) and effective (Effect) governance with both conditions being necessary and thus allowing no alternative paths. When compared with the complex model in Table 4, it provides a significant increase in coverage, namely from 0.700 to the quite persuasive 0.879, while its consistency of 0.920 remains fully satisfactory (i.e. only 0.027 lower than for the much more complex model presented above). The parsimonious solution dropping the aspects of culture and socio-economic development as rather irrelevant is thus much sounder than the complex one.
Consistency and coverage of the consistent causal paths to digital social innovation and sustainability of the parsimonious solution
1 | Voice * Effect | 0.879 | 0.920 |
Even when only observing the two aspects of governance, we should note that our population of countries includes no countries with high effectiveness and no high inclusiveness. Whether the non-existence of the high effectiveness–low inclusiveness combination among the selected European countries is just a coincidence, is beyond the scope of this research. It implies, however, that the causal effects of effectiveness cannot be tested separately but only combined with inclusiveness – as nobody can check the effect of a nonexisting phenomenon.
The results of causal Model 2 are presented in Figure 2. We can observe a high level of consistency between lower governance results and lower social innovation on the one hand and the high governance results and high social innovation performance on the other hand. Estonia is the only major exception in this regard – with comparatively good results in terms of effectiveness/inclusion but weak digital social innovation.
As inclusiveness and effectiveness of governance are necessary and sufficient for digital social innovation (with a high level of consistency), there can be no cases with inclusive and/or effective governance but with no high digital social innovation. The final parsimonious model that drops the conditioning by culture and development that could not be confirmed is summarised in Figure 3.
European societies have been facing several societal challenges, the success of which will have a major impact on the position of the ‘old continent’ in a global context. The response of political institutions, both at the national and EU levels, to the crises of this millennium – from the financial to the migrant and the current epidemiological crisis – has been far from optimal. Inefficiency and irresponsibility of traditional political parties and their lack of leadership lead to poor performance in tackling these problems, contributing to a decrease of political trust in many European countries. There is a need for improving the quality of governance to maintain a high level of systemic competitiveness as well as a high level of well-being and social cohesion.
Governance is strongly linked with social innovation. As shown by our explanatory model, dimensions of governance, inclusiveness and effectiveness are necessary and sufficient causes of digital social innovation to take place. This means that existence of channels for widespread participation of the people, including vulnerable and marginalised groups, in political life, on a basis of their autonomous choice, as well as effective and transparent conduct of political institutions, staffed by competent people with a high level of professional and personal integrity, lead to a boom of creativity and utilisation of human capital in all key areas of society. This brings more optimal solutions for resolving political, economic, social and technological problems that burden contemporary societies. The tight relationship between governance and social innovations is particularly relevant regarding the pursuit of sustainability in terms of the development and implementation of new innovative policy ideas to tackle key environmental challenges (Baker and Mehmood 2015; Parra 2013).
When we talk about the role of democratic governance and its societal effects, emphasis is needed on the existence of a balance between its dimensions. Neither participation alone nor sheer effectiveness is sufficient. They only contribute to the development of innovation through mutual interactions.
It has turned out that neither cultural features nor the level of socio-economic development can be proven as relevant as separate factors in stimulating digital social innovations. A note of caution is needed in this regard. First, it should be stressed that this conclusion may only be drawn for the European or even more specifically EU context, where the disparities either in terms of culture or in terms of socioeconomic development are not much less significant than at the global level. We are dealing with countries that are culturally still relatively similar and relatively well-developed when compared with most other parts of the world. According to the UNDP HDI (United Nations Development Programme n.d.), all countries in our analysis are ‘highly developed’ despite their differences. The lack of explanatory potential of the socio-economic development and culture can thus be related to these facts. And second, the lack of direct conditioning of digital social innovations by culture and development does not imply that there are no background effects of these factors on the inclusiveness and effectiveness of governance. Our additional fuzzy sets analyses indicate that culture (but not socio-economic development) may be necessary and sufficient condition for effective and inclusive governance of the European countries.
Consequently, it might be argued – though with some caution – that high inclusiveness and effectiveness as components of quality of governance presuppose particular cultural characteristics since these factors strongly overlap. However, again we have to state that the importance of particular cultural characteristics as detected in our analysis holds for European societies and thus one shall avoid generalisation since it is possible that in non-European environments, different cultural combinations are more relevant.
At least in the EU context, our results provide some reasons for optimism. Among the EU member states, the highest levels of socio-economic development are not necessary for impressive performance in terms of digital social innovation. Even the comparatively less developed EU countries and EU countries with a significant cultural variety are capable of high levels of social innovation – as a major source of sustainable development.