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WP10

Socio-cultural determinants of innovation

TECHNOPOLIS

Nelly Bruno, Michal Miedzinski

Alasdair Reid, Miriam Ruiz Yaniz

February 2008

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INTRODUCTION ..........................................................................................................................................................3

1. THE CONCEPTUAL FRAMEWORK OF THE STUDY............................................................................................5

2. OVERVIEW OF NATIONAL SOCIO-CULTURAL PROFILES ...............................................................................7

2.1. Methodological approach for national data ............................................................................................7 2.2. Cultural capital and consumer behaviour................................................................................................8 2.3. Human capital..........................................................................................................................................10 2.4. Social capital............................................................................................................................................12 2.5. Organisational capital and entrepreneurship........................................................................................14 2.6. Summary ...................................................................................................................................................16 2.7. Socio-cultural environment and innovation performance: an overview..............................................18

3. OVERALL RESULTS OF THE SURVEY .............................................................................................................24

3.1. General overview .....................................................................................................................................24 3.2. Cultural capital and consumer behaviour..............................................................................................25 3.3. Human capital..........................................................................................................................................26 3.4. Social capital............................................................................................................................................28 3.5. Organisational capital and entrepreneurship........................................................................................29

4. ANNEXES .........................................................................................................................................................30

5. REFERENCES ...................................................................................................................................................42

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Introduction

Work package 10 of the SYSTEMATIC project aimed at identifying socio-cultural barriers and driving forces to innovation across ten sectors: food/drink, automotive, energy production, biotechnologies, textiles and clothing, chemicals, information and communication technologies, aerospace, machinery and equipment, and eco-innovation. Our work extends the analysis carried out in the previous work-packages on the barriers to innovation and the driving forces, by identifying and analysing other sector specific characteristics such as for example consumer habits, tradition and culture, organisational rigidities, and mobility of the workforce. In the literature on innovation, socio-cultural factors are most often used to characterise a geographically defined community (e.g. a nation, a region, etc.) rather than a sector of economic activity. Nevertheless, management literature and simple observation suggests that behaviour is also shaped by factors inherent to belonging to a professional group (with shared educational, work experience, social networks such as engineering associations, etc. trajectories). Equally, the increasingly integrated European market with large companies operating across the Single Market 'imposing' their management 'culture' on operating divisions and their standards and practices on suppliers may also lead to cases where the 'culture of innovation' in enterprises diverges from that of their 'home' region or country. Equally from the demand side, we know from economic literature that not all products have the same inherent characteristics and that they are more or less sensitive to consumer demand. Sophisticated marketing can change perceptions of a product in wider society but equally interest groups and lobbies and changing social values can equally undermine a products position or image in the eyes of consumers / buyers. These societal forces, the subtle, or sometimes dramatic, changes in the balance of market power can heavily influence the position of certain sectors more than others (most simply one could imagine that sectors producing essentially final consumer goods, food or automotives for instance, would be more sensitive to such factors than sectors producing 'intermediary goods' such as machinery and equipment).

In this respect the analysis of socio-cultural factors goes to the very heart of what the SYSTEMATIC project set out to examine: are there sectoral innovation systems operating at national level or even across national boundaries; and if so does this require a distinct policy approach on the part of national or European institutions. If the hypothesis is correct that there are socio-cultural factors which influence innovation in specific sectors to a greater or lesser extent and that these factors transcend national boundaries, then the SYSTEMATIC project will have come closer to elucidating the policy issues related to sectoral innovation systems.

In consequence, the approach of the study team has been twofold: first, we have analysed similarities and differences across national socio-cultural profiles and then we have explored socio-cultural factors relevant for each of the specific sectors.

The study has been conducted in several steps:

! The first step provides a review of academic literature on socio-cultural factors influencing innovation. Based on this literature review a selection was made of

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generic socio-cultural factors relevant to innovation. This led us to develop a novel conceptual framework entitled the “four capitals” approach (Chapter 1).

! Following a review of available surveys dealing with relevant socio-cultural aspects (e.g. Eurobarometer, European Social Survey, Community Innovation Survey), relevant indicators have been collected with an objective to characterise national socio-cultural environments. A framework of socio-cultural indicators at country level and a database of national data were developed. Presentation and analysis of the data was undertaken following the “four capitals” approach (Chapter 2).

! To gather further sectoral insights, a survey has been prepared and submitted to the Europe Innova panels and other key sectoral stakeholders (Chapter 3).

! In parallel socio-cultural characteristics especially relevant for specific sectors have been scanned in the literature and strengthened with the views of the experts of the Europe Innova panels1. This resulted in sectoral socio-cultural profiles presented following the “four capitals” approach (Chapter 4).

Finally policy implications of socio-cultural aspects of innovation have been discussed by the experts of the Europe Innova panels and are presented in the last chapter of this report.

1 No panel of expert took place for the following sectors: food and drink, chemicals and machinery and equipment.

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1. The conceptual framework of the study

Socio-cultural barriers or drivers to innovation are socio-cultural factors that influence sectoral innovativeness. In this study, it is assumed that innovation processes are influenced by a range of socio-cultural factors.

Various dimensions can be used to describe the socio-cultural characteristics of a community (whether it be geographically or professionally defined). Each of these dimensions of the socio-cultural value system can be described by (a set of) socio-cultural factors. In this project four dimensions2 are used to identify the socio-cultural characteristics of communities relevant to innovation:

! cultural capital & consumer behaviour,

! human capital,

! social capital, and

! organisational capital & entrepreneurship.

Within each of these dimensions specific elements can be distinguished that are considered relevant to innovation. Cultural capital was defined by Pierre Bourdieu (1981) as “the inherited and acquired properties of one’s self. Inherited not in the genetic sense, but more in the sense of time, cultural, and traditions bestowed elements of the embodied state to another usually by the family through socialisation. It is not transmittable instantaneously like a gift. It is strongly linked to one's habitus - a person's character and way of thinking”. The definition refers to the cultural background and basic value system that is shared by the individuals in a community and manifests in their attitudes and habits, including consumption. In this context, demand is composed of individual consumers and firms characterised by different attributes, knowledge and competencies, and is affected by social factors and institutions. As has been underlined the evolution of demand specific to sectoral communities is likely to influence sharply the dynamics of sectoral systems (Malerba 2005).

The cultural capital and consumer behaviour category encompasses factors (and related indicators) which describe certain basic attitudes of people that influence innovation: the interest in and the attitude towards science and technology, the attitude towards risk from new technologies, the attitude towards the future, the attitude of people towards the environment, the attitudes towards other cultures3, the consumer’s responsiveness to new technologies.

Human capital, a more familiar concept, is defined by the OECD (2005) as the knowledge, skills and attributes derived from education, training and work experience. The literature on knowledge economics (Cowan, David, Foray, 2000) emphasises the point that knowledge plays a central role in innovation and production; while the evolutionary literature (Nelson, 1995) underlines that sectors

2 Three of these dimensions have been used previously at European level (Ricardis project, 2006) but in a different context to define the intellectual capital of a company. To emphasise the importance of individual habits and attitudes, cultural capital and consumer behaviour have been added to the analysis. 3 See also for instance the work of Florida R. (2003), who considers the level of a gay population in a region as a good proxy for the openness of society.

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and technologies differ greatly in terms of the knowledge base and learning processes related to innovation.

Several factors linked to human capital may influence innovation in companies, namely: the availability of human resources in science and technology (HRST) (national and international resources) and in knowledge-intensive services; the provision of higher educated people in the country; the job-to-job mobility of HRST.

It is widely acknowledged that firms accumulate knowledge not only by managing knowledge flows from formal institutions through research, educating and training but also through fostering learning by interacting, within the workforce but also with suppliers and consumers (Schienstock, Hamalainen, 2001). The concept of social capital has many differing definitions. In "The Forms of Capital" (1986) Pierre Bourdieu defines social capital as "the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition." The OECD (2001) defines social capital as ‘networks together with shared norms, values and understanding that facilitate cooperation within or among groups.’ Social capital is therefore about the nature and intensity of relationships. The essential assumption is that social networks have value, which means that social contacts affect the productivity of individuals and groups.

The impact of social capital on innovation can be measured though different aspects: the cooperative behaviour of firms; the main sources of information for innovation; the extent of collaboration with competitors and academia, the level of trust in other people, the importance of informal networks. Our explicit hypothesis is that social capital (propensity to network) differs from sector to sector and hence is a more or less important barrier or driver to innovation. This assumption is comforted by a relatively large literature on co-operation and networking of enterprises.

Finally, organisational capital may have an important impact on the innovation capacity of a company. Organisation’s resources are not just the obvious ones like cash flow or R&D personnel, but also the company’s culture, routines, structure, morality and management styles. Organisational learning "amplifies the knowledge created by individuals and crystallises it as part of the knowledge network of the organisation" (Nonaka and Takeuchi 1995).

Several aspects may reflect the organisational capital as well as the entrepreneurship dimensions: the attitude towards work of individuals; the relation between employers and employees; the attitude towards risk; the extent to which companies implement organisational innovations, the level of organisational rigidities.

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2. Overview of national socio-cultural profiles

2.1. Methodological approach for national data

Constructing indices

Available surveys and studies dealing with relevant socio-cultural aspects have been screened in order to collect indicators characterising national profiles with respect to the four capitals approach adopted. This resulted in a framework with socio-cultural indicators at country level and a corresponding database of national data. Each capital is here represented by several factors illustrated by a limited number of indicators. A choice has been made to retain a limited number of specific indicators for each capital, which best reflect the socio-cultural environment of a country. The choice was made based on availability of data and also to avoid keeping indicators for each capital which were closely correlated. The annex 1 will provide the reader with the full list of selected indicators and their sources.

In order to be able to draw comparisons between national socio-cultural frameworks, values adopted by each indicator in each country have been indexed, with the average for the EU25 countries as the basis reference (=100). Each country (c) is therefore positioned in comparison to the EU mean.

!

Ic /EU 25 =100* (

Vc

VEU 25

)

In case values were missing for some countries, the method of regression imputation has been used to compute estimates. Missing values are substituted by the predicted values obtained from a regression. The dependent variable of the regression is the sub-indicator hosting the missing value and the regressor is the sub-indicator showing the strongest correlation with the dependent variable.

In case of indicators where high values indicated a badly performing country, initial values have been reversed. Therefore the higher the index, the better the performance of the country in comparison to the European mean.

A synthetic index has then been compiled for each factor and each capital. For the determination of the human capital index for each country (H), we have:

!

H = h1;h

2;h

3;...;h

i;...;h

k{ } where each elementary index is like:

!

Ic /EU 25(h

i) =

hc

i

hEU 25

i*100 .4

We therefore have a range of k elementary indexes that we have to summarise numerically with a synthetic index

!

Ic /EU 25(H) which is calculated as an arithmetic

average of the elementary indexes. The computation of elementary indexes before the one of the mean of indexes allows us to suppress the influence of the unit of

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!

hc

1 should be read as : the value of the simple indicator

!

h1, first component of H, in the country c.

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measurement of each indicator. Each indicator has been attributed the same weight in the calculation of the synthetic index.

!

Ic /EU 25(H) =100*

1

n

hc

i

hEU 25

i

i=1

n

"

This procedure has been applied to the national data for each capital. As a result each EU25 country has been positioned against the other countries with respect to its human, social, cultural and organisational capital (see annex 2 to 5 for the full list of indexes).

A synthetic index called “socio-cultural environment index” has then been compiled using the same procedure and giving the same weight to each capital.

Cluster analysis

A cluster analysis has been performed to group information on countries based on their similarity on different sub-indicators. This analysis allows notably disseminating information on the composite indicator without losing that on the dimensions of the sub-indicators. In order to perform the analysis and to avoid results being influenced too much by scores of countries over-performing, the dataset has been normalised for outlier’s scores with the next best values5.

The method of k-means clustering has been used for cluster analysis6. This method is useful when the aim is to divide the sample in k clusters of greatest possible distinction. The parameter k has been decided in advance. The k-means algorithm supplies k clusters, as distinct as possible, by analysing the variance of each cluster. The aim of the algorithm is to minimise the variance of elements within the clusters, while maximising the variance of the elements outside the clusters.

While acknowledging the limits (data availability, subjective choice of indicators, etc), the results of the analysis provide an interesting and novel overview of the differences across the EU member states in terms of the socio-cultural environment, which may in turn influence the sectoral innovativeness of companies.

2.2. Cultural capital and consumer behaviour

The results indicate that the cultural capital in the Netherlands is by far the most favourable for innovation across the EU25 Member States, whereas it appears as a barrier to innovation in comparison for other European countries especially in Greece and Italy. The result for the Netherlands is strongly influenced by its score for the attitude of citizen towards the environment, their interest in science and technology as well as their attitude towards risky technologies. Across the new Member States, Hungary, Malta and Cyprus are the only countries, which have better than average results.

5 Values representing the mean (100) plus two standards deviations have been normalised with the next best value considering that 68% of the values drawn from a normal distribution are within one standard deviation " > 0 away from the

mean µ; about 95% are within two standard deviations and about 99,7% lie within tree standard deviations 6 The analysis conducted with the hierarchical clustering method provides similar results as the k-means analysis.

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Exhibit 1: Indicators selected for cultural capital

Capital Indicator Sources

Interest in science

and technology

Eurobarometer 224, QA1, interest in new inventions and technologies: very

interested

Attitude towards

science

Eurobarometer 224, QA15b.6, The benefits of science are greater than any

harmful effects it may have: agree

Attitude towards risk

from new

technologies

Eurobarometer 224, QA12a.1, If a new technology poses a risk that is not

fully understood, the development of this technology should be stopped even

if it offers clear benefits: disagree

Attitudes towards

future

Eurobarometer 225, Q7.2, The next generation will enjoy a better quality of

life than we do now: agree

Attitudes towards

environment

Eurobarometer 217, Q11, Categorisation of the attitudes of European citizens

towards the environment: convinced

Attitudes towards

other cultures

Resistance to multicultural society 2003 (index constructed by EUMC based

on Eurobarometer 59.2): "It is a good thing for any society to be made up of

people from different races, religions or cultures" and "(COUNTRY)'s

diversity in terms of race, religion or culture adds to its strengths" ; reversed

values

Cultural

capital

and

consumer

behaviour

Customer

responsiveness

Eurostat, CIS3, Enterprises reporting the following factor as highly important

in hampering innovation activities, as percentage of all innovative

companies: customer responsiveness, reversed values

Exhibit 2: Cultural capital across EU25 countries (basis 100: European average)

Cluster analysis

The cluster analysis performed on cultural capital indicators provides an interesting overview of the differences between countries. The analysis classified EU25 countries into four groups, which we have labelled as follows:

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! Conscious conservatives: these are countries in which citizens show a strong interest towards science and technology but are not especially open towards novelty, other cultures and who are especially reluctant about the development of new risky technologies. Moreover in these countries citizen seem to be less environmentally committed.

! Passive believers: in these countries citizen show a low interest towards science and technology issues but are positive and optimistic about the future and the benefits of science.

! Middle road: these are countries showing average values for all indicators.

! Active pessimists: in these countries, citizen are highly open towards novelty, and are ready to take risk. Interest in science and technology is high as well as the environmental commitment. Nonetheless, in these countries, citizen seem to be more pessimistic about the future and about the benefits of science and technology.

Exhibit 3: Typology of EU25 countries for cultural capital

Conscious Conservatives Passive Believers Middle Road Active Pessimists

Cyprus Czech Republic Austria Denmark

Greece Estonia Spain Luxemburg

Malta Poland Ireland The Netherlands

France Hungary Italy Belgium United Kingdom Portugal Germany

Slovenia Finland

Lithuania Sweden

Latvia

Slovak Republic

2.3. Human capital

With respect to human capital, the best environment is found Luxembourg, which a particularly high share of foreign-born human resources in science and technology (46,2%). This result should be interpreted with caution since this small country is performing relatively bad in terms of people active in knowledge-intensive services. The good relative results of Denmark and Spain are partly due to a high share of the population involved in life-long learning; whereas the performance of Poland, Slovenia and Slovakia is weak in terms of the diversity of nationalities in HRST. Portugal performs well below the EU25 average for all indicators of human capital.

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Exhibit 4: Indicators selected for human capital

Capital Indicator Sources

Human resources in

science and

technology

Eurostat, HRST statistics 2005, Human resources in science and technology

as a share of labour force , Total

Nationality of HRST Eurostat HRST database, Share of non-national human resources in science

and technology (HRST), aged 25-64 years

Higher education

(provision of higher

educated people)

Eurostat Labour Force Survey Percentage of people with tertiary education

qualifications (ISCED 5 and 6) in the population aged 25-64, 2006

Human resources in

knowledge-intensive

services

Eurostat HRST database, Share of total employment in knowledge-intensive

service sectors, 2005

Job-to-job mobility

of employed HRST Eurostat HRST database, 2006 / job-to-job mobility of employed HRST in %

Participation in life-

long learning

Eurostat, Labour Force Survey, Population participating in life-long learning

aged 25-74, 2006

Human

capital

Availability of

qualified personnel

Eurostat, CIS3, Enterprises reporting the following factor as highly important

in hampering innovation activities, as percentage of all innovative

companies: lack of qualified personnel, reversed values

Exhibit 5: Human capital across EU25 countries (basis 100: European average)

Cluster analysis

The cluster analysis on EU25 countries led to the following four groups:

! Rigid labour market: in these countries, the provision of higher educated is low as well as the share of human resources in science and technology. The workforce is particularly immobile and less involved in life-long learning. In this country, the share of non-national human resources in science and technology is also particularly low.

! Middle road: this group of countries share an average performance for all indicators of human capital in comparison to the EU25.

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! Open labour market: these countries show a particularly high share of non-nationals in their human resources in science and technology. These are typically small EU countries, clearly dependent on temporary or permanent immigration of skilled labour if they are to compete globally.

! Strong human capital: These countries perform well in terms of availability of human resources in science and technology, the provision of highly educated people and score particularly well on the mobility of the workforce and the share of individuals involved in life-long learning.

Exhibit 6: Typology of EU25 countries for human capital

Rigid labour market Middle road Open labour market Strong human capital

Czech Republic Austria Cyprus Denmark

Germany Belgium Estonia Spain

France Greece Ireland Sweden

Hungary Finland Luxembourg United Kingdom

Italy Lithuania

Malta Latvia Poland The Netherlands

Portugal

Slovenia

Slovak Republic

2.4. Social capital

With respect to social capital, the most favourable environment seems to be in Finland, where companies are highly open to collaboration, be it with competitors or academics. Customers are an important source of information, the corruption level is low and the level of trust between people is fairly high. The Baltic countries are performing relatively well for the indicators related to the cooperation of companies with competitors. The situation of Italy with a very low openness to the external world and a high level of corruption is noteworthy.

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Exhibit 7: Indicators selected for social capital

Capital Indicator Sources

Cooperation with

competitors

Eurostat, CIS4, 81/2007, Different types of cooperation partners of enterprises

by country, as a percentage of innovative enterprises: Competitors or other

enterprises of the same sector

Cooperation with

the academic

world

Eurostat, CIS4, 81/2007, Different types of cooperation partners of enterprises

by country, as a percentage of innovative enterprises: Universities or other higher

education institutions

Customer as a

source of

information

Eurostat, CIS4, 81/2007, Highly important sources of information for innovation

by country, as a percentage of innovative enterprises: clients or customers

Trust Eurobarometer 225, QB8, In general, would you say that you trust other people

almost always, often, only sometimes, rarely, or almost never?Total answer Trust

Social

capital

Corruption Transparency international, CPI 2006, Corruption Perceptions Index EU-27

(from 10=clean to 0=highly corrupt)

Exhibit 8: Social capital across EU25 countries (basis 100: European average)

Cluster analysis

A cluster analysis was again performed on indicators of social capital and EU25 countries can be classified into four groups:

! Closed networks: countries in this group share in general a low level of trust and a high level of corruption. Cooperation with academia is as well particularly low.

! Weak business links: in these countries, cooperation of companies with other actors along the value chain is especially low, especially with competitors.

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! Strong business links: countries in this group share a high level of cooperation with competitors, and tend to consider the customer as an important source of information for innovation.

! High performance networks: in these countries, cooperation of companies with academics is highly developed, customers are considered as a particularly important source of information for innovation and cooperation with competitors is also fairly high. Trust between actors is particularly high and corruption is not an issue.

Exhibit 9: Typology of EU25 countries for social capital

Closed networks Weak business links Strong business links High performance networks

Cyprus Austria Czech Republic Denmark

Greece Germany Estonia Finland

Poland Malta France The Netherlands

Spain Lithuania Sweden

Portugal Luxemburg

Ireland Latvia

Italy Slovenia Slovak Republic

United Kingdom

Belgium

Hungary

2.5. Organisational capital and entrepreneurship

With respect to organisational capital, the best innovation environment appears to be the ones of Ireland and Denmark. Ireland demonstrates particularly strong entrepreneurial attitudes in comparison to other EU Member States. The number of SMEs introducing organisational innovation is particularly high in Ireland as well as in Denmark, Luxemburg and Germany, whereas the performance of Slovakia for this indicator is well below the EU average. Empowerment of employees by management is especially high in Sweden and low in the Mediterranean countries. The relationship between employers and employees appears to be particularly confrontational in France in contrast to Denmark.

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Exhibit 10: Indicators selected for organisational capital

Capital Indicator Sources

Initiative at work

European Value Study.- Here are some more aspects of a job that people say

are important. Please look at them and tell me which ones you personally

think are important in a job?Q.13H An opportunity to use initiative,

mentioned, in%

Enpowerment

WEF, Readiness of the management to delegate decisions to subordinates is

low (=1) (the management takes all decisions by itself) to 7=high (a lot of

decisions are taken by domains and respective levels)

Relation between

employers and

employees

WEF, Labor-employer relations in your country are 1=generally confrontational to 7= generally cooperative

Risk

aversion/entreprene

urship

Eurobarometer Flash 160 Q12: one should not start a business if there is a

risk it might fail: disagree

SMEs which

introduced an

organisational

innovation

CIS3, Number of SMEs who have either introduced “new or significantly

improved knowledge management systems”, “a major change to the

organisation of work within their enterprise” or “new or significant changes in their relations with other firms or public institutions”

Organisat

ional

capital

and

entrepren

eurship

Organisational

rigidities

Eurostat, CIS3, Enterprises reporting the following factor as highly important

for innovation activities, as percentage of all innovative companies:

organisational rigidities, reversed values

Exhibit 11: Organisational capital across EU25 countries (basis 100: European average)

Cluster analysis

The cluster analysis performed on indicators of organisational capital provides an interesting overview of the differences between countries. Based on this analysis EU25 countries can be classified into four groups:

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! Rigid organisations: this group gathers countries in which the level of empowerment of employees by employers is low. Individuals appear particularly adverse to risk even if they demonstrate a high importance given to initiative at work.

! Low personal engagement: these countries share a particularly low level of importance given to initiative at work by individuals.

! New ventures: in this group of countries individuals tend to be particularly open to start a business even if there is a risk of failure and the relationship between employers and employees is particularly confrontational.

! Flexible organisations: these countries share a high level of empowerment, have in general particularly collaborative relations between employers and employees as well as a high share of SMEs introducing organisational innovations.

Exhibit 12: Typology of EU25 countries for organisational capital

Rigid organisations Low personal engagement New ventures Flexible organisations

Hungary Czech Republic Belgium Austria

Malta Estonia Cyprus Germany

Slovenia Spain Greece Denmark

Lithuania France Finland

Latvia Italy Ireland

Portugal The Netherlands Luxemburg Slovak Republic Poland Sweden

United Kingdom

2.6. Summary

The aggregated results for each country across all capitals allows an overview of the general socio-cultural environment, which might have an impact on the innovativeness of companies. The variances of indexes of cultural and organisational capitals appear smaller across EU countries than for social and human capitals.

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Exhibit 13: The four socio-cultural capitals across EU25 countries (basis 100: European mean)

Finland, Denmark, Luxemburg, Sweden and the Netherlands have the most favourable socio-cultural environments for innovative activities when compared to all EU25 Member States. At the other extreme, Italy, Portugal, Poland, Malta and Greece have the least favourable socio-cultural environments for innovation.

Exhibit 14: Indexes of socio-cultural environment across EU25 countries (basis 100: European mean)

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Cluster analysis

Grouping the countries in terms of their socio-cultural environment through a cluster analysis provided the following three groups:

! Rigid socio-cultural environment: these countries do not attain high performance for neither cultural, human nor organisational capital.

! Closed socio-cultural environments: this group of countries share a low performance in terms of social capital.

! Strong socio-cultural environment: these countries perform well on average for all four socio-cultural capitals considered by this study.

Exhibit 15: Typology of EU25 countries for socio-cultural environment

Rigid socio-cultural

environment

Closed socio-cultural

environment

Strong socio-cultural

environment

Czech Republic Austria Belgium

France Cyprus Denmark

Hungary Germany Estonia

Lithuania Greece Finland

Latvia Spain Ireland

Portugal Italy Luxembourg

Slovakia Malta The Netherlands

Poland Sweden

Slovenia

United Kingdom

2.7. Socio-cultural environment and innovation performance: an overview

In order to examine if the differences visible in socio-cultural capitals has an impact on innovation performance, the following charts provide an overview of the relationship between several performance indicators, namely labour productivity7, EPO patents8 and business expenditures in research and development9 and the four capitals of this study.10 For the sake of this exercise, normalised values have been used, in order for the results not to be influenced by the performance of outliers11.

Socio-cultural environment and labour productivity

Organisational capital has the strongest correlation with labour productivity (see chart below). Ireland is the highest performing country for organisational capital and the second, after Luxembourg, for labour productivity. Whereas Lithuania performs worst for both aspects.

7 GDP per person employed, US$ EKS, Total Economy Database, Conference Board, 2007 8 European Patent Office (EPO) patents per million population, Eurostat, 2003 9 Business expenditures in research and development, Eurostat, 2005 10 Since the social capital appears to have a very weak correlation with each of these indicators, charts for this capital are not displayed here. 11 For details on the normalisation method used, see methodological part

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Exhibit 16: Labour productivity and organisational capital12

When considering the socio-cultural environment as a whole, a slightly positive relation with the labour productivity is apparent.

12 The red line in each graph represents the linear regression line, when the indicator of innovation performance is considered as the dependent variable and the capital under focus as the independent variable. The R square value (coefficient of determination) displayed on each graph represents the part of the variance of the dependent variable that can be explained by the linear regression. The closer R square from 1, the best suited is the linear relation for describing the dataset. It appears that relations between each capital and each output indicator seem to be positive.

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Exhibit 17: Socio-cultural environment and labour productivity

Socio-cultural environment and EPO patents

When considering the relation between EPO patents and cultural and organisational capitals, the picture appears rather clear, with the Nordic countries (plus Luxembourg and Germany) performing well in terms of both capitals and having a high share of EPO patents per million population.

Exhibit 18: Cultural capital and EPO patents

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Exhibit 19: Organisational capital and EPO patents

At the aggregated level of the socio-cultural environment as a whole, the relationship with the number of EPO patents per country is even clearer. The weaker the socio-cultural environment, the lower the number of EPO patents per million population.

Exhibit 20: Socio-cultural environment and EPO patents

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Socio-cultural environment and business expenditures in research and development (BERD)

The capital showing the highest relation with business expenditure for research and development (BERD) is organisational capital. The Nordic countries are the best performer for both aspects. Ireland is an interesting case where the level of organisational capital is relatively high but the level of BERD low.

Exhibit 21: Business expenditures in research and development and organisational capital

Looking at the correlation between cultural capital and BERD, the picture that emerges is also interesting even if the relation is weaker than with organisational capital: a group of countries perform particularly well in both aspects namely Sweden, Finland, Denmark, Luxembourg, Germany and the Netherlands.

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Exhibit 22: Business expenditures in research and development and cultural capital

As a whole, the socio-cultural environment and the level of BERD in each country seem to be positively correlated. Sweden and Denmark in particular show high levels of BERD associated to strong scores for the index of socio-cultural environment. The cases of Germany and Austria are here also interesting since they perform very well in terms of BERD while the index of socio-cultural environment are just below the European average. This could indicate that economic structure, sectoral composition of an economy, can compensate for weaknesses in socio-cultural characteristics.

Exhibit 23: Business expenditures in research and development and socio-cultural environment

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3. Overall results of the survey

As data allowing a similar analysis for sectors at European level was not available, a survey was conducted between October and December 2007 in order to gather the views and opinions of leading European sectoral experts’13 on the relevance of socio-cultural factors for innovation performance in their sector. For the sake of this exercise socio-cultural factors were defined as the elements of the value systems, traditions and habits, norms and institutions that are relevant for sectoral innovation performance and manifest in the attitudes, behaviour and choices made by individuals, organisations and societies.

In total, 76 responses were received. Accordingly, it is not relevant to develop any sector specific conclusions. Nonetheless, the results give indications on the extent to which sectoral innovation performance is in general determined by the socio-cultural environment.

3.1. General overview

A large majority of respondents (87%) agreed that socio-cultural factors are important determinants of innovativeness in their respective sectors of expertise. Almost three quarters of them believe that their sector has it's own distinct culture, which determines sectoral innovation processes in a different way than in other sectors.

Exhibit 24: Do you agree with the statement that socio-cultural factors are important determinants of innovativeness in your sector?

Experts were asked to rank the different socio-cultural factors in their order of importance for innovation performance in their respective sectors. It appears that the capital having the strongest influence on innovation performance across all sectors is human capital (30% of respondents); followed by organisational capital and

13 Respondents to the survey have been the experts of the Europe Innova Panels as well as additional experts sources via the Europe Innova network

25

entrepreneurship (28%) and cultural capital (25%). Social capital was generally indicated as the least important capital (35% of respondents). At the same time, organisational and cultural capital were also indicated as the least important capitals by a large number of respondents (respectively 33% and 25% of respondents), which reflects a strong division of opinions.

Exhibit 25: Which of the four general areas of socio-cultural factors has the strongest influence on the innovation performance of your sector? Rank from 1 to 4 where 1 is the most important and 4 the least important area (n=69).

Answer Options 1 2 3 4

Rating

Average

Human capital 21 21 22 5 2,2

Cultural capital and consumer behaviour 17 17 18 17 2,5

Organisational capital and entrepreneurship 19 16 11 23 2,6

Social capital 12 15 18 24 2,8

3.2. Cultural capital and consumer behaviour

According to the Eurobarometer opinion survey14 views of Europeans are divided on whether decisions concerning science and technology development should be based more on risks and benefits analysis rather than taking into account moral and ethical considerations. In the framework of the Innovation Watch survey experts were first requested to assess whether in their sectors decisions are taken considering risk and benefit analysis or rather moral and ethical considerations. Nearly two thirds of all respondents (63%) considered that while decisions in their sector are mainly taken considering risk and benefit analysis, moral and ethical considerations are also taken into account. None of the respondents was of the opinion that decisions are influenced by only moral and ethical considerations.

Attitudes towards other cultures can be captured, in part, by peoples’ opinion on the impact of immigration. The vast majority of respondents (77%) agreed that openness towards other cultures creates a more favourable environment for innovation in their sectors.

According to the third Community Innovation Survey15, customer responsiveness is considered as a relevant factor hampering innovation activity. The vast majority of experts (59%) agreed that customer responsiveness to innovative services or products may be explained by cultural factors in their respective sector.

Experts were requested to rank 13 factors influencing consumer responsiveness to innovation in their sector in order of importance (from 1 = least important to 5 = most important). A table presenting the results per sector (within the limits of interpretation imposed by the number of responses by sector) can be found in annex 6.

14 Eurobarometer n°255 15 Eurostat, Community innovation survey, Third Wave, 2000-2001

26

Exhibit 26: Please assess the importance of the factors influencing consumer responsiveness to innovation in your sector. Assign a value to each factor using a 5-rank scale where 5 is the most important and 1 least important (n=73)

The overall results indicated that the five most important factors are the following:

! “income level” (average rank: 3.74);

! “availability of information” (3.59) (most important factor for biotech experts);

! “product/service originality (differentiation)” (3.55)

! “quality of marketing/advertising” (3.52) (most important factor in ICT sector);

! “education level” (3.48).

“Fashion” was given an average rating by almost all respondents (3.18), but it was included, perhaps not suprisingly, in the top two most important factors influencing consumer responsiveness to innovation in the automotive and textile sectors. The factor “ethnic origin of consumer” has been consistently low rated (2.21).

3.3. Human capital

The literature on the knowledge economy emphasises that knowledge plays a central role in innovation and production. Evolutionary literature underlines that sectors and technologies differ greatly in terms of the knowledge base and learning processes related to innovation. According to the results of third Community Innovation Survey16

16 Eurostat, Community innovation survey, Third Wave, 2000-2001

27

the lack of qualified personnel was one of the most important factors hampering innovation activities. In the framework of the Innovation Watch survey, respondents were requested to rank the reasons explaining the lack of qualified personnel in innovation activities in their sector in their order of importance.

The results indicated that the higher rated factor is the limited supply of highly specialised labour followed by the lack of people with interdisciplinary skills.

Exhibit 27: According to you what are the most important reasons explaining the lack of qualified personnel involved in innovation activity in your sector? Please assess importance of each of the following factors using a 5-rank scale where 5 is very important and 1 - least important (n=72).

When looking closer at the sectoral level (acknowledging the limitations) some differences are noticeable17:

! “low number of students and graduates in relevant disciplines” was rated as one of the top 5 most important factors in all sectors, except for the food & drink sector where it was low rated;

! two specific factors “difficult working conditions” and “inappropriate gender balance” were consistently low rated;

! ”unattractive image of the sector” although generally given a low ranking, was the highest ranked factor by the textiles experts;

! “education programmes not adapted to industry needs” was rated as the most important factor explaining the lack of qualified personnel in innovation activities by the experts of the eco-innovation, ICT and biotechnology sectors but was relatively lowly ranked by the respondents of automotive and textile sectors.

17 The table with results splitted per sector can be found in annex 7

28

3.4. Social capital

Social capital in the context of this study is the capacity to network and collaborate between individuals and companies facilitating the generation and accumulation of knowledge and innovation activity. Generation and accumulation of social capital is based on trust and made possible as actors share common norms, values and understanding. In line with what might be expected, the vast majority of respondents (91.5%) agreed that trust is an important factor determining collaborative innovation activity in their respective sectors of expertise.

Comparing the level of trust between key sectoral stakeholders, it can be noticed that the majority of respondents gave an average ranking to trust for the relationships between companies (38% of responses), between companies and research organisations (51%), between companies and consumers (44%) and between research organisations and government (47%). One noticeable exception concerns business-government relationships which was ranked as low or very low by 48% of respondents.

Sectoral experts were requested to rank a set of factors driving innovation collaboration in order of their importance. Previous collaborations and/or shared experience was considered as the most important factor in all sectors followed by funding opportunities for collaborative projects. The least important factors appear to be sharing the same language or having the same nationality or being member of formal associations. Annex 8 provides a rapid overview of differences per sector.

Exhibit 28: According to you what factors are the main drivers of innovation collaboration? Please assess importance of each of the following factors using a 5-rank scale where 5 is very important and 1 - least important (n=71)

29

3.5. Organisational capital and entrepreneurship

The aim of the fourth set of questions of the survey was to gather experts’ views on organisational rigidities, economic risk and corporate culture as factors influencing innovation in their sectors. In the framework of this exercise organisational capital includes company’s culture, routines, structure, ethics and management styles. It determines the organisation’s ability to deal with risks and uncertainty, to unlearn (lock-in phenomenon) as well as remain open for change.

When considering the overall results for all sectors, it appears that views on how well companies in each sector adapt to challenges accompanying innovation activities in terms of organisation and management are widely spread. While 31% of respondents were of the opinion that companies adapt well, 27% disagreed with this statement and 31% neither agreed nor disagreed. The same spread arises for the question on whether managers in each sector are open to taking risk and pursuing new risky projects (34% agreed, 34% disagreed and 17% nor agreed nor disagreed).

Nonetheless more than half of the respondents (55%) evaluated that in their sectors individuals are in general ready to establish a new business even if there is a risk of failure. The percentage is even higher for the biotechnology (75%), eco-innovation (70%), ICT (63%) and aerospace (67%) sectors.

Sectoral experts were finally asked whether they agree that corporate culture (e.g. internal code of conduct, corporate social responsibility, environmental statements, etc.) of the companies in their sector has an influence on their innovative activity. The vast majority (79%) of respondents evaluated indeed that corporate culture is a key factor for innovative activity in their respective sector of expertise.

30

4. Annexes

Annex 1: Data sources for each indicator in each capital

Capital Indicator Sources

Interest in science

and technology

Eurobarometer 224, QA1, interest in new inventions and technologies: very

interested

Attitude towards

science

Eurobarometer 224, QA15b.6, The benefits of science are greater than any harmful effects it may have: agree

Attitude towards risk

from new

technologies

Eurobarometer 224, QA12a.1, If a new technology poses a risk that is not

fully understood, the development of this technology should be stopped even

if it offers clear benefits: disagree

Attitudes towards

future

Eurobarometer 225, Q7.2, The next generation will enjoy a better quality of life than we do now: agree

Attitudes towards

environment

Eurobarometer 217, Q11, Categorisation of the attitudes of European citizens

towards the environment: convinced

Attitudes towards

other cultures

Resistance to multicultural society 2003 (index constructed by EUMC based

on Eurobarometer 59.2): "It is a good thing for any society to be made up of

people from different races, religions or cultures" and "(COUNTRY)'s

diversity in terms of race, religion or culture adds to its strengths" ; reversed

values

Cultural

capital

and

consumer

behaviour

Customer

responsiveness

Eurostat, CIS3, Enterprises reporting the following factor as highly important

in hampering innovation activities, as percentage of all innovative

companies: customer responsiveness, reversed values

Human resources in

science and

technology

Eurostat, HRST statistics 2005, Human resources in science and technology

as a share of labour force , Total

Nationality of HRST Eurostat HRST database, Share of non-national human resources in science

and technology (HRST), aged 25-64 years

Higher education

(provision of higher

educated people)

Eurostat Labour Force Survey Percentage of people with tertiary education

qualifications (ISCED 5 and 6) in the population aged 25-64, 2006

Human resources in

knowledge-intensive

services

Eurostat HRST database, Share of total employment in knowledge-intensive

service sectors, 2005

Job-to-job mobility

of employed HRST Eurostat HRST database, 2006 / job-to-job mobility of employed HRST in %

Participation in life-

long learning

Eurostat, Labour Force Survey, Population participating in life-long learning

aged 25-74, 2006

Human

capital

Availability of

qualified personnel

Eurostat, CIS3, Enterprises reporting the following factor as highly important

in hampering innovation activities, as percentage of all innovative companies: lack of qualified personnel, reversed values

Cooperation with

competitors

Eurostat, CIS4, 81/2007, Different types of cooperation partners of

enterprises by country, as a percentage of innovative enterprises: Competitors

or other enterprises of the same sector

Cooperation with the

academic world

Eurostat, CIS4, 81/2007, Different types of cooperation partners of

enterprises by country, as a percentage of innovative enterprises: Universities

or other higher education institutions

Social

capital

Customer as a

source of

information

Eurostat, CIS4, 81/2007, Highly important sources of information for

innovation by country, as a percentage of innovative enterprises: clients or

customers

31

Trust

Eurobarometer 225, QB8, In general, would you say that you trust other

people almost always, often, only sometimes, rarely, or almost never?Total

answer Trust

Corruption Transparency international, CPI 2006, Corruption Perceptions Index EU-27

(from 10=clean to 0=highly corrupt)

Initiative at work

European Value Study.- Here are some more aspects of a job that people say

are important. Please look at them and tell me which ones you personally

think are important in a job?Q.13H An opportunity to use initiative,

mentioned, in%

Enpowerment

WEF, Readiness of the management to delegate decisions to subordinates is

low (=1) (the management takes all decisions by itself) to 7=high (a lot of

decisions are taken by domains and respective levels)

Relation between

employers and

employees

WEF, Labor-employer relations in your country are 1=generally confrontational to 7= generally cooperative

Risk

aversion/entreprene

urship

Eurobarometer Flash 160 Q12: one should not start a business if there is a

risk it might fail: disagree

SMEs which

introduced an

organisational

innovation

CIS3, Number of SMEs who have either introduced “new or significantly

improved knowledge management systems”, “a major change to the

organisation of work within their enterprise” or “new or significant changes in their relations with other firms or public institutions”

Organisat

ional

capital

and

entrepren

eurship

Organisational

rigidities

Eurostat, CIS3, Enterprises reporting the following factor as highly important

for innovation activities, as percentage of all innovative companies:

organisational rigidities, reversed values

32

Annex 2: Cultural capital and consumer behaviour, indexes (Basis 100 = European mean)

Cultural

capital and

consumer

behaviour

Interest in

science

and

technology

Optimism

towards

science

Attitude

towards risk

from new

technologies

Attitude

towards

future

Attitude

towards

environment

Attitude

towards

other

cultures

Customer

responsive

ness

Cultural

capital

index

AT 77 93 85 88 135 100 102 97

BE 112 103 113 76 117 86 102 101

CY 173 107 80 105 70 83 101 103

CZ 77 85 174 111 35 74 103 94

DE 118 89 108 80 170 90 100 108

DK 109 101 155 76 147 115 107 116

EE 90 112 113 136 59 61 97 95

EL 131 93 47 91 53 50 97 80

ES 86 111 56 118 117 121 95 101

FI 96 97 117 93 158 112 104 111

FR 128 97 80 56 70 112 105 93

HU 109 122 122 125 70 113 90 107

IE 86 97 80 115 94 122 95 98

IT 51 111 61 116 41 104 101 84

LT 45 122 85 144 94 92 101 98

LU 131 95 160 61 153 119 102 117

LV 83 81 66 129 88 71 102 89

MT 147 103 66 111 76 102 102 101

NL 134 76 193 58 229 110 104 129

PL 67 126 103 125 59 103 99 97

PT 58 116 61 120 129 111 99 99

SE 125 99 136 70 112 117 102 109

SI 83 78 38 75 117 119 100 87

SK 77 91 66 123 53 99 97 86

UK 106 95 136 98 53 115 92 99

33

Annex 3: Human capital, indexes (Basis 100 = European mean)

Human

capital

Human

resources

in science

and

technology

Nationa

lity of

HRST

Higher

education

(provision

of higher

educated

people)

Human

resources in

knowledge-

intensive

services

Job-to-job

mobility

of

employed

HRST

Participa

tion in

life-long

learning

Availabili

ty of

qualified

personnel

Human

capital

index

AT 102 130 74 112 104 135 94 107

BE 121 121 133 129 94 76 99 110

CY 100 218 127 24 117 74 93 107

CZ 90 17 56 177 67 57 105 81

DE 112 98 99 194 88 69 85 107

DK 127 63 145 119 190 317 112 153

EE 117 233 139 75 112 65 97 120

EL 76 52 125 44 156 119 92 95

ES 101 133 147 94 138 238 96 135

FI 126 23 109 104 109 77 104 93

FR 104 63 90 115 58 18 109 80

HU 82 14 74 128 62 37 103 71

IE 102 158 125 74 93 79 97 104

IT 85 52 54 140 81 60 99 82

LT 99 20 112 40 89 49 104 73

LU 114 709 100 20 83 82 103 173

LV 89 18 88 33 114 67 103 73

MT 81 67 50 78 83 57 106 75

NL 130 52 122 59 112 146 101 103

PL 77 5 75 100 84 47 106 71

PT 56 51 56 63 80 38 93 62

SE 123 81 127 121 135 198 97 126

SI 98 5 89 188 54 154 98 98

SK 80 6 61 170 57 42 107 75

UK 108 110 123 100 143 199 96 126

Nationality of HRST : For LV in 2004; for LT in 2003, IT value missing estimated based on the method of

regression imputation

Job to Job mobility : Values estimated for IE and SE based on the method of regression imputation

34

Annex 4: Social capital, indexes (Basis 100 = European mean)

Social capital

Cooperation

with

competitors

Cooperation

with the

academic world

Customer as

a source of

information Trust Corruption

Social

capital

index

AT 30 89 39 104 128 78

BE 72 118 105 106 114 103

CY 98 20 21 43 82 52

CZ 117 117 129 83 64 102

DE 33 76 40 102 125 75

DK 113 122 137 156 144 134

EE 141 77 113 106 91 105

EL 86 57 39 69 65 63

ES 23 42 21 115 108 62

FI 261 296 204 137 147 209

FR 107 90 98 96 108 100

HU 104 122 97 109 73 101

IE 46 90 124 111 114 97

IT 37 42 25 94 73 54

LT 193 107 170 87 70 126

LU 113 89 110 104 128 109

LV 191 123 142 87 61 121

MT 43 38 82 63 103 66

NL 94 111 108 148 132 118

PL 65 55 81 72 53 65

PT 52 67 57 83 96 71

SE 82 155 138 156 140 134

SI 155 174 163 83 91 133

SK 161 132 149 85 61 118

UK 85 89 110 102 131 103

35

Annex 5: Organisational capital and entrepreneurship, indexes (Basis 100 = European mean)

Organisational

capital and

entrepreneurship

Initiative

at work Enpowe

rment

Relation

between

employers

and

employees

Risk

aversion/

entrepreneur

ship

SMEs which

introduced an

organisational

innovation

Organisa

tional

rigidities

Organisati

onal capital

index

AT 101 117 122 87 126 96 108

BE 102 113 78 109 100 102 101

CY 102 78 103 104 112 95 99

CZ 62 86 97 87 92 104 88

DE 99 117 105 89 140 98 108

DK 103 133 130 111 150 106 122

EE 72 100 105 60 103 101 90

EL 117 75 85 135 104 97 102

ES 73 86 93 121 72 100 91

FI 100 124 99 130 123 102 113

FR 89 102 68 135 94 103 99

HU 128 82 105 39 50 100 84

IE 123 115 113 167 130 97 124

IT 134 75 76 118 84 101 98

LT 66 89 95 63 62 96 78

LU 102 113 113 104 153 99 114

LV 29 86 103 82 94 101 83

MT 144 80 95 60 85 104 95

NL 129 126 117 130 69 102 112

PL 117 84 87 97 51 101 89

PT 74 82 95 82 107 95 89

SE 108 140 109 121 115 100 115

SI 163 89 89 60 133 101 106

SK 65 89 107 82 35 102 80

UK 95 117 111 128 114 99 111

Initiative at work: Value estimated for CY based on the method of regression imputation

SMEs which introduced organisational innovation : Value estimated for UK based on the method of regression

imputation

36

Annex 6: Please assess the importance of the factors influencing consumer responsiveness to innovation in your sector - (values from 1-least important to 5 -most important) - average assigned values - survey results

Automotive (15

respondents)

Biotechnology (16

respondents)

Eco-Innovation (10

respondents)

Food/Drink (8

respondents)

ICT (9 respondents) Space (6

respondents)

Textile (9

respondents)

All sectors (76

respondents)

Factors Average Factors Average Factors Average Factors Average Factors Average Factors Average Factors Averag

e Factors Average

income level 4,00

availability

of

information

3,81 income level 4,00

product/service

originality

(differentiation

)

4,00

quality of

marketing/adve

rtising

3,88

product/servi

ce originality

(differentiati

on)

4,00 Fashion 4,33 income

level 3,74

Fashion 3,92 income level 3,75

environmenta

l

consideration

s

3,90

quality of

marketing/adve

rtising

3,88

product/service

originality

(differentiation

)

3,75

attitudes

towards

science

3,67 income

level 4,00

availabilit

y of

informati

on

3,59

product/servi

ce originality

(differentiatio

n)

3,69 education

level 3,69

education

level 3,70 education level 3,50

age of

consumer 3,75

availability

of

information

3,67

product/ser

vice

originality

(differentiat

ion)

4,00

product/se

rvice

originality

(differenti

ation)

3,55

availability of

information 3,69

attitudes

towards

science

3,50

availability

of

information

3,60 scale of

distribution 3,50

availability of

information 3,50

education

level 3,50

quality of

marketing/a

dvertising

4,00

quality of

marketing

/advertisi

ng

3,52

age of

consumer 3,62

quality of

marketing/ad

vertising

3,44

quality of

marketing/ad

vertising

3,40 income level 3,38 income level 3,38 income level 3,33 age of

consumer 4,00

education

level 3,48

quality of

marketing/ad

vertising

3,38 scale of

distribution 3,25

attitudes

towards

science

3,20

place of

residence

(urban/rural)

3,38 education level 3,25

environment

al

consideration

s

3,33 education

level 3,67 Fashion 3,18

place of

residence

(urban/rural)

3,31

product/servi

ce originality

(differentiati

on)

3,19

product/servi

ce originality

(differentiati

on)

3,10 Fashion 3,13 Fashion 3,25

quality of

marketing/ad

vertising

2,83

availability

of

information

3,67

environm

ental

considerat

ions

3,18

environmenta

l

consideration

s

3,23

ethical

consideration

s

3,06

ethical

consideration

s

3,00 environmental

considerations 3,13

attitudes

towards

science

3,13 scale of

distribution 2,50

scale of

distribution 3,33

age of

consumer 3,18

37

education

level 3,00

environmenta

l

consideration

s

2,94 Fashion 3,00 age of

consumer 3,13

place of

residence

(urban/rural)

3,13

ethical

consideration

s

2,33

place of

residence

(urban/rural

)

3,00

attitudes

towards

science

3,16

attitudes

towards

science

2,85 age of

consumer 2,88

scale of

distribution 3,00

availability of

information 3,00

scale of

distribution 2,75 Fashion 2,33

environmen

tal

considerati

ons

2,89

scale of

distributio

n

3,00

scale of

distribution 2,85 Fashion 2,69

age of

consumer 2,80

ethical

considerations 2,88

environmental

considerations 2,38

age of

consumer 2,33

attitudes

towards

science

2,89

place of

residence

(urban/rur

al)

2,77

ethical

consideration

s

2,54

place of

residence

(urban/rural)

2,19 ethnic origin

of consumer 2,70

attitudes

towards

science

2,75 ethnic origin of

consumer 2,25

place of

residence

(urban/rural)

2,17

ethical

considerati

ons

2,78

ethical

considerat

ions

2,73

ethnic origin

of consumer 2,15

ethnic origin

of consumer 2,00

place of

residence

(urban/rural)

2,40 ethnic origin of

consumer 2,50

ethical

considerations 2,13

ethnic origin

of consumer 1,33

ethnic

origin of

consumer

2,56

ethnic

origin of

consumer

2,21

Other: yearly

driven

distances

5,00

Other:

medical

reimburseme

nt

n.a.

Other:

financial or

practical

benefit

n.a.

Other: the

product meets

customer needs

better than

competitive

products

5,00

Other: early

adopter

attitude

5,00

Other: family 5,00

Other:

innovation

champions/th

ought leadres

n.a.

Other: town

or country

design

n.a.

Other: sex 4,00

Other: early

adopter

community

n.a.

Other: fear of

failure within

business

culture

n.a.

38

Annex 7: Most important reasons explaining the lack of qualified personnel involved in innovation activity in each sector - (values from 1-least important to 5 -most important) - average assigned values - survey results

Automotive (15

respondents)

Biotechnology (16

respondents)

Eco-Innovation (10

respondents)

Food/Drink (8

respondents)

ICT (9 respondents) Space (6 respondents) Textile (9 respondents) All sectors (76

respondents)

Factors Aver

age Factors Aver

age Factors Aver

age Factors Aver

age Factors Ave

rage Factors Aver

age Factors Aver

age Factors Aver

age

lack of people

with

interdisciplinary

skills

3,62 education

programmes not

adapted to

industry needs

3,73 education

programmes not

adapted to

industry needs

3,80 limited supply

of highly

specialised

experienced

labour

3,75 education

programmes

not adapted to

industry

needs

3,75 limited supply

of highly

specialised

experienced

labour

3,33 unattractive

image of the

sector

4,56 limited supply of

highly specialised

experienced

labour

3,51

low number of

students and

graduates in the

relevant

disciplines

3,54 lack of people

with

interdisciplinary

skills

3,73 limited supply

of highly

specialised

experienced

labour

3,60 lack of

appropriate

professional

training

3,75 lack of people

with

interdisciplina

ry skills

3,75 low number of

technicians

3,00 low number of

students and

graduates in the

relevant

disciplines

4,00 lack of people

with

interdisciplinary

skills

3,47

low number of

technicians

3,54 inability to

attract

international

talent

3,67 lack of people

with

interdisciplinary

skills

3,50 low job-to-job

mobility of

personnel

involved in

innovation

process

3,50 limited

supply of

highly

specialised

experienced

labour

3,75 education

programmes

not adapted to

industry needs

2,83 limited supply

of highly

specialised

experienced

labour

3,67 education

programmes not

adapted to

industry needs

3,32

limited supply of

highly specialised

experienced

labour

3,31 limited supply

of highly

specialised

experienced

labour

3,20 low number of

students and

graduates in the

relevant

disciplines

3,50 lack of people

with

interdisciplinary

skills

3,50 low number

of technicians

3,50 lack of

appropriate

professional

training

2,67 low number of

technicians

3,44 low number of

students and

graduates in the

relevant

disciplines

3,32

low job-to-job

mobility of

personnel

involved in

innovation

process

3,23 low number of

students and

graduates in the

relevant

disciplines

3,13 lack of

appropriate

professional

training

3,30 low number of

technicians

3,13 low number

of students

and graduates

in the relevant

disciplines

3,25 low number of

students and

graduates in

the relevant

disciplines

2,33 inability to

attract

international

talent

3,44 low number of

technicians

3,26

lack of

appropriate

professional

training

3,00 low job-to-job

mobility of

personnel

involved in

innovation

process

3,13 low number of

technicians

3,00 education

programmes not

adapted to

industry needs

3,13 inappropriate

gender

balance

2,88 low job-to-job

mobility of

personnel

involved in

innovation

process

2,33 lack of people

with

interdisciplinar

y skills

3,33 low job-to-job

mobility of

personnel

involved in

innovation

process

3,04

39

education

programmes not

adapted to

industry needs

2,77 low number of

technicians

3,07 inability to

attract

international

talent

3,00 inability to

attract

international

talent

3,00 inability to

attract

international

talent

2,75 lack of people

with

interdisciplina

ry skills

2,33 education

programmes

not adapted to

industry needs

3,11 inability to attract

international

talent

3,00

inability to attract

international

talent

2,77 difficult

working

conditions

3,00 low job-to-job

mobility of

personnel

involved in

innovation

process

3,00 unattractive

image of the

sector

2,88 low job-to-

job mobility

of personnel

involved in

innovation

process

2,75 inappropriate

gender balance

2,00 low job-to-job

mobility of

personnel

involved in

innovation

process

3,00 lack of

appropriate

professional

training

2,96

inappropriate

gender balance

2,77 unattractive

image of the

sector

2,93 inappropriate

gender balance

2,40 low number of

students and

graduates in the

relevant

disciplines

2,88 lack of

appropriate

professional

training

2,63 difficult

working

conditions

1,83 lack of

appropriate

professional

training

2,89 unattractive image

of the sector

2,63

difficult working

conditions

2,23 lack of

appropriate

professional

training

2,87 unattractive

image of the

sector

2,30 difficult

working

conditions

2,88 unattractive

image of the

sector

2,25 inability to

attract

international

talent

1,67 difficult

working

conditions

2,22 difficult working

conditions

2,38

unattractive image

of the sector

1,85 inappropriate

gender balance

2,07 difficult

working

conditions

2,00 inappropriate

gender balance

2,75 difficult

working

conditions

2,13 unattractive

image of the

sector

1,00 inappropriate

gender balance

1,56 inappropriate

gender balance

2,35

Other: language

skills

5,00 Other:

regulatory

environment

5,00 Other: lack of

effective

learning tool

5,00 Other: limited

R&D finance

resources of

SMEs

4,00 Other: non-

competitive

income level,

emigration

5,00

Other: proper

financing of

start-ups in

biotech

5,00 Other: Lack of

knowledge as to

what eco-

innovation

means (same in

industry)

n.a.

Other: lack of

career

development

from science to

management

n.a. Other: risks

linked to the

acivity

5,00

Other: revenue 4,00

40

Annex 8: What factors are the main drivers of innovation collaboration (values from 1-least important to 5 -most important) - average assigned values - survey results

Automotive (15

respondents)

Biotechnology (16

respondents)

Eco-Innovation (10

respondents)

Food/Drink (8

respondents)

ICT (9 respondents) Space (6

respondents)

Textile (9

respondents)

All sectors (76

respondents)

Factors Aver

age Factors Aver

age Factors Aver

age Factors Aver

age Factors Aver

age Factors Aver

age Factors Aver

age Factors Aver

age

previous

collaboration/s

hared

experience

4,46

previous

collaboration/sha

red experience

4,53

funding

opportunities for

collaborative

projects

3,78

previous

collaboration/share

d experience

4,50

previous

collaboration/sha

red experience

4,50

previous

collaboration/sha

red experience

4,33

previous

collaboration/s

hared

experience

4,89

previous

collaboration

/shared

experience

4,38

informal

contacts 3,54

funding

opportunities for

collaborative

projects

4,33

previous

collaboration/share

d experience

3,33

funding

opportunities for

collaborative

projects

4,13 informal contacts 3,63

funding

opportunities for

collaborative

projects

4,33

funding

opportunities

for

collaborative

projects

4,56

funding

opportunities

for

collaborative

projects

3,89

belonging to

the same sector 3,15

participation in

conferences,

seminar, fairs

etc.

3,27

same

nationality/shared

language

3,11

same

nationality/shared

language

3,38

funding

opportunities for

collaborative

projects

3,38 informal contacts 3,83 informal

contacts 3,78

informal

contacts 3,37

belonging to

the same

professional

community

3,08 informal contacts 3,20 geographic

proximity 3,11

belonging to the

same sector 3,25

participation in

conferences,

seminar, fairs

etc.

3,38 belonging to the

same sector 3,50

participation in

conferences,

seminar, fairs

etc.

3,44

participation

in

conferences,

seminar,

fairs etc.

3,03

funding

opportunities

for

collaborative

projects

3,08 labour mobility 2,73 informal contacts 2,78 informal contacts 3,25

same

nationality/share

d language

3,13

belonging to the

same

professional

community

3,33

membership of

formal

associations

3,00

belonging to

the same

sector

2,94

labour

mobility 3,00

belonging to the

same

professional

community

2,73 belonging to the

same sector 2,67 labour mobility 3,00

belonging to the

same sector 3,13

participation in

conferences,

seminar, fairs etc.

3,17

belonging to

the same

professional

community

3,00

belonging to

the same

professional

community

2,90

participation in

conferences,

seminar, fairs

etc.

2,77 geographic

proximity 2,73

participation in

conferences,

seminar, fairs etc.

2,67

belonging to the

same professional

community

2,75 geographic

proximity 3,00

membership of

formal

associations

2,67 geographic

proximity 3,00

geographic

proximity 2,77

41

same

nationality/sha

red language

2,62 belonging to the

same sector 2,47

belonging to the

same professional

community

2,56 geographic

proximity 2,63

belonging to the

same

professional

community

3,00 labour mobility 2,50 labour mobility 2,78 labour

mobility 2,73

geographic

proximity 2,62

membership of

formal

associations

2,40 labour mobility 2,56

participation in

conferences,

seminar, fairs etc.

2,50 labour mobility 2,75 geographic

proximity 2,33

belonging to

the same sector 2,67

same

nationality/s

hared

language

2,69

membership of

formal

associations

2,54

same

nationality/share

d language

2,13 membership of

formal associations 2,22

membership of

formal associations 2,50

membership of

formal

associations

2,13

same

nationality/share

d language

2,00

same

nationality/sha

red language

2,67

membership

of formal

associations

2,48

Other:

inspiring

environment

5,00 Other, Necessity 5,00 Other: information 5,00

Other: innovation

planned with mid-

long term

objectives

5,00

Other: clear

commercial

benefits to both

parties with alow

level of risk

5,00

Other:

complementaritie

s of interest

5,00

Other:

creativeness of

the actor

n.a.

Other: decision

making

capability of

leaders

5,00

Other: Animation

and facilitation

of

collaboration/opp

ortunities

n.a. Other: financial

support 3,00

Other: existing

internal

resources

4,00

Other: potential

to get

commercially

useful results

5,00

Other: sharing the

same vision on the

future of the (sub)

sector

5,00

42

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