Skills Report

8/12/2019 Skills Report

1/43

European Trend Chart on Innovation

Trend Chart Policy Workshop

"Skills for Innovation: Ensuring the competitive future of

companies"

Developing Indicators for Skills and Innovation

Edward Lorenz

IDEFI-CNRSUniversity of Nice-Sophia [email protected]

The contents of this paper have not been verified by the European Commission and donot necessarily express the position of the European Commission.


2/43


Page 2 of 40

1. Introduction

The importance of human capital and skills as drivers of innovation in the

knowledge-based economy is increasingly recognised. At the same time, as the

consultation document for the New European Innovation Action Plan observes, the

European Union is not always sufficiently prepared and adapted to the specific

needs of the knowledge economy and innovation. There is a clear need to develop

policies that support more effective investment in human resources and this

requires identifying the kinds of skills enterprises need for innovation. Developing

indicators for these skills can play an important role in this process by tracking

progress across member states and creating a common framework for dialogueamong the different stakeholders in the innovation system. It sets the stage for

defining skills-enhancing policies aimed both at increasing the innovative

capabilities of nations that lag far behind the average and sustaining the innovative

capabilities of the leaders.

Innovation depends on the skills and expertise of scientists and engineers with

third-level education, but formal science and engineering training are not the only

kinds of skills that firms require. Successful innovation also depends on skills

developed by employees on-the-job in the process of solving the technical and

production-related problems they encountered in testing, producing and marketing

new products and processes.1Developing these sorts of skills in turn depends not

just on the quality of formal education, but on having the right organisational

structures and work environments. Work environments need to be designed to

promote learning through problem solving and to effectively use these skills for

innovation. This implies that indicators of skills for innovation need to do more than

capture the quality of the available pool of skills by measuring years of education.

Indicators also need to capture how these skills are used and appropriate work

environments for their further development. Section 2 below presents a framework

developed in Jensen et al. (2004) for analysing knowledge management and skills

development for innovation that takes into account both of these aspects. Section 3

illustrates the framework with some case study evidence that goes beyond the

1

The importance of linkages and information feedback between the R&D, production and salesdepartments is one of the key points developed by Kline and Rosenberg (1985) in their well-

know chain-link model of innovation.


3/43


Page 3 of 40

simple measure of the pool of scientific and technical labour. The fourth section

identifies 12 main indicators of skills for innovation. Section 5 explores the relation

between the indicators and measures of innovative capacity at the national level.

The final section draws conclusions with a stress on policy implications.

1. A Framework for Identifying Skills for Innovation

Jensen et al. (2004) develop a distinction originally proposed by B.A. Lundvall

between two different, but complementary, modes of learning and skills

development: the STI-mode (Science, Technology and Innovation) and the DUI-

mode (Doing, Using and Interacting).2The STI-mode most obviously depends on

explicit know-why and the R&D-departments in big firms play a key role in STI-

processes. Specific R&D-projects will often be triggered by practice (problems with

a product, new user needs, problems with producing) but almost immediately

attempts will be made to restate the problem in an explicit and codified form that

potential users can understand since there is a need for interaction with and feed-

back. This mode depends on the skills of engineers, scientists and technicians with

formal university training and maintaining the absorptive capacity of the enterprise

often requires continuous renewal of their knowledge through lifelong learning.

The DUI-mode of learning and innovation (Doing, Using, Interacting) most obviously

relies on employee know-how which is tacit and often highly localized. This mode

involves building structures and organisational practices which enhance and utilize

learning by doing, using and interacting. Knowledge and skills are developed

through on-going problem-solving and when the process is complex it will involve

interaction within and between teams and it may result in new shared routines for

the organization. Learning by doing and learning by using are promoted through

decision-making autonomy that allows employees to explore new novel possibilities.

This is why such practices as self-managing teams and the delegation of authority

2The distinction between STI and DUI-mode learning was originally developed by B.A. Lundvall in

a series of workshop reports for our joint EU Accompanying Measures Project,Labour,

Organisation and National Innovation Systems(Loc Nis). See the projects web page and

notably Lundvall, et al. (2004) and Lorenz and Lundvall (2004).http://www.business.aau.dk/loc-nis/


4/43


Page 4 of 40

tend to show a positive relation to learning and innovative performance (see the

case study evidence presented in section 2 below).

Innovations can be competence-enhancing or competence-destroying (Henderson

and Clark, 1990). Competence-enhancing innovations build on a firms existing

competences and tend to be especially characteristic of innovation in more

traditional or mature technologies (textiles, autos, mechanical engineering).

Innovative activity is mostly incremental in nature and typically involves small

improvements in existing technologies or new combinations of exiting technologies.

Incumbent firms are typically well-placed to carry out such innovations eitherthrough investing in in-house skills or through recruiting workers on the labour

market with complementary knowledge and skills. This corresponds to the phase of

exploiting existing knowledge and competence in Nootebooms (2000) analysis of

innovation cycles.

Competence-destroying innovations, on the other hand, mark significant breaks in

the technological architecture or paradigm and typically require new types of skills

and knowledge. This is characteristic of innovative activity during the early phases

of development of a new technological field (e.g. biotechnology). Innovative activity

will be characterised by intensive forms of exploration that are favoured by looser,

network forms of organisation. Incumbent firms may be at a disadvantage relative

to start-up firms in part because of internal resistance to the organisational and

manpower changes required to radically reconfigure their competence-base

(Chesbrough, 1999).3

As Jensen et al. (2004) observe, the importance of the STI-mode of learning and

knowledge management is most obvious in fast moving technology fields such as

ICT, bio- and nano-technology and it is these sectors which are the focus of most

3It would be a mistake to identify innovations that are new for the market with those that are

competence destroying or, conversely, to identify innovations that are new to the firm but not to the

market with those that are competence enhancing. The development of products or processes that

are new to the market may be based on incremental improvements in existing technologies. The

diffusion of competence destroying innovations, on the other hand, can pose a major challenge to

economies precisely because incumbent firms find it difficult to undertake radical reconfigurations oftheir competence base.


5/43


Page 5 of 40

current policy efforts both at the national and the European level. This reflects the

combination of respectively path dependency in relation to science and technology

policy and the combined pressure emanating from the science community and the

big science-based firms. But other traditional industries such as food, clothing

and furniture as well as many service sectors also draw upon science when it

comes to innovating production processes, the use of materials and designing new

products. Econometric analysis for the Danish case, for example, demonstrates that

small and medium-sized firms operating in such sectors tend to be the ones that

benefit the most in terms of innovative capability from a stronger connection to

science (Vinding, 2002).

And DUI-learning is crucial in high-technology sectors. Experience-based learning

takes place in daily production and in the implementation and use of advanced

technologies. The speed up of science-based innovation tends to run into

bottlenecks whenever the capability to absorb and efficiently use new technologies

is limited.

Empirical evidence shows not only that the DUI-mode of learning contributes

positively to innovation across sectors, but also that the most promising results are

obtained when the two modes are combined. This implies the need for some

realignment of policy at the national and European levels.


6/43


Page 6 of 40

2. Case Study Results

There exists a large HRM literature looking at the relation between enterprise

performance and the use of new managerial practices such as problem-solving

groups, enhanced autonomy in decision making and individual responsibility for

quality assessment that support DUI forms of learning. A central question raised in

this recent literature is whether there exist complementarities among the individual

HRM practices resulting in performance benefits from adopting a set or bundle of

practices simultaneously. Underlying this notion of HRM complementarities is the

idea that the core high involvement work practices (problem-solving groups,

autonomous team organisation, etc.) are more likely to be effective if they are

supported by substantial investments in training and by forms of pay linking

employees compensation to their effort and to company performance. Training can

be seen as a natural complement to work arrangements that provide increased

opportunities for employee participation in decision-making. Collective incentive

schemes, as profit sharing and gain sharing, and individual incentive schemes, as

pay for knowledge and compensation for suggestions, are seen as complementary

pay devices which encourage employees to commit themselves to the goal of

improving company performance. Such payment arrangements promise employees

a share of the increased returns from their enhanced effort (Appelbaum et al., 2000;

Becker and Gerhart, 1996; Becker and Huselid , 1998; Ichniowski et al., 1997;

Guest, 1997).

Enterprise performance in this literature has traditionally been measured as financial

performance or labour productivity and little attention has been given to innovative

performance. Yet, there are good reasons to suppose that the firms capacity for

innovation can be increased by the use of such practices as job rotation,

interdisciplinary teams, and shop or service meetings. For example, these practices

can positively contribute to the sort of interdepartmental information flows and

feedbacks which Kline and Rosenbergs (1986) chain-link model of innovation

identifies as critical to the firms capacity for technological innovation. A key idea in

the model developed by Nonaka and Takeuchi (1995) is that innovation involves a

knowledge spiral, in which tacit knowledge is converted into more explicit and


7/43


Page 7 of 40

codified forms that are then embodied in new product and services. HRM practices

such as team organisation, quality circles, suggestion schemes and shop meetings

can be used in order to provide a framework within which employees can articulate

and make more explicit their tacit knowledge.

More recently, a number of scholars interested in the relation between organisation

and innovation have drawn on nationally representative data sets to explore the

relation between HRM bundling and innovation. The results consistently show that

the likelihood of innovating is increased by the use of managerial practices that

support DUI forms of learning.

One of the first empirical studies exploring these links is that of Michie and Sheehan

(1999) using private sector establishment-level data for the UK from the 1990

Workplace and Industrial Relations Survey (WIRS3). Two indirect measures of the

firms innovative activities are used: R&D expenditures and whether the firm has

introduced advanced technological change. HRM practices are divided between 3

basic categories: practices which increase opportunities to participate in decision-

making (e.g. teamwork, flexible job assignments), practices which increase

information sharing across the enterprises (e.g. consultation, use of shop meetings)

and practices which provide incentives for participation (e.g. profit sharing, job

appraisal). Establishments are grouped into one of three HRM systems going from

traditional to modern. Modern systems are characterised by teamwork, innovative

incentive systems, implicit employment security pledges, flexible job assignments

and regular information-sharing. The regression analysis, which controls for firm size

and industrial sector, shows that firms that use more innovative work practices are

more likely to engage in R&D and to introduce technical change.

Two studies, which use data from the Danish DISKO survey, use more direct

measures of innovation to test the relation between HRM practices and innovative

performance (Laursen and Foss, 2003; Lundvall and Nielsen, 2003). The DISKO

survey identifies firms which have introduced a new product or process over the last

three years and further distinguishes between innovations that are new to the firm,

new to the Danish market and new to the world. Laursen and Foss (2003) use

principal components analysis to identify different HRM systems based on 9

measures: interdisciplinary groups, quality circles, suggestion schemes, job rotation,


8/43


Page 8 of 40

delegation of responsibility, integration of functions, performance-related pay, firm-

internal training and firm-external training. They identify two HRM systems that are

positively related to the likelihood of innovating, one that combines the two training

variables and a second that combines the other 7 organisational practices.

Moreover, their results point to important sector effects with those firms belonging to

the wholesale and ICT sectors tending to be associated with the first system and

firms belonging to the manufacturing sectors tending to be associated with the

second system.

The study by Lundvall and Nielsen (2003), in addition to exploring the relation

between innovation and HRM bundling, looks for possible effects of employee

representation and cooperation with clients, subcontractors and knowledge

institutions (universities and research institutes). Their results support those of

Laursen and Foss in showing that the likelihood of successful product innovation

increases when the firm promotes learning through the bundling of innovative work

and pay policies. There results also point to positive complementarities between

HRM practices on the one hand and the use of systems of employee representation

and cooperating with external institutions on the other.

The study by Lorenz et al. (2004) is the first internationally comparative empirical

investigation of the HRM/performance link. The analysis draws on data from two

nationally representative surveys of public and private sector establishments: the

WERS98 survey which covers UK workplaces with 10 or more employees, and the

REPONSE98 survey which covers French establishments with 20 or more

employees. The analysis is restricted to the trading sector and excludes public

services (government, health, education, etc) resulting in samples of 2086

establishments for France and 1165 establishments for the UK.4In common with the

study by Lundvall and Nielsen (2003), the econometric analysis includes measures

of employee representation as well as the more conventional HRM variables

(problem-solving groups, job rotation, teams, information sharing). Innovative

performance is measured by a question asking whether the firm has introduced a

4In both cases the samples of workplaces were arrived at through a process of stratified random

sampling using variable sampling fractions. The response rates for WERS98 and REPONSE97

were 83 and 65 per cent respectively. These rates compare well to those achieved for most US-


9/43


Page 9 of 40

new product or service over the last 3 years in the case of France and over the last 5

years in the case of the UK.

The econometric results show positive effects of HRM bundling on innovative

performance in the both countries, while revealing important differences for the

effects of systems of employee representation. Employee representation has neutral

effects on innovative performance in France, while in the UK those firms which

combine employee representation with innovative HRM practices are more likely to

innovate. The authors account for this by differences in the regulatory environment

between the two countries, which imply that that the indicators of employeerepresentation are capturing different levels of commitment of employers to

representation. As they observe, A possible explanation of this (difference) is simply

that since representation is a legal requirement in France all or most firms will have

it, while commitment to involving employees in decision-making will vary much more

widely and in many cases may be quite low. On the other hand, where

representation is not a legal requirement, as in the UK, only those firms that are

seriously concerned to involve their employees in decision making are likely to have

it.

The above studies explore the role of the largely neglected DUI learning dynamics in

innovative performance. The first study to explicitly examine the importance of

complementarities between the science and technology mode of skills development

and the informal experience-based mode is Jensen et al. (2004). Drawing on the

DISKO data set for Danish enterprises, they use latent cluster analysis to identify

four groups of firms according to mode of skills development: those using neither the

DUI nor the STI mode, those using the DUI-mode alone, those using the STI mode

alone and those combining both modes of learning. Logit regression analysis shows

that firms that use one of the two modes alone are about twice as likely to develop a

new product or service as those using neither of the modes. Those combining both

modes are about five times as likely to innovate as a firm using neither of the modes.

The results points to strong complementarities between informal experience-based

based surveys which rarely top 25%. UK workplaces in the 10 to 19 employee size range were

excluded from the descriptive statistics and econometric analysis.


10/43


Page 10 of 40

ways of acquiring knowledge and improving skills and more formal scientifically-

based ways of getting access to and using knowledge.


11/43


Page 11 of 40

3. Indicators for skills and innovation

This section draws on the framework developed in Jensen et al. (2004) to propose

twelve skills- for-innovation indicators. The indicators fall under four different

categories:

STI-mode indicators. Four of the indicators capture the STI-mode of

learning and skills development. They are strongly overlapping with S&T

indicators used in other TrendChart reports and I explain their particular

relation to skills development.

DUI-mode indicators. Four are indicators of the DUI-mode. They are a

subset of the indicators used in Lorenz and Valeyre (2003) to develop a

taxonomy of organisational forms for the EU-15. I use the indicators that are

most clearly relevant to skills development.

Skill maintenance indicators. Two indicators capture lifelong learning that

contributes to the development and maintenance of the skills of adult

workers.

Foundation skills indicators. Two indicators capture foundation skills that are

relevant to workers capacity for lifelong learning.

Paragraph 3.5 discusses obstacles to successful development and deployment of

skills for innovation. The section concludes with a table summarising the 12

indicators, comparing the means for the EU-15 and the new member countries.

3.1. STI-mode indicators

3.1.1. HRSTC (Human Resources in Science and Technology Core)The innovative performance of enterprises depends both on the societys production

of highly trained science and technology (S&T) human resources and on the firms

capacity to integrate such human resources into innovative activities involving the

production and use new knowledge. There are a number of ways to measure such

human resources and the HRSTC measure based on the Canberra manual is used

here. HRST is defined as people who fulfil one or other of the following conditions:


12/43


Page 12 of 40

- successfully completed education at the third level in a S&T field of study5

- not formally qualified as above but employed in a S&T occupation where the above

qualifications are normally required.

A weakness of HRST as a STI-mode indicator is that it includes all people with a

third level-degree in a S&T field including those who are inactive or employed in a

non-science and technology occupation. For this reason HRSTC is chosen as the

indicator. HRSTC is restricted to those who have a third level degree and are

employed in an S&T occupation.

Figures for HRSTC are available for the EU-25 and are provided annually to

Eurostat. The figures for 2000 presented in Figure 1.1 below show that amongst the

EU-15, the Scandinavian countries, Belgium and the Netherlands are leaders.

Amongst the new member countries, Lithuania, Latvia and Cyprus are leaders.

Figure 1.1

HRSTC as per cent of e mp loyed popu lation aged 24-65

24

222 1

20

1918

18 1817 17 16

16 1515

14

1312

11

10 10 10 9 98 8

0

5

10

15

20

2 5

30

SE FI DK B E NL LU LT CY UK DE FR EE IE ES EL SI HU LV PL M T A T CZ IT PT SK

5S&T fields of study are defined to include: natural sciences, engineering and technology, medical

sciences, agricultural sciences and social sciences.


13/43


Page 13 of 40

3.1.2. BERD (Business Expenditures on R&D) as a percent of GDP

Since the publication of Cohen and Levinthals (1990) classic article on absorptive

capacity it has been appreciated that an important by-product of in-house R&D

activity is the development of the skills and competence engineers and technicians

need to access new external sources of scientific and technical knowledge for

innovation. While developing this absorptive capacity is especially important in

technologically fast-moving sectors such as pharmaceuticals and biotechnology, it

also plays a role in the ability of firms in more traditional sectors to absorb new

scientific and technical developments. As an indicator of this sort of skills

development, we use business expenditures on R&D which provides arguably thebest measure of how much in-house R&D activity is being performed by

enterprises. Figures on BERD are provided annually to Eurostat and can be found

on Newcronos. The figures for 2000, as presented in Figure 1.2, are available for all

member countries with the exception of Austria.

Figure 1.2

BERD as a pe rcen t of GDP

3,1

2,4

1,8

1,61,5 1,5

1,2

1,1

0,8 0,80, 7

0,5 0,50,5

0,40,4

0,30,2 0,2 0,2

0,1 0,10, 1 0,1

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

SE FI DE LU DK BE UK NL FR SI CZ IE IT ES SK HU PT PL EL LV EE LT M T CY

As in the case of indicator 1, amongst the EU-15 Sweden and Finland are the

leaders. Germany ranks third on this scale. Amongst the new member countries,

Slovenia and the Czech Republic are leaders.


14/43


Page 14 of 40

3.1.3. HRST job-to-job mobility as a percentage of employed HRST

Science and technology skills for innovation can be developed internally through

formal R&D and knowledge management activities or they can be acquired on the

labour market. HRSTC provides a measure of the degree to which those with third-

level training are employed in science and technology occupations. Firms can also

acquire science and technology skills through the mobility of mid-career scientists,

engineers and technicians and the third STI indicator provides a measure of how

active the labour market is for science and technology human resources. Such

mobility may be of especial importance in fast moving technological sectors where

tight competitive conditions call for a quick reconfiguration of the firms competencebase. Such mobility may also be important for relatively mature technological

sectors confronted with a need to incorporate new technologies (e.g. ICT) into

established products.

Figure 1.3

HRST M obility as a per cent of em ployed HRST

13,3

12,2

11,6

8,3 8,2

7, 4

6,86,6 6,6 6,4

6,2 6,25,8 5,7

5,5 5,5 5,5 5,4 5,24,9

4, 0

2,5

0

2

4

6

8

10

12

14

16

DK UK FI EE FR DE ES BE LV CY MT LU LT PT SE IT AT CZ PL SI HU SK

Mobility is defined as the movement of individuals aged 24 to 65 between one job

and another from one year to the next and does not include inflows into the labour

market from a situation of unemployment or inactivity. People must fulfil the

condition of belonging to HRST in both periods of time. HRST job-to-job mobility is

provided to Eurostat on an annual basis and can be found on Newcronos. The


15/43


Page 15 of 40

figures for 2000, as presented in Table 1.3, are available for all EU-member

countries with the exception of Ireland, Greece and the Netherlands. The ranking in

suggests that two types of regulatory environments are adapted to high levels of

HRST mobility: those combining strong systems of unemployment protection with

relatively low levels of employment protection (Denmark and Finland), and those

with overall weak systems of labour market regulation (the UK).

3.1.4. Computer training

This indicator measures the percent of the actively employed population using a

computer in work that has received computer training. We use this as an STImeasure, rather than figures on ICT expenditures or the level of internet access of

enterprises, because such training is arguably a prerequisite for the use of

advanced forms of ICT in the innovation process, such as computer simulation and

computer-aided design. The figures, which are taken from a November 2001

Eurobarometer survey6, are only available for the EU-15. Denmark and Finland are

leaders.

6Eurobarometer 56.0, Les Europens et les technologies de la communication et de linformation

dans le cadre de lemploi, 2001. This is a non-periodic survey. The new European survey

instrument on ICT, which will be undertaken for the first time in 2006, will contain detailedinformation on the various forms of ICT use by firms and should provide the basis for the

construction of an alternative measure of computer skills.


16/43


Page 16 of 40

Figure 1.4

Percen t of actively em ployed population using com puters in w ork having

received computer training

70

66 66 65 65

6058

5755

51

44 44

3938

36

0,00

10,00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

DK FI IE DE SE AT UK NL LU FR EL ES PT BE IT

3.2. DUI indicators

As discussed above, there are a number of national enterprise-level surveys of

organisational innovation that can be used to develop measures of DUI forms of

learning and skills development. However, at present there are no EU-wide

harmonised survey data on organisational innovation. In order to develop measures

of informal experience-based learning and skills development, I draw here on the

results of the third European Survey on Working Conditions undertaken by the

European Foundation for the Improvement of Living and Working Conditions7. The

survey, which was carried out in March 2000, covers only the EU-15. The survey is

carried out every 5 years and the next version in 2005 will be addressed to all

member nations of the EU-25.

The survey questionnaire was directed to approximately 1500 active persons in

each country with the exception of Luxembourg with only 500 respondents. The

7The initial findings of the survey are presented in a European Foundation report by D. Merlli and

P. Paoli [2001].


17/43


Page 17 of 40

total survey population is 21703 persons, of which 17910 are salaried employees.

The four DUI-indicators are based on the responses of the 8081 salaried

employees working in establishments with at least 10 persons in both industry and

services, but excluding agriculture and fishing, public administration and social

security, education, health and social work, and private domestic employees.

The use of employee-level data presents advantages and disadvantages compared

to enterprise-level data. An advantage is that it allows for a much richer

characterisation of actual work content, the sorts of skills required and the learning

that results. A disadvantage is the lack of enterprise performance measures whichcan only be derived by matching the survey with other surveys. An arguably best-

practice approach to this problem is to use joint survey instruments addressed both

to firm representatives and to a representative sample of their employees.8

The first two indicators capture employee involvement in decision-making, the third

captures autonomy in work and the fourth is a general measure of employee

learning in work.

3.2.1. Individual responsibility for quality assessment

A key indicator of employee involvement is individual responsibility for quality

assessment. This form of employee involvement plays a central role in the

development of the sorts of skills and knowledge that contribute to the feedbacks

and knowledge flows that Kline and Rosenberg identify as fundamental to

innovative capacity. The percentage of employees engaged in such activity ranges

from a high of 86 percent in Denmark to a low of 51 percent in Greece.

8This method is applied by the French Changement Organisationnel and Informatisation (COI)

survey. See Greenan and Mareisse (1999).


18/43


Page 18 of 40

Figure 2.1

Percent of em ployee s res ponsible for quality asse ssm ent

86

82

78 7775

71 70 7068 67 67

65 65 65

51

0,00

10, 00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

DK NL FR FI UK SE IE AT BE PT DE ES IT LU EL

3.2.2. Employee involvement in problem-solving

Since the classic study by Newell and Simon (1972) cognitive scientists, and social

scientists more generally, have appreciated the close links between problem-solvingactivity and the development of new knowledge. The indicator presented in Figure

2.1 captures problem-solving emerging out of learning-by-doing and learning-by-

using in daily work activity.


19/43


Page 19 of 40

Figure 2.2

Percental of employees w hose w ork entails problem-s olving

939 1

89

8 583

8079

77 7775

73 73

6867

59

0,00

10, 00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100, 00

NL DK SE FR BE UK ES AT LU DE IT FI IE EL PT

This form of problem-solving contributes to development of the tacit forms of

knowledge that can contribute to innovation in a subsequent phase of articulation

(e.g. through off-line problem-solving groups), as developed in Nonaka and

Takeuchis (1995) model of product innovation. Amongst the EU-15, the

Netherlands and the Scandinavian countries are leaders on this indicator.

3.2.3. Autonomy in determining work methods

Autonomy contributes to innovation because it increases the scope for the

exploration of new knowledge. Greater autonomy increases the likelihood of a

creative response to unanticipated problems that will usefully add to the stock of in-

house knowledge. The importance of autonomy has been documented by a number

of authors including Lam (2003) who has analysed the role of teams in the product

development process. The case studies referred to above all find a positive relation

between innovative performance and such factors as the delegation of responsibility

or the use of autonomous team organisation. The measure used here is the

percentage of employees exercising control over their work methods.


20/43


Page 20 of 40

Figu re 2.3

Percentage of em ploye es e xercising control over their wo rk m ethods

8179

72

68

6362

60 60 6057

56

4948

4341

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

SE NL DK DE FI IT A T LU FR UK BE IE ES EL PT

3.2.4. Learning new things in work

The fourth DUI indicator provides a general measure of whether work is organised

in a manner that promotes learning.

Figure 2.4

Percentage of em ployees th at learn new things in work

8885

79 7875 75 74

7169 68

6663

61

5149

0,00

10, 00

20,00

30,00

40,00

50,00

60,00

70,00

80,00

90,00

100,00

FI DK NL SE BE L U UK FR AT IT IE ES DE PT EL

This indicator captures informal learning dynamics in the broadest sense and is

highly associated with the other three DUI-indicators (see the discussion of the DUI-

scale and its relation to the innovation taxonomy in Section 4 below). Netherlandsand the Scandinavian countries are leaders.


21/43


Page 21 of 40

3.3. Lifelong learning indicators

Two indicators of lifelong learning relevant to the maintenance of the skills of older

employees are included. The first, available for the EU-25 and taken from the

annual Labour Force Survey, is the percentage of the population aged 24-65 in

2001 engaged in any form of training during the four weeks prior to the survey.9

This indicator captures learning activity both on and off the job and includes

learning that though not directly related to employment could be of importance for

maintaining or improving future learning capacity and skills development. The

second, based on the 1999 Continuous Vocational Training Survey, is thepercentage of all enterprises providing training of any type in 1999. This survey,

which is undertaken every 6 years, provides a measure of the importance of

enterprise investments in the skills development of their employees. The figures,

taken from Newcronos, are available for the EU-25 with the exception of Malta,

Cyprus and the Slovak Republic.

9Although more recent data are available for indicator 3.1, figures for 2001 are used in order to

keep all indicators at roughly the same time period.


22/43


Page 22 of 40

Figure 3.1

Percen tage of the w ork ing-age population e ngaged in training, 2001

2221

2 1

20

16

9

8 88 8

7

6 66

5 55 5

4

33 3

3 3

1

0,00

5,00

10 ,00

15,0 0

20,00

25,00

SE UK DK FI NL SK A T LV IE SI B E EE CZ IT DE ES LU PL M T PT CY HU FR LT EL


23/43


Page 23 of 40

Figure 3.2

Percentage of enterprises providing training in 199996

9188 87

8279

76 7572 71 70 69

63

53

48

43

3937 36

2422

18

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

DK SE NL UK FI IE FR DE AT LU BE CZ EE LV SI LT PL HU ES IT PT EL

3.4. Foundation skills

In common with Innovation Scoreboard Report 5, indicators of foundation skills are

included based on the OECDs Programme for International Student Assessment

(PISA). The PISA survey is carried out every 3 years and the next assessment will

be in 2006. PISA measures basic literacy and numerical skills that play a central

role in the ability of individuals to continuously learn throughout their lives. PISA

aims to assess not only what students know but also their capacity to apply that

knowledge to real world issues including those at the workplace. As the OECD

report on the first results observes, PISA is based on a dynamic model of lifelong

learning in which new knowledge and skills necessary for successful adaptation to a

changing world are continuously acquired throughout life (OECD, 2000, p. 14).

Foundation skills are especially important to sustaining dispersed forms of DUI-

mode learning which concern employees at all levels of the hierarchy and across

functional services. Such forms of skills development are particularly important for

incremental innovation which draws on the full in-house knowledge base to

progressively improve product design and product quality. Enterprises operating in

countries where a large fraction of population has such foundation skills will be

better placed to sustain DUI forms of learning and skills development.


24/43


Page 24 of 40

Two PISA-based measures from 2000 are used10. The first, a measure of reading

literacy, is the percentage of 15 year olds reading at levels 4 or 5. This requires a

capacity to locate and sequence multiple pieces of deeply embedded information,

possibly in accordance with multiple criteria.

Figure 4.1

PISA rea ding literacy: pe rcentage o f 15 yr. olds r eading at levels 4 or 5

50

4140

3837

34

32

30

2827

25 2524

23 23

21

18

0,00

10,00

20,00

30,00

40,00

50,00

60,00

FI IE UK BE SE AT FR DK DE CZ ES PL IT EL HU PT LV

The second, a measure of mathematical literacy, is the percentage of 15 yr. olds

scoring 600 or above. This requires a capacity to connect and integrate more than

one piece of material and to translate and create appropriate models within

unfamiliar context. Both types of literacy are arguably important to the sorts of

problem-solving capabilities required in learning organisations. The two measures

are available for all member nations of the EU-15 with the exception of the

Netherlands and Luxembourg. Amongst the new member countries, the measure of

reading literacy is unavailable for Hungary, the Czech Republic, Latvia, and Poland

and the measure of mathematical literacy is available for Hungary, the Czech

Republic and Poland.

10The OECD released the results of PISA 2003 in early December 2004, but these results were

available too late to be incorporated into this report.


25/43


Page 25 of 40

Figure 4.2

PISA m athem atical liter acy: per centage o f 15 yr. olds s coring 600 or bette r

24

23

22

18 18

16 16

15

14

13

12

109

7

5

4 4

0,00

5,00

10,00

15,00

20,00

25,00

30,00

BE UK FI FR AT DK SE CZ DE HU IE PL ES EL IT LU PT

3.5 Obstacles to the successful development and deployment of skills for innovation

At present there exist no European-wide data that would allow us to develop

indicators of the obstacles to the development and deployment of skills for

innovation. The 3rd Community Innovation Survey asks whether the lack of

qualified personnel hampers innovative activity. However, it doesnt address the

factors which impede the development and deployment of those skills.

Some indication of the nature of the obstacles to DUI-forms of skills development

can be gained from a recent survey of 800 European firms with 50 employees or

more carried out by the European Commission, DG Employment and Social

Affairs.11The survey classified firms into three groups based on the progress they

have made towards implementing new forms of work organisation: Non-users,

Transitional or partial users, and System users. System users are defined as firms

having introduced a wide range of new working practices and account for 10

percent of the population of firms. Non-users account for 40 percent, while

Transitional or partial users account for the remaining 50 percent. The results show

11

DG Employment and Social Affairs, 2002, New Forms of Work Organisation: The Obstacles toWider Diffusion.


26/43


Page 26 of 40

that the main obstacles are linked to the attitudes of employees and management,

and more generally to resistance to major changes in the companys culture. A

change in work organisation thus requires a change in understanding as well as in

behaviour, and there is a perceived need for tools to help change the behaviour of

management as well as employees.

3.6 Summary statistics and European leaders

Table 1 summarizes the 12 indicators for skills and innovation, comparing the

means for the EU-15 and for the new member countries.


27/43


Page 27 of 40

Table 1

Summary Statistics: Skills for Innovation Indicators

Indicator

Mean:

EU-25

Mean:

EU-15

Leaders: EU-15 Mean:

New

member

countries

Leaders: New

Member

countries

STI-Mode Indicators

1.1 HRSTC as a percentageemployed population aged 24-65, 2000

14.82 16.42 SE(23.81)

FI(22.06)

12.41 LT(17.66)

CY(17.61)

1.2 BERD as a percent of GDP,2000

0.84 1.22 SE(3.12)

FI(2.41)

0.31 SI(0.81)

CZ(0.74)

1.3 HRST mobility as apercentage of HRST, 2000

6.80 7.86 DK(13.34)

UK(12.18)

5.51 EE(8.31)

LV(6.55)

1.4 Percentage of employeesusing a computer havingreceived computer training,2000

NA 54.13 DK(69.90)

FI(66.00)

NA NA NA

DUI-Mode Indicators

2.1 Percentage of employeesresponsible for qualityassessment, 2000

NA 70.52 DK(86.44)

NL(82.38)

NA NA NA

2.2 Percentage of employees

whose work involves problemsolving, 2000

NA 77.80 NL

(92.59)

DK

(91.38)

NA NA NA

2.3 Percentage of employeesexercising control of workmethods, 2000

NA 59.87 SE(80.85)

NL(78.54)

NA NA NA

2.4 Percentage of employeeswhose work involves learningnew things, 2000

NA 70.00 FI(87.71)

DK(84.5)

NA NA NA

Skill Maintenance Indicators

3.1 Percentage of the workingage population engaged intraining of any type four weekprior to survey, 2001

8.17 9.96 SE(21.6)

UK(21.1)

5.49 LV (8.2) SI (7.6)

3.2 Percentage of enterprisesoffering training of any type,1999

60.86 55.87 DK (96) SE (91) 50.29 CZ (69) EE (63)

Foundation Skills Indicators

4.1 PISA Reading literacy:percentage 15 yr. olds readingat levels 4 or 5, 2000

30.35 32.54 FI (50) IE (41) 23.251 CZ (27) PL (25)

4.2 PISA Mathematical literacy:percentage of 15 yr. oldsscoring 600 or over, 2000

13.53 13.71 BE (24) UK (23) 12.672 CZ (15) HU

(13)

1. Average for four member countries: Poland, Czech Republic, Hungary and Latvia2. Average for three member countries: Poland, Czech Republic and Hungary


28/43


29/43


Page 29 of 40

Figure 5

Skills-for-Innovation Index: EU15

0,88

0,770,74

0,59 0,56

0,200,14 0,14

0,02

-0,11 - 0,13

-0,59

-0,79

- 1,14

-1,22

-1,5000

-1,0000

-0,5000

0,0000

0,5000

1,0000

FI DK SE NL UK LU DE BE AT FR IE ES IT PT EL

Two of the indicators used in the 12-indicator Skills-for Innovation index are also

used in the construction of TrendCharts 2001 Summary Innovation Index for the

EU-15. Figure 6 below shows the relation between the Skills Index, calculated

without these two indicators, and the Summary Innovation Index. The correlation

coefficient between the two indices is 0.93 and significant at the .001 level. This

positive relationship supports the view that that the skills indicators impact positively

on innovative performance.


30/43


31/43


Page 31 of 40

Figure 7 shows that the three Scandinavian countries, which are leaders on the

scale of the 12-indicator index, are also leaders on the scale of the 5-indicator

index. Amongst the new member countries, Estonia, Cyprus and the Czech

Republic are leaders.

Figure 7

Skills-for -Innnovation Index b ase d on 5 indicators : EU-25

1.64 1.621.55

1.22

0.90

0.430.37

0.26

0.12 0.10

-0.28-0.33

-0.38 -0.38 -0.39 -0.42-0.46

-0.73-0.78 -0.81 -0.82

-0.87

-0.96 -0.98

-0.08

-1.5

-1

-0.5

0

0.5

1

1.5

2

D K FI SE UK N L B E DE LU FR IE EE A T CY CZ SI ES LV LT M T PL IT HU SK EL P T

4.3. Relating the Skills Indices to a taxonomy of modes of innovation

The relation between the Skills-for-Innovation Indices and enterprise innovative

performance can be explored further for the EU-15 and the EU-25 by using a

taxonomy of innovation modes developed by TrendChart in conjunction with

Eurostat. The taxonomy, which draws on the work of Tether (2001) and Arundel

(2003), is derived from CIS-3 data. It covers 12 member nations of the EU-15 and 7

new member countries. Four categories of innovators are distinguished: strategic

innovators, intermittent innovators, technology modifiers and technology adopters.

The two criteria upon which the classification is based are the degree of novelty of

the innovations and the creative effort that the firm expends on in-house innovative

activities. The taxonomy also distinguishes non-innovators.


32/43


Page 32 of 40

For strategic innovators, innovation is a core component of their competitive

strategy and they perform R&D on a regular basis. Intermittent innovators develop

innovations in-house when necessary or favourable but innovation is not a core

strategic activity. Technology modifiers modify existing products or processes

mainly through non-R&D based innovative activities. Technology adopters innovate

primarily by adopting innovations developed by other firms or organisations.

Table A.2 in the Annex gives the percentage distribution of enterprises for the four

innovation modes and non-innovators for the 19 EU nations for which data are

available. In order to summarise this data, cluster analysis is used to group nationsthat are close in terms of the percentage distribution of strategic innovators,

intermittent innovators, modifiers, adopter and non-innovators. Table 2 presents the

outcome of this analysis which resulted in four distinct clusters of nations.

Table 2

Percentage Distribution of the 4 Innovation Modes and

Non-innovators for Each Cluster

Countries incluster

Cluster 1

BE, DE, FR,

NL, AT, FI,

SE

Cluster 2

IT, PT, LU

Cluster 3

CZ, EE, ES,

LT

Cluster 4

EL, LV, SI,

SK, HU

EU-19

STRATEGIC 13% 6% 4% 5% 8%

INTERMIT 17% 17% 11% 9% 13%

MODIFIER 15% 17% 5% 5% 11%

ADOPTER 9% 5% 16% 6% 9%

NON-INNOVATOR

46% 55% 64% 75% 59%

The first cluster groups the Scandinavian countries, the Netherlands, France,

Belgium and Austria. This cluster is distinctive both for the over-representation of

the three innovation modes that are highest in terms of novelty requirements and in-

house creative effort and for being the only cluster in which the percentage of


33/43


Page 33 of 40

strategic innovators is above the average for the 19 nations. The second cluster

groups Italy, Portugal and Luxembourg. In this cluster both intermittent innovators

and modifiers are over-represented relative to the population average and this is the

cluster with the highest percentage of modifiers.

Cluster 3 groups together Czechoslovakia, Estonia, Spain and Lithuania. Cluster 4

groups Greece, the Slovak Republic, Latvia, Slovenia and Hungary. These two

clusters can be distinguished from the first two by the over-representation of non-

innovators and the under-representation of the three innovation modes that are

highest in terms of novelty requirements and in-house creative effort. Cluster 3 canbe distinguished from Cluster 4 by the over-representation of adopters, while cluster

4 stands out for being the one with the highest percentage of non-innovators.

In order to explore the relation between the taxonomy and DUI and STI-modes of

learning, separate DUI and STI-indices, as well as a combined DUI/STI index, are

constructed for the twelve members of the EU-15 for which the taxonomy could be

applied. As with the overall skills index, these indices are constructed by first

creating standardised variables for each indicator with mean 0 and standard

deviation 1. The standardised variables are summed and the result is divided by the

number of indicators. Table 3 shows the correlation coefficients between the modes

of innovators in percentage of all firms and the DUI, STI and combined DUI/STI

indices.


34/43


Page 34 of 40

Table 3

Correlations between Types of Innovators and Dimensions of Skills for

Innovation: EU-12

DUI/STIIndex

DUIIndex

STIIndex

STATEGIC INTERMIT MODIFIER ADOPTERNON-INNVATOR

DUI/STIIndex

1.00

DUI Index .95 1.00

STI Index .92 .76 1.00

STRAG .82 .75 .77 1.00

INTERM .59 .51 .59 .62 1.00

MODIF .21 .26 .11 .33 .73 1.00

ADOPT -.34 -.27 -.35 -.43 -.61 -.38 1.00

NON-INV -.55 -.53 -.48 -.65 -.78 -.79 .10 1.00

1. Correlations in bold are significant at the .05 level or better.

The results show positive and significant correlations between the relative

importance of strategic innovators and each of the three skills indices. Although the

correlations with adopters and non-innovators are not significant they are negative.

The positive correlation between the percentage of strategic innovators and the

STI-index is to be expected to some extent, since all strategic firms perform R&D

on a continuous basis and BERD is one of the four indicators used in the STI-index.

The positive and significant correlation with the DUI-index suggests that these very

same strategic firms draw a critical advantage for their innovative activities by

combining high levels of R&D with an emphasis on organisational forms and

practices that foster experience-based learning. Further support for the idea that the

best results are achieved where the two modes of learning are combined at a high

level is given by the larger size of the coefficient on the combined DUI/STI scale

when compared with the coefficients on the DUI or the STI-scales alone.

Table 4 shows the correlation coefficients between the 5-indicator Skills-for-

Innovation Index and the different types of innovators for all 19 EU nations for which

the innovation taxonomy could be applied.


35/43


Page 35 of 40

Table 4

Correlations between types of innovators and the 5-indicator

Skills-for-Innovation Index: 19 nations

Skillsindex: 5

indicators

STRATEGIC INTERMIT MODIFIER ADOPTER NON-INNVATOR

Skillsindex: 5indicators

1.00

STRAG .78 1.00

INTERM .65 .61 1.00

MODIF .39 .54 .74 1.00

ADOPT -.18 -.36 -.27 -.19 1.00

NON-INNOV

-.62 -.68 -.83 -.87 -.09 1.00

1. Correlations in bold are significant at the .05 level or better.

The correlations between the skills index and both the strategic and intermittent

modes of innovators are positive and significant. The higher value of the coefficient

for the strategic mode points to a positive relation between the two dimensions of

skills development captured in the index and the importance of novelty

requirements in innovative activity.


36/43


Page 36 of 40

6. Conclusion

There exist large differences in the development of skills for innovation across EU-

nations. Portugal, Greece, and to a lesser extent Italy, as well as a number of the

new member countries, show an enormous lag. The science and technology-based

perspective implies that scientifically-based ways of getting access to, producing

and utilizing knowledge dominate the innovation process.12This STI-perspective,

along with the combined pressure emanating from the science community and the

big science-based firms, accounts for the fact that such high-tech fields as ICT, bio

and nano-technology are most often the focus of current policy efforts at the

national and European levels. But other traditional industries such as food, clothing

and furniture as well as many service sectors also draw upon science when it

comes to innovating production processes, the use of materials and designing new

products.

At the same time, it would clearly be a mistake to assume that STI-mode learning is

sufficient and that DUI-mode learning will take care of itself. DUI-mode initiatives

are needed to overcome the bottlenecks high-tech faces in absorbing and using

new technologies. Low-tech needs DUI to sustain its capacity for incremental

improvements in product quality and design. Moreover, the empirical evidence

presented in this report highlights both the considerable variance in capacity for

DUI-mode learning that exists across European nations and the important

differences in this capacity that exist across firms within the same nation.13

Irrespective of whether the focus is on promoting catch-up among the nations that

lag behind, or promoting the sustained development of the leaders, this implies a

need for a realignment of policy. On the one hand, there is a need to give more

attention to developing the science and technology base of the services and low

and medium-tech manufacturing. These are the sectors which account for the bulk

of employment and growth in Europes economies. On the other hand, DUI-mode

12For a further discussion of the points raised in this concluding section, see Lundvall et al. (2004).

http://www.business.aau.dk/loc-nis/

.13See pp. 6-9 above for a discussion of detailed national survey evidence for France, the UK and

Denmark.


37/43


Page 37 of 40

learning cannot be taken for granted and initiatives are need to actively promote

this experience-based skills development in both low and high-tech sectors.

One way in which these issues can be brought to the fore in policy is through the

development of more adequate DUI-mode measures and benchmarks. There

currently exist specialised Manuals setting norms and guidelines for collecting

harmonised data at the European level on innovation (Oslo Manual), R&D (Frascatti

Manual) and HRST (Canberra Manual). A preliminary step towards developing

European-wide harmonised data on DUI-mode skills development would be a

Manual on Organisational Innovation. The European Survey on Working

Conditions, while providing some useful characterisations of individual workexperience in terms of problem-solving, responsibility for quality assessment and

degrees of autonomy, is first and foremost a survey of working conditions and it

cannot substitute for a focused survey on organisational innovation. Adequate DUI

measures would require complementary establishment-level data providing

information on the use of such collective organisational forms as problem-solving

groups, shop and department meetings and the way knowledge flows and sharing

is organised among different services and departments.

Finally, while the skills indices developed in this report clearly identify leading and

lagging nations, it would be a mistake to try to identify a best practice method for

creating learning organisation on the basis of these aggregate measures. Although

the indicators identify common features of learning organisations, as Lorenz and

Valeyre (2004) discuss in more detail, there are multiple ways to build them and

whats best in a particular case will depend both on sector-specific features and on

the national institutional context. This bench-marking should be interpreted in the

spirit of the open-method of coordination with the idea that each nation will develop

the organisational practices and skills that support innovation in a way that is

adapted to its distinctive cultural and institutional resources.


38/43


Page 38 of 40

References

Applebaum, E., T. Bailey, P. Berg, and A. Kalleberg, 2000, ManufacturingCompetitive Advantage: the effects of High Performance Work Systems of PlantPerformance and Company Outcomes, Cornell University press, Ithica, NY.

Arundel A. The Knowledge Economy, Innovation Diffusion, and the CIS.Proceedings of the 21st CEIES Seminar, Innovation Statistics - More than R&DIndicators, Athens, April 10-11, 2003, Eurostat, General Statistics, EuropeanCommission, Luxembourg, 2003.

Becker, B. and B. Gerhart, 1996, The Impact of Human Resource Management onOrganisational Performance: progress and prospects, Academy of ManagementJournal, 39, pp. 779-801.

Becker, B. and M. Huselid, 1998, High-performance Work Systems and FirmPerformance: a synthesis of research and managerial implications, in G. Ferris(ed.) Research in personnel and Human Resources, Vol. 16, Greewick, Conn., JAIPress, pp. 53-102.

Chesbrough, H. (1999). The Organisational Impact of Technological Change: aComparative Theory of National Institutional Factors, Industrial and CorporateChange,

Cohen, W.M. and Levinthal, D.A., 1990. Absorptive capacity A new perspectiveon learning and innovation. Administrative Science Quarterly, 35, pp. 128-52.

Greenan, N. and J. Mairesse (1999) Organizational Change in FrenchManufacturing: What do we learn from firm representatives and from theiremployees? Paper prepared for the NBER conference on organisational changeand performance, April, 1999.

Guest, D. (1997) Human Resource Management and Performance: a review andresearch agenda, International Journal of Human Resource Management, 8: 3, pp.263-26.

Ichiniowski, C., K. Shaw and G. Prennushi, "The Effects of Human ResourceManagement Policies on Productivity: A Study of Steel Finishing Lines", AmericanEconomic Review, June 1997.

Jensen, M., B. Johnson, E. Lorenz and B.A. Lundvall (2004). Absorptive Capacity,Forms of Knowledge and Economic Development, paper presented at the 2ndInternational Globelics Conference, Beijing, China.

Kline, S. and N. Rosenberg 1986. An Overview of Innovation, in: R. Landau andN. Rosenberg (eds), The Positive Sum Strategy: Harnessing Technology forEconomic Growth, Washington, D.C., National Academy Press.

Lam, A. (2003) Organisational Learning in Multinationals: R&D Networks of

Japanese and U.S. MNEs in the UK, DRUID Working Paper,http://ideas.repec.org/s/aal/abbswp.html.


39/43


Page 39 of 40

Laursen, K. and N. Foss (2003) New HRM Practices, Complementarities, and theImpact on Innovation Performance, Cambridge Journal of Economics,

Lorenz, E. and B.A. Lundvall, Final Report, Labour, Organisation and Competencein National Innovation Systems, (Loc Nis), EU Accompanying Measures Project,Contract nHPSE-CT-2001-60004

Lorenz, E., J. Michie and F. Wilkinson (2004) HRM Complementarities andInnovative Performance in French and British Industry, forthcoming in J. LChristensen & B-A Lundvall (eds.) Product Innovation, Interactive Learning andEconomic Performance, Esivier Ltd.

Lorenz, E. and A. Valeyre (2003) Organisational Change in Europe: NationalModels or the Diffusion of a New One Best Way? DRUID Working Paper,

http://ideas.repec.org/s/aal/abbswp.html.

Lundvall, B.A. and P. Nielsen (2003). Knowledge creation and innovation in the

context of learning organizations and industrial relations, DRUID Working Paper,

http://ideas.repec.org/s/aal/abbswp.html.

Lundvall, B. A., Lorenz, E. and I. Drejer (2004) Report from the Loc Nis Policy

Workshop, How Europes Economies Learn, European Commission, Brussels,http://www.business.aau.dk/loc-nis/

Merlli D. and Paoli P., 2001, Third European Survey on Working Conditions

(2000), Luxembourg: Office for official publications of the European Communities,

2001.

Michie, J. and M. Sheehan (1999a). HRM Practices, R&D Expenditure andInnovative Investment: Evidence from the UK's 1990 Workplace Industrial RelationsSurvey, Industrial and Corporate Change, vol. 8, 211-234.

Newell, A. and Simon, H., 1972. Human Problem-Solving, Prentice-Hall, NewJersey.

Nonaka, I. and Takeuchi H., 1995. The Knowledge Creating Company, Oxford

University Press, Oxford.

Tether B. Identifying Innovation, Innovators, and Innovation Behaviours: A CriticalAssessment of the Community Innovation Survey, CRIC Discussion Paper 48,UMIST, December 2001.

Vinding, A., 2002. Absorptive capacity and innovative performance: A humancapital approach, Ph.-D.-dissertation, Department of Business Studies, AalborgUniversity, Aalborg.


40/43


Page 40 of 40

Annex

Table A.1: Skills-for-Innovation Indicators and IndicesIndicators BE CZ DK DE EE EL ES FR IE

1.1 HRSTC as a percentage employedpopulation aged 24-65, 2000

20.41

9.48 21.12

16.57

15.51

14.02

14.91

16.25

154

1.2 BERD as a percent of GDP, 2000 1.48 .74 1.51 1.75 .14 .223 .5 .83 .5

1.3 HRST mobility as a percentage ofHRST, 2000

6.64 5.35 13.34

7.36 8.31 NA 6.79 8.16 NA

1.4 Percentage of employees using acomputer having received computertraining, 2000

37.7 27.0 69.9 64.8 NA 44.3 43.7 51.1 65

2.1 Percentage of employeesresponsible for quality assessment,2000

68.3 NA 86.4 66.6 NA 50.9 65.3 77.9 70

2.2 Percentage of employees whosework involves problem solving, 2000

83.0 NA 91.4 74.7 NA 66.8 79.0 85.0 68

2.3 Percentage of employees exercisingcontrol of work methods, 2000

55.7 NA 72.0 67.7 NA 43.0 48.1 59.5 49

2.4 Percentage of employees whosework involves learning new things, 2000

74.9 NA 84.8 61.3 NA 49.1 62.8 71.2 65

3.1 Percentage of the working agepopulation engaged in training of any

type four week prior to survey, 2001

6.8 5.9 20.8 5.2 6.0 1.1 5.1 2.8 7.

3.2 Percentage of enterprises offeringtraining of any type, 1999

70 69 96 75 63 18 36 76 79

4.1 PISA Reading literacy: percentage15 yr. olds reading at levels 4 or 5, 2000

38 15 30 28 NA 23 25 32 41

4.2 PISA Mathematical literacy:percentage of 15 yr. olds scoring 600 or

24 NA 16 14 NA 7 9 18 12


41/43


Page 41 of 40

over, 2000

Indices

12 Indicator skills index .14 NA .77 .14 NA -.12 -.59 -.11 -.1

5 Indicator skills index .43 -.38 1.64 .37 -.08 -.96 -.39 .12 .1

Table A.1 (contd) Skills-for-Innovation Indicators and Indices

Indicators LU HU MT NL AT PL PT SI

1.1 HRSTC as a percentage employed

population aged 24-65, 2000

18.19 12.17 9.80 19.13 9.8 9.93 8.48 12.58

1.2 BERD as a percent of GDP, 2000 1.58 .35 .07 1.11 NA .24 .29 .811

1.3 HRST mobility as a percentage ofHRST, 2000

6.17 3.99 6.20 NA 5.49 5.22 5.71 4.90

1.4 Percentage of employees using acomputer having received computertraining, 2000

55.33 NA NA 56.7 59.6 NA 39.1 NA

2.1 Percentage of employeesresponsible for quality assessment,2000

65.1 NA NA 82.4 69.8 NA 66.7 NA

2.2 Percentage of employees whose

work involves problem solving, 2000

76.9 NA NA 92.6 77.0 NA 58.5 NA

2.3 Percentage of employees exercisingcontrol of work methods, 2000

60.0 NA NA 78.5 60.3 NA 41.0 NA

2.4 Percentage of employees whosework involves learning new things, 2000

74.5 NA NA 78.7 68.7 NA 50.8 NA

3.1 Percentage of the working agepopulation engaged in training of any

4.8 33.1 4.4 15.6 8.3 4.8 3.4 7.6


42/43


Page 42 of 40

type four week prior to survey, 2001

3.2 Percentage of enterprises offeringtraining of any type, 1999

71 37 NA 88 72 39 22 48

4.1 PISA Reading literacy: percentage15 yr. olds reading at levels 4 or 5, 2000

NA 23 NA NA 34 25 21 NA

4.2 PISA Mathematical literacy:percentage of 15 yr. olds scoring 600 orover, 2000

4 13 NA NA 18 10 4 NA

Indices

12 Indicator skills index .20 NA NA .58 .02 NA -1.1 NA

5 Indicator skills index .26 -.82 -.73 .90 -.28 -.78 -.98 -.38


43/43


Table A.2

Taxonomy of Innovation Modes1

(percentage of all enterprises)StrategicInnovators

IntermittentInnovators

Modifiers Adopters Non-innovators

BE 11.0 16.0 17.0 15.0 41.0 100.0

DE 13.0 21.0 22.0 10.0 34.0 100.0

EL 4.0 8.0 5.0 10.0 73.0 100.0

ES 3.0 7.0 6.0 22.0 62.0 100.0

FR 12.0 14.0 10.0 11.0 53.0 100.0

IT 7.0 13.0 17.0 4.0 59.0 100.0

LU 7.0 22.0 18.0 3.0 50.0 100.0

NL 12.0 19.0 16.0 8.0 45.0 100.0

AT 13.0 15.0 18.0 8.0 46.0 100.0

PT 3.0 15.0 17.0 10.0 55.0 100.0

FI 17.0 20.0 11.0 2.0 50.0 100.0

SE 12.0 15.0 15.0 6.0 52.0 100.0

CZ 5.0 8.0 3.0 16.0 68.0 100.0

EE 5.0 13.0 9.0 13.0 60.0 100.0

HU 4.0 6.0 7.0 6.0 77.0 100.0

LT 2.0 16.0 4.0 14.0 64.0 100.0

LV 3.0 10.0 3.0 7.0 77.0 100.0

SI 11.0 11.0 5.0 1.0 72.0 100.0

SK 4.0 8.0 6.0 5.0 77.0 100.0

1. Based on CIS-3 data.

Date post:	03-Jun-2018
Category:	Documents
Upload:	carlos-henrique-menezes-garcia
View:	217 times
Download:	0 times

Skills Report

Documents