![Correspondence analysis between occupation categories and frequency of learning new things at work. Source: authors calculation on data from ESJ (Wave 1) [70]](https://www.researchgate.net/publication/363588519/figure/fig2/AS:11431281084709116@1663339012326/Correspondence-analysis-between-occupation-categories-and-frequency-of-learning-new_Q320.jpg)
![Correspondence analysis between occupation categories and frequency of job autonomy. Source: authors calculation on data from ESJ (Wave 1) [70]](https://www.researchgate.net/publication/363588519/figure/fig3/AS:11431281084709117@1663339012406/Correspondence-analysis-between-occupation-categories-and-frequency-of-job-autonomy_Q320.jpg)
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Electronics 2022, 11, 2927. https://doi.org/10.3390/electronics11182927 www.mdpi.com/journal/electronics
Article
Skill Needs among European Workers in Knowledge
Production and Transfer Occupations
Adriana Grigorescu 1,2,*, Ana-Maria Zamfir 3, Hallur Thor Sigurdarson 4 and Ewa Lazarczyk Carlson 4
1 Department of Public Management, National School for Political and Administrative Studies, 30A Expozitiei
Bdl., 012104 Bucharest, Romania
2 Academy of Romanian Scientists, Str. Ilfov nr. 3, Sector 5, Bucharest 050044, Romania
3 Department of Education, Continuing Training and Labour Market Department,
National Scientific Research Institute for Labour and Social Protection, 6 Povernei Street,
010643 Bucharest, Romania; ana.zamfir@incsmps.ro
4 Department of Business Administration, Reykjavík University, Menntavegur 1, 102, 101 Reykjavík, Iceland;
hallursig@ru.is (H.T.S.); ewalazarczyk@ru.is (E.L.C.)
* Correspondence: adriana.grigorescu@snspa.ro; Tel.: +40-724253666
Abstract: Skills needed in jobs and skills mismatches are important topics for research and policy in
the field of economic development and the labour market. Understanding skill needs is essential
for improving education and training policies, as labour markets experience dynamic transfor-
mation driven by rapid technological progress and increased complexity of work. On the other
hand, knowledge economy is considered an important driver force of economic growth. This paper
aims to assess skill needs in knowledge production and transfer occupations. We analyse data from
online job advertisements and from the European Skills and Jobs Survey in order to provide a
comprehensive picture of skills needed in occupations related to science, technology and ICT, as
well as teaching positions from higher education in Europe. We find that workers involved in
knowledge production and transfer activate in highly changing and challenging working envi-
ronments. They differentiate themselves by other professionals and technicians mostly by the
increased need for ICT skills, problem-solving, communication and learning skills, the ability to
collaborate and adaptability. Our results are relevant for designing better education and training
programs targeting occupations supporting knowledge production and transfer.
Keywords: knowledge economy; digital economy; knowledge management; knowledge transfer;
skills need; high skilled workers
1. Introduction
The skills forecast for 2030 highlights an expected increase in employment for high
skilled jobs, such as managers, legislators, senior officials, professionals, technicians and
other positions associated with professionals [1,2]. The number of high qualification jobs
is expected to grow at the expense of those with low qualification; the demand for me-
dium qualification is expected to remain fairly stable and the highest number of new jobs
will be for professionals [2]. Moreover, macro trends, such as globalisation, accelerated
technological change, climate change, instability and social risks, fuel important disrup-
tions in the labour market, increasing the importance of atypical forms of work and
bringing significant changes in the nature of performed work. In addition, changes in-
duced by the pandemic have the potential to become structural, including the drop in
working hours, increase in remote and temporary work and wage disparities as well as
widening of the digital divide [3]. In the context of important macroeconomic shifts to the
technologized domains and other dynamic transformations in the labour market, the
topic of skills needed and skills mismatches on the labour market grows in importance.
Citation: Grigorescu, A.; Zamfir,
A.-M.; Sigurdarson, H.T.; Lazarczyk
Carlson, E. Skill Needs among
European Workers in Knowledge
Production and Transfer
Occupations. Electronics 2022, 11,
2927. https://doi.org/10.3390/
electronics11182927
Academic Editor: Yue Wu
Received: 20 August 2022
Accepted: 11 September 2022
Published: 15 September 2022
Publisher’s Note: MDPI stays
neutral with regard to jurisdictional
claims in published maps and
institutional affiliations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/license
s/by/4.0/).
Electronics 2022, 11, 2927 2 of 14
Skill needs reflect the demand for particular types of knowledge and skills existing
in economies, sectors and occupations [4]. One aspect of skill needs is given by skills
mismatches found when the level or type of skills available does not correspond to ex-
isting needs. Skill mismatch can be analysed at individual, enterprise, sectoral and
economy levels, consisting of a surplus or a lack of knowledge and competencies [4].
On the other hand, the knowledge economy is a key element of the modern world
and relies extensively on innovation and advancements in education and science, as it
supports the creation and transfer of knowledge [5]. The knowledge economy is consid-
ered an important driving force of economic growth [6,7]. For the further development of
the knowledge economy, it is essential that the skill supply matches the demands and
challenges involved in the 4th industrial revolution [8]. Knowledge is produced, im-
proved and shared through the collaboration, social process and cognitive processes of
individuals, such as reflection [9]. Previous studies have been more focused on disen-
tangling the processes and mechanisms involved in knowledge creation and transfer
[10,11] and less on which skills and competences are needed in this respect. However,
preparing future and current workers for knowledge management becomes of crucial
importance [12], with a special view on providing skills for knowledge creation and
transfer. According to OECD, supplying the labour force with high levels of education is
necessary but not sufficient in order to support the knowledge economy development.
Still, more research is needed in order to inform education policy makers with respect to
which skills are needed for the knowledge economy [13].
Our study brings together two important areas of research: skills need in an in-
creasingly changing labour market and knowledge creation and transfer. We consider
that the labour market relevant for knowledge creation and transfer is mainly repre-
sented by knowledge-rich jobs, such as teaching positions in higher education and jobs in
science, technology and ICT (researchers, engineering, ICT workers, technicians in sci-
ence and engineering). Thus, this paper aims to assess skill needs in knowledge produc-
tion and transfer occupations. We analyse data from Skills-OVATE and European skills
and jobs survey (Wave 1) in order to provide a comprehensive picture of skills needed in
occupations related to science, technology and ICT, as well as teaching positions in Eu-
rope. Our results are relevant for designing better education and training programs tar-
geting occupations supporting knowledge production and transfer in the European la-
bour market.
2. Literature Review
Worldwide, economies have moved into the Information Age. The new model of the
employee working in the current labour market is equipped with competences that are
built on knowledge, skills and attitudes, with problem solving and motivation playing
important roles [14]. This is why the knowledge management and the knowledge trans-
fer have become very important in the process of education and skills formation. Li-
yanage et al. [15] describes a process model of knowledge transfer in six steps, while the
process could be simplified if the source and receptor have a grade of similarity. Univer-
sities are representative environments for knowledge transfer, as their activity is an ex-
plicit transfer of knowledge from professors to students, with implicit knowledge trans-
fer being overshadowed but should be encouraged [16].
One of the most important channels of knowledge transfer is the usage of the re-
search results obtained in universities by companies or other organizations, but the first
step is to create the link between the universities and their potential beneficiaries [17–20].
A network to facilitate the creation and development of the education–work eco-system
is needed to better understand the need for skills and to stimulate proper knowledge
transfer [21]. Universities are using various models of knowledge transfer, being con-
sidered dedicated organizations for knowledge production and transfer [22–24], even if
they are competing for research funds within the research institutes and benefiting from
Electronics 2022, 11, 2927 3 of 14
knowledge transfer from industry. The COVID-19 vaccine can be used as the best exam-
ple of knowledge transfer for large scale production [25].
People interact and more easily facilitate knowledge transfer than organizations,
internal transfer has an advantage in comparison to external transfer and reservoirs and
networks are its vehicles [26–29]. Knowledge production in the academic versus produc-
tion environment has similarities but also differences [30], with the networks of all
stakeholders contributing to homogenize them.
With respect to knowledge production and transfer, we expect the concept to be
clear and properly used. The study of Thompson et al. [31] concluded that for the five
roles studied (opinion leader, facilitator, champion, linking agent and change agent)
there are inconsistencies and confusions in use, but they appreciate the similarities and
suggested the bridge building. At the same time, it seems that the knowledge transfer
and knowledge sharing also have similar but different meanings; the level and consist-
ence of the transfer being the main difference and the confusion is related to the infor-
mation, data and knowledge of conceptual ambiguities or improper usage [32].
Knowledge barriers were added to the previous analysis, being linked with both
knowledge transfer and sharing and generally considered in terms of the failure of the
process or the factor that blocks the process, which is often more related to a lack of ed-
ucation [32–34].
Chen et al. [35] consider knowledge transfer to be an accelerator of learning and
proposed a neural net2net model, Jacobian matching model or other types of networks
[36–40]. Knowledge transfer has been valued in the last few years for cross-domain
transposition [41,42], with open science offering an opportunity for the increased access
to literature, data, tools, etc. There is an increased interest in stimulating knowledge
transfer and its positive effects in regard to performances, as well as the factors that in-
fluence it [43,44]. Thomas Duve [45], in the first chapter of his book, presents, in detail,
the evolution of the School of Salamanca as a dedicated organization to producing
knowledge, he describes the mechanism and the mixture that are involved in this
co-creation process. A perspective on knowledge production in Arab world is given by
Hanafi and Arvanitis [46], who analyse the dynamics of scientific research using the
knowledge index [47].
Knowledge production and transfer occupations have a large international base and
the skills needed should comply with the mainstream, as reflected in publications [48].
The international scale of knowledge transfer is influencing organization performance
and culture and is related to employee retention [49]. In our opinion, employee volatility
is higher than in other sectors and knowledge transfer opportunities could influence the
decision of highly skilled professionals in accepting a job or a long-term commitment.
A new dimension of the globalization of knowledge production is the
trans-disciplinary coproduction, involving different domains and various regions, al-
lowing the shifting of the methods between actors [50,51]. Mixed teams with diverse
knowledge are the suggested strategy for companies to stimulate innovation and the
co-creation of knowledge in partnership between the research and practice [52–55].
There are opinions regarding the monopoly of very well ranked US and UK journals
attracting the main knowledge production and the Anglicisation of knowledge produc-
tion and transfer occupation [56], as very good skills in English are becoming a necessity
for these positions.
Digitalization is the new engine of knowledge production and transfer [57,58], gen-
erating the premise of the job delocalization and developing digital skills are now com-
pulsory for new generations [59].
In a recent work by Philipson [60], the analysis of the knowledge production and
transfer of phenotypes synthetizes the needs, consequences, transformational and em-
bodied knowledge for the 10 identified typologies.
Defining the skill needs for knowledge production and transfer occupations is a pre-
requisite condition for quality scientific research and engaging in the knowledge economy
Electronics 2022, 11, 2927 4 of 14
which has to answer the challenges of the society [61–65]. The gap between the required
and acquired skills should be as small as possible, in accordance with the knowledge and
skills needed by the current knowledge economy and Industry 4.0. [66,67]. Previous re-
search showed that three elements of knowledge and skill are essentially contributing to
innovation and knowledge production, namely problem solving, communication and
beneficiary involvement [68], and teamwork abilities could be also added.
3. Methodology
First, the need to adopt new competences and to perform the work autonomously
are important proxies for the skill needs of workers. Exposure to learning new things and
work autonomy are relevant non-economic characteristics of jobs that characterise some
working environments more than others. They are among the aspects that are taken into
consideration when assessing the job strain incidence that is reflected by situations with
high job demands and low job resources [69]. Hence, our first research question is:
RQ1: Are the working environments of knowledge production and transfer jobs characterised by
learning new things and by autonomous work more than other professionals and technicians?
Skill needs are reflected by the skill gaps driven by changes in technologies, working
methods and practices, products and services, as well as by the incidence of skill mis-
match or skills that have to be updated due to changes in the performed work [4]. Thus,
the second research question addressed by this study is:
RQ2: What are the sources for skill gaps that characterise the workers in knowledge production
and transfer jobs more than other professionals and technicians?
Finally, a significant part of the research on recent and expected evolutions of jobs and
skills is related to which skills are important for performing specific occupations or job po-
sitions. Evidence regarding skill use and demand is very useful for designing better edu-
cation and training programs that provide the needed skill supply, in this case, for occupa-
tions supporting knowledge production and transfer. Our third research question is:
RQ3: What skills profiles are required for performing knowledge production and transfer activities?
Figure 1 presents the conceptual model of this study considering that characteristics
of the working environments, such as frequent exposure to learning and work autonomy,
and skill gaps and skill profiles required at the workplace reflect skill needs specific for
knowledge production and transfer jobs.
Electronics 2022, 11, 2927 5 of 14
Figure 1. Conceptual model of the research study.
We analyse two important sources of data provided by the EU Agency European
Centre for the Development of Vocational Training (CEDEFOP) [70]. First, we analyse
data reflecting the supply side from the European skills and jobs survey ESJ (Wave 1).
Data were collected in 2014 from 48676 adult employees in the 28 EU Member States. The
survey examined the skill needs in occupational and sectoral profiles, as well as skill
mismatches driven by changing technologies.
In order to explore skills needed in knowledge production and transfer occupations,
we restricted our sample to workers in professional jobs (based on International Standard
Classification of Occupations ISCO-08: major group 2) and technician jobs and associate
professionals (based on ISCO-08: major group 3). The restricted sample included 17249
adult employees. Furthermore, we focused our analysis on two categories of workers
employed in: (1) science, technology and ICT and (2) teaching positions.
In order to answer our research questions, we employed correspondence analysis
and logistic regression on the restricted data set in order to provide a comprehensive
picture of the working environments and skills needed for performing the targeted oc-
cupations.
First, we performed correspondence analysis with the purpose of exploring the as-
sociation between the analysed occupational categories and the characteristics of the
working environments. Correspondence analysis is a data visualisation technique that is
very useful for revealing relationship between categories. It allows users to explore sim-
ilarities between objects (in our case, occupational categories) and their relative rela-
tionships with attributes. Thus, we performed two correspondence analyses examining
the relation between one variable reflecting three groups of occupations (science, tech-
nology and ICT; teaching positions; other professionals and technicians) and two varia-
bles measuring the frequency of the following circumstances at work:
Learning new things
Choosing the way of performing the work.
Second, logistic regressions were performed in order to profile targeted occupations
from the point of view of skills used at work and skill mismatches. We preferred to con-
struct two different binary logistic regressions for the two occupational categories of in-
terest instead of a multinomial logistic regression, as this approach allowed us to better
identify factors that predict the classification into one of the occupational categories of
interest. Thus, two models were constructed: Model 1 for science, technology and ICT
Electronics 2022, 11, 2927 6 of 14
occupations (the dependent variable takes value 1 for science, technology and ICT oc-
cupations and 0 for other professionals and technicians) and Model 2 for teaching posi-
tions (the dependent variable takes value 1 for teaching positions and 0 for other profes-
sionals and technicians). The independent variables are presented in Table 1.
Table 1. Independent variables included in the logistic regressions.
Items
Variables
Measurement
In the last five years or since you started your
main job, have any of these changes taken
place in your workplace?
Changes in the used technologies Dummy variables:
1 = yes, 0 = no
Changes in the working methods and practices
Changes in the products/services delivered
How important are the following for doing
your job?
Advanced literacy skills
Scale from:
0 = Not at all important
5 = Moderately important
10 = Essential
Advanced numeracy skills
Advanced ICT skills
Technical skills
Communication skills
Team-working skills
Foreign language skills
Customer handling skills
Problem solving skills
Learning skills
Planning and organisation skills
Overall, how would you best describe your
skills in relation to what is required to do your
job?
Skills (mis)match
1 = My skills are higher than re-
quired by my job
2 = My skills are matched to what
is required by my job
3 = Some of my skills are lower
than what is required by my job
and need to be further developed
Was your main reason for doing training…? To stay up-to-date with changing skill needs of the
job
Dummy variable: 1 = yes, 0 = no
Source of the variables: European skills and jobs survey ESJ (Wave 1) [70].
Additionally, for the third research question, we extracted data reflecting the de-
mand side from Skills-OVATE [71] portal, which provides information on the skills de-
manded by employers based on online job advertisements (OJAs) collected from EU
member states and UK in the past four quarters. Skills-OVATE collects information on
the jobs and skills from the millions of OJAs extracted from numerous private job portals,
portals of public employment service, recruitment companies’ websites and online me-
dia. By relying on a huge amount of collected data, the Skills-OVATE system comple-
ments the skills intelligence that is based on traditional sources of statistical data. This
new source of data provides evidence on labour market trends in real time, offering a
way to collect and analyse skills-related data that are not available from other sources
[72]. For this paper, data are extracted from online job advertisements posted in 2021 for
five occupational groups that we selected as being relevant for knowledge production
and transfer activities: researchers and engineers, ICT professionals, science and engi-
neering technicians, ICT technicians and university and higher education teachers.
4. Results
Data collected via the questionnaire-based survey from employees (ESJ) show that
teaching positions and science, technology and ICT occupations are more likely to expe-
rience the frequent learning of new things than other professionals and technicians. In
fact, teaching positions are associated to the highest extent with the permanent learning
of new things, while science, technology and ICT occupations are most likely to experi-
ence the learning of new things on a regular basis. The visual representation of corre-
Electronics 2022, 11, 2927 7 of 14
spondence analysis suggests that other professionals and technicians experience learning
of new things sometimes (Figure 2). The results of the correspondence analysis indicate
that occupations relevant to the production and transfer of knowledge, especially the
teaching staff, are more exposed to learning new things than other professionals and
technicians.
Figure 3 presents the correspondence analysis between occupational category and
the degree of work autonomy. The results show that workers in science, technology and
ICT occupations are more those who ‘usually’ choose the way they do their work, while
teaching staff is likely to ’always’ make such choices. The other professionals and tech-
nicians experience less autonomy at work, compared to the other groups.
Figure 2. Correspondence analysis between occupation categories and frequency of learning new
things at work. Source: authors calculation on data from ESJ (Wave 1) [70]
Figure 3. Correspondence analysis between occupation categories and frequency of job autonomy.
Source: authors calculation on data from ESJ (Wave 1) [70]
Electronics 2022, 11, 2927 8 of 14
The results of logistic regressions for profiling the existing skills needs for targeted
occupations are released in Table 2. According to Model 1, science, technology and ICT
workers are more likely than other professionals and technicians to experience changes
in the technologies they work with. On the other hand, they are less likely to be con-
fronted with changes in working methods and practices. In addition, for science, tech-
nology and ICT occupations, advanced ICT skills, technical skills, foreign language skills
and problem-solving skills are more likely to be essential than for other professionals and
technicians. On the other hand, professionals and technicians for whom advanced liter-
acy and numeracy skills, communication and customer handling skills are more essen-
tial, these workers are less likely to be found in science, technology and ICT occupations.
Additionally, workers employed in science, technology and ICT occupations have a
higher probability than other professionals and technicians of having their skills matched
to what is required by their job rather than being higher. Additionally, they have an even
higher probability of having lower skills than is required than having a higher skill
mismatch. Moreover, workers who participated to training in order to stay up to date
with changing the skill needs of the job have higher odds of succeeding in science, tech-
nology and ICT occupations than other professionals and technicians.
Table 2. Logistic regression for profiling skills needs in science, technology and ICT jobs (Model 1)
and teaching positions (Model 2).
Model 1 Model 2
Exp(B) Sig. Exp(B) Sig.
Changes to the technologies you use (e.g.,
machinery, ICT systems) (reference category
= No)
Yes 1.432 0.004 0.868 0.465
Changes to your working methods and prac-
tices (e.g., how you are managed or how you
work) (reference category = No)
Yes 0.747 0.019 1.370 0.098
Changes to the products/services you help to
produce (reference category = No)
Yes 1.137 0.305 0.592 0.009
Advanced literacy skills 0.809 0.000 1.221 0.028
Advanced numeracy skills 0.850 0.002 1.111 0.198
Advanced ICT skills 1.365 0.000 0.989 0.894
Technical skills 1.395 0.000 0.749 0.000
Communication skills
0.734
0.000
1.956
0.000
Team-working skills 1.051 0.374 0.832 0.019
Foreign language skills 1.110 0.000 0.938 0.045
Customer handling skills 0.932 0.012 0.977 0.543
Problem solving skills 1.250 0.002 0.582 0.000
Learning skills
0.917
0.163
1.415
0.001
Planning and organisation skills 0.849 0.005 1.075 0.409
Overall, how would you best describe your
skills in relation to what is required to do
your job? (reference category = My skills are
higher than required by my job)
0.009 0.028
My skills are matched to
what is required by my
job 1.404 0.004 0.630 0.011
Some of my skills are lower than what is required
1.639 0.077 0.554 0.170
Electronics 2022, 11, 2927 9 of 14
by my job and need to be further developed
Training undergone in order to stay
up-to-date with changing skill needs of the
job
(reference category = No)
Yes 1.506 0.001 0.959 0.832
Constant 0.888 0.813 0.012 0.000
Note: Model 1 Nagelkerke R Square = 0.208, Model 2 Nagelkerke R Square = 0.166. Source: authors’
calculation on data from ESJ (Wave 1) [70].
According to the results of Model 2, teaching staff register a higher probability than
other professionals and technicians to experience changes in the working methods and
practices they use. On the other hand, they are less likely to confront with changes in the
products/services they provide. In addition, professionals and technicians for whom
advanced literacy skills, communication and learning skills are more essential are more
likely to be found in teaching occupations. On the other hand, professionals and techni-
cians for whom technical skills, team-working, foreign languages and problem-solving
skills are more essential are less likely to be found in teaching occupations. Additionally,
workers employed in teaching occupations have a lower probability than other profes-
sionals and technicians of having their skills matched to what is required by their job
with a high skill mismatch. This suggests that workers in teaching positions are more
likely to possess skills that are higher than those required by their job than other profes-
sionals and technicians.
According to data extracted from a high volume of online job advertisements (OJA)
posted in European countries in 2021, occupations related to science, technology and ICT
require good knowledge in engineering, manufacturing and construction; knowledge in
information and communication technologies; skills in working with computers; as well
as communication, collaboration and creativity. In addition, software and applications
development and analysis, accessing and analysing digital data and teamwork skills are
required for performing such occupations. On the other hand, teaching positions in
higher education require communication, collaboration and creativity, knowledge in in-
formation and communication technologies and capacity to adapt to changes (Table 3).
Table 3. Top three skills requested in knowledge production and transfer occupations.
Level 1 ESCO Skills
Level 3 ESCO Skills
Researchers and
engineers
Communication, collaboration and creativity (67.1% of
OJA)
Working with computers (60% of OJA)
Knowledge in engineering, manufacturing and construc-
tion (51.9% of OJA)
Personal skills and development (48.9% of OJA)
Accessing and analysing digital data (46.9% of OJA)
Working in teams (46.4% of OJA)
Science and engi-
neering technicians
Communication, collaboration and creativity (58.7% of
OJA)
Knowledge in engineering, manufacturing and construc-
tion (54.7% of OJA)
Working with computers (44.7% of OJA)
Working in teams (36% of OJA)
Languages (34.2% of OJA)
Accessing and analysing digital data (32% of OJA)
ICT professionals
Working with computers (82% of OJA)
Knowledge in information and communication technolo-
gies (81.6% of OJA)
Communication, collaboration and creativity (81.6% of
OJA)
Software and applications development and analysis
(73.8% of OJA)
Adapt to change (63.6% of OJA)
Accessing and analysing digital data (62.1% of OJA)
ICT technicians
Communication, collaboration and creativity (73.2% of
OJA)
Working with computers (73% of OJA)
Knowledge in information and communication technolo-
gies (62.6% of OJA)
Adapt to change (62.7% of OJA)
Accessing and analysing digital data (50.7% of OJA)
Working in teams (47.9% of OJA)
Electronics 2022, 11, 2927 10 of 14
University and
higher education
teachers
Communication, collaboration and creativity (59.5 % of
OJA)
Knowledge in generic programmes and qualifications
(45.9% of OJA)
Knowledge in information and communication technolo-
gies (40.4% of OJA)
Adapt to change (60.2% of OJA)
Personal skills and development (45.9% of OJA)
Working in teams (43.8% of OJA)
Source: data extracted from Skills-OVATE, Data on: Quarter 1 2021–Quarter 4 2021[71].
5. Discussions
Our results show that working environments related to knowledge creation and
transfer are highly demanding as they are more characterised by the frequent exposure of
workers to learning new things and by autonomous work, as highlighted by Figures 2
and 3. Such characteristics of a working environment require workers who possess the
right mix of abilities and attitudes in order to effectively carry out their tasks. From this
point of view, such workers need to be highly adaptable with strong learning abilities in
order to possess strong decision-making capacity, motivation, good perceived-self effi-
cacy and self-organisation capacity.
With respect to sources of skill gaps among workers who create or transfer
knowledge, technological advancement is one of the main drivers for change in the case
of science, technology and ICT jobs, as shown by the empirical results of Model 1 in Table
2. This complements previous findings that indicate that technological change is one of
the main determinants of increasing demand for highly educated workers [73]. So,
technological advancement induces more than the increased demand in the number of
highly skilled workers but also changes the skills they use at the workplace. Results of
Model 1 and Model 2 presented in Table 2 indicate that changes affecting the way or-
ganisations interact, develop their networks, collaborate and exchange experiences and
knowledge represent other sources of change in the skills needed by workers involved in
knowledge creation and transfer.
Participation in training for keeping workers up to date with the changing skill
needs of the job is more necessary in science, technology and ICT occupations, as high-
lighted by the results of Model 1 in Table 2. As a result, workers in these jobs are better
matched from the point of view of their skills. On the other hand, teachers in higher ed-
ucation are more often over-skilled, suggesting that their potential is not fully used (see
results of Model 2 in Table 2).
According to the results of Model 1, with respect to skills that are important in the
workplace, science, technology and ICT occupations require more advanced ICT skills,
technical skills, foreign languages and problem-solving skills. On the other hand, teach-
ing positions require advanced literacy skills, communication and learning skills to a
higher extent (see results of Model 2 in Table 2). These results are consistent with expec-
tations regarding a high growth rate of demand for highly specialized skills [74] and a
shift in the labour market towards more autonomy, more ICT and more social and intel-
lectual tasks in the years to come [2]. Our results are consistent with previous conclusions
of OECD showing that employers in the knowledge economy rely more on “workplace
competencies” as compared with technical skills that refer mostly to cognitive compe-
tencies. Workplace competencies include inter-personal skills, such as communication,
ability to collaborate, teamwork and leadership, and intra-personal skills, such as ability
to learn, problem solving, analytical skills and motivation, as well as ICT skills [13].
Confirming these previous findings, we found that workers involved in knowledge cre-
ation and transfer mostly differentiate themselves from other professionals and techni-
cians through the increased need for ICT skills, problem-solving, communication and
learning skills, collaboration and adaptability.
The empirical findings of our study clearly demonstrate that the skills of workers in
teaching and research need to be higher than for other jobs, and knowledge production and
transfer positions have the role of pushing transformation in all socio-economic sectors. On
Electronics 2022, 11, 2927 11 of 14
the other hand, we highlighted the consistent importance of digital skills. The main gain
and contribution of the present study is an improved knowledge of the skills needed by the
most future-oriented jobs, considering that the next generation labour force is educated in
this environment. As skills are directly linked with labour productivity, improving skill
matches in knowledge-based sectors would boost growth and development. Developing
the right mix of skills that respond to the needs of knowledge production and transfer
sectors would be highly beneficial for economic and social progress.
Our results contribute to the literature related to the competencies required to par-
ticipate effectively in the knowledge economy. Improving the understanding of the skill
needs is important for updating curricula, developing appropriate actions and providing
incentives focused to promote the formation of needed skills. Mapping skills needed by
workers of the analysed sectors will help to the design of future digital education meant to
address the challenges of the digital transformation and the knowledge economy. Dedi-
cated educational and training programs have to provide strong ICT skills, prob-
lem-solving, communication and learning skills, the ability to collaborate and adaptability.
Compared with other studies, our contribution has pointed out and placed the
spotlight on workers in knowledge production and transfer, mainly higher education
and scientific research, who are considered the spearhead of the evolution of society.
Based on our findings, the selection, training, evaluation and promotion of these occu-
pational categories could be reshaped.
6. Conclusions
The expansion of the knowledge economy is changing the landscape of the labour
market demands with respect to competences and skills. The demand for workers to
perform jobs that involve the production and use of knowledge is increasing and they
require specific skills profiles. Occupations related to knowledge production and transfer
are more exposed to dynamic transformation than other professionals’ and technicians’
jobs. Science, technology and ICT workers experience permanent changes in the tech-
nologies they use and products and services they deliver, while the teaching staff from
higher education faces more changes in the methods and practices they work with. They
are more frequently required to learn new things and are in a position to choose the way
of performing their work. The participation of workers to training helps them to stay
up-to-date and match their skills with changing requirements. Strong ICT skills, prob-
lem-solving, communication and learning skills, the ability to collaborate and adaptabil-
ity are key skills for workers in jobs involving knowledge management. Our findings
could be useful for improving the content of both short-term training programs as well as
educational programs targeting such jobs. The main limitation of the study is related to
difference between the reference periods for the two sources of data. However, results
obtained from the two sources are consistent and convergent. Future research could fo-
cus on assessing the evolution in the skills needed for jobs in knowledge production and
transfer as new comparable data are collected and become available.
Author Contributions: Conceptualization, A.G. and A.-M.Z.; methodology, A.-M.Z.; software,
A.-M.Z.; validation, A.G., A.-M.Z., H.T.S. and E.L.C.; formal analysis, A.-M.Z.; investigation, H.T.S.
and E.L.C.; resources, A.G., A.-M.Z., H.T.S. and E.L.C.; writing—original draft preparation, A.G.
and A.-M.Z.; writing—review and editing, A.G., A.-M.Z., H.T.S. and E.L.C.; supervision, A.G. and
A.-M.Z.; project administration, A.G.; funding acquisition, A.G. All authors have read and agreed
to the published version of the manuscript.
Funding: This research was funded by EEA Grants/Norway Grants, grant number 18-COP-0032.
Acknowledgments: This publication was realised with the EEA Financial Mechanism 2014–2021
financial support through the Project School of Knowledge Production and Transfer for Global
Economy and Governance, contract number 18-COP-0032. Its content does not reflect the official
opinion of the Programme Operator, the National Contact Point and the Financial Mechanism Of-
fice. Responsibility for the information and views expressed therein lies entirely with the authors.
Electronics 2022, 11, 2927 12 of 14
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the
design of the study; in the collection, analyses, or interpretation of data; in the writing of the man-
uscript; or in the decision to publish the results.
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