Motivating employees to participate in adult learning - Lise Meylemans, Lise Szekér & Ezra Dessers - KU ...
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Motivating employees to participate in adult learning RESEARCH REPORT ON THE FUTUREFIT BELGIUM PROJECT Lise Meylemans, Lise Szekér & Ezra Dessers
MOTIVATING EMPLOYEES TO PARTICIPATE IN ADULT LEARNING Research report on the FutureFit Belgium project Lise Meylemans, Lise Szekér & Ezra Dessers Research commissioned by NESTA
Abstract The FutureFit project aims to gain insight in the learning motivation of employees in order to tackle the challenge of adult learning and to encourage employees to participate and to successfully complete adult education. The central research question is ‘how to motivate workers whose roles are at risk of automation to engage in adult learning?’. The project tool place in five countries. This report presents the research that was carried out in Belgium. Three companies were studied who implemented a learning trajectory on digital skills and knowledge, together with Mtech+, City of Ghent and three trade unions. The aim of this trajectory was first to motivate employees to engage in learning activities, and second to strengthen employees’ digital abilities. The trajectory included not only (digital) training modules, but also a digi-fair in which employees were introduced to new digital technologies in an informal and interactive way. To gain insights in the various elements which may support (and predict) employees’ motivation in learning activities, both a quantitative (surveys) and qualitative (interviews and focus groups) approach was adopted. From the results we notice several factors significantly affecting employees’ autonomous motivation and future learning intentions, including an autonomy-supporting work climate in which the learning activity is promoted, (previous) positive learning experiences, affinity with the topic, and intrinsic outcome expectations. All findings were in line with the Self-Determination Theory (SDT) on which the conceptual model for the research was built. A first implication of these results is that triggering autonomous motivation helps to engage employees in learning activities. Secondly, providing informal and interactive learning experiences regarding digitalisation can lower the threshold for employees in strengthening their digital skills. Thirdly, special attention for various characteristics of the target group is crucial. Finally, COVID-19 has accelerated the transition towards digital learning, for which it is crucial that employees have access to a supporting infrastructure. Published by KU Leuven HIVA - RESEARCH INSTITUTE FOR WORK AND SOCIETY Parkstraat 47 box 5300, 3000 LEUVEN, Belgium hiva@kuleuven.be http://hiva.kuleuven.be © 2021 HIVA-KU Leuven Niets uit deze uitgave mag worden verveelvuldigd en/of openbaar gemaakt door middel van druk, fotokopie, microfilm of op welke andere wijze ook, zonder voorafgaande schriftelijke toestemming van de uitgever. No part of this book may be reproduced in any form, by mimeograph, film or any other means, without permission in writing from the publisher.
Contents
List of abbreviations 7
List of tables 9
List of figures 11
List of graphs 13
Introduction 15
1 | Adult education in Belgium 17
2 | The FutureFit project in Belgium 19
2.1 Involved stakeholders and their role 19
2.2 Main parts of the FutureFit project in Belgium 19
3 | Conceptual framework 23
4 | Impact COVID-19 pandemic on the project 27
5 | Research questions 29
6 | Methodology 31
6.1 Qualitative research method: interviews and focus groups 32
6.1.1 Stakeholder interviews and focus groups 32
6.1.2 Interviews digi-fair 32
6.1.3 Interviews digital training program 33
6.2 Quantitative research methods: three cross-sectional surveys 34
6.2.1 Survey 1 (T1) 35
6.2.2 Survey 2 (T2) 37
6.2.3 Survey 3 (T3) 38
7 | Findings 41
7.1 Digital literacy and digital skill needs 41
7.1.1 Digital literacy 41
7.1.2 Digital skill needs 46
7.2 Overview available training courses 48
7.3 RQ 1 - Autonomy-supportive work context 49
7.4 RQ 2 - Participation in learning activities regarding sociodemographic, job and
personality characteristics 52
7.5 RQ 3 - Future learning intentions regarding the digi-fair as a previous learning
experience 55
7.6 RQ 4 - Learning motivation regarding the trainee’s attitude and expectations of the
training 58
7.7 RQ 5 - Learning motivation and future learning intentions regarding the learning
experience 61
7.8 Training success 64
CONTENTS 57.9 COVID-19: digital needs and impact of training on coping with the pandemic 66
8 | Discussion 69
8.1 Main take-aways 69
8.2 Practical contribution 70
8.3 Limitations and future research 71
9 | Conclusion 73
References 92
6 CONTENTSList of abbreviations
ABC Anglo Belgian Corporation
SDT Self Determination Theory
VCG Volvo Car Ghent
LIST OF ABBREVIATIONS 7List of tables
Table 2.1 Timing of the FutureFit project 21
Table 2.2 Practical details for digi-fair and training program per company 21
Table 6.1 Overview interviews and focus groups 34
Table 6.2 Sample size and response rates on the three surveys for the three involved
companies 37
Table 7.1 Overview available training courses ABC (response ABC T3 = 11) 48
Table 7.2 Overview available training courses Niko (cleaned response Niko T3 = 46) 49
Table 7.3 Overview available training courses VCG (response VCG T3 = 32) 49
Table 7.4 Linear regression with autonomous regulation as dependent variable and
perceived autonomy support in company as independent variable with
standardised coefficients (N=89) 52
Table 7.5 Description of the socio-demographic characteristics, job characteristics and
personality traits (in T1) 54
Table 7.6 Linear regression with future learning intentions as dependent variable and
perceived usefulness of digi-fair and interest and enjoyment scale as
independent variables with standardised coefficients. (N = 218) 56
Table 7.7 Linear regression with autonomous regulation as dependent variable and
digital technology affinity as independent variable with standardised
coefficients (N=89) 59
Table 7.8 Linear regression with autonomous regulation as dependent variable and sum
of intrinsic outcome expectations and sum of extrinsic outcome expectations as
independent variables with standardised coefficients (N=89) 60
Table 7.9 Linear regression with sum of autonomous reasons to enrol in training as
dependent variable and sum of intrinsic outcome expectations and sum of
extrinsic outcome expectations as independent variables with standardised
coefficients (N=89) 60
Table 7.10 Linear regression with sum of controlled reasons to enrol in training as
dependent variable and sum of intrinsic outcome expectations and sum of
extrinsic outcome expectations as independent variables with standardised
coefficients (N = 89) 60
Table 7.11 Linear regression with future learning intentions as dependent variable and
perceived usefulness of training and interest and enjoyment scale as
independent variables with standardised coefficients (N = 89) 62
Table 7.12 Linear regression with future learning intentions as dependent variable and
autonomous and controlled regulation, and sum of autonomous and
controlled of reasons to enrol in training as independent variables with
standardised coefficients. (N = 89) 63
LIST OF TABLES 9List of figures
Figure 2.1 Phases in the Belgian FutureFit project 20
Figure 3.1 The self-determination continuum 23
Figure 3.2 Conceptual framework 25
Figure 6.1 Planning of the surveys (orange) and qualitative research methods (green)
used throughout the project 31
LIST OF FIGURES 11List of graphs
Graph 7.1 Employees owning digital devices (%) 42
Graph 7.2 Employees using online applications and websites (%) 43
Graph 7.3 Employees using computer software and programs (%) 44
Graph 7.4 Employees using company specific digital tools and software in ABC (%) 45
Graph 7.5 Employees using company specific digital tools and software in Niko (%) 45
Graph 7.6 Employees’ opinions on how ABC deals with digital opportunities and digital
skills (%) 46
Graph 7.7 Employees’ opinions on how Niko deals with digital opportunities and digital
skills (%) 47
Graph 7.8 Employees’ opinions on how VCG deals with digital opportunities and digital
skills (%) 47
Graph 7.9 Employees’ perceived usefulness of the training in ABC on short term and long
term (%) 65
Graph 7.10 Employees’ perceived usefulness of the training in Niko on short term and long
term (%) 65
Graph 7.11 Employees’ perceived usefulness of the training in VCG on short term and long
term (%) 65
Graph 7.12 Employees’ training needs after participating in the FutureFit training program
(%) 66
Graph 7.13 Impact of COVID-19 on the use of digital applications (%) 67
Graph 7.14 Impact of the FutureFit training program on coping with the COVID-19
pandemic (%) 67
LIST OF GRAPHS 13Introduction
The labour market is undergoing radical changes and facing multiple challenges, including evolving
skill needs. A major trend which affects this change in skill needs is digitisation and emerging new
technologies at the workplace and in society in general. The recent COVID-19 crisis may even have
accelerated this need for digital skills, both at work and in private life. This trend is expected to have
enormous consequences for jobs and needed competencies (Hughes et al., 2019).
A critical element for companies is their human capital: the knowledge, experience, and compe-
tences of employees. Companies will need employees who are able to use these digital technologies
and adapt to evolving methods and new digital approaches (Strack et al., 2017). Therefore, lifelong
learning through adult education and employee training is key, in order to upskill and to strengthen
digital competences in the workforce (Hughes et al., 2019).
The objective of the FutureFit research project is to gain more insight in the learning motivation
of employees in order to tackle the challenge of adult learning in the workplace and to encourage
employees to participate and to successfully complete adult education. Adult learning is defined as
‘all learning activity undertaken throughout life, with the aim of improving knowledge, skills and
competence, within a personal, civic, social and/or employment-related perspective’ (Kapetaniou,
2019, p. 10). By developing and setting up a training program FutureFit had the opportunity to look
into different stages of the learning activity and various factors which could influence employees’
motivation and participation. The project addresses the following questions: (1) what (de)motivates
employees to engage in and to successfully complete adult education programs regarding digital skills?
(2) Which factors support (and may predict) employees’ motivation in learning activities regarding
digital skills?
FutureFit is carried out in five countries labelled as digital frontrunners by the European Commis-
sion (2019a): Sweden, Finland, The Netherlands, Denmark and Belgium. This report concerns the
Belgian case and consists of the following parts: an overview of adult education in Belgium, clarifica-
tion of the FutureFit project in Belgium, conceptual framework, impact of the COVID-19 pandemic,
research questions, methodology, findings, and discussion and conclusion.
INTRODUCTION 151 | Adult education in Belgium
To set the scene, we will present an overview of adult education in Belgium in this first chapter of
the report. The emphasis of this overview lies on the northern, Dutch-speaking region of Flanders
as the three companies studied in the FutureFit project are all located in this region.
Recent OECD research shows that about 14% of employees in Flanders (and more general in
Belgium) are employed in jobs at high risk (70%) to become automated and 29% of jobs in Flanders
will change significantly as a result of automation (OECD, 2019). Secondly, demographic trends such
as an ageing population and thereby an extension of the working career will challenge the labour
market. To ensure sustainable employability and a digital competent workforce a culture of lifelong
learning and adult education must be encouraged, according to the OECD.
Belgium has a three-level governmental structure which includes the federal state, the communities
(Flemish, French and German-speaking) and the regions (Flemish, Wallonia and Brussels-Capital),
all three are equal from a legal viewpoint, but have powers and responsibilities for different fields
(Belgian Federal Government, 2020). Adult education and lifelong learning programs are mainly a
responsibility of the regional governments.
In all three Belgian regions (Flanders, Brussels-Capital and Wallonia) policies have been developed
regarding (lifelong) learning and adult education. Flanders has elaborated the most comprehensive
policy in this regard, as lifelong learning is included as one of the key transition priorities in the long-
term strategy ‘Vision 2050’ (Flemish Government, 2016). In this policy, Flanders identified five top
priorities (OECD, 2019). Developing a learning culture is a first priority. The development of a strong
learning culture is necessary to make sure everyone is willingly to participate in learning activities.
Currently, some groups are left behind, such as low-skilled, older employees, employees in flexible
forms of employment and immigrants. Paradoxically, these groups have higher learning needs. A
second priority is matching competences more effectively with the labour market. The labour market
is confronted with an imbalance in the supply and demand of skills. This results in extra costs which
can be avoided by matching competences more effectively. Thirdly, this policy wants to make better
use of competences in the workplace by encouraging companies to think critically about the design
of workplaces, adapting wages to the complexity of tasks and involving employees in the organisation
of work and training. Strengthening the provision of lifelong learning for adults is a fourth priority.
For achieving this it is crucial to have a global approach at all policy levels, involving all relevant
departments, government levels and social partners. Finally, the Flemish government wants to
enhance the financing of adult learning by dividing the costs among individuals, employers and
government. However, the financing of lifelong learning might not reach the groups that can benefit
most from it (low-skilled, older employees, etc.) (OECD, 2019).
This policy was translated in concrete actions. Recently, the Flemish government introduced a
reform of the Flemish educational leave, with 125 hours of paid educational leave per year for each
employee (OECD, 2019). In addition to this paid educational leave system, the Flemish government
also provides career and training vouchers which employees can request to co-finance training pro-
grams, which are not paid by the employer, and career counselling (Flemish Government, n.d.).
Variations on these training vouchers are also offered by the Walloon government and the govern-
ment of Brussels-Capital (Forem, n.d.; Brussel Economie en Werkgelegenheid, n.d.). Compensations
also exist for companies. Companies which are unable to find suitable labour forces can, through the
CHAPTER 1 | ADULT EDUCATION IN BELGIUM 17mediation of the VDAB (the public employment service of Flanders), train a jobseeker themselves
within the company on favourable conditions (VLAIO, 2020a). Also, partial or total exemption from
payment may be obtained for certain categories of workers for training provided by VDAB (VLAIO,
2019). Subsequently, each Flemish SME receives an annual subsidy which it is free to use for training
and/or advice (VLAIO, 2020b).
In addition to regional policy and actions, social partners are undertaking initiatives to stimulate
this learning culture as well. Agoria, which is an employers’ federation representing companies in the
manufacturing, construction, materials, digital and telecom industries, has developed a research pro-
ject ‘shaping the future of work’ (Agoria, 2018). Here, upskilling and lifelong learning are pushed
forward as a first strategy for a sustainable labour market. Proactively upgrading digital and other
related skills should enhance workers’ employment prospects in this digital transformation process.
Belgian trade unions also anticipate on this by transforming their education funds to career funds.
In contrast to the former education funds, career funds concentrate on a broader mission; to create
sustainable employability, taking into account the individual career and training preferences of the
employees themselves.
In both regional policy and initiatives of social partners in Belgium the focus lies on co-creation,
which anticipates on a shared responsibility for government, employers and employees. In this
objective, it is the government’s duty to provide a framework, employers should provide resources
and learning programs for its employees and it is the employee’s responsibility to be motivated and
committed to invest their time (Agoria, 2018).
Regarding this co-creating perspective, it will be crucial to motivate and encourage the workforce
in developing and shaping training and education as part of their career path. However, participation
in adult learning programs in Belgium seems rather low compared to other European countries
(Djait & Boey, 2014; Eurostat; OECD, 2019). In 2016, 6.8% of the Belgian population between the
ages of 25 and 64 was participating in training and education (in the last 12 months); 9.8% in Brussels-
Capital, 6.8% in Flanders and 5.8% in Wallonia. However, the national and European objective for
2020 is set at a participation rate of 15% (European Commission, 2019b; Statbel, 2018; Flemish
government, 2009). More than half of former participants in formal education programs in Belgium
would rather not participate again and 76.1% of non-participants would still not participate in the
future (Eurostat).
Looking at the obstacles and main reasons to not participate in educational programs, data shows
that a great majority says they do not want to engage in adult learning programs (76.2%). Mostly,
older employees are not willingly to participate in adult education (85.3%). Others who wanted to
but could not participate mainly gave ‘schedule’ as a reason or obstacle not to participate (Eurostat).
Also, differences can be noticed between the participation rate in formal and non-formal education.
While formal education is provided by public organisations and recognised private bodies, usually
with a set curriculum, non-formal education is organised by education providers and leads mostly to
qualifications that are not recognised by the relevant national or local education authorities, or no
qualifications at all (Kapetaniou, 2019). In comparison with formal education, the participation rate
in non-formal educational programs is higher: in 2016, 41.1% of the Belgian population between the
ages of 25 and 64 participated in non-formal education during the last 12 months. Almost 3 out of
4 non-formal training courses are work-related (Statbel, 2018). In addition to these organised courses
(formal and non-formal), there is also informal learning in which people acquire new knowledge by
talking to others, reading, or visiting museums, for example (Kapetaniou, 2019).
18 CHAPTER 1 | ADULT EDUCATION IN BELGIUM2 | The FutureFit project in Belgium
In this second chapter we will provide an overview and further clarification of the FutureFit project
in Belgium. We will first present all involved actors and their role in this project. Secondly, the main
features of the project will be discussed.
2.1 Involved stakeholders and their role
The FutureFit training project in Belgium consists of a cooperation between nine partners. This col-
laboration includes:
1. Mtech+
2. City of Ghent
3. Companies: Anglo Belgian Corporation (ABC), Niko and Volvo Cars Gent (VCG)
4. Trade unions: ACV-CSC METEA, ABVV Metaal and ACLVB
5. HIVA KU Leuven
The project is coordinated by Mtech+, which was formerly known as TOFAM, a training fund for
the metal and technology sector in the province of Eastern Flanders (in Dutch: Oost-Vlaanderen).
During the FutureFit project, Mtech+ was transforming into a career fund, where they aim to shift
their focus from a reactive towards a proactive approach in strengthening employees' careers. The
FutureFit project exemplifies this new proactive strategy.
Secondly, the city of Ghent was involved as they identified digitalisation as a general, shared
problem for the main sectors in Eastern Flanders for which Ghent is the capital city. Therefore, the
involved companies locate in the province of Eastern Flanders.
The project was organised in the technology and metal sector, more specifically in the following
three firms: Anglo Belgian Corporation (ABC), Niko and Volvo Cars Gent (VCG). ABC is a manu-
facturer of medium speed engines. Niko is engaged in the manufacture of switchgear, socket outlets
and home automation. VCG is a car factory integrated in the Volvo Car Corporation.
Furthermore, trade unions which were present in the companies, were involved. These trade unions
included: ACV-CSC METEA, ABVV Metaal and ACLVB representing the three largest Belgian trade
unions: ACV-CSC, ABVV-FGTB and ACLVB-CGSL.
Finally, HIVA KU Leuven was included as research partner.
Additionally, three external partners were involved in the project: iDrops, TEO and Bit by bit.
iDrops is a social innovation agency in Ghent who tailor innovation trajectories for companies and
organisations. iDrops was involved in the project to design and create a digi-fair (explained below).
TEO and Bit by bit are training providers and were included in the FutureFit project to provide and
set up tailormade training programs in the companies (Bit by bit in ABC and TEO in Niko and
VCG).
2.2 Main parts of the FutureFit project in Belgium
The aim of the FutureFit project is to understand what could motivate employees to enrol in and
successfully complete adult education, by examining adult learning before, during and after training.
The project for Belgium will consist of two elements: the digi-fair, an interactive fair, which stimulates
CHAPTER 2 | THE FUTUREFIT PROJECT IN BELGIUM 19employees to engage with and learn about new digital technology; and custom-made training pro-
grams designed according to the needs of the participants and of their employers. The involved com-
panies chronologically ran through three phases: kick-off, the digi-fair and the training program (see
Figure 2.1).
Figure 2.1 Phases in the Belgian FutureFit project
During the first phase of the project, several kick-off meetings within the companies were organised.
Here, the digi-fair and training programs were presented to the company’s management, trade union
representatives, and supervisors. The aim of these kick-off meetings was to announce the project and
to communicate next steps and timing within the companies. The kick-off meetings were scheduled
in early July 2020 (see Table 2.1).
Also, during this first phase ‘digi-ambassadors’ were appointed within the companies. A digi-
ambassador acts as the employees’ contact person for digital matters within the company. For
example, a digi-ambassador can help colleagues with small digital problems, answer digital related
questions and ensure employees becoming more self-reliant in this respect (without taking over the
role of IT). As a colleague, a digi-ambassador can lower the threshold for employees to ask questions
related to digital matters. Digi-ambassadors also played a role in communicating the digi-fair and the
FutureFit training program and encouraging employees to participate.
In second phase of the project the digi-fair was organised in all three companies. The aim of this digi-
fair was to arouse employees' interest in new digital technologies, digital skills and knowledge.
Employees were introduced to new technologies and digitalisation in an informal way. In this way,
employees were expected to become more intrinsically, or autonomously, motivated to participate in
later training modules concerning digital and technical skills and knowledge. The digi-fair could there-
fore lower the threshold for employees who fear digitalisation and have low digital skills. Due to
COVID-19 measures, in two out of three companies the digi-fairs had to be organised online instead
of physically. Only in Niko, the digi-fair was organised physically taking into account the social dis-
tancing measures (see Table 2.2).
Both at the physical and the online fair, participants could visit various booths. For example, at the
fair in Niko participants could visit a booth where the online learning platform and learning boxes
were presented. A second booth included a digital quiz with questions about digitalisation. Thirdly,
participants could visit the brainstorm wall where inspiring videos about digitalisation and digital
transformation were shown, after which participants could leave their ideas, concerns and needs
behind. Another booth showed a demo of disassembling and reassembling an engine block using
AR. Finally, participants could visit a booth on the conscious use of digital applications. Any addi-
tional booths included company specific technology or software. For example, at the fair in Niko an
additional booth was presenting ‘OMETA’, a company specific software used as a digital production
assistant.
The third phase in the FutureFit project included the custom-made training modules in each com-
pany. These modules were customised to the employer’s and employees’ needs. The aim of this
training program is to get employees with low digital skills engaged in training to strengthen their
20 CHAPTER 2 | THE FUTUREFIT PROJECT IN BELGIUMdigital skills and knowledge. This training could be on digital skills or on technical skills, all using a
digital format.
ABC opted for a collective, classroom experience focusing on Microsoft Office software. The
training courses took place in a virtual classroom where an instructor provided an online live class of
three hours, which was partly theoretical training and partly workshop. These trainings were organ-
ised during the so-called ‘digital Mondays’ (see Table 2.1).
Niko and VCG had a flexible, mixed-learning, individual, and digital, online training approach
focusing on technical skills. A digital learning platform and practical training boxes were provided, in
which employees learn theoretical insights through short videos and can apply these directly in exer-
cises in the box, such as constructing electrical circuits. The employee can thus choose when and
where to learn. This flexibility was expected to enable companies to engage more employees in
training programs. Employees had to go through the training course individually but were also fol-
lowed up by a coach. The training coach corrected the exercises and provides feedback. In this way,
employees can learn at their own speed and receive personalised support. The training programs in
Niko and VCG focused mainly on the technical aspects of digitalisation, enabling workers to operate
new machines and technologies, or were promoted to technicians.
Table 2.1 Timing of the FutureFit project
ABC Niko VCG
FutureFit project kick off June 29, 2020 June 29, 2020 June 29, 2020
Internal kick off in companies July 2, 2020 July 1, 2020 July 6, 2020
Digi-fair January 11-18, 2021 September 29, 30 and January 2021
Online October 1, 2020 Online
Training program January 25 and February 1, September 2020 - ongoing December 2020 - ongoing
8, 15 and 22, 2021
Table 2.2 Practical details for digi-fair and training program per company
ABC Niko VCG
Digi-fair
Format Website Physical fair Website
Participants White and blue collar Blue collar White and blue collar
Participation Voluntary Obligatory Voluntary
During working time During working time Outside working time
Training program
Format Digital classroom setting Digital learning platform Digital learning platform
and practical training boxes and practical training boxes
Participants White collar Blue collar Blue collar
Participation Voluntary Co-determined by worker Voluntary
During working time and supervisor Both inside and outside
During working time working time
CHAPTER 2 | THE FUTUREFIT PROJECT IN BELGIUM 213 | Conceptual framework
In this research project participation in adult education and lifelong learning will be examined using
a broad perspective. At the macro level adult education and lifelong learning is implemented through
various national and regional policies taken by governmental as well as social actors. The main
objective of this research is to gain insight in what motivates employees into participating in learning
programs. Therefore, the focus in this report will be on meso (company) and micro (individual) level.
The Self Determination Theory (SDT) of Ryan and Deci (2000) provides us with a framework to
examine which factors can influence participation and learning among employees. SDT is a broad
and frequently used motivation theory which explains motivation as an outcome of the fulfilment of
three universal basic psychological needs: autonomy, competence, and relatedness. The more these
needs are satisfied, the more people will experience autonomous motivation. ‘Autonomous motiva-
tion is defined as engaging in a behaviour because it is perceived to be consistent with intrinsic goals
or outcomes and emanates from the self. In other words, the behaviour is self-determined. Con-
versely, controlled motivation reflects engaging in behaviours for externally referenced reasons such
as to gain rewards or perceived approval from others or to avoid punishment or feelings of guilt’
(Hagger et al, 2014, p. 566).
Autonomous and controlled motivation must be interpreted as positions on a continuum in which
an individual can fluctuate from amotivation (a lack of motivation) which situates at one end of the
continuum over controlled motivation to autonomous motivation which situates at the other end of
the continuum (Figure 3.1). According to SDT autonomous motivation is considered to be of higher
quality than controlled motivation. More qualitative motivation is linked to better outcomes for the
person. Regarding learning, persons who are more autonomously motivated to participate in learning
programs tend to perform better, learn more and make better use of what they have learned (Deci &
Black, 2000; Giesbers et al., 2013). Also, quality of motivation tops quantity of motivation. In other
words, the type of motivation people experience is more important in determining outcomes than
the level or amount of motivation that learners display for a particular learning activity (Vansteenkiste,
Lens & Deci, 2006).
Figure 3.1 The self-determination continuum
Source Adaptation from Deci and Ryan (2000)
The meso or company level generates the social context in which a learning goal is promoted. How-
ever, these social contexts can differ in the way the learning activity is promoted, introduced and
communicated. In a controlling context people can feel pressured to engage in a learning activity,
while in an autonomy supportive context the environment is supportive and people are encouraged,
but can decide for themselves whether they think it is worthwhile to pursue this learning goal
CHAPTER 3 | CONCEPTUAL FRAMEWORK 23(Vansteenkiste et al., 2005). Autonomy supportive (vs. controlling) learning climates improve learning,
performance and participation in learning programs (Kyndt & Baert, 2013; Lee, Pate & Cozart, 2015;
Reeve & Jang, 2006; Vansteenkiste et al., 2004). This autonomy supportive environment can be
expressed through employer support and autonomy supportive communication styles (Kyndt &
Baert, 2013; Tharenou, 2001; Vansteenkiste et al., 2005). Also, the attitude of other stakeholders
involved, such as trade unions, may affect this autonomy supportive context and thus the level of
participation (Esteban-Lloret, Aragón-Sánchez & Carrasco-Hernández, 2018; Kyndt & Baert, 2013).
At the individual microlevel sociodemographic factors, job characteristics and personality traits can
influence motivation and participation in adult education. Firstly, sociodemographic factors such as
gender, age and educational level have an effect. Regarding gender, women tend to have higher learn-
ing intentions than men (Sanders et al., 2011). However, talking about actual participation some
studies point out women tend to participate less in formal training than men (Albert et al., 2010).
Especially when the duration of the training is long (Greenhalgh & Mavrotas, 1994). Yet, other
research contradicts this by arguing that women have caught up with men and that differences have
disappeared over the years (Kyndt & Baert, 2013; Rothes et al., 2014). Concerning age, younger
employees tend to have higher learning intentions and to participate more in formal learning activities
than older employees. Especially employees in the highest age group (50+) seem to have low learning
intentions and the lowest participation rates compared to other age categories (Sanders et al., 2011).
While level of education is not a strong predictor for employee’s learning intentions, it predicts par-
ticipation rate rather well. Employees with a higher level of education seem to participate more in
formal learning activities than employees with lower educational levels (Albert et al., 2010; Rothes et
al., 2014). Rothes and colleagues (2014) suggested that people with characteristics such as, unemploy-
ment, lower educational level and male could be more at risk of failure and drop-out. Besides these
factors, also marital status, children, ethnicity, and social class were mentioned as possible deter-
minants for participation.
Secondly, also certain job characteristics may affect motivation and participation in learning activi-
ties. Besides tenure, which highly correlates with age and thus has the same relation with learning
intentions and participation, the occupational level influences participation rate in formal learning
activities. Non-manual workers tend to participate more in formal learning activities than do non-
manual workers. Further, employees with temporary contracts tend to participate more in training
opportunities than employees with permanent contracts and participation often is lower among
employees who work part-time (Kyndt et al., 2014; Kyndt & Baert, 2013; White, 2012).
Thirdly, we look at personality traits to predict participation and motivation. Multiple researches
found a positive relation between self-efficacy, learning intentions and participation. Self-efficacy can
be defined as ‘the belief individuals have in their own capacities, in this case the capacity to learn’
(Kyndt & Baert, 2013, p. 286). The more an individual believes in their own capacities, the higher
their autonomous or intrinsic motivation to participate (Kyndt & Baert, 2013; Rothes et al., 2014).
Self-efficacy tends to be positively correlated with level of education (Rothes et al., 2014). Addition-
ally, goal orientation is considered, which can be distinguished into performance goal orientation and
learning goal orientation. Individuals with a performance goal orientation want to show their com-
petence through performance on tasks and gain favourable judgements or avoid negative judgements
about their capabilities. Individuals with a learning goal orientation find it important to learn new
things or to increase their knowledge or capabilities when doing tasks (Button, Mathieu & Zajac,
1996). Learning goal orientation is a direct predictor of actual participation (Hurtz & Williams, 2009).
Also, persons whose goals are to learn (compared to performance goals) seem to have more learning
success (Schulz & Roβnagel, 2010).
Finally, characteristics of the learning activity and the trainee’s attitudes and expectations about the
(outcomes of) the training should be considered. Previous or recent learning experiences influence
intentions to take up future learning. Therefore, people who negatively experienced past learning tend
to avoid participating in future learning (White, 2012). Regarding attitude and expectations of the
24 CHAPTER 3 | CONCEPTUAL FRAMEWORKtrainees themselves, if employees believe the training outcomes are desirable for them, for example
they might expect to get a higher wage, this leads to training success (Colquitt, LePine & Noe, 2000).
Also feeling connected to the content of a learning program and thinking the training is relevant
enhances motivation and engagement in learning (Sibold, 2016; Assor, Kaplan & Roth, 2002).
Interest-based learning, or the ‘focused attention and/or engagement with the affordances of a par-
ticular content’ (Krapp, 2005, p. 382), is positively related to intrinsic motivation (Müller & Louw,
2004).
Based on SDT, we expect these factors (autonomy-supportive context, sociodemographic charac-
teristics, job characteristics, personality traits, characteristics of learning activity, trainee’s attitude and
expectations) to influence motivation (autonomous or controlled motivation). Autonomous motiva-
tion tends to have positive outcomes regarding learning; people who are more autonomously moti-
vated to participate in learning programs tend to perform better, learn more, make better use of what
they have learned, and have higher future learning intentions (Deci & Black, 2000; Giesbers et al.,
2013; White, 2012).
Figure 3.2 Conceptual framework
CHAPTER 3 | CONCEPTUAL FRAMEWORK 254 | Impact COVID-19 pandemic on the project
The FutureFit project was heavily affected by the COVID-19 pandemic. As a result of the measures
taken to contain the virus, the project planning had to be modified several times. Also, companies
were confronted with new safety measurements, more sick leave among employees, a new work
organisation, etc. which resulted in other priorities for the companies. Due to these constant modifi-
cations and changed priorities for companies the project deviated from the initial research plan in
some areas and had to be postponed. This complicated the research process and development of the
training program.
First of all, the project was confronted with several delays due to COVID-19 restrictions. The first
kick-off phase was moved from March to July 2020. The second phase, in which the digi-fairs were
to be organised, was postponed until autumn 2020 (Niko) and January 2021 (ABC and VCG). The
third phase in which employees could participate in customised trainings was organised starting from
February 2021 in most companies.
Secondly, due to further COVID-19 restrictions in January the digi-fairs in ABC and VCG had to
organised in an online setting (using a website) instead of a physical setting. Although both companies
made efforts to reach as many employees as possible, the main target group, workers with low digital
skills, was not fully reached. This website version of the digi-fair in ABC and VCG contrasts with the
physical fair at Niko in which all blue-collar workers were invited to participate. This fair took place
at the workplace and was organised during working time. This allowed workers to experience directly
and interact with new technologies such as VR and AR. These differences in the way the fair was
organised (online vs. physical) could also affect the motivation and number of employees to partici-
pate in the actual training courses in phase three.
Thirdly, the customised training programs per company were modified several times in order to
organise them in February 2021, taking into account the COVID-19 measures. In ABC the training
courses were planned to be carried out in a physical classroom setting, which had to be reorganised
to an online setting and the target group was narrowed to only white-collar workers. Training for
blue-collar workers will be postponed until September 2021 in ABC (which is beyond the scope of
this report). Apart from several delays, the training modules for Niko and VCG have continued as
planned.
COVID-19 restrictions not only affected the planning of the project, but also hindered the research
evaluating the FutureFit project. Due to constant modifications and changed priorities for companies
during the pandemic, the project deviated from the initial research plan. Firstly, the main target group
were employees with low or no digital skills. This group was not fully reached because of the website
version of the digi-fair in ABC and VCG. Secondly, the pandemic made it more difficult for compa-
nies to include workers in training programs. This resulted in fewer workers being able to participate
in training courses than expected, which led to low response rates on the questionnaires. Therefore,
it was not possible to link the data from the first survey with the data from the third survey as initially
planned. As a result, some of the analyses we had originally planned could not be performed for every
company (VCG was not included in the T2 analysis). We attempted to compensate for this by col-
lecting additional data via the interviews and focus groups with the partners, participants, and digi-
ambassadors in the companies. Thirdly, in the initial research plan an additional survey (T4) was going
to be set up after 6 months to assess long-term learning success as one of the main outcomes in this
CHAPTER 4 | IMPACT COVID-19 PANDEMIC ON THE PROJECT 27study. Autonomous motivation tends to have a positive influence on learning success. Due to post-
ponements regarding the COVID-19 pandemic, this fourth survey could not be carried out within
the timeframe of the FutureFit project.
Despite this COVID-19 pandemic and the limitations and changes described above, the project
still managed to develop meaningful training programs and collect relevant data. This is mainly thanks
to the engagement of all the partners in the project.
28 CHAPTER 4 | IMPACT COVID-19 PANDEMIC ON THE PROJECT5 | Research questions
This research part of the FutureFit project aims to gain insights in the broad question: ‘What
(de)motivates employees to engage in and to successfully complete adult education programs regarding digital skills?’.
We will explore a set of more specific research questions, which address factors at the meso and
micro level using a multi-method approach.
At the macro level we focus on the role of various stakeholders. Several stakeholders were involved,
such as: Mtech+, city of Ghent, companies, and trade unions. How these stakeholders fulfilled their
role in the project can have an impact on the creation of a supportive environment. For example,
trade union involvement can have a significant influence on the training opportunities which are
provided in an organisation (amount of trainings and topics of training) and the conditions under
which trainings are organised (Desmedt et al., 2006; Esteban-Lloret et al., 2018). As Mtech+ and city
of Ghent coordinated the project they too can have an impact on the conditions under which train-
ings are organised.
At the meso/organisational level, creating a work environment which is supportive and encourages
employees to participate in trainings has also been found to positively influence participation in
training activities (Kyndt & Baert, 2013; Tharenou, 2001). In this project, each organisation appointed
digi-ambassadors among the employees, who act as a support and contact person concerning digital
matters. They can thus play an important role in creating a supportive work environment. This
autonomy supportive environment can also be expressed through employer support and autonomy
supportive communication styles used by supervisors (Kyndt & Baert, 2013; Tharenou, 2001;
Vansteenkiste et al., 2005). In this research project it is therefore interesting to look at this cooperation
between various stakeholders and the possible advantages of the cooperation and gains for the
motivation of employees. A first research question is therefore:
RQ1: How can the cooperation between different stakeholders create an autonomy-supportive social context to
encourage employees to participate in the training program?
At the microlevel we look at the influence of some sociodemographic, job and personal characteris-
tics. First of all, literature on participation in adult education is unanimous that there are differences
in participation in adult education depending on sociodemographic characteristics. Women tend to
participate in training more often, as do younger employees (Kyndt & Baert, 2013; Rothes et al., 2014).
Participation rate is also higher among employees with higher qualifications (Kyndt et al., 2014;
Kyndt & Baert, 2013; Rothes et al., 2014).
Certain job characteristics can also have an influence on the willingness of the employee to partici-
pate in training. Employees with temporary contracts tend to participate more in training opportuni-
ties than employees with permanent contracts. Further participation often decreases with tenure and
is lower among employees who work part-time. Also occupational level influences participation rate
so that managers and non-manual workers seem to participate more than blue collar workers (Kyndt
et al., 2014; Kyndt & Baert, 2013).
Personality traits may also play a role in the motivation of employees to participate in learning
activities. Many studies provide evidence for a positive relation between self-efficacy and learning
motivation and participation. Self-efficacy can be defined as ‘the belief individuals have in their own
CHAPTER 5 | RESEARCH QUESTIONS 29capacities, in this case the capacity to learn’ (Kyndt & Baert, 2013, p. 286). Further, goal orientation
predicts participation in learning activities. Persons with a learning goal orientation tend to participate
more and experience more learning success than persons with a performance goal orientation
(Hurtz & Williams, 2009; Schulz & Roβnagel, 2010).
RQ2: Are there differences in participation in learning activities between employees depending on sociodemographic
characteristics (age, gender and educational level), job characteristics (contract type, function, tenure, full-time or part-
time work), and personality traits (self-efficacy and goal orientation)?
Previous learning experiences will also affect motivation and participation for the current learning
activity. Previous learning experiences affect future intentions to participate in learning (White, 2012).
In this project, employees had the opportunity to participate in the digi-fair, a technology fair where
employees were introduced to new technologies and digitalisation in an informal way. If employees
experienced the digi-fair as positive, we expect it to have a positive influence on their motivation to
participate in the FutureFit training program.
RQ3: Are there differences in future learning intentions between employees depending on previous learning experiences
(digi-fair)?
The trainee’s attitude and their expectations on the (outcomes of) the training program can also have
an influence on their motivation and participation. Employees who believe the training will lead to
desirable outcomes for them, such as higher wage, promotion etc., experience more training success
(Colquitt, LePine & Noe, 2000). Also, if employees feel connected, identify with, and are interested
in the learning topic enhances intrinsic motivation to learn about it (Sibold, 2016; Müller & Louw,
2004). Here, the learning activity is focusing on strengthening digital skills and learning about new
digital technologies. Therefore, technology affinity could be a possible determinant for predicting the
motivation and participation of employees in this training. Technology affinity is defined as ‘the way
people approach (new) technical systems, meaning whether users tend to actively approach inter-
action with technical systems or, rather, tend to avoid intensive interaction with new systems’ (Franke,
Attig & Wessel, 2019, p. 456). We expect people with a high technology affinity to be more autono-
mously motivated and thus to participate more in this training than persons with a low technology
affinity.
RQ4: Are there differences in motivation between employees depending on the trainee’s attitude and their expectations
of the training (technology affinity and expectations on outcomes of the training)?
Furthermore, we will look at the employees’ learning experience during the FutureFit training pro-
gram to evaluate their motivation, and future training intentions on digital skills. As discussed for the
third research question, previous or recent learning experiences influence intentions to take up future
learning (White, 2012). We expect employees who enjoyed the trainings to have higher future learning
intentions than employees who did not enjoy the training program. Also, we expect motivation to
have an influence on future learning intentions.
RQ5: Are there differences in future learning intentions between employees depending on their learning experience
during the FutureFit training program?
30 CHAPTER 5 | RESEARCH QUESTIONS6 | Methodology
To address the research questions described in the previous chapter, we use a multiple method
approach, combining surveys, semi-structured interviews, and focus groups. A multi method research
design has many advantages to address a broad research question, like the one this study is focusing
on. A broader perspective can be taken, investigating both organisational and personal aspects at the
meso and micro level which might influence the motivation of employees to participate in training
activities. It also allows to consider contextual elements and get a richer picture of the processes
involved. Another advantages of multi method research is triangulation, which allow to validate
finding through different sources and methods (Esteves & Pastor, 2004; Tashakkori & Teddlie,
2003). Figure 6.1 gives an overview of the specific research activities throughout the project’s main
activities.
Figure 6.1 Planning of the surveys (orange) and qualitative research methods (green) used throughout
the project
The surveys focus on the individual motives, characteristics, and experiences of the employees. The
first survey (T1) is conducted among all involved employees about three to four months after the
kick-off of the project. The second survey (T2) was integrated in the digi-fair. The third survey (T3)
was done after employees finished at least one of the training modules of the digital skills training
program. Due to impact of COVID-19 measures, as discussed in the previous chapter, the digi-fair
for ABC and VCG and training modules for all companies were postponed. This resulted in a delay
for T2 and T3.
Qualitative methods are used to get insights in the elements that are important at the organisational
level and to explore individual motives and experiences of participants more in depth. To this purpose
stakeholders are interviewed individually or brought together in a focus group at the beginning of the
project and during the digital skill training program. Further interviews were done with employees
who participated in the digi-fair, training modules of the digital skills training programs, as well as
with digi-ambassadors. Interviews were mostly conducted using video-conference software or by
CHAPTER 6 | METHODOLOGY 31telephone. Furthermore, all communication documents of the companies were requested in order to
gain insight in the way of communicating the digi-fair and digital training programs to the employees.
The documents were analysed for autonomous versus controlling language and other factors, such
as rewards/penalties, timing, and management attitude towards training, which could influence
motivation of employees.
A General Data Protection Regulation (GDPR) assessment and ethical review has been completed
with reference number ‘G-2020-2219-R2(MAR)’ by KU Leuven.
6.1 Qualitative research method: interviews and focus groups
The main objective of the interviews and focus groups is to gain a richer picture of the macro and
meso level processes involved and to help interpret the results of the surveys at a micro level. Inter-
views were conducted in various stages of the FutureFit project and included various actors, such as
stakeholders (Mtech+, city of Ghent, trade unions, and companies), participants and digi-
ambassadors. All interviews and focus groups were structured by using semi-structured interview
guides.
6.1.1 Stakeholder interviews and focus groups
The stakeholders are the main actors involved in determining the focus and direction of the project.
Stakeholders of the FutureFit project include Mtech+, city of Ghent, the companies: Anglo Belgian
Corporation (ABC), Niko and Volvo Cars Gent (VCG) and trade unions: ACV-CSC METEA,
ABVV Metaal, ACLVB. To create an overview of their role and contribution to the project stake-
holders’ interviews or focus groups were organised in several phases during the project.
In a first phase, July-August 2020, two interviews (Mtech+ and city of Ghent) and two focus groups
(companies and trade unions) were carried out. Each interview/focus group took about 1 hour. The
aim of these interviews and focus groups was mainly to assess their views on digital skill needs,
expectations regarding the training program and their role as stakeholder in the project. Also, some
practical elements were discussed (such as timing, framework of the digi-fair and communication and
motivating strategies). Due to COVID-19 measures these interviews/focus groups were conducted
using video conference.
In a second phase, March 2021, three focus groups (Mtech+ and city of Ghent; companies; and
trade unions) were organised in which an evaluation was made of the digi-fair (online versus physi-
cally) and training sessions. Also, their views on COVID-19 impact, communication and motivating
strategies, future steps needed were discussed. Due to COVID-19 measures the focus groups were
as well conducted using video conference. The focus groups were of approximately 30 minutes up
to 1 hour. In total, 7 interviews and/or focus groups were organised with various stakeholders (see
Table 6.1)
6.1.2 Interviews digi-fair
In the autumn of 2020 and in January 2021 the digi-fairs were organised in all participating companies.
The aim of this technology fair was to arouse employees' interest in new digital technologies and
learning digital skills and knowledge. After the digi-fair, interviews with participants and digi-
ambassadors were conducted. The main purpose of the interviews was to gain insight in how
employees experienced this digi-fair and how the digi-fair was communicated to them.
At Niko, the digi-fair was organised in a physical setting as was initially intended, while the digi-fair
was organised using a website at ABC and VCG. The interviews at Niko were conducted face-to-face
at the last day of the digi-fair (1st of October) where 4 participants, 1 digi-ambassador and 1 supervisor
were interviewed. The interviews at ABC and VCG were conducted using video conference or by
32 CHAPTER 6 | METHODOLOGYphone and were limited to only 1 participant and 1 digi-ambassador as a result of the revised research
plan due to the COVID-19 pandemic. In total, 10 interviews were conducted in this stage of the
project (see Table 6.1). The interviews took approximately 10 to 15 minutes. The interviews with
participants focused on their experience with and motivation to participate in the digi-fair. Interviews
with the digi-ambassadors and supervisor focused more on the meso level, asking about their role as
a digi-ambassadors/supervisor in this project, communication and motivating strategies, and reac-
tions from employees to the digi-fair. Respondents participating in the interviews were selected ran-
domly by the project coordinator in the firm and/or based on who was participating in the digi-fair.
6.1.3 Interviews digital training program
The training programs at ABC, Niko, and VCG were organised from January 2021 until mid-March
2021. The aim of these interviews was to look into employees’ experience with the training, their
motivation to participate in learning activities at work, and impact of the digi-fair on their motivation
to learn about digital technologies.
In total, 9 interviews were conducted, of which 6 with participants and 3 with digi-ambassadors
(see Table 6.1). The interviews took about 10 minutes. Because of the COVID-19 measures the
interviews were organised by phone or video conference. The interviews with participants consisted
of three topics: their motivation to participate in the training session(s), an evaluation of the content
and form of the session and their participation in the digi-fair and how the digi-fair affected their
motivation to participate in the trainings. The interview with the digi-ambassador included questions
on a meso level, such as communication and motivating strategies, past and future training and skill
needs, etc. Respondents participating in the interviews were selected randomly by the project coor-
dinator at the firm.
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