The self-reinforcing effect of digital and social exclusion: The inequality loop

Abstract

Since an increasing number of daily activities are carried out online, an exclusion or limited access to the Internet prevent citizens from entering a world full of opportunities that cannot be accessed otherwise; in this sense, inclusion in the digital realm is strictly connected to social inclusion. Digital inclusion is not conceived as a mere dichotomy, access versus no access, but in terms of the degree to which e-inclusion improve wellbeing for individuals, community and society. Using a quantitative method based on a multivariate analysis, multiple correspondence analysis and cluster analysis, applied to a representative sample of UK citizens, this article sheds light onto the gradual process of digital inclusion, highlighting how social and digital inclusion are intertwined and how people who have one or more social or economic vulnerabilities are more likely to be in the group of those who are digitally excluded.

Full text

Telematics and Informatics 72 (2022) 101852
Available online 16 June 2022
0736-5853/© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
The self-reinforcing effect of digital and social exclusion: The
inequality loop
Massimo Ragnedda
a
,
*
, Maria Laura Ruiu
a
, Felice Addeo
b
a
Northumbria University, Newcastle upon Tyne NE1 8ST, UK
b
Department of Political, Social and Communication Science, University of Salerno, Italy
ARTICLE INFO
Keywords:
Digital inclusion
Social inclusion
Digital society
Digital inequalities
Digital divide
ABSTRACT
Since an increasing number of daily activities are carried out online, an exclusion or limited
access to the Internet prevent citizens from entering a world full of opportunities that cannot be
accessed otherwise; in this sense, inclusion in the digital realm is strictly connected to social
inclusion. Digital inclusion is not conceived as a mere dichotomy, access versus no access, but in
terms of the degree to which e-inclusion improve wellbeing for individuals, community and so-
ciety. Using a quantitative method based on a multivariate analysis, multiple correspondence
analysis and cluster analysis, applied to a representative sample of UK citizens, this article sheds
light onto the gradual process of digital inclusion, highlighting how social and digital inclusion
are intertwined and how people who have one or more social or economic vulnerabilities are
more likely to be in the group of those who are digitally excluded.
1. Introduction
This paper contributes to the digital divide research by conceptualising digital inclusion as a result of the combination of the three
levels of the digital divide and suggesting a need for multidimensional approaches (both online and ofine) to tackle the rapidly
changing digital inequalities. By using data from an online survey - stratied by age, gender, income, and level of education - con-
ducted in the UK (N =868), it investigates the socially inclusive use of the Internet in the UK and how this is related to socio-
demographic and socioeconomic features. Although there exist multiple denitions of digital inclusion, a commonly accepted
denition comes from the International Telecommunication Union (ITU, 2019), which refers to digital inclusion as all the different
initiatives implemented not only to provide citizens with equal access but also to provide them with the competencies that they need to
benet from digital technologies. These are commonly recognised by scholars as the three levels of the digital divide related to the
inequalities in access to ICT, digital skills and life chances (Ragnedda, 2017). Even though the number of ICT has constantly increased
in the UK, forms of digital exclusion persist among those who access the internet. Digital inclusion is not limited to closing the gap in
accessing the Internet in itself (rst level of the digital divide), but also comprises a certain level of digital competencies and motivation
to use digital technologies (second level of the digital divide) to improve personal wellbeing (third level of the digital divide). An
estimated 11.7 million (22%) people in the UK, despite access to the Internet, do not possess the digital skills needed for everyday life
(Lloyds, 2020). Given the intertwined relationship between social and digital exclusion (Clayton and Macdonald, 2013; Alam and
Imran, 2015) the digital divide increases the risk for socially disadvantaged groups of being “left behind”, thus widening social
* Corresponding author.
E-mail address: massimo.ragnedda@northumbria.ac.uk (M. Ragnedda).
Contents lists available at ScienceDirect
Telematics and Informatics
journal homepage: www.elsevier.com/locate/tele
https://doi.org/10.1016/j.tele.2022.101852
Received 30 November 2021; Received in revised form 25 May 2022; Accepted 11 June 2022

Telematics and Informatics 72 (2022) 101852
2
inequality gaps. Understanding digital inclusion has become essential due to the increasing number of activities - e.g., learning,
shopping, paying bills, keeping in touch with friends and family, and working – migrated online (Good Things Foundation, 2021;
Townsend et al., 2020. Therefore, exclusion or even limited access to the digital arena means missing chances, resources, and op-
portunities in different areas of daily life, which cannot be accessed otherwise. The main axes of social inequalities, such as socio-
economic status, gender, age, and level of education (Van Dijk, 2020; Bol et al., 2018; Van Deursen et al., 2021) inuence the ways
individuals access ICTs, use the Internet and their digital competencies to get benets/outcomes from the digital experience.
This paper interprets digital inclusion by conceiving the digital divide as a multi-layered phenomenon, which involves techno-
logical access, differentiated uses, competencies, social contexts, support, and measurable outcomes (Helsper, 2017; Gangneux, 2019).
Rooted in this approach, this paper investigates what contributes to creating multi-layered levels of digital inclusion and how such
digital inclusion is related to social inequalities. In analysing the different degrees of digital inclusion, we will focus on the second level
(digital competencies) and the third level (tangible outcomes) by paying particular attention to socially disadvantaged categories. It is
indeed widely accepted that socially disadvantaged individuals also tend to be discriminated against via digital technologies (Rag-
nedda et al., 2020; P´
erez-Escolar and Seale, 2022), further marginalising their position in society (Helsper and Reisdorf, 2017; Reisdorf
and Rhinesmith, 2020; Goedhart et al., 2019).
This paper, therefore, tries to understand how socially disadvantaged individuals, such as the elderly, uneducated people, and those
with low incomes, are discriminated against when using the Internet in the UK. Through a multivariate analysis approach, based on
multiple correspondence analysis and cluster analysis, we shall identify those social groups who have the highest level of digital
competencies and, therefore, benet the most from using the Internet. More specically, this paper explores the social benets that
disadvantaged groups get from the Internet in ve different contexts: political, economic, social, cultural, and personal. While plenty of
research on the lack of digital skills and different types of Internet use exists (Blank and Groselj, 2014; Brandtzæg, 2010; Helsper, 2010;
Van Deursen and Van Diepen, 2013), not as many studies analyse the relationship between socially disadvantaged populations and
social inclusion through the lens of the “benets” obtained by using the internet. To ll this gap, the paper will rst introduce the
phenomenon of the digital divide by focusing specically on the concept of digital inclusion. It will then describe the methods adopted
to analyse the data collected through an online survey. The third section will report the results of the analyses, with particular focus on
some groups who are at high risk of social exclusion. The nal section will discuss the results of the research and formulate some
conclusions.
2. Theoretical background
Since the very beginning of ICTs studies, the Internet has been seen as a tool to access services, information, resources and help in
different areas of social life, from economy to political engagement, from socialization to educational activities (Castells, 2000; Chen
and Wellman, 2004; Norris, 2001). Inclusion in the digital arena could, therefore, provide the possibility of increasing individuals’ job
opportunities, business activities, political and civic engagement, education, and socialisation (Townsend et al., 2020; Reddy et al.,
2020). The process of digital inclusion has gradually become vital for policy actions aimed at tackling social inequalities and reducing
social disparities (European Commission, 2018). It emerged quite quickly that socio-economically disadvantaged groups, such as older
people, uneducated, ethnic minorities, and citizens with disabilities, tend to have limited capacity to access ICTs and elementary
digital competencies (Armitage and Nellums, 2020). At this point, studies on the digital divide mainly adopted a binary perspective in
terms of those who have access (“haves”) and those who have not (“have-nots”). Therefore, these preliminary efforts focused on
physical access (e.g., devices) and Internet access (e.g., penetration, costs, infrastructure, etc.). However, even though scholars have
been paying less attention to the rst level of the digital divide, given the increasing penetration of the Internet, especially in developed
countries (Helsper, 2012; Brandtzæg, 2010), more recent studies have shown that this level of digital divide still requires attention
according to differences in material access (Van Deursen and Van Dijk, 2019). In fact, even though the “haves” might have out-
numbered the “have-nots”, there is still a disparity issue in relation to accessing the rapidly changing technologies that provide
different experiences (and outcomes) of the online arena (Gonzales, 2016; Van Deursen and Van Dijk, 2019). While scholars return to
the roots of the digital divide by redening this rst level, the increasing diffusion of digital technologies has led to increasing attention
to the second level of the digital divide (Ruiu and Ragnedda, 2020), which is directly connected to differences in terms of skills
possessed by the Internet users. From this point, researchers have increasingly focused on analysing the complex interconnection
between ICT usage and existing socio-cultural and economic backgrounds (Dobrinskaya and Martynenko, 2019; Robinson et al., 2015;
Goedhart, et al., 2019). Policymakers and researchers pointed out how traditional axes of social inequalities such as gender (Arroyo,
2020;), class and status (Hassan and Beverly-jean, 2020; Yoon et al., 2020; Ragnedda and Muschert, 2013), age (Calder´
on G´
omez,
2019; Walker et al., 2020; Yoon et al., 2020), level of education (Haddon, 2000), and race (Elena-Bucea et al., 2021; Walker et al.,
2020) have a strong impact on digital inequalities. All these socioeconomic and sociodemographic dimensions inuence the degree of
e-inclusion, and the depth and breadth of Internet use (Al-Muwil et al., 2019; Zdjelar and Hrustek, 2021).
Evidently, given the complexity and multifaceted nature of digital inclusion (Borg and Smith, 2018; Katz and Gonzales, 2016;
Mubarak, 2015), many other features need to be taken into consideration while analysing the digital inclusion process, such as
experience and differentiated uses of the Web (Ruiu and Ragnedda, 2020), the autonomy of use (Asmar et al., 2020), motivation (Borg
and Smith, 2018; Van Dijk, 2005), and digital skills (van Laar et al., 2020). Digging deeper into the relationship between the socio-
economic dimension and digital inequalities, scholars found how individuals from lower socioeconomic statuses tend to use
simpler applications for communication and entertainment compared to their counterparts who, by contrast, use the Internet for
educational, economic or service-oriented purposes (Van Laar et al., 2020). This brings our attention to the replication and repro-
duction of inequalities through the use of the Internet. Several sociological studies over the years have pointed out, for instance, how
M. Ragnedda et al.

Telematics and Informatics 72 (2022) 101852
3
socioeconomic inequalities and social and cultural capital are maintained and transmitted across generations in a family (Bertaux,
1981; Bourdieu, 1984; Biblarz and Raftery, 1993; Putney and Bengston, 2002). In the same vein, individuals’ dispositions toward
digital technologies as well as their knowledge and skills are also transmitted within families (Dulay et al., 2019) and reproduced
across generations (Straubhaar et al., 2012).
To help disadvantaged groups to overcome other social inequalities and be prepared for the digital society (European Commission,
2020; European Commission, 2016), digital inclusion policies have proliferated at both national and international levels. The UN’s
Sustainable Development Goals (SDGs), for instance, include a commitment to ‘Leave No One Behind’ which pays particular attention
to already disadvantaged citizens. In the same vein, the 2016 World Development Report on ‘Digital Dividends’ pointed out different
ways of adopting digital technologies to increase income levels and empower citizens around the world (World Bank, 2016). Following
this strategy, since the end of the millennium, UK governments have implemented digital inclusion policies aimed at bringing every
citizen, company, and school online (Cabinet Ofce, 2012). The overall aims are to provide all citizens with the digital competencies
needed to be fully engaged citizens in a digital society. They advocate a focus on the most marginalised populations (van Deursen and
van Dijk, 2014), such as women, low skilled or elderly people (Arroyo, 2018), because those at the margins of society and the most
vulnerable categories need to be at the core of the digital inclusion process (Alam and Imran, 2015; Menger et al., 2015; P´
erez-Escolar
and Seale, 2022). In this vein, as underlined by the Department for Communities and Local Government (DCLG) in the UK, ‘digital
equality matters because it can help mitigate some of the deep social inequalities derived from low incomes, poor health, limited skills
or disabilities’ (DCLG, 2008: 5), by promoting a society where everyone benets from digital technology, mitigating digital in-
equalities and empowering citizens through the use of digital technologies.
The UK Digital Strategy 2017 (Department for Digital Culture, Media and Sport, 2017) still advocates for simultaneously providing
both access and skills to the UK population. Programmes that aim to simultaneously tackle both access and lack of skills have shown to
be successful in providing those basic skills necessary to gain the basic benets of the digital realm (Good Things Foundation, 2019). In
2019 the Good Things Foundation highlighted that the Online Centres Network realised thanks to the Future Digital Inclusion pro-
gramme (funded by the Department for Education) supported more than 1 million people to learn basic digital skills. However,
beneciaries of such programmes also showed to have short-term goals in mind when they rst attended online training. By contrast,
the rapidly changing state of digital technologies requires a constant and continuous engagement with digital skills. Moreover, such
programmes recognise the need for “one to one” support for personalised needs (and this need increases with age, lower educational
attainment, and unemployment) and this requires further resources and support from public bodies, especially in terms of consistency
of support.
While digital technologies are often depicted as a way to reduce traditional inequalities, they have been frequently found to
replicate and amplify existing inequalities (Fleming, Mason and Paxton, 2018; Van Deursen and Helsper, 2015; Ragnedda and Ruiu,
2020), further underlining how barriers to digital inclusion are connected with social exclusion (Clayton and Macdonald, 2013). This is
directly connected to the identication of the third level of the digital divide, which is intertwined with the different opportunities
provided by ICT access and usage (Ragnedda, 2017; Scheerder et al., 2017).
Moving across these lines, this paper will shed light on this inequality loop in which those already (socially) marginalised have
limited chances to use the Internet as a tool of social inclusion, thus being further marginalised.
Fig. 1. Digital inclusion conceptual map.
M. Ragnedda et al.

Telematics and Informatics 72 (2022) 101852
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3. Research design and data collection
This study explores how digital inclusion varies among people and how social and economic factors may help to understand its
variation. The denition of digital inclusion adopted here considered the three levels of the digital divide. Specically, we included
digital behaviour - how people stay online in terms of access and skills used to perform online activities - and the digital benets - what
advantages/outcomes people have gained by being online. This led us to formulate two research questions aiming at both grouping users
according to their level of digital inclusion and investigating the relationship between these clusters and some socioeconomic and
sociodemographic factors.
RQ1: How can users be grouped according to different levels of digital access, digital competencies, and digital benets?
Table 1
Operational denition of digital competencies.
Component Description Operational Denition Response
Options
Measure
Information and data
literacy
Cronbach’s Alpha
=0.713
Browsing, searching, ltering
data, information and digital
content
I am condent in browsing, searching and ltering data,
information and digital content
- Not at all true
of me
- Not very true
of me
- Neither true
nor - untrue
- Mostly true of
me
- Very true of
me
Ordinal
Evaluating data, information
and digital content
I regularly verify the sources of the information I nd
Managing data, information and
digital content
I regularly use cloud information storage services or external hard
drives to save or store les or content
Communication and
collaboration
Cronbach’s Alpha
=0.778
Interacting through digital
technologies
I actively use a wide range of communication tools (e-mail, chat,
SMS, instant messaging, blogs, micro-blogs, social networks) for
online communication
- Not at all true
of me
- Not very true
of me
- Neither true
nor - untrue
- Mostly true of
me
- Very true of
me
Ordinal
Sharing through digital
technologies
I know when and which information I should and should not share
online
Engaging in citizenship through
digital technologies
I actively participate in online spaces and use several online
services (e.g. public services, e-banking, online shopping)
Managing digital identity I have developed strategies to address cyberbullying and to
identify inappropriate behaviours
Digital content
creation
Cronbach’s Alpha
=0.828
Developing digital content I can produce complex digital content in different formats (e.g.
images, audio les, text, tables)
- Not at all true
of me
- Not very true
of me
- Neither true
nor - untrue
- Mostly true of
me
- Very true of
me
Ordinal
Integrating and re-elaborating
digital content
I can apply advanced formatting functions of different tools (e.g.
mail merge, merging documents of different formats) to the
content I or others have produced
Copyright and licences I respect copyright and licences rules and I know how to apply
them to digital information and content
Programming I am able to apply advanced settings to some software and
programs
Safety
Cronbach’s Alpha
=0.732
Protecting devices I periodically check my privacy setting and update my security
programs (e.g. antivirus, rewall) on the device(s) that I use to
access the Internet
- Not at all true
of me
- Not very true
of me
- Neither true
nor - untrue
- Mostly true of
me
- Very true of
me
Ordinal
Protecting personal data and
privacy
I use different passwords to access equipment, devices and digital
services
Protecting health and well-being I am able to select safe and suitable digital media, which are
efcient and cost-effective in comparison to others
Problem solving
Cronbach’s Alpha
=0.903
Solving technical problems I am able to solve a technical problem or decide what to do when
technology does not work
- Not at all true
of me
- Not very true
of me
- Neither true
nor - untrue
- Mostly true of
me
- Very true of
me
Ordinal
Identifying needs and
technological responses
I can use digital technologies (devices, applications, software or
services) to solve (non-technical) problems
Creatively using digital
technologies
I am able to use varied media to express myself creatively (text,
images, audio and video)
Identifying digital competence
gaps
I frequently update my knowledge on the availability of digital
tools
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RQ2: Is there a relationship between the digital inclusion typology and gender, age, education, income, and occupation?
The originality of our conceptual framework relies upon the fact that we included the three levels of the digital divide (access-
usages-benets) in a holistic model. Specically, our research considers access to multiple devices important to determine the level of
digital inclusion. Secondly, our conceptual model adopts the European Digital Competence Framework for Citizens”, named DigComp
2.1 to identify the different levels of digital competencies. The focus on specic macro-dimensions is further justied in the following
section by focusing on the literature on the relevant aspects to be included in digital inclusion research. Finally, it includes also the
digital benets, namely how respondents take advantage of the access and use of the Internet. The dimensions considered in our
conceptual model are shown in Fig. 1.
1. Digital Behaviour: the operational denition of digital behaviour relies on two sub-dimensions, a) Digital Access and b) Digital
Competencies. Digital Access takes into account the number of digital devices owned by respondents, which we dene here as the
depth of the digital equipment (Murphy et al., 2016; Napoli and Obar, 2014; Ragnedda et al., 2020; Van Deursen and Van Dijk,
2019; Van Dijk, 2005). Digital Competencies was developed upon the proposal from “The European Digital Competence Frame-
work for Citizens”, named DigComp 2.1 (Carretero et al., 2017). We identied ve areas of competence: (a) information and data
literacy, (b) communication and collaboration, (c) digital content creation, (d) safety, and (e) problem-solving;
2. Digital Benets: this dimension explores if and how respondents took advantage of the Internet experience by evaluating externally
measurable outcomes in ve areas: political, economic, social, cultural, and personal.
3. Sociographic: information about social and economic characteristics (gender, age, education, income, and occupation).
3.1. Sampling
The unit of analysis is represented by individuals living in the UK, aged over 18, who can connect to the Internet and perform at
least some basic digital activities. The UK context is an appropriate setting to study digital inclusion processes because it is charac-
terised by a high Internet penetration but varying degrees of adoption and digital competencies. The sample adopted in this study is
stratied in terms of age, gender, income, and education, and was selected by Toluna digital market research group. The sample size
(868 respondents) was calculated as having a 3.33% margin of error at a 95% condence level. Toluna recruited this sample online by
extracting respondents from its panel members (participation rate 98%). Software checked for missing responses and then prompted
users to respond. An online pre-test survey was conducted with 20 Internet users over two rounds. Amendments were made based on
the feedback provided. The average time required to complete the survey was 25 min. In total, 868 responses were collected in
January/February 2019.
3.2. Data collection
We measured users’ digital competencies by focusing on ve areas of competence (DigComp 2.1). Relying on previous research
(Ragnedda et al., 2020), we adopted a different set of statements about the use of the Internet, and respondents were asked to indicate
how accurate the statements were on a ve-point Likert-type scale, from “Not at all true of me” to “Very true of me” (Table 1). The
reliability of each scale was tested with the Cronbach’s Alpha: all the values are over 0.7, which implies that there is an overall
adequate internal consistency and that the measures could be considered reliable (Cortina, 1993; Taber, 2017). As regards the validity,
using the simple factor structure criterion (Garson, 2016), we performed a factor analysis for each set of items: results showed that all
the variables composing a scale highly contribute to one factor. These results suggest that each set of items could be considered a
reliable measure of the areas they are intended to represent.
Each area of competence was transformed into a single variable by combining the answers of the respondents into a composite
index. Five indexes were then created: information and data literacy (IDL), communication and collaboration (CC), digital content
creation (DCC), safety (S), and problem-solving (PS); each index has three modalities: Basic experience (majority of answers are “not at
all true of me” and “not very true of me”), Intermediate experience (the answers are mixed) and Advanced experience (majority of
answers are “mostly true of me” and “very true of me”).
The operational denition of digital access took into consideration differences in material access (Gonzales, 2015; Van Dijk, 2005),
by including different devices used to access the Internet, such as desktop PC, smartphones, tablets, and Smart TVs (Table 2). Digital
Table 2
Operational denition of Digital Access.
Component Description Operational Denition Response options Measure
Digital equipment Depth, i.e., number of devices used to access the Internet Multiple responses - Smartphone
- Notebook
- Tablet
- Desktop PC
- Media player
- Game player
- Smart TV
- Other devices
Nominal
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Telematics and Informatics 72 (2022) 101852
6
access was then operationalised as multiple response variables: respondents were asked to indicate as many devices as they wanted.
The digital access variable was then created by counting how many devices each respondent has specied; four options were created:
1) one device; 2) two devices; 3) three devices; 4) four or more devices.
The DISTO model (Helsper et al., 2015; van Deursen et al., 2016) and the Digital Capital Index (Ragnedda et al., 2020), led us to
identify the digital benets falling into ve areas: social, political, economic, cultural, and personal. As a consequence, the operational
denition of digital benets relies on ve different sets of items (Table 3), measured with a 5-point Likert scale from “Strongly
Disagree” to “Strongly Agree”. The respondents were asked to evaluate how much they agree or disagree with each statement con-
cerning how the use of the Internet may have improved their capacities in performing several online activities.
The results of the reliability assessment show high Cronbach’s Alpha values (over 0.8) for each set of items, suggesting internal
consistency. In terms of validity, the results of the simple factor structure criterion procedure show that, in each scale, all the items
converge on a unidimensional meaning.
As with digital competencies, digital benets were summarized into variables by developing ve different composite indexes -
Political benets (PoB), Economic benets (EB), Cultural benets (CB), Social benets (SB), and Personal benets (PeB) - each one
with three modalities: Low (the majority of answers are “strongly disagree” and “disagree”), Medium (the answers are mixed) and High
(the majority of answers are “strongly agree” and “agree”).
3.3. Data analysis
RQ1 was investigated by adopting the “French way” to multivariate analysis (Di Franco, 2006; Holmes, 2007; Migliaccio et al.,
2011); this approach originated from the “Analyse des Donn´
ees” developed by Benzecri (1973) and was made well-known by Bourdieu
(1984).
RQ1 required a clustering procedure, and the French approach offers an effective way of achieving this goal by applying two
multivariate techniques in sequence (Di Franco, 2006; Delli and Addeo, 2011): rst, a multiple correspondence analysis (MCA) to
synthesise nominal variables into single factors, then a clustering method to group cases according to the MCA results. The result of this
procedure is a typology of respondents according to their level of digital inclusion.
To analyse the relationships between digital inclusion and sociodemographic features - gender; age; education; income – and
answer the RQ2, we performed a multinomial logistic regression analysis. The choice of this analysis strategy depends on the nature of
both the independent and dependent variables, which are categorical with more than two levels (Hosmer, Lemeshow, and Sturdivant,
2013). The multinomial logistic regression allows us to estimate the probability of categorical membership.
Data analysis was carried out using two software packages, SPSS 23 for data cleaning, univariate and multinomial regression
analysis, and SPAD to perform the multivariate analysis.
4. Results
To respond to RQ1 a digital inclusion typology was created by performing the MCA and then the Cluster Analysis. The rst step
Table 3
Operational denition of Digital Benets.
Component Operational Denition Response Options Measure
Political
Cronbach’s Alpha =0.897
Look for information about national government services
Look for information about an MP, local councillor, political party or candidate
Ask a representative of a public institution for advice on public services
Organize a claim and or protest
Launch or sign a petition
Strongly disagree
Disagree
Neither agree or disagree
Agree
Strongly agree
Ordinal
Economic
Cronbach’s Alpha =0.816
Sell something I own
Expand my business activities
Look for information on insurance policies
Look for information on interest rates
Look for a better job
Strongly disagree
Disagree
Neither agree or disagree
Agree
Strongly agree
Ordinal
Cultural
Cronbach’s Alpha =0.899
Find a course or course provider
Interact with and understand other cultures
Check others’ opinions about a course or place to study
Learn or practice a new language
Read new books or articles
Strongly disagree
Disagree
Neither agree or disagree
Agree
Strongly agree
Ordinal
Social
Cronbach’s Alpha =0.851
Keep in touch with family who lives further away
Keep in touch with friends who live further away
Enlarge my network and meet new friends
Look for information on clubs or societies
Interact with people who share my personal interests and hobbies
Strongly disagree
Disagree
Neither agree or disagree
Agree
Strongly agree
Ordinal
Personal
Cronbach’s Alpha =0.870
Improve and change my lifestyle
Improve my tness
Ask others about a training program
Improve my understanding about problems or issues that interest me
Consult others’ opinions on problems or issues that interest me
Strongly disagree
Disagree
Neither agree or disagree
Agree
Strongly agree
Ordinal
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Telematics and Informatics 72 (2022) 101852
7
implemented in the MCA was to select the active variables, i.e., those having an active role in dening the factors (Delli and Addeo,
2011). Specically, the MCA was carried out using the following active variables:
1) Depth of possessed digital equipment as a proxy of digital access;
2) The ve variables representing digital competencies: IDL, CC, DCC, S, PS;
3) The ve variables representing digital benets: PoB, EB, CB, SB, and PeB.
The MCA extracted two factors, reecting the 30.5% of inertia (a concept similar to variance in the Factor Analysis), which is a
satisfying value considering the high number of options involved in the analysis (Di Franco, 2006). After the extraction, we ran a
procedure called “Description of Factors” (DEFAC) to rene the interpretation of the extracted factors through a selection of the most
representative options according to their test value.
The rst factor (Table 4) recalls the distinction between basic and advanced digital competencies; in fact, a closer look at the most
representative modalities underlines a contrast between the highest value (positive semi-axis) and the lowest value (negative semi-
axis) of the four variables representing the following skills: Problem-solving, Digital content creation, Communication and Collabo-
ration, and Information and Data Literacy.
The second factor (Table 5) represents the digital benets dimension, as it emerges from the comparison between the negative semi-
axis (mostly the middle modalities of the benets’ variable) and the positive axis (highest modalities of the benets’ variables)
(Table 6).
The two factors were then used as criterion variables to carry out the cluster analysis; the procedure applied to group the re-
spondents is called SEMIS, i.e., a clustering procedure based on an algorithm that applies rst a non-hierarchical technique and then a
hierarchical one. We used the clustering procedure to have three solutions with 3, 4, and 5 clusters (Table 7).
The evaluation of the number of clusters took into account the criterion of achieving the best compromise between parsimony,
intelligibility, and sharpness of the cluster solution. Therefore, when choosing the number of clusters, we observed that the increase in
variance did not compensate for the loss of parsimony (Biorcio, 1993; Di Franco, 2006).
The solution with three clusters seemed to be the most reliable both from a semantic point of view (the groups appear homogeneous
within them and heterogeneous to the others) and for the reduced share of inertia explained by the solutions with 4 and 5 clusters. In
fact, from three clusters – 70.9% of inertia – to ve clusters – 77.9% of inertia – there is a net gain of 7.0% that could be considered not
sufcient to justify additional clusters (Biorcio, 1993; Di Franco, 2006). Moreover, following the French approach (Di Franco, 2006),
cluster 4 and cluster 5 are a subset of the 3-cluster solution, and therefore, do not add valuable insights to the interpretation of the
phenomenon under study (Table 8).
Below is the detailed analysis of each cluster. More specically, VALUE TEST is a signicance measure: the highest is the most
signicant as a modality. The Value Test threshold is 2, all the values higher than 2 are signicant; CLA/MOD % is the percentage of the
overall respondents with a specic modality and who are actually in the cluster; MOD/CLA % indicates the percentage of people in the
cluster that have been classied into a modality; GLOBAL % indicates the overall percentage of respondents in the whole sample
assigned to a specic modality; MODALITY is the modality of the variable that characterises a cluster, and nally, VARIABLE indicates
the variable the modality comes from.
The rst group, the largest one, comprises 43.8% of participants who are at the midpoint for digital inclusion: throughout their life,
they have acquired an adequate level of digital competence; they also evaluate as ‘good’ the digital benets developed from their
online experience. This cluster is completely characterised by the middle values, “intermediate” or “medium”, of all the active mo-
dalities: for example, 73.13% of respondents in the overall sample classied as “intermediate” on the Problem-Solving Index belonging
to this cluster.
The second cluster (28.9%) comprises respondents with a high level of digital inclusion: they have a high level of digital access, as
they use four or more devices to connect to the Internet; they have been classied as “Advanced” in all ve indexes created to measure
digital competencies; moreover, respondents from this cluster are those who have been classied as high achievers in all ve indexes to
measure digital benets.
Table 4
First Factor Description. Distinction between Basic and Advanced Experience in relation to skills.
NEGATIVE SEMI-AXIS
Test value Modality Active Variable
−19.24 Basic experience Problem solving
−19.23 Basic experience Digital content creation
−18.38 Basic experience Communication and collaboration
−18.11 Basic experience Information and data literacy
CENTRAL ZONE
POSITIVE SEMI-AXIS
Test value Modality Active Variable
17.05 Advanced experience Digital content creation
17.34 Advanced experience Problem solving
17.66 Advanced experience Communication and collaboration
18.88 Advanced experience Information and data literacy
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Table 5
Second Factor related to Digital Benets.
NEGATIVE SEMI-AXIS
Test value Modality Active Variable
−15.51 Medium benets Cultural Benets
−14.81 Medium benets Social Benets
−14.36 Medium benets Problem solving
−14.32 Medium benets Personal Benets
CENTRAL ZONE
POSITIVE SEMI-AXIS
Test value Modality Active Variable
9.87 High benets Social Benets
11.03 High benets Cultural Benets
11.20 High benets Personal Benets
11.44 High benets Political Benets
Table 6
First Cluster: Intermediate experience with ICTs.
VALUE TEST CLA/MOD % MOD/CLA % GLOBAL % MODALITY VARIABLE
12.53 73.13 56.58 33.87 Intermediate Problem-solving
12.19 67.29 66.05 42.97 Intermediate Communication and collaboration
11.17 67.36 59.74 38.82 Intermediate Information and data literacy
10.45 70.15 49.47 30.88 Intermediate Digital content creation
10.33 65.22 59.21 39.75 Intermediate Safety
9.98 63.71 60.53 41.59 Medium Social Benets
9.93 64.97 57.11 38.48 Medium Cultural Benets
9.69 63.23 59.74 41.36 Medium Personal Benets
8.78 62.65 54.74 38.25 Medium Economic Benets
4.86 55.52 42.37 33.41 Medium Political Benets
Table 7
Second Cluster: Advanced experience with ICTs.
VALUE TEST CLA/MOD % MOD/CLA % GLOBAL % MODALITY VARIABLE
17.51 69.63 74.90 31.11 Advanced Problem solving
17.05 65.12 78.09 34.68 Advanced Information and data
16.83 72.46 68.13 27.19 Advanced Digital content creation
16.59 81.01 57.77 20.62 High Personal Benets
16.17 63.85 75.30 34.10 Advanced Communication and collaboration
14.68 56.29 78.49 40.32 Advanced Safety
13.59 57.23 70.92 35.83 High Social Benets
12.42 57.09 64.14 32.49 High Cultural Benets
11.43 50.00 71.31 41.24 High Economic Benets
11.38 64.48 47.01 21.08 High Political Benets
7.47 42.86 60.96 41.13 4 or more Devices
Table 8
Third Cluster: Basic experience with ICTs.
VALUE TEST CLA/MOD % MOD/CLA % GLOBAL % MODALITY VARIABLE
19.18 82.41 69.20 22.93 Basic Communication and collaboration
18.03 64.47 82.70 35.02 Basic Problem-solving
17.91 58.24 89.45 41.94 Basic Digital content creation
17.59 73.04 70.89 26.50 Basic Information and data literacy
14.22 72.83 53.16 19.93 Basic Safety
14.04 54.55 75.95 38.02 Low Personal Benets
13.61 67.35 55.70 22.58 Low Social Benets
12.69 58.33 62.03 29.03 Low Cultural Benets
12.21 65.73 49.37 20.51 Low Economic Benets
8.44 64.15 28.69 12.21 1 device Devices
8.09 40.76 67.93 45.51 Low Political Benets
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The third cluster encompasses 27.3% of respondents and is populated by those who are on the edge of digital exclusion. As evi-
denced by the traits that characterise this cluster, respondents within this group have restricted digital access, 64.1% of them use only
one device to connect to the Internet; the general level of digital competence is very unsatisfactory: most users with basic digital skills
belong to this cluster. Moreover, people from this cluster are by far the ones who have obtained the fewest digital benets from their
online activities. Table 9 summarises the distribution of the digital inclusion typology. Therefore, this group is sitting on the wrong side
of the digital divide, in terms of access (rst level), in terms of digital competencies (second level), and nally in terms of digital
benets (third level).
To explore the confounding effects of socio-demographic variables (gender, age, occupation, education and income) on each
cluster, we performed a multinomial logistic regression (Table 10). The occupation variable was excluded because it was not signif-
icant (and it is likely to be already captured by the income variable) and the goodness of t table showed statistically signicant values
for both the Person chi-square statistic and the Deviance, suggesting that the model did not t the data well. The category “basic
experience” was adopted as a reference.
For “intermediate experience”, holding age, education, and gender constant, income does not play a signicant role in differen-
tiating intermediate and basic experiences. The same applies to gender, which in the multivariate analysis does not have a signicant
distinctive effect. Considering age, the multinomial log-odds for users who are equal or under 55 years old to belong to the inter-
mediate experience cluster instead of basic, would be expected to increase by 7 times for the age group 18–24, 5 times for the age group
25–34, 3 times for 35–44 and 2 times for 45–55. The multinomial log-odds for a user with some high school qualications (compared to
a user with a master’s degree or a PhD) to belong to this group compared to basic skills are expected to decrease by 74% (b =-1.337).
This suggests that the lower the education qualication the more basic will be the online experience.
Considering the cluster “advanced experience”, and considering incomes while holding the other variables constant, those users
with an income less than £10000 have 81% (b = − 1.666) fewer chances to have an advanced experience compared to those with higher
incomes. As for the intermediate experience, those who are equal to or younger than 55 years old are more likely to be included in this
cluster compared to basic experience. Moreover, the chance to have an advanced experience decreases for those less educated users by
80% (b =-1.583) compared to the basic experience. Also, in this case, there is no signicant difference between female and male digital
experiences.
5. Conclusion and discussion
This paper investigated how inclusive the use of digital technologies is amongst some socially disadvantaged categories in the UK.
We rst carried out a multivariate analysis following the “French Way” - rst MCA and then a cluster analysis - to create a typology
with three different types of users according to their level of digital inclusion, and then we explored the role of some social and
economic vulnerabilities in interpreting the differences among the clusters.
This analysis showed the different ways in which users exploit the Internet to increase their social inclusion through, for instance,
reinforcement of existing ofine social relationships, purchase of essential goods, and updates on current affairs. The results reinforce
the idea of an intertwined relationship between social and digital exclusion and how socially disadvantaged people are the most
affected by digital exclusion (P´
erez-Escolar and Seale, 2022; Reisdorf and Rhinesmith, 2020; Goedhart et al., 2019).
More specically, this research is in line with those studies that underline how, despite their access to the Internet, those at risk of
social exclusion (especially less educated, older and low-income users) are more likely to have a digital experience that does not fully
exploit the possibilities that the Internet can offer. At the same time, it suggests that the three levels of the digital divide are profoundly
interconnected and generate a vicious circle that might be difcult to break if each level is considered separately and independently
from the ofine backgrounds. In fact, weak competencies, deriving from scarce opportunities to improve individual digital literacy,
will inevitably affect the outcomes of the Internet experience.
At the same time, our study did not consider the qualitative value added to the rst level of the digital divide in terms of material
access to the internet (Van Deursen and Van Dijk, 2019). This is an aspect that deserves further investigation to show how stratied
material access to ICTs might affect a stratied conguration of outcomes in terms of benets. In this direction, in a qualitative study,
Groˇ
selj (2021) found that different accesses to the Internet correspond to diverse roles played by internet technologies in individuals’
daily lives. However, Groˇ
selj’s study focused on specic types of access and the value that users attribute to them. There is still a need
to explore the concrete outcomes in socio-economic terms and how these outcomes stratify users also in relation to pre-existing
conditions. In this direction, our preliminary ndings reveal that digital inclusion should be tackled as a complex interplay be-
tween different inequalities at multiple levels. Furthermore, our study shows that those individuals who are already socially mar-
ginalised and need the Internet the most to become socially included are also those who experience a low degree of digital inclusion.
This paper argues that policymaking should focus on traditional socially disadvantaged categories (such as less educated and
Table 9
Digital Inclusion of users included in each cluster.
Digital Inclusion Frequency Percentage
Basic experience of ICTs/users on the edge of exclusion 237 27.3
Intermediate experience of ICTs/average achievers 380 43.8
Advanced experience of ICTs/higher achievers 251 28.9
Total 868 100.0
M. Ragnedda et al.

Telematics and Informatics 72 (2022) 101852
10
economically disadvantaged users), not only to facilitate their access to the Internet/devices and the acquisition of advanced skills but
also to create favourable ofine conditions that facilitate the continuous update and the access to opportunities in both online and
ofine realms (Townsend et al., 2020). Therefore, this suggests that digital policies need to be embedded in a wider strategy that
combines efforts from different Governmental Departments to develop concerted plans of action that simultaneously tackle ofine and
online inequalities.
The Levelling Up whitepaper announced by the Government (Gov.uk, 2022), introduced 12 “missions” to address geographical,
economical, and societal inequality by 2030, however, it only supercially addresses the importance of technologies to societal
progress and the reduction of inequalities. The Digital Poverty Alliance emphasised that Mission 4 focuses on broadband, and 4G and
5G coverage, but it does not consider issues such as broadband affordability and device accessibility. Moreover, Mission 1 related to
levelling up education should also consider the acquisition of digital skills as essential together with achieving the basic standards in
reading, writing and maths (Drinkwater, 2022).
Furthermore, the acquisition of basic skills in a specic moment (through occasional intervention) cannot itself ensure the success
of the intervention if cultural, economic and social conditions do not create the foundations for independent personal development.
The introductory sections of this paper highlighted that some programmes in the UK have been successful to provide basic skills and
recognise the need for tailored support, especially for older, less educated, and unemployed users to adapt to the rapid pace of
technological change. This requires resources and support from public bodies, especially in terms of consistency of support.
While the Government strategy is that of mimicking the approach taken for adult literacy and numeracy training (Gov.uk, 2017),
technological advances might require “boost programmes” that work in multiple directions to avoid some categories will be left
behind.
The UK Government has created various partnerships with private bodies (such as Lloyds Banking Group, Barclays, Google, BT,
Accenture, HP, Cisco and IBM), but these partnerships have a specic focus (e.g., promoting cybersecurity or organising summer
schools) that might support those who already have intermediate digital skills and access to valuable information.
By highlighting how various levels of digital inclusion are related to socioeconomic and sociodemographic features, this paper
contributes to reinforcing the idea that ofine social structures and practices inuence individuals’ ability to use digital technologies as
an empowering tool for social inclusion. This is in line with Dobrinskaya and Martynenko (2019) who emphasise how the rst level of
the digital divide can stratify according to different uses of ICTs, but differences in access alone cannot create social inequality. By
contrast, digital inequalities are intertwined with social inequalities, social mobility, and life chances.
Socially disadvantaged citizens, even when they access the Internet, tend to not fully exploit the benets offered by it, therefore
missing the opportunity to use the Internet as a tool of social inclusion. Being online, therefore, is not an end in itself, but it is the rst
step to being a fully participating citizen in an increasingly digital world (Ragnedda, 2020).
Specically, in the COVID-19 and post-COVID-19 era the role of digital technologies is more important than ever. Therefore, any
policy interventions thought to improve wellbeing must take into consideration the role and effect of digital technology in an in-
dividual’s everyday life. This poses some challenges for policy intervention, in light of today’s necessities for digital inclusion,
especially for those most vulnerable, in that to be effective digital technology needs to provide transformative wellbeing benets. The
results of this research might help policymakers to identify the interconnections between different levels of the digital divide, and what
digital competencies need to be reinforced.
Table 10
Effects of socio-demographic variables on clusters (95% CI).
Characteristics Intermediate Digital Experience Advanced Digital Experience
Income Group
Over £100000 ref ref
Under £10000 0.463 (0.103, 2.091) 0.189** (0.43, 0.835)
£10000-£25000 1.151 (0.280, 4.720) 0.371 (0.094, 1.456)
£26000-£50000 1.221 (0.303, 4.924) 0.511 (0.134, 1.952)
£51000-£10000 1.506 (0.350, 6.480) 0.701 (0.170, 2.883)
Age Group
Over 55 ref ref
18–24 7.118* (3.157, 16.046) 19.069* (8.033, 45.266)
25–34 4.702* (2.485, 8.895) 15.370* (7.680, 30.763)
35–44 2.999* (1.714, 5.247) 8.101* (4.287, 15.308)
45–55 1.815* (1.146, 2.875) 3.987* (2.277, 6.981)
Education
Master or PhD ref ref
Some high school, no diploma 0.263* (0.112, 0.616) 0.205* (0.077, 0.550)
High school graduate 0.466 (0.212, 1.021) 0.444 (0.189, 1.044)
Some college credits 0.591 (0.269, 1.299) 0.559 (0.240, 1.303)
Bachelor’s degree 0.530 (0.245, 1.146) 0.630 (0.278, 1.429)
Gender
Female ref ref
Male 0.949 (0.665, 1.352) 1.501 (0.991, 2.273)
Note: The reference category is Basic Digital Experience. Model Fit: chi-square =195.480 (p <0.001); Goodness of Fit: Person chi-square =
340.989 (p =.160); Deviance chi-square =338.920 (p =.180); Nagelkerke =0.228.
M. Ragnedda et al.

Telematics and Informatics 72 (2022) 101852
11
This study does not come without limitations. Our model of digital inclusion looks at the three levels of the digital divide and,
therefore, incorporates digital access, digital competencies, and meaningful uses of digital media to obtain tangible outcomes.
Providing and measuring a full spectrum of accesses, competencies, and benets individuals might get from using the Internet has been
a challenge and is acknowledged as a limitation. Furthermore, in line with most of the research on digital skills (Litt, 2013), we asked
our respondents to evaluate their level of competence. This, as suggested by van Dijk (2006), introduces a validity problem. Finally,
this paper set the background for further research development in the COVID-19 era, which might have exacerbated the gap between
digitally equipped and digitally excluded users.
In conclusion, our results show that socially disadvantaged individuals in the UK (mainly in terms of income, education, and age)
are those who are disempowered, with less digital competencies and fewer benets acquired by using the Internet. This self-reinforcing
effect of digital and social exclusion is what we have dened as the inequality loop. The level of digital inclusion is a key factor in terms
of (re)producing social inequalities. At the same time, digital inclusion may help reduce social inequalities and improve life chances
and the overall quality of life. The paradox is that even though socially disadvantaged categories are those that more than anyone else
would benet from socially inclusive use of the Internet (e.g., to nd a job, a public service, or resources), they are those who are the
least digitally included.
Declaration of Competing Interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
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