Theo Lynn · Pierangelo Rosati · Edel Conway Declan Curran · Grace Fox · Colm O'Gorman

**Digital Towns** Accelerating and Measuring the Digital Transformation of Rural Societies and Economies

Digital Towns

Theo Lynn • Pierangelo Rosati Edel Conway • Declan Curran Grace Fox • Colm O'Gorman

# Digital Towns

Accelerating and Measuring the Digital Transformation of Rural Societies and Economies

Theo Lynn DCU Business School Dublin City University Dublin 9, Ireland

Edel Conway DCU Business School Dublin City University Dublin 9, Ireland

Grace Fox Lithia, FL, USA Pierangelo Rosati DCU Business School Dublin City University Dublin 9, Ireland

Declan Curran DCU Business School Dublin City University Dublin 9, Ireland

Colm O'Gorman DCU Business School Dublin City University Dublin 9, Ireland

#### ISBN 978-3-030-91246-8 ISBN 978-3-030-91247-5 (eBook) https://doi.org/10.1007/978-3-030-91247-5

© The Editor(s) (if applicable) and The Author(s) 2022. This book is an open access publication.

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# Foreword

In February 2020, the European Commission presented its strategy for a Digital Society based on three pillars:

The European Union's vision is no less than the digital transformation of Europe by 2030. This will be achieved through secure, performant and sustainable digital infrastructures, the digital transformation of businesses, the digitalisation of public services, and the upskilling of citizens. As such, this book is timely as it explores the digital transformation of one part of society, smaller and rural towns.

In 2020, the OECD estimates 80% of all territory worldwide is rural in which 30% of the world's population resides. These communities face signifcant societal and economic threats not only from increased urbanisation and climate change, but the very attribute that defnes them, their rurality. Low population mass and density combined with geographic remoteness presents rural communities with significantly different challenges than their urban counterparts. Access to markets, tertiary education, and a critical mass of skilled labour, as well as high transportation costs, are just some of the factors that sustain the urban-rural divide. And while digital technologies present rural communities with the opportunity to overcome the challenges presented by remoteness, there remains a danger of being left behind.

In 1997, Ireland was one of the frst countries in the world to announce the establishment of an information age town, Ennis. At the same time, similar initiatives were active in Aveiro, Portugal and Parthenay, France, amongst others. And yet, since then while the policy and scholarly discourse on smart cities has accelerated and grown, the digital transformation of towns has faltered and discourse has faded into the background.

The Irish scientist, Lord Kelvin, famously said:

*When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind.*

In this regard, while we might speak about the Digital Society or Digital Economy, unless we can express it in measurable units, we can neither fully understand it, diagnose it, monitor progress, nor evaluate decisions and alternatives. The research presented in this book was partly funded by .IE, the national registry for .ie domain names. A key aspect of the .IE mission and corporate purpose is to help everyone in Ireland to thrive online. In much the same way that there is an urban-rural digital divide, there is a measurement divide. The overwhelming majority of international frameworks and composite indices for measuring the evolution and development of digital progress focus on countries and cities. The research presented in this seventh book in the series, "Advances in Digital Business and Enabling Technologies", was motivated by the desire to address this gap.

The book is organised around seven dimensions based on the four sectors of an economy—individuals (citizens), government (public services), business, and civil society (non-profts)—and three enabling infrastructures—connectivity, education, and governance. In each chapter, the relevant dimension is defned and the benefts and challenges to adoption and use of technologies are discussed. Each chapter includes a discussion of how that dimension is measured in existing frameworks for digital society and the digital economy, if at all. The book concludes in Chap. 9 with an overview of a digital town measurement framework, including indicators and their potential sources, for each dimension.

**Chapter 1** introduces key concepts and terms in digital society literature. Based on a review of literature and digital town initiatives and projects, ten rationales for adopting digital technologies in towns are identifed, and discussed and a working defnition of a digital town is proposed. The chapter concludes with a discussion of commonly cited international frameworks and composite indices for measuring digital society and the digital economy and the need for a discrete measurement framework for digital towns.

**Chapter 2** discusses the participation of individual citizens in the digital economy and digital society, and the factors that contribute to digital inequalities and the so-called 'digital divide'. Following an exploration of the ways in which digital technologies and digital literacy can be used to reduce social and digital exclusion, existing international frameworks for measuring the digital literacy of individual citizens are discussed.

**Chapter 3** explores how public services can be delivered using digital technologies to deliver greater transparency, effciency and responsiveness from public sector organisations. This chapter defnes digital public services, discusses the existing challenges for the implementation of these services in the rural context, and summarises existing frameworks for assessing the adoption and use of e-Government, e-Health technologies, and open data.

**Chapter 4** introduces and defnes the concept of the digital economy and digital business. It discusses the main benefts and challenges in the adoption and use of digital technologies by enterprises in general and by those in a rural context. The chapter concludes with a discussion of how international frameworks and composite indices measure the digital adoption and use of digital technologies by businesses.

**Chapter 5** outlines the important contribution civil society makes to the economy and society in general. Largely absent from the literature on the digital society and digital economy, digital technologies can transform how civil society organisations operate and interact with their stakeholders, and meet their mission. This chapter defnes civil society, discusses the role they play in society, and the opportunities and challenges for digital adoption and use in civil society.

**Chapter 6** shifts the discussion from the basic sectors in an economy to enabling conditions. This chapter provides an overview of the growing body of evidence that now documents the positive impact of digital connectivity across a number of different economic indicators. However, it also highlights and discusses the signifcant challenges that continue to impede the delivery of comprehensive digital connectivity across all social groups and geographical contexts. Digital connectivity is a common feature of most international measurement frameworks for digital progress and key indicators are discussed and presented.

**Chapter 7** defnes digital education and discusses the rationales, benefts and challenges in integrating digital technologies in education, a major pillar of education policy worldwide. Despite widespread optimism, digital inequalities remain in education—with these inequalities impacting the most vulnerable in society, including those who are socio-economically disadvantaged and/or residing in rural areas. While there is substantial data on the adoption and use in formal education systems, this cannot be said for non-formal education provision.

**Chapter 8** assumes that the adoption and use of digital technologies is an essential component of town resilience, growth, and competitiveness in the Digital Society and a Digital Economy. This implies that towns need to integrate a digital layer, comprising technology and non-technology actors, into their existing physical, economic and social ecosystems. This chapter discusses key concepts and terms in relation to building rural community digital resilience and specifcally the nature and need for vertical and horizontal integration in digitalisation plans and governance.

**Chapter 9** seeks to synthesise the preceding chapters into a Digital Town Readiness Framework with associated indicators that can be used for measuring the evolution and development of a digital town and benchmarking progress against historic and international benchmarks. The chapter discusses issues related to data collection and methodology, as well as communication requirements and challenges. The chapter concludes with recommendations for future research.

This book provides a frst attempt at a comprehensive framework and set of indicators for measuring digital town initiatives. We believe it provides useful and practical knowledge for scholars, policymakers and rural communities that can be acted on. In doing so, it can help inform policy making and implementation at national, regional, and local levels so that all parts of society can leverage the opportunities that a Digital Society presents.

Dublin, Ireland David Curtin

# Acknowledgement

Research in this book was partially funded with the fnancial support from .IE, the national registry for .ie domain names.

# Contents

# About the Authors

**Theo Lynn** is Full Professor of Digital Business at Dublin City University. He was formerly the Principal Investigator (PI) of the Irish Centre for Cloud Computing and Commerce, and Director of the Irish Institute of Digital Business and LINK Research Centre. Professor Lynn specialises in the role of digital technologies in transforming business processes with a specifc focus on cloud computing, social media and data science.

**Pierangelo Rosati** is Assistant Professor in Business Analytics at Dublin City University and a Co-Deputy Director of the Irish Institute of Digital Business. He previously worked as Post-Doctoral Researcher of the Irish Centre for Cloud Computing and Commerce (IC4). Dr Rosati specialises in measuring the business value of digital technologies such as big data analytics, cloud computing, social media and blockchain.

**Edel Conway** is Professor in HRM and Organisational Psychology and Director of Doctoral Studies at DCU Business School. Her research focuses on employee perspectives on work, particularly employee engagement and wellbeing and employee reactions to work intensifcation and organisational change. She was awarded a Fellowship of the Irish Academy of Management for her contribution to the advancement of management studies in Ireland.

**Declan Curran** is Associate Professor of Economics at Dublin City University. Dr Curran's research focuses on the areas of industrial economics, economic geography, and regional development.

**Grace Fox** is a Postdoctoral Researcher attached to the Irish Institute of Digital Business. Formerly an Assistant Professor of Digital Business at Dublin City University Business School, her research interests intersect the broad interdisciplinary areas of information privacy and digital technology adoption and assimilation.

**Colm O'Gorman** is Full Professor of Entrepreneurship at Dublin City University. He is former Associate Dean for Research (DCU), Director of the MBA programme (DCU), and Department Chair (Management) (UCD). Professor O'Gorman specialises in research on entrepreneurship, innovation, and growth in new frms and in small and medium sized enterprises (SMEs). He is an author of the annual Global Entrepreneurship Monitor (GEM) for Ireland.

# Abbreviations

# List of Figures

# List of Tables

#### xxiv List of Tables

# Defning, Rationalising and Measuring Digital Towns

## 1.1 Introduction

The European Union's (EU) 'Digital Society' is the latest in a long line of 'revolutions', 'ages' and societal forms proposed by policymakers, academics and industry for over ffty years (Martin, 2008; Lynn et al., 2018). Critics note that it is at best inaccurate and at worst incorrect to describe society as digital or of technological origin, and is not by and large subject to sudden unexpected phase transitions inherent in revolutions (Martin, 2008). Nonetheless, digital technologies are infuencing, and in many cases transforming, how society operates and how social actors interact with each other (Martin, 2008; Reis et al., 2018). Furthermore, there is a well-established literature base regarding the potential benefts of digital technologies for society (Mossberger et al., 2007). The EU's vision of a European digital society is an inclusive one based on "building smarter cities, improving access to eGovernment, eHealth services and digital skills" (European Commission, 2021), and yet for many such a digital society can seem ambiguous, distant, and beyond their technical abilities and imagination.

Over the same time that our conceptualisation of a society permeated and transformed by technologies evolved, there was and continues to be a parallel shift in where and how we live. Since 1975, there has been a rise in the proportion of the global population that live in cities from 37% in 1975 to 48% today (OECD and European Union, 2020). Attracted by the perceived economic opportunities and quality of life in cities, rural populations have migrated to cities contributing to their expansion and densifcation (OECD and European Union, 2020; Lerch, 2017). The socio-cultural, political, and economic benefts of urban agglomeration bring signifcant challenges in sustainable development, not least pollution, crime, and health issues (OECD and European Union, 2020). So-called 'smart city' technologies are touted as solutions to modern urban problems but what about the rest, the other 52% who don't live in cities?

For those who live in and depend on rural towns to participate fully in a digital society requires an understanding of what digitalisation means in its widest sense, and to imagine alternatives to the current city-centric narrative (Dufva & Dufva, 2019). The remainder of this chapter begins with a brief overview of key terms and concepts followed by a discussion of the urban-rural digital divide. This is followed by a review and discussion of the rationales for increased adoption and use of digital technologies in rural areas and specifcally towns. Based on this review, we propose a working defnition of a Digital Town. We conclude with a discussion on the need for a discrete measurement framework to measure the digital readiness of a digital society.

# 1.2 Digital Society—Key Concepts and Terms

Understanding and conceptualising what constitutes a digital society is made more complex by its situation at the intersection of the virtual, physical, and social. To make sense, exist fully, and imagine a future society permeated by digital technologies requires understanding not only the digital and physical world in themselves, but the relationships between the various entities in each of these worlds and between them, a space which is a form of mixed reality. Furthermore, the perspectives taken by different actors can vary substantially, from macro to micro levels.

## *1.2.1 What Do We Mean by Digital?*

When we refer to the digital society or even a digital town, we do not mean that, as Martin (2008) states, it is "[…]made by the digital, and that its essential characteristics have been created because of the development of digital technology." But what do we mean? The answer to this question is not simple.

A signifcant challenge noted in recent reviews is that terms like Industry 4.0 and Digital Transformation, while widely cited, lack agreement on meaning (Reis et al., 2018; Vial, 2019; Nosalska et al., 2019; Culot et al., 2020). Indeed, whether one is referring to the information society or digital society etc., there would appear to be two major categories. To paraphrase Webster (2006), there are those who endorse or promote the idea of a digital society or information society, and those who see digitalisation or informatisation as the continuation of pre-established relations, a subordinate feature of established social systems. As Webster (2006) puts it, the former emphasise change while the latter emphasise persistence. These need not be binary. Table 1.1 below briefy summarises highly cited and prominent defnitions of common terms and concepts with respect to digital society.

**Table 1.1** Key terms and concepts in the digital society literature

(*continued*)

**Table 1.1** (continued)

#### *1.2.2 Mainstream vs Frontier Technologies*

When one considers digital technologies, we are faced with what Chambers (2010) calls a 'cornucopia of potentials' much more than can be covered within the confnes of this chapter. As such, it is important to differentiate between mainstream technologies and frontier or emerging technologies. While the former are widely used in society and are considered relatively normal and conventional, frontier technologies represent technological advancements on previous generations of technologies and offer potential disruption. They are defned by their emergent use, their potential. Mainstream technologies include offce productivity software, mobile technologies (incl. smartphones), websites, social media, and basic forms of cloud computing. Frontier technologies are often referred to as emerging technologies as they lack widespread adoption in society. In their most recent report, UNCTAD references eleven such technologies summarised in Table 1.2 below. The use of these technologies by society as a whole, by defnition, is at a nascent stage although they represent signifcant markets already (UNCTAD, 2021). Similarly, some technologies are further along the adoption cycle than others. In addition to native digital technologies, frontier technologies are often enabled by digital technologies (e.g., gene editing), enter mainstream use through incorporation into general purpose technologies (e.g., artifcial intelligence and nanotechnology), or enable (more effcient) access and use to digital technologies (e.g., solar photovoltaic power).

**Table 1.2** Selected frontier technologies and defnitions (adapted from UNCTAD, 2021)

# 1.3 What Is a Town?

While national and global defnitions tend to agree on what cities are, national defnitions tend to disagree on the classifcation of towns, semidense areas and rural areas (OECD and European Union, 2020). These defnitional challenges reduce comparability and do not recognise governance differences (Lynn et al., 2020). Recently, a consortium of international organisations addressed this issue through the introduction of two new defnitions, the degree of urbanisation and the functional urban area (FUA) (OECD and European Union, 2020). The FUA recognises that cities are metropolitan areas comprising the city itself and surrounding areas that are connected to the city in terms of labour market interactions (commuting zones) (Dijkstra et al., 2019). The degree of urbanisation refects an urban-rural continuum and proposes three classes:

# 1.4 The Urban-Rural Digital Divide

While commonly used, the term digital divide, in reality, refers to a variety of interrelated digital divides. Philip et al. (2017) highlights two such divides—(i) divides resulting from inequalities in the technological infrastructure required to support digital connectivity, and (ii) socio-economic digital divides. These aspects have been explored in the urban-rural context for over two decades (Philip et al., 2017; Hindman, 2000; Townsend et al., 2013). Others view the digital divide across three levels—Internet access (frst-level digital divide), Internet skills and use (second-level digital divide), and tangible outcomes of Internet use (third-level divide) (Scheerder et al., 2017; Wei & Hindman, 2011). These two perspectives are clearly not mutually exclusive.

We defne the urban-rural divide as an inequality between urban and rural areas with respect to the adoption and use of digital technologies, and the benefcial outcomes resulting from such adoption and use. The hypothesis underpinning this divide is that rural areas present lesser access and use of technologies, and consequently experience less benefcial outcomes, than urban areas. Firstly, inequalities in access and use are not disputed. There is substantial evidence that rural areas experience less availability and less access to infrastructure (Philip et al., 2017; Ashmore et al., 2015; Ali et al., 2020). Furthermore, studies in highly digitised countries such as South Korea and Australia suggest that the digital divide extends to a difference in use by and perceived benefts for rural users (Park & Kim, 2015; Park, 2017). In addition to broadband availability, geographic remoteness and suitability, and social exclusion are some of the factors that have been cited as barriers to digital adoption and use in rural areas (Park & Kim, 2015; Park, 2017; Ali et al., 2019). This is consistent with Philip et al. (2017). Unfortunately, as Scheerder et al. (2017) point out there is a general lack of research on the third-level digital divide i.e., relating to the benefcial outcomes of digital adoption and use. What research exists is fragmented. The limited literature on the urban-rural digital divide does present regional differences but is not comprehensive in scope or particularly current. Regional differences, supporting the urban-rural digital divide hypothesis, are reported for economics and other daily activities (Stern et al., 2009), e-payment and online shopping (Hsieh et al., 2013), parental mediation of adolescent internet use and adolescent exposure to internet risk and harm (Chang et al., 2016), institutional outcomes (Van Deursen & Helsper, 2015), and educational outcomes (Li & Ranieri, 2013). In many cases, these are related to socio-economic factors including age, income, gender, and education.

These interrelated factors may not be capable of being addressed by the market or government intervention alone, particularly where structural and geographic conditions make broadband deployment commercially infeasible or unattractive. Community-led multi-stakeholder initiatives have been suggested as a solution to the urban-rural digital divide however such initiatives need to overcome access to technical expertise, volunteerism, and funding arrangements, as well as geographical conditions to ensure success (Ashmore et al., 2015).

# 1.5 Rationalising and Defining Digital Towns

Borrowing from Hawkridge (1990) and based on analysis of existing community network and digital town projects, we identify at least ten rationales for digital town initiatives (Table 1.3). Eight of these can be organised along a socio-economic spectrum—Social, Accessibility, Pedagogical, Vocational, Sustainability, Quality of Service, Catalytic, Economic. The proposed Reactive rationale differs in that it represents a short term response to a crisis such as COVID-19; if continued it would likely be rationalised using one of the other rationales. The Opportunistic rationale differs in that it is over-riding.

(*continued*)

**Table 1.3** (continued)

These rationales are refected in three prevailing perspectives on digital towns in the literature, which we label as infrastructure-centric, servicecentric, and community-centric. The Infrastructure perspective of a digital town emphasises the local availability and appropriation of ICT infrastructure as a prerequisite for the connection of a town as a node in a national/ global network. The Service perspective emphasises the provision of local information services for citizen's everyday lives and visitors. Finally, the Community perspective emphasises platforms for communities of interest to support work in a geographical and information space where users can interact, sharing knowledge, experience and mutual interests (Hervé-Van Driessche, 2001). In reality, a digital town is all of these things.1

Consequently, we defne a digital town as:

*A geographic and information space that adopts and integrates information and communication technologies in all aspects of town life where a town consists of contiguous grid cells with a density of at least 300 inhabitants per km2 , are at least 3% built up, and have a total population of at least 5,000.*

# 1.6 The Need for <sup>a</sup> Discrete Digital Town Measurement Framework

Performance management and measurement literature suggests that making strategy more measurable enables decision makers to take corrective actions to keep the organisation on track (de Waal, 2007). Furthermore, by providing critical success factors and indicators necessary for success, organisations and individuals can set goals which, in themselves, may infuence performance particularly when specifc feedback is provided on progress towards achieving said goals (de Waal & Kourtit, 2013).

Measurement frameworks and composite indices are used widely in policymaking and in particular to measure performance, relative progress or competitiveness through benchmarking, and identify areas of excellence or areas for improvement (Foley et al., 2018). As the G20 Digital Economy Task Force (DETF) (2018, p. 4) stated in the introduction to the G20 Toolkit for measuring the digital economy:

<sup>1</sup>Given the renewed interest in the metaverse, it is important that any defnition of digital town is suffciently broad to accommodate the physical world, virtual reality, and the comingling of both through augmented reality or other forms of hyper-reality.

*Sound measurement is crucial for informing and guiding policymaking, as it helps policymakers produce precise diagnostics, assess the potential of alternative policy options, monitor progress, and evaluate the effciency and effcacy of implemented policy actions.*

The measurement of digital progress is not a new idea. Since the turn of the century, a wide range of frameworks and composite indices have been proposed for assessing digital adoption and use by policymakers, scholars, and international organisations (G20 Digital Economy Task Force, 2018).2 Table 1.4 summarises commonly cited international frameworks and composite indices; links to each framework are provided in the Useful Links section at the end of the book. Initially, these measures were dominated by the desire to quantify the economic impact of digital technology adoption and use. While approaches and indicators to measure the progress towards a digital society as a whole have emerged, for example, the European Union (EU) Digital Economy and Society Index (DESI), the economic imperative has remained the dominant perspective as evidenced in the recent G20 DETF roadmap for measuring the digital economy (G20 Digital Economy Task Force, 2020). As such, existing approaches and indicators mostly focus on national economic indicators. Notwithstanding this, there have been recent efforts to assess the state and evolution of digital progress at more granular levels. For example, both the IMD-SUTD Smart City Index (Bris et al., 2019) and CityKeys framework present a set of city-level indicators (Bosch et al., 2017). These proposals are largely in the smart city domain and as such often confate both digital and environmental sustainability themes.

In general terms, there are pros and cons to using rankings and composite indicators. As well as informing both policy making and administration, they can also seek to inform and guide the public on the relative success of policy and/or initiatives (Berger & Bristow, 2009). Furthermore, rankings and composite indicators can help summarise complex issues and reduce complexity thereby improving interpretability (Berger & Bristow, 2009). At the same time, such rankings and indicators have been criticised for being too simplistic and condensed and presenting an objective and

<sup>2</sup> Section 4 of the G20 Toolkit for Measuring the Digital Economy includes overviews of frameworks and indicators for measuring various aspects of the digital economy from Argentina, Australia, Brazil, Canada, China, France, Germany, Japan, Korea, Mexico, Russia, Saudi Arabia, Singapore, Turkey, the United Kingdom, and the United States of America.

**Table 1.4** Selected international digital society and digital economy measurement frameworks and composite indices

(*continued*)

**Table 1.4** (continued)

a ITU has proposed a change in the methodology behind the IDI but these have not been implemented at the time of writing. See ITU (2020) for more details

representative view, while sometimes being based on relatively small samples or subjective judgments (Berger & Bristow, 2009). Often data is aggregated or weighted without commonality of approach. Indeed, this largely refects the motivation for the G20 roadmap for a common framework for measuring the digital economy (DETF, 2020). In these cases, there is a signifcant risk of comparing apples to oranges (Berger & Bristow, 2009). Finally, many of these indices are constructed on available data rather than required or ideal data.

These issues also arise in composite indices seeking to measure the digital society or digital economy. Firstly, towns and rural communities typically do not have the same agency as national governments or urban municipal authorities. For example, towns and rural communities may have little or no (a) autonomy with respect to decision making, and/or (b) revenue generation ability. As discussed earlier, successful digital town initiatives require a broad concept of community governance that, as per Leach and Percy-Smith (2001), involves multi-agency working and selforganising networks that cut across organisational and stakeholder boundaries. If this is a critical success factor then measurement frameworks must capture and make such governance measurable in a way that is not done so today.

Secondly, even where data is collected nationally, it may use sampling strategies which are not useful for decision-making at a town level. Indeed, town-level data may not be available at all, or, where available, is not representative due to the sampling strategy employed. For example, small-tomedium sized enterprises (SMEs) in rural communities are largely skewed towards micro-enterprises, those with less than ten employees. There is evidence to suggest that rural SMEs may be under-represented in international composite indices due to reliance on frm-level data from sources, such as Eurostat, that only collect data on enterprises with greater than ten employees. For example, the G20 Toolkit for Measuring the Digital Economy (G20 DETF, 2018), DESI (Digital Economy and Skills Unit, 2018, 2020, 2021), and I-DESI (Foley et al., 2018) all feature indicators that exclude micro-enterprises based on this criteria.

Thirdly, these national and city-level frameworks do not fully recognise the important role that all sectors of society play in rural towns and communities. For example, despite the signifcant role that civil society plays in modern economies and society as a whole, it does not feature as a discrete sector in digital measurement frameworks. As will be discussed in Chap. 5, civil society organisations are major employers and generators of signifcant economic value through expenditure. Digital technologies present nonproft organisations and micro-enterprises in rural areas with a signifcant opportunity to overcome the limitations of their location yet are excluded or under-represented from critical policy making indicators.

Fourthly, rural towns and communities face specifc limitations due to their geographic location. As well as poorer broadband infrastructure, skills and human capital are generally lower in rural areas than in urban areas. Even in more developed economies, rural education attainment can signifcantly lag urban areas across education levels (OECD, 2017; Campbell, 2019). As higher education institutions are typically located in urban areas, rural communities often experience an out-migration of skilled individuals from rural to urban areas for tertiary education, many of which do not return due the greater employment opportunities and higher wages available in cities. Undoubtedly, digital business and remote working offer rural communities the opportunity to reverse out-migration trends however enabling infrastructure is required. This includes both local access to high-speed broadband and the provision of local education opportunities in and through ICTs. While digital infrastructure is prominent in all measurement frameworks, education is not. In measuring digital town readiness, we suggest these dimensions require specifc attention.

To summarise, a comprehensive framework for measuring digital towns must be methodologically appropriate for the specifc policy- and decisionmaking context. Consequently, it needs to be suffciently fexible and scalable to allow for different local priorities and resources. When considering what factors should be measured with respect to the evolution and development of a digital town, it should not only include comprehensive data on the basic sectors of the local economy—individuals and households, government, business, and nonproft organisations—but enabling infrastructure i.e., digital infrastructure, education, and community governance. It should be noted that these factors should not be considered fxed in stone. Additional thematic areas may be added or removed, or weighted differently, depending on the local context priorities. For example, more emphasis may be placed on a specifc sector or set of economic activities e.g., tourism. Similarly, as technology advances, access, use and outcomes will change. Furthermore, the framework should allow for national and international comparison by including commonly used indicators. The OECD has called for rural areas to drive their own economic development rather than rely on the national government, specifcally with respect to identifying and mobilising assets to improve economic performance (OECD, 2014). For both comprehensiveness and local planning, we argue that data needs to be collected at a local level thus the framework needs to be suffciently easy to use, understand, and be communicated to support a bottom up community approach.

#### 1.7 Conclusion

This chapter introduces key terms and concepts in the digital society literature and emerging defnitions of what a town is. From this literature, we defne a digital town as "a geographic and information space that adopts and integrates information and communication technologies in all aspects of town life where a town consists of contiguous grid cells with a density of at least 300 inhabitants per km2 , are at least 3% built up, and have a total population of at least 5,000." Our review of existing academic literature and digital town projects suggests a wide range of perspectives and rationales for adopting digital technologies at a town level. Notwithstanding this, it remains a relatively under-researched area particularly with respect to the longitudinal measurement of impact. We present a brief overview of commonly cited frameworks and composite indices for measuring digital society and digital economy, and discuss their applicability for rural towns and communities.

The remainder of this book is organised around seven dimensions based on the four sectors of the economy and three enabling infrastructures outlined in Sect. 1.4 above. Chapter 2 discusses the adoption and use of technologies by citizens and is followed by a similar discussion for public services (Chap. 3), businesses (Chap. 4) and civil society (Chap. 5). Then each of the enabling infrastructures are discussed i.e., infrastructure for digital connectivity (Chap. 6), education (Chap. 7), and governance (Chap. 8). In each chapter, the relevant dimension is defned and the benefts and challenges to adoption and use of technologies are discussed. Each chapter includes a discussion of how that dimension is measured in existing frameworks for the digital society and the digital economy, if at all. The book concludes in Chap. 9 with an overview of a digital town measurement framework including indicators and potential benchmarks.

#### References

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# The Digital Citizen

#### 2.1 Introduction

New technology is changing the way in which individuals and societies communicate, learn, work and govern (Meyers et al., 2013).  Digital citizenship is not only the technical ability to participate online but to behave in an appropriate, responsible way with regard to such digital technology use (Mossberger et al., 2007; Ribble & Bailey, 2007). Such regular, appropriate and responsible use implies a level of technical competence and digital literacy skills, as well as access to both technology and to the internet. However, the 'digital divide', representing 'the gap between individuals, households, businesses and geographic areas at different socio-economic levels with regard both to their opportunities to access information and communication technologies (ICTs) and to their use of the internet for a wide variety of activities' (OECD, 2001, p. 5), still persists due to the systemic inequalities that have emerged in societies worldwide (Beaunoyer et al., 2020). Evidence suggests that these inequalities are not only evident in developing economies, but also exist within richer economies, where signifcant disparities remain between urban centres and rural communities. Overcoming these inequalities requires equal emphasis on digital infrastructures and the development of digital skills. However, it is possible that the COVID-19 pandemic has served to widen rather than narrow this divide as more and more services are only available online and because access to workplaces, schools and libraries have been severely restricted, particularly in rural areas (Lai & Widmar, 2021). At the same time, rural towns have become the preferred locations for many to live and work, presenting some optimism that previously struggling rural communities and economies can thrive in the future.

As discussed in Chap. 1, there are two important factors in the defnition of towns—people and place. To paraphrase Marshall (1950, p. 149), to be a citizen is to be a member of a community. If digital citizenship is the ability to participate online then citizens must have the access, competences, and skills to use digital technologies. This chapter begins with a discussion on how and where digital inequalities, so-called 'digital divides', surface. It then discusses what digital literacy is and how competencies and skills might be categorised. Next, opportunities and challenges associated with remote and other new forms of work are considered. The fnal section discusses how extant international frameworks and composite indices measure access, competences, and use of digital technologies by individuals and households.

#### 2.2 The Digital Divide

A signifcant proportion of the population worldwide either do not have access to the internet or the skills to leverage the opportunities presented by digital technologies. This is not limited to the developing world. In 2019, approximately 15% of European households did not have internet access in 2019 citing insuffcient skills (44%), equipment costs (26%) and high cost barriers (24%) (Digital Economy and Skills Unit, 2020). This is consistent with fndings from the OECD which suggests that 11.7% of adults aged 16 to 65 reported having no prior computer experience and a further 4.7% of adults did not possess basic ICT skills (OECD, 2019b).

As discussed in Chap. 1, the digital divide is a multi-faceted topic. It can be viewed across multiple levels including the availability of and access to technological infrastructure including computing equipment, software, and the internet, digital skills and use, and tangible outcomes of internet use (Scheerder et al., 2017; Philip et al., 2017; Wei et al., 2011). While the main focus of this book concerns bridging the urban-rural divide, rural communities, like their urban counterparts, may experience digital inequalities, typically related to age, income, education, and to a limited extent, gender.

#### *2.2.1 The Grey Digital Divide*

Populations are ageing across developed economies worldwide (Rouzet et al., 2019). Economists anticipate that this will impact GDP per capita growth, labour market conditions, earnings, as well as potentially increasing inequality and poverty risk on future generations (Rouzet et al., 2019). Rural areas are not only, on average, older than urban areas but ageing is progressing faster in rural areas (OECD, 2019a). Furthermore, the ruralurban divide with respect to demographic profles is expected to grow (Daniele et al., 2019).

Social isolation is defned as "a state in which the individual lacks a sense of belonging socially, lacks engagement with others, has a minimal number of social contacts and they are defcient in fulflling and quality relationships" (Nicholson, 2009, p. 1346). While it may be a personal choice, where it is not so, individuals may experience loneliness (Havens et al., 2004). Social isolation and loneliness represent a substantial diffculty for older populations worldwide. They are associated with a variety of factors associated with poorer well-being and a lower quality of life (Murthy, 2020; Kaye, 2017; Havens et al., 2004). While research on rural and urban differences in social isolation and loneliness in older adults is mixed (Havens et al., 2004), older people in rural areas are impacted signifcantly from greater physical isolation and associated transportation issues (Kaye, 2017; Davidson & Rossall, 2015).

Digital technologies can mitigate the negative outcomes of physical isolation, social isolation, and loneliness by connecting older adults to meaningful social network support provided they have access and the skills to use these technologies effectively (Francis et al., 2019). This sense of isolation was particularly exacerbated during the COVID-19 pandemic (Garcia et al., 2021), where the lack of ICT access, usage and skills among older populations became more apparent. In one recent study, increasing age was signifcantly and negatively associated with use of e-services (e.g., e-banking, e-government, e-health and e-learning) and social networking across the EU-28 (more so than education, gender or income) (Elena-Bucea et al., 2020). As Friemel (2016, pp. 12–16) notes, the differences in internet use among those older than 70 years 'seems not to be linear but rather exponential'. Friemel notes that "with every additional year of age, the likelihood of usage decreases by 8% in fve-year differences when considering a range of 65–90 years or more" (p. 328).

The key determinants of using new technologies among older adults represent a combination of attitudinal, functional, and physical factors (Neves et al., 2018). Attitudinal factors include interest, anxiety (e.g., technophobia), perceived usefulness and perceptions of being too old to use it. Functional factors include access to devices, levels of education and digital skills, and ease of use depending on technology design and size. Finally, physical factors include limitations due to poor health or other age-related impairments such as visual acuity, reduced dexterity and memory. Access to education is regarded as an effective strategy in reducing social isolation among older people, as well as providing them with new knowledge and helping them to adapt to a changing society (Blažic & ̌ Blažic, ̌ 2018).

#### *2.2.2 The Income Digital Divide*

Extant research suggests that an urban-rural income gap exists (Young, 2013). Level of income is regarded as a further reason for the digital divide (United Nations, 2012; World Bank, 2016) and is regarded as an important driver of the digital development of countries (Cruz-Jesus et al., 2017). Income, which impacts both internet access (Van Deursen et al., 2016) and usage (Robinson & Williams, 2015; Zhang, 2013), was regarded as particularly important during the emergence of new technologies because owning a computer was regarded as an optional luxury (Lindblom & Räsänen, 2017). In a study of 110 countries, Cruz-Jesus et al. (2017) found that 82.7 percent of the variance in the digital divide was explained by GDP alone. Other recent studies, however, show that lower income inequalities are associated with increased internet usage and mobile phone subscriptions, suggesting that the digital divide can be narrowed if income disparities reduce further (Richmond & Triplett, 2018).

#### *2.2.3 The Education Digital Divide*

The use of ICT is sometimes complex, which is regarded as a signifcant obstacle to its adoption (Van Deursen et al., 2016). Pick and Azari (2008) report that government spending on education directly impacts access to ICT, which is positively related to ICT usage in both developed and developing countries. Recent evidence from across the EU-28 reports that education is strongly linked to the adoption of e-services and social networking (Elena-Bucea et al., 2020). Compared to age, gender or income, education was the strongest driver of the adoption of e-services. Van Deursen et al. (2016) also found that more educated respondents were consistently more confdent about all dimensions of internet skills. In addition, competency in the English language represents a further barrier to access and usage (United Nations, 2012), as well as to decent prospects for future employment and life opportunities (Pick & Nishida, 2015). Because digital exclusion has broader implications for accessing education, work and other opportunities, this creates what is termed the 'digital vicious cycle' (Baum et al., 2014). Education will be discussed in greater detail in Chap. 7.

#### *2.2.4 The Gender Digital Divide*

Dixon et al. (2014) suggest that 'the phenomenon of technology itself cannot be fully understood without reference to gender' (p. 993). Differences in gender equality across nations refect complex familial, institutional, religious, societal and stereotypical beliefs, which can also impact ICT access and usage (Cooper, 2006). Despite this complexity, the 'Women in Digital' scoreboard (European Commission, 2020b) suggests relative parity between men and women regarding the 'use of internet' across most EU states. This declines slightly with regard to internet skills among women compared to men. It further declines regarding 'specialist skills and employment'; a fnding which refects the lack of female participation in science, technology, engineering and mathematics (STEM) and ICT occupations. In their analysis across the EU-28, Elena-Bucea et al. (2020) found no evidence to suggest differences in levels of adoption between the genders. Van Deursen et al. (2016), however, found that men rated their skills consistently higher than females, with the exception of 'information navigation skills'. This fnding perhaps refects the so-called 'confdence gap', which has been reported across a range of studies on gender differences (Guillen, 2018).

#### *2.2.5 The Digital Divide and Other Vulnerable Parts of Society*

In addition to older adults, the vulnerable in society include a wide range of people including minors, disabled people, persons with serious illnesses or mental disorders, amongst others (European Commission, 2021). Vulnerable people are typically more likely to suffer from social exclusion and social isolation than the general population. The consequences of social exclusion can result in lower social and civic participation and representation, lower social standing, poverty, low human capital endowments, restricted access to employment and services (Tangcharoensathien et al., 2018). Digital participation may beneft vulnerable people by avoiding or mitigating the effects of social isolation, reducing stigma, and allowing these people to perform activities that are unavailable to them (Dobranski & Hargittai, 2006; Duplaga, 2017). Unfortunately, there is evidence of digital divides for many vulnerable populations. For example, studies have found evidence of a disability digital divide in the UK (Offce of National Statistics, 2019), Poland (Duplaga, 2017), Sweden (Johansson et al., 2021), amongst others (Kim et al., 2018; Tuikka et al., 2018). Similarly, digital divides have been found amongst refugees, displaced persons, and specifcally asylum-related migrants (Merisalo & Jauhiainen, 2020; Lynn et al., 2021). It is worth noting that inequalities can exist in those parts of society we assume are digitally native. While minors often have physical access to digital technologies, inequalities may exist with respect to mediation and contextualisation of use (Talaee & Noroozi, 2019; Smahel et al., 2020). Talaee and Noroozi (2019) call for a reconceptualization of how we think about the digital divide with respect to children. They argue that access to a supportive 'social envelope', for example active mediation by parents and other family members, is the area in which most digital inequalities exist rather than the physical access to the hardware or even usage time. If a true measure of any society can be found in how it treats its most vulnerable members then digital initiatives and associated measurement frameworks must include those at most risk.

## 2.3 Digital Literacy

Globally, the lack of progress in the development of digital skills has been associated with diffculties in defning and measuring digital literacy (Van Deursen et al., 2016). While historically, the digital literacy literature has focussed on the prevalence and sophistication of (i) computer skills and (ii) internet skills (Hargittai, 2005), recent conceptualisations are more nuanced seeking to differentiate, between digital knowledge, skills and competencies (Iordache et al., 2017). Unfortunately, these nuances are not widely understood or applied, and as such are often confated and used synonymously. Based on a review of 13 digital literacy models, Iordache et al. (2017) attempt to unravel these concepts into a practical set of interlinking defnitions as per Table 2.1.

While there are a wide range of digital literacy models in the literature, there is signifcant commonality over the high-level categories of skills and

**Table 2.1** Defnitions of digital knowledge, skills, competence, and literacy (Iordache et al., 2017)

competences. Iordache et al. (2017) classify digital skills and competences into fve categories, largely based on van Deursen et al. (2014), as per Table 2.2.

Care needs to be taken that digital literacy and digital citizenship are not viewed in isolation. The skills required to participate fully online depend on the specifc tasks to be performed e.g., e-government, e-health, online learning etc. These are discussed in Sect. 2.5 below and subsequent chapters. Furthermore, individuals acquire and develop these competences and skills through informal, formal and nonformal means. The extent to which educational opportunities are provided to all members of a given community varies. While the formal education system has made signifcant strides to integrate digital technologies, such provision may not be uniform and may struggle to maintain pace with technological change. Similarly, depending on the size of a town, comprehensive nonformal education provision may not be comprehensive or exist at all.

# 2.4 New Forms of Work and Rural Towns

COVID-19 and associated public health measures resulted in a temporary transition to remote working. Recent studies suggest remote work and virtual meetings are likely to continue, albeit less intensely than at the pandemic's peak (OECD, 2020; McKinsey, 2021). There is some optimism that this increased acceptance of teleworking combined with lower cost of living will help reverse population trends and increase the sustainability of smaller and rural towns. In addition to remote working, the emergence of online platforms to support the sharing economy and gig economy, are providing new markets, income, and economic opportunities to individuals and households in rural communities.

#### *2.4.1 Remote Working*

Remote working is the partial or total substitution of technology for the daily commute to and from work. Remote working options include working from home, working from a regional offce close to home, or using coworking spaces (Spinuzzi, 2012). The COVID-19 pandemic saw an unprecedented surge in the numbers of people working remotely, with reports of up to half of the entire EU workforce working from home (Eurofound, 2020). The reported benefts of remote working can be organised at three levels: (i) societal (e.g., less traffc congestion and lower air and noise pollution, better opportunities for the disabled); (ii) organisational (e.g., cost savings due to lower infrastructural costs); and (iii) individual (e.g., greater fexibility, job satisfaction, lower transport costs, work-balance etc.) (Bloom et al., 2015; Martin & MacDonnell, 2012; Morganson et al., 2010). Research suggests that remote working can be community-friendly (Kamerade & Burchell, 2004), that it can increase productivity and improve organisational performance (Martin & MacDonnell, 2012), and is associated with greater work-family balance and less work-family confict (e.g., He & Hu, 2015). Furthermore, research suggests that those working remotely even half-time can save between \$2000 and \$6800 a year (Lister, 2010). For organisations, remote work also offers an opportunity to cut costs arising from the reduced need for offce space and associated running costs (Popma, 2013; Bloom et al., 2015). It should be noted that remote working need not take place in the home but in remote working hubs. While there is renewed interest and support for remote working hubs and telecenters in rural communities as a result of COVID-19 (Tomaz et al., 2021; Department of Rural and Community Development, 2021), they are not necessarily predicated on digital strategies.

Gallardo and Whitacre (2018) note the lack of research examining the impact that remote work has on local economic indicators such as income. They theorise that if remote working leads to higher levels of worker satisfaction and productivity, these outcomes should in turn lead to higher levels of income. They propose that workers who work remotely in 'outside locations' potentially increase the number of jobs available for local residents, as well as for residents of nearby areas who may commute in. Their study reported a positive relationship between remote working and median household income, suggesting that it can have a positive impact on local area income. They conclude that the traditional economic development approach of industry attraction and geographic clustering fails to consider other strategies and that remote working has the potential to become a community economic development tactic. This suggests a need to modify existing industrial incentive systems to focus more on placing workers in remote jobs (Erard, 2016). This would offer opportunities to rural communities to attract both workers and customers, while continuing to promote existing attractions (e.g., natural amenities, housing costs), which would help to level the playing feld between urban and rural economies (Gallardo, 2016b). Despite the many reported benefts of remote working, a number of drawbacks have been noted including the potential for greater social isolation, stress and burnout. (Golden et al., 2008) and increased mental health symptoms (Mann & Holdsworth, 2003; Bloom et al. (2015). One report from the EU suggests that while remote working can afford some fexibility, autonomy and empowerment, there is also a risk of work intensifcation, increased stress, longer hours, and blurring of the boundaries between work and private life (Eurofound, 2015; Popma, 2013). Some EU countries including France and Ireland have already developed a code of practice on 'the right to disconnect' and the EU parliament is paving the way for the enactment of legislation on this issue.

Prior to the pandemic, Hynes (2016) noted a lack of interest and/or commitment from employers to more fully embrace opportunities for remote work. From an infrastructural perspective, he noted the poor quality of broadband and a lack of policy or regulation for remote work as further impediments. Gallardo (2016a) suggests that a remote work-friendly policy framework should: (i) make it easier for businesses to offer remote work through subsidies and tax credits, which help retain fast-growth companies that may otherwise leave due to lack of labour supply, (ii) modify existing workforce development programmes to be better aligned with remote work (e.g., self-motivation, self-management, teamwork and other soft skills), and (iii) improve broadband availability and access for remote workers through multiple tax credit mechanisms (e.g., subsidising monthly access costs). Increased rural digitalisation offers a potential win-win for frms seeking labour but also workers seeking improved quality of life.

#### *2.4.2 The Gig Economy*

The gig economy relates to the intermediation of labour typically via an online platform. Gig economy platforms are typically two-sided platforms that match workers with customers, those who require work done on a per-service basis (Schwellnus et al., 2019). They include a wide range of services including transportation (e.g., Uber), home delivery (e.g., Deliveroo), home cleaning and maintenance (e.g., Care.com), data processing and other crowd work (e.g., Fiverr.com and Amazon Mechanical Turk), amongst others (Schwellnus et al., 2019). The popularity of such gig economy platforms can be explained by their virtual nature and low barriers to entry (Huang et al., 2018; Lynn et al., 2021). Consequently, work is both location- and time-agnostic enabling fexible, temporary, adhoc working arrangements (Huang et al., 2018; Lynn et al., 2021).  Gig economy work is not without drawbacks including competition from low-income countries, discomfort associated with working for strangers, and traditional drawbacks associated with freelance work e.g., lack of employment and retirement benefts (Huang et al., 2020; Lynn et al., 2021). While gig work not only provides work that fts the needs and capabilities of workers and overcomes the limitations of rurality, research suggests it does not necessarily value the expertise and experience of, for example, older workers (Cook et al., 2019).

#### *2.4.3 The Sharing Economy*

Bartering, renting, swapping and sharing equipment and space are long standing market behaviours in rural communities, typically performed with friends, families and neighbours. In recent years, digital technologies and online platforms have transformed and revitalised these activities by enabling such transactions between strangers in what is widely referred to as the 'sharing economy' (Puschmann & Alt, 2016; Laurenti et al., 2019). Whereas the gig economy focuses on the intermediation of labour, the sharing economy involves "[…] consumers granting each other temporary access to under-utilized physical assets ("idle capacity"), possibly for money" (Frenken & Schor, 2019, pp. 121–122). The sharing economy is disrupting and transforming a number of sectors, not least the short stay and holiday accommodation sector e.g., AirBnB and Couchsurfng. The gig economy shares many of the same benefts of the sharing economy for rural communities in terms of access to income-earning opportunities and employment (Dreyer et al., 2017). However in many cases it is less impacted by competition from low-income countries and has potential additional benefts in terms of environmental sustainability derived from the increased utilisation of resources (Mi & Coffman, 2019). To date, short stay accommodation and associated direct, indirect and induced economic effects has been the primary focus of sharing economy research, and anecdotally activity, in smaller and rural towns. However, recent research suggests that the attitudinal changes the COVID-19 pandemic has wrought with respect to scarcity, community living, and online participation, provides substantial opportunities for rural communities to overcome their local challenges through socio-economic sharing in other sectors including retail sustainability and poverty prevention (Buheji, 2020).

# 2.5 Measuring Individual and Household Access and Use of Digital Technologies

Unsurprisingly, given the central role citizens play in society, individual and household access and use of digital technologies is a signifcant feature of the majority of international frameworks and composite indices for measuring the evolution and development of the digital society and the digital economy. There are a number of common themes. These include access to broadband and the internet which we address in Chap. 6, use of digital technologies, and the prevalence and sophistication of digital competences and skills. As can be seen from sources cited in Table 2.3 and later in Chap. 7, there is signifcant overlap with these themes and those used for measuring the integration of digital technologies into formal and

**Table 2.3** Common themes and selected international sources for digital technology access and use by individuals and households

nonformal education. While equity data is widely collected, it is somewhat myopic focussing on gender balance rather than other potential targets of inequality e.g., older adults, people with disabilities, refugees and other displaced persons, amongst others.

Two signifcant themes in extant frameworks are digital competences and use of digital technologies in general and by activity. There are a number of different frameworks for assessing digital skills competences sometimes from the same organisation (e.g., the UNESCO Media and Information Literacy (MIL) Curriculum for Teachers (UNESCO, 2021) and the UNESCO Global Media and Information Literacy Assessment (GMIL) Framework (UNESCO, 2013), the European skills/competences, qualifcations and occupations (ESCO) transversal ICT skills list (European Commission, 2020b), and the European e-Competence Framework(e-CF) (CEN, 2019). Even where the source may be from the same or related organisations, the defnition and gradation of skills and use may vary. In this regard, the European Digital Competence Framework (DigComp 2.0) is useful in that not only does it seek to provide a high level conceptual digital competence framework that synthesises many existing frameworks, it provides a useful mapping with MIL, GMIL, ESCO, and e-CF frameworks (Carretero Gomez et al., 2017). Table 2.4 summarises DigComp 2.1; at the time of writing the consultation for DigComp 2.2 was underway. It is important to note that the extent to which these competences, in any comprehensive way, feature in international frameworks and composite indices for the digital society and the digital economy is limited.

ICT skills are included in a number of international frameworks, typically by type of activity gradated from basic to advanced (Digital Economy and Skills Unit, 2020; G20 Digital Economy Task Force (DETF), 2018; Partnership on Measuring ICT for Development (ITU, 2018). For example, Table 2.5 outlines the main ICT skills indicators for DESI, the data for which is sourced from the Eurostat Community survey on ICT usage in Households and by Individuals (Digital Economy and Skills Unit, 2020).

Regarding ICT use, and more specifcally ICT use for digital activities, there is signifcant variation across frameworks and composite indices. As well as access to digital infrastructure (Chap. 6), and the use of digital technologies for accessing and interacting with government and health services (Chap. 3), working and conducting business (Chap. 4), and for formal and nonformal education (Chap. 7), a wide range of activities are used as indicators. For example, the Partnership on Measuring ICT for Development (ITU, 2016) includes a comprehensive list of activities:

Not all international frameworks are as comprehensive. Digital Economy and Skills Unit (2020) employs three sub-dimensions—(i) internet (non-) users, (ii) online activities (news, music, videos and games, video on demand, video calls, social networks, and doing an online course), and (iii) transactions (banking, shopping, and selling). DESI also measures access to infrastructure and e-Government interactions separately in other dimensions (Table 2.6).

**Table 2.4** DigComp 2.1 competence areas and competences (adapted from Carretero Gomez et al., 2017)

**Table 2.5** Digital skills indicators used in the EU Digital Economy and Society Index (Digital Economy and Skills Unit, 2020)

Extant international frameworks for measuring the evolution and development of digital society and the digital economy, can also be distinguished by the extent to which they focus on individuals and households as opposed to society or an economy as a whole. For example, the Digital Capital Index and the IMD-SUTD Smart City Index exclusively focus on the perceptions of individuals, whereas as others, for example, the EU Digital Economy and Society Index (DESI), the G20 Toolkit for Measuring the Digital Economy, and Partnership on Measuring ICT for Development focus on a variety range of indicators including businesses and government.

While one would not expect the presence of remote working hubs in a set of digital indicators, it may be a proxy for a type of online work. New places of work and ways of working are typically under-represented or absent from commonly cited international frameworks. Similarly, uses of digital technologies associated with online work, the gig economy, and sharing economy have not featured prominently to date. In response to the increased prevalence of such practices during the COVID-19 pandemic, one would anticipate that this is likely to change.

As a fnal note, it is important to note that collecting data on those parts of society that do not use the internet or digital technologies is a substantial challenge. By defnition, more modern methods of data collection such as online survey panels will not capture these cohorts thereby requiring on-site manual data collection from people who may be diffcult to identify.

**Table 2.6** Selected indicators on digital technology skills and use by individuals and households in selected international digital society and digital economy measurement frameworks and composite indices excluding general access to digital infrastructure

## 2.6 Conclusion

This chapter has identifed many of the key challenges and opportunities for greater participation by individuals and households in a digital society. It considers a range of factors related to accessibility, affordability, and skills as key challenges. While new forms of work and working present opportunities for many living in rural communities, it is critical that those most vulnerable in society are not left behind in the digital society or digital economy. In measuring the evolution and development of digital progress in any community, whether a rural town, a city, or nation, only by understanding the extent of digital inequality can we take action to eradicate it.

#### References

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# Digital Public Services

#### 3.1 Introduction

Governments play a key role in our society by providing citizens and businesses with access to a range of essential public services. As such, there is a constant demand for ways to improve transparency, responsiveness and effciency in the delivery of these services. The adoption and use of digital technologies provides a number of obvious benefts in this regard and the digitalisation of public services has been a constant item on the agenda of policymakers for over a decade.

The potential benefts generated by the adoption of digital public services have become even more visible during the Covid-19 pandemic when the public were forced to move much of their daily activities online due to restrictions put in place to contain the spread of the virus (European Commission, 2020). In this context, any service that was not online was not accessible, so public organisations were forced to accelerate the adoption of digital technologies and to fnd more innovative uses of existing e-Government solutions to manage the crisis (United Nations, 2020).

The remainder of this chapter defnes digital public services and discusses the benefts and the existing challenges for the implementation of these services in the rural context. Three main types of public services, namely e-Government, e-Health and open data, are then discussed together with extant attempts to measure their adoption and use.

# 3.2 What Do We Mean by Digital Public Services?

Digital public services, often termed e-Government, refer to public services provided using digital technologies wherein the interaction with a public sector organisation is mediated by an IT system (Jansen & Ølnes, 2016; Lindgren & Jansson, 2013; Lindgren et al., 2019). While most of the focus around e-Government is on public service delivery, the concept of digital public services is broader than that, as it encompasses all interactions between citizens and public bodies.

Lindgren et al. (2019) discuss the impact of the digitalisation of public services from the perspective of the *public encounter* as conceptualised by Goodsell (1981). The public encounter is defned as "the interaction of citizen and offcial as they communicate to conduct business" (Goodsell, 1981, p. 4) and is characterised by four general aspects: (1) nature and purpose of the encounter, (2) the actors involved, (3) the communication form and setting in which the encounter occurs, and (4) the encounter's initiation, duration and scope. The shift from traditional to digital public services has an impact on all these characteristics of the encounter, as summarised in Table 3.1.

With regard to the nature of the encounter, digital technologies have mostly been adopted to mimic traditional paper-based processes (Heeks, 2006) and act as mediators of public services. This means that the technology is typically used to provide citizens with access to a public service but the technology does not deliver the service itself (Lindgren & Jansson, 2013). From the perspectives of communication and ease of access, this generates clear effciencies. However most services still rely on human

**Table 3.1** Summary of changes to the public encounter (adapted from Lindgren et al., 2019)

intervention, so the impact on the average lead time is marginal. Recent advancements associated with machine learning and artifcial intelligence offer clear opportunities for seamless automated service provisioning and associated benefts in terms of shorter lead time and higher transparency (Wihlborg et al., 2016; Matheus et al., 2020). Automation may also introduce new risks. These are mostly related to the potential bias in the algorithms that could exclude specifc groups of citizens from accessing a service (Wihlborg et al., 2016), and the introduction of new actors, technology providers, who are typically private institutions and multi-tenant in nature. As such, they are responsible for securing and maintaining multiple different service delivery platforms—thus introducing additional risks (Janssen & Klievink, 2009; Lindgren et al., 2019).

The adoption of digital technologies may also change how the provisioning of public services is initiated. In a traditional setting, one of the actors involved would initiate the encounter, but now the use of algorithms and predictive analytics may lead to proactive service provisioning based on a constant incoming data fow (Scholta et al., 2019). In this context, the defnition of a start and an end point becomes blurry and potential concerns regarding government surveillance may arise. Furthermore, digital public services introduce a major change compared to the traditional public encounter with regard to *where* the service is actually accessed or provisioned. The fact that citizens can access digital services from a digital device instead of a physical public offce provides obvious benefts but it is still unclear whether there may be negative outcomes associated with detaching public services from the traditional places of government (Pollitt, 2012).

Despite some concerns, some of which are briefy mentioned above, the increasing adoption of digital public services promises to deliver enormous benefts for both public organisations and citizens. This promise however is based on two major assumptions. First that citizens will have equal and widespread access to the Internet, and second that they will possess the skills required for interacting with public bodies online (Pors, 2015; Williams et al., 2016; Almeida et al., 2019; Lindgren et al., 2019). Previous studies suggest that e-Government initiatives can be hindered by the digital divide and even contribute to it in some cases (Ebbers et al., 2016). Bélanger and Carter (2009), for example, demonstrate that demographic factors such as income, education and age have a signifcant impact on the intentions of citizens to use e-Government services. This is mostly related to the so-called "access divide" where specifc cohorts of the population have access to the Internet and digital services while others do not. While enabling widespread access to connectivity has traditionally been one of the main objectives of public and private initiatives (Salemink et al., 2017), research suggests that the physical access divide has evolved into a skills divide where citizens' ability to use the internet and online search experience represents a key determinant of adoption and use of online public services (Bélanger & Carter, 2009; Van Deursen & Van Dijk, 2011). This is particularly important in rural areas as they are typically characterised by lower than average levels of education and skills (Salemink et al., 2017) and may therefore be left behind when governments pursue greater digital provision of public services (Van Deursen & Van Dijk, 2011; Ebbers et al., 2016).

## 3.3 E-Government

A number of frameworks have been proposed to measure the maturity and sophistication of e-Government solutions. An early framework was that proposed by Layne and Lee (2001) comprising four main stages:

One of the most referenced follow-up frameworks to Layne and Lee (2001) is that adopted by the United Nations Global e-Government Survey (United Nations, 2003). First presented in the early 2000s, like Layne and Lee's (2001), the UN model comprises four stages:

1. Emerging: this stage is somewhat comparable to Cataloguing in Layne and Lee (2001) as the government simply provides information to citizens via digital means.

As Heeks (2015) points out, maturity models are a product of their time and are often context-related. In fact, these initial frameworks are mostly focused on technology adoption refecting the early stage of development of Internet technologies at the time and are not particularly concerned about the real impact, use, and usefulness of e-Government solutions (Kawashita et al., 2020). Similarly, these initial models are quite rigid and are not able to take into account changing requirements, conditions and developments related to contextual or technological changes (Bertot et al., 2016). A number of frameworks have tried to overcome such limitations by using a variable number of maturity levels which makes a direct comparison quite diffcult. Table 3.2 provides a summary and comparison of these models.

The EU eGovernment Framework Benchmark (European Commission, 2020) departs from the concept of *maturity.* Rather, it "is built on the foundation of the EU policy priority areas in the feld of e-Government" (van der Linden et al., 2020, p. 8)—user empowerment, preconditions and the digital single market—and translates them into four key dimensions:

*Underlying concepts*

*Source/*

*Presenting*

*Interaction*

*Transaction*

*Integration*

*Transformation*

*e-governance*

*Policy driven*

Furthermore, most indicators included in the EU eGovernment Framework Benchmark are collected by "mystery shoppers" who are "trained and briefed to observe, experience, and measure a service process by acting as a prospective user" (van der Linden et al., 2020, p. 15). As such, the EU framework represents a shift from supply-side maturity (government) to demand-side experience (citizens).

This user-centricity is also refected in the UN e-participation index which emphasises citizen participation as the cornerstone of socially inclusive governance. As such, it focuses on the provision of information by governments to citizens ("e-information sharing"), interaction with stakeholders ("e-consultation"), and engagement in decision-making processes ("e-decision making") (UN, 2021). Links to additional information on selected indicators are provided in the Useful Links section at the end of the book.

#### 3.4 E-Health

The e-Government maturity frameworks presented in the previous section consider digital public services in their entirety, as if they represent an homogenous group of services delivered by public organisations. In reality though, public services are not all the same and some are more suitable for digital interaction/delivery than others (Lindgren et al., 2019). Among all public services, healthcare is arguably one of the most important for citizens. Unsurprisingly, e-Health has been on the agenda of policy makers for a long time and has been the focus of a number of digitisation initiatives (Domenichiello, 2015).

e-Health can be defned as "the use of Information and Communication Technologies (ICT) across the whole range of healthcare functions" (European Commission, 2004) and comprises a wide range of applications that can beneft citizens, healthcare professionals and organisations, and public authorities by improving medical practices, simplifying the prescription of diagnostic procedures, producing alerts and reminders, and reducing errors (Bodell et al., 2004; Delpierre et al., 2004; Kaushal et al., 2006; Øvretveit et al., 2007). Cowie et al. (2016) summarise these domains as follows:

• Telemedicine and telecare: disease management services, remote patient monitoring, teleconsultations, and homecare.

Even before the Covid-19 pandemic, healthcare systems were already facing signifcant pressure due to increasing demand and costs, inconsistent quality of care, and ineffcient, poorly coordinated processes. As the population in developed countries becomes older, questions have been raised regarding the sustainability of traditional healthcare systems. In the European Union, for example, public health expenditure is expected to represent 8.5% of GDP by 2060, a 16% percent increase compared to 2010 (European Commission, 2012b).

More pervasive use of ICT in healthcare has been proposed as a way to overcome these challenges (WHO, 2016). At a macro level, studies suggest that e-Health solutions can result in signifcant improvements in terms of system productivity, ease of access, and quality of service (Hackett et al., 2019). Successful implementations seem to support this argument. Canada, for example, launched its frst eHealth plan in 2001 (Canada Health Infoway, 2021); current estimated savings amount to approximately CAD 119–150 million per annum with a concurrent increase in service quality (Hackett et al., 2019). In an attempt to achieve similar results, the European Commission issued a frst eHealth action plan in 2004, followed by a revised version issued in 2012 (European Commission, 2012a). The main objective of these plans was to increase the adoption of e-Health across different countries but this has proved to be challenging mostly due to lack of awareness of e-Health services, interoperability issues, legal barriers, and high start-up costs (European Commission, 2012a).

General practitioners (GPs) play a central role in facilitating access to and delivery of care as they represent the main point of contact between the healthcare system and citizens, particularly in rural areas (Macinko et al., 2003; Atun, 2004). As such, they are in a position to gather important information which would constitute the basis of an IT-enabled integrated healthcare system (European Commission, 2013). For this reason, the EU mostly focuses on the adoption of e-Health services such as electronic prescriptions (e-prescribing) and data exchanges by GPs when it comes to measuring the digitalisation of healthcare across different countries. However, other actors like pharmacies and specialised health professionals (e.g. physiotherapists, dentists, psychiatrists etc.) may also play a critical role in fostering the adoption of eHealth services within communities (Gregorio et al., 2013; Vorrink et al., 2017; Baines et al., 2018).

Digital Economy and Skills Unit (2018, 2019) represent one of the frst attempts to include an explicit measure of e-Health adoption in the context of digital public services. However, the framework only includes three indicators—i.e. e-prescription, online consultations, and use of electronic medical data exchange—and results are only presented at a country level so it does not have the necessary level of granularity to assess adoption across different regions or in rural areas. This may ultimately be due to the diffculty of collecting timely data, as the exclusion of e-Health indicators from Digital Economy and Skills Unit (2020) seems to suggest.

#### 3.5 Open Data

Another aspect of digital public services that is often not considered explicitly is the availability of open government data (OGD). This involves making public sector information (PSI) freely available in open formats and ways that enable public access and facilitate exploitation (Kalampokis et al., 2011). The main benefts of open data are summarised in Table 3.3 It is important to note that open data on its own has little intrinsic value, as value is only created by its use (Janssen et al., 2012).

PSI is a strategic resource that can generate benefts for a number of actors (Ubaldi, 2013) including:

**Table 3.3** Overview of benefts of open data (Janssen et al., 2012)

• Governments: open data provides the scope for faster decisionmaking, better resource allocation and effcient, and effective delivery of more personalised public services.

The total direct economic value of PSI is expected to increase from a baseline of €52 billion in 2018 for the EU28 to €194 billion in 2030 (Barbero et al., 2018) and a similar trend can be expected in other economies. There are a number of initiatives that track open government data initiatives worldwide such as the Global Open Data Index1 and the OECD OURdata Index.2 However, these frameworks present information at a country level, providing little room for identifying more local initiatives that take place in rural areas. Walker et al. (2020) clearly show that rural open data has a massive economic potential but it is often overlooked by government policies due their focus on smart cities and urban areas.

## 3.6 Measuring Digital Public Services

As noted earlier in Sect. 3.3, there is a wide range of frameworks for measuring e-Government and the use of digital technologies by the public services. The recent G20 Digital Economy Task Force roadmap for measuring the digital economy specifcally includes the government as a producer and consumer of economic activity reliant on or enhanced by the use of digital inputs (G20 DETF, 2020). Notwithstanding this, the inclusion of specifc public service indicators in international frameworks and composite indices is limited (see Table 3.4).

<sup>1</sup> https://index.okfn.org

<sup>2</sup> https://www.oecd.org/gov/digital-government/open-government-data.htm

**Table 3.4** Selected international e-Government measurement frameworks and composite indices

Where public services are included in international frameworks and composite indices, they are limited to three main themes—access, employment, and use. It is worth noting that access in this context focuses on the access by the public as opposed to government itself (Table 3.5).

E-health is rarely included as a discrete segment in international frameworks and composite indices on the digital economy or digital society. Where it is included, it is typically included in the context of measuring individual or household access and use of the Internet. For example, the Partnership on Measuring ICT for Development utilizes data on individuals and households using the Internet to seek health information or make an appointment with a medical practitioner (ITU, 2016). In addition to similar indicators on usage, DESI has previously reported on the availability of e-prescriptions and online medical consultation (DESU, 2018). National data on individual and household use of the Internet for health purposes is available through a variety of sources including Eurobarometer, Eurostat and the ITU.

**Table 3.5** Themes measured in international digital society and digital economy measurement frameworks and composite indices and indicative benchmark data sources

Other than DESI, open data is not included in any other major international framework on the digital society or digital economy. DESI measures the maturity of open data in a given country based on:

Data on open data maturity is available from the European Data Portal.3

# 3.7 Conclusion

Public services underpin economic activity and are essential for the functioning of society. Despite this, public service delivery has historically been ineffcient and, for many, lacks suffcient accessibility—particularly for those in rural and remote areas. The adoption and use of digital

<sup>3</sup> https://data.europa.eu/en/impact-studies/open-data-maturity

technologies can generate substantial benefts in this context by making services more accessible, convenient and effcient. A large number of initiatives have been put in place by national governments to provide public services via digital channels. In line with these initiatives, a similarly large number of maturity frameworks to classify these initiatives have been proposed. Unfortunately, many of these lack commonality or do not address relatively smaller towns and rural communities. As a consequence, very little is known about the adoption and use of digital public services at a rural or municipal government level. It would seem that even for digital public services, an urban-rural digital divide remains.

#### References

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# The Digital Economy and Digital Business

## 4.1 Introduction

Small and rural communities face greater challenges than metropolitan areas in generating economic growth. In addition to access to physical and industrial resources and global market competition, rural communities face further obstacles due to their distance from urban centres, low population levels, and associated low population density (OECD, 2014; Liu, 2021). These factors result in signifcant differences in the attributes of rural economies compared to urban areas; with rural communities characterised by employment in services and manufacturing (often limited to consumer and basic producer services, and either small scale and/or relatively unsophisticated manufacturing), low skilled and ageing workforces, and low levels of innovation and productivity (OECD, 2014; Liu, 2021). In some areas, these factors are exacerbated by geographic impediments such as unfavourable climate and topographies (Freshwater, 2018). While small and medium-sized enterprises (SMEs) represent approximately 90% of businesses and more than 50% of employment worldwide (World Bank, 2021), in rural areas, the vast majority of frms are SMEs or, more precisely, micro-enterprises (i.e., frms with less than ten employees). These companies are traditionally reliant on external markets for their growth but at the same time face greater challenges than their urban counterparts such as more limited access to fnance, labour shortages, and higher transport costs to external markets (Freshwater et al., 2019).

Digital technologies present rural enterprises with the opportunity to overcome the constraints of their location. The commercial benefts generated by these technologies such as websites, e-commerce, digital marketing and advertising, and social media for enterprises in general, and for SMEs in particular, are clear and have been documented by a large number of studies (see, for example, Walczuch et al., 2000; Mehrtens et al., 2001; Jones et al., 2003; Claffey & Brady, 2014; Tiago & Verissimo, 2014; Jeansson et al., 2017). However, recent research suggests that there exists a digital divide between urban SMEs and rural SMEs in terms of adoption of digital technologies and digital business practices (Richmond et al., 2017). Furthermore, micro-enterprises are often under-represented in offcial statistics (see, for example, Digital Economy Skills Unit, 2020) therefore making it diffcult for policymakers to make informed and effective decisions to close the digital divide in rural communities by both enterprises and individuals. At the same time, the OECD, amongst others, believe that rural areas should drive their own economic development rather than rely on national government, specifcally with respect to identifying and mobilising assets to improve economic performance (OECD, 2014). This being in the case, more local and granular data and indicators are needed to guide such local bottom up strategies.

The remainder of this chapter defnes the digital economy and digital business and discusses the differences between the two. This is followed by a discussion of the benefts and challenges in adoption and use of digital technologies by enterprises, with an emphasis on SMEs and rural SMEs. The chapter concludes with a discussion of international frameworks and composite indices for measuring the evolution and development of digital technology adoption and use by businesses.

## 4.2 What Is the Digital Economy?

The digital economy, as a phenomenon, is relatively recent, particularly in developing countries and rural areas (World Economic Forum, 2015) even though the technological underpinnings of the digital economy started to be laid out in the 1990s with the initial adoption of enterprise computing and computerised manufacturing (Sturgeon, 2021). The advent of the Internet in the early 2000s represented a stepping stone toward the digital economy as we know it today. The widespread organisational adoption of the Internet has enabled the development and adoption of a number of technologies and services that are at the core of the digital economy. A number of defnitions of digital economy have been proposed over time. However, as Bukht and Heeks (2017, p. 4) put it, "defnitions are always a refection of the times and trends from which they emerge" and therefore need to adapt as the technological landscape and users' sophistication and knowledge evolve. Table 4.1 provides an overview of how the defnition of digital economy has evolved since the early 2000s.

**Table 4.1** Selected defnitions of digital economy (adapted from Bukht and Heeks, 2017)

(*continued*)

**Table 4.1** (continued)

As can be seen from Table 4.1, extant defnitions of the digital economy vary signifcantly in terms of scope highlighting a clear lack of agreement around what should be included in or excluded from the digital economy. The G20 Digital Economy Task Force has been active in proposing a common defnition for the digital economy, reviewing and proposing new measurement frameworks and composite indicators for the digital economy. For the G20 Digital Economy Task Force (2020, p. 114), the digital economy is defned as:

All economic activity reliant on, or signifcantly enhanced by the use of digital inputs, including digital technologies, digital infrastructure, digital services and data. It refers to all producers and consumers, including government, that are utilising these digital inputs in their economic activities.

This defnition was designed to allow for a fexible approach to measurement including top down and bottom up approaches. Echoing Bukht and Heeks (2017), it allows for a fexible and gradated defnition of the digital economy across three tiers (Fig. 4.1):

**Fig. 4.1** Tiered defnition of the digital economy. (Adapted from G20 Digital Economy Task Force, 2020)

3. The broad measure of the digital economy—the frst two measures plus economic activity from frms signifcantly enhanced by the use of inputs.

Furthermore, the G20 Digital Economy Task Force recognise that other parts of the digital society perform digital interactions and activities that add value and impact the economy, which while not strictly part of a digital economy, are important for economic and regional development policies, amongst others (G20 Digital Economy Task Force, 2020).

## 4.3 What Is Digital Business?

SMEs are arguably the backbone of the economy in many countries and often represent a catalyst for economic growth. This is particularly the case in rural communities where rural enterprises provide a key contribution to local economic and social resilience, and infuence the life of rural communities in both direct and indirect ways (Eachus, 2014; Steiner & Atterton, 2015). Direct effects include more obvious benefts such as the creation of local employment and local product/service availability (Eachus, 2014). Indirect effects are somewhat less visible and yet extremely benefcial for rural communities (Bruce et al., 2006). These include, for example, a reduced risk of migration toward urban areas thanks to local employment opportunities and the development of a more active and higher value local economy that leads to higher quality of life (Hegney et al., 2008).

Unsurprisingly, SME support and rural development are constant items on the agenda of policy makers (Bennett, 2008; Mole et al., 2011; Skerratt & Steiner, 2013; Lyee & Cowling, 2015) however the level of sophistication and effectiveness of such interventions varies depending on their geographical location (European Commission, 2019; Phillipson et al., 2019). In fact, the evidence suggests that despite rural SMEs being comparable to (if not better than) their urban counterparts in terms of longevity, exports and economic growth, they receive signifcantly less attention and support than urban SMEs (Phillipson et al., 2019).

Some of the challenges affecting rural enterprises may be similar to those faced by urban SMEs but others are typical of rural environments. These challenges can be summarised in the following fve categories (Interreg Europe, 2020):

tively impact the ability of rural enterprises to innovate and grow (Phillipson et al., 2019).

The roll out of connectivity infrastructure in rural areas is mostly being driven by national governments (Salemink et al., 2017). However, merely providing the infrastructure is not suffcient to make an impact. Adoption and actual usage are the next steps that need to be taken for digital connectivity to have an impact on rural communities in general and businesses in particular (Hage et al., 2013). The adoption of digital technologies could mitigate all the other challenges presented above by facilitating entry into new markets, providing access to training and skills, and ultimately by enabling growth (Price et al., 2018).

The adoption of infrastructure for digital connectivity is also a necessary prerequisite for enabling digital business practices in rural enterprises. Digital business is a relatively new concept and in the academic literature it is often referred to as e-business or electronic commerce. A number of different defnitions of e-business have been proposed over time and are presented in Table 4.2.

Most defnitions link digital business to the use of Internet technologies or mobile technologies, while others (e.g., Rayport & Jaworski, 2001) provide a technology-agnostic defnition of e-business. A common trait across these defnitions is the transactional nature of e-business. However, digital business is much broader than transaction-based commerce conducted via digital means (Chaffey et al., 2019). Chaffey et al. (2019, p. 15) defne digital business as "how businesses apply digital technology and media to improve the competitiveness of their organisation through optimising internal processes with online and traditional channels to market and supply". As such, digital business is not only limited to buying and selling online but it encompasses a range of processes and activities enabled by digital technologies that aim to integrate the digital and physical worlds (Gartner, 2021).

**Table 4.2** Selected defnitions of e-business (adapted from Wirtz, 2019)

Digital business defnitions rarely distinguish between mainstream and frontier technologies, however by and large they focus on the former. Frontier technologies represent a signifcant opportunity for businesses with some market estimates as high as US\$3.2 trillion by 2025 (UNCTAD, 2021). The adoption of such technologies is not uniform. For example, the fnance and manufacturing sectors were early adopters of AI, IoT, big data and blockchain, and the US and China dominate frontier technology supply (UNCTAD, 2021). While the opportunity for frontier technologies for economies is signifcant in terms of jobs and expenditure, capacity to exploit these technologies depends heavily on human capital, access to fnance, and other structural factors to support scale (UNCTAD, 2021). Unfortunately, these are less likely to be found in rural communities (UNCTAD, 2021).

Finally, it must be noted that some commentators suggest that it is important to differentiate between (1) businesses that make *extensive* use of digital technologies (their existence depends on digital technologies), and (2) businesses that make *intensive* use of digital technologies (applying digital technology to enhance their productivity) (Bukht & Heeks, 2017). Consequently, one can conceptualise the adoption and use of digital technologies across a spectrum of competencies, from core and distinctive to common and subordinate as per Fig. 4.1.

## 4.4 Benefits and Challenges of Digital Technologies for Businesses

Digital business leverages a wide range of digital technologies including website technologies, digital advertising, social media and social commerce, email marketing, mobile and e-commerce, and analytics, amongst others. The emergence of cloud computing in particular provides SMEs with the opportunity to outsource their technology infrastructure to support their web operations therefore providing greater reliability and scalability (Trigueros-Preciado et al., 2013; Leimbach et al., 2014). Similarly, the availability of user-friendly digital advertising platforms and content management systems (CMS) with support for mobile responsive themes has resulted in websites that are easy to navigate on both desktop and mobile devices therefore providing users with a more seamless experience. These technologies can support a range of commercial objectives including information dissemination and exchange (Daniel et al., 2002; Jeansson et al., 2017), demand generation (Jones et al., 2015; Richmond et al., 2017), sales (Drew, 2003; Jones et al., 2015; Jeansson et al., 2017; Tiwasing, 2021), and customer relationship management (McCann & Barlow, 2015; Richmond et al., 2017).

The potential benefts of digital technologies to SMEs are wellestablished. These include, reductions in distance-related barriers (Walczuch et al., 2000; Sinkovics & Sinkovics, 2013), cost savings and operational effciency (Walczuch et al., 2000; Trigueros-Preciado et al., 2013), IT resilience and scalability (Trigueros-Preciado et al., 2013; Leimbach et al., 2014), easier access to new markets (Walczuch et al., 2000; Jones et al., 2003; Pergelova et al., 2019), marketing effectiveness (Jones et al., 2015), customer service and engagement (Claffey & Brady, 2014), and market and customer intelligence (Tiago & Verissimo, 2014). From a strategic perspective, digital technologies can represent the basis for a competitive advantage for SMEs and enable them to compete with larger frms (Mehrtens et al., 2001; Richmond et al., 2017). Despite the wide range of benefts provided by digital technologies, SMEs continue to lag in terms of adoption and usage when compared to their larger counterparts, particularly with regard to the adoption of more sophisticated technologies (OECD, 2021). This is due to a number of reasons including diffculty accessing skills and skilled resources, perceived risk associated with greater investment to adopt and support more advanced technologies (McDowell et al., 2016), or security (Trigueros-Preciado et al., 2013; Leimbach et al., 2014).

The digital divide between urban and rural communities has been a focus of research for a number of years (Hindman, 2000; Townsend et al., 2013; Philip et al., 2017; OECD, 2021). However, the divide between urban and rural SMEs in general and micro-enterprises in particular has not attracted the same level of interest from researchers. Previous studies reported relatively low broadband access, website and social media use by rural SMEs in developed countries (Michaelidou et al., 2011; Daun & Muessig, 2012; Townsend et al., 2013). More recently, Richmond et al. (2017) found that, while broadband access has become more widespread in rural communities, rural SMEs still lagged urban SMEs in online marketing practices, website sophistication, and e-commerce and social media usage.

The need for more widespread adoption of digital technologies by rural SMEs has become even more evident since the breakout of the COVID-19 pandemic and the associated disruption of the normal operations of businesses worldwide. In fact, SMEs have been disproportionately impacted by the restrictions put in place by national governments in order to contain the spread of the virus (OECD, 2021). This is because SMEs (1) are over-represented in sectors that have been impacted by lockdowns, (2) have lower productivity and weaker supply chain capabilities, (3) are more fragile than large frms from a fnancial perspective due to limited cash reserves, and (4) typically lack the managerial capabilities to navigate through the evolving challenges presented by the pandemic (Bartik et al., 2020; Cowling et al., 2020; Humphries et al., 2020; OECD, 2021). Despite the large number of initiatives launched by different governments to promote and support digitalisation, innovation and technology development, upskilling and reskilling, and fnding new alternative markets for SMEs (OECD, 2021), a large number of SMEs reported making signifcant layoffs during the COVID-19 pandemic, and expect that it could take up to two years for them to recover (Bartik et al., 2020; Humphries et al., 2020). Rural SMEs are likely to be more adversely affected due to their less diversifed economic activity, disproportionate reliance on local markets, disruption to national and international supply chains and, as mentioned above, their lower adoption and use of digital technologies (Richmond et al., 2017; OECD, 2020).

Against the backdrop of COVID-19, the pandemic has contributed to a signifcant acceleration of digital initiatives and may lead to a radical change in consumer behaviour (Guo et al., 2020; McKinsey, 2020; Riom & Valero, 2020). Such a change may represent an opportunity for businesses (Klein & Todesco, 2021) so it is crucial for rural SMEs to be fully equipped to fully seize this opportunity.

#### 4.5 Measuring Digital Business

As is evident from Chap. 1, there is a large number of measurement frameworks and composite indices for measuring digital business, typically labelled under the 'Digital Economy'. Not only do these include frameworks specifc to the digital economy but those seeking to measure the evolution and development of the Digital Society e.g., DESI (Digital Economy and Skills Unit, 2020). The proliferation of these frameworks is largely due to the priority given to economic research and the availability of data at a national level.

There are a wide range of digital economy indicators included in extant frameworks and composite indices. These can be categorised into a number of major themes as per Table 4.2; links to sources and indicators can be found in the Useful Resources section at the end of the book. The majority of the indicators that compose digital economy indices are sourced from national sources and statistics and are aggregated by a variety of sources for international comparison as indicated in Table 4.3. Data for a specifc rural town and environs may not be available at a national level depending on the methodology employed for data collection. Furthermore and as discussed in Chap. 1, such statistics may only include enterprises with more than ten employees. As a result, micro-enterprises, that make up the bulk of rural SMEs, may not be included. Furthermore, extant measures typically do not include new paradigms and business models enabled by digital technologies such as the so-called 'sharing economy' and 'gig economy'. Measuring these activities is more diffcult as traditional sources of national statistics may not probe suffciently to uncover such work arrangements, workers may not consider these

**Table 4.3** Common themes and selected international sources for digital business

(*continued*)

**Table 4.3** (continued)

activities as their primary employment or even a job, or workers may not wish to report such activities for tax avoidance reasons (Riggs et al., 2019). Similarly, statistics based on the digital platforms that enable these activities may not be comprehensive, representative, or consistent over time (Riggs et al., 2019). Most international frameworks do not include evaluations of laws and regulations related to digital business, one would assume due to the diffculty in reducing these variables to a numeric indicator. That is not to say that such indicators do not exist. The World Bank Digital Business Indicators (Chen, 2019) includes indicators for data privacy and security (individual rights, cross-border data fows, and data security and enforcement) and digital market regulations (consumer protection, intermediary liability, and e-signatures). Similarly, Chakravorti et al. (2019) include a measurement of institutional barriers (wholesale foreign direct investment (FDI) regulatory restrictiveness indicator, anti-monopoly policy, and FDI regulations). In the context of rural towns, these are less important measurements in that local decision-makers do not typically have the requisite agency to effect change in laws and regulations.

Despite the wide range of indicators included in these frameworks, the extent to which they assess the penetration and the use of a comprehensive and relevant range of technologies by businesses is limited. There are exceptions. DESI, the G20 Digital Economy Task Force, and the Partnership on Measuring ICT for Development all propose indicators using ICTs other than internet infrastructure and computer equipment (see Table 4.4), some of which can be inferred by activities reported. Furthermore, data on other relevant indicators may be collected but not used in these frameworks. For example, Eurostat's Digital Economy and Society Statistics includes data on computer-based tasks performed by individuals at work including e-mail, data entry, electronic document creation or editing, received tasks via apps, occupation-specifc software, social media use for work purposes, and developed or maintained IT systems or software (Eurostat, 2021). This data can be used to infer digital technologies used by businesses.

## 4.6 Conclusion

The potential benefts of digital business technologies adoption for enterprises including SMEs are well established, and yet adoption and use by rural enterprises still lag their urban counterparts, particularly for more advanced technologies and sophisticated uses. This is an important differentiation as it is through leveraging emerging technologies that the

**Table 4.4** Selected indicators for digital business in digital society and digital economy measurement frameworks and composite indices

**Table 4.4** (continued)

greatest benefts and opportunities may be realised. While policymakers have been mostly focused on connectivity, there are also a number of other reasons behind this digital divide that have partly been overlooked. These include, for example, limited access to fnance and skilled resources that put rural enterprises in a disadvantaged position compared to urban enterprises, largely due to their size and location. There are a number of initiatives that aim to measure the level of adoption and sophistication of digital business technologies by SMEs but they tend to leverage secondary data that are only available at a national or regional level. These assessments are typically based on samples where rural SMEs and micro-enterprises are under-represented compared to urban SMEs due to information availability and ease of access. As such, any decision taken on the basis of aggregated statistics may ultimately widen the urban-rural divide even further.

#### References

Laudon, K. C., & Traver, C. G. (2013). *E-commerce*. Pearson.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Digital Technologies and Civil Society

## 5.1 Introduction

'Civil society' is a term increasingly used to refer to social institutions outside of the confnes of households, the market and the state. Such social institutions are typically characterised by varying degrees of selfgovernance, voluntarism, and not-for-proft operation (Salamon et al., 1999). Civil society includes a wide range of voluntary, social and community organisations including charities, social and sports clubs, political parties, religious bodies amongst others (see Table 5.2). These civil society organisations (CSOs) often play an integral role in rural society providing an important underlying social fabric in a community and addressing issues that have not been satisfactorily addressed by the market or state. Like commercial and indeed government institutions, digital technologies have the potential to transform organisational capacity and stakeholder engagement in and with civil society institutions yet CSOs are rarely included in indices seeking to measure digital progress in society.

The remainder of this chapter defnes civil society and discusses the role of CSOs in the context of rural communities. The opportunities and challenges for the digital transformation of CSOs are then discussed followed by a discussion of extant attempts to measure digital adoption and use by civil society.

#### 5.2 Defining Civil Society

'The Third Sector', 'the independent sector', 'the nonproft sector', and CSOs are just a few of the terms used loosely to refer to civil society (United Nations, 2003). In many respects there are broad and narrow perspectives to defning civil society. Anheier et al. (2001, p. 21) defne civil society as:

the sphere of ideas, values, institutions, organizations, networks, and individuals located between the family, the state, and the market and operating beyond the confnes of national societies, polities, and economies.

In this respect, they conceive civil society as a broad, global and somewhat abstract concept, which has been critiqued as lacking in rigor and precision (Taylor, 2002). In contrast, Salamon and Anheier (1998, p. 216) defne civil society as a collection of entities that share fve characteristics:

While providing specifc criteria for inclusion in a civic society or nonproft 'sector', this defnition is also suffciently broad. As such, it encapsulates a wide range of organisations including those involved in culture and recreation, education and research, health, social services, education, environmental protection and conservation, human rights advocacy, religion, and politics, amongst others. Furthermore, as an operational defnition, it clearly distinguishes between households, the market, and the private sector, the other three economic units defned in the System of National Accounts by the United Nations (United Nations, 2003). Internationally, civil society both varies in presence, composition, fnancing and scale with specifc CSO categories more or less prominent depending on context (Salamon et al., 1999). Notwithstanding this, education and research, health, social service, and culture and recreation are historically dominant in most countries (Salamon et al., 1999; Indecon, 2018). Given that many of these activity categories are infuenced by government, Salamon et al.'s (1999) fve defning organisational characteristics serve to distinguish CSOs from facilities and services provided from entities other than households, the market or the state.

#### 5.3 The Role of Civil Society

A number of theories have been posited to explain the pattern of civil society growth in a given region (Salamon, 1999). These are summarised in Table 5.1. Although the most common and prevalent theory is the market failure/government failure theory, it does not fully explain the patterns for growth although the remaining anomalies can be explained by a combination of other theories, namely supply-side and social origins theory (Salamon, 1999).

At a practical level, civil society institutions play a number of important roles. Firstly, CSOs play an important role in the context of society. They play a key role in not only encouraging community involvement but promoting citizenship values, skills and attitudes and motivating citizens to use in the public interest (Salamon, 1997; Edwards & Foley, 2001). Secondly, they play a representative and contestory role for presenting and advocating distinct interests and diverse points of view (Ben-Ner & Van Hoomissen, 1992; Salamon, 1997; Edwards & Foley, 2001). Thirdly, they perform a variety of public and quasi-public functions through

**Table 5.1** Major theories explaining the presence of civil society institutions (Salamon et al., 1999)

service delivery (Salamon, 1997; Edwards & Foley, 2001). These roles are refected in the wide range of activities they perform and are summarised in Table 5.2. As discussed, education and research, health, social service, and culture and recreation are historically dominant CSO activities in most countries (Salamon et al., 1999; Indecon, 2018).

Secondly, the nonproft sector is a signifcant employer. For example, recent data suggests that the sector is the third largest employer in the US with 12.5 million paid workers (Salamon & Newhouse, 2020). Similarly, in Europe the sector employs 28.3 million full-time equivalent (FTE) workers (paid and volunteer) in the EU28+ countries, accounting for c. 13% of the European workforce (Salamon & Sokowlowski, 2018). Thirdly, in addition to social impacts and employment, these institutions create signifcant economic value through expenditure. Even in relatively small countries the impact can be signifcant. For example, a recent report on the social and economic impact of the nonproft sector in Ireland estimated that charities in Ireland resulted in direct, indirect and induced expenditure of €24.98 billion in 2017 (Indecon, 2020). Signifcantly, the economic value of volunteering alone was estimated at €649 million per year driven by more than 300,000 volunteers working over 67 million hours in 2017 (Indecon, 2020).

Unfortunately, the COVID-19 pandemic has signifcantly impacted the nonproft sector. As well as rising demand for services pre-pandemic, CSOs have experienced an increase and intensifcation of demand resulting from the pandemic (Pro Bono Economics, 2020, 2021; EFA and Salesforce.org, 2020). Service delivery and fundraising were adversely impacted by increased demands from other service closures and exacerbated by social distancing requirements (EFA and Salesforce.org, 2020; Pro Bono Economics, 2020, 2021). Unsurprisingly, many CSOs have had to reduce their workforce due to COVID-19 restrictions (Salamon & Newhouse, 2020; EFA and Salesforce.org, 2020; Pro Bono Economics, 2020, 2021). At the same time, CSOs have reported lower income levels due to COVID-19 restrictions while also encountering diffculties in reaching and engaging volunteers and supporters (EFA and Salesforce. org, 2020; Pro Bono Economics, 2020, 2021).

(*continued*)

# 5.4 Digital Technologies and Civil Society

Information and communication technologies (ICT) are used widely in civil society refecting, albeit lagging, commercial organisations as a whole. There is a long established body of literature on the topic, often referred to as ICT4D (Walsham, 2017).

#### *5.4.1 Mainstream Technologies*

Like commercial organisations, CSOs can generate value and exploit the same opportunities from mainstream digital technologies improved organisation capacity and stakeholder engagements, cost savings, process effciencies, new revenue generation, and improved quality of service (Dufft & Kreutter, 2018; O'Grady & Roberts, 2019; Ehnold et al., 2020; Walker et al., 2020). Indeed, there is pressure on nonproft organisations to adopt the methods and values of the market (Eikenberry & Kluver, 2004). Increasingly, this includes the adoption of digital technologies and

**Table 5.3** Selected scholarly research on civil society usage of digital technologies

platforms. Furthermore, ICT, and internet-based technologies more specifcally, are changing how civil society organisations organise themselves locally, regionally and globally (Williams, 2018). As can be seen from Table 5.3, there is a well-established literature on the use of digital technologies by nonproft organizations for information sharing and promotion, community building, fundraising, recruitment, and advocacy. This literature cites a wide range of potential advantages including increasing organisation capacity (Sun & Asencio, 2019), improved transparency (Dumont, 2013), access to market and targeting (Shier & Handy, 2012; Saxton & Wang, 2014), message amplifcation and reach (Saxton & Wang, 2014; Briones et al., 2011), faster service delivery (Briones et al., 2011), and payment (donation) effciency (Shier & Handy, 2012). Notwithstanding these benefts, academic literature suggests goals and overall organisational capacity are signifcant barriers to adoption, and specifcally leadership, skills and training, privacy concerns, and budgetary constraints (see, for example, Campbell et al., 2014; Sun & Asencio, 2019; Mogus & Levihn-Coon, 2018; Ehnold et al., 2020).

More recently there have been a number of surveys by private organisations seeking to benchmark use of digital technologies. Notwithstanding the promise of digital technologies, extant literature published prior to COVID-19 suggested that digital adoption by CSOs is limited (Dufft & Kreutter, 2018), with a substantial focus on the use of digital technologies for communication (Ehnold et al., 2020; Dufft & Kreutter, 2018; Skills Platform, 2019). In their 2019 survey of 5721 NGOs, Nonproft Tech for Good (2019) found that NGOs used a wide range of digital technologies including websites, emails, online payment systems, social media, paid advertising, customer relationship management (CRM) systems, internal communications and project management tools. However, usage varied across regions and by level of intensity and sophistication. For example, only 40% use a CRM and while 68% utilise recurring/monthly giving, only 31% use some form of crowdfunding. Similarly, while 90% use social media, respondents overwhelmingly use Facebook with less than 30% using LinkedIn, WhatsApp or YouTube.