Information society and socioeconomic characteristics in Europe: a typology of regions

233 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 Cuadernos Geográficos 62(1), 233-254 DOI: https://doi.org/10.30827/cuadgeo.v62i1.25334 Information society and socioeconomic characteristics in Europe: a typology of regions Sociedad de la Información y características socioeconómicas en Europa: una tipología de regiones Purificación Crespo-Rincón1 orcid 0000-0002-6321-0537 Rosa Jordá-Borrell1 orcid 0000-0002-4680-4771 Francisca Ruiz-Rodríguez1 orcid 0000-0002-5409-3631 1 Departamento de Geografía Física y Análisis Geográfico Regional. Universidad de Sevilla, España. Fechas • Dates Recibido: 2022.07.09 Aceptado: 2023.02.13 Publicado: 2023.03.21 Autor/a para correspondencia Corresponding Author Francisca Ruiz-Rodriguez [email protected] Artículo original Original Article Abstract Research on the Information Society and its socio-economic context is limited at the European regional level. This research aims to identify the socio-economic and technological factors that define the underlying structure of household-individual (H+i) digital development, at NUTS 2 level, using Factor Analysis, with data that have been collected from Eurostat, and characterise the socio-economic environments associated with advanced ICT use using Cluster Analysis. We show that, at the regional level in Europe, the use of advanced ICT_H+i is related to GDP pc, the qualification of the population and high-tech employment; broadband internet access is associated with employment and the average educational level of the population; and the degree of innovation, business growth and ICT employment are decoupled from the level of ICT_H+i. The model defines a spatial performance that contrasts the southern and eastern European regions, with high availability of ICT infrastructures but low advanced use of them; with the central-northern regions, the most advanced in the EU in terms of ICT access and use, with adequate infrastructure and high upper-middle education levels that allow for the improvement of ICT skills and regional digital transformation. Keywords: Information and communication technologies (ICTs); households and individuals (H+i); socio-economic and technological factors; digital development; NUTS 2. 234 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 Resumen La investigación sobre la Sociedad de la Información y su entorno socioeconómico es reducida a escala regional europea. Esta investigación pretende: identificar los factores socioeconómicos y tecnológicos que definen la estructura subyacente del desarrollo digital de los hogares-individuos (H+i), a nivel NUTS 2, mediante Análisis Factorial, con datos obtenidos de Eurostat; y caracterizar los entornos socioeconómicos asociados al uso avanzado de las TIC con Análisis Clúster. Mostramos que, a nivel regional, en Europa, el uso de las TIC avanzadas de los H+i se relaciona con el PIB pc,la cualificación de la población y el empleo en alta-tecnología; el acceso a internet por banda ancha se asocia al empleo y al nivel educativo medio de la población; y el grado de innovación, crecimiento empresarial y el empleo TIC se desvinculan del nivel de TIC_H+i. El modelo define un comportamiento espacial que contrapone las regiones europeas del sur y este, con alta disponibilidad de infraestructuras TIC, pero reducido uso avanzado de ellas; frente a las del centro-norte, las más avanzadas de la UE en el acceso y uso de las TIC, con adecuada infraestructura y alto nivel educativo superior-medio que permiten el perfeccionamiento de destrezas TIC y la transformación digital regional. Palabras clave: Tecnologías de la Información y Comunicación (TIC); hogares e individuos; factores socioeconómicos y tecnológicos; desarrollo digital; NUTS 2. 1. Introduction The digital development of a region is regarded as the process that drives the transformation of the region towards the information and knowledge society and makes it possible to modify conditions through the generation and processing of information and knowledge in order to, from then on, improve competitiveness, innovation and the adoption of information and communication technologies (ICTs) (Gónzalez-Relaño, R. et al., 2021). In this context, such processes are reliant on the provision of ICT equipment and Internet access, both being fundamental elements to achieve the digitalisation of a region (Hernández & Maudos, 2021). ICTs access and usage has produced the so-called digital society. This is when the public, companies and institutions establish their relationships (social, administrative, employment, consumption, etc.), while simultaneously generating data and information via digital devices and platforms, giving rise to digital development (Ruiz-Rodríguez, et al., 2018). Therefore, digital technologies are a constant component of the lives of individuals and companies, with these becoming dependent on digital technologies and their specific infrastructures. The rapid progress of ICTs at global level is sparking the interest of many researchers, motivating them to focus their research on the impact of the diffusion of ICTs on the socio-economic growth of regions. According to Stanley et al. (2018), the socio-economic characteristics of a region may or may not favour access to, and advanced use of, ICTs in that region. In this context, outstanding modern theories such as neo-Schumpeterian ones (Schumpeter & Nichol, 1934; Malacarne, 2018; Garbin & Marini, 2021) and the neoclassical growth theory (Solow, 1956), have pointed out the positive relationship between the socio-economic characteristics of a territory/region and the development of technology, including ICTs. Consequently, these theories suggest that ICTs constitute a key input for a region to improve the production process, modernise technology and enhance workforce skills. They also entail a result (collection of data and complex information) that makes competitiveness and complex technology essential elements of the regional business fabric (Jordá-Borrell, 2021). 235 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 The EU policy to achieve the digitalisation of European society dates back to 1999 with the “e-Eu- rope” plan, which intended to create an information society for all. In the first decade of the twenty-first century, within the framework of the Lisbon Strategy, the policy focused on increasing Internet connections; development of services, applications and contents; and fostering knowledge and information through Plan i2010. In the second decade of the century, under the “Europe 2020 Strategy for smart, sustainable and inclusive growth” the European Digital Agenda was implemented, with the aim of reducing the digital divide, and also a Digital Single Market Strategy (Muñoz & Pérez, 2017). Recently, the EU approved the so-called second digital strategy 2020-30, whose objectives are specified in the “2030 Digital Compass”: a society with citizens and professionals prepared for the digital future; sustainable, secure and efficient digital infrastructures; a digital transformation of companies; and an extensive digitalisation of public services. Consequently, in Europe, the use of basic ICTs reveals uniform regional behaviour (Ruiz-Rodrí- guez et al., 2020). European regions have high values of usage of basic ICTs, with those of a social nature predominating, while the use of more complex or advanced ICTs by European users (educational, healthcare, e-commerce, administrative, etc.) vary between regions (Alzahrani et al., 2018; Berkowsky et al., 2017); and are in keeping with their socio-economic environment (educational level and degree of access to the technology, infrastructures, financing, business dynamics, etc.) (Lee & Porumbescu, 2019; Yera et al., 2020). Thus, Ragnedda & Kreitem (2018) points out that the socio-cultural and economic background of the population is an important factor in the digital inequalities between the households and individuals of European regions, as these are determined by the different uses and abilities that people may have developed to obtain greater benefits from ICTs. Accordingly, for the use of digitalisation to be effective, the public, companies and institutions must utilise and take advantage of ICTs investment. This means that the human resources must be equipped with sufficient technological and digital skills (Hernández & Maudos, 2021). Similarly, different authors indicate that both the most advanced uses of ICTs (healthcare, finances, research, etc.) ( Lucendo-Monedero et al., 2019)DD and the production of ICTs (companies with ICTs specialists) require people with medium-high levels of education (Alfaro et al., 2017). The successful introduction of ICTs in a region therefore requires an adequate level of training; a sufficiently high GDP per capita to generate wealth and innovation (creation of software, human capital management processes, etc.) (Erazo & Castro, 2011); a diversified and potent business development and significant employment in telecommunication companies (Jordá-Borrell, 2021). Ultimately, this leads one to think that the ability of regions to access ICTs and use them efficiently (individuals, institutions, companies, etc.) is linked to their socio-economic characteristics (Moyano, 2020). Hence, in this research, the use of ICTs by the public is defined as a multifactorial and multicausal phenomenon; so different factors and causes concur when it comes to explaining their implementation in correlation with regional socio-economic dynamics. It is now known that the influence of the ICT factor is maximised when this interacts with other economic, social and innovation factors, etc. (Jordá-Borrell & López-Otero, 2020). Currently, empirical research on the information and communication society at regional scale within the EU (NUTS 2 level) is insufficient, especially the analysis of its relationship with the socio-economic environment. Moreover, when this topic has been addressed, studies have focused above all on the factors that determine basic digital development (Cruz-Jesus et al., 2016). Iden- 236 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 tification of this research gap has led to the proposition of the following hypotheses, and their corresponding research questions: 1. There are few differences between regions in terms of Internet access for the European population because the policies of the EU and national governments on communications equipment and infrastructures have helped significantly reduce such possible inequalities. This leads us to ask the following question. Are the regions with good telecommunications infrastructure coverage, such as the availability of broadband, those that most use advanced ICTs? 2. The distance between regions, in terms of the use of social networks by the population, is small, while the dissimilarities between regions in the use of advanced ICTs are important. Are these latter differences associated with the socio-economic characteristics of the regions? Which socio-economic factors or elements are more important? Is the innovation and business capacity of a region associated with the advanced use of ICTs by the population of that region? In this context, the aim of this study is, first, to identify the socio-economic (related to training, business and regional wealth) and technological (innovation, digitalisation, etc.) factors that may define the underlying structure of the digital development of households and individuals (ICT_ H+i); and second, to characterise the socio-economic environments that are associated with the advanced use of ICTs at NUTS 2 regional level in Europe. To this end, the following are necessary: 1) Identify the interaction between the socio-economic variables and ICT_H+i access and usage by means of factor analysis (FA); 2) Establish a typology of regions (NUTS 2) according to the socio-economic environment and ICT access and usage by means of a cluster analysis (CA). 2. Theoretical framework In recent decades, the great diffusion of ICTs has given rise to a transformation of the world towards an information society. Thus, the physical limits of geographical storage capacity, transmission of information (universality) and simultaneity and speed of information have been surpassed (García, 2013). Thanks to ICT infrastructures and equipment, individuals and governments now have much better access to information and knowledge than before in terms of scale, scope and speed. Hence, in order for such diffusion of ICTs to take place, equipment that facilitates high-quality access to Internet is needed. The EU and national and regional governments have taken a number of actions to modernise telecommunications infrastructures and, more specifically, provide broadband. This helps foster and drive its use by the public, companies and institutions, and the availability of such infrastructures has a positive effect on regional economies (Appiah-Otoo & Song, 2021).These infrastructures, along with the corresponding devices and associated applications, are becoming increasingly diverse, and now it is not so much “those who have” and “those who do not have” but rather the present analysis focuses on the degree of use and its different patterns (Salemink et al, 2017). To date, usually, the greater the frequency of Internet use, the greater the associated skills, and vice versa. However, the level of digital skills of human capital is also related to the educational and training levels attained by the population, with training being one of the keys of the digital transformation of Europe (European Commission, 2021). Human capital, as is known, is the basis of regional competitiveness, stimulating employment, technological and business growth and, accordingly, GDP growth (Hernández & Maudos, 2021). 237 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 In this respect, the European Commission (2016) has proposed that everybody should have the opportunity to prosper, choose freely and participate, with security, in the information society, regardless of age, gender or professional situation. To this end, the population need to acquire certain digital capabilities and skills related to information (information on goods and services, looking for information on health, organising and saving information), communication (sending/ receiving e-mails, participation in social networks, etc.), resolution of problems (online services, online sales and purchases, Internet banking, etc.) and use of software (use of spreadsheets, photo editing programs, video or audio files, creation of presentations or documents that include text, images, tables or graphs, etc.) (Hernández & Maudos, 2021). Aptitudes and skills are considered to be advanced if the population is capable of carrying out more than one of these activities.The digital transformation of society will take place when the advanced use of ICTs is adopted by large sectors of the population, as information and knowledge are decisive variables for increasing the technological and human capacity to generate, process and produce(Castells, M. 2019). This fact is correlated with an educational level (medium and high) which enables the development of the skills necessary to use the technology more efficiently (Wirtz & Kurtz, 2017). This implies that a large part of society benefits from the ICTs, and is more disposed to accept the risks of using the technology (Billon, et al., 2021). The digital transformation of society will take place when the advanced use of ICTs is adopted by large sectors of the population, as information and knowledge are decisive variables for increasing the technological and human capacity to generate, process and produce(Castells, M. 2019). This fact is correlated with an educational level (medium and high) which enables the development of the skills necessary to use the technology more efficiently (Wirtz & Kurtz, 2017). This implies that a large part of society benefits from the ICTs, and is more disposed to accept the risks of using the technology (Billon, et al., 2021). It should not be overlooked that digitalisation is transforming jobs, threatening the stability of some with its automation and demanding the renewal of many of them, in order to make work more productive, while creating opportunities for workers whose education, training and digital skills and abilities allow them to better take advantage of the new technologies (Grigorescu et al., 2021). Therefore, the regions that invest in their human capital, and in the link between R&D and technology, will see their wealth increase. Digital aptitudes/skills are becoming increasingly primordial in order for people to improve their social position, work from home or start up their own business. They are also fundamental in allowing companies to increase their productivity and competitiveness, and they open up new business possibilities on the basis of the quality of their human resources; and consequently, provide new opportunities for European regions (Lamberti et al., 2021). Investment in new technologies, including ICTs and digital technologies, will improve the working and economic conditions of the population and of companies, optimising the validity of resources, reducing production costs, initiating demand and stimulating much greater investment in all socio-economic sectors at state and regional level (Jorgenson & Vu, 2016; Pradhan et al., 2018). On the other hand, it is also associated with an increase in the production of R&D. Investment in R&D and innovation tends to be concentrated in knowledge-intensive sectors, also known as high-technology sectors. In these sectors, companies mainly use R&D to obtain a competitive advantage and the skills of human capital constitute a fundamental input, including their ability to use ICTs (Sandu & Ciocanel, 2014). 238 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 Nonetheless, it is still necessary to further investigate the interaction between the use of advanced ICTs by individuals and households (ICT_H+i) (Jordá-Borrell et al., 2018) and the regional socio-economic environment, as the results of previous studies (with both large and small samples) continue to be partial and disparate. Some authors (Caridad et al., 2014) affirm that access to and use of advanced ICTs are indispensable in order to intensify the competitiveness of regions. Meanwhile, other studies indicate that ICTs alone do not increase regional GDP but rather need to be accompanied by other factors (Bahrini & Qaffas, 2019; Iammarino et al, 2018). Therefore, the ideas put forward in this section justify the treatment of variables such as GDP per capita, educational and training levels of the population, employment, R&D expenditure, the generation of patents, business dynamics, etc.; as variables representative of the regional socio-economic environment associated with the population’s capacity to access and use ICTs in the region. They also reveal the need to analyse the interaction, or the different relationships that exist, between the socio-economic variables and ICT access and usage in order to provide a typology of regions at NUTS 2 level in accordance with the intensity and characteristics of these relationships. 3. Methodology 3.1. Territorial scope of study, data and variables This paper analyses the relationship between socio-economic characteristics and Internet access of households and the advanced/basic use of ICTs by individuals (ICT_H+i) in the NUTS 2 regions (nomenclature of territorial units for statistics, a hierarchical system for dividing the economic territory of the EU, and the spatial unit of reference in the present study). On the one hand, using FA, 15 variables have been chosen from a total of 26 (Appendix A) which, according to theory, show a relationship between socio-economic characteristics of a territory (level of wealth, educational level, innovative capacity and business dynamics) and ICTs access and usage by households and individuals (Table 1). The data of these variables have been obtained from the regional statistics of Eurostat for 2019, and when data for this year is not available, data for 2018 has been selected. On the other hand, a population of 333 regions of the European Union has been used, which includes the NUTS 2 regions of the 27 countries belonging to the European Union (EU), the 26 regions of Turkey (country in accession negotiations with the EU), the 41 regions of the United Kingdom, as at the time of study they belonged to the EU, the 7 regions of Norway (country belonging to the European Economic Area) and the 8 regions of Switzerland (member of the European Free Trade Association - EFTA). However, the lack of data at NUTS 2 level in some countries made it necessary to use: i) the NUTS 1 level for Germany, Greece, Poland and the United Kingdom; ii) and NUTS 1 or NUTS 0 level for some regions of Turkey, and regions that make up NUTS 1 in eastern Anatolia and in eastern Europe. 239 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 Table 1: Description of Variables TYPOLOGY VARIABLES YEAR DESCRIPTION AND UNIT OF MEASURE EUROSTAT SOURCE LEVEL OF WEALTH GDP per capita compared to the EU28 average in PPP 2019 Regional gross domestic product (PPS per capita as % of EU28 average). https://ec.europa.eu/eurostat/ databrowser/view/tgs00006/ default/table?lang=en Unemployment rate in the population aged 15-74 years old 2019 Percentage of the unemployed population aged 15-74 years old. https://ec.europa.eu/eurostat/ databrowser/view/lfst_r_lfu3rt/ default/table?lang=en LEVEL OF EDUCATION Population aged 25-64 with tertiary education 2019 Percentage of the population aged 25-64 with tertiary education (Tertiary education ISCED 5-8). https://ec.europa.eu/eurostat/ databrowser/view/EDAT_ LFSE_04__custom_2315836/ default/table?lang=en Working population aged 15-64 with secondary and tertiary education 2019 Percentage of the working population aged 15-64 with secondary and tertiary education (Tertiary education ISCED 3-8). https://ec.europa.eu/eurostat/ databrowser/view/LFST_R_ LFP2ACTRC__custom_2338730/ default/table?lang=en LEVEL OF INNOVATION Employment in High Technology Sectors 2019 Employment in high-technology sectors as a percentage of total employment. (Statistical classification of economic activities in the European Community; NACE Rev. 2 codes- 2-digital level: 09, 19, 21, 26, 51, 58-79 and 90) https://ec.europa.eu/eurostat/ databrowser/view/HTEC_EMP_ REG2__custom_2338815/default/ table?lang=en Patent applications as a share of GDP 2012 Number of patent applications with respect to GDP in billions. https://ec.europa.eu/eurostat/ databrowser/view/PAT_EP_ RTOT__custom_2339933/default/ table?lang=en Intramural spending on R&D 2017 Domestic R&D expenditure as a percentage of GDP https://ec.europa.eu/eurostat/ databrowser/view/RD_E_ GERDREG__custom_596580/ default/table?lang=en BUSINESS DYNAMICS Growth rate of employment in telecommunications 2018 Percentage of net employment growth of in telecommunications sectors (with respect to previous year). https://ec.europa.eu/eurostat/ databrowser/view/SBS_R_ NUTS06_R2__custom_2340087/ default/table?lang=en Net Growth of the Number of Companies 2018 Percentage of net growth of number of companies in industry, construction and services sectors (with respect to previous year). https://ec.europa.eu/eurostat/ databrowser/view/BD_ESIZE_ R3__custom_597161/default/ table?lang=en LEVEL OF ACCESS ICTs Households with internet access 2019 Percentage of total number of households where anyone in the household has the possibility to access Internet from home. https://ec.europa.eu/eurostat/ databrowser/view/isoc_r_iacc_h/ default/table?lang=en Households with broadband 2019 Percentage of total number of households that have broadband Internet access. https://ec.europa.eu/eurostat/ databrowser/view/ISOC_R_ BROAD_H__custom_2341129/ default/table?lang=en 240 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 TYPOLOGY VARIABLES YEAR DESCRIPTION AND UNIT OF MEASURE EUROSTAT SOURCE LEVEL OF USE ICTs Individuals who access the Internet on a daily basis 2019 Percentage of total number of individuals who use Internet on a daily basis for any type of consultation. https://ec.europa.eu/eurostat/ databrowser/view/ISOC_R_ IUSE_I__custom_2341207/default/ table?lang=en Individuals who used the Internet for online banking 2019 Percentage of total number of individuals who access Internet to conduct banking transactions. https://ec.europa.eu/eurostat/ databrowser/view/ISOC_R_ IUSE_I__custom_595421/default/ table?lang=en Individuals who used the Internet for the sale of goods and service 2019 Percentage of total number of individuals who access Internet for the sale of goods and services. https://ec.europa.eu/eurostat/ databrowser/view/ISOC_R_ IUSE_I__custom_2315292/default/ table?lang=en Individuals who used the Internet to interact with public authorities 2019 Percentage of total number of individuals who access Internet to interact with public authorities. https://ec.europa.eu/eurostat/ databrowser/view/ISOC_R_ GOV_I__custom_595365/default/ table?lang=en Source: Prepared by the authors 3.2. Statistical analysis To confirm the hypotheses and the objectives set out, the research was undertaken in two stages: 1. First, a factor analysis (FA) was conducted, using the IBM SPSS v24 program, to identify the interdependencies between the ICT_H+i variables and the socio-economic variables (Table 1) on the basis of a total of 26 variables available for the NUTS 2 regional level. The FA is a multivariate statistical technique that identifies the interdependencies between a set of variables, joining them by means of common factors that are not directly observable (Pérez & Méndez., 1995); and therefore show, in this research, the underlying dimensions between the socio-eco- nomic aspects of European regions and the digital development of their households and population (ICT_H+i). An exploratory type of FA was conducted using the principal components method, which is based on the principle of maximising the variance explained, and the first factor is that which justifies better and greater variance, the second accounts for the largest amount of the remaining residual variance, being independent of the first factor, and so on with the successive factors which explain small amounts of the total variance (Frías-Navarro & Pascual, 2012). Likewise, and with the same aim, a factor matrix was obtained rotated by the Varimax method, an orthogonal rotation method that reduces the number of essential factors to a minimum to justify and interpret the set of variables observed and studied. 2. On the basis of the resulting factor model, a cluster analysis (CA) was performed using the IBM SPSS v26 program, to establish a typology of regions according to the interaction between ICT access and usage by households and individuals and the socio-economic characteristics of European regions. The CA makes it possible to group the regions according to similarity of their characteristics (variables). Using the factor scores obtained in the FA, two types of analysis of conglomerates were performed: i) hierarchical (with the Ward method and squared Euclidean distance), apt for establishing the optimum number of conglomerates in the data, and subsequently carrying out the analysis of the dendrogram, opting for a distance of 10 with 5 groups; ii) subsequently, with the non-hierarchical k-means method (making it possible to process an unlimited number of cases), the regions were assigned to the resulting groups and their main characteristics defined (final averages or centres of the conglomerates) according to the variables of classification (factors). Moreover, for each of the clusters, a statistical analysis 241 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 (centrality and dispersion) was performed for each of the primary variables used in the FA (15) in order to characterise each of the clusters in greater detail. 4. Results 4.1. Factor analysis The structure of interrelationships between the socio-economic aspects of European regions and the digital development of households and individuals (ICT_H+i), resulting from the factor analysis, is made up of 4 factors (Tables 2 and 3) based on 15 variables. The resulting factor model explains 70.036 % of the total variance, with a KMO of 0.858, and a determinant of 0.00003601, a very low level. These factors are: Factor 1. This factor (36.45 % of the variance explained) encompasses all the variables of Internet access and usage, associated with GDP per capita, employment in high-technology sectors and the population with higher education level (degrees, masters and doctorates). The European Commission (2021) revealed the importance of the level of education for the acquisition of digital skills; such is the relevance of having or not having higher studies that, of the total population with studies, 93.9 % of this population developed digital skills, compared with the population without studies, where only 59.3 % have managed to develop such skills. This factor could be called ICT_H+i and GDP per capita. Factor 2. This accounts for 11.76 % of variance explained and comprises the positive association of the generation of patents and R&D expenditure variables. This component would explain the relationship between R&D expenditure in all sectors and the generation of patents, and could be termed R&D and Patents. Factor 3. This accounts for 11.69 % of variance explained, combining the population with the secondary and tertiary education (medium-high), households with broadband and the unemployment rate variables. This component presents different signs in its correlation; thus, when the unemployment rate falls it is because the number of households with broadband and the population with a medium-high level of education increase. This factor could be designated Human Capital and ICT infrastructure. Factor 4. This accounts for 10.14 % of variance explained, and associates the following variables: growth rate of employment in telecommunications and net growth of the number of companies. It indicates that in regions with employment growth in the telecommunications sector this is linked to growth of the business fabric. Both of these have positive factor loads, implying that these variables are not directly associated with ICT usage by households and individuals. This factor could be called Business growth and employment in telecommunications. 4.2. Cluster analysis A cluster analysis of k-means was performed on the basis of 4 complex variables (factors), identifying 5 homogeneous groups of European regions, which are set out ordered from lesser to greater intensity of access and advanced use of ICTs by households and individuals (Table 4 and Map 1). 248 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 Vu, 2016; Lee et al., 2017; Pradhan et al., 2018). Currently, these are regions that are leading the so-called fourth industrial revolution, those that stand out due to technological transformations, artificial intelligence and the digital revolution (Wang L, et at., 2020). These regions are leaders in R&D and technological development with great pulling power and influence over other regions (Maneejuk & Yamaka, 2020). In fact, the growth of employment in the telecommunications sector is linked to the growth of the business fabric in Factor 4. Therefore, this factor shows that in Europe it is not only necessary to have ICT infrastructures, but a population with a high digital skills is also required to benefit significantly from ICTs. Both elements are necessary in order to produce a transformation and innovation in regions and, consequently, an increase in the growth of both the business population and the corresponding job creation (Lahiguera, et al., 2020). Given that employment in telecommunications and the growth of the business fabric may be considered very relevant for the progress of European regions, it is worth considering whether it would have been convenient to complete the interaction between the socio-economic and ICT access and usage by households and individuals variables in the factor model with ICT access and usage by companies. Actually, the cluster analysis shows that those European regions that have a higher level of GDP per capita also have strong business dynamics along with an important volume of employment in telecommunications, which predisposes these regions to make a greater investment in R&D and to develop more patents (Cluster 5). On the other hand, it should be borne in mind that an increase in research expenditure may give rise to an increase in the number of patents and new technologies, as well as value added. This leads one to affirm that the capacity for investment in R&D is related to knowledge creation (Choi & Yi., 2018; Kuchiki, A. 2021) and the digital transformation of companies. However, as the clusterisation indicates, on the basis of the factor analysis performed, an association between the advanced uses of ICTs (interaction with public authorities, sale of goods and services and online banking), high rates of growth of employment in telecommunications, high investments in R&D and a high volume of patent applications are only identified in a tiny part of European regions (1.22 %), specifically the German regions Oberfranken, Münster and Detmold and Mazowiecki regionalny in Poland (Cluster 5). The existence of a cluster with these characteristics supports the hypothesis that the regions with a higher GDP per capita have, or have managed to ensure that their population has, greater ICT access and the acquisition of sufficient digital skills to take advantage of an advanced use of ICTs (Stanley, Doucouliagos & Steel, 2018; Ali et al., 2020). According to the studies of Nair, Pradhan & Arvin (2020), although they are at country level, Germany is one of the leading economies in Europe and among the OECD countries with the highest degree of R&D investment, and European regions benefit from this, driven by the dynamism of Germany. It could be said that these are the regions that are leading the digital revolution in Europe. These regions are closely accompanied by the rest of the German länders, the regions of Austria, southern Finland and Sweden and the central nucleus of France (13.98 %, Cluster 4), where the level of investment in R&D is actually higher than in the previous regions of Cluster 5, benefiting from the physical proximity of the regions of Cluster 5, which are those that lead said digital revolution. This research, therefore, is a clear contribution to the study of the digital, technological and socio-economic inequalities of European regions, and highlights the concentration of wealth, technological skills and knowledge in the central area of Europe and the need to increase the diffusion from these to peripheral areas (Balland & Boschma, 2021; J. Freeman et al., 2016). This fact evi- 249 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 dences the heterogeneity and territorial disparities of European regions, spotlighting the existing digital divide (Lucendo-Monedero et al., 2019). A typology of (NUTS 2) regions is provided, with regard to the assimilation and basic and advanced uses of ICTs. This taxonomy and the factor model developed constitute a basis for the implementation of European policies, with the intervention of national and regional actors, on the diffusion of the advanced use of ICTs. Nevertheless, this study has its limitations, given that the number of variables included in the analysis is conditioned by the availability of regional data provided by Eurostat at NUTS 2 level. Further research is therefore highly recommended in order to increase the number of study variables related to the use of advanced digital technologies (artificial intelligence, big data, robots, drones, etc. associated with companies and households) which, moreover, should be regionalised and homogenised at regional level (NUTS 2). To this end, it would be convenient if Eurostat could publish more data on ICT_H+i, and especially on companies at NUTS 2 level, as this is the main level used by the European Union that may contribute to the promulgation of new territorial-regional policies for development and the reduction of inequalities (Pick & Nishida, 2015). Bibliography • Alfaro, J. L., Lopez, V. R., & Nevado, D. (2017). The effect of ICT use and capability on knowledge-based cities.Cities,60, 272-280. doi: 10.1016/j.cities.2016.09.010 • Alzahrani, L., Al-Karaghouli, W., & Weerakkody, V. (2018). Investigating the impact of citizens’ trust toward the successful adoption of e-government: A multigroup analysis of gender, age, and internet experience. Information Systems Management, 35(2), 124-146.doi:10.1080/10580530.2018.1440730 • Ali, M. A., Alam, K., Taylor, B., Rafiq, S. (2020). Does ICT maturity catalyse economic development? Evidence from a panel data estimation approach in OECD countries. 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Interdisciplinary Description of Complex Systems - Scientific Journal, 9(2), 119- 133. https://hrcak.srce.hr/76730 Appendix A: The 26 variables initially included in the analysis TYPOLOGY VARIABLES YEAR DESCRIPTION AND UNIT OF MEASURE EUROSTAT SOURCE LEVEL OF WEALTH GDP per capita compared to the EU28 average in PPP 2019 Regional gross domestic product (PPS per capita as % of EU28 average). https://ec.europa.eu/eurostat/databrowser/ view/tgs00006/default/table?lang=en Unemployment rate in the population aged 15-74 years old 2019 Percentage of the unemployed population aged 15-74 years old. https://ec.europa.eu/eurostat/databrowser/ view/lfst_r_lfu3rt/default/table?lang=en Unemployment rates educational level 5-8 between 15 and 64 years old 2019 Percentage of unemployed rates of educational level 5-8 and with ages between 15 and 64 years https://ec.europa.eu/eurostat/databrowser/ view/LFST_R_LFU3RT__custom_4538670/ default/table?lang=en Activity rates 2019 Activity rates https://ec.europa.eu/eurostat/ databrowser/view/LFST_R_LFP2ACTRC__ custom_4562366/default/table?lang=en Activity rates education level 5-8 among 15-64 year olds 2019 Participation rates in selected education levels 5-8 at regional level https://ec.europa.eu/eurostat/databrowser/ view/educ_uoe_enra15/default/ table?lang=en Working population aged 15-64 with secondary and tertiary education 2019 Participation rates in selected education levels 3-5 at regional level https://ec.europa.eu/eurostat/ databrowser/view/LFST_R_LFP2ACTRC__ custom_4562432/default/table?lang=en Working population aged 15-64 with secondary and tertiary education 2019 Percentage of the working population aged 15-64 with secondary and tertiary education (ISCED 3-8). https://ec.europa.eu/eurostat/ databrowser/view/LFST_R_LFP2ACTRC__ custom_2338730/default/table?lang=en Unemployment rates by age, (25-65 years) and NUTS 2 regions 2019 Percentage of the unemployed population aged 25-65 years old. https://ec.europa.eu/eurostat/databrowser/ view/LFST_R_LFU3RT__custom_4538510/ default/table?lang=en LEVEL OF EDUCATION Population aged 25-64 with tertiary education 2019 Percentage of the population aged 25-64 with tertiary education (ISCED 5-8). https://ec.europa.eu/eurostat/databrowser/ view/EDAT_LFSE_04__custom_2315836/ default/table?lang=en Participation rates in education levels (5-8) 2019 participation rates of citizens in education levels 5-8 https://ec.europa.eu/eurostat/databrowser/ view/educ_uoe_enrt05/default/ table?lang=en Population aged 25-64 with tertiary education 2019 Percentage of population between 25 and 64 years old with tertiary education https://ec.europa.eu/eurostat/databrowser/ view/edat_lfse_04/default/table?lang=en % Population with studies 3-8 2019 Percentage of population with tertiary studies https://ec.europa.eu/eurostat/ databrowser/view/LFST_R_LFP2ACTRC__ custom_4562502/default/table?lang=en LEVEL OF INNOVATION Employment in High Technology Sectors 2019 Employment in hightechnology sectors as a percentage of total employment. https://ec.europa.eu/eurostat/databrowser/ view/HTEC_EMP_REG2__custom_2338815/ default/table?lang=en Patent applications as a share of GDP 2012 Number of patent applications with respect to GDP in billions. https://ec.europa.eu/eurostat/databrowser/ view/PAT_EP_RTOT__custom_2339933/ default/table?lang=en Intramural spending on R&D 2017 Domestic R&D expenditure as a percentage of GDP https://ec.europa.eu/eurostat/databrowser/ view/RD_E_GERDREG__custom_596580/ default/table?lang=en 253 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 TYPOLOGY VARIABLES YEAR DESCRIPTION AND UNIT OF MEASURE EUROSTAT SOURCE BUSINESS DYNAMICS Growth rate of employment in telecommunications 2018 Percentage of net employment growth of in telecommunications sectors (with respect to previous year). https://ec.europa.eu/eurostat/databrowser/ view/SBS_R_NUTS06_R2__custom_2340087/ default/table?lang=en Net Growth of the Number of Companies 2018 Percentage of net growth of number of companies in industry, construction and services sectors (with respect to previous year). https://ec.europa.eu/eurostat/databrowser/ view/BD_ESIZE_R3__custom_597161/ default/table?lang=en LEVEL OF ACCESS Households with Internet access 2019 Percentage of total number of households where anyone in the household has the possibility to access Internet from home. https://ec.europa.eu/eurostat/databrowser/ view/isoc_r_iacc_h/default/table?lang=en Households with broadband 2019 Percentage of total number of households that have broadband Internet access. https://ec.europa.eu/eurostat/databrowser/ view/ISOC_R_BROAD_H__custom_2341129/ default/table?lang=en LEVEL OF USE Individuals who access the Internet on a daily basis 2019 Percentage of total number of individuals who use Internet on a daily basis for any type of consultation. https://ec.europa.eu/eurostat/databrowser/ view/ISOC_R_IUSE_I__custom_2341207/ default/table?lang=en Individuals who used the Internet for online banking 2019 Percentage of total number of individuals who access Internet to conduct banking transactions. https://ec.europa.eu/eurostat/databrowser/ view/ISOC_R_IUSE_I__custom_595421/ default/table?lang=en Individuals who used the Internet for the sale of goods and service 2019 Percentage of total number of individuals who access Internet for the sale of goods and services. https://ec.europa.eu/eurostat/databrowser/ view/ISOC_R_IUSE_I__custom_2315292/ default/table?lang=en Individuals who used the Internet to interact with public authorities 2019 Percentage of total number of individuals who access Internet to interact with public authorities. https://ec.europa.eu/eurostat/databrowser/ view/ISOC_R_GOV_I__custom_595365/ default/table?lang=en Individuals who used the Internet for civic participation 2019 Percentage of total number of individuals who access Internet to interact for civic participation. https://ec.europa.eu/eurostat/databrowser/ view/ISOC_R_IUSE_I__custom_4538591/ default/table?lang=en Individuals who used the Internet to participate in social networks 2019 Percentage of total number of individuals who access Internet to participate in social networks. https://ec.europa.eu/eurostat/databrowser/ view/ISOC_R_IUSE_I__custom_4538615/ default/table?lang=en Source: Prepared by the authors Authorship contribution stament Purificación Crespo-Rincón: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing-Original draft preparation, Writing-Reviewing and Editing, Visualization, Funding acquisition. Rosa Jordá-Borrell: Conceptualization, Methodology, Investigation, Data curation, Writing-Original draft preparation, Writing-Reviewing and Editing, Visualization, Supervision, Funding acquisition. Francisca Ruiz-Rodríguez: Conceptualization, Methodology, Investigation, Data curation, Writing- Original draft preparation, Writing-Reviewing and Editing, Visualization, Supervision, Project administration, Funding acquisition. 254 Creative Commons Reconocimiento-No Comercial 3.0 e-ISSN 2340-0129 DOI: http://dx.doi.org/10.30827/cuadgeo.v62i1.25334 Crespo-Rincón, P. et al. (2023). Information society and socio-economic characteristics in Europe… Cuadernos Geográficos 62(1), 233-254 Funding This article is a result of the R+D+i project PID2019-107993GB-I00 funded by MCIN/ AEI/10.13039/501100011033. Conflict of interest The authors of this work declare that there is no conflict of interest