Information and Communication Technologies of households and individuals, geographical proximity and regional competitiveness: distribution, clusters and spatial patterns of technological capacity in Europe

Boletín de la Asociación de Geógrafos Españoles, (90) eISSN: 2605-3322 How to cite this work: González-Relaño, R., Lucendo-Monedero, Á. L., & Ruiz-Rodríguez, F. (2021). Information and Communication Technologies of households and individuals, geographical proximity and regional competitiveness: distribution, clusters and spatial patterns of technological capacity in Europe. Boletín de la Asociación de Geógrafos Españoles, (90). https://doi.org/10.21138/bage.3118 Information and Communication Technologies of households and individuals, geographical proximity and regional competitiveness: distribution, clusters and spatial patterns of technological capacity in Europe Tecnologías de la Información y Comunicación de los hogares e individuos, proximidad geográfica y competitividad regional: distribución, clústeres y patrones espaciales de la capacidad tecnológica en Europa Reyes González-Relaño [email protected] Ángel Luis Lucendo-Monedero [email protected] Francisca Ruiz-Rodríguez [email protected] Department of Physical Geography and Regional Geographical Analysis University of Sevilla (Spain) Received: 19.03.2021 Accepted: 11.07.2021 Published: 09.08.2021 Published under the terms and conditions of an Attribution-NonCommercial 4.0 International license. Abstract Information and Communication Technology (ICT) has been consolidated as a fundamental pillar of the new model of competitiveness and sustainable development of territories. However, there has still not been sufficient in-depth study of the role that geographical proximity plays in the digital inequalities that affect regional competitiveness. In this context, this paper aims to identify and characterise the clusters and patterns that define the spatial behaviour of ICT access and usage variables at household and individual level in the Technology Readiness (TR) pillar of the Regional Competitiveness Index (RCI). A database of 280 NUTS2 has been created in order to perform: i) an exploratory statistical analysis to show the regional behaviour of the ICT variables; and ii) a spatial autocorrelation analysis to detect the presence of spatial dependence. The results at regional level in Europe demonstrate that: i) the greatest inequalities occur in the ICT usage variables and not in those of access; ii) the indicators of TR create a spatial pattern with a gradient from the regions of the extreme north-west to south-western and eastern Europe; and iii) there is evidently an opportunity to incorporate other regional variables of ICT usage for future editions of the RCI. Key words: Regional Competitiveness Index (RCI); Technological Readiness (TR); spatial analysis, European regions (NUTS2). Resumen Las Tecnologías de la Información y Comunicación (TIC) se han consolidado como pilar fundamental para el nuevo modelo de competitividad y desarrollo sostenible del territorio. Sin embargo, aún no se ha profundizado suficientemente sobre el papel que la proximidad geográfica ejerce sobre las desigualdades digitales que inciden en la competitividad regional. En este contexto, el objetivo de este trabajo es identificar y caracterizar los clústeres y patrones que definen el comportamiento espacial de las variables de acceso y uso de las TIC de los hogares-individuos en la dimensión Technological Readiness (TR) del Regional Competitiveness Index (RCI) europeo. Se ha creado una base de datos de 280 NUTS2 para realizar: i) un análisis estadístico exploratorio para mostrar el comportamiento regional de las variables TIC; y ii) un análisis de autocorrelación espacial para detectar la presencia de dependencia espacial. Los resultados a nivel regional en Europa muestran que: i) las mayores desigualdades se dan en las variables de usos de TIC y no en las de acceso; ii) los indicadores del TR dibujan un patrón espacial con un gradiente desde las regiones del extremo noroccidental hacia el suroccidental y Boletín de la Asociación de Geógrafos Españoles, (90) 2 oriental; y iii) se evidencia la oportunidad de incorporar otras variables regionales sobre usos de TIC para futuras ediciones del RCI. Palabras clave: Índice de Competitividad Regional (RCI); Preparación Tecnológica (TR); análisis espacial; regiones europeas (NUTS2). 1 Introduction There is no doubt today that the Information Society and technological services such as the Internet and other Information and Communication Technologies (ICTs) have been consolidated as a fundamental pillar of the new model of competitiveness and sustainable development of territories (Bisk & Bołtuć, 2017; Ziemba, 2019; Sáiz Peña, 2019). Hence, Agenda 2030 for Sustainable Development explicitly recognises the potential of ICTs to facilitate interconnection and accelerate global human development (Perez Castro, Mohamed-Maslouhi & Montero-Alonso, 2021). To date, the scientific literature has concentrated on studying and demonstrating that ICTs play an important role in growth and economic development at national level (Inklaar, O'Mahony & Timmer, 2005; Koutroumpis, 2009; Rohman & Bohlin, 2014; Jordá-Borrell & López-Otero, 2020; Banerjee, Rappoport, & Alleman, 2020). In this respect, there is abundant literature on the concepts and indices that reflect the different dimensions of development and competitiveness (Möbius & Althammer, 2020; United Nations Development Programme, 2017), although some authors still assert that the notion of competitiveness is ambiguous and lacks a common definition (Möbius & Althammer, 2020). On the other hand, most of these indices are treated at national level, such as the ICT Development Index (IDI) published by the United Nations International Telecommunication Union; the E-Government Development Index (EGDI) of the United Nations Department of Economic and Social Affairs and the Network Readiness Index (NRI) published by the World Economic Forum (Perez-Castro et al., 2021), among others. Nevertheless, many researchers defend the inclusion of the regional dimension in the analysis of competitiveness in Europe (Malecki, 2007; Keating, 2017) despite the fact that the state of research at this level of analysis is still embryonic (Möbius & Althammer, 2020). In general, regions are considered to be an intermediate level between nations and municipalities (Keating, 2017), they have their own regulations and institutions and their own historical and geographical environment (Sbeppard, 2003) and they compete for production factors such as a highly skilled Boletín de la Asociación de Geógrafos Españoles, (90) 3 workforce, risk capital, foreign direct investments and the establishment of large companies (Camagni, 2002; Malecki, 2004). One of the indices widely accepted by the scientific community for the analysis of the competitiveness of European regions is the Regional Competitiveness Index (RCI), published by the European Union, which follows the methodology of the Global Competitiveness Index of the World Economic Forum (GCI-WEF) (Annoni & Dijkstra 2013; Annoni & Dijkstra, 2019). The index measures a region’s capacity to provide an attractive and sustainable environment for firms and residents to live and work in. It uses more than 70 indicators to measure different aspects and dimensions of regional competitiveness by means of sub-indices. Among these, the RCI Innovation sub-index includes pillar 9, Technological Readiness (TR), which is composed of 9 indicators: 3 related to ICT and households and individuals; 3 related to the availability, absorption and transfer of technology; and another 3 on ICT usage by enterprises. Of these 9, only 3 have a regional data source (those concerning ICT usage and access by households and individuals), while the other 6 are based on national level data which, in order to be comparable, have been regionalised (Annoni & Dijkstra, 2010; Annoni & Dijkstra, 2019). Thus, and related to the difficulty of obtaining data at regional level, the main indices created by diverse institutions to measure the technological capacity of enterprises and/or individuals have mainly been applied at country level. One example of this is measuring digitalisation, which is done by using the ICT Development Index (IDI) of the International Telecommunication Union (2018), the Networked Readiness Index (Portulans Institute, 2019) or the Digital Economy and Society Index (DESI) (European Commission, 2020), among others. However, and in relation to these, recent studies (Ruiz-Rodríguez, Lucendo-Monedero & González-Relaño, 2018) have shown that these national indices are not appropriate for measuring regional differences in digital readiness, given that they also entail a series of restrictions. It is worth mentioning, for example, a simplification of the complex interrelationships between ICT variables; the inadequate selection of the variables related to ICT technologies; or the random weight assigned to each indicator or variable when calculating the indices (OECD,2008; Bruno, Esposito, Genovese & Gwebu, 2011; Corrocher & Ordanini, 2002). In this respect, European regional data (at NUTS2 level) may be viewed via Digital Economy and Digital Society Statistics at Regional Level (Eurostat, 2020), although the conclusions drawn do not reflect European regional diversity. Furthermore, and although the RCI has been used in a number of studies (Snieška & Bruneckienė, 2009; Aiginger & Firgo 2017; D’Urso, De Giovanni, Massari & Sica, 2019; Aria, Boletín de la Asociación de Geógrafos Españoles, (90) 4 Gaeta & Marani, 2019), there has still not been sufficient in-depth study of the role that geographical proximity plays in the digital inequalities that affect regional competitiveness. As Haefner and Sternberg (2020) indicate, there is very little scientific and empirically valid evidence as to what the spatial implications of digitalisation are. In fact, very little is yet known about the spatial behaviour of ICT access and usage by European citizens (households and individuals) on the basis of the geographical proximity of territories (Ruiz-Rodríguez, González- Relaño & Lucendo-Monedero, 2020). In this context, an analysis of the spatial disparities of the ICT variables of households and individuals included in the RCI may reveal the existence of spatial patterns and structures, which would enable the formulation of prior hypotheses for econometric modelling and, where appropriate, spatial regression research on this aspect. In this respect, some studies have been conducted on ICT usage at national level in Europe by means of spatial analysis techniques (spatial autocorrelation) (Grubesic & Murray, 2005; Van Dijk, 2006; Ruiz-Rodriguez et al., 2020). These demonstrated that geographical proximity is relevant in explaining the diffusion of ICT usage and virtual space. As Pick and Nishida (2015) note, a country with a high level of ICT usage could be influential on neighbouring countries leading them to raise their levels of digital usage. However, for the regional context, and specifically in the case of Europe, there are very few references to an in-depth analysis of the effects of geographical proximity as a factor to take into account in analyses of ICT usage by households and individuals (Billon, Lera-Lopez, & Marco, 2017). Therefore, this paper is motivated by the need to better understand the spatial behaviour of the variables of ICT access and usage by households and individuals in relation to European regional competitiveness. The spatial analysis of the ICT variables included in the Technological Readiness pillar of the European Regional Competitiveness Index should facilitate the understanding of digital inequalities from a regional perspective. Taking the arguments set out above as a starting point, the following questions are posed: what is the spatial behaviour of the variables of ICT access and usage by households and individuals included in the Technological Readiness pillar? And, are there any other ICT access and usage variables at household and individual levels in Europe that could be included in the Technology Readiness pillar to shed light on regional differences? Hence, this paper aims to identify and characterise the clusters and patterns that define the spatial behaviour of ICT access and usage variables by households and individuals in the Technology Readiness pillar of the RCI. To answer the aforementioned research questions, we will: i) explore the regional differences in Europe for all ICT access and usage variables by Boletín de la Asociación de Geógrafos Españoles, (90) 5 households and individuals through descriptive and spatial dependency statistical analysis; and ii) analyse and compare the spatial distribution of the Technology Readiness pillar with the Technology Readiness-Households and Individuals sub-pillar and its component indicators by identifying clusters of regions and spatial patterns. In the following sections the paper addresses the state of play with regard to the digital inequalities and spatial analysis of ICT infrastructure and usage by households and individuals and, in particular, technological readiness for regional competitiveness. Below, the methodology used to ascertain territorial distribution and the existence of clusters of regions and spatial patterns in Europe is explained. Subsequently, the results obtained are set out and discussed. A final section presents the conclusions based on results that would be useful for future editions of the RCI and ensuing research, for the scientific literature and for drawing up regional policies that could help to reduce pre-existing spatial inequalities, taking into account the spatiality of the phenomenon. 2 Theoretical framework International organisations frequently affirm that greater ICT adoption and usage helps nations, societies, businesses and people increase progress and welfare (Bahrini & Qaffas, 2019; Avgerou, 2010; Ramírez-Correa, Grandón & Rondán-Cataluña, 2020), and even more so when the transformation of services is accelerating, driven by current technologies that are rapidly increasing in velocity, capacity, connectivity, functionality and ease of use (Parasuraman & Colbi, 2015). The rhythm of technological change has accelerated with the dissemination of high-speed Internet access, e-commerce, social networks and cloud computing, among other uses (Biagi & Falk, 2017). Thus, now well into the twenty-first century, there is no doubt that the relationship between innovation and digital revolution is more than consolidated, both for enterprises and for individuals. A clear example of this is what occurs in countries where the impact of technological innovation is particularly noteworthy (Finland, Switzerland, Sweden and the Netherlands, for example) and where there are high levels of ICT usage both within the business fabric (Ramírez- Correa et al., 2020; Hunady, Pisar & Durcekova, 2020; Añón Higón, 2012) and by households and individuals (Lucendo-Monedero, Ruiz-Rodríguez & González-Relaño, 2019). For its part, the concept of technological readiness contemplates the propensity of people to adopt and use new technologies to achieve beneficial objectives, both in their private lives and at work (Parasuraman, 2000; Parasuraman & Colbi, 2015). This perspective is relevant because it analyses the behaviour of individuals in relation to technology and reveals that if the population of Boletín de la Asociación de Geógrafos Españoles, (90) 6 a country or region is not open to new ICTs, the future development of its territory may be affected (Ramírez-Correa et al., 2020). People’s access to and use of ICTs plays an important role in a competitive environment (Peña-Vinces, Cepeda‐Carrión & Chin, 2012) since the use of ICTs in the business sector would not be possible if there were not personnel qualified in ICT. Hence, the innovative capacity of individuals and the technological readiness of households and individuals directly affects such specific actions as the intention to purchase technology (Jeong, Yoo & Heo, 2009), the adoption of electronic devices, the purchase of mobiles (Saprikis, Markos, Zarmpou & Vlachopoulou., 2018) and diverse uses of ICTs (Schmidthuber, Maresch & Ginner, 2020). Furthermore, other studies argue that people’s experience with ICTs has a positive impact on technological readiness (Blut & Wang, 2020), and especially on innovation, so that regional competitiveness partly depends on the capacity of individuals to take advantage of the opportunities offered by ICT (Castellacci, Consoli & Santoalha, 2020). This therefore underlines the usefulness of studies that address e-readiness, understood as the ability of a country to be prepared, willing to adopt, use and benefit from e-innovations (Aboelmaged, 2014). Therefore, the focus on the concept of technological readiness is in accordance with the agility with which an economy adopts existing technologies to improve the productivity of its industries, with special emphasis on the capacity to fully take advantage of ICTs in daily activities and production processes to increase efficiency and enable innovation (World Economic Forum, 2018). Currently, the European Union intends to contribute to the technological readiness of territories by implementing the European Digital Agenda (European Commission, 2010; Vicente & López, 2011) and adopting the Digital Single Market Strategy (European Commission, 2015) as one of its 10 main political priorities. This strategy has 16 initiatives that cover three major areas: promoting better online access to goods and services across Europe; designing an optimal environment for digital networks and services to develop; and ensuring that the European economy and industry take full advantage of the digital economy as a potential driver for growth (Ruiz-Rodriguez et al., 2020). At present, however, each European country implements and manages its digitalisation strategy differently and at a different pace, which does not really help to reduce spatial inequalities in digital matters (Uljas, 2019; Cruz-Jesus, Oliveira & Bacao, 2012). Thus, recent studies have revealed the existence of disparities between northern and southern Europe in the ICT usage of households and individuals and, to a lesser extent, in the availability of ICT infrastructures (Billon, Ezcurra & Lera-López, 2009; Tranos & Gillespie, 2009). Countries in the north have concentrated on widespread availability and use of e-government and other Boletín de la Asociación de Geógrafos Españoles, (90) 7 public services, which has led to massive broadband deployment (Falch & Henten, 2018). Countries in south-eastern Europe, however, are still in the phase of developing infrastructures through the implementation of policies to develop the broadband network (Ragnedda & Kreitem, 2018). Regional differences, therefore, exist today due to different advanced uses and the socioeconomic benefits that individuals obtain from ICTs (Hargittai, 2010; Ragnedda, 2017). Accordingly, some authors such as Haefner and Sternberg (2020) affirm that despite efforts from the public sphere to counterbalance digital inequalities there is still no evidence of a reduction in the socio-economic disparities between different territories in Europe (Iammarino, Rodríguez- Pose & Storper, 2019). Hence it is necessary to promote measures that foster digitalisation and take access to ICTs to all territories while simultaneously encouraging their use. However, this digital decentralisation driven by ICT access and usage has been questioned by other researchers (Camagni & Capello, 2005; Cairncross, 2018) because there is still little scientific and empirically valid evidence with regard to the spatial implications of digitalisation in regions’ economic, political and social spheres (Castellacci et al., 2020; Haefner & Sternberg, 2020). The divide in ICT access and usage (Briglauer, Dürr & Gugler, 2019; Lobo, Alam & Whitacre, 2020) still hinders competitiveness and only the regions whose firms and individuals have sufficient digital skills to use ICTs efficiently can successfully meet the challenges of digitalisation, seize their opportunities and reduce the gap to other more advanced regions (Haefner & Sternberg, 2020). A regional and spatial analysis of digital skills continues to be relevant as long as these are not introduced or activated uniformly in all territories (Corrocher & Ordanini, 2002), with different consequences for their technological readiness, innovation and competitiveness (Richardson & Bissell, 2019; Castellacci et al., 2020). Nonetheless, as Otioma, Madureira & Martinez (2019) indicate, most of the literature on measuring and explaining ICT usage does not include spatial aspects despite the fact that geographical distance and the degree of spatial agglomeration determine the probability of being connected (Tranos, 2012). With this argument, studies have been undertaken on ICT usage by applying spatial analysis techniques (spatial autocorrelation) at national and regional level in Europe, the United States, Japan and China, among others (Grubesic & Murray, 2005; Van Dijk, 2006; Uljas, 2019). All these coincide in indicating that space and geographical proximity help explain the territorial diffusion of digitalisation. Consequently, as Pick and Nishida (2015) point out, a territory with a high level of ICT usage could influence neighbouring territories, inducing them to raise their levels of digital usage. It is thus necessary to know whether the differences and similarities in the digitalisation of regions are related to geographical Boletín de la Asociación de Geógrafos Españoles, (90) 8 proximity (Schlichter & Danylchenko, 2014; Novo-Corti & Barreiro-Gen, 2015) and, specifically, whether these differences can be discerned with an analysis of the spatial behaviour of the variables of ICT access and usage by households and individuals used in the Technological Readiness pillar of the European RCI. 3 Methodology 3.1 Data The data necessary to undertake this study come from two European Union sources: i) Eurostat's "ICT Use by Households and Individuals" survey database. From this source 8 ICT variables of households and individuals in European regions have been selected (Table 1). These variables have been chosen based on the criteria of type of access, frequency and use of ICTs. The data are from 2018 and their unit of measurement is the percentage of households and individuals in each region. Table 1. Variables used from “ICT usage by households and individuals” of the Eurostat Regional Information Statistics (at NUTS2 level) VARIABLES MEASUREMENT REFERENCE YEAR Use internet banking % of individuals 2018 Submitting completed forms (last 12 months) % of individuals 2018 Interaction with public authorities (last 12 months) % of individuals 2018 Online purchases: travel and holiday accommodation % of individuals 2018 Individuals who accessed the internet away from home or work % of individuals 2018 Individuals who accessed the internet daily % of individuals 2018 Selling goods or services % of individuals 2018 Participating in social networks % of individuals 2018 Source: elaborated by the authors ii) The European Union's Regional Competitiveness Index1 (RCI). Four variables have been selected from the Technology Readiness pillar: one corresponds to the Technology Readiness pillar (which is composed of two sub-pillars, see Annex 1); and three others form the Technology Readiness-Households (TR-HI) sub-pillar: Households with broadband access (indicator), Individuals purchasing via the Internet (indicator), and Household Internet access 1 The RCI is made up of three sub-indices that are organized into 11 dimensions or pillars, which in turn total 74 different indicators (https://ec.europa.eu/regional_policy/en/information/maps/regional_competitiveness/). Boletín de la Asociación de Geógrafos Españoles, (90) 9 The indicators individuals buying over internet and households with access to broadband have very low spatial concentration values (0.68 and 0.64, respectively), whereas household access to internet has a high value (0.82). The first two indicators, which have less geographical concentration, also show a smaller divide between the values of European regions (standard deviation of 12.5 % and 8.1 %, Table 3), indicating that there is a greater convergence of regions in terms of broadband access to ICTs compared with the spatial behaviour of regions in the TR pillar. Meanwhile, although household access to internet is the ICT indicator that has the smallest gap between the values of European regions (standard deviation of 7.7 %, Table 3), the high spatial autocorrelation value indicates that it has a different spatial behaviour, as there is a great geographical concentration of regions with high values and low values. On the other hand, as regards the variables of ICT access and usage by households and individuals not included in the RCI, the use internet banking variable stands out due to its high spatial dependence (0.87) and because it has the greatest divide and dispersion of regional values of all the ICT variables analysed (a difference of 91 % between the regions with the highest and lowest values; and a standard deviation of 21.2 %, Table 3). This shows that the use of internet banking by households and individuals in European regions is not generalised, which reflects the inequalities in ICT usage. According to the autocorrelation index value, this variable is followed by online purchases travel and holiday accommodation and who accessed the internet daily (0.83 and 0.82, respectively). These two variables, although they have similar spatial behaviour, show significant statistical differences. Hence, while the use of ICTs for online purchases of travel and holiday accommodation has values that are very unequal between regions (standard deviation of 17.9 % and a divide of 69 %), the access the internet daily variable is more homogeneous at regional level (standard deviation of only 12 % and a range of 51 %). The variables who accessed the internet away from home or work, interaction with public authorities and submitting completed forms have an average level of spatial autocorrelation (with values between 0.77 and 0.70). On the contrary, individuals participating in social networks shows the least concentrated spatial behaviour of all the ICT variables studied (Global Moran’s I of 0.62), associated, in turn, with very little divergence of regional values (standard deviation of 11.1 % and a range of 58 %). It follows from this analysis that, from among all the variables included in the TR, access to broadband may no longer be sufficient to show digital inequalities or progress as this infrastructure is now widely established in European households. In fact, there has been widespread use of broadband and digital devices in the European Union since the onset of the Boletín de la Asociación de Geógrafos Españoles, (90) 16 second decade of the twenty-first century (Scheerder, van Deursen & van Dijk, 2017), and hence most individuals and households have access to ICTs. Despite the fact that this indicator has been used to explain the differences between having or not having ICT access since the 1990s (Yu, 2006; Contreras-Cabrera, 2012; Tsetsi & Rains,2017), studies have recently focused on measuring digital inequalities via the ICT usage of individuals and the social and economic benefits that these produce (Van Deursen & Van Dijk, 2019). At present, aspects related to the advanced use of ICTs (commercial purposes or interaction with the public administration) are becoming necessary and appropriate variables to include in indicators to measure the technological capacity of regions (Ruiz-Rodriguez et al., 2020). Moreover, up until now, other technologies allow provision of the same services as broadband to households, and so the limitations that a substantial part of regions have in terms of broadband to households does not prevent their citizens accessing Internet and buying online. Therefore, aspects related to the advanced use of ICTs (for personal activities, commercial purposes or interaction with the public administration) are becoming necessary and appropriate variables for inclusion in indices that measure the inequalities and geographical concentration of the technological capacity of regions. 4.2 Spatial analysis of the TR pillar, the TR-HI sub-pillar and its component indicators In relation to the analysis of the spatial distribution of the values of the TR pillar, of the TR-HI subpillar and of its component indicators, in European regions is shown in the maps of Figure 1. With regard to the TR pillar, on the basis of the z-score intervals used in Figure 1a, the existence of two great concentrations of regions with similar TR values, which illustrate a clear digital divide or inequality at spatial level between the regions of northern and central Europe and the southernwestern-eastern regions of the periphery of the European Union, can be clearly seen. Indeed, all, or nearly all, the regions of Ireland, the United Kingdom, Netherlands, Germany, Austria, Denmark, Sweden, Finland, Estonia and France have TR values above the European average. On the contrary, southern and eastern Europe, which includes most Spanish regions and all the regions of Portugal, Italy, Poland, Hungary, the Czech Republic, Slovakia and, especially, the Greek, Romanian and Bulgarian regions, have TR values below the average. In the spatial distribution, for both high and low values, the national dimension is present and yet, for intermediate values (0.5 above and below the average) the national component is diluted in the regions of some western countries, as occurs in the case of France, Spain and Belgium. Boletín de la Asociación de Geógrafos Españoles, (90) 17 Meanwhile, the values of the sub-pillar related to ICT access and usage by households and individuals (TR-HI) in European regions show a more random geographical distribution (Figure 1b). Thus, the high values of ICT access and usage continue appearing in almost all the regions of the countries in northern Europe (United Kingdom, Netherlands, Denmark, Lithuania and Estonia); yet this also true for some regions of Eastern European countries. Likewise, there is a more random spatial distribution in the values below the average, except in regional capitals that have higher values than their surrounding regions. Hence, while there are few regions with values below the average in northern Europe (in Ireland and Sweden), most of such regions are to be found in the central European corridor (French and Czech regions) and also in the western (Spain and Portugal) and north-eastern (Poland) peripheries. The lowest values for ICT access and usage are spatially concentrated in the southern regions of Italy, Romania, Bulgaria and Greece. The analysis of the spatial distribution of the 3 indicators that make up the TR-HI sub-pillar (Figures 1c, 1d and 1e) show the following spatial pattern: above-average values in all or almost all the regions of northern and central Europe (Ireland, United Kingdom, Netherlands, Denmark, Finland, Lithuania and Estonia); and a large part of the regions of France (except for the Households with access to broadband indicator, Figure 1c), and also the regions of Austria and the Czech Republic. Once again the regions with below-average values are those of the south and south-east of Europe, with a clear difference between: a) the regions of Hungary, Romania, Bulgaria, Greece, Croatia and Italy, which have the lowest values; and b) the regions of Slovakia, Poland, Spain and Portugal, with average negative values. It is therefore evident that the spatial distribution of the regional values of the TR pillar of the RCI reveal the existence of imbalances in ICT access and usage and technology adoption and absorption at regional level in Europe (Corrocher & Ordanini, 2002; Moroz, 2017). However, the results of the comparative analysis of the spatial behaviour of the TR pillar and the TR-HI subpillar show that the TR of regions is influenced by a national component, that is, all the regions of the same country have similar values. This may be due to the fact that the TR pillar comprises not only the TR-HI sub-pillar but also another sub-pillar (TR-E) which itself is made up of 6 indicators constructed with data at national level whose values have been extrapolated to the regions. This may explain why the TR pillar shows the aforementioned spatial homogeneity of a national character in the values of the regions. This form of constructing the TR pillar may limit the use of analyses whose conclusions reflect the regional reality. Specifically, it is possible to study the convergence of regions in ICT access and usage by households and individuals (based on regional data) but with regard to the adoption of new technologies and ICT by firms (based on Boletín de la Asociación de Geógrafos Españoles, (90) 18 national data) the results may be misleading. This, in turn, raises the need to have regional statistics for all the indicators included in the TR pillar of the RCI, with the aim of improving understanding of the regional reality regarding ICT access and usage and technological capacity in general. Figure 1. Spatial distribution of the Technological Readiness Pillar (TR) and the ICT access and usage by households and individuals sub-pillar (TR-HI) at regional level (in z-scores) of the RCI a) Technological Readiness Pillar b) Households and Individuals Sub-pillar c) Households with access to broadband d) Households with access to internet Boletín de la Asociación de Geógrafos Españoles, (90) 19 Figure 1. Continuation e) Individuals buying over internet Source: elaborated by the authors The spatial distribution maps of the variables of ICT access and usage reveal that this phenomenon does not occur in an isolated fashion, but rather there is a concentration of groups of regions with high and low values for both the TR pillar and the TR-HI sub-pillar and its indicators and, therefore, demonstrates a possible spatial autocorrelation. Regarding local spatial autocorrelation, the analysis of the spatial dependence of the TR pillar, the TR-HI sub-pillar and its indicators by means of the Anselin local Moran’s I index (LISA) and the Getis-Ord Gi* index makes it possible to discover where and what kind of spatial clusters of European regions comprise these variables; and to show the existence of spatial patterns with different levels of technological capacity and, specifically, levels of ICT access and usage of households and individuals at regional level in Europe. The Anselin local Moran’s I index (LISA) for the Technological Readiness variable (Figure 2a) shows the presence of three large spatial clusters that configure a spatial pattern in Europe with a north-east-south-east gradient in line with a higher to lower level of TR of regions: 1. A High-High (HH) cluster of regions comprising those with high values of TR in northern and central Europe (109 NUTS2, 39.2 % of the total). It includes all the regions of Sweden and Finland; those of Denmark, Netherlands, United Kingdom and Ireland; and most German regions. Boletín de la Asociación de Geógrafos Españoles, (90) 20 2. A Low-Low (LL) cluster of regions comprising those with lower levels of TR (24.1 %, 67 NUTS2). This cluster is concentrated in eastern and south-eastern Europe, encompassing all the NUTS2 of Cyprus, Greece, Romania, Bulgaria and Lithuania; practically all the regions of Italy, eastern regions of Hungary; the central and eastern regions of Poland; and some regions of Croatia and Slovakia. 3. The cluster comprising the regions whose value on the LISA index has not been significant due to having values around the average. It is the second largest cluster with 100 NUTS2, 36 % of the total. It is made up of the western European regions of Portugal, Spain, France and Belgium; the central European regions of northern Italy, Slovenia, Croatia, Austria and the Czech Republic; and the regions of eastern Germany, western Poland, Slovakia and Hungary; and to the north, Estonian and Latvian regions. With regard to the results of the local Getis-Ord Gi* index for the TR pillar, a series of hot spots and cold spots can be identified that create a spatial distribution pattern of European regions with a north-west-south-east gradient with a higher to lower level of TR of regions (Figure 2b). The concentration of hot spots of European regions with high levels of TR (with a statistical certainty of over 95 %) occurs in a territorial corridor that runs from Ireland and the United Kingdom to Sweden and Finland, passing through the Netherlands, north-western Germany and Denmark. Meanwhile, the cold spots or clusters of European regions with lower levels of TR are made up of the regions of eastern Europe, especially those of Romania, Bulgaria, Greece, Italy and Cyprus, and some regions of Hungary, Slovakia, Croatia and the centre and east of Poland and Lithuania. Finally, and without statistical significance on the local Getis-Ord Gi* index, a cluster of regions can be detected that borders on the hot spots: regions of Portugal, Spain, France, Belgium, northern Italy, Austria, the Czech Republic, eastern regions of Germany, western regions of Poland, Estonia and Latvia. The TR-HI sub-pillar creates a similar pattern to the TR variable (Figures 2c and 2d) as it maintains the gradient of higher to lower value from the regions of the extreme north-west to south-western and eastern Europe, but with the difference that for this TR-HI sub-pillar, there are more outlier regions. Hence, on the one hand, the map of the Anselin local Moran’s I index (Figure 2c) reflects a cluster of regions with High-High (HH) values that contains 93 European regions (33.5 % of the total) of the United Kingdom, the Netherlands, Ireland except the capital region, the French region of Brittany, the regions to the north and centre of Germany, Denmark and the regions of southern Sweden. Another cluster of Low-Low (LL) regions is made up of the regions Boletín de la Asociación de Geógrafos Españoles, (90) 21 of Portugal, Extremadura in Spain, most of the regions of Italy (except the north) and Croatia, all the regions of Cyprus, Greece, Romania and Bulgaria, and the central regions of Poland and Hungary (66 NUTS2, 23.7 % of the total). As mentioned above, the spatial autocorrelation analysis of the TR-HI sub-pillar has thrown up a particularity, which is a greater number of regions with outlier values than in the TR pillar, as there are 2 NUTS2 with Low-High (L-H) indices in Ireland and Sweden; and 3 NUTS2 of the High-Low (H-L) type in Malta, Hungary and Romania. Additionally, as a result, the spatial cluster of regions with non-significant values or values close to the average for ICT access and usage by households and individuals is greater (41% of the total) than in the TR pillar. The results of calculating the local Getis-Ord Gi* index for the ICT access and usage by households and individuals variable (Figure 2d) show spatial clusters of both hot spots and cold spots, with a confidence level over 90 %. These clusters reflect a spatial distribution pattern similar to that defined by the LISA index, as the regions that form the hot spots are again located in north-western Europe, in the regions of Ireland, United Kingdom, Netherlands, central-southern Germany, Denmark and Sweden (73 NUTS2, 26.0 1% of the total); while the cold spots (97 NUTS2, 34.6 % of the total) are basically concentrated in the regions of south-eastern Europe (all the regions of Cyprus, Greece, Romania and Bulgaria; central-southern Italy and Croatia; and the regions of eastern Poland, Slovakia and Hungary) plus the western periphery (Portugal and the Spanish region of Extremadura). Furthermore, as well as the location of the clusters, the local Getis-Ord Gi*index makes it possible to see that the regions with a greater concentration of higher values of ICT access and usage by households and individuals (hot spot - 99 % confidence) correspond to all the regions of the United Kingdom, Denmark and the Netherlands, as well as the regions of north-western and central Germany and southern Sweden. On the contrary, the regions where there is less ICT access and usage by households and individuals (cold spot - 99 % confidence) are located in the south-eastern periphery of Europe, and include all the regions of Cyprus, Greece, Romania and Bulgaria; central-southern Italy; and the Algarve, in southern Portugal. Boletín de la Asociación de Geógrafos Españoles, (90) 22 Figure 2. Spatial clusters according to the Anselin local Moran’s I index (LISA) and the Getis-Ord Gi* index for the Technological Readiness pillar (TR) and the Households and Individuals sub-pillar (TR-HI) TECHNOLOGICAL READINESS PILLAR a) Anselin local Moran’s I index (LISA) b) Getis-Ord Gi* index HOUSEHOLDS AND INDIVIDUALS SUB-PILLAR c) Anselin local Moran’s I index (LISA) d) Getis-Ord Gi* index Source: elaborated by the authors With regard to the spatial autocorrelation local analysis of the three indicators included in the TRHI sub-pillar (Share of individuals who used internet to order goods/services - Individuals-buying- Boletín de la Asociación de Geógrafos Españoles, (90) 23 internet; Share of households with internet access - Households-access-internet; and Share of households with access to broadband - Households-access-broadband), it shows that there is only a spatial distribution pattern or gradient in European regions similar to that already described for TR and TR-HI in the first two indicators (Figure 3a, 3b, 3c and 3d). Indeed, for both the Individuals-buying-internet and the Households-access-internet indicators the Anselin local Moran’s I index (LISA) and the Local Getis-Ord Gi* index show a distribution of the HH/LL spatial clusters and hot-cold spots with a gradient of regions that starts from the highest values located in north-western Europe (United Kingdom, Ireland, Denmark, Netherlands and Germany, except its NUTS2 in the east) and runs to the lowest values of regions located in the east and the southern European periphery (Portugal, southern Spain, Italy, Croatia, Greece, Cyprus, Bulgaria, Romania and a few Hungarian, Slovak and Polish NUTS2). As for the size of the clusters, these are larger for the Individuals-buying-internet indicator than for Householdsaccess-internet, both for the HH and for the LL (made up of 33.5 % and 24.5 % of the total NUTS2 for the former; and 32 % and 17.6 % for the latter). The same occurs with the size of the hot spots and cold spots (35.7 % and 28.6 %, and 26.1 % and 26.4 %, respectively). Meanwhile, the spatial distribution clusters and patterns obtained for Households with access to broadband (Figure 3e) show a number of differences from the other two indicators, in relation to both their size and their location. First, with regard to the size of the HH and LL clusters, they are smaller, that is, there is a smaller number of European regions with values above or below the average (26.2 % and 19.8 % of the total number of regions); or, otherwise, most European regions have similar values for Households-access-broadband (almost half, 49.6 % of the total). This in turn means that there is a higher number of outlier regions, above all NUTS2 of the HL type (almost 4 %), in comparison with the other ICT variables analysed so far. Second, all of this is reflected in the location of these clusters and the appearance of spatial patterns for Households-access-broadband given that: i) the higher numbers of HL outlier regions are distributed throughout southern Europe, from Portugal, France and Italy to Slovenia, Hungary, Romania, Bulgaria and Cyprus; ii) the HH NUTS2 clusters remain in the northern European regions, especially in the United Kingdom and the Netherlands, but now there are less NUTS2 in Germany, Ireland and Denmark that have above-average values; and iii) the LL cluster is once again located in south-eastern Europe, especially in all the regions of Greece and Bulgaria and a few regions in Italy and Romania (those in the south of both countries) and in Poland (those that form a central corridor). However, on top of these, as an important anomaly, there is a fair number of regions in central-southern France with low percentages for the Households-access- Boletín de la Asociación de Geógrafos Españoles, (90) 24 broadband indicator, which have values close to the average in the LL clusters of TR, TR-HI, Individuals-buying-internet and Households-access-internet. The same characteristics with regard to size and spatial distribution that we have indicated in the spatial clusters of Households with access to broadband obtained with the Anselin local Moran’s I index (LISA) occur in the spatial clusters resulting from the local Getis-Ord Gi* index. Indeed, as can be seen in the map in Figure 3f, the hot spots are concentrated in the British Isles (Great Britain and Ireland), the Netherlands and a few regions in northern and central Germany, Denmark and southern Sweden (with the highest values or 99 % confidence only clustering in the United Kingdom, the Netherlands and the German region of Weser-Ems). The cold spots continue to be concentrated mainly in south-eastern Europe (Cyprus, Greece, Bulgaria, Romania, Poland and southern Italy) and the odd western region (in Spain and Portugal). As occurred previously with the LL clusters, the regions of central France also have below-average values (cold spots). In short, the analysis of spatial dependence has made it possible to verify the existence of statistically significant territorial clusters. Moreover, as regards the TR pillar, the clusters identified show that this indicator is conditioned by the use of data on economic and business characteristics at national level, although these data are weighted and extrapolated to each of the regions. As a result of this, the predominance of a spatial homogeneity of the behaviour of the TR of the regions within their national limits is observed. On the contrary, the resulting spatial clusters for the TR-HI sub-pillar reveal a greater differentiation in the digital behaviour of regions. On the one hand, the clusters identified make it possible to know intra-national digital differences in greater detail. On the other hand, the clusters also indicate the existence of atypical digital behaviour in some regions with respect to their neighbours. Consequently, unlike for the spatial behaviour of the TR pillar, the TR-HI sub-pillar is created on the basis of regional data, which makes it possible to discern the real territorial inequalities in the digital development of Europe. Thus, the results lead to the interpretation that the spatial behaviour of TR would be due to the influence of the sub-pillar based on national data, that is, on the TR-E made up of indicators of ICT access and usage by European enterprises and their capacity of technology adaptation and absorption. However, when spatially and individually analysing the only 3 indicators based on regional data, the existence of a regional convergence is perceived, underpinned by Households-access- broadband due to the fact that a large part of European regions have very similar values, a result Boletín de la Asociación de Geógrafos Españoles, (90) 25 References Aiginger K., & Firgo M. (2017). Regional competitiveness: connecting an old concept with new goals. In R. Huggins & P. Thompson (Eds.), Handbook of regions and competitiveness, contemporary theories and perspectives on economic development (pp. 155-191). Cheltenham and Massachusetts: Edward Elgar Publishing Limited. https://doi.org/10.4337/9781783475018 Aboelmaged, M. G. (2014). Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms. International Journal of Information Management, 34(5), 639- 651. https://doi.org/10.1016/j.ijinfomgt.2014.05.002 Annoni, P., & Dijkstra, L. (2010). EU Regional Competitiveness Index: RCI 2010. Luxembourg: Publications Office of of the European Union. Retrieved from https://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/6th_report/rci_20 13_report_final.pdf Annoni, P., & Dijkstra, L. (2019). EU Regional Competitiveness Index: RCI 2019. Luxembourg: Publications Office of the European Union. European Union. Retrieved from https://ec.europa.eu/regional_policy/sources/docgener/work/2019_03_rci2019.pdf Añón Higón, D. (2012). The impact of ICT on innovation activities: Evidence for UK SMEs. International Small Business Journal, 30(6), 684- 699. https://doi.org/10.1177/0266242610374484 Aria, M., Gaeta, G. L., & Marani, U. (2019). Similarities and differences in competitiveness among European NUTS2 regions: an empirical analysis based on 2010–2013 data. Social Indicators Research, 142(1), 431-450. https://doi.org/10.1007/s11205-018-1909-0 Avgerou, C. (2010). Discourses on ICT and development. Information Technologies & International Development, 6(3), 1- 18. Retrieved from http://eprints.lse.ac.uk/35564/1/Discourses_on_ICT_and_development_%28LSERO%29. pdf Bahrini, R., & Qaffas, A. (2019). Impact of Information and Communication Technology on Economic Growth: Evidence from Developing Countries. Economies, 7(1), 21. https://doi.org/10.3390/economies7010021 Banerjee, A., Rappoport, P. N., & Alleman, J. (2020). A Cross-Country Analysis of ICT Diffusion, Economic Growth, and Global Competitiveness. In J. Alleman, P. Rappoport, M. Hamoudia Boletín de la Asociación de Geógrafos Españoles, (90) 32 (Eds.), Applied Economics in the Digital Era. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-40601-1_3 Biagi, F., & Falk, M. (2017). The impact of ICT and e-commerce on employment in Europe. Journal of Policy Modeling, 39(1), 1- 18. https://doi.org/10.1016/j.jpolmod.2016.12.004 Billon, M., Ezcurra, R., & Lera-López, F. (2009). Spatial effects in website adoption by firms in European regions. Growth and change, 40(1), 54-84. https://doi.org/10.1111/j.1468- 2257.2008.00461.x Billon, M., Lera-Lopez, F., & Marco, R. (2017). Patterns of combined ICT use and innovation in the European regions. Journal of Global Information Technology Management, 20(1), 28- 42. https://doi.org/10.1080/1097198X.2017.1280302 Bisk, T., & Bołtuć, P. (2017). Sustainability as growth. In L. Zacher (Eds.), Technology, Society and Sustainability (pp. 239-250). Springer, Cham. https://doi.org/10.1007/978-3-319-47164- 8_16 Blut, M., & Wang, C. (2020). Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, 48(4), 649-669. https://doi.org/10.1007/s11747-019-00680-8 Briglauer, W., Dürr, N. S., & Gugler, K. (2019). A retrospective study on the regional benefits and spillover effects of high speed broadband networks: Evidence from German counties. Mannheim: ZEW‐Centre for European Economic Research (Discussion Paper No. 19‐ 026). http://dx.doi.org/10.2139/ssrn.3423864 Bruno, G., Esposito, E., Genovese, A., & Gwebu, K. L. (2011). A critical analysis of current indexes for digital divide measurement. The Information Society, 27(1), 16- 28. https://doi.org/10.1080/01972243.2010.534364 Buzai, G. D., & Baxendale, C. A. (2009). Análisis exploratorio de datos espaciales. Geografía y Sistemas de Información Geográfica, 1. Retrieved from https://ri.unlu.edu.ar/xmlui/bitstream/handle/rediunlu/702/Buzai_An%c3%a1lisis%20Ex ploratorio%20de%20Datos%20Espaciales.pdf?sequence=1&isAllowed=y Cairncross, F. (2018). The Death of distance‐has distance died? RSAI Newsletter. New Series, 17, 8–9. Retrieved from https://www.regionalscience.org/images/PDF/RSAI%20Newsletter November_2018.pdf Boletín de la Asociación de Geógrafos Españoles, (90) 33 Camagni, R. (2002). On the concept of territorial competitiveness: sound or misleading? Urban studies, 39(13), 2395-2411. https://doi.org/10.1080/0042098022000027022 Camagni, R., & Capello, R. (2005). ICTS and territorial competitiveness in the era of internet. The Annals of Regional Science, 39, 421–438. https://doi.org/10.1007/s00168-005-0244-y Castellacci, F., Consoli, D., & Santoalha, A. (2020). The role of e-skills in technological diversification in European regions. Regional Studies, 54(8), 1123- 1135. https://doi.org/10.1080/00343404.2019.1681585 Cava-Ferreruela, I., & Alabau-Munoz, A. (2006). Broadband policy assessment: A cross-national empirical analysis. Telecommunications Policy, 30(8-9), 445- 463. https://doi.org/10.1016/j.telpol.2005.12.002 Chasco, C. (2009). Análisis exploratorio de datos espaciales al servicio del Geomarketing. III Seminario sobre Nuevas Tecnologías de la Investigación, en el Marketing y la Comunicación. e- Participación. Retrieved from https://docplayer.es/9477800-Analisis-exploratorio-de-datos- espaciales-al-servicio-del-geomarketing-1.html Contreras-Cabrera, G. (2012). Asociacionismo en red para la integración sociodigital en Andalucía. Revista de Estudios Andaluces, 29, 105- 126. https://doi.org/10.12795/rea.2012.i29.05 Corrocher, N., & Ordanini, A. (2002). Measuring the digital divide: a framework for the analysis of cross-country differences. Journal of Information technology, 17(1), 9- 19. https://doi.org/10.1080/02683960210132061 Cruz-Jesus, F., Oliveira, T., & Bacao, F. (2012). Digital divide across the European Union. Information & Management, 49(6), 278-291. https://doi.org/10.1016/j.im.2012.09.003 D’Urso, P., De Giovanni, L., Massari, R., & Sica, F. G. (2019). Cross Sectional and Longitudinal Fuzzy Clustering of the NUTS and Positioning of the Italian Regions with Respect to the Regional Competitiveness Index (RCI) Indicators with Contiguity Constraints. Social Indicators Research, 146(3), 609-650. https://doi.org/10.1007/s11205-019-02134-8 ESRI (2020). Analyzing Patterns toolset. Retrieved from https://desktop.arcgis.com/es/arcmap/10.3/tools/spatial-statistics-toolbox/modeling-spatial- relationships.htm#GUID-DDDFA1EE-4ED2-4126-9BB0-3E57E01D7885 European Commission (2010). A digital agenda for Europe. Retrieved from https://eurlex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52015DC0192&from=EN Boletín de la Asociación de Geógrafos Españoles, (90) 34 European Commission (2015). A Digital Single Market Strategy for Europe. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52015DC0192 European Commission (2020). Digital Economy and Society Index- DESI. Retrieved from https://ec.europa.eu/digital-single-market/en/digital-economy-and-society-index-desi Eurostat (2020). Data. Main Tables. Retrieved from https://ec.europa.eu/eurostat/web/regions/data/main-tables Falch, M., & Henten, A. (2018). Dimensions of broadband policies and developments. Telecommunications Policy, 42(9), 715-725. https://doi.org/10.1016/j.telpol.2017.11.004 Grubesic, T. H., & Murray, A. T. (2005). Geographies of imperfection in telecommunication analysis. Telecommunications Policy, 29(1), 69-94. https://doi.org/10.1016/j.telpol.2004.08.001 Haefner, L., & Sternberg, R. (2020). Spatial implications of digitization: State of the field and research agenda. Geography Compass, 14, e12544. https://doi.org/10.1111/gec3.12544 Hargittai, E. (2010). Digital na (t) ives? Variation in internet skills and uses among members of the “net generation”. Sociological inquiry, 80(1), 92-113. https://doi.org/10.1111/j.1475- 682X.2009.00317.x Hunady, J., Pisar, P., & Durcekova, I. (2020). R&D Investments in the European ICT Sector: Implications for Business Performance. Business Systems Research: International journal of the Society for Advancing Innovation and Research in Economy, 11(3), 30- 44. https://doi.org/10.2478/bsrj-2020-0025 Iammarino, S., Rodríguez-Pose, A., & Storper, M. (2019). Regional inequality in Europe: evidence, theory and policy implications. Journal of economic geography, 19(2), 273- 298. https://doi.org/10.1093/jeg/lby021 Inklaar, R., O'Mahony, M., & Timmer, M. (2005). ICT and Europe's productivity performance: industry‐level growth account comparisons with the United States. Review of Income and Wealth, 51(4), 505-536. https://doi.org/10.1111/j.1475-4991.2005.00166.x International Telecommunication Union (2018). Measuring the Information Society Report 2018. Geneva: ITU. Retrieved from https://www.itu.int/en/ITU- D/Statistics/Documents/publications/misr2018/MISR-2018-Vol-1-E.pdf Boletín de la Asociación de Geógrafos Españoles, (90) 35 Jeong, N., Yoo, Y., & Heo, T. Y. (2009). Moderating effect of personal innovativeness on mobile-RFID services: Based on Warshaw's purchase intention model. Technological Forecasting and Social Change, 76(1), 154-164. https://doi.org/10.1016/j.techfore.2008.08.007 Jordá -Borrell, R., & López-Otero, J. (2020). Economic growth factors in developing countries: the role of ICT. Boletín de la Asociación de Geógrafos Españoles, (86). https://doi.org/10.21138/bage.2979 Jordá‐Borrell, R., Ruiz‐Rodríguez, F., & González‐Relaño, R. (2015). Factors and taxonomy of technology purchase (TP) by internationalized innovative companies in peripheral European regions. Papers in Regional Science, 94, S139-S174. https://doi.org/10.1111/pirs.12172 Keating, M. (2017). Contesting European regions. Regional Studies, 51(1), 9- 18. https://doi.org/10.1080/00343404.2016.1227777 Koutroumpis, P. (2009). The economic impact of broadband on growth: A simultaneous approach. Telecommunications policy, 33(9), 471-485. https://doi.org/10.1016/j.telpol.2009.07.004 Lobo, B., Alam, M., & Whitacre, B. (2020). Broadband speed and unemployment rates: Data and measurement issues. Telecommunications Policy, 44(1), 101829. https://doi.org/10.1016/j.telpol.2019.101829 López Hernández, F.A., y Palacios Sánchez, M.A. (2000). Distintos modelos de dependencia espacial. Análisis de autocorrelación. In ASEPELT-España, Reunión Anual (Actas de la XIV Reunión). Oviedo: ASEPELT. Retrieved from https://repositorio.upct.es/bitstream/handle/10317/1513/dmd.pdf?sequence=1&isAllowed=y Lucendo-Monedero, A. L., Ruiz-Rodríguez, F., & González-Relaño, R. (2019). Measuring the digital divide at regional level. A spatial analysis of the inequalities in digital development of households and individuals in Europe. Telematics and Informatics, 41, 197- 217. https://doi.org/10.1016/j. tele.2019.05.002 Malecki, E. (2004). Jockeying for position: what it means and why it matters to regional development policy when places compete. Regional studies, 38(9), 1101- 1120. https://doi.org/10.1080/0034340042000292665 Malecki, E. J. (2007). Cities and regions competing in the global economy: knowledge and local development policies. Environment and Planning C: Government and policy, 25(5), 638- 654. https://doi.org/10.1068/c0645 Boletín de la Asociación de Geógrafos Españoles, (90) 36 Möbius, P., & Althammer, W. (2020). Sustainable competitiveness: a spatial econometric analysis of European regions. Journal of Environmental Planning and Management, 63(3), 453- 480. https://doi.org/10.1080/09640568.2019.1593005 Moroz, M. (2017). The level of development of the digital economy in Poland and selected European countries: a comparative analysis. Foundations of management, 9(1), 175- 190. https://doi.org/10.1515/fman-2017-0014 Novo-Corti, I., & Barreiro-Gen, M. (2015). Public policies based on social networks for the introduction of technology at home: Demographic and socioeconomic profiles of households. Computers in Human Behavior, 51, 1216-1228. https://doi.org/10.1016/j.chb.2014.12.040 OECD-Organization for Economic Cooperation and Development (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide. Paris: JRC European Commission, OECD. Retrieved from https://www.oecd.org/std/42495745.pdf Otioma, C., Madureira, A. M., & Martinez, J. (2019). Spatial analysis of urban digital divide in Kigali, Rwanda. GeoJournal, 84(3), 719-741. https://doi.org/10.1007/s10708-018-9882-3 Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of service research, 2(4), 307- 320. https://doi.org/10.1177/109467050024001 Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of service research, 18(1), 59- 74. https://doi.org/10.1177/1094670514539730 Peña‐Vinces, J. C., Cepeda‐Carrión, G., & Chin, W. W. (2012). Effect of ITC on the international competitiveness of firms. Management decision, 50(6), 1045-1061. https://doi.org/10.1108/00251741211238328 Perez-Castro, M. Á., Mohamed-Maslouhi, M., & Montero-Alonso, M. Á (2021): The digital divide and its impact on the development of Mediterranean countries. Technology in Society, 64, 101452. https://doi.org/10.1016/j.techsoc.2020.101452 Pick, J. B., & Nishida, T. (2015). Digital divides in the world and its regions: A spatial and multivariate analysis of technological utilization. Technological Forecasting and Social Change, 91, 1-17. https://doi.org/10.1016/j.techfore.2013.12.026 Boletín de la Asociación de Geógrafos Españoles, (90) 37 Portulans Institute (2019). Network Readiness Index 2019. Washington D.C., USA. Retrieved from https://networkreadinessindex.org/2019/wp-content/uploads/2020/03/The-Network- Readiness-Index-2019-New-version-March-2020-2.pdf Ragnedda, M., & Muschert, G. W. (Eds.). (2017). Theorizing digital divides. London: Routledge. https://doi.org/10.4324/9781315606002 Ragnedda, M., & Kreitem, H. (2018). The three levels of digital divide in East EU countries. World of Media. Journal of Russian Media and Journalism Studies, 1(4), 5- 26. https://doi.org/10.30547/worldofmedia.4.2018.1 Ramírez-Correa, P., Grandón, E. E., & Rondán-Cataluña, F. J. (2020). Users segmentation based on the Technological Readiness Adoption Index in emerging countries: The case of Chile. Technological Forecasting and Social Change, 155, 120035. https://doi.org/10.1016/j.techfore.2020.120035 Richardson, L., & Bissell, D. (2019). Geographies of digital skill. Geoforum, 99, 278– 286. https://doi.org/10.1016/j.geoforum.2017.09.014 Rivera, N., & Galicia, L. (2016). La escala geográfica como concepto integrador en la comprensión de problemas socio-ambientales. Investigaciones Geográficas, Boletín del Instituto de Geografía, (89), 137-153. https://doi.org/10.14350/rig.47515 Rohman, I., & Bohlin, E. (2014). Decomposition analysis of the telecommunications sector in Indonesia: What does the cellular era shed light on? Telecommunications Policy, 38(3), 248- 263. https://doi.org/10.1016/j.telpol.2013.10.006 Ruiz-Rodríguez, F., González-Relaño, R., & Lucendo-Monedero, A.L. (2020). Comportamiento espacial del uso de las TIC en los hogares e individuos. Un análisis regional europeo. Investigaciones Geográficas, (73), 57-74. https://doi.org/10.14198/INGEO2020.RRGRLM Ruiz-Rodríguez, F., Lucendo-Monedero, A. L., & González-Relaño, R. (2018). Measurement and characterisation of the Digital Divide of Spanish regions at enterprise level. A comparative analysis with the European context. Telecommunications Policy, 42(3), 187- 211. https://doi.org/10.1016/j. telpol.2017.11.007 Sáiz Peña, J. (2019). La Sociedad Digital en España 2018. Madrid: Fundación Telefónica. Sánchez-Moral, S. (2015). Territorio, transformación productiva, equidad y redes. Revista de Estudios Andaluces, 32, 49-75. http://dx.doi.org/10.12795/rea.2015.i32.03 Boletín de la Asociación de Geógrafos Españoles, (90) 38 Saprikis, V., Markos, A., Zarmpou, T., & Vlachopoulou, M. (2018). Mobile shopping consumers’ behavior: An exploratory study and review. Journal of theoretical and applied electronic commerce research, 13(1), 71-90. https://doi.org/10.4067/S0718- 18762018000100105 Scheerder, A., van Deursen, A., & van Dijk, J. (2017). Determinants of internet skills uses and outcomes. A systematic review of the second-and third-level digital divide. Telematics and informatics, 34(8), 1607-1624. https://doi.org/10.1016/j.tele.2017.07.007 Schlichter, B. R., & Danylchenko, L. (2014). Measuring ICT usage quality for information society building. Government Information Quarterly, 31(1), 170- 184. https://doi.org/10.1016/j.giq.2013.09.003 Schmidthuber, L., Maresch, D., & Ginner, M. (2020). Disruptive technologies and abundance in the service sector-toward a refined technology acceptance model. Technological Forecasting and Social Change, 155, 119328. https://doi.org/10.1016/j.techfore.2018.06.017 Serrano, R. M., & Vayá, E. V. (2002). Econometría espacial: nuevas técnicas para el análisis regional. Una aplicación a las regiones europeas. Investigaciones Regionales-Journal of Regional Research, (1), 83-106. Retrieved from https://www.redalyc.org/pdf/289/28900104.pdf Sbeppard, E. (2003). Competition in Space and between Places. In E. Sheppard & T. J. Barnes (Eds.), A Companion to Economic Geography (pp. 169-186). Oxford: Blackwell Publishing Ltd. https://doi.org/10.1002/9780470693445.ch11 Snieška, V., & Bruneckienė, J. (2009). Measurement of Lithuanian regions by regional competitiveness index. Engineering economics, 61 (1). Retrieved from https://etalpykla.lituanistikadb.lt/object/LT-LDB- 0001:J.04~2009~1367168617711/J.04~2009~ 1367168617711.pdf Soja, E. W. (2009). Taking space personally. In B. Warf and S. Arias (Eds.), The spatial turn: Interdisciplinary perspectives (pp. 11-35). London and New York: Routledge. Szeles, M. R. (2018). New insights from a multilevel approach to the regional digital divide in the European Union. Telecommunications Policy, 42(6), 452-463. https://doi.org/10.1016/j.telpol.2018.03.007 Tobler, W. (2004). On the first law of geography: A reply. Annals of the Association of American Geographers, 94(2), 304-310. https://doi.org/10.1111/j.1467-8306.2004.09402009.x Boletín de la Asociación de Geógrafos Españoles, (90) 39 Tranos, E., & Gillespie, A. (2009). The spatial distribution of Internet backbone networks in Europe: a metropolitan knowledge economy perspective. European urban and regional studies, 16(4), 423-437. https://doi.org/10.1177/0969776409340866 Tranos, E. (2012). The causal effect of the internet infrastructure on the economic development of European city regions. Spatial Economic Analysis, 7(3), 319- 337. https://doi.org/10.1080/17421772.2012.694140 Tsetsi, E., & Rains, S. A. (2017). Smartphone Internet access and use: Extending the digital divide and usage gap. Mobile Media & Communication, 5(3), 239- 255. https://doi.org/10.1177/2050157917708329 Uljas, S. (2019). The European digital single market strategy: Local indicators of spatial association 2011-2016. Telecommunications Policy, 43(5), 393-410. https://doi.org/10.1016/j.telpol.2018.10.003 United Nations Development Programme (2017). Human Development for Everyone (Vol. 2016). New York: UNDP. Retrieved from http://hdr.undp.org/sites/default/files/2016_human_development_report.pdf Van Deursen, A. J., & Van Dijk, J. A. (2019). The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New media & society, 21(2), 354- 375. https://doi.org/10.1177/1461444818797082 Van Dijk, J. A. (2006). Digital divide research, achievements and shortcomings. Poetics, 34(4-5), 221-235. https://doi.org/10.1016/j.poetic.2006.05.004 Vicente, M. R., & López, A. J. (2011). Assessing the regional digital divide across the European Union-27. Telecommunications Policy, 35(3), 220- 237. https://doi.org/10.1016/j.telpol.2010.12.013 Vicente, M. R., García-Muñiz, A. S., & Billon, M. (2020). The regional shaping of ICT: Exploring the links between ICT research, innovation and diffusion. In Society and Technology (pp. 66-78). Routledge. World Economic Forum (2018). The Global Competitiveness Report 2018. Retrieved from http://www3.weforum.org/docs/GCR2018/05FullReport/TheGlobalCompetitivenessRep ort2018.pdf Boletín de la Asociación de Geógrafos Españoles, (90) 40 Yu, L. (2006). Understanding information inequality: Making sense of the literature of the information and digital divides. Journal of Librarianship and Information Science, 38(4), 229- 252. https://doi. org/10.1177/0961000606070600 Ziemba, E. (2019). The contribution of ICT adoption to the sustainable information society. Journal of Computer Information Systems, 59(2), 116-126. https://doi.org/10.1080/08874417.2017.1312635 Boletín de la Asociación de Geógrafos Españoles, (90) 41