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This article investigates the interaction between digital capital and some offline components (economic, cultural, political, social and personal) that represent the background against which we access and use the Internet. Based on a stratified sample of the UK population (868), six indexes (one for each component) were generated through factor analysis and univariate analysis. We summarised them into a unique model by performing a multiple linear regression to evaluate the role-played by offline components in the development/reinforcement of digital capital. The interaction between these new indexes and the digital capital index shows that, with the exception of the political component, all offline backgrounds positively contribute to digital capital. Moreover, the multiple regression analysis shows that the economic and social components have the strongest influence on digital capital. [ABSTRACT FROM AUTHOR]

This paper explores Digital Poverty (DP) in England by adopting the DP Alliance's theoretical framework that includes both Individual Determinants (individual capability and motivation) and Circumstantial Determinants (conditions of action). Such a framework is interpreted as an expression of Strong Structuration Theory (SST), by situating the connection between social structure and human agency in an intertwined relationship. We focus on new potential vulnerabilities that are connected to DP in England by drawing on a survey conducted on a randomised stratified sample (n = 1988) of parents aged between 20–55 with children at school. Exploring parents' experience in the COVID-19 era, we identified economic factors and having children with disabilities as important predictors connected to Digital Poverty. Additional socio-demographic traits (such as age and education), parental status, lifestyles and digital behaviours also play a role in predicting some of the determinants linked to Digital Poverty. This paper adds to SST by empirically exploring how individuals use the Internet according to their metabolised embodiment of external determinants. [ABSTRACT FROM AUTHOR]

The paper draws linkages between ethnic diversity of the eight federal districts of Russia and their technological development (access and use of ICTs, digital literacy, etc.). We show that although there is no universal correlation between ethnic composition of the regions and the level of their technological advancement, regions where Russians constitute the majority (i.e. Central and Northwestern) more often tend to be the country's leaders in terms of technological development. Following up on this, we use purposive sample of 398 Internet users based in Russia, showing how the level of digital capital of users varies depending on their ethnicity (here we will distinguish between two large groups – Russians and non-Russians, based on self-identification of survey participants) and their place of living. Results of the digital capital study, despite being indicative, show that those belonging to the ethnic majority (in our case Russians) and those living in big cities tend to have a higher level of digital capital. We argue that although ethnicity solely does not define the level of users' digital capital, it is still an important and understudied issue. This is particularly true for big multiethnic societies, such as the Russian society, where digital divide across various groups and regions remains a serious problem. [ABSTRACT FROM AUTHOR]

This article develops a Digital Capital Index by adopting the definition provided by Ragnedda, who defines Digital Capital as the accumulation of digital competencies and digital technologies, and the model for measuring it developed by Ragnedda and Ruiu. It aims to develop a measure that can be replicated for comparison in different contexts. This article contributes both theoretically and empirically to the literature by (a) consolidating the concept of Digital Capital as a specific capital and (b) empirically measuring it. A Digital Capital Index is developed through an exploratory factor analysis (EFA) and validated with a representative sample survey of 868 UK citizens. The validation procedure shows that the Digital Capital Index is associated with socioeconomic and sociodemographic patterns, such as age, income, educational level and place of residence, while it appears not to be related to gender. [ABSTRACT FROM AUTHOR]