Measuring Digital Capital: An empirical investigation

nms-cover-socialMassimo Ragnedda, Maria Laura Ruiu, and Felice Addeo (2019). Measuring Digital Capital: An empirical investigation. New Media and Society. 1-24.


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.


This article develops a Digital Capital Index (DCI) by adopting a definition of Digital Capital as ‘“a set of internalised abilities and aptitudes” (digital competencies) as well as “externalised resources” (digital technology) that can be historically accumulated and transferred from one arena to another’ (Ragnedda, 2018). This definition conceptualises Digital Capital as a specific capital (though intertwined with other capitals). Moving on from this conceptualisation, Ragnedda and Ruiu (2019) proposed some indicators to measure Digital Capital. However, this model construct has hitherto never been tested. This article fills this gap in the literature by exploring the empirical application of these indicators which were developed only at a theoretical level.

To date, a multiplicity of conceptualisations of Digital Capital have been provided. Some definitions simply embed this capital within the framework of other capitals (Emmison and Frow, 1998; Seale et al., 2006), whereas other scholars, despite recognising the independence of this capital, do not provide a clear-cut definition of it (Morgan, 2010). Another line of research refers to a variety of concepts such as techno-capital (Rojas et al., 2004), information capital (Hamelink, 2000) and informational capital (Prieur and Savage, 2013: 261–262), which include those skills and competencies that a user develops through engagement with new technologies. However, although these concepts include some constitutive features of Digital Capital, such as its technological component and its informational character, they still need to be unified under a comprehensive framework and empirically operationalised. Park (2017) moved one step further, introducing Digital Capital as a specific capital, however without providing an empirical model to operationalise it. In a similar way, Ragnedda (2018) provides a definition of Digital Capital as ‘the accumulation of digital competencies (information, communication, safety, content creation and problem solving), and digital technology’. Following this conceptualisation, Ragnedda and Ruiu (2019) proposed a model to measure Digital Capital. Therefore, the aim of this article is twofold: first, to apply this model (Ragnedda and Ruiu, 2019) to test the empirical application of its construct to measure Digital Capital and second, to observe its potential interaction with the main axes of social inequalities. Accordingly, this article investigates whether or not Digital Capital can be empirically measured and characterised by specific components that can be quantified. The identification of the constitutive components of Digital Capital is directly connected to the second aim, related to the interplay between this specific capital and a set of demographic and socioeconomic patterns. As further described in the following sections, this research shows the construct validity of the DCI by first applying an exploratory factor analysis (EFA) and then testing the hypotheses of the research by applying bivariate analyses on a representative sample of the UK population (868 UK citizens).

To fulfil these aims, the article is structured in five sections. The first section provides the theoretical background upon which the empirical design has been drawn. The second describes the method used to operationalise the indicators already identified by Ragnedda and Ruiu (2019). The third reports the results of both an EFA, used to investigate constitutive components of Digital Capital, and a bivariate analysis, used to check the construct validity of the DCI by observing its relationship with socioeconomic and sociodemographic variables. The fourth section discusses the main findings of this study. Finally, some conclusions will highlight the implications and limitations of this study.


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