Who are the limited users of digital systems and media? An examination of U.K. evidence

Abstract

This paper presents findings on the correspondence of levels of digital systems and media use with a range of socio-economic and demographic measures in the U.K. Most research on inequalities in regard to digital systems and media has focused on access and skills. Building on prior work (Yates and Lockley, 2018; Yates, et al., 2015) we argue that inequalities in regard to digital systems and media are better understood around types of user and their correspondence to other key social variables — rather than solely individual skills and access. The analysis presented here covers a range of key demographic variables, especially those that are markers of distinct social disadvantage. We find that those not using the Internet have distinct characteristics — predominantly around age, education and deprivation levels. We also find that those undertaking limited uses (overall limited use or a very narrow range of uses) are all predominantly from lower socio-economic status backgrounds with variations due to age and education. The data used for the analysis is the recent U.K. Ofcom 2018–19 (n = 1,882) media literacy survey. The paper uses latent class analysis methods to inductively define user types. Multinomial and binary logistic regression are used to explore the correspondence of latent class group membership to key demographic variables. These insights have direct U.K. and international policy relevance as they are key to the development of strategies to tackle ongoing digital inequalities in U.K. society.

The Second-Level Smartphone Divide: A Typology of Smartphone Usage Based on Frequency of Use, Skills, and Types of Activities

“…Similarly, broad non-commercial users use their smartphone for a large variety of activities with the exception of online banking and making online purchases. Advanced users have also been identified in previous typologies (e.g., De Canio et al, 2016: "supersmartphoners"; Reisdorf and Groselj, 2017: "broad users"; Yates et al, 2020: "extensive users") as well as broad non-social-media users (e.g., Yates et al, 2020: "general (no social media) users"). Smartphone owners who belong to one of these usage types tend to be younger and have higher levels of educational attainment, which is in line with previous research in this area (Andone et al, 2016;Fortunati & Taipale, 2014;Serrano-Cinca et al, 2018).…”

“…Finally, basic general users use their smartphone to take photos, browse websites, use GPS/location-aware apps, install new apps, and read and/or write emails. Social media and information users have also been identified in previous typologies (e.g., Yates et al, 2020: "social and entertainment media only users") as well as basic general users (e.g., Yates et al, 2020: "utility users").…”

“…To create the usage typologies, prior studies often classified Internet users based on their amount, variety, and types of use (Blank & Groselj, 2014;Holmes, 2011;Horrigan, 2007;Livingstone & Helsper, 2007;Reisdorf & Groselj, 2017;Selwyn et al, 2005;van Deursen & van Dijk, 2014;Zillien & Hargittai, 2009) While a large number of typologies have been proposed for general Internet usage, smartphone usage has received less attention in prior research on the digital divide. In a few of the existing studies, mobile phone and smartphone use has been employed as an indicator for general Internet usage (Herzing & Blom, 2019;Horrigan, 2007;Yates et al, 2020) but has not been studied on its own. Two related areas of research where smartphone user typologies have previously been developed, however, are smartphone addiction research (Bian & Leung, 2015;Elhai & Contractor, 2018;Kim, Nam, Oh, & Kang, 2016) and marketing research (Calvo-Porral & Otero-Prada, 2020;Chen, Zhang, Chu, & Yan, 2019;De Canio, Pellegrini, & Aramendia-Muneta, 2016;Hamka, Bouwman, De Reuver, & Kroesen, 2014;Petrovčič, Slavec, & Dolničar, 2018;Sell, Mezei, & Walden, 2014).…”

“…To identify different types of smartphone users, we conduct a latent class analysis (LCA) which classifies individuals based on their similarity in response patterns on a set of categorical variables (McCutcheon, 1987). LCA has been used in a number of previous studies to construct typologies of Internet usage (Elhai & Contractor, 2018;Hamka et al, 2014;Herzing & Blom, 2019;Holmes, 2011;Sell et al, 2014;Yates et al, 2020).…”

Data Citizenship: Data Literacies to Challenge Power Imbalance

“…Throughout October until March our team conducted 15 focus groups with UK citizens via digital literacies centres across the UK, who were divided according to their literacy levels (indicated by the centres) as well as age. Low digital access and skills closely correspond and correlate with other key demographics such as age, income, poverty, being in social housing, low educational attainment, long term ill-health and ethnicity (Yates et al, 2020). Often in complex and intersectional ways.…”