Abstract
The Internet is central to the new media, but the Internet is itself a dynamic technology that is constantly evolving as users adopt and reject new features, devices and applications and use them in ways that are often unanticipated. This article is anchored in longitudinal survey data on how Britons use the Internet, which illuminates the emergence of new patterns of accessing the Internet over multiple devices—some of which are portable—in everyday life and work. We call those who adopt this new approach ‘next generation users’. In contrast, first generation users remain anchored to one or more personal computers in the household or workplace for accessing the Internet. The analysis shows how this emerging pattern of access is reshaping the use and impact of the Internet, such as in supporting the production of user generated content. The analysis also shows how next generation access is socially distributed; creating a new digital divide that reinforces socioeconomic inequalities. Future research needs to move beyond the study of access to the Internet to track the diffusion of next generation access and its implications across a wider array of nations.
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Notes
The case for viewing Lessig’s argument as technologically determinist is developed by Mayer-Schönberger (2008).
These correspond to common stages in the adoption of an innovation, from adoption to implementation to routinization (Rogers 1962).
For a more complete overview of the OxIS methodology, see: http://microsites.oii.ox.ac.uk/oxis/methodology
Using response rate formula RR1 defined by the American Association of Public Opinion Researchers (AAPOR) (2011:44).
For a more detailed description of the sample and methodology see Dutton and Blank (2011).
For example, Wei’s (2001) study of mobile phones.
The four applications are: browsing the Internet, using email, updating a social networking site, or finding directions.
Interestingly, education does not have a zero-order effect on being a Next Generation User. See below for the effect in a multivariate model.
The variables are defined as age, a continuous variable ranging from 14–92 years; income, an 8 category variable; higher education degree, a dummy variable indicating whether or not a respondent has a higher education degree; gender, a dummy variable using males as the comparison group; retired, a dummy variable indicating retired people; use the Internet at work, a dummy variable indicating if the respondent uses the Internet in their job; and married, a dummy variable indicating the respondent is married. We also tried a variable measuring urban–rural residence but it was not statistically significant. For several variables in the model we tried numerous specifications. For marital status the full variable had five categories, but only the ‘married’ category was statistically significant. Similarly, for education only the higher education category was significant, and for ‘life stage’ only ‘retired’ was significant.
Strength is measured by the size of the odds ratio. With an odds ratio of only 0.33, retired respondents are the most important variable in the regression. Similarly, having a higher education degree and using the Internet at work both have odds ratios near 1.50, making them the second most important variables in the regression.
Note, however, that when we substituted a direct measure of occupation in this model it was not statistically significant.
Information about the World Internet Project (WIP) and other national samples can be found online at: http://www.worldinternetproject.net/#news
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Dutton, W.H., Blank, G. The emergence of next generation internet users. Int Econ Econ Policy 11, 29–47 (2014). https://doi.org/10.1007/s10368-013-0245-8
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DOI: https://doi.org/10.1007/s10368-013-0245-8