Much like my previous post, I’m cutting this from my chapter because it’s not good enough and doesn’t really progress my overall argument. I’m still keen to develop the point though so any feedback is much appreciated.
It helps us move beyond the increasingly influential notion of techno-genesis, in which human beings and technological artefacts are understood to be co-evolving (Hayles, p. 10). This idea is not inaccurate so much as it is unhelpful: invocations of the “co-evolutionary spiral in which humans and tools are continuously modifying each other” (ibid p. 30) correctly recognise mutual causation but lack any specificity about the sequencing of the process or the operative mechanisms underlying it. In so far as that we frame empirical questions in this way then the conceptual instruments used will tend to render specifically social referents opaque, leaving us mired in generalities about co-evolution and impeding our capacity to investigate why specific technologies produce specific effects in specific people under specific circumstances. The concept of techno-genesis or co-evolution lends itself to tracking co-occurrence of change rather than isolating the mechanisms responsible for particular changes under particular circumstances. We lose sight of the properties and powers of the interacting entities as our attention becomes ever more embroiled in the ceaseless dance of co-constitution, leaving us caught between general claims of tendencies and particular examples of cases, while leaving the domain of the real positioned between the two frustratingly free from interrogation.
The difficulty with such central conflationary approaches to the relationship between human beings and digital technology is that so much of what is at stake sociologically arises from the variability with which they can be seen to obtain. As will be discussed below, even if the socio-technical infrastructure can be (cautiously) claimed to be exercising structural effects at the global level, the diffusion of the consumer technology so integral to claims about the transformation of agency varies greatly both within nations and between them. For instance, to take the UK as an example, between 2006 and 2014, the proportion of adults using a computer daily rose from 45% top 73%. This was heavily segmented by age, with 25-34 and 35-44 year olds (86% and 86%) most frequently using a computer daily compared to only 42% of 65+ year olds. With regards to ubiquitous computing, 68% of adults had used mobile computing devices to access the internet when away from home or work in the last 3 months. Amongst 16 to 24 year olds this was true of almost 96%, contrasting to only 23% amongst those aged 65 or over (ONS 2014). The Media Consumer 2014 survey, conducted by IPSOS MORI for Deloitte but restricted to an “online methodology with 2,000 consumers” and unhelpfully lacking further methodological detail, found that 49% of households surveyed “owned at least one smartphone, tablet or personal computer and that these households are 12 times more likely to own six or more computing devices” than those households outside this ‘mass-geek’ category delineated by the survey.
Leaving aside the methodologically limitations of the findings, the survey nonetheless highlights an important polarisation in ownership of consumer technology: acquisition of consumer technologies tend towards intensification and these ‘mass-geek’ households are, perhaps unsurprisingly, relatively privileged, “three-quarters (72 per cent) of the mass-geek category were ABC1, only 28 per cent were C2D” (Deloitte 2014) So claims about the effects of mobile computing need to be understood in terms of the structured diffusion of these technologies amongst different groups within the population and, through doing so, we begin to move beyond the empiricism which too frequently characterises research in this area. The mechanisms conditioning this diffusion could easily constitute a paper in their own right: sufficient disposable income to purchase expensive consumer technologies is clearly necessary but there is clearly more at work here than income alone. Such an explanatory project must also address constraints upon the diffusion as well as the enablements. The categories in which this diffusion is measured quantitatively should be treated cautiously as statistical artefacts (particularly as they appear in market research) but the actual patterns they fallibly track invite explanation in terms of real mechanisms that a realist sociology is well resourced to provide. Some of these mechanisms may be resolutely infrastructural but these in turn invite sociological explanation because technical infrastructure can only be sustained as a socio-technical system. Furthermore, the mechanisms responsible for diffusion within a particular national context should not be assumed to hold internationally (though nor should this be rejected out of hand).
 With ecological constraints upon the continued diffusion of mobile computing devices, particularly given the dramatically sharp cycles of obsolescence built into each new generation of products (Featherstone 2012), representing one important factor which should not be overlooked.
 The 4G network launched in the UK in 2012 offered vast increases in speed over existing 2G and 3G networks. The accessibility of public wireless hotspots increased at the same time, increasingly becoming an expectation in many popular consumer locations and with companies such as The Cloud (owned by BskyB who already dominate satellite television and have expanded aggressively in home internet access) continually expanding their coverage by courting businesses with the promise of reliably outsourcing the provision of their wireless access (ONS 2014). However explaining the implications of these changes for the diffusion of mobile computing rests at least in part upon use case studies of the advantages they afford for users of the technology e.g. the ability to connect to the internet while commuting, the possibility for streaming video on mobile devices.