How cyberpsychology could support the UK Government's ambitions about tech adoption

The UK Government published its Industrial Strategy yesterday and I was reassured to see much discussion about tech adoption particularly in relation to driving and supporting innovation. As part of the strategy, reference is made to the Technology Adoption Review: a Review by the Government Chief Scientific Adviser and the National Technology Adviser to explore the barriers to adoption of transformative technologies. This review (which was also published yesterday), outlines a range of barriers to tech adoption in the UK. Among these include internal barriers such as financial constraints, lack of workforce, and managerial resistance to change and skills gap, as well as external barriers such as lack of information on technology and its benefits, policy and regulation uncertainty and lack of access to tech infrastructure.

I read this review with interest and couldn’t help but feel that an important piece of the puzzle was missing: what about people’s attitudes and perceptions surrounding the tech they are being asked to use? Although it was good to see that workforce skills were mentioned, there was little acknowledgement about the range of other psychological factors that are known to be related to tech acceptance and adoption. This is where cyberpsychology can contribute some valuable insights. At the heart of it, cyberpsychology is the study of the digitally-connected human experience. Essentially, this refers to understanding the human experience associated with technology use and online behaviour.

Indeed, there is a wide literature on technology acceptance. Technology acceptance generally refers to factors which determine the likelihood of individuals accepting and therefore using certain technologies. There are various widely-used frameworks which underpin this including: the Technology Acceptance Model (TAM; Davis, et al., 1989; Marangunić & Granić, 2015), the Unified Theory of Technology Acceptance and Use (Venkatesh et al., 2012) most recently, the Technology Integration Model (Shaw et al., 2018). Broadly, these are socio-cognitive models. This means they incorporate social factors (e.g., peer attitudes) as well as cognitive or individual factors (e.g., perceptions of usability and usefulness) which can collectively explain the acceptance of a particular technology in a given context (Davis, et al., 1989; Venkatesh et al., 2003). Users’ perceptions of these determine their intentions to use and subsequently continue to use a given technology (Davis et al., 1989).

Tech acceptance models have the potential to significantly advance our abilities to understand and measure the range of important user-centred factors which are associated with tech acceptance and therefore adoption. In many cases, barriers at the workforce level may not just be about skills (or there lack of): instead there may well be psychological barriers such as a lack of appreciation about the perceived usefulness of a given technology to fulfil its intended use or purpose. As such, rolling out technical training to fill skills gaps and improve literacy is not necessarily going to be the solution to these sorts of barriers. Instead, there need to be integration of more user-centred approaches to more fully understand the individual-level barriers, which can support wider organisational-level strategies around tech adoption.

Some of the work we have been doing recently is focused on exactly this, but specifically in the context of digital health innovation (see Liverpool et al., 2024 for a recent example). Moving on from this, some of our ongoing work is exploring how we can apply the Technology Integration Model as a basis for assessing digital readiness and maturity within contexts such as health trusts. This can help assess potential psychological barriers to MedTech adoption at an individual level, but also present useful organisational level data on wider digital readiness.

As is often the case, the human angle will always draw in additional complexities to a given issue. The example of tech adoption is no different. However, it’s not too late to use the knowledge out there to harness more tailored, psychologically-informed training, to make sure we aren’t missing an important piece of the tech adoption puzzle.

 Find out more about my work on digital acceptance, adoption and innovation here.

References

Davis, F. D., Bagazzie, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical model authors. INFORMS, 35 (8), 982-1003

Liverpool, S., Fletcher, K., Kaur Chopra, T., Jay, D., Walters, F., & Kaye, L. K. (2024). Implementing a mental health app intervention for university students: Multi-methods evaluation study. Mental Health and Digital Technologies. https://doi.org/10.1108/MHDT-07-2024-0015

Marangunic, N., & Granic, A. (2015). Technology acceptance model: a literature review from 1986 to 2013.. Universal Access in the Information Society, 14 (1), 81-95. doi: 10.1007/s10209-014-0348-1

Shaw, H., Ellis, D. A., & Ziegler, F. V. (2018). The Technology Integration Model (TIM): Predicting the continued use of technology. Computers in Human Behavior, 83, 204-–214. https://doi.org/10.1016/j.chb.2018.02.001

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540