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reduce transaction costs for patients (e.g. direct and indirect costs of travel) but increase transaction costs for the health system (e.g. costs of installing the videoconferencing equipment); conversely, a self-monitoring app may increase transaction costs for patients (e.g. forgone income due to time spent on monitoring by the patient or their family) but reduce transaction costs for the health system (e.g. less expense on staff time for patient monitoring). Domain 4 is the adopter system: the staff, patients and carers who will be expected to use the technology (but who may refuse to use it or find they are unable to use it). Relevant theories here include theories of how people learn to use technology (one example is Bandura’s social learning theory, which emphasises on-the-job learning and the importance of respected role models [22]). But non-use of a technology is rarely solely due to lack of knowledge or skill. We may also need to invoke sociological theories of why professionals resist new technologies, (see for example Greenhalgh, Stones and Swinglehurst’s adaptation of Giddens’ structuration theory to explore professional resistance to nationally mandated software programmes [23]). Complexity in Domain 4 occurs not only when using the technology requires knowledge or skills the user does not have but also when the roles and practices assumed by the technology threaten deeply held values or norms – for example, when a staff member is expected to do something she feels is against her professional code of conduct or work in a way that provides what she feels is a lower standard of care. In relation to adoption of technologies by patients, May’s burden of treatment theory (like transaction costs theory) proposes that shifting the work of care from clinician to patient places new demands on the sick, hence may be disempowering rather than empowering [24]. Such work may include taking readings and entering data (e.g. in many telehealth applications), making judgements (e.g. about what is an emergency or whom to contact in a crisis) or adjusting medication (for example, in response to a treatment titration algorithm). Domain 5 is the healthcare organisation(s). The theoretical underpinning of this domain was summarised in an earlier paper, ‘Diffusion of Innovations in Health Service Organizations’ [25], which included an extensive systematic review of the characteristics of organisations that support innovation. These included theories of organisational structure and climate (for example, the well-documented findings that well-led organisations with flat hierarchies, devolved decision-making, slack resources and a risk- taking climate find it easier to innovate than those lacking these features), theories of absorptive capacity (preconditions for capturing knowledge from outside the organisation and disseminating it internally), theories of organisational readiness (especially the notion of innovation-system fit and the potential ‘wrecking power’ of strategically-placed opponents) and various theories of assimilation and implementation. In addition, theories of incremental versus disruptive change are relevant (‘disruptive innovation’ of the rip-and-replace school succeeds far less often than a more incremental approach to change [26]). Finally, May’s normalisation process theory6 unpacks the work of implementing a technology in an organisation, including coherence work (the work that people do to make sense of a practice), cognitive participation (work to enrol and engage other people in relation to that practice), collective action (work to enact the new practice), and reflexive monitoring (the work involved in evaluating the impact of the technology) [27]. 6 See Chapter 15, “Implementing and embedding health informatics systems – understanding organisational behaviour change using Normalization Process Theory (NPT)”. T.GreenhalghandS.Abimbola /TheNASSSFramework–ASynthesisofMultipleTheories198