NHS top managers, knowledge exchange and leadership: the early development of Academic Health Science Networks a mixed-methods study

Article history

The research reported in this issue of the journal was funded by the HS&DR programme or one of its preceding programmes as project number 12/5002/19. The contractual start date was in September 2013. The final report began editorial review in April 2016 and was accepted for publication in January 2017. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HS&DR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.

Declared competing interests of authors

none

Disclaimer

This report contains transcripts of interviews conducted in the course of the research and contains language that may offend some readers.

Copyright statement

© Queen’s Printer and Controller of HMSO 2017. This work was produced by Ferlie et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

The policy and organisational context

First, we outline the policy and organisational contexts before considering academic literatures that surfaced in our early protocol (although we develop our literature review in Chapter 3). The diagnosis at the policy level is that the UK NHS has been good at the invention of new treatments and products, but poor at their diffusion: ‘while the NHS is recognised as a world leader in invention, the spread of those inventions within the NHS has often been slow, and sometimes even the best of them fail to achieve widespread use’. 1

Accelerating the diffusion of promising innovations and more effective knowledge mobilisation across the health-care field has been a recurrent concern in recent English health policy (we review these policy texts more fully later). This policy-level concern has helped to create a new supporting institutional architecture for knowledge mobilisation, of which the AHSNs are an important part.

An emergent academic literature reflects on evolving UK health research policy. Walshe and Davies’ historical analysis of policy documents notes a shift from initial goals of knowledge production to later ones of ‘knowledge mobilisation’, which could embed the results of the research now produced more quickly into practice. 2

From the mid-2000s onwards, significant health and economic policy documents encouraged better translational research capacity at a macro level. The Cooksey Review outlined novel wealth generation goals for the health-care sector in addition to traditional health improvement goals. 3 It urged better system capacity in KT so that new scientific knowledge could flow ‘from bench to bedside’ speedily. The Cooksey Review’s characterisation of the so-called translation 2 gap – the long time taken for a promising innovation to roll out from pilot sites across the health-care field – is of particular interest here. 3

The Darzi Review4 further sought to support more rapid roll-out of evidence-based innovations in health care. It helped to invent a new organisational form (or, rather, imported it to England from well-known American sites, such as Johns Hopkins Medicine). It was stated that a ‘small’ number of leading edge and internationally competitive Academic Health Sciences Centres (AHSCs) would be created (only five in the first 2009 tranche) to bring together basic science, clinical practice and education and training and to stimulate more and speedier interactions between these three traditionally loosely coupled domains. 4

Further institutional change came with the first tranche of regionally based Collaborations for Leadership in Applied Health Research and Care (CLAHRCs) (2008) to encourage applied health research to improve patient outcomes across their areas. They should develop collaborations between NHS organisations and local universities and work to narrow the second translation gap. We review this important policy stream fully later.

The academic and theoretical context

Any empirically grounded study of AHSNs should be informed by academic and theoretical literature so that it has a conceptual basis. We now draw on the literature review in our protocol to outline the initial academic emplacement of the study (www.nets.nihr.ac.uk/projects/hsdr/12500219), although our arguments are developed further in the full literature review later undertaken (see Chapter 3).

First, our protocol argued that the well-established diffusion of innovations literature6,7 needs consideration. Coleman et al. ’s6 classic early study of the diffusion of medical innovations (the timing of a clinician’s decision to prescribe a new antibiotic) highlighted the role of informal clinical networks in spreading knowledge about innovation. This study also found an important group of clinical ‘opinion leaders’ as influential in adoption decisions and explored their characteristics.

Rogers7 distinguishes the role of ‘early adopters’ as individuals well integrated into a local social system who act as the first port of call for advice and information about innovation. They play an important role in the ‘take-off’ stage of the diffusion of an innovation by spreading positive evaluations to colleagues. They draw on strong interpersonal networks and credibility to persuade others, and are also moderately more ‘cosmopolitan’ (e.g. attending more scientific conferences or having a national professional leadership role) than followers, while combining these attributes with strong local credibility. This perspective suggests that agency from credible opinion leaders may be important in innovation take-off.

A further issue concerns the long-term sustainability of health-care innovations. 8 Innovations may be adopted but later unadopted rather than undergo routinisation into everyday practice. A study of NHS service improvement, for example, noted pervasive ‘improvement evaporation’,9 with rapid reported improvements associated with short-term bursts of change management activity but followed by regression to the status quo. An implication is that longitudinal analyses of the career of innovations over time is required to assess any evaporation effects.

Second, the initial protocol suggested that literature on knowledge leadership in health-care organisations was important. Specifically, we built on two recently completed studies on the leadership of knowledge in health-care organisations previously undertaken by researchers on the proposal. Nicolini et al. 10 explored knowledge mobilisation practices undertaken by CEOs in the NHS trusts, using shadowing methods. Although the study sheds useful light on how information and knowledge enter these senior managers’ thinking, by its nature it offers limited evidence as regards how such knowledge is shared more widely. This current project extends this earlier study by retaining a focus on senior managerial agency, but examines the wider knowledge networks of what we originally termed AHSN very senior managers (VSMs), who could be either general managers or clinical–managerial hybrids.

More recently, Swan et al. ’s edition11 explores knowledge mobilisation processes in health care from a practice perspective. ‘Knowledge mobilisation’ is construed as a proactive process that involves efforts to transform practice through the circulation of knowledge within and across different practice domains. Knowledge is not a thing that people ‘have’, but it is what they ‘do’ and involves questions of who they are (or identity).

Dopson et al. 12 explored health-care managers’ accessing and use of management knowledge, rather than the more conventional focus on the implementation of guidelines by clinicians. Despite some calls for an evidence-based management movement that replicated evidence-based medicine (EBM), Dopson et al. 12,13 found few texts based on evidence-based management. Instead, radically different forms of management knowledge (such as business school faculty or management consultant-authored texts, often from American authors) were present. So, knowledge leadership may be exercised internationally, as influential texts may be written by international authors (often American) and imported. A second implication is that codified texts (such as bestselling books) could be important,14 in addition to local knowledge practices.

Dopson et al. 12 identified opportunities for local knowledge leadership within the health-care organisations studied. One important competence was brokering the movement of knowledge across distinct knowledge domains, epistemic boundaries and different institutions, encouraging its absorption by more than one organisation or profession. This finding is relevant to the AHSNs, as they too operate in a multisectoral and multiprofessional context.

Fischer et al. 14 ask how leaders influenced by research-based management knowledge mobilise such knowledge. The KLs found12 often had a strong desire for formal knowledge or a ‘will to know’, apparent throughout their personal biographies and over time. They might complete PhDs (Doctors of Philosophy degrees) in a related area or write and publish articles and books. These ‘KLs’ were not merely facilitators and translators of management knowledge, but rather personally deeply immersed in – and committed to – producing and diffusing such knowledge.

A third theoretical perspective explored in our protocol was that of ‘absorptive capacity’ (ABCA) drawn from the resource-based view of the firm in strategic management. The resource-based view sees the firm as a bundle of tangible and also intangible assets where a key intangible asset is the (variable) corporate ability to develop and exploit its fundamental knowledge base. 15 High ABCA may partly depend on well-positioned individuals who ‘stand at the interface of either the firm and the external environment or at the interface between subunits within the firm’16 as boundary spanners. In reading ABCA articles, we will explore how they conceive of agency.

Knowledge networks in health care and related settings were a fourth and final area of academic literature in our protocol. Within network-based organising, less formal, more interactive and interpersonal forms of communication and knowledge exchange across traditional boundaries assume greater prominence.

A generic management literature suggests that social and informal networks17 help staff to acquire and process knowledge in and across organisations. A recent study examined knowledge flows empirically in eight NHS managed networks5 (e.g. Managed Cancer Networks). The ASHNs are another example of the managed network form.

Strong interpersonal networks can potentially spread information and knowledge and build high ‘social capital’. Yet very dense networks also create closed cliques that stifle innovation, so one argument is that a network with more ‘weak ties’ may be more functional than one with fewer strong ones. 18 So, we shall be interested to map different networks in the AHSNs, to explore variation (how open/closed they are and how they evolve over time) and to explore any influence on innovation processes. We also noted that academic literature on knowledge flows in biotechnology clusters19 will be important.

Research aims and research questions

Following our early review of policy and academic literatures, our overall aims were defined in the initial protocol as follows:

1. to shed light on the dynamics of knowledge circulation, sharing and exchange that take place within and around newly formed AHSNs

2. to deepen understanding of the role of VSMs in triggering and instigating the knowledge mobilisation activities that are the core of the remit of the AHSNs

3. to explore how and why certain VSMs develop a strong engagement with knowledge exchange events and mobilisation strategies and how they have become ‘KLs’ within the research utilisation network instituted by the AHSN.

The specific research questions (RQs) are:

1. What role does ‘knowledge networking’ play both formally (in national and regional AHSN knowledge exchange fora) and informally (i.e. in VSMs’ professional and local networks) within knowledge mobilisation strategies and practices in AHSNs?

2. How is the ‘knowledge’ (in particular about knowledge mobilisation strategies and practices) discussed at these fora diffused by a group of engaged VSMs in their AHSNs?

3. Is there a subgroup of VSMs emerging who are highly engaged with such knowledge mobilisation events and who appear to act as KLs in their AHSNs?

4. If so, what explains such knowledge leadership behaviours?

We will report the results of the study by the various work packages (WPs) outlined in our protocol [note that there were some unavoidable amendments, as noted in our methods (see Chapter 2)]. Chapter 3 is the literature review, and this is followed by the findings from our policy-level interviews and texts presented in Chapter 4. In Chapters 5 and 6, we introduce the regional case studies and use social network analysis (SNA) to investigate the national and regional ‘knowledge networking’ supporting AHSNs at their early stage of development. Here, we highlight the role played by AHSN boards but also by other key actors as knowledge ‘brokers’. To build on this work, Chapter 7 explores the diffusion of this knowledge by AHSN senior managers, teams and stakeholders, and illustrates the models of knowledge mobilisation and innovation spread in action through an analysis of our ‘innovation tracers’. Finally, Chapter 8 sheds light on the role and activities of KLs.

Having outlined the policy, organisational and academic contexts of the study, we move on to specify further our research design in Chapter 2.

Introduction and overview

Overall, the study uses a mixed-methods design to study the early development of AHSNs, including an analysis of the wider health-care policy landscape, a SNA of knowledge networks, comparative case studies of the evolution of five AHSNs, including 10 ‘micro tracers’, and an examination of leadership dynamics. We used four types of research methods:

1. qualitative semi-structured interviews and case studies

2. SNA and accompanying surveys

3. observations of national and regional meetings and events

4. analysis of secondary documents (i.e. policy papers, AHSN official publications and website materials, board minutes and grey literature).

The project aim was to assess the early development of AHSNs and their specific knowledge exchange efforts; in addition, we aimed, to explore senior managers’ knowledge leadership strategies, specifically in relation to knowledge mobilisation. We tracked developments within five AHSNs, tracing their espoused knowledge mobilisation strategies and practices in use to meet health improvement and wealth creation policy objectives. Below, we provide an overview of our WPs, followed by a more detailed discussion of the methods used. A few approved adaptations from the original protocol are also described and the rationale for these is explained.

Research ethics and NHS governance

In advance of the study start date, we contacted the NHS Research Ethics Service (now located in the Health Research Authority) to establish whether or not NHS Research Ethics approval was required. An e-mail from the National Research Ethics Service (August 2013) confirmed that our study was not defined as ‘research’ for NHS purposes and that NHS Research Ethics Committee (REC) review would not be necessary because our study involved staff only and would not access patients or patient-sensitive data.

Our host university still required ethics review. We therefore submitted our research plans and early data collection materials to the King’s College London (KCL) REC, which granted full approval in November 2013 [reference REP(EM)/13/14-12]. Ethics permission from KCL was later extended to reflect a short (6-week) extension granted by the National Institute for Health Research (NIHR). Furthermore, as we progressed with the study and developed new materials (e.g. the SNA survey and interview protocols), we submitted these texts as ‘modifications’ to the KCL REC as supplementary documents, which were also granted approval.

Although the study was deemed to be low risk for NHS purposes, owing to the nature of the study (exploring networks with a wide geographic reach), we needed NHS governance permission across many NHS sites that we might visit to undertake face-to-face interviews. However, AHSNs invariably cut across large areas, with typically 10–30 NHS trusts and 5–25 Clinical Commissioning Groups (CCGs) per region, depending on local arrangements. To manage this complexity, we sought guidance from KCL’s local Research and Development (R&D) Governance Team located at Guy’s and St Thomas’s Foundation Trust (FT)/KCL Biomedical Research Centre (BRC). With support from this team, we applied for our study to be added to the NIHR Clinical Research Network (CRN) portfolio and completed relevant forms via the Integrated Research Application System. Site-specific access was granted, along with appropriate research passports for the researchers visiting NHS trusts when these were required locally. Only a very small number of NHS trusts did not provide governance permission and we were careful to avoid visiting these sites.

We had to deal with multiple NHS trust R&D offices and submit study accruals centrally, yet these were also sometimes requested by individual sites. In this way governance was highly complex in a network-based study of this kind and we often encountered duplication of processes (e.g. recruitment reporting). The Health Research Authority may now have simplified the NHS governance process for more recent similar studies; certainly we would welcome streamlined NHS permission systems, especially for low-risk studies involving staff and service evaluations.

We are especially grateful to the research and governance team based at Guy’s and St Thomas’s FT, who advised us throughout the course of this study.

Scoping work, site selection and policy analysis (work package 1, leading to work package 5)

First of all, we accessed the original prospecti from all 15 AHSNs, securing an initial overview of network objectives and their remit. Following our protocol, our sampling strategy was to ensure variation in our sample of five AHSNS, both geographically (rural/urban and north, midlands and south) and according to whether or not they had an AHSC at the time of licensing. We also wanted to include some regions with a well-developed health/biosciences capacity, given the strong policy and academic interest in regional innovation systems. In addition, there was a pragmatic consideration of which AHSNs would provide access for our 30-month study.

With our partner at Universities UK (UUK) and our Study Steering Committee (SSC) members, we discussed as a team which sites to approach. We were aware that AHSNs were being established in different ways post licensing: some were hosted by NHS trusts, others as companies limited by guarantee. We successfully achieved a balanced sample of five AHSNs following our selection rules, including a spread of AHSNs with different regional economies and hosting arrangements.

Literature review (work package 2)

The team originally proposed a literature review methodology based on the Chartered Association of Business Schools list of business management top ranked journals. We were encouraged by NIHR expert reviewers to amend this approach to a wider and ‘evidence-informed’ form of review. We then devised an alternative, more thematically focused, review methodology to identify high-quality and/or relevant papers from various disciplinary sources and to capture ‘grey literature’, such as commentary pieces about UK health policy.

A four-phase structured review method was devised. This approach was driven by our RQs and was intended to allow adequate coverage across a range of disciplines including inter alia, management, health care, business and political science. A consequence of broadening the search parameters – and steering away from the Chartered Association of Business Schools ranked journals – was that this WP expanded vastly, given the complexity of searching a dispersed literature base that also included journals of varying quality (see our later discussion of study limitations). We found – as have other health researchers20 – that it is most challenging to synthesise such social science, policy and health-care literatures without investing very significant resources, expertise and time. In the ABCA field, for example, there are a few very well-cited and enduring articles by key authors (e.g. Cohen and Levinthal;16 Zahra and George15) that are foundational texts. Where these articles take the form of theory development or a critical synthesis, the conventional health services research (HSR) notion of making a judgement about ‘high-quality evidence’ becomes problematic.

Social network analysis (work packages 3 and 4)

Qualitative data collection and analysis (work packages 3–5)

Case studies of five Academic Health Science Networks (work package 5)

The qualitative modules also included five comparative cases of AHSNs. The design of the case study module reflected basic principles of organisational process analysis,30–32 which is a well-known school within management research. Process scholars are interested in how organisations as a whole evolve over time and how they achieve (generally intermediate) organisation-wide outcomes such as varying performance levels. There is often an interest in exploring the organisational correlates of high performance. Organisations are also seen as embedded in their wider contexts, which creates conditions of path dependence.

Although process research is not sectorally specific, it has been widely applied in UK and also in international studies to investigate processes of change in health-care organisations. 33 One of the research team has used these process methods in previous large-scale NHS-based organisational studies. 5 This process approach encourages us to generate a holistic understanding of the AHSN as a whole, over time and seen as embedded in a regional context. ‘Stronger’ case study designs are here seen as those that are comparative and longitudinal in nature rather than a single case, and as relating well to theory and conceptualisation as well as local empirics. These basic principles underpinned our approach to case study work.

We generated a basic template to organise the material in the same manner across the five cases to facilitate comparisons; thematic recognition and pattern recognition (see Appendix 17). The researchers engaged in the case study write-ups met face to face to present and discuss drafts of cases to encourage debate, critique and then movement to a more shared understanding. Long initial descriptive case reports were prepared, which were then radically shortened and made more consistent to fit with tight length constraints here.

As well as writing these macro-level cases, we also undertook two ‘micro’ studies of specific tracers in each case to reveal concrete knowledge mobilisation activity in action (see Chapter 7 and Appendix 17 for a list of interview questions). The tracers enabled us to explore each AHSN’s strategic approach to promoting innovation spread at pace and scale as enacted at the local level and over time (and thus help us assess the extent of knowledge mobilisation achieved in practice). We note that the tracers were selected in consultation with the AHSNs so there may be some positive bias, in that they may have been perceived as areas where the AHSN would be likely to make good progress.

Dissemination and engagement (work package 6)

Reflecting our study protocol, the team also engaged in numerous engagement activities during the study, as follows.

Interim face-to-face feedback was provided to all five participating AHSNs during the summer of 2015 at board/executive level. The findings were well received and validated demerging empirical findings (post-T1 SNA). The feedback from AHSN leaders subsequently informed the T2 SNA design. Presentations and short summaries were provided to the five sites.

A well-attended end of study event took place (2 June 2016 at UUK and organised in conjunction with UUK; see below for more details). Delegates were invited from industry, academic and NHS communities, and also included our SSC members. Members of AHSN teams were also invited (across the 15 nationally), and some AHSN sites had direct input, with AHSN leaders speaking on panels.

The team were made aware of various reports and studies about AHSNs/IHW that emerged in the period 2014–16, such as an Institute for Public Policy Research report, ‘Unleashing innovation across the NHS’,34 and a later project by RAND Europe – University of Manchester35 that evaluated IHW implementation. Contact was made with the latter research team and arrangements were discussed for co-operation between the studies, which the Manchester team may hopefully be able to take forward as their study will end at a later date than ours. Conversations with a relevant HSDR project team were also conducted early in the study (Professor Alison Bullock at Cardiff University, HSDR 12/5002/04).

Two members of the research team gave a well-attended seminar at Imperial College Health Partners in late 2015 to communicate academic thinking to this important grouping. Two researchers (Jean Ledger and Daniela D’Andreta) presented at the Health Services Research Network conference in 2015. From this event, links were made with NHS Improving Quality (NHS IQ) and The Health Foundation (the latter held a SNA event). Daniela D’Andreta also attended NHS Expo 2015.

The team presented a short summary of their research to the 15 AHSN leads in their national meeting in May 2016. The team presented a draft version of the policy chapter at the Organisational Behaviour in Health Care Conference at the University of Cardiff in April 2016.

Study Steering Committee and patient and public involvement

We are grateful for a very active SSC that met three times during the course of the project and provided very helpful advice. It was chaired by Professor James Barlow of Imperial College London, an expert in health-care innovation. Our members included an AHSN MD, a NHS director of research and innovation, a management school professor and two patient/public representatives.

Our patient and public involvement (PPI) representatives were recruited through the University of Warwick’s University/User Teaching and Research Action Partnership (UNTRAP) group, which organises service user involvement on research projects. We found the involvement of patient representatives useful for providing insights about how new health interventions and developments might be geared more towards patient benefit. One of our representatives was aware of other relevant knowledge exchange initiatives (e.g. digitech) linked to their local university and brought with them a breadth of knowledge owing to their engagement with the local health research community and patient experience.

Variation against study protocol

In the initial protocol, we proposed attending an ‘AHSN forum’ to provide access to national-level events. Owing to developments beyond our control, and reflecting the fact that AHSNs were only just emerging and coming to fruition in 2013, which was the time of our study launch, this forum was no longer meeting. Instead, the team negotiated access to attend some meetings of a later national grouping of AHSN MD/CEOs, hosted by ZPB Associates, facilitated by a member of the SSC. We remain grateful for the permission from AHSN leaders to do so and also to the AHSN commercial directors who granted similar access to their national meetings.

Our SSC advised the research team that AHSN boards and executives would be especially interested in (1) how AHSNs compare; (2) what health/wealth networks look like visually; and (3) the regional and national picture. When the team engaged in face-to-face meetings with AHSNs, they reflected these helpful observations. Indeed, the end of study event has been expanded to include diverse stakeholders.

The May 2015 SSC supported the research team’s suggestion to undertake some qualitative interviews by telephone given the wide spread of actors across geographically dispersed AHSN regions to expedite the logistics of data collection.

Owing to various delays encountered, the NIHR kindly granted the team a no-cost, 6-week extension.

Concluding remarks

Our study design is complex and covers a number of (interlinked) WPs. We now have doubts about our earlier decision to adopt a systematic review methodology, given the diffuse nature of the field we encountered. However, the triangulation of SNA and our qualitative data can be seen as a strength. The comparative and longitudinal nature of the case studies/tracers module can also be seen as a strength, enabling us to plot the spread of selected innovations over time. The set of 10 tracers is an interesting and distinctive database.

The design also enabled us to identify a small but interesting set of ‘knowledge beacons’ for interview. There was much interest found in the field about the study and also in the final conference, which helped stimulate active engagement. Finally, we have found it difficult to contain the mass of qualitative material we secured with the 50,000 word limit for final NIHR reports, which the editorial office has helpfully relaxed for the present draft.

Plan of the chapter

The aim of this study is to examine the early development and impact of five AHSNs and to deepen the understanding of how networking processes and knowledge leadership can support and accelerate health improvement and wealth creation. In this section we report the results of our structured literature review (WP 2, see also Chapter 2), which underpins the rest of the research. We start by examining three bodies of literature, which constitute the background of our research: work on promoting innovation and wealth creation at the intersection of industry, public sector and academic institutions; studies on how knowledge circulates in health care; and studies on how collaboration for innovation unfolds in the sector. We then move to an analysis of literature that is directly relevant to our study. First, we examine the literature on existing experiences such as AHSCs and CLAHRCs. These initiatives share some interesting common dimensions with AHSNs and for this reason they constitute relevant precedents that are worth considering. Second, we examine the research on managed networks as ways to govern innovation and quality improvement in health care. As we will see, these new developments require the deployment of new research methods, such as SNA, but also raise questions around ‘agency’, such as ‘where is the source of action in promoting and sustaining knowledge mobilisation and the diffusion of innovation?’. What is the role and contribution of leaders and managers in these initiatives? Finally, we turn our attention to the literature on the nature of leadership processes and forms supporting knowledge mobilisation in networked initiatives. In setting the scene, we also examine how the promotion of innovation and wealth creation occurs at the intersection of industrial, public sector and academic actors and institutions.

Literature review

One of the central remits of AHSNs is to deliver a step-change in identifying, developing and adopting innovations in the field of health care. The activities of AHSNs are predicated on partnership working and collaboration between the NHS, academia, the private sector and other partners. What models are available in the literature to make sense of this complex process of collaboration?

How to conceptualise the collaboration between NHS, academia, the private sector and other stakeholders?

Our review suggests that one of the most widespread ways to frame the issue is through the idea of ‘triple helix’ as conceptualised by Etzkowitz and Leydesdorff. 36 The basic ‘triple helix’ model suggests that innovations in many sectors are increasingly co-produced innovations that arise through the interaction between university, governmental agencies and business organisations. The ‘triple helix 3’ model is university centred: ‘the triple helix thesis states that the university can play an enhanced role in innovation in increasingly knowledge based societies’ (p. 109),36 unlike other models that privilege the state or the market. The university may itself be undergoing internal change with a new third mission of economic development, alongside research and teaching.

Triple helix 3 is consistent with strong trilateral networks and hybrids, which strongly involve universities. New organisational forms (such as technology transfer offices, spinouts and science parks) emerge across traditional boundaries. The evolution of the triple helix is not linear or predictable but, rather, is interactive, reflexive and emergent. The university is integral to the regional innovation systems that power economic growth. According to this model, the role of universities should shift towards acting as ‘incubators of incubators’. Etzkowitz,37 for example, argues that the contemporary university incubator model (e.g. technology transfer office, spinouts and the science park) builds on the classic venture capital firm but adds in the co-location of firms on green-field sites to encourage cross-fertilisation: ‘the university of the future will include an incubator as a regular feature along with classrooms, faculty offices and laboratories in each department or academic unit’ (p. 127). 37 The university is becoming more permeable and ‘outwards facing’, with more external alliances. By exploring ‘science city’ initiatives in regions searching to accelerate economic growth (e.g. Barcelona), Etzkowitz38,39 outlines transformations associated with the triple helix. First, there is no single dominant power within the triple helix but rather emergent, negotiated and cross-boundary development. Second, various organisational and ‘hybridising’ inventions are as important as physical devices in speeding innovation. These organisational forms include the entrepreneurial university, the innovative region (where Silicon Valley, CA, USA, and Route 128 in Boston, MA, USA, are cited as prime exemplars), the incubator, the science park, the venture capital firm and the knowledge intensive network.

The triple helix is not the only model used to conceptualised the collaboration between university, government and business. Fromhold-Eisebith and Werker40 discuss alternative academic literatures on the changing economic and social role of universities. As well as triple helix, they consider the new production of knowledge model, which is based on the distinction between mode 1 and mode 2 of knowledge production. Gibbons et al. ’s41 influential text argued that there was a long-term shift from so-called mode 1 to mode 2 knowledge production. Mode 1 includes the primacy of academic basic research, the strong agency of individual scientists, the importance of scientific peers, and a hierarchical and university-centred governance system. Mode 2, by contrast, exhibits transdisciplinary knowledge production, engagement from a wider range of sites, the formation of temporary knowledge networks and the production of applied knowledge for use within specific social and economic contexts. The message, therefore, is not only that to foster innovation one has to act at institutional and policy level, but also that other types of network-based forms are necessary to foster the mode 2 type of knowledge production. From this perspective, AHSNs are an early attempt to facilitate and foster, rather than to plan and govern, processes that fall within the new production of knowledge model. Schoonmaker and Carayannis42 propose a further model of the ‘quadruple helix/mode 3’ with an activated civil society. Carayannis and Campbell43 suggest that in a knowledge-orientated society, a ‘creative class’ of highly educated workers shapes a mode of public discourse that favours a future orientation, the production of knowledge and support for innovation as positive societal values. This notion of ‘civil society’ suggests the presence of ‘third-sector’ organisations lying between the state and the market not captured in the classic triple helix model.

One of the common themes of these models is that they shift attention from high-level policy interventions aimed at creating top-down institutional and economic change towards the promotion of specific networking and collaboration initiatives ‘on the ground’. For example, taking a perspective from economic geography, Fromhold-Eisebith and Werker40 review academic literature(s) on universities in the KT process. They explore cases in which university/regional knowledge flows have fallen short of expectations. Their message is that this happened because these initiatives remained at a traditional ‘policy’ level and did not consider or address the issues of networking and creating links within the system. In their words ‘we find that social network analysis and its theoretical basis, i.e., social network theory, will give more body to understanding knowledge transfer functions of universities’. 40

Learning from case studies of regional innovation and governance

Over the past decade or so, authors have utilised the triple helix and other models to examine the experience of regional efforts to promote innovation through collaboration and multilateral partnerships. Some have found, for example, that triple helix arenas are facilitated by intermediary organisations, including university incubators and industrial consortia. For example, Johnson’s44 study of PRECARN (‘PRE-Competitive Advanced Research Network’ – an industrial consortium in the field of intelligent systems) suggests that the network successfully acted as an intermediary organisation and was instrumental in the effort to lower the transaction costs associated with network-based working (e.g. acting as a mediator in case of dispute) and to generate new revenue streams for partners (p. 502). 44

Smith and Bagchi-Sen’s45 survey of the UK Oxfordshire biotechnology sector cluster explored the proposition that regional contexts for triple helices may vary. A metropolitan region, for example, may have greater scope and scale than smaller cluster-type regions. Labour market aspects of such regions, such as the availability of skilled labour, should be considered further. Although this region included a premier research-intensive university (Oxford), it played a less clear-cut role than triple helix theory would suggest. Leading universities operate in national and international networks as much as they do in regional ones. Their survey responses indicated that a strong role was also played by national funders and by extraterritorial linkages. The role of the university was more indirect (at least in firms not spun out by it) in helping to ensure the ready supply of skilled labour, as they found ‘that the role of Oxford University, a world centre for biomedical research, is secondary at the regional level rather than being dominant as might be expected and that the availability of skills, underplayed in traditional presentations of the model, is far more significant’ (p. 805). 45

Etzkowitz and Klofsten46 go a step further and question whether or not certain conditions can and should be replicated elsewhere. They do so on the basis of a study of the Swedish Linköping region. They wonder if the well-known US cases of Silicon Valley and Boston’s Route 128 reflect special historical circumstances that are difficult to replicate elsewhere, even in superficially promising locations: ‘Cambridge UK has generated a significant number of niche firms but has had difficulty in creating high growth firms, common in Silicon Valley’ (p. 244). 46

Goldstein and Glaser47 examined the governance of local and regional development in science parks developed from two research-intensive universities (University of North Carolina at Chapel Hill and Utrecht University). Drawing on political science work on ‘network governance’,48 they broaden the narrow focus on technology transfer in the current literature. Governance theory suggests that the direct command role of the nation state is declining as a broader range of policy actors develops from various sectors and tiers, including strong regions. Goldstein and Glaser47 distinguish between possible governance modes. The first, traditional, category is hierarchical governing. The second category shows movement to hierarchical governance in which non-governmental actors are more actively involved in collaborative decision-making, yet the final decision-making power still rests with the government actor (p. 161). 47 Third, ‘heterarchic’ governance reflects high interdependence and more equal power relations with more networks, public–private partnerships and co-management. Their two case studies were cautious and inconclusive (p. 172). 47 There was variation found in the governance mode, with Utrecht University showing greater heterarchy. Their lesson is that there is no ‘one model fits all’ solution on how to govern regional collaboratives and that one of the critical factors is aligning the mode of governance with expectations in the local institution and cultural norms (in their case, Utrecht’s heterarchic mode of governance was possible in view of the local political and cultural conditions that were absent in the USA). 47

Finally, some authors examined collaborations in a context more akin to that of the AHSNs. Feldman,49 for example, analysed the development of a science park in Linköping, Sweden, with a strong medical industrial sector. An interesting feature was the shift of innovation platform over time (pp. 1027–8). 49 The key growth point moved from being an incubator in a large bureaucratic firm to a small start-up. The shift depended on new strategies deployed by regional actors and wider changes to the administration and resources available to such actors. New actors (from the county and city councils and the local university) came into the field and constructed networks that made additional resources available. At the same time, these new actors were themselves experiencing internal policy and even ideological shifts (as in a growing ‘enterprise culture’ in the university).

In summary, the common theme emerging from these studies is that, although no ‘silver bullet’ exists, collaboration and triple helix effects at regional level are better served by initiatives run by agile, intermediary organisations with flexible mode of governance that can act as contained facilitators of processes, rather than trying to engineer and govern them. This in turn raises the issue of which types of agency and leadership processes are appropriate in such newly emerging organisational contexts. We return to these topics later in this review.

Understanding knowledge transfer, mobilisation and innovation in health care: theoretical framings

The topic of how knowledge and innovation circulate in health care, and how the process should be conceptualised, has attracted increasing attention among authors. This is reflected in a growing body of literature on the topic. Authors propose a variety of models and different ways of understanding the processes through which novel ideas, technologies and delivery practices are circulated and translated among those involved in health-care activities (including patients). Overall, the tendency is to abandon simplified ‘transfer’ linear models that see the process of the circulation of knowledge in terms of a rather mechanical sequence of production, transfer and utilisation. In their stead, authors favour more nuanced, ecological and socialised views. 11 This shift is usually signalled by the use of terms such as knowledge mobilisation rather than knowledge utilisation or knowledge transfer. The notion of mobilisation is seen as reflecting the fact that the circulation of knowledge and innovation in health care is dynamic, processual, moved by a number of diverging interests, and happens at different levels at the same time. 11

Different reviews of this literature on and mobilisation in health care exist (for a recent review see the introduction in Swan et al. , p. 201). 11 For example, Kitson50 presents a theoretical overview of social literature that is critical of received approaches to KT in the health-care sector in general and in the field of nursing in particular (the paper was published in an academic nursing journal). She argues that dominant KT models are too often linear and ‘machine like’, sitting badly with more organic models, perhaps drawing on more iterative organisation process work developing in management studies (e.g. Van de Ven et al. 51), and even complex adaptive systems (CASs) thinking. Her most general of five propositions states that ‘the (healthcare) system is best viewed as a complex, interactive, organic entity where experimentation, experiential learning and reflection are central to creating a culture of innovation, improvement and consequently effectiveness’ (p. 218). 50

Kitto et al. 52 seek to reconceptualise KT processes in health care, given what they see as the problem of the ‘vested interest’ of projects undertaken by KT specialists, which assume a goal of desired behaviour change, supported by the adoption of planned change models. They suggest that this framing is too rationalistic, individualistic and linear and argue for a new approach based on the sociology of KT: which is unburdened by the desire to demonstrate intervention successes ‘such an approach fully embraces the notion that “new” knowledges are generated in the activities between KT practitioners and the chosen communities in which they work, requiring an actively reflexive approach to KT intervention design and practice’ (p. 290). 52 They advocate a theoretical framing based on actor–network theory (ANT) and its tracing of associations and networks. They then apply their approach to the concrete analysis of a surgical safety checklist, of which some of the authors had strong professional knowledge, so that: ‘all aspects of the work to make connections between all contextual factors, newly introduced techniques of change (i.e. checklists, guidelines), KT practitioners and knowledge users are open to scrutiny’ (p. 296). 52

Nicolini et al. 53 presented a systematic literature review of health-care sector innovations based on knowledge management (KM) (a narrower phrase than that of ‘knowledge mobilisation’ used here). They identified three major themes in the literature. The first was an exploration of the ‘nature of knowing’ in the sector, which could be seen as typically fragmented and distributed. There seemed to be a proliferation of medical knowledge, information and data, which presented sectoral conditions of ‘knowledge overload’. There also seemed to be a preference for the use of local knowledge in the making of clinical decisions. The second theme covered a range of KM tools and techniques that could be used in the health-care sector, covering both ‘hard’ approaches (electronic libraries and repositories; data mining) and ‘soft’ social learning-based approaches, which Nicolini et al. 53 argued might fit the nature of the sector better. The third theme was an examination of what the literature saw as the enablers of and barriers to KM in the health-care sector (which they comment appeared similar to the literature on other sectors). Enablers included shared common values and culture, weaker power and status differences, and stronger interdisciplinarity. Barriers included overmanagement and interference from the political sphere, clinical managerial conflict, professional barriers and low levels of trust. They commented that, at the time of the review, some of the health-care literature appeared to present conditions of sectoral isolation from important concepts found in mainstream management literature, such as ‘social capital’ and ‘communities of practice’ (p. 258). 53

Best et al. 54 present a realist review of literature on large-scale transformation and innovation in health-care systems using CAS theory, previously developed in the health-care context by Plsek and Greenhalgh. 55 The literature on CAS is relevant in the present context, as it was developed to explain the behaviour of non-hierarchical and partially emergent organisations not unlike AHSNs. Best et al. 54 argue: ‘although CAS are complex and unpredictable, they are amenable to guided transformation by applying simple rules that are sufficiently flexible to allow for adaptation’ (p. 432). Their literature review enabled them to specify five such simple rules (and then middle-range propositions) as follows: (1) engage individuals at all levels in leading the change process (leadership should be both designated and distributed), (2) establish feedback loops, (3) attend to history (as change mechanisms are context sensitive), (4) engage physicians and (5) involve patients and families. They also called for more comparative case study analysis of change processes in health-care systems to build learning and a theoretical base.

Absorptive capacity and ‘dynamic capabilities’

A further promising approach to understand the process of knowledge mobilisation in health care is provided by the ABCA perspective. The approach, which is based on the resource-based view of strategic management, argues that firms even within the same sector (e.g. biotechnology firms) often generate distinctive collective competences (e.g. the ability to learn, absorb knowledge or change), which produce idiosyncratic profiles and positions in the marketplace. The firms’ assets include intangible elements (such as tacit knowledge) as well as traditional tangible elements (such as manufacturing plants). ‘ABCA’ constitutes one of these core competences. 56 Zahra and George15 define ‘ABCA’ as ‘a dynamic capability pertaining to knowledge creation and utilization that enhances a firm’s ability to gain and sustain a competitive advantage’ (p. 185). 15 They further distinguish between two dimensions of ‘potential’ ABCA (namely, acquisition and assimilation) and two of ‘realised’ ABCA (transformation and exploitation). The third transformation stage encompasses: ‘the firm’s capability to develop and refine the routines that facilitate combining existing knowledge and the newly acquired and assimilated knowledge’ (p. 190). 15 From this perspective routines and processes of the firm are seen as being of extreme significance for the capacity of organisations to learn and innovate. The third ‘transformation’ stage may be particularly interesting to study because it opens up the ‘black box’ of the firm at the critical stage of creative innovation. It is at this stage that the genesis of new competences may become apparent.

Agency and the role of the individual in knowledge mobilisation

Although the ABCA literature focuses on processes and routines, other scholars are more interested in the dimension of ‘agency’ in the process of knowledge mobilisation. Agency refers to the capacity of an entity to act in any given environment and more generally to particular source of action and change. In other terms, the literature asks where the agency is in the knowledge mobilisation process. Who or what has the capacity to make knowledge mobilisation happen?

The interest for the agential dimension of knowledge mobilisation has led to the identification of a number of specific roles that individuals play in promoting and sustaining knowledge creation and innovation diffusion. These roles are critical in our study as they provide the necessary concept by which to understand the role of managers and senior leaders in the activity of AHSNs:

• Brokers: the idea of the broker refers to individuals who mobilise knowledge across professional, cultural and institutional boundaries, and is explored by authors such as Obstfeld. 57 He writes about the ‘tertuis iungens’ or the ‘third player’, or an entrepreneurial strategy connecting people who are disconnected (i.e. closing ‘structural holes’). This paper links to Burt’s work on brokerage, structural holes and innovation. 58 Long et al. 59 use SNA to explore if these individuals share characteristics (e.g. powerful, influential, well connected). Interestingly, there was empirically a mismatch between actual and perceived brokerage. Individuals with formal knowledge broker roles (i.e. formally designated directors and managers) were not necessarily the individuals who network members perceived as key in knowledge mobilisation.

• Boundary spanners: Liu and Stuart60 investigate boundary spanning in a for-profit biotechnology and pharmaceutical research laboratory. They emphasise how innovation in the life sciences context arises in a context of porous organisational boundaries (such as between universities, research centres, private companies, spinouts and commercial laboratories). They paradoxically suggest that individuals in less central roles may find themselves in more central positions. They found that actively publishing researchers are much better connected to external (to the company) members of a knowledge-exchanging scientific community. Jones et al. 61 suggest a major role for the ‘knowledge champion’. This formulation places knowledge activists and change agents at the informational centre, where they provide regulative and communicative capacity. Such observations are pertinent to our study’s examination of ‘KLs’.

• Institutional entrepreneurs: institutional entrepreneurship refers to the ‘activities of actors who have an interest in particular institutional arrangements and who leverage resources to create new institutions or to transform existing ones’ (p. 657). 62 Examining the conditions that facilitate institutional entrepreneurship in health care and encourage individuals that drive radical change. Lockett et al. 63 suggest that those individuals with limited structural legitimacy under prevailing institutional conditions (i.e. who are more peripheral and have limited access to resources) may be best placed to engender change, but least able to given prevailing conditions. Individuals with strong structural legitimacy, however, are most able to produce change but may be less willing to do and challenge the status quo if ‘privileged by existing institutional arrangements’ (p. 361). 63

• Activists and champions: Käser and Miles64 argue that knowledge-intensive firms are increasingly relying on ‘knowledge activists’ to facilitate knowledge flows supporting innovation through effortful, creative and less formal means, such as obviating organisational hierarchies and building trust through communities of practice.

Overall, the literature we reviewed confirms the growing interest in non-linear views of the innovation processes that go beyond simple ‘technology push’ models to consider the operation of wider social–technical systems and the local enactment of knowledge exchange. It signals the importance of professional networks and interpersonal trust building that supports ‘knowledge driven innovation’ (p. 25). 64 The frameworks and theories in the literature also demonstrate the benefits of social science (including management)-based approaches to the study of KT processes, including important concepts such as situated learning (knowing rather than knowledge), communities of practice, ACBA and CASs.

Empirical studies of knowledge mobilisation

A number of papers offer more developed empirical views on the process of knowledge mobilisation, although still with a theoretical framing. Pope et al. 65 studied the introduction of treatment centres in the NHS as an organisational innovation after the 2000 NHS plan designed to reduce waiting lists for elective procedures (they studied eight different sites). They draw on a ‘negotiated order’ perspective to argue that there was great local variation in the way in which the innovation was instantiated locally, reflecting local systems of cultural meaning and interpretation. The implication is that other health-care innovations (including perhaps the case of AHSNs) could be constructed in very different fashions locally, reflecting local histories and meaning systems.

Greenhalgh et al. 66 used a multiple case study design (of four early adopter locales including both primary care and unscheduled care sites) to study the introduction of electronic patient records in the English NHS in the national Connecting for Health programme, informed by the diffusion of innovation theory. 67 These health-care innovations to information technology (IT) systems should be seen as involving both social and technical components rather than just operating as a simple ‘technology push’ (which is how policy discourse tended to cast them) so they were: ‘not plug in technologies. They are complex innovations that must be accepted by individual patients and staff and embedded in organizational and system levels and routines’ (p. 1043). 66 Although some early sites had strong leadership and ABCA, there could well be a need to build up such competences in later adopters.

Waldorff68 examined the local instantiation of a health-care innovation (a municipal health-care centre) in case studies from 18 local authorities in Denmark. The abstract concept was found to take different forms in different locales: ‘some municipalities conceptualised the centre as a building that is open for patients with chronic diseases to visit, while other municipalities created the centre as a collaborative network of local organizations which promote health for citizens in various places such as schools and workplaces’ (p. 219). 68 Theoretically, the paper drew on both institutional logics theory (as multiple logics were evident and co-existed in the broader municipal field) and ANT, with its interest in the translation process: “the concept of ‘translation’ from ANT enabled analysing the reform as a process in which local rationales of health care centres were constructed” (p. 230). 68

Other studies examined in depth the mechanisms and conditions of, and obstacles to, knowledge mobilisation in health care. For example, Gabbay and Le May’s69 well-cited ethnographic exploration of the social construction of evidence-based practice in two English primary care settings found that that the formal literature search/adoption model espoused in EBM models was not evident in these sites. There was a key role for professional networks (including opinion leaders) in spreading knowledge for primary care doctors, although nurses relied more on interactions within local teams. Pharmaceutical representatives and patients were also important sources of information. The authors presented a social constructionist model of learning and introduce the notion of ‘collective mindlines’ to describe how an especially adaptive mode of knowledge and understanding arises within professional communities of practice: ‘mindlines were therefore iteratively negotiated with a variety of key actors, often through a range of informal interactions in fluid communities of practice, interactions with and experience of patients and practice meetings. The result was day to day practice based on socially constituted knowledge’. 69

Their findings are echoed in Waring and Bishop’s70 ethnography of informal knowledge mobilisation and sharing activities in ‘the backstage’ of two NHS day surgery units. The study moved away from formal KM accounts to look at learning through informal ‘water cooler’ moments: ‘although these semi-private and casual situations constitute a potent vehicle for knowledge sharing, learning and the maintenance of organizational and occupational values based on privacy, trust and mutual understanding. They could also be seen as aligned with views on situated learning within COPs [communities of practice] or networks of practice’ (p. 326). 70 Waring and Bishop70 argue that the challenge is to try to align these informal processes with more formal patient safety and learning systems, but not in such a way that over formalisation drives out these more interpersonal ‘water cooler’ moments.

Currie and Suhomlinova’s71 well-cited paper explored limits to processes of knowledge sharing across various organisational and professional boundaries in a case study of an English academic health centre that sought to bring together the health and higher education sectors. Using a ‘neo-institutionalist’ framing, which distinctively is interested in organisational imitation or processes of field convergence across the health-care sector, they distinguished between three possible institutional and professional ‘boundaries’ that could influence knowledge-sharing processes: (1) between the higher education and health sectors, (2) between hospital medicine and primary care in the health sector and (3) between the different health-care professions. Strong institutional forces were found to segment knowledge and restrict its flow across these boundaries, allied to power inequalities. Hospital doctors remained dominant within the health sector. There appeared to be negative forces in operation driving academic professors and medical consultants further apart, as performance indicators and career-building strategies in higher education placed strong emphasis on publishing in top journals rather than engaging with local practice. These structural forces need to be taken into account in the policy domain: ‘in conclusion, we highlight cultural and political dimensions of knowledge sharing not hitherto recognised by policy makers’ (p. 26). 71 We comment that their use of the concept of an organisational and professional ‘boundary’ may be an important and helpful one. What creates these boundaries? Can knowledge move across them (as suggested in a mode 2 knowledge production framework), for example such as through the use of ‘boundary objects’ or ‘knowledge brokers’? Are these boundaries impermeable or semi-permeable?

Similar conclusions were arrived at by Kislov et al. 72 The authors explored KT processes in service improvement projects undertaken in four English general practices, in an initiative sponsored by Greater Manchester CLAHRC. They drew on community of practice (COP) theory73 to study knowledge flows in these settings, defining a COP as: ‘work related communities of individuals created over time through sustained collective pursuits of shared enterprises’ (p. 2). 72 Knowledge may flow easily within COPs, but ‘stick’ when it encounters a COP boundary. Kislov et al. 72 suggested that within the individual practices, interprofessional boundaries were not pronounced and teamwork was often good, but that boundaries were sometimes encountered between the practices and the specialist service improvement teams. In addition, communications between different general practices were sometimes poor. They concluded: ‘it is possible to view primary care organizations as a constellation of COPs united by a shared organizational culture and identity. As a result, constituent (multi professional) intra organizational COPs tend to become tighter than those (predominantly uniprofessional) networks of practice that cut across organizational boundaries in primary care’ (p. 10). 72 The configuration of COPs was found to have a significant influence on the KT process. They argued that a developmental approach to fostering such COPs was indicated, rather than a manipulative approach to try to create managed COPs for KM purposes, which would be unlikely to ‘stick’.

Finally, Jacobson et al. 74 presented a new model of KT in Canadian health care based on consulting activity from academic units to complement existing ‘interactive’ models. Given the increased attention on ‘impact’ and KT activity involving academics now being displayed in many academic settings, this model of highly applied working is of interest. They drew on three cases of projects undertaken by a health systems research and consulting unit working on mental health services and based at the University of Toronto. They concluded that ‘consulting appears to be an effective strategy for carrying out an interactive model of KT to enhance the use of research based knowledge in decision making environments’ (p. 317). 74

In summary, the literature sensitises us to the complexity of knowledge mobilisation and confirms the need to adopt a multilevel approach that is aware of how different values, interests and expectations shape knowledge and innovation flows in practice. There are also suggestions that stronger interactions between knowledge producers and consumers can aid knowledge mobilisation activities. 52,74,75 The literature adds a strong concern for the operation of ‘knowledge networks’, a topic that we discuss below. By contrast, there is much less attachment to formal hierarchy as an effective governance mode to support innovation. Finally, the literature suggests that we should search for novel disciplinary perspectives that cross conventional disciplinary boundaries (see, for example, Shaffique76).

Academic Health Science Networks: learning from previous experience

Academic Health Science Networks were established on the back of a number of previous national and international initiatives aimed at fostering the emergence of ‘systems for delivery of innovation’. 1 These initiatives included the AHSCs and the CLAHRCs. What lessons can be learnt from such previous experiences?

Networks as forms of governance in the UK health service

We signalled the potential significance of regional governance arrangements to promote innovation earlier in this review. There is also an established literature on managed networks that have been promoted as a novel governance mode in UK public policy and applied in English health service settings. This reflects a policy turn from principles of market competition to network governance and collaboration, and became increasingly evident during the New Labour period (1997–2010). The use of this form of organisation for innovation and health improvement is also increasingly underpinned by a solid evidence base showing the capacity of networks to deliver results.

Harvey et al. ,87 for example, used a comparative case study-based approach to examine the correlates of high academic performance in four health-care research groups in one NHS region. They found that strong network connectedness was associated with high performance levels in elite health research groups. Their inductive model identified five factors behind this positive outcome: (1) strong strategic research leadership from a group director (so there was an individualistic element of leadership in this case), as well as specific ‘product champions’ lower down the hierarchy; (2) finding, rewarding and motivating talent; (3) strategies of related diversification; (4) strongly linked theory and practice; and (5) network connectedness, which could be seen as a key competence and which was placed in the centre of the visualisation of the model. The study corroborated the argument from the science policy literature (examined earlier) that there is a general shift proceeding in the dominant mode of knowledge production from an academically based and centralised mode 1 to a more dispersed, transdisciplinary and pluralist mode 2. The underpinning message is that networked organisations are the institutional form of choice to address the societal changes in a mode 2 knowledge production system.

The importance and operation of knowledge-orientated ‘managed’ networks in health-care settings was explored in several papers (e.g. Ferlie et al. 5). In a comparative case-based study, Addicott et al. 88 looked at the (early) evolution of the five Managed Cancer Networks set up across London in the early 2000s. One argument for such managed networks was that they should promote more effective knowledge mobilisation: ‘networks in health care were initially proposed inter alia as effective forums for sharing knowledge about ‘best practice’ and developing educational opportunities across organizational and professional boundaries’ (p. 95). 88 Empirically, the cases suggested the dominance of more policy-sensitive issues of structural reorganisation (especially the centralisation of specialist services) over KM. Of the five cases, however, one network appeared to be developing as a positive outlier, where there was a more developed focus on learning evident in the nursing subgroup and some of the clinical subgroups. These themes were picked up in a large-scale study of the evolution and impact of a set of eight managed health-care networks (four pairs drawn from different policy sectors, typically involving multiple players with different interests), all set up in the early 2000s. 5 The two Managed Cancer Networks studied were both assessed as higher performers. The argument was that, in order to make an impact, these networks would need first of all to develop competences within three different areas: (1) a move from vertical management to more lateral leadership, (2) stronger shared information and communication technologies (ICTs) and databases and (3) well-developed interorganisational learning.

Therefore, the emergence of managed networks as a novel governance mode in English health services raises different issues, one of which is how can we best study such forms of organising: an issue to which we now turn.

How to study networks and networked initiatives

The increasing attention on social networks and the recognition that they play a central role in the creation and diffusion of innovative knowledge has put social network theory and SNA, the methodological toolkit for studying social networks, on the research map in management and organisation studies. 89,90 Traditionally, this perspective was focused at the individual level of analysis (the personal or ‘ego-networks’ of managers) and prioritises competition and exploitation of opportunity over collaboration, and has spurred a wealth of subsequent management research. A typical example is the central notion of structural holes theory, which claims that individuals positioned at ‘gaps’ in a social networks are provided ‘structural advantage’ because they are better able to tap into heterogeneous sources of knowledge emanating from these fragmented networks, thus supporting their capacity to innovate. 91,92 More recently, however, the approach has been extended to broader social formations and is increasingly used to study and shed light on the dynamics of collaboratives and alliances. For example, Powell et al. 93 illustrated that the locus of innovation can be found in networks of learning and alliances, so it is not sufficient for firms to develop external networks, but social networks must offer some form of learning or new information in order to support innovation.

Social network analysis is increasingly used to examine innovation and improvement in health care. SNA is in fact particularly suitable to study phenomena that are dispersed, fluid and emergent. For example, Cunningham et al. ’s94 systematic review of literature on the structure of health professionals’ networks noted a growth of interest in network-based health-care delivery and in the use of SNA techniques to investigate them (given advances in computing capacity and SNA software). They accessed and rated published articles in this area using systematic methods. Of the 26 articles that survived their quality sift, 19 used SNA methods. They reviewed key concepts in the networks literature such as brokerage (bridging activity across conventional boundaries) and network density (how contained/intense a network is; it should be noted that highly dense networks might produce cliques). They observed that much existing literature has explored the structural features of such networks rather than making links to outcomes, and so recommend: ‘using multi methods approaches and exploiting advances in SNA . . . research should examine the relationships between professionals’ network structures and health outcomes in a range of different care settings’ (pp. 247–8). 94 Although their study operated at the micro level of service delivery, this recommendation could be applicable to macro-level health organisational research. How this might be done is demonstrated by a study by Lewis. 95 Here, power dynamics in the health policy-making process are explored within an Australian context. Respondents were asked to nominate influential actors in the policy field. Lewis found that ‘those who top the list are health academics, senior bureaucrats and people located in research institutes and NGOs [non-governmental organisations]. Most of the influential actors are medically trained and the vast majority of them are men’ (p. 2130). 95 These persons also had long standing interpersonal ties and were closely interconnected (e.g. serving on the same major committees). Importantly, SNA techniques were used to map and analyse underlying network structures. Although various clusters were discerned, two were of notable interest. First was a core block consisting of the minister, senior bureaucrats and medical deans (i.e. senior clinical academics who also had senior managerial roles). There was also a second and cohesive public health grouping. Overall, elite but invisible segments of medicine (as opposed to its institutionalised expression in visible interest groups) appeared to have regrouped and recaptured influence in the policy domain from what earlier on had been a challenging economist/management block.

In summary, with the growth of network-based forms of organisation in health care, SNA emerges as an important analytic technique. Network concepts and SNA techniques are a promising way to study initiatives such as AHSNs, in that they ‘provide novel and useful means for understanding the structures of influence which impact on the health policy process’ (p. 2135). 95 They may, therefore, shed light on the professional groups and actors that influence policy, but also on local processes of interpretation and implementation.

Research on leadership, knowledge and networks for innovation

Knowledge-intensive networks tend to bring together a highly educated and professionalised work force potentially good for organisational learning, but also environments within which informal leaders emerge. As such, leadership may be distributed across a wider spectrum of individuals, organisational settings and hierarchies. 101 In complex organisational environments such as health care, leadership requires, first and foremost, the management of social relationships. Accordingly, informal ‘personal networks’ (i.e. friendship or support ties) are important, and research has shown that the maintenance of long-term relationships supports the flow of knowledge across organisational boundaries. 102 In the next four subsections we ask what types of leadership agency support knowledge-intensive innovation networks? What makes leadership effective in networked forms of organisation? In particular, we focus on the types of leadership agency that support knowledge-intensive innovation networks like AHSNs (such as in science, technology and health-care settings).

How to lead mandated networks

In our review of existing research on leadership agency in knowledge and innovation networks we found that very little research has been published on formal leadership in mandated networks; studies on formal leaders in knowledge-intensive firms are much more common. As we noted above, questions about formal managerial leadership and agency in mandated networks remain the ‘800 lb gorilla in the room’. 100

Early research in this area tended to be exploratory. A qualitative study of leadership in four community care networks found that leadership in networks can be differentiated from that found in traditional hierarchical organisations because, in the former, membership is voluntary and egalitarian and often entails co-operation by organisations with different cultures and agendas, and network leaders often lack formal control over members and their actions. 128,129 A literature review of cross-sectoral interorganisational partnerships in health-care domains warned, in particular, that the form and functioning of a collaboration is contingent on its ‘strategic intent’ (the reason for its organising), which may change over time,130 especially given the probable internal epistemic plurality and network decentralisation of these collaborative forms. 131 Leadership processes and style should therefore align with the strategic intent according to the principle that forms should follow function.

More recently, the focus of research has turned towards how formal and informal leaders collaboratively shape the work of the network. Eglene et al. 109 explored leadership issues in four public knowledge network-based initiatives. They suggest that leadership style (charisma and adaptability), formal authority and perceived authority all affect networks’ ability to achieve substantive goals. Silvia and McGuire100 studied the differences between the behaviours of leaders when leading networks and when leading in traditional organisations, finding that when in network leadership mode these senior managers focused more on soft skills (‘people-oriented behaviours’) and less on hard skills (‘task-oriented behaviours’). Their findings are corroborated by Forno and Merlone,103 who argue that the skill sets required to lead networks are different from those needed to lead firms. The authors conceptualise leadership through the entrepreneurial agency of network members and argue that the orchestration of innovation networks involves collective interplay between structure and agency. In another study, Considine and Lewis132 found that formal leaders (political bureaucrats) in innovation networks recognise the potential leadership opportunities and role agency that their network positions offered to them in terms of decision-making, advice and ‘co-ordination of effort and relevant input across domains’. Brokerage potential was clearly understood as a role facilitating ‘strategic linkages’. The authors conclude that the leaders’ perceptions of their roles and associated agency accord with both the potential inherent in their network positions as well as actual activities known to increase the success of collaborative activity. 132

Studies on informal emergent leaders therefore suggest that one of the skills critical for leaders to operate in network conditions is a clear and accurate perception of the social system they operate in. In other words, how leaders understand and interpret their networks is paramount to their effectiveness. This point is argued quite strongly by Balkundi and Kilduff,133 who emphasise the importance of being aware of one’s own network position and the social structure one operates within. They suggest that networks are both actual structures of relationships that link individuals and cognitive structures in the minds of individuals (p. 434). 133 The (cognitive) perception of the state, and of personal organisational and interorganisational networks, leads individuals to pursue and maintain certain relationships, or to launch or avoid certain initiatives. Similarly, Cross and Sproull118 claim that social networks and cognitive motivation to realise network opportunities are linked to individual differences in personality. Finally, Long et al. 134 call for attention to be paid to the leaders’ self-perceptions in order to help explain differences in managers’ motivation to realise the potential benefits that exist in their social networks (i.e. social capital plus personality).

Other research involving the role of formal leaders in mandated networks has investigated the link between leadership and strategic intent in terms of the development and performance of mandated knowledge networks. Martin et al. 99 studied leadership and public–service networks (a comparative case study of cancer–genetics pilots in the English NHS) to highlight the importance of dispersed leadership in achieving change in a networked public–service setting. They argue that this dispersed model allows leaders to be effective in achieving change not only through the network’s formal structure, but also to facilitate complementary action in other organisational forms that coexist alongside the mandated network.

Last, in more recent studies, again relating to scientific research collaborations (CLAHRCs), there is evidence that the epistemological leanings of formal leaders combined with the antecedent conditions around the initial bid and senior team involved in this conception, influenced how the network evolved. 135 CLAHRC directors and senior teams actively built governance mechanisms into their organisational models that subsequently influenced how the network came to be shaped, co-ordinated and enacted. 135 These leaders have been conceived as ‘institutional entrepreneurs,’ possessing a demonstrable degree of creative licence, albeit with more credibility and privilege than enjoyed by leaders with traditional biomedical professional backgrounds. 86 This suggests that, despite various challenges, formal leaders of mandated networks do have sufficient agency in carving out a mission or vision for the network. However, although formal leadership plays a key role in agenda-setting for mandated networks, what is more important perhaps is how this is recognised and responded to by network members and how this mission subsequently shapes the actual programmes of work through which particular social network structures are constituted. 98 Similarly, Bond et al. 136 show how members’ social identification with the mandated network creates positive knowledge-transfer benefits for central organisations and symbolically affects network members’ perceptions of the network’s overall performance in light of knowledge-transfer benefits received, and as an antecedent to affective commitment to the network.

In summary, although some studies address the issue of the role, skill set and recommended conduct of those who aspire to guide or govern networked initiatives, a significant research gap exists around the actual experiences of formal leaders of mandated innovation networks such as AHSNs. In fact, relatively little is known about how the individual leadership competencies and personal networking strategies of formal leaders impact a network’s knowledge mobilisation and innovation activities. This evidence vacuum around how to lead mandated innovation networks has meant that there is no operational blueprint to accompany policy initiatives and learning is thus likely to be experiential, ‘on the job’ and subject to trial and error. More research is required to support leaders of mandated innovation networks who are tasked with engaging stakeholders without any formal power and influence, and often with limited resources.

Leadership and knowledge mobilisation

One of the main aims of the present research is to shed light on the process of knowledge mobilisation, here defined as ‘any attitude or action – joint or individual, observed or imputed – that prompts new and important knowledge to be created, elicited, shared and utilized in a way that ultimately brings a shift in thinking and collective outcomes’ (p. 2451). 137 As we have seen above, the literature considers a number of roles that different actors play in the process (institutional entrepreneurship, brokers, boundary spanners, knowledge activists, change agents and opinion leaders). However, less is known about how to trigger, stimulate, energise and govern such processes, all of which are tasks that we could subsume under the idea of ‘knowledge leadership’. We thus know little about whether or not formal leadership roles and informal knowledge leadership tend to overlap.

A recent exception is worth mentioning. Dopson et al. 12 identified processes of knowledge leadership among managers and clinical hybrids at different levels in the UK NHS and non-NHS health-care organisations. They found that an important competence was brokering the movement of knowledge across distinct knowledge domains, epistemic boundaries and institutions, which encouraged knowledge use by more than one organisation, unit or profession. According to the authors, ‘epistemic boundaries’ refer to boundaries between different knowledge domains and ways of knowing associated with, for example, different professional backgrounds, cultural provenance and research practices. In health care, epistemic boundaries can be found between administrators and clinicians12 as well as between different professional groupings with different research orientations and backgrounds. The researchers therefore go on to ask: how do leaders influenced by management research and ideas mobilise such knowledge into practice?14 The leaders they identified12 often had a strong desire for formal knowledge or a ‘will to know’, apparent throughout their personal biographies and developed over time. For example, KLs might complete PhDs or write and publish articles and books on a topic of special interest. However, these individuals were not merely facilitators and translators of formal management knowledge and research, but rather, personally deeply immersed in – and committed to – producing and diffusing knowledge to drive change in health-care organisations and systems.

In summary, an increasing number of authors are now focusing on formal and also emergent forms of network leadership, that is, how to lead the organisations that are charged with facilitating, brokering and supporting the development and mobilisation of innovations. However, almost no attention has been devoted to how to lead and facilitate these processes and we also know little about the role and characteristics of KLs in such settings.

Discussion and implications for the research

The literature review provides a number of valuable indications for our research. These include both the issue to be investigated and approaches and sensitising concepts that can be deployed to deepen our understanding of such issues. Among others, the following are worth noting.

Commentary on policy documents (2003–15): the NHS innovation challenge and progressing economic growth through the life sciences

We first comment on the key policy documents (2003–15) and their construction of a narrative about health-care innovation. The health-care sector (and the NHS) is here progressively construed as being of economic importance, with recent policy papers increasingly referring to wealth creation in conjunction with health improvement objectives. 1 This strand of policy appears consistent across governments, although post 2010 there is stronger reference to supply side incentives, such as tax concessions and deregulation.

Importantly, health-focused policy texts feature contributions from three central departments of state: not only the DH, but also the then Department of BIS141 (since reorganised, summer 2016) and Her Majesty’s Treasury. 3 Some DH papers1,142 are also open to economic objectives and the strategic role of the life sciences industry, complementing what might be seen as conventional health improvement aims: ‘the NHS has a crucial role in alleviating the burden of ill health, but it also has a wider role in contributing to economic growth, specifically growth in the life sciences industry for which it is the largest customer’ (p. 8). 1

Theme 1: perspectives on health care and life sciences innovation policy

Respondents’ views were generally well aligned with key arguments in the policy texts discussed above. They talked about this stream being on the long-term policy agenda since the early 2000s, but having now gained increasing importance, as reliance on mainstream efficiency measures is not sufficient to transform old NHS service delivery patterns at the pace and scale required through Quality, Innovation, Productivity and Prevention.

Respondents often stated that the NHS was good at inventing new treatments or products, but poor at spreading them. Scaling up innovation remained a challenge. Therefore, weakness in innovation diffusion was a long-term problem that had provoked various policy responses, now including the AHSNs:

. . . you could see AHSNs as just being the latest manifestation of a way of trying to address this issue of how do you reach the value, how do you address the very poor uptake of innovation . . .

Three concrete examples of the slow diffusion of even evidence-based innovative treatments in the NHS were given by respondents: novel anticoagulants, denosumab (a new drug for secondary bone cancers) and sofosbuvir (a radical new drug for hepatitis C). Some respondents reported frustration that pharmaceutical companies are bringing to market some evidence-based products with the potential to provide important health gains [and that have successfully gone through National Institute for Health and Care Excellence (NICE) appraisal], but that the NHS as a system is too slow to adopt them.

Interestingly, as explored later, some respondents noted a switch of interest from conventional incremental innovation to more radical, open or disruptive mode of innovation (Christensen143,144 was seen as a key author) promoting ‘. . . innovation in the radical sense of innovation, innovation outside a drugs, devices and software type innovation.’

Radical innovation included new service delivery modes, such as telemedicine or primary care polyclinics. The self-management of long-term conditions (LTCs) was cited as a significant area in which medical applications could transform service delivery. Concrete examples of science-led disruption were given:

Some of the new bio pharma technology is now disruptive because the science has got better . . . NOACs [new oral anticoagulants] are a case in point, they disrupt a pathway of care.

More radical innovation was advocated because traditional efficiency-based measures are insufficient to deal with the current challenges: ‘what would be achieved by simply making the system more efficient, dragging everyone up to the top decile or top quarter of performance?’ The argument was for bolder innovation, although we comment that reliance on disruptive technology to drive change143,144 may appear unfamiliar to a NHS system that has historically prized system stability and that has strict guidance around the procurement of new medicines and technology.

Theme 2: achieving a co-ordinated innovation strategy for the NHS

Respondents also reflected on challenges faced in building a co-ordinated innovation strategy in the NHS. One problem is that there appears to be a recurrent pattern of rapid agency creation and then dismantling in this field. AHSNs1 built on the earlier AHSCs, set up just 3 years earlier. 4 The older Health Innovation and Education Clusters (HIECs) lasted only a short period of time. Smith146 recently recommended that the national service improvement agency NHS IQ (set up only in 2013) be abolished and some of its functions absorbed by the AHSNs.

Shortly after the establishment of AHSNs, NHS England147 proposed two new programmes to encourage radical health-care innovation further: innovation ‘Test Beds’ to host experiments alongside AHSCs and AHSNs; and ‘Vanguards’ trailing new care models (some 50 high-profile sites have now been established across England). In addition, ‘green-field’ sites in health and social care ‘new towns’ were to provide opportunities for radical service redesign.

It is perhaps for this reason that interviewees (in both this WP and the study more widely) were sometimes candid about the lack of joined-up innovation policy:

. . . the history is frankly of inconsistent funding, rapid institutional change and a reoccurrence of the sort of policy loop where institutions which seem to do a particular job are disestablished and then re-established with a different name.

. . . a very messy landscape of interlocking and overlapping institutions whose job it was to promote innovation.

The NHS was often seen as too short-termist, which created weaknesses in the innovation space. The long-term nature of the ‘innovation journey’51 was highlighted, including the development of a body of tacit knowledge as well as formal evidence perhaps taking place over a 20-year period:

. . . so what you have is a journey of an innovation or an idea which can take 20 years and often does, which includes the accumulation of evidence that it works but also the accumulation of knowhow about how to do it, and particularly complex innovations and service innovations that have multiple components.

Short-termism reflected intense operational management overload: ‘the pressures on the system, the incentives to do imminent things rather than longer term things. There are very few true incentives on people working in an NHS context to think long term, although the system as a whole has to . . .’. This pattern was evident in the innovation policy and regional development space too: ‘this whole thing of setting up institutions to do innovation and then killing them off again’. Frequent reorganisation eroded the organisational memory and core competences needed to foster a good innovative idea over the longer term. Overall, the picture was of a NHS innovation landscape that was ‘unnecessarily crowded’ and too often reinvented.

Rather, there was a need to align health policy better between different streams, including the financial regime (the present tariff-based system – Payment by Results – can pull activity into hospitals rather than away from them, undercutting innovation policy and models).

Theme 3: commentary on Academic Health Science Networks – opportunities and challenges

We consider some positive features of the AHSNs reported to us in interviews and then some challenges. We note that some observations were made in the T1 period (early 2014) when AHSNs were very young. We incorporate later insights from T2 interviews and also comment elsewhere in the report on AHSN development over time.

Theme 4: preferred Academic Health Science Network leadership style and knowledge mobilisation/diffusion of innovation models and approaches

Overall conclusions

Our first conclusion of the policy module refers to policy content. We detect a cumulative policy stream about how the health-care sector can contribute to wealth generation as well as traditional goals of health improvement. Such policy discourse has survived various changes of government. At central departmental level, Her Majesty's Treasury and the Department of BIS have authored important policy documents, as has the DH, two of whose recent texts1,142 also strongly feature wealth improvement objectives. We suggest that ‘wealth AND health’ constitutes an important policy narrative.

Biopharmaceuticals is here construed as a research-intensive sector that could produce high-skill, high value-adding jobs in a stressed economy in which they are much needed. This economic/science focus then produces a cross-sectoral overlap of strategic themes and objectives, scattered across business, university and health sector-orientated policy documents. The creation of OLS was seen as a significant institutional development.

Second, the process of policy-making in this emergent health and life sciences ‘policy community’ deviates from the traditional analysis of the UK health policy community, as described by Ham,140 which narrowly centres on the DH and its close links with senior clinicians and medical bodies. The greater variety of players active in this health policy process is more consistent with the more recent network governance-based accounts of English public policy-making. 149,150 Our work may then be able to inform a current debate on whether or not the nature of the English health policy process is becoming more pluralist. 151

Our (admittedly early) analysis suggests that central economic departments have become important policy actors nationally in this policy stream alongside the DH, and the life sciences sector is increasingly construed as a key partner for government in health and economic policy. This policy field strongly includes industrial ‘peak organisations’ such as the Association of the British Pharmaceutical Industry, which appears influential in the contemporary policy community. There is also some early evidence of advice and involvement by ‘think tanks’ (notably The Young Foundation and Wellcome Foundation) as well as traditional DH civil servants. There is then some evidence of greater pluralism in this particular policy stream than in traditional models of the health policy-making process in Britain, reflecting an increase in the number of actors found and a broadening of policy networks.

There is some evidence of new levels of governance emerging below the national (English) level. The MedCity initiative, specifically, suggests a growing metropolitan and even supraregional level of co-ordination. Our study was too early to assess the impact of city-level devolution arrangements, but this theme should be followed up.

Theme 2 (achieving a co-ordinated innovation system) highlighted the danger of constant agency reorganisation, the multiple agencies still operating in this policy space and the danger that the remit of the AHSNs would not be clearly understood.

Theme 3 outlined some key opportunities and challenges that the AHSNs were seen to be facing.

Theme 4 outlined some leadership competences seen to be needed in AHSNs: a strength network and relationship building, a small team or distributed approach, vision and energy and also a strong cognitive authority were all highlighted as important. Some doubted that the AHSNs have a clear model of innovation and knowledge mobilisation. The work of Rogers7 on diffusion and of Christiansen143,144 on disruptive innovation, alongside triple helix theory, might be the most significant influences on the field.

Introduction

Our first RQ focuses on the role of ‘knowledge networking’ to support the knowledge mobilisation strategies and practices of AHSNs, both nationally and regionally, with an emphasis on leadership. This chapter and Chapter 6 attempt to answer this question in part to present an analysis of knowledge networking and AHSN knowledge mobilisation at two levels: national (see Chapter 5) and local (see Chapter 6). Both chapters draw on our social network and qualitative interview data. Chapter 2 outlines these data in more detail. The social network survey and qualitative interview schedules can be found as appendices to this report (see Appendices 11 and 12 for SNA and Appendices 13–17 for qualitative interview questions).

In this chapter we provide a macro-level analysis of the knowledge networking taking place nationally within and around newly formed AHSNs. In the absence of the previously planned national AHSN knowledge exchange fora (see Chapter 2 for study adaptions), we explore the macro-level role of knowledge networks that evolved nationally across the UK to support knowledge mobilisation linked to the AHSN strategic remit around health improvement and wealth creation. We also identify who was involved in this early knowledge mobilisation and highlight the role of leadership in accessing and diffusing knowledge through networking by studying the networking activities of AHSN boards. Finally, we explore how knowledge generated through these national networks was implemented into practice by AHSNs and their stakeholders.

Insights from the literature

We develop the current literature on knowledge, networks and leadership in a number of ways.

To study knowledge mobilisation around AHSNs, we build on literatures on networked innovation systems (ecosystems, regional innovation systems, CASs) and networked leadership (distributed leadership, complex leadership). Here, the mobilisation of knowledge is conceptualised in these theories as being an interactive, collaborative and iterative process evolving through networks and network building among multiple actors. 50,152 A networked innovation systems perspective offers a broad framework through which to consider how leadership functions in national or region-wide knowledge-sharing networking and is a helpful lens through which to understand networked initiatives, such as AHSNs, which are tasked with responding to a ‘macro-level’ national policy challenge.

We thus develop these approaches using SNA to shed light on the importance of agency by highlighting the role of multiple actors (formal and informal leaders, i.e. AHSN boards and knowledge ‘brokers’) who together make a contribution to the national and regional knowledge-sharing system. In particular, the policy remit for AHSNs to deliver a step change in the adoption and spread of innovation around health improvement and wealth creation inevitably involves collaboration between NHS, academia, the private sector and other stakeholders. Capturing the structure (shape) and composition (distribution of actors and knowledge types) of knowledge networking around AHSNs can offer insights in to the highly complex nature of systems leadership as being relational and distributed because it emerges from the interaction and interdependencies among many individuals actors within these systems. 103,153

Where some previous SNA studies have focused on small/micro-scale knowledge networking (i.e. project teams, within and across firms), we attempt to capture here how such knowledge is mobilised at a national (macro) or regional level to support innovation across sectors and industries to satisfy a particular innovation remit or mandate. This reifies the importance of cross-sector hybrid networked organisational forms for transdisciplinary knowledge mobilisation involving a wider group of stakeholders (as highlighted in our literature review, i.e. triple helix, mode 2 and other). In this chapter we pay particular attention to the types of actors and types of knowledge being mobilised in the early stages of AHSN development to address the system-level challenges of health improvement and wealth creation.

We now provide a macro-level analysis of the knowledge networking found to be taking place nationally within and around newly formed AHSNs.

Early knowledge networking to support health and wealth

Creating a culture for collaboration: who was involved with knowledge mobilisation to support Academic Health Science Networks?

Key finding 2

Knowledge to support early-stage AHSN knowledge mobilisation mainly came from senior leaders and middle managers in the NHS, academia and industry, with a later growing involvement of individuals from other public sector domains and non-management roles.

Our social network survey peer-referral sampling strategy surfaced a national cohort of 818 individuals perceived as being key knowledge mobilisers supporting the AHSN remit at T1. The majority of these knowledge contacts were either very senior leaders (i.e. director, CEO, very senior clinician) or middle managers/clinical managers (45.5% and 42.3%, respectively), with a smaller proportion of non-managers (i.e. junior researchers, developers, trainee medics) also named (12.2%) (Figure 1).

FIGURE 1.

Important knowledge contacts by organisational role.

Looking at institutional composition, the NHS made up the largest component, at 41.5% (tenure from 3 months to 41 years, mean 9.4 years), followed by academia (26%), industry (19%; 9.6% from SMEs and 9.4% large commercial companies) and the third/not-for-profit sectors (8%), with smaller representation from central government (4.4%) and local authorities (1%) (Figure 2). This three-way network building and knowledge sharing across institutional boundaries was seen by many of our interviewees (AHSN leaders and stakeholders) as ‘the start of the blurring of the boundaries’, which would especially influence new connections between the NHS and SMEs.

FIGURE 2.

Important knowledge contacts by sector.

The professional expertise base of these key knowledge influencers was varied (Figure 3): 18.1% were experienced health-care managers, 16.4% were clinical and medical practitioners, 10% had experience in general business operations and management, 7.8% had skills in public health policy, 7.8% were skilled in digital health or health informatics, 7.7% had skills in commissioning and 5.6% had other, unclassified business expertise. In terms of research expertise, 14.4% were trained in clinical, medical or health sciences, 4.4% were biotechnology/life science researchers, 4% were pharmaceutical researchers and 3.8% were social scientists. Note that individuals often worked in more than one sector and had expertise across multiple disciplines (‘boundary spanners’) and were able to self-identify as such; thus, we provide percentages of the total number of selections across categories.

FIGURE 3.

Important knowledge contacts by professional expertise.

Who was mobilising knowledge to support wealth creation?

Nearly half of all new contacts nominated were VSMs/leaders [i.e. CEO/director-level persons (45.5%)], 33.2% were middle managers and a smaller percentage were non-managers (21.3%) (Figure 4). We note the diversification of contacts across organisational roles, especially the increased involvement of non-managers, compared with earlier data. Very senior leaders were regarded as being most knowledgeable across all types of knowledge (products, services, research), but were especially important for the creation of new ideas to support wealth creation. Knowledge sources for wealth were mainly from academic health industry bases, with a growth in industry and local authority contacts from our earlier survey [37.6% NHS, 27.8% industry (14.5% large commercial companies and 13.3% SMEs), 20% HEIs, 6.6% central government, 4.2% third sector and 3.8% local authority] (Figure 5). This is indicative of a ‘triple helix-style’ mode, with government-funded institutions (in this case the NHS and HEIs) and private industry playing a pivotal role in health-care market innovation and growth. 46

FIGURE 4.

Important knowledge contacts supporting wealth by organisational role.

FIGURE 5.

Important knowledge contacts supporting wealth by sector.

In our qualitative interviews, industry actors were signalled as being crucial to the success of AHSNs and significant work was under way to create new relationships in this area: ‘I think if we are successful and make a significant contribution to those two aspects of health and wealth, both of which I mean, . . . personally I think the NHS is just sunk basically without innovation, without industry playing a role and reducing some of those barriers’ (AHSN commercial director).

Academic Health Science Network board members and knowledge networking around wealth creation

Key finding 3

During the early development of the AHSNs, AHSN board members played an important role in triggering the circulation of knowledge to support activities at the core of the AHSN remit, especially for knowledge circulations relating to wealth creation.

We investigated the early role that AHSN leadership played in mobilising national networks for wealth creation. We highlight the knowledge mobilisation activity of AHSN board members in their formal networking capacity as a leadership collective. Figures 6 and 7 are visual sociograms depicting national knowledge circulations around health and wealth relevant to AHSNs in their initial development.

FIGURE 6.

National knowledge networking around health improvement (Health-NET).

FIGURE 7.

National knowledge networking around wealth creation (Wealth-NET).

Individual knowledge contacts are represented as points in the sociograms (arrowheads and shapes) and the connecting lines between points represent knowledge exchanges between individuals. AHSN board members from each of our case regions are represented by shapes and smaller arrow heads represent other agents circulating knowledge in the knowledge mobilisation system. The positioning of individuals as points on a sociogram is determined ‘relationally’ by their knowledge-sharing relationships. The similar two individuals’ networks are (i.e. if they exchange the same type of knowledge with the same contacts), the closer together they will appear on the graph. AHSN board members that are positioned close together on the graph are therefore tapping in to similar knowledge contacts.

The network sociograms show that to access health-related knowledge, AHSN board members mostly networked within their own region (see scatter of AHSN leaders). For example, in Figure 6, AHSN 1 board members scatter most widely across the sociogram, a configuration that reveals that some board members are connecting with entirely different contacts from their colleagues; this is also suggestive of a geographically dispersed set of knowledge contacts (see Figure 7). In contrast, there is a much tighter configuration of board members for AHSN 4, who are tapping into the same knowledge sources as their regional colleagues.

The extent to which AHSN board members hold central positions in both sociograms indicates their centrality to national knowledge circulations related to the AHSN health/wealth remit. We see that the AHSNs with AHSCs (AHSNs 1, 3 and 4) appear as central and most tightly grouped (see also regional-level case studies in Chapter 6). AHSN 5 board members hold distinctly different positions from other AHSN leaders, suggesting that these individuals are mobilising knowledge separately. We also see this pattern to a much lesser extent for AHSN 2, whose leaders, although connecting with the dominant AHSC–AHSN grouping, position themselves at some distance and focus on within-region knowledge exchanges to support wealth creation.

Figure 6 reveals that AHSN board members are leading knowledge networking to support wealth creation by collectively networking with each other and similar sets of contacts. These shared interactions pull together some AHSN leaders into a more clustered display in the figure to provide a ‘critical mass’ of knowledge sharing around wealth creation. The centralised and clustered shape/structure of the network itself suggests a ‘problem-solving’ or strategising formation observed in other civic networks in mobilisation mode acting on behalf of collective and public interests. 154

Our interviews with AHSN commercial directors highlighted an ethos of national-level collaboration to promote national policy alongside regional competition:

So I think there’s a really good, healthy spirit of we’re all in this together and we want the whole movement to succeed, because if the whole movement doesn’t succeed what we’ll end up with is, is totally locally funded entities in the future . . . So I think it’s really important for us that the whole movement succeeds, not just us. And I think most of the 15 of us get that, so we do want to collaborate and we’ve collaborated with the Small Business Research Initiative and other AHSNs. But there’s also a bit of competition and, you know, I want us to be the best, you know, everyone does, don’t they?

National-level networking between AHSN leaders also supported on-the-job learning about network leadership: ‘So there are things I admire about a lot of the others and I’d like to, you know, openly pinch some of that and learn from them’.

Some AHSN commercial directors felt that monthly networking meetings with their counterparts from other regions were very helpful in formulating their own strategy around wealth:

. . . what was really helpful in terms of its formulation was conversations with other people doing my own job, other Commercial Directors from the other AHSNs. So we have monthly meetings which are a great opportunity in terms of, you know, checking how each other’s doing, sharing sort of best practice and ideas as well as, because in terms of what we’re here to deliver, you know, what does success look like because we’re such, we’re like a start-up organisation really ourselves, there’s a lot of debate about, well, how do we actually measure that, so there’s an ongoing debate around that and that sort of really sort of informed the strategy.

Commercial director

Opportunities for AHSN collaboration and networking were further evident at the national level, in the form of the AHSN Network and its meetings for CEOs/MDs, which we were invited to observe. Over time, there appeared to be an increasing emphasis on understanding AHSNs’ collective contribution and opportunities for joined-up working, such as by pooling ‘expertise and resources’. Certainly, at the national level, there was momentum for AHSNs to function as a collaborative movement, with alignment of AHSNs activities with NHS priorities and new initiatives that came on board after the inception of AHSNs (e.g. Vanguards, Patient Safety Collaboratives and Test Beds), as well as with small business research initiatives (SBRIs). 


Academic Health Science Network knowledge networking and implementation into practice

Discussion and implications

This chapter has explored the role of national ‘knowledge networking’ to support AHSN knowledge mobilisation. We summarise the main findings in Box 2.

Our approach to knowledge mobilisation and networking here, drawing on literature on networked innovation systems, illustrates that knowledge mobilised through networks is a type of ‘social capital’ resource, that is, a collective form of ‘capital that is captured through social relations’ providing instrumental gains for the wider collective community in this domain,22 at both national and regional levels. This knowledge develops as a relational resource and is generated through interaction between multiple actors and becomes part of the regional fabric in the same way as its infrastructure and assets. This process will inevitably create contextual differences between regions. 36 Building on our review of literature on knowledge, networks and leadership, we support the view that pro-innovation initiatives such as AHSNs, although responding to a top-down policy mandate, have then to navigate and position themselves as an ‘overlay’ to pre-existing knowledge networking structures.

Our research also provides an emerging example of ‘systems’ and ‘distributed’ forms of leadership to support innovation and change where multiple actors with a collective impetus for action interact across sectors and industries, so that knowledge mobilisation evolves from the bottom up to produce new modes of operating. 155 In particular, knowledge networking to support wealth creation appears to be a key strategic area for AHSNs with AHSN leaders already demonstrating concerted efforts in this area to support national policy. We note at our point of data capture, at the early stages of AHSN development, that national knowledge networking around wealth creation was largely ideas based, but we were able to identify some tangible outcomes from ‘actionable’ knowledge, particularly those linking to pan-regional initiatives.

This research also teases out points of note concerning ‘how’ AHSN stakeholders network to support innovation. Despite the ‘double-headed’ policy agenda positioning health and wealth as mutually reinforcing, in practice we found that, rather than health and wealth being tackled in total alignment, different knowledge networks were activated and built to support health improvement (established, old contacts/’strong ties’) and wealth creation (newer relationships/’weak ties’). This is important because if individual stakeholders network differently to support their health and wealth innovation work, targeted networking strategies may be more appropriate for AHSN knowledge mobilisation activity, and, indeed, some but not all AHSNs were cognisant of this. Alignment between health and wealth may be achieved incrementally, over time, as personal networking supporting health and wealth begins to overlap. Notwithstanding interests and inclinations, we emphasise that changes in networking practices take time and this may be at odds with the short-termism highlighted in our earlier policy chapter (see Chapter 4).

We also point to the dynamic nature of networks and networking in the sense that contacts, relationships and knowledge utilisation are evolving. Our SNA capture is based on networking ‘snapshots’. Looking at two such snapshots (earliest networking to support health and wealth innovation and then later networking to support wealth creation) we illustrate a change in who was involved in such knowledge networking and how knowledge mobilised through networks was being implemented by AHSN stakeholders. The next chapter in this report focuses more on innovation process, but future studies might be able to take a more longitudinal, in-depth approach to the study of networking practices when AHSN activity is further off the ground.

Introduction

To further investigate the role that ‘knowledge networking’ plays to support AHSN knowledge mobilisation, this chapter moves down to a regional level to consider the five AHSNs as studied within their local contexts. It draws on both the SNA material and the qualitative interviews with AHSN leaders and stakeholders to characterise the regional knowledge mobilisation systems that emerged and evolved. We provide a comparative analysis of the five case study AHSNs as knowledge mobilisation systems through a focus on their academic health science infrastructures, the ‘structure’ (shape) of their regional knowledge networks, the types of knowledge being shared through networking in each region and an exploration of knowledge leadership through identification of key knowledge ‘brokers’.

Insights from the literature

In this chapter we further develop the current literature on knowledge, networks and leadership as highlighted in the previous chapter (see Chapter 5). In particular, we study regional knowledge mobilisation systems through a comparative analysis of AHSNs, building on relevant literatures and theories emphasising the iterative, relational and collaborative dimensions of networks – such as networked innovation systems (ecosystems, regional innovation systems, CASs), networked leadership (distributed leadership, complex leadership, systems leadership) and the agency of actors in networks (i.e. AHSN boards and other knowledge ‘brokers’). In light of our literature review, we view these aspects of networking and interactions as structural components of ‘social capital’ that support knowledge mobilisation and innovation. 104

Where some previous SNA studies have focused on small-micro-scale knowledge networking (i.e. project teams, within and across firms), we attempt to capture here how such knowledge is mobilised at a national (macro) or regional level to support innovation across sectors and industries to satisfy a particular innovation remit or mandate. This reifies the importance of cross-sector hybrid networked organisational forms for transdisciplinary knowledge mobilisation involving a wider group of stakeholders (as highlighted in our literature review, i.e. triple helix, mode 2 and other). In this chapter we pay particular attention to the types of actors and types of knowledge being mobilised in the early stages of AHSN development to address the system-level challenges of health improvement and wealth creation.

We take this existing research as a broad framework through which to study, compare and contrast AHSNs as regional networked initiatives working under the same policy remit.

The five Academic Health Science Networks, their regional economies and academic health science infrastructures

In this chapter we provide a regionally based and comparative analysis of knowledge networking to characterise the knowledge mobilisation systems observed for each AHSN region. We first draw on our qualitative interviews with AHSN leaders and regional stakeholders and consultation of documents to provide an account of the five regional case study AHSNs in terms of their inherited infrastructure supporting academic health science innovation (from least to most developed).

The structure of Academic Health Science Network regional knowledge networking

The type of knowledge being circulated by stakeholders through networking

Linking regional knowledge networking structures with the implementation of knowledge

Knowledge leadership and agency in networked systems: who are the key actors and institutions acting as knowledge brokers in each region?

Discussion and implications

This chapter has compared the five regional AHSN knowledge mobilisation systems studied.

Our research suggests that regional knowledge networks to support health-care innovation emerge from antecedent relationships as well as existing academic health science infrastructure (top-down and bottom-up influences). Networked initiatives, such as AHSNs, with an innovation, change or improvement mandate that relies on knowledge mobilisation should therefore be understood as being situated within these differing contexts.

As we have seen, AHSN regions have variant structures of knowledge networking (connected, loose and hybrid integrated types) and AHSN leaders must navigate these networks to generate locally emergent models for knowledge mobilisation under the same policy mandate. Moreover, building new networks around a policy ‘problem’ takes time and change is incremental. These factors will influence how networks, knowledge mobilisation and leadership emerge from the ground up and create variant paths for innovation adoption and spread. Further research could investigate whether or not infrastructure and ‘old networks’ provide an advantage for some AHSNs or whether or not it means different innovation pathways. Chapter 7 explores the issue of path dependency, but other studies might tackle this issue at later stages of AHSN evolution when AHSN metrics and measures might be available for comparative analysis.

We also explored ‘agency’ and leadership in terms of the different actors contributing to knowledge networking. Our literature review highlighted the importance of knowledge ‘brokers’ for networking and the absorption and cross-fertilisation of knowledge. 57,91,92 Senior leaders (including AHSN leaders) with ‘boundary-spanning’ backgrounds (professional expertise spanning across NHS, HEI and industry) were most prominent as knowledge ‘brokers’ in AHSN regions with more connected or hybrid networking systems and developed infrastructure. Our qualitative research showed that these knowledge brokers were also able to take a systems perspective and operate well within networks in terms of being able to pull people and ideas together. This supports a broad, flexible conceptualisation of ‘knowledge leadership’ as operationalised both through the activities of official leaders who occupy formal roles and key network positions and through individuals in less visible and perhaps less senior organisational roles but who nonetheless occupy key network positions and are recognised as KLs by their peers.

Introduction

In this chapter, we present a thematic analysis of knowledge mobilisation strategies for spreading innovations traced over the course of the study (two per AHSN site). These innovations reveal the role of AHSNs in selecting, supporting and mobilising health-care innovations across regional health economies. We present a range of approaches found within the AHSNs we studied and reflect on the role of individuals and AHSN teams in supporting knowledge mobilisation. We discuss four specific knowledge mobilisation strategies or ‘models’ and provide illustrative vignettes of the innovations that we tracked over time. The data presented below form part of a wider case study WP and draw on a range of qualitative interviews, field work observations and documentary analysis [such as supporting evidence, e.g. NICE Technology Assessments (TAs)], wherever possible. Later in the chapter we touch on issues surrounding innovation performance and measurement in AHSNs, before summarising our key findings and their implications.

We point out that AHSN leaders and members of their operational teams – specifically those with roles incorporating responsibilities for innovation adoption and spread – guided us to these innovation ‘tracers’. The concrete innovations we followed (n = 10) were therefore selected in dialogue with the AHSN managers interviewed during the early phase of the study and so these tracers were seen locally as innovations (although they may have already been adopted in other sites so were not necessarily inventions). These persons were asked to nominate streams of activity where issues related to processes of such innovation adoption would be apparent and also researchable, for example, innovations that had been particularly successful in their view or, conversely, those that illustrated systemic barriers to wider regional adoption in the NHS and local health economy. We were aware that these were early days (2014/15) and we (or the local AHSNs) were not in a position to make a full impact assessment of the success of specific innovations, either in terms of evaluating associated health improvement outcomes within specific populations or in terms of economic and efficiency gains at scale. Instead, we collected available information on the types of performance metrics and evidence important to AHSNs and used internally for mobilising selected innovations, remaining attentive to the issues encountered by AHSNs as they attempted to monitor the uptake and progress of selected innovations across local health-care geographies. Our main point of focus was therefore less on the substantive and technical content of the specific innovations we followed, and more on the processes used to mobilise these innovations within AHSNs and the key persons engaged in this type of activity.

Innovations within regional health systems: insights from the literature

In the two previous chapters we described variations between AHSN regions and compared the structures of knowledge exchange networks within this context. Now we look at more ‘micro’ data about specific innovations to identify some general approaches to innovation adoption and scale-up found across our sample of AHSNs.

Two observations are worth restating before examining the specific details of the knowledge mobilisation processes we identified. First, from the literature we recognise that knowledge mobilisation dynamics in health care are multilevel and influenced by individual, organisational and regional factors,11 as well as contemporary policy priorities. This observation was reinforced by the empirical data we collected during this study given that we found knowledge interactions arising between multiple organisations, institutions and agencies, both public and private (e.g. industry, local authorities, universities, national charities and NHS organisations).

Furthermore, local innovation pathways were being actively mediated by national and regional influences, such as the variety of technological and knowledge-intensive firms embedded within geographic regions. As discussed in our literature review, a regional innovation system perspective emphasises the spatial dimension of innovation adoption and the strategic relationships that arise between institutions – such as collaborations between knowledge producers (e.g. universities) and intermediary agencies (e.g. university spinouts) – all of which occur within a context of shifting regional and national innovation policy. 44 Regional innovation systems can be characterised as inward facing and distinctive (consider Silicon Valley or Route 128 in Boston), yet at the same time remain highly porous to global networks and markets. So even where technical innovation systems are responding to similar policy mandates regionally (as in the case of AHSNs), one would expect to find heterogeneity given that different clusters of innovation activity, knowledge assets and forms of investment are found within regional zones. 158 Indeed, attention to underpinning ‘micro-innovation systems’ is argued to help illuminate processes (such as local knowledge sharing and exchange, organisational practices) that support the functioning of larger regional and national innovation systems as a whole. 158 This view is closely related to concept of the triple helix, which relies on regional and national case studies to illustrate the workings of innovation systems from the ‘bottom up’. Etzkowitz and Klofsten,46 for example, discuss how ‘collective entrepreneurship’ between government, business and academic sectors drives ‘techno-economic’ change and growth within a region and emphasise a prominent role for the ‘entrepreneurial university’ (pp. 246–8),46 which provides the capacity and expertise for new innovations, transforming an innovation system from its traditional base through investment in niche areas of research expertise with the potential for commercialisation. 46

This leads on to a second point, namely that networks and networking practices support regional collaborative initiatives, which again highlights the relational nature of knowledge mobilisation. We found in our innovation ‘tracers’ that relationship building and investment in networks of contacts was paramount for enabling innovation spread and regional connectivity. Importantly, this included the NHS but also went beyond the NHS or any single institution or sector. As such, the vignettes outlined in this chapter provide a snapshot (as with our network analysis) of some of the ways in which AHSNs were found to be engaging in innovation-focused relationship building and knowledge mobilisation activities, in a manner that went beyond a linear product adoption pipeline within the NHS. Indeed, our case study data and tracers raised interesting questions about the types of innovations and system interventions that AHSNs can realistically prioritise and support (or should support), given restrictions around funding, local system capacity and delivery time scales, and also the types of targeted knowledge mobilisation mechanisms that might effectively bring about innovation adoption at ‘pace and scale’.

Finally, we briefly find it useful to refer back to the definitions of innovation that inform this study. A central concern in the literature and policy is how to define innovation, which can be a highly overused term. Rogers’ classic definition identifies innovation as a ‘an idea, practice, or object that is perceived as new by an individual or other unit of adoption’ (p. 12),7 so the key aspect is the perception of novelty. An invention may be old, but if it appears new to a recipient or a social system, then it is deemed innovative. IHW1 retained this emphasis on novelty in its view of health innovation as ‘An idea, service or product, new to the NHS or applied in a way that is new to the NHS, which significantly improves the quality of health and care wherever it is applied’. 1 When examining innovation tracers in practice, we referred back to this broad policy definition, but we also remained attentive to how innovation itself was conceived of and discussed by individuals involved with the AHSNs, in keeping with our research objectives.

We also take a processual approach to the study of the career of an innovation over time, building on Van de Ven et al. 51 who tracked the long-term evolution of a number of innovations in US work settings, including in the health-care sector. They found that their trajectories were often non-linear in nature rather than following a prescribed template, going through distinct periods and involving shifting groups of actors and leaders with different interests and visions.

Innovation selection and adoption approaches are far reaching within Academic Health Science Networks

Table 9 provides a brief overview of some the innovations that we encountered in AHSNs. The innovations varied by clinical domain and whether or not wealth creation/business or industry engagement was a predominant point of focus. Many AHSN innovations were addressing long-term and chronic conditions, as these were common NHS priorities for improving population health. AHSN innovations that had been prioritised for spread typically fell within specified work streams and thematic areas that had been agreed at the outset – such as mental health, digital technology, diabetes mellitus, wealth creation or cancer – usually in consultation with an AHSN board and/or membership committee or council. AHSNs were especially mindful of population health needs and local priorities (e.g. through accessing knowledge from an earlier Joint Strategic Needs Assessment), so innovations needed to fit with locally defined needs and recommendations as well as the concerns of a far-reaching range of AHSN stakeholders (local authorities, acute trusts, community providers, industry, charities, etc.). However, the innovations we observed varied qualitatively in terms of their content and approach, reflecting both contextual variations that had influenced their development and the different mobilisation strategies used by AHSNs.

Knowledge mobilisation in practice: typology of four models for health innovation uptake and spread

Academic Health Science Networks were engaged in multiple types of knowledge mobilisation activities and thematic programmes, many of which overlapped with both health and wealth goals. Furthermore, AHSNs were intervening at different points of a long innovation journey, for example moving from early commercialisation and product development to NHS procurement, deployment and sustainable spread. We therefore conceptualise AHSN knowledge mobilisation strategies along a continuum ranging from more targeted initiatives to regional or systems-focused proposals (Figure 8). These strategies cover both knowledge ‘exploration’ processes (searching and discovering innovations and ideas) and knowledge ‘exploitation’ processes (using pre-existing understanding and practices) as outlined by Oborn et al. 85

FIGURE 8.

Typology of AHSN knowledge mobilisation strategies (four models).

We now describe four main ‘knowledge mobilisation models’ seen as ‘ideal types’ of knowledge mobilisation process directed at innovation adoption and spread within and across AHSNs, which we place in a general typology. These approaches frequently overlapped in practice but, nonetheless, had distinguishable features. The four models are:

1. investment in new products and ‘clinical entrepreneurship’ (co- or direct ownership/sponsorship)

2. improvement science and spread of evidence-based interventions (NHS change management and service improvement)

3. innovation networking and matchmaking (stakeholder events and industry/business interface)

4. cross-institutional regional brokerage and support for regional innovation systems (‘triple helix’ and regional economic growth).

Model 1: investment in new products and ‘clinical entrepreneurship’ (co- or direct ownership/sponsorship)

Academic Health Science Network core teams were engaged in actively ‘scouting’ for innovations within their regions. Many of the innovations we identified during our early data collection had originated locally, in research-intensive universities or small businesses (especially universities, spinouts or SMEs), usually with the involvement of a local clinician, academic or product champion who had developed the idea. The innovation might be a product (such as a medical technology device) or a treatment intervention.

One key type of activity found in AHSNs was therefore the mobilisation of health-care innovations by lending support to an interesting and important group of clinicians (who we term ‘clinical entrepreneurs’) and local innovators who had invested substantial time in developing a new product or service improvement idea, but who lacked the business skills or knowledge for commercialisation and/or scale-up across the NHS. Some individuals had commercial experience (e.g. they may have founded an associated company), yet, nevertheless, struggled to get their particular innovation rolled out across multiple NHS sites. So many promising innovations failed to gain traction within the NHS system and had remained dependent on the efforts of their original inventors to drive adoption. This issue did not appear to stem from only a lack of time on the part of clinicians or NHS managers; it was frequently noted that there was generally low capacity and expertise within NHS trusts – which acted independently – to successfully identify, commercialise and scale up ‘bottom-up’ innovations originating from within local services and the wider health economy. There was also the perception that it remained difficult to inculcate a genuinely innovative internal NHS culture with new ideas often remaining in silos, as one core AHSN team member explained:

So there was a lot of that, so things were happening in isolation or clinicians were saying, you know, over time we’ve developed this product because actually it’s better, you know, we’ve found that it works with patients, so it’s just become best practice. We recognise we could develop it but, you know, we’re working with this company, they’ve got the IP but we don’t really know what to do next.

Manager, AHSN 5

It was also observed that the ‘NHS mavericks’ and clinical entrepreneurs present in local health economies would benefit from training and support to help develop their innovations for use within the NHS. One AHSN, for example, had developed a short, intensive training course for clinicians (doctors, nurses and biomedical scientists) covering the basics of innovation development and topics such as marketisation, IP, regulation and manufacturing. The AHSN had offered funded places for a small cohort and drew in different knowledge agencies to develop the training programme, namely external industry experts, local educational providers, as well as levering the business expertise within the AHSN itself (several members of the AHSN had previous health and life sciences commercial experience). There was recognition that in order to create wider cultural impact across the NHS, such learning would need to extend its reach to NHS managers and ‘decision-makers’ in order to bring about greater understanding of innovation as a process; however, small-scale regional programmes provided a positive starting point.

Academic Health Science Networks were therefore directing different types of capital into the local health economy (e.g. financial, intellectual, social) to further promising products and ideas selected for their fit with AHSN clinical themes and license priorities. One hospital consultant involved in a diabetes mellitus innovation reflected as follows:

NHS mavericks by nature are not embraced, that’s always been the case, but you need your mavericks to make things happen . . . So where do the networks come in? The network might be able to harness that maverick power and say right, we’ll give it some structure so that you guys can play in a more bigger palace, a bigger field, and try and lead the change. You’ve done it where you are, can you now lead those changes across bigger areas, bigger patches, bigger networks, there’s mavericks who like that. That is where I think the network has got to play.

Knowledge contact, AHSN 5

Academic Health Science Network support for health innovation arose at different points along an innovation pathway, depending on how developed an innovation was. An innovation might require a high degree of AHSN sponsorship or ownership/co-ownership to get it off the ground. In such cases, AHSN-led open competitions, themed events and ‘scouting’ activities helped to identify local innovations and provide a point of entry for their progress across the NHS–industry interface. An AHSN could ‘pump prime’ an innovation or provide accelerator funding to move a product forward commercially or in terms of establishing its efficacy (e.g. cost-effectiveness, scale-up potential, etc.).

However, given our research interest in knowledge mobilisation as instigated by AHSNs, our tracers were especially attentive to examples of AHSNs supporting ‘roll-out’ and spread. We found AHSNs to be highly active in this space, selecting and pushing health-care innovations that had already secured some level of IP protection and/or research evidence base, but struggling to transform local services and provide benefits to patients at scale (i.e. beyond one or two NHS sites). Typically, such innovations were being promoted locally – and sometimes further afield – by clinical and academic entrepreneurs (‘clinical–academic hybrids’), in ways that were opportunistic and non-systematic. The spread of innovations was in this way highly reliant on an individual’s capacity to promote their particular product or idea through their professional network, but many such persons had full-time roles and limited resources (and time) to promote NHS uptake regionally. There was recognition of this challenge within AHSN teams:

. . . if you think that they used to talk about innovation taking 15 to 17 years to get in to the NHS, we’ve now got an innovation in, with this one, within months. It’s been a year and we’ve got three [Trusts] in, by the end of this financial year we intend to have the rest in. But the point is that there are system blocks that you have to overcome and I wouldn’t want to be a Trust trying to overcome those, or a clinician, if you’re a ground up, I wouldn’t want to be a clinician trying to overcome that.

AHSN 4

Vignettes 1 and 2 (Boxes 3 and 4) offer some concrete examples of innovations being mobilised by AHSNs and some of the processes and issues encountered when an innovation is pushed across the NHS by a network. The first innovation (vignette 1; see Box 3) is an example of a medical technology product – a diabetes mellitus-monitoring device – originally developed within the regional knowledge economy by an academic and further developed by a NHS clinical specialist. Like many other innovations that we traced (i.e. devices and service improvement innovations), the innovative technology in vignette 1 (see Box 3) had been developed over a significant time period (10+ years). Furthermore, there was a published research base emerging about its efficacy, which drew on different research methods [e.g. randomised controlled trials (RCTs)].

The medical technology innovation nevertheless had a long and protracted history around spread: the inventor had attempted to push the original technology into the NHS largely through using their personal and well-connected professional network and sheer determination. They reported a high degree of interest in the particular innovation at multiple levels over the years, including from senior health leaders, policy-makers and health-care professionals.

However, successive periods of NHS reforms had meant that ‘all the people that had been behind setting up the project had moved on . . . I mean a lot of people had given up then and, and I didn’t’.

With the advent of regional academic science infrastructure – an AHSN and also a BRC – real change arose when funding became available to renew work on the innovation and invest in its active diffusion in the NHS. At this point, a local clinical specialist approached the inventor (via their own professional network) to develop the technology further and produce a prototype product that could help respond to pressures arising within local clinical services.

The innovation was later picked out by an AHSN with a focus on scale up and spread. It handled issues such as the manufacture of the technology, NHS procurement and the promotion of technology to local NHS trusts and health professionals. It thus provided support for both the inventor and local clinical leadership to scale up the innovation by navigating ‘complexities’ within NHS contractual and supplier systems, as well as managing relationships with industry (who had potential to scale up production).

Of our 10 tracers, several touched on chronic LTCs, such as osteoarthritis or type 2 diabetes mellitus. In one AHSN, for example, there was a spectrum of AHSN interventions aimed at improving clinical outcomes for MSK patients and up-skilling local health professionals’ knowledge of MSK disorders as an illness. The AHSN reported training ‘over 200 GPs’ about how to deal more effectively with patients presenting MSK issues within 10-minute GP consultations and sponsored a second innovation, which we explored in depth, designed to enable better patient self-management and education as well as clinical outcomes (vignette 2; see Box 4).

Similar to the innovation described earlier, the MSK innovation had originated regionally within a HEI, led by a clinical academic. However, it ‘hadn’t been rolled out at all actually in this neck of the woods’, and in one local area the innovation had ‘died a death’, leading to a modified version being used. For this reason, innovation fidelity and NHS uptake were deemed problems for a very promising intervention.

The innovation highlighted dynamics around innovation ownership and scale-up as some tensions emerged around knowledge mobilisation at scale. The originator of the innovation had traditionally been the first port of call for early adopters and visited sites to promote uptake and to ‘get this thing out there on the streets’. The innovation had therefore been traditionally mobilised through a direct and highly personalised approach, such as through professional networking and ad hoc interest, a theme that recurs across our tracers. Consequently, the innovation had been applied in pockets of health-care practice without additional capacity to monitor innovation fidelity or sustainability.

The AHSN had a view to ‘get this going’ and ‘push the boundaries a little bit’. It was perceived as providing the ‘kudos’, ‘credibility’ and ‘push’ required to take forward implementation, offering a momentum previously lacking within local HEIs (whose interest in the innovation had been primarily research focused). Yet despite several evident successes, the AHSN had also struggled to promote regional adoption across the NHS as a result of prevailing cost pressures. This had resulted in a stronger community adoption focus and a focus on non-NHS providers.

We return to some common difficulties for knowledge mobilisation and innovation barriers later in the chapter (see Issues encountered and knowledge mobilisation barriers), but note the major challenges faced in both of these examples of an innovation that has already been mobilised locally on a small scale, but has struggled to gain acceptance across the NHS more widely (such as becoming incorporated in NHS provider or commissioning plans). This part of the spread process was found to take a huge amount of effort from persons knowledgeble about NHS decision-making processes and able to actively liaise between many organisational boundaries.

Therefore, we found that locally derived innovations that showed a great deal of promise for patient impact and for which there was evidence of efficacy (such as a high-quality research base of RCT standard, but also smaller pilot studies) were selected by AHSNs for wider adoption. Mobilisation processes that AHSNs engaged in included supplying expert knowledge about the NHS or innovation process, funding, project management support and capacity at service delivery level (e.g. through backfilled clinical posts). AHSN operational teams and middle managers also played a key role in promoting adoption across a larger number of NHS trusts by putting together NHS business plans and dealing with NHS supply procedures.

In the main, these types of innovations had been in existence for some time and stemmed from academic infrastructure and spinouts. They had been initiated by a key cluster of academic innovators and clinical entrepreneurs who were research active and highly motivated by goals of service change and modernisation in the NHS. However, these innovations had not necessarily been NICE endorsed (although they may have started the process), and were requiring further evaluation and research if they were to be rolled out across different contexts. As such, they were very much ‘bottom-up’ innovations endorsed and sponsored by AHSNs but requiring investment to realise their full potential.

Model 2: improvement science and spread of evidence based interventions (NHS change management and service improvement)

National Institute for Health and Care Excellence TAs were sometimes selected as an important area as AHSNs responded to a need for system-wide, sustainable spread of innovations that had already passed rigorous tests of economic and clinical efficacy. In this way they were addressing inconsistent usage of treatment guidelines and proven interventions and the challenge of ‘unwarranted regional variation’ in the NHS. The NICE TAs we observed addressed a variety of clinical areas such as diabetes mellitus, stroke, arthritis, nutrition, cancer and dementia.

Innovation, Health and Wealth1 promoted the wide-scale adoption of innovations that would strengthen NHS performance and add value and, above all, were replicable across settings (p. 9). Possibly for this reason, support of evidence-based and approved NICE TAs was viewed as an important inclusion by some AHSNs, in addition to their receptivity towards novel innovations originating within their regions.

This type of knowledge mobilisation had strong links with the theme of medicines optimisation at regional and pan-regional level and intranetwork knowledge sharing was important for knowledge mobilisation, such as co-operation with regional CLAHRCs (where present), SCNs and possibly other AHSNs. At the national level, we noted that AHSNs were discussing the NICE Implementation Collaborative, indicative of support for national partnership working.

Often the ‘knowledge contacts’ and inventors we spoke with about the health-care innovation landscape recognised the need for a positive and collaborative relationship with NICE and its associated programmes. However, there were also some frustrations mentioned about NHS evidential requirements for innovations, especially around medical technology and new technologies arising from smaller firms or spinouts, which lacked RCT-type trial capacity, as one product developer explained:

. . . the other barrier for us at the moment is the level of evidence required is, it’s almost in line with a new drug . . . we’re medtech but actually we’re not a medical device because we don’t actually provide any diagnostic or therapeutic impact, we’re effectively a better delivery mechanism . . . we know that there’s a great case for this and it fits perfectly with the NHS need to take some costs out of the system and to provide more support around mental health. . . . we’ve done some trials but they’ve been service trials, but we have evidence that it will improve the service delivery. What we don’t have and what I’m very keen for and what we asked the AHSNs was to have the clinical side of that trial.

Interestingly, the Five Year Forward View147 discussed the intention for the NHS to expedite the health innovation adoption pathway by supporting ‘lower cost RCTs’ and evaluations in cases in which full RCT trials were not feasible (p. 34). 147 There was also acknowledgement by the NHS that medical technology devices (as opposed to drug interventions) required support, for example through ‘operational pilots to generate evidence on the real world financial and operation impact on services’. 147 Nevertheless, in practice, we found that the resourcing of such trials was still difficult for innovators to leverage, even with AHSNs active in this space.

For AHSNs, sponsoring NICE TAs were a ‘win/win’ given that they were institutionally backed by the NHS and in an area where AHSN–industry collaboration was possible: ‘I’m getting a lot of requests to say can you help us with the uptake and reduction in variation in the NICE guidelines essentially . . . we’ve got best practice that’s been identified, you know, evidence based and we’ve got industry saying we’d like to engage with you on this, how do we work collectively to do that’.

However, some AHSNs had encountered resistance when attempting to mobilise NICE TAs in their regions:

I think people want different things from the AHSNs. So the commissioners don’t want us to get more NICE TAs used, they absolutely don’t want us to, they don’t want us to make them do that or persuade them to do it . . . Because they see it as NICE TAs are expensive.

AHSN 4

Three of our tracers focused on NICE TAs and covered clinical practice in CVD and stroke care, and diabetes mellitus (we present only two here for reasons of space; Boxes 5 and 6). They illustrate several core issues in the knowledge mobilisation of evidence-based interventions: (1) even where there is a convincing evidence base, regional and local knowledge mobilisation is particularly effortful, requiring boundary-spanning activities across organisations, sectors and networks and relationship building for maximum impact; passive dissemination is highly likely to be inadequate; (2) in a climate of severe financial restraint in the NHS at trust and commissioner levels, short-term cost drivers and concerns about decommissioning can present difficulties for the adoption of best practice within local health economies; and (3) changes or controversies in the evidence base at national level may slow down implementation and adoption processes at the local level.

Interestingly, the attempted spread of NICE TAs by AHSNs had engendered a fair degree of multiagency working and opportunities for shared learning. The interventions were embedded within clinical systems; therefore, in many respects they were attempts at service quality improvement along established care pathways or, if more ambitious, regional attempts to reconfigure an entire treatment pathway to improve clinical outcomes and realise cost savings. Compared with some other innovations selected by AHSNs, NICE TAs tended to have clear, pre-established outcome metrics, and, therefore, performance measurement and impact assessment were altogether more straightforward.

Model 3: innovation networking and matchmaking (stakeholder events and industry/business engagement)

Early on, health improvement networks were found to be more mature in our SNA than wealth-orientated ones (as previously discussed in Chapter 5), indicating that AHSNs were tapping into previous networks of relationships and initiatives (largely NHS and academic contacts) to support innovation adoption and spread around health priorities. By contrast, the emergent wealth networks differed in their composition, suggesting that new contacts and relationships needed to be accessed to address their objective of wealth creation.

Our tracers illustrated the degree to which AHSNs needed to invest in relational and networking efforts across sectors and different parts of the NHS to support knowledge mobilisation, as indicated by one AHSN manager: ‘I think that we do have a great role in pushing the new technologies and all that type of stuff too, but we shouldn’t forget that actually it’s that forging the relationships and building the networks’.

So ‘managing’ and ‘brokering’ relationships for innovation was a core activity, one that frequently involved areas in which traditionally NHS engagement was perceived as poor or difficult to overcome, such as linkages to SMEs and industry:

. . . from a pharma point of view what has been interesting is that they have been allowed almost a different insight into the health-care market. So this is about them being seen as collaborators and partners. It is trying to change the relationship of pharma and health care. And I think that is our role as a knowledge broker to go they have a skill set. It was interesting because you get the health-care professionals in a room and they know their part of the pathway very well whereas pharma appreciate the whole pathway because of their interactions across all of the different pieces.

AHSN 2

Some AHSNs had invested resources mapping regional assets and businesses in the local economy early on, particularly in the health and life sciences and SMEs and spinouts, so they were acquiring knowledge on the key firms and partners within their regions and potentially how to connect them.

Some AHSNs that we observed were acting as innovation ‘matchmakers’ with the commercial sector, for example by creating spaces and forums where interested parties could meet, share knowledge and showcase new products and innovations. Knowledge mobilisation in this way centred around the practice of digital communications (websites, e-mail updates, newsletters, online platforms, etc.), events and behind-the-scenes knowledge brokering. To further support brokering with the commercial sector, events were organised by AHSNs targeted at specific health innovation subsectors such as the pharmaceutical, digital health and medical technology industries.

At the national level, we noted that building ‘a culture of partnership and collaboration’ was a collective objective for all AHSNs. In practice, we found this being promoted locally in the AHSNs we studied through inclusive governance structures (e.g. large membership councils and oversight committees with a direct influencing role) and an array of AHSN-hosted or jointly hosted events, sponsorship and leadership of collaborative efforts around service improvement.

Many of our tracers touched on these knowledge mobilisation strategies of network building, partnership and matchmaking (where individuals or groups were connected to progress particular innovations). Indeed, model 3 underpins many core AHSN activities.

To illustrate how AHSNs attempted to grow networks, we provide one example: a diabetes mellitus programme led by an AHSN. This aimed to create a cohort of ‘champions’ who were recruited to act as regional ‘change makers’ and then roll out improvements in diabetes mellitus care through their own professional networks. Several large pharmaceutical companies were involved, in addition to the third sector and local branch of Health Education England. Champions included individuals from primary care and CCGs, community pharmacists, hospital consultants and pharmaceutical representatives. Knowledge mobilisation in this case relied on the collective enthusiasm of champions; however, this meant that quantifying the overall impact of the programme was inherently problematic. However, in our qualitative interviews with champions, we found that improved knowledge mobilisation and self-confidence were considered important outcomes of the programme.

Vignette 5 (Box 7) illustrates how AHSNs were also engaging with SMEs during the course of the study. Very often, they were helping SMEs and other non-NHS organisations to negotiate NHS procedures in ways similar to model 1: ‘When I first got into post I put me and the SME in the middle and I drew lines out to all the people, all the organisations that I felt would be of use to my role and I made an effort to go out and explain to them what the AHSN was doing and what my role within the AHSN was doing’ (AHSN 3).

Model 4: cross-institutional, regional brokerage and support for regional innovation system development

The vision for AHSNs, set out in policy, was that they would serve as a ‘gateway’ between academia, industry and the NHS, and bring together disparate stakeholders from local authorities, the third sector and research communities (pp. 18–19). 1 AHSNs were tasked with disseminating impactful and effective innovations by working closely with other NHS innovation and research organisations (such as AHSCs). We found examples of cross-regional brokerage undertaken by AHSNs that brought together an array of non-NHS organisations. This type of intervention often built on antecedent networks (such as local HIECs or innovation hubs) or a member of an AHSN’s own professional network, especially in the early phases of the AHSNs. Compared with model 3, they show more prominent features associated with the concept of the ‘triple helix’ stream of literature (as discussed in Chapter 3) and were more university and/or growth focused.

One such ‘macro’ example is provided by vignette 6 (Box 8). The AHSN that we studied had funded an expert role to support the development of a life sciences cluster in the regional economy. The concept of a cluster was not in itself new and predated the AHSN. The AHSN provided a suitable ‘vehicle’ to take a concept further and helped to facilitate collaborative working with local partners – notably county councils, a strong regional university and LEPs. Across these partners, there was ‘strategic alignment’: for the AHSN, the concept of a life sciences cluster aligned with its wealth creation objectives (in this instance, the creation of new jobs locally and capacity building the life sciences); for external agencies, they had access to some funds and business support and expertise via the AHSN (the brokering of relationships and concept was led by an individual with academic and commercial experience and funded by the AHSN). In this way, innovations supported by an AHSN might extend beyond the NHS and have an especially strong economic, commercial or geographic focus in terms of investing in regional infrastructure for growth.

Vignette 6 can be considered an outlier given the scale of the regional ambition and less immediate focus on population health, yet the brokering of strategic relationships across sectors and agencies (such as LEPs and councils) was a common activity in the AHSNs we studied.

An interesting feature of AHSN activity in this model was how networks were functioning as intermediary boundary-spanning vehicles by finding shared strategic interests across industry, universities and government agencies – in ways outlined by ‘triple helix’ theory (vignette 7; Box 9). Nevertheless, it was frequently boundary-spanning individuals with knowledge of each of the university, the NHS and commercial sectors in health care or life sciences who were able to effectively broker institutional relationships given their personal networks and knowledge. Agency within the triple helix, as well as institutional relations, therefore appears highly important.

Our findings further suggest that local councils and other publicly funded innovation agencies may have been overlooked by triple helix theory given how strongly the concept emphasises the role of entrepreneurial universities in driving economic growth. Indeed, multiple external sources of external funding (e.g. such as from the European Union, local enterprises or industry) lent support to regional innovation in the health and life sciences, not simply HEI funding. Some of the AHSNs we studied were proving highly successful at leverage such funding streams within their regions (vignette 7; see Box 9).

Academic Health Science Network learning about knowledge mobilisation over time

Academic Health Science Networks should be seen as dynamic networks, with the implication that that their knowledge mobilisation strategies were also evolving over time. Some interviewees noted how at the outset of AHSNs they had perhaps been naive about innovation spread or attempted a more directive role aimed at diffusing high-impact innovations. Over time, greater cross-sector collaboration, matchmaking and regionally focused activities appeared prominent. There was less emphasis on direct AHSN ownership of innovations and a greater sense of matching local problems to promising innovations, some of which might more radically shape care pathways:

. . . we have moved away from pushing innovation to let’s work with our partners to define their needs and their challenges and then bring in industry, but not for the sake of bringing in industry but for the sake of solving the problems.

AHSN MD

There also appeared to be a growing understanding of effective approaches to knowledge mobilisation and the pervasive system challenges that needed addressing, such as NHS complex decision-making processes, along with issues around evaluation and evidence needed for innovation adoption, scale-up and replication:

I suppose joining the network it’s been quite a steep learning curve to go forget the traditional hierarchies that you’re used to and now start exploring and it doesn’t matter what position they are. And I suppose that’s, certainly I’ve been very conscious of in how I’ve approached things.

AHSN 2

In particular, negotiating NHS efficiency savings and engagement of CCGs was reported to have been difficult across the AHSNs that we studied and these efforts had not always worked out so well. AHSNs were also at times struggling to find a balance between target-led knowledge mobilisation strategies and the need to ensure wider membership coverage and stakeholder engagement which risked spreading their resources too thinly:

The one thing that I probably did not realise when I came into post, when I started looking at it, you know, this would be bought in an acute trust and you can see where products belong, probably sometimes better than the manufacturers and producers of them. But the most difficult people to get hold of, sit around the table with has been CCGs and it is the CCGs who really hold the purse strings to a lot of these products. . . . But the focus of the NHS at the moment especially with innovation is about saving money. Innovation has to demonstrate it can save money or else it’s just not going to get in there because there is no money to buy innovation.

AHSN 3

One of our principles is that of inclusivity. We have not yet spread our tentacles as it were as widely as we want to. But I think that’s important that we do that. However, there are an awful lot of organisations out there like CCGs, primary care, or acute trusts or mental health trusts or community trusts. I think pragmatically one has to try and get on board those that are most likely to come on board and do it as a sort of slowly incremental process.

AHSN 2

Knowledge mobilisation influences

. . . now we need to think slightly differently about the spread.

AHSN MD

Academic Health Science Networks in the early days were not drawing on explicit academic models or knowledge (e.g. from the social sciences) to support innovation adoption at pace and scale, although Rogers’s influential text7 was sometimes cited. Service improvement, collaborative and social movement methodologies also appeared to have had some influence (e.g. from Health Foundation and IHI texts), as one MD confirmed:

Yes, I don’t like diffusion, it sounds a bit passive but I know it’s in the textbooks. So I think we’d be open to a whole range of methodologies. . . . And I guess the two things which are most common that we’re looking at are the collaborative methodologies, so the breakthrough collaborative, but also then social movement.

AHSN 5

Academic Health Science Networks 1 and 2 showed more academic approaches to knowledge mobilisation, reflecting the influence of their local CLAHRCs. AHSN prospecti and documents outlined service improvement models, which included robust outcome measurement, better translational research capacity and evaluation and placed heavy emphasis on the academic base. Other sources sometimes viewed an AHSN in some ways as more linear – as a ‘downstream’ pipeline for the new science-/evidence-based innovations coming from an AHSC: ‘we have essentially defined a boundary between the two, they do the origination of technology and we do the spread of technology’.

However, our tracers illustrated that such a pipeline model often tended to have already been proven as ineffective in practice, with a long lag time for even locally developed innovations to enter health practice (empirically shown in our diabetes mellitus and MSK examples). The pipeline model was by itself too narrow and communicated badly with some stakeholders weary of a purely ‘academic science’ approach to spread.

Knowledge mobilisation based on ‘pushing’ evidence-based interventions and/or guidance (e.g. those originating from NICE) was apparent (as illustrated in model 3) and sat neatly with AHSN concerns to provide evidence of innovations that were impactful, effective and measurable (‘we are very data driven’). Yet in practice, service improvement and networking was often evolving through ‘bottom-up’ forms of collaboration and communication with stakeholders to create an ‘architecture’ for knowledge exchange and opportunities for ‘dialogue and learning’.

Academic Health Science Network 3 demonstrates the importance of tacit knowledge and antecedent networks for learning about knowledge mobilisation and innovation. Most AHSN directors in this site had previously worked together in some capacity within the region and so had a good understanding of each other’s roles, responsibilities and strengths. Some held other formal and informal roles spanning clinical, research and commercial engagement functions and so were boundary spanners and networkers. The directors spent much time out of the AHSN office at other sites – such as AHSC, university, NHS. In this sense, the leadership approach was distributed. This AHSN would align common assets and networks (e.g. leadership academy, improvement initiatives, along with a complex mix of a CRN, CLAHRC, AHSC, HIEC, clinical networks and local education and training boards) to deliver structured implementation, and used the official NHS Change Model to try to drive change. Networks in the region shared intelligence through ‘knowledge ambassadors’, which signalled a wider shared intention to develop an innovation system. The AHSN had identified three conditions for faster spread:

1. alignment of priorities for improvement

2. implementation using a disciplined improvement method for change [the AHSN enlisted a NHS innovation hub and a health research and technology facility (a translation of a US model), Advancing Quality Programme, university evaluations and the CLAHRC approach were referenced]

3. incentivisation.

Academic Health Science Network 4 referenced some academic literature reviews of innovation uptake, plus Rogers’ model. 7 During interviews, it emerged that the team was engaged with specialist university-based knowledge transfer agencies and tapping into HEI networks to widen its reach. A general AHSN knowledge mobilisation strategy was less easy to discern during the early period and ‘top-down’ adoption of evidence-based innovations was prioritised (model 2). Yet by the mid-licence period, the AHSN was exploring how to best to evaluate models of innovation adoption and capture its tacit knowledge and learning about effective innovation adoption drawn from examples within the region. There were indications that greater strategic thinking in this area was coming together. There was also more proactive engagement and co-ordination over time with other translational networks in the region (i.e. a CLAHRC, LEP and BRC).

Finally, in AHSN 5 various approaches were detected, which further developed through time. The AHSN prospectus had positioned the network as a ‘strategic space’, where learning and well-chosen risks would be encouraged across a large regional system. Some early thinking around assessing ‘system resilience’ as a concept did not survive, but ideas about an approach that was collaborative, multisectoral, and learning and change orientated did. Links with more basic academic work undertaken by the CLAHRC were still in development, even at a late stage. AHSN had staff, and also visited staff, from NHS IQ in search of ‘best practice’ ideas, such as social movement-based approaches including NHS Change Day (referenced in a presentation to the local Patient Safety Collaborative).

However, by 2016, it was clear that promising innovations needed extra push with ‘spread’. With help from an external consultancy, the AHSN (2016) produced a text outlining evidence-based guidelines on potential for ‘spread’, drawing on the basic work of Rogers7 and Greenhalgh et al. ,67 along with a structured process including a regional panel, for assessing spread potential. More internal resource was put into knowledge mobilisation work, as well as the directly clinically related programmes. The text was ‘road tested’ with members, bringing in local, tacit and experiential knowledge, and has been trialled with three or four programmes. It was reported that a dementia-related innovation in primary care was now spreading rapidly, previously having been ‘stuck’ (the target was for 75% or 80% of the practices in the region to adopt the innovation by 2018).

Issues encountered and knowledge mobilisation barriers

Every trust has their own financial instructions. They all have to follow public procurement roles. The only quick way to buy this at the moment is through supply chain.

AHSN 3

So I called NHS supply chain and said right, you know, the people that you have on the framework, it doesn’t work for us, we need another supplier and this is the supplier. We’ve done our market research, here it is and these are the reasons why they don’t have certain things that we need. And they said well I’m sorry but that’s tough because basically we won’t be going through that round again till 2017.

AHSN 4

Our tracers revealed a number of barriers to and enablers of innovation spread, yet complex NHS supply and procurement processes often stood out as common issues requiring much expertise and time to be negotiated for each innovation a AHSN took on board. In summary, the following issues repeatedly came up as impacting negatively on effective innovation spread:

• NHS procurement and complex institutional architectures/processes

• funding for sustainability

• shifting evidence base/guidance (e.g. regulations, NICE)

• low trust/cultural barriers (both industry and NHS)

• time and resource restraints (e.g. for relationship building, sourcing innovations, supported implementation)

• standardised metrification given diversity, attribution

• engagement with clinical commissioners (‘product sell’, ‘battle’, lack of responsiveness).

Innovation and performance: the complexities of monitoring uptake, measuring success

In practice, the processes around innovation were often highly complex as AHSNs were attempting to find synergy and crossover between their health and wealth goals, ensuring that streams of activity and projects generated wealth but were also underpinned by clinical themes and core values (such as population health improvement). The tensions between the two sets of goals were not always easy to resolve. In many instances it was far from straightforward to manage an innovation process over the short term, especially where innovations were very early in development, or had a more large-scale or regional focus, meaning that potential returns on investment could take years or even decades to manifest (e.g. regional science park vignette). In the more triple helix-orientated vignettes (e.g. MSK group), an additional complication was the presence of multiple stakeholders within a regional network (e.g. AHSN, HEI, LEPS), each keen to attribute success for themselves but often with different indicators.

At the same time, AHSNs were under severe pressure to measure and demonstrate impact. Some necessarily crude indicators were being used in practice in the early days, such as the number of NHS trusts/primary care practices claiming to have adopted an AHSN sponsored innovation – such as a NICE TA – or, in the wealth creation domain, the number of new jobs or amount of internal investment claimed to have been secured.

As AHSNs progressed we observed greater co-ordinated attempts across AHSNs (at the national level) to demonstrate a collective impact through aggregating metrics and activities across the 15 networks. Metrics were being developed against core AHSN objectives, for example health improvement (e.g. the number of interventions resulting in patient benefit and/or fewer secondary care admissions); partnership and collaborative working (e.g. the number of organisations activity engaged in AHSN work streams and projects); adoption and diffusion (e.g. the number of sites adopting specific AHSN-sponsored innovations); and industry engagement (e.g. the number of introductions brokered by AHSNs between industry and the NHS). A publicly available impact report in 2015 also highlighted wealth creation achievements with SBRI involvement, for example > 100 contracts being awarded for new product development (The AHSN Network 2015; www.ahsnnetwork.com/wp-content/uploads/2014/12/AHSNs-impact-report-2015.pdf). Nevertheless, this important theme of success and performance measurement was still a complex and evolving area given the multifarious nature of the innovations and activities being supported by AHSNs in practice.

Discussion: key themes across the tracers

Insights from the literature

To better understand the characteristics that are important for persons leading innovation networks or in leadership roles in networked innovation settings, we first conducted a review of literature relating to leadership, knowledge and innovation networks (see Chapter 3). We found little on knowledge leadership in general and specifically on the formal and informal leaders of mandated innovation networks such as AHSNs. In addition, although an extensive literature exists on knowledge brokers and their role in promoting knowledge mobilisation, little is known about their individual leadership competencies and personal networking strategies.

In particular, we found that the existing literature on knowledge leadership is usually restricted to the organisational level and identifies generic behaviours that apply to managers and leaders of groups and small units. 159,160 Viitala,159 for example, suggests that knowledge leadership stems from orientation to learning, support of individual- and group-level processes, acting as a role model and creating a wider climate that supports learning. These behaviours are traditionally associated with leading teams and groups for innovation. 161 Little is known about the behaviour of individuals who need to animate broader and less structured networks such as the AHSNs.

More useful insights can be derived from the small body of literature on the leadership of networks. As discussed in Chapter 3, authors suggest that, in dispersed organisations and networks, leadership is derived from a combination of formal status, experience, power, being well connected and informal leadership. The last stems from perceived status and leaders being perceived by their peers as well connected across boundaries (i.e. geographies, sectors and hierarchies). The literature therefore warns against confusing formal authority and informal capacity to act as catalyst of knowledge mobilisation. Leaders of mandated networks are seen to have influence and authority but not necessarily knowledge leadership. This may require other characteristics, although what these are varies. As Mabey and Nicholds162 put it, in large networked organisations, knowledge leadership is unlikely to be ‘a manifestation of traditional “top-down” hierarchical mechanisms’ (p. 43). 162

Although the literature provides few indications of how and why certain individuals are perceived as playing a leadership role, it also suggests that much is to be gained if we cast our net widely and adopt an open view with regard to who could be considered a KL in an inherently loosely bounded initiative such as AHSNs.

Identifying local knowledge leadership

To explore the concept of knowledge leadership in AHSNs, we first used the survey of key knowledge contacts in the UK health-care innovation domain named through a peer referral sampling methodology (see Chapter 2).

In our social network survey we asked respondents to provide detail on the characteristics of their nominated knowledge contacts. This would provide some indications of who stood ‘out of the crowd’. The results are illustrated in Table 11. The top two categories across all AHSNs were ‘leader in their field’ (33.1%) and ‘an expert in their field’ (31.6%). This suggests that peer recognition of a person’s leadership quality as well as possessing specific expert knowledge are key prerequisites of being a KL. Being ‘well connected’ or ‘influential in their field’ were regarded as secondary characteristics (18.7% and 16.6%, respectively).

The perceived characteristics of knowledge contacts varied between AHSNs (Table 12 and Figure 9). We asked our survey respondents about the knowledge contacts they had named: Do you consider this person to be a leader in their field; an expert in their field; a well-connected person; influential in their field (please select all that apply)?

FIGURE 9.

Perceived characteristics of knowledge contacts (multiple responses allowed).

In all AHSN regions respondents were more likely to name as ‘important knowledge contacts’ for health-care innovation people who they perceived as ‘leaders’ and ‘experts’ in their field (except for AHSN 1 where respondents named contacts in relatively equal proportions across response categories). Discounting AHSN 1 and comparing the other regions, a knowledge contact’s ‘influence’ was most important in AHSN 3 (the region with an academic health science ‘ecosystem’ with a ‘hybrid’ knowledge network structure). Being ‘well connected’ seems to be an important characteristic of a knowledge contact in AHSNs 2 and 4 (regions with ‘connected’ knowledge network structures).

It is important to note that these findings reflect how individuals perceive their knowledge contacts, and are not necessarily accurate reflections of the nominated person’s leadership quality, expert status, personal networks or influence.

Findings

Apart from a small number of respondents who stated that they could not identify obvious KLs regionally, interviewees could identify specific individuals who in their view were especially engaged in the spread or adoption of health-care innovations and instrumental in making this happen. Individuals were attributed knowledge leadership for a number of reasons.

First, individuals were identified as KLs because they were at the top of their organisation. There was a sense that the combination of their social position and the qualities that allowed them to occupy the office in the first place enabled them to fulfil specific roles within the network; for example, this turned them into powerful gatekeepers and brokers. In the words of one of our informants:

They have traditional power because of the organisations that they are in and they have personal power as well because they are all charismatic individuals . . . It is an interesting thing about networks, isn’t it?

It is worth adding that some informants emphasised that occupying a formal position often is not enough and, indeed, innovation and transformation comes from individuals who ‘sit outside’ the formal structure. Formal position and leadership tended to reinforce each other.

Second, individuals were considered KLs due to their capacity to give access to material, cognitive and social resources. Most responses fell within this broad category. A small number of respondents associated KLs with the capacity to give access to material resources in terms of funds and money. KLs were thus recipients of large grants or individuals who had secured substantial investments. Much more attention, however, was given to the other two types of resources. Many respondents suggested that KLs were individuals who could provide access to critical knowledge and understanding. Particularly important were the span of knowledge (e.g. having a national outlook and a broad overview), breadth of skills (including business skills) and capacity to provide alternative views and act as carriers of new ideas. KLs were thus nominated because they had in-depth understanding of how the system worked; they ‘. . . look at things on a national basis’; they have experience and carry a historical memory; and ‘they have an ability to think outside the box’. By giving access to crucial knowledge and understanding, these individuals ‘helped [to] contextualise . . . where an AHSN can add value’.

Equally important was the sense that the nominated individuals could provide access to social resources in terms of contacts, access and relationship. KLs were thus especially well connected, in constant touch with other influential people, and provided valuable contacts:

. . . [He] has been instrumental in setting up a range of networks both locally, regionally and nationally.

. . . His network is extremely good and he has helped us to establish partnerships that otherwise we were struggling to do.

One recurrent characteristic was that these individual wore ‘multiple hats’ and thus could span different worlds and bridge local and national levels of activity:

He is managing to combine very rigorous world-class research with a local implementation.

They might have strong views and they might be able to offer them very eloquently but they are happy to listen to other views and to know how you could put them together.

Importantly, social brokering, that is, the capacity to provide a link between individuals and groups, was considered as a mean to an end. What made these individuals stand out was not so much having a vast and influential network as making this available as a resource for the AHSN:

If you want somebody to go and talk to local MPs [Members of Parliament], the Minister, well they’re always talking to them, they’re always on the bloody phone to them. So you have to recognise that he occupies a space that is much more than his job, just by the nature of who he is.

Third, many respondents suggested that KLs needed not only to be very well connected, but also accessible. Respondents often identified KLs as people who could act the ‘first port of call’ and who were able to provide support:

. . . it is those people who are interested in sharing and supporting others raise their grain rather than compete.

Not all respondents agreed on the last point. Some respondents told us that KLs were considered as such because of their distance and influence in spite of the absence of personal ties.

Fourth, several respondents associated KLs with specific personal qualities. These included empathy, ‘great personality’, capacity to engage with different public and charisma:

Now [this person] is charismatic in the extreme, he’s funny, he knows what he wants to do and I love working with him.

Other notable traits mentioned were visibility in the public sphere, credibility within their professional group, integrity and personal history and being considered a source of experience and wisdom. Communication capacity and an ability to transmit a vision or share passion were also mentioned as reasons for considering a specific individual as a KL:

[He] has the ability to engage very well and to sell the message and see it as an opportunity, and he will have particular traction with academics because he is one, he’ll have traction with the NHS because he works in the NHS and he has a delightful personality and he understands it.

Finally, individuals were considered KLs because of their capacity to make things happen or because of their effectiveness. Their leadership quality therefore stemmed from the results of their actions, which made them positive role models for what could be achieved:

She makes things happen and she’s constantly bringing learning from other programmes to the attention of whoever she happens to be speaking to, so I’d regard her as a good model and a very effective operator in that respect.

Interestingly, not only demonstrating effectiveness but being able to communicate it was associated with knowledge leadership. Effectiveness therefore derived not only from personal action, but also from the capacity to create narratives that allowed different stakeholders to find points of contacts and act together:

I think probably the most effective [KLs] are the ones who are able to tell a good story about why something is working.

The findings from the knowledge contact interviews are summarised in Figure 10.

FIGURE 10.

Findings from the knowledge contact interviews.

Discussion

Our findings align with the existing literature, which suggests that knowledge leadership in networks and collaboratives stem from a combination of formal status, experience and being well connected. Our findings also align with some of the existing literature on leadership. Leadership scholars suggest that in Western societies leadership is attributed according to five different sets of criteria or discourses. 163 These include position (where people operate makes individual leaders); personality (who the leaders are makes them so); process (it is what individuals do and how they do it that make them leaders); purpose (it is why leaders act that makes them leaders); and results (what people achieve connotes them as leaders). Each of these discourses of leadership focuses on and foregrounds some aspects over others. Not unexpectedly, all five discourses were utilised by our respondents, although a clear awareness exists that agency derived from status and leadership are two different things that may or may not go together. Some interesting novel aspects, however, emerge from our analysis:

• Knowledge leadership is complex and derives from a combination of traditional traits (position and personal characteristics; personality and contacts; results and access). One-dimensional leaders who may thrive in traditional organisations are unlikely to be recognised as KLs in the context of a highly networked initiative such as AHSNs.

• Different types of capital164 need to be carefully balanced. For example, structural capital (the capacity to establish connections between distant individuals and networks) needs to go hand in hand with cognitive capital (the capacity to create shared interpretation and narratives that enable effective communication across networks). Equally relational capital (the creation of network of trust reciprocity and obligations) need to be supported by symbolic capital (professional recognition, proven expertise). Carriers built out of one form of capital only are unlikely to be perceived, or be successful, as KLs.

• Social and cognitive capitals are still prevalent, whereas access to material resources is mentioned and considered desirable but is not central. The finding aligns well with the rest of our research and the unequal difference from the health and wealth dimensions of the AHSNs’ agendas. The finding can be read in optimistic terms as a glass half full (the capacity to give access to material resources is considered as one of the traits of KLs) or in pessimistic terms as a glass that is half empty (this type of leadership is partly based on intangible assets).

• Knowledge leadership requires a broad outlook and an in-depth understanding of the existing system of relations as one of their critical functions is to enable change or trigger innovation by creating new and novel links (they are enablers). However, they also need to be – and be perceived as being – rooted in a specific field of competence. Complete generalists are unlikely to thrive as KLs.

• KLs are judged more for what they do and less by their prescribed status. KLs are therefore by definition not only doers but visible doers. Communication skills are therefore essential, although KLs must also demonstrate that they are able to make a difference.

In summary, our study suggests that in a large networked initiative knowledge leadership is likely to emerge as a property of horizontal network interactions, networking practices and networked effects. KLs appear less as heroic doers and more as enablers. Knowledge leadership itself appears less as a stable individual characteristic and more as a circulating function that certain individuals take on themselves for specific reasons and due to specific personal and biographical circumstances. What these reasons and circumstances might be was the object of the last steps of our research into KLs.

From regional knowledge leaders to ‘national beacons’

To shed further light on KLs we identified individuals who according to our survey were ‘hyperconnected’ and highly influential not only within regions but also across them. We selected in particular individuals nominated as KLs with a 10% degree centrality score and who had been named in three or more AHSN regions (n = 14). In clearer terms, these were individuals considered as KLs in multiple parts of the country and had a national-level reputation, prestige and visibility. To differentiate them from the KLs discussed above, we call this selected group of individuals ‘national-level knowledge mobilisation beacons’ or ‘national beacons’ for short. National beacons are therefore interesting in that they are widely recognised as KLs and in a sense they constitute referent models worth investigating in depth. Our aim was not to investigate whether or not they were in fact ‘super-KLs’, but rather what led them to become so prominent in the public eye. So we interviewed them, focusing in particular on their biographies, what they do and what they did to become and remain influential and credible. We also asked them about the activities and skills that they considered central to enable them to act and be perceived as KLs.

Discussion

Zooming in on the biography and work activities of a small sample of national knowledge mobilisation beacons allowed us to further the understanding derived from our interviews with regional KLs. Among others, our findings suggest that:

• Acting as KLs across regions and connecting part of the health-care landscape requires building on personal networks that are accumulated during an entire career. New connections thus build and expand existing ones.

• Managing networking and knowledge leadership is a balancing act that requires one to remain grounded in a specific domain of activity and be perceived as adding value. Although KLs need to create narratives that allow people to find some common ground and work together, they also need to be seen as the authors and owners of those narratives. Alternatively, they may be perceived simply as a ‘gabber’ and lose credibility. In this sense, the traditional dichotomy between generalists and specialists do not apply to the individual as their perceived effectiveness depends on balancing the two.

• Knowledge leadership is not the name of a set of abstract competencies as much the description of a specific type of concrete work. This work is conducted through a number of rather ordinary activities built on the principle that effective KLs are matchmakers rather than broadcasters and, therefore, their success is based on listening rather than talking.

• The idea that KLs are hyperconnected thanks to the extensive use of social media is misplaced. In fact, knowledge leadership and cross-regional brokering is again the result of a careful mix of traditional and contemporary ways of communicating that are strategically chosen in view to the characteristics of the audience.

Core study findings

Although the focus of the study broadened from an early emphasis on AHSN senior managers, our core themes of the strategies and practices of knowledge mobilisation and knowledge leadership endured. So what we have learnt?

Study achievements and limitations

We recognise that the study has some limitations as data collection (SNA surveys, interviews, observation and case studies – including of the mobilisation of specific innovations – see Chapter 7) proceeded over a relatively short time period (early 2014 to early 2016); therefore, it did not cover the entire 5-year licence period. We examined the early development and ‘forming’ phase of AHSNs, meaning that their concrete knowledge mobilisation strategies and practices emerged relatively late in the life of the study. We reiterate that the study was not intended as an impact evaluation of AHSNs against their licence objectives; the research protocol originally developed was in response to a call for greater understanding of knowledge exchange and leadership dynamics in the NHS, having noted an important gap about NHS senior leadership behaviours that might lend support to innovation. So it was beyond our brief to include an impact evaluation and indeed comment that this would be challenging at the national level and require substantial, longer-term resources (see Future research agenda). Our SNA captured snapshots of networking but only at two relatively close time points rather than over a more extensive period.

Achievements of the study include (1) early mapping of the types of networks emerging around AHSNs as mandated networks, with indications of how these networks may be developing over time (e.g. to include a wider pool of actors); (2) a contribution to the currently limited literature on knowledge leadership – both in health delivery research but also in management as a disciplinary field; (3) insights about regional innovation systems and potential opportunities for economic growth within them, which is arguably a topic of significance given economic shifts in knowledge economies internationally (e.g. the expansion of biomedical and life sciences sectors); (4) SNA that helpfully enabled us to identify the multiple actors operating in the health innovation landscape beyond central AHSN teams and to plot emerging wealth creation networks. Finally, we undertook a collection of 10 innovation tracers across a set of five different AHSNs on which is a good comparative data set about knowledge mobilisation practices and innovation pathways on which future studies may be able to build.

Future research agenda

As already mentioned, our study was not set up as an evaluation of AHSN impact, yet there was clearly a desire apparent in the policy and practice fields for such a study. Such a study would not be without methodological challenges in terms of unpicking causal processes around the careers of innovations in what is a highly complex organisational setting, and also in terms of the time lag that might be involved in making such an assessment and the sophistication of the indicators that are readily available. Nevertheless, we suggest that highest priority consideration should be given to such a study, in consultation with AHSNs and their members.

We further suggest that the wealth creation role of AHSNs should also be a high priority for future research; there is as yet a relatively contained volume of research on the wealth creation role of English health-care organisations – despite its rising importance in the policy domain which was apparent in Chapter 4 – as opposed to a more traditional health improvement and clinically orientated focus. For example, the SNA picked up some early evidence on the evolution of wealth networks but over a relatively short period of time. It would be useful to have more longitudinal survey data on the presence of SMEs and industry in these networks and how this is evolving. We found the health innovation landscape diverse, with different levels of innovation being developed in terms of scale and scope. So, large pharmaceutical companies had a presence and role to play alongside smaller start-ups and SMEs, which lacked substantial experience of navigating the NHS. Future research may wish to explore in more depth the consequences of such high diversity in terms of a pipeline of innovation relevant to the NHS and patient populations.

We have as yet very few English case studies of the operation of triple helix-style regional innovation systems, especially where the life and health sciences sector are rapidly developing; therefore, this is also a high-priority area in our view. New partnerships and strategic alliances may be forming between sectors and agencies that are novel and need to be explored.

In terms of what might be seen as longer-term research priorities, there are several further suggestions that flow from our other findings.

Chapter 4’s analysis of national policy around the health and life sciences suggested a broadening of the actors actively engaged as partners in the policy-making process – so possible movement beyond a traditional lobbying role for professional organisations and businesses to greater incorporation of a variety of stakeholders (including industry bodies) within the policy-making process. The role of the OLS was seen as significant, again reiterating the rapid changes under way in the wider institutional landscape. The chapter also raised the question of where national leadership for AHSN development and progress sits, since innovation policy risks remaining poorly integrated and disconnected. This policy stream could be an important candidate for more political science-informed research, which takes into account developments at both national and regional levels.

Chapter 7 began to explore the processes of knowledge mobilisation in relation to the scale-up of innovations selected by AHSNs from within their regions. A few authors and institutes were identified as having some influence on AHSN understandings of knowledge mobilisation (e.g. Rogers7), but this is an area where further work to understand the models in use would be helpful (beyond solely an AHSN or quality improvement focus).

Chapter 7’s other suggestion was that an important role was being played by a small but important group of clinical entrepreneurs and health inventors (including academic hybrids) in the health innovation process. This insight should be explored further: what roles do these individuals hold, what is their career trajectory and skill set? We noted that many innovators had a primary professional identity (e.g. nurse, doctor, engineer, academic), but had acquired a more entrepreneurial approach on the job and broadened their remit into other – often more commercial – arenas. Therefore, the active agency driving health-care innovation could be explored in greater depth, such as how and why individuals select specific problems for improvement and what sustains personal investment in a particular idea, product or service.

Finally, Chapter 8 suggested that there was a distinction to be drawn between actively managing and leading networks and enacting a knowledge leadership role that goes beyond formal role positions. KLs can help spread new ideas about innovation and knowledge mobilisation across health systems and beyond regional boundaries. Our idea of a national ‘knowledge beacon’ is an intriguing one, which could be explored empirically.

Generally speaking, these insights suggest the greater use of longitudinal rather than snapshot research designs, perhaps drawing on the present study as an early baseline. They also suggest that we need a greater awareness of systems-based approaches to the analysis of what are complex and regional innovation ecosystems.

Contributions of authors

Professor Ewan Ferlie (Principal Investigator) was responsible for data collection (policy interviews), data analysis, literature review and was co-ordinator for the report (lead author Chapter 4).

Professor Davide Nicolini (Co-investigator) was responsible for data collection (one site, KLs), data analysis, literature review and writing (lead author Chapter 8).

Dr Jean Ledger (Research Associate, Project Co-ordinator) was responsible for data collection (policy and three sites), literature review, data analysis and writing (lead author Chapter 7).

Dr Daniela D’Andreta (Research Fellow) was responsible for data collection (SNA lead and two sites), literature review, data analysis and writing (lead author Chapters 5 and 6).

Mr Dmitrijs Kravcenko (Part-time Research Assistant) was responsible for data collection (one site), literature review.

Mr John de Pury (Policy Advisor and Partner) was responsible for public engagement and dissemination strategy, feedback on analysis and was a SSC member and final event co-ordinator.

Data sharing statement

This is a mixed-methods study: the qualitative study and SNA component (driven by peer nomination) could identify participants. Therefore, the data generated are not suitable for sharing beyond those contained within the report. Further information can be obtained from the corresponding author.

Disclaimers

This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health.

Search string strategy devised with librarian and based on key terms in relation to research questions

Who?

1. Early adopter* OR leader* OR entrepreneur* OR executive* OR director*

What about them?

1. Agency, social capital, absorptive capacity, institutional design, networks, knowledge transfer

Where/contexts?

1. ‘institutional entrepreneurship’, ‘science parks’, ‘science AND cluster’, university collaborative, Triple Helix, ‘knowledge transfer partnership’. bio-tech, SME, ‘knowledge-intensive networks’, ‘knowledge-intensive firms’, Management, partnership, healthcare, innovation AND policy, university R&D

6a leadership and network agency stream: research questions

1. What role do formal and informal leaders play in mobilising and circulating knowledge in networked organisations? How much agency do these KLs have?

2. What are the antecedent conditions under which leadership agency in knowledge networks happens or is hindered? In particular, what effect do formal leaders’ external connections (personal network) have on the networked organisation’s strategy/capacity for knowledge mobilisation?

3. How does the network organisation itself act as a knowledge leadership system?

Analysis

Subnetworks forming the SNA data set:

1. knowledge networks to support health improvement (Health-Net)

2. knowledge networks to support wealth creation (Wealth-Net)

3. strong tie networks of contacts who regularly exchanged knowledge and had known one another for 10 years or more (Old ties-Net)

4. knowledge networks leading to implementation (Implem-Net)

5. knowledge networks providing access to contacts (Broker-Net)

6. knowledge networks based on new ideas (Ideas-Net)

7. knowledge networks providing political influence or leverage (Power-Net)

8. knowledge networks providing trustworthy advice, guidance or information (Trust-Net).

It is important to note that many standard inferential statistics do not apply because social network data violate case independency criteria as respondents are not sampled independently from their population. Instead, metrics are based on permutation approaches, which are applied to calculate sampling distributions directly from observed networks using random assignment/matrix manipulation across thousands of trials under the assumption that null hypotheses are true (in other words, to calculate the likelihood that the observed network would have occurred by chance).

Objectives of the ‘tracers’ case studies

• Understand each AHSN’s strategic approach to promoting innovation spread at pace and scale at the local level (mobilisation in practice).

• Understand how the AHSN deploys its strategy in practice.

• Understand the nature of the relationship between the project and the AHSN and its development over time as an example of how AHSN strategy in practice has evolved/is evolving.

• Understand how innovation adoption may relate to the contextual features of AHSNs (e.g. regional innovation economy investments, AHSC/non-AHSC, science park/pharmaceutical presence, etc.).

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