Diverse Academic Health Science Network knowledge mobilisation strategies and practices
First, we highlight strong AHSN-level diversity in the pre-existing assets of AHSNs that then influences their knowledge mobilisation strategies. These assets included the strength of inherited academic health sciences infrastructure (e.g. AHSC, Biomedical Research Centre) and the relative development of science parks and clusters, alongside other health networks [e.g. Collaborations for Leadership in Applied Health Research and Care (CLAHRC)]. These inherited regional characteristics shape the development of regional innovation ecosystems.
Academic Health Science Networks had different types of knowledge mobilisation networks: some were loose and others were more tightly organised. Although there is no ‘one size fits all’ formula for success, each of these different types has advantages and disadvantages. For example, looser networking systems may draw in more and varied new contacts to support health and wealth objectives; however, implementation of newly acquired knowledge may here require more effort, given that networks remain highly dispersed. Conversely, tighter networks may expedite implementation through their strong interconnections, but be less open to new ideas and actors.
Academic Health Science Networks were engaged in a spectrum of knowledge mobilisation activities, which we plotted in a typology of four models or ideal types. We emphasise diverse strategies found, reflecting the wide remit of AHSNs and their multiple stakeholders, which now cross public and private sector boundaries. AHSNs were involved in very differently scaled discussions about how to scale up a regional innovation ecosystem with other partners [e.g. with local enterprise partnerships (LEPs) and higher education institutions (HEIs)], but also how to support individual clinical entrepreneurs to scale up research-based innovations.
We reiterate the complexity of AHSN performance measurement, given the many agencies and stakeholders involved (e.g. AHSNs, LEPs and HEIs). These bodies may well all claim the attribution of any innovation success, but do so according to their distinct key performance indicators (KPIs). In addition, AHSNs have different involvement in and ownership levels of the tracer innovations studied, which may affect the returns that can be realistically expected to go back to them.
Academic Health Science Networks’ approaches to knowledge mobilisation were often pragmatic. There was some use of Rogers’ diffusion model, as well as CLAHRC-related service and quality improvement approaches (Rogers E. Diffusion of Innovations. 5th edn. New York, NY: Free Press; 2003). Texts were used from the Institute for Healthcare Improvement and occasionally from NHS Improving Quality. However, a theoretically well-developed and empirically grounded framework for their knowledge mobilisation activity was generally lacking.
The shape of knowledge networks within and around Academic Health Science Networks
We identified two different forms of knowledge networks, which differed in the types of contacts and knowledge being exchanged. Early knowledge exchanges and networking were more linked to the implementation of national policy and local projects. They were associated with pre-existing ties and established relationships. In the later phases, the knowledge being exchanged around wealth objectives suggested the emergence of new AHSN connections and activities, linked more to pan-regional developments and initiatives. These emergent wealth networks were less mature and based on newer relationships and contacts. AHSN board members importantly helped bridge new contacts. Our later survey suggested that the knowledge being implemented was becoming more specific and ‘joined up’ across the region. We further note that across the five AHSNs, different network types were found locally.
The construction of knowledge leadership
An important finding is that those in formal leadership positions (i.e. in AHSNs or in associated organisations) may not necessarily be the most effective knowledge brokers, as leadership in a complex health system may well be dispersed. The holding of formal authority by itself does not always lead to effective knowledge mobilisation, as it may arise at different points and be undertaken by those with less formal role power.
We identified some attributes of individuals seen as effective KLs: acting as powerful gatekeepers and brokers (indeed easier for those in senior formal positions); having strong access to material, cognitive and social resources or capital; and operating with a broad outlook and breadth of skills. Such individuals might wear multiple hats and/or act as skilled social brokers with strong interpersonal networks. They were good communicators, able to transmit their vision or passion widely. So, we suggest that effective knowledge leadership involves strong relational capital (i.e. strong networks, high-trust relationships). Although personality traits (e.g. communication skills and drive) have a role to play alongside formal role position in knowledge leadership, access to social capital also plays an important role.
We then explored a subset of national knowledge ‘beacons’. These individuals were hyperconnected and influential beyond their own region. We differentiated them from region-specific KLs and contacts. These were high-profile individuals at the top of their profession. Many had long tenure in the NHS or public service; fewer came from private industry. Their generally non-linear careers could explain their high degree of connectivity, as over time they had moved across sectors and professional communities. These peer-nominated beacons were not directly involved in operational-level AHSN work but had wide indirect influence with AHSN leaders and/or teams. They also needed to be visibly rooted in a specialism to have the necessary legitimacy to ‘spread the gospel’. In short, complete generalists, hypernetworkers and celebrities – perhaps with a lot of Twitter (Twitter, Inc., San Francisco, CA, USA) followers but with no NHS track record – are unlikely to be effective as KLs. We found that these beacons grew their network organically and used their position actively to increase their influence, so these mechanisms at some point become self-reinforcing. This finding is different from the traditional SNA argument that one becomes important simply because of structural positioning, and stresses the role of activity.
