Dr Jennie Jordan and Professor Graham Hitchen, CRAIC, Loughborough University London
This essay outlines the case for reframing innovation in the creative industries as a form of Doing-Using-Interacting (DUI) knowledge exchange (Parrilli and Alcalde Heras, 2016). Drawing attention to some distinctive aspects of the sector’s working processes – in particular, the unusually large proportion of freelancers and projects (e.g. Freelancers Make Theatre Work 2023; Cornell et al. 2022; The Musicians Union 2013) – we argue that using DUI knowledge exchange as a lens for examining flows of tacit knowledge, expertise and skills in freelancer-heavy creative ecosystems provides a logic for structuring data collection and analysing the dynamics of innovation in these sectors.
Drawing on earlier research, this essay argues that there are glitches and gaps in both collecting and structuring data to inform evidence-based policy and investment and that these gaps are a consequence of unclear understanding of how innovation processes in creative industries ecosystems differ from other sectors, leading to data that misses key elements of tacit knowledge exchange.
“There is this, what I’ve sometimes referred to as, the kind of ‘dark matter’ of the [creative industries] sector, all this stuff that’s going on, this activity, but we don’t see it, we don’t understand it” (Horizon 5: Data Data Everywhere interviewee)
This essay therefore proposes a theoretically grounded framework for identifying the people, places and processes that facilitate DUI knowledge exchange in the creative industries; and recommends further research to develop and validate indicators which could be used as common data standards for collecting innovation data within creative ecosystems.
Data, Data Everywhere
Horizon 5: Data, Data Everywhere was a collaborative research project undertaken in 2022. It asked what data was being collected on innovation in the creative industries, who by and to what ends (McDonald and Jordan, 2022). It confirmed that data played a pivotal role in shaping policy decisions concerning innovation, but exposed a series of inconsistencies, gaps and disjointed approaches in the available data landscape on the creative industries. This echoes findings from an earlier CRAIC study which noted the weakness in official data in reflecting the reality of creative R&D. Consequently, policymakers and practitioners involved in fostering innovation ecosystems in the creative industries find themselves compelled to gather their own data or explore alternative sources. For creative practitioners involved in the Horizon 5 Data Innovation research project, this was a vital way of understanding and connecting with novel practices and opportunities; one interviewee expressed this as wanting to know how “to navigate ‘dense’ data networks to find what connects me to other creatives” in other sub-sectors.
The intermediaries, connectors and freelancers not captured by formal data-collection were referred to in that Horizon 5 research project as “pockets of knowledge” whose skill-based, embodied know-how and know-who (Parrilli and Alcalde Heras, 2016) played an important role in facilitating innovation. They catalyse innovation and generate spillovers as they move from project to project and between companies, and play a vital role in creative innovation ecosystems.
“What does innovation look like in the creative industries is a really interesting issue. My understanding is that it probably looks quite different for the creative industries than it does for other industrial sectors… for example, the role of freelancers, as pockets of knowledge that get moved through supply chains or across networks… hasn’t reached mainstream policy articulation and it certainly isn’t falling back into creative businesses who really need to understand what innovation looks like.” (Horizon 5: Data Data Everywhere interviewee)
These networks, processes and activities are forms of tacit knowledge which could be described as Doing-Using-Interacting (Jensen et al., 2007; Parrilli and Alcalde Heras, 2016). These knowledge types are embodied and iterative, and skill based – unlike the Science-Technology-Innovation (STI) knowledge that is written down and codified. Being codified and written down in this way, with clear data structures, STI knowledge forms the basis of the data collection models used to evaluate innovation in the economy.
As part of a programme of work to support and understand the working experiences of Edinburgh’s creative freelancers in the wake of the pandemic, Creative Informatics undertook research on their role in the local eco-system. They identified complex patterns of work with freelancers operating as employees, contractors and producers of their own products and services, and sometimes being unemployed. The picture was of curious individuals, keen to learn, entrepreneurial but precarious, without the resources of a large firm to support their innovative ideas to accelerator stage. Key institutions and interventions were identified as important in enabling freelancers to succeed. Edinburgh festivals were open and welcoming to a diverse range of workers to practise their skills and learn new ones; Creative Edinburgh’s networks such as Creatives with Children provided supporting places to interact; and Creative Informatics’ Creative Bridge programme was cited as important in enabling one participant to take time to develop their creative practice and develop their business (Connell et al., 2022). However, data gaps hindered effective policymaking aimed at simultaneously supporting freelancers to thrive while addressing the challenges associated with self-employment, such as job insecurity and irregular income. Consequently, in the subsequent Horizon 5 study, we found many regional and cluster-based policymakers were individually attempting to map these key workers, largely through analogue means such as surveys. Meanwhile, the Digital Creative RTIC developed during the course of the research by The Data City, is an alternative means of trying to capture dynamic data which is not captured in official data-collection.
Since neither the analogue survey methods nor the TDC data-analysis are satisfactory in capturing or assisting an understanding of the knowledge flows highlighted in the Creative Informatics research, an alternative approach is required.
Reconceptualising knowledge flow data
Several attempts have been made to resolve how creative work is conceptualised, most notable is the idea of creative intensity, defined as ‘the proportion of people within an industry engaged in creative occupations’ (Bakhshi, Freeman and Higgs, 2013, p. 3). Assuming creative work is highly correlated with R&D, this definition means Standard Occupation Codes can be used to analyse how much different firms and sectors are investing in innovation. This creative intensity framework forms the basis for categorising the creative economy and has informed the DCMS’s use of data in its economic estimates (DCMS Sector Economic Estimates Methodology, 2022).
However, creative intensity does not account for spillover effects. Spillovers are new ideas or systems that derive from an individual or organisation’s knowledge or expertise but can be used by others without the original creator being fully compensated. Knowledge transfer of this sort is central to most innovation entrepreneurship theories and indicators that attempt to measure it can be found in national and international innovation surveys and analysis (e.g. OECD and Eurostat, 2018; Department for Business, Energy and Industrial Strategy, 2021). In a recent scoping study for DCMS, Quantifying knowledge spillovers from the UK creative industries, Frontier Economics argued that ‘much innovation is derived from everyday activity and problem solving’ (2022, p. 3) rather than specific investment into innovation or R&D. The authors addressed the issue by using an economics concept that categorises knowledge transfer as an input that augments a firm’s own R&D. This framing meant they could estimate the positive impacts of creative industries knowledge by calculating if innovating firms’ outputs surpassed that expected from their internal investments into R&D.
While a useful step forward in trying to track ‘pockets’ of DUI activity, this model focuses on measuring spillover effects outside the creative industries, leaving open questions about innovation within creative ecosystems. What is distinctive about creative industries’ knowledge types that means innovation is ‘derived from everyday activity’, and what does that mean for how knowledge is transferred?
A framework for structuring knowledge exchange data in the creative industries
This essay follows Alhusen et al. (2021) in focusing on indicators of knowledge being exchanged as a core part of the DUI process. In Alhusen’s model, ‘innovative activity’ can be conceptualised as either knowledge flows themselves, or the institutions, processes or people that facilitate knowledge exchange. The authors argue this ‘allows for a better understanding of how knowledge is generated, as well as how it is collected, augmented and distributed within and between organizational units. Consequently, a more fine-grained assessment of how firms acquire new knowledge and use it internally can be established’ (ibid 2021, p. 12). Based on research on SMEs in Germany’s highly successful Mittlestand, a cluster of light engineering companies, they developed a set of indicators that focus on the boundary points where knowledge from outside the firm meets knowledge inside the firm, and on the internal processes that facilitate communication between departments.
In contrast to the formalised structures of an economic cluster such as the Mittlestand, the creative industries are dominated by micro-businesses and freelancers operating in clusters and networks (Bakhshi, Freeman and Higgs, 2013; Easton and Beckett, 2021). Consequently, Alhusen et al’s framing of the majority of interactions taking place between medium sized firms and other entities, supported by formal decision-making processes, requires some adaptation to fit the creative industries fluid and informal ecosystem structures. Relational sociology, an approach that highlights interactions, social ties (‘relations’), and networks, indicates these networks are formative in cultural innovations such as music scenes and creative cities (Crossley, 2015; Fuhse and Mische, 2023). In fields undergoing rapid technological change, some organisations end up moving between established practices across fields, and in the process producing new products, services, ways of working, evaluative standards and regulations (Padgett and Powell, 2012). The synergies currently emerging between the screen industries, gaming and live event sectors are a case in point and provide suggestions as to where indicators of innovation might be identifiable.
Data sources and analysis
An earlier CRAIC study – drawing on research from SQW and Belmana – noted: “There is a need to capture evidence of innovation activity in the ‘joins’ as well as the ‘dots’ of clusters of activity. For example, evidence of networks of relationships and supply-chain activity ought to provide some insights into cluster interaction and that in turn could provide useful insights into R&D activity”
The table below suggests an approach to understanding those ‘joins’, by using the DUI model. It builds on Alhusen et al’s insights that DUI knowledge exchange can be captured and measured by identifying indicators of exchange processes themselves (the points where different perspectives, new technologies interact), or new uses being proposed and tested, and of doing promoting efficiencies. While far from finalised, the table below uses this theoretical framing to suggest common data structures and sources.
Evidence of innovation
|Average years in role
|ONS Employment and Labour Market/SIC code
|increased productivity via skills
|Number of improvements suggested
|Business Innovation Survey
|Process improvements implemented
|Practical training days
|Business Innovation Survey/ONS Employment and Labour Market/SIC code
|Number of skilled creatives employed
|Patents/designs/ trademarks registered
|Successful registration of new designs and trademarks
|Value of registered copyrights
|Copyright Collection Societies
|Increased value of copyrights
|New equipment introduced to a creative ecosystem
|Innovate UK funding/Business Innovation Survey
|New designs/patents registered
|Number of users of new equipment
|New designs/patents registered
|Participation in product trials
|Business Innovation Survey/Designers
|New designs/patents registered
|Number of attendees at relevant conferences
|Event listings/sales data
|Conferences are well attended
|Social contact places
|Number of creative workspaces/hubs/venues in an ecosystem
|ONS High Street data
|Increases or decreases in social spaces in creative clusters
|Qualifications acquired in an ecosystem’s workforce
|ONS Qualification data / SOC code
|Increases in qualifications
|Number of short courses being run
|Event listings/sales data
|Short courses well attended
|Number of network members in an ecosystem
|Network membership lists/event listings
|Network engagement is high
These indicators are a sample and require further refinement in consultation with creative innovators themselves and with data collectors to discuss the feasibility of undertaking such work.
One benefit of this approach is that data can be collected on activity – the ‘joins’ where knowledge exchange is at its most intense – rather than from individual freelancers. This will enable a degree of alignment between creative practice and the models used to evaluate innovation in technology fields. Then, because these indicators are theoretically informed they can be used to standardise data collected. This means data collected through different mechanisms, including qualitative methodologies, will be reliable and interoperable. They could be added to existing innovation surveys such as those undertaken by the OECD or the Department for Business and Trade (Department for Business, Energy and Industrial Strategy, 2021), or used to develop supplementary surveys that focus on the creative sectors.
The theoretical framework is also highly promising in structure, informing the development of data systems as the roll out of AI technologies gathers speed. The significance of informal knowledge exchange via networks and hubs points to these as sites of focus. The key will be to identify the publicly available data on informal learning, for example professional networking events or short courses and coming this with data on facilities providers and with formal data on innovation investment and companies registered.
Options for developing this research
The findings from Horizon 5: Data Data Everywhere showed there was a clear need for timely, reliable and standardised data on creative industry ecosystems to deliver dynamic and nuanced analyses of what is happening on the ground. The consequences of so much ‘dark matter’ in relation to working practices, particularly among the large freelancer cohorts, means that policies aimed at supporting creative R&D are largely based on models that are not a good fit.
The work undertaken in collaboration with The Data City supports the theory that knowledge exchange operates following the Doing-Using-Interacting model more than the Science-Technology-Innovation model. This offers the potential to collect and interpret data according to a common framework across and within innovation ecosystems. The table above suggests some indicators as a starting point for further research to test the potential of this methodology.
To that end we are developing a new research programme which will:
- Consult with business and freelancers in the creative industries to refine the model above, by identifying the ‘links’ that catalyse their innovation and to get a better understanding of where they see this data being captured.
- Consult and partner with data platforms, funding bodies and governmental research organisations to define common data standards for collecting DUI innovation data within creative ecosystems.
- Pilot the indicators across platforms and innovation surveys to test their efficacy and reliability.