Behavioural models like COM-B are indispensable tools for understanding what drives behaviour. However, alternatives like ISM may be a better fit for certain issues. And as practitioners, it’s vital that we use models sensitively and remain aware of their potential pitfalls.

One particular briefing session sticks with me from my early years at Kantar’s Behavioural Practice. Our client was new to behavioural work and, eager to understand what they were buying, had asked us to talk them through the model that we were using to structure our analysis: the COM-B and Theoretical Domains Framework, which draws together 83 theories of behaviour change into a comprehensive array of 14 ‘domains’ of influences split across 84 different ‘constructs’. Following our introduction, they looked across and asked – “Isn’t there a danger that we miss something in amongst all this detail?”

Behavioural models are undoubtedly a valuable tool – for formulating hypotheses, thinking through research design and analysing data. They provide a useful checklist, drawing from a large body of literature, of the factors that might be influencing behaviour. The exercise with our client that day went on to prove this, helping to surface interesting thoughts about what might be driving the behaviour in question that may not otherwise have arisen.

However, our client’s question also lands on an insightful point. Behavioural models are maps to the messy territory of behaviour as it is enacted in everyday life. And whilst maps are incredibly useful, simplifying and flattening complexity to help us navigate effectively, they are also necessarily incomplete. In bringing forth some features, they cannot but take attention away from others. And by focusing in on detailed representations of reality, they also risk obscuring the richness of the life that we’re ultimately aiming to understand.

The widespread application of theoretical models to the understanding of behaviour within government social research can arguably be traced back to a 2008 GSR knowledge review conducted by Andrew Darnton. His paper synthesises over 60 academic theories, models and frameworks of behaviour, to produce a set of nine principles for developing behaviour change interventions. It draws a clear distinction between models of behaviour, for diagnosing behavioural drivers, and theories of change, for identifying which intervention techniques might best bring about change. Notably, Darnton explicitly advocates drawing on a bank of each of these when considering any specific behaviour, to determine which provides the best theoretical and empirical fit. The review ends with a call to retain flexibility and a salutary quote from the authors of an early paper on Intervention Mapping that “one of the potential drawbacks of any policy model is that it will be used as a cookbook”.

Since then, the use of models has become entrenched and practice has coalesced around one model in particular – COM-B and the Behaviour Change Wheel. Developed in 2011 by Susan Michie and colleagues at UCL based on a review of the existing literature, this framework is based on the core idea that behaviour occurs when an individual has the Capability to do so, the Opportunity to do so and – assuming these prior conditions are met – the Motivation to do so. It neatly brings together this model of what drives behaviour with a theory of what intervention and policy categories might serve to change that behaviour. It has also been linked with the aforementioned Theoretical Domains Framework, incorporating many more concepts under the model’s Capability, Opportunity and Motivation categories.

The COM-B framework has become virtually synonymous with behavioural diagnosis. The original paper has 9,678 citations and counting on Google Scholar and, in the world of applied research, many briefs are designed with it in mind. In other words, it has become a default for many practitioners, a position that has been reinforced by the number of studies using it to effectively model behaviour, further building confidence in its validity, perhaps particularly amongst those with less familiarity with the field. This popularity, along with the assumed comprehensiveness of the model, may though mask some of the assumptions that it contains.

The COM-B model is squarely designed around the individual as the locus of analysis. This means that, whilst it does consider the role of environmental and social factors, both major influences on behaviour, these are conceptualised as the Opportunity available at an individual level, focusing attention there as opposed to on the design of systems or make-up of networks. Similarly, whilst the Behaviour Change Wheel does point towards systemic interventions and policy categories, such as coercion or regulation, it provides little direction for a user on how to start unpicking the role of systemic factors on behaviour. None of this is necessarily wrong, but it does subtly direct the attention of the user. And given agreement around the need for a greater consideration of how systemic factors shape behaviour, is there a risk that the model therefore reinforces the discipline’s focus on the individual?

ISM is an alternative model, which was developed for the Scottish Government by Darnton himself alongside colleagues from the University of Manchester. In contrast to COM-Bs theory-based origins, ISM was developed based on a review of 30 cases of behaviour change initiatives aimed at promoting pro-environmental behaviour, focusing on six of these specifically to draw out insight into the potential effectiveness of different kinds of interventions. The resulting model is designed around a set of concentric circles representing behavioural influencers – and potential levers for intervention – at the Individual, Social and Material levels. The categories within each level work both to model behaviour and to inspire thought about what interventions might drive change. It was created as a tool for bringing together stakeholders to co-design around complex behavioural challenges with the suggestion, based on the evidence of the review, that sustained behaviour change is best achieved when there is intervention at all three levels in the model.

The ISM model has undoubtedly received less attention than COM-B. The paper on which it is based has only 98 citations and, in my experience, it is not well known amongst Government policymakers (except for presumably in Scotland). In my experience though, it is a valuable tool that deserves greater use, especially in the context of a shift within the discipline to a greater use of systems mapping. Its contextual model prompts a clear focus on the ways in which individual behaviour is nested in wider networks of people, meaning and physical infrastructure. By doing so, it informs a more complex and nuanced analysis of what supports or prevents behaviour, how these different influences at different levels fit together and what might need to change at what level to best support change. Whilst developed specifically around pro-environmental behaviours, it has particular relevance for complex behavioural challenges in which no one individual or organisation controls all of the levers over behaviour. Which is arguably most behaviours, from insulating a home to eating more healthily to saving for retirement.

To be clear, I’m not arguing that we should all abandon COM-B in favour of ISM, though there are many cases where, in my experience, ISM is the more useful tool. It is more to reinforce the point, made by Darnton himself, that “there is no winning model” and that there is a case for giving greater thought to the use of behavioural models. For example, COM-B was originally developed with a focus on health behaviours and it may still be the most appropriate model to use when considering a very specific behaviour, such as washing hands, in a particularly controlled environment, such as a particular hospital setting. ISM, on the other hand, may be a more appropriate model when considering more sticky or diffuse problems, such as upgrading to low-carbon heating systems, in which behaviour is complex and relies on input from multiple actors influencing such things as such as the state of the technology, the provision of grants, the availability of trusted information, the supply of installers, the behaviour of neighbours and friends, and a whole host of other inter-connected factors.

It’s also worth keeping in mind that no model is perfect. For example, Kantar Public’s proprietary behavioural model, though now little used, contained an explicit reference to ‘legitimacy’, referring to the extent to which an individual perceives it to be fair for them to perform a behaviour. Although Darnton also highlights the importance of this in his 2008 review, it is not explicitly referenced as a factor in either ISM or COM-B, meaning that it could be missed with strict adherence to the model. Yet legitimacy and perceived fairness come up time and again as a factor shaping behaviour and adherence to policies – “Why should I take action to change my behaviour if big business / the government / other people aren’t playing their part?” As I’ve previously argued, as the need for societal change becomes more pressing, there is likely to be an increasing role for participatory and deliberative research in helping to build consensus and a sense of legitimacy around challenging policy shifts.

Above all, it is important to keep in mind that models are representations of reality and should not be mistaken for reality itself. They are enormously useful analytical tools for ensuring a systematic consideration of different influences and, if used properly, for inspiring creative thinking about policy solutions. However, if used too rigidly they risk strait-jacketing thinking or stripping the richness from our understanding, as anyone who has sat through a presentation structured literally around a model will be able to attest to. In presenting the findings of our work, it is our duty as researchers not just to categorise findings, but to synthesise them into insight that illuminates how influences at different levels interplay within specific contexts to drive behaviour. Models can help bring some order to this richness, but it is in the colourful details of everyday life that the best inspiration for connection and intervention can be found.

Recognising the limits of ‘nudge’ interventions to drive positive behavioural change at scale prompts challenging questions about how research can effectively drive action within the structures of government decision making. Participatory methods offer an alternative vision of ‘evidence’ that can complement behavioural science to help spur action.

From individuals to systems

Last year, Nick Chater and George Lowenstein published a paper challenging behavioural science’s focus on individual levels interventions, arguing that:

• Behavioural policy making has been focused on interventions aimed at ‘nudging’ individual behaviour

• An evaluation of the evidence demonstrates “disappointingly modest” results for individual level interventions across a wide range of policy areas

• This focus has crowded out a proper consideration of more systemic reform, via traditional policy levers, such as regulation or taxation

• Whilst this has been carried out in good faith, it has also unwittingly played into the hands of corporate lobbyists wishing to block systemic reform

• The discipline should therefore refocus on informing more systemic interventions, via the application of behavioural insight and research to regulatory and tax regimes

The paper prompted a range of responses – some more receptive and others more critical (and all worth reading!). For me, as someone coming into the field from a background in anthropology and qualitative research, it was a welcome public airing of conversations that otherwise seemed to be happening largely in private. Given the scope of work taking place under the behavioural banner, this felt like an overdue corrective to the gap between the discipline’s public image and the actual concerns of practitioners.

And at its core lies a fundamental point around which everyone seems to agree: the accumulated evidence from years of behavioural work points towards a need for greater system-level intervention to effectively drive behaviour change at scale across a range of important issues.

Structural and political challenges

The implications of this shift in focus to system-level interventions are profound – and are made more searching in the context of the systems and structures that helped embed behavioural science so firmly into policy making.

This fascinating piece of qualitative work carried out amongst Government Social Research colleagues helps to illustrate how the discipline’s appeal within government has been driven in part precisely by its promise of enacting change without recourse to systemic reform. Policymakers are typically operating within constrained budgets and timescale, with limited scope for influence, and pressure to provide evidence of their impact. In this context, interventions focused on nudging individual behaviour that can be tested using experimental methods and rolled-out incrementally have a natural appeal.

By contrast, systemic reform is hard! It requires long-term commitment and carries with it substantial risk. It may rely on coordination across multiple individuals, teams or departments. And it is generally not amenable to the kind of generalisable and replicable quantitative evidence of ‘what works’ favoured within government. Even more fundamentally, the idea of shifting focus from individual to systemic level intervention is itself deeply political, given that it requires by necessity an appetite for greater state intervention. This adds to the challenge of carrying out reform – and takes researchers and practitioners into tricky territory when making recommendations.

In their paper, Chater and Lowenstein propose a series of ways in which behavioural science can be brought to bear on the design of regulation, including a better consideration of ergonomics in service design, the application of psychological principles to improve group interactions amongst policymakers and a more nuanced consideration of how citizens may respond to incentives. However, beyond calls to aim to maximise voter turnout, they do little to propose how research might help to overcome the structural and political factors that could otherwise be a block to passing this legislation in the first place, instead claiming that this will need to be shaped by ‘normal democratic processes’.

For a discipline whose impact to date has been in part due to the way that it has been able to neatly sidestep questions of politics and work with the grain of the constrained policymaking process, this shift into more political territory therefore presents something of an existential challenge.

Participation and collaboration

There are no easy answers to this impasse. Systemic reform will continue to need political will, which will in large part – and quite properly – depend on the actions of political parties as voted for by the public. However, if we look beyond the traditional toolbox of behavioural science, then there are also ways in which research may be able to help.

In their paper, Chater and Lowenstein talk about a shift from the ‘libertarian paternalism’ associated with behavioural approaches. This term reflects two founding tenets of the discipline:

• It is best to avoid legislation where possible so as not to curtail individual freedoms (the ‘libertarian’ bit)

• Individuals are prone to making predictable errors of judgement and therefore do not always act in their own best interests (the ‘paternalism’ bit)

In place of this, the authors suggest the need to shift to a more ‘heavy-handed paternalism’, in support of a shift away from libertarianism in light of evidence about the limited effectiveness of attempts to change behaviour in the absence of systemic reform. However, perhaps because of the paternalistic assumptions that continue to be built into the DNA of behavioural policy, they don’t seem to consider alternatives to this part of the model.

Participatory research methods are one such alternative, in which research and policy making is less about ‘doing to’ and more about ‘doing with’. And they have the potential to effectively bring in all of the parties involved in and affected by policy reform:

• Public Participation: Voting will be vital to achieving democratic consensus around systemic reform, but it is a blunt instrument in relation to specific proposals. Deliberative research methods offer the means to meaningfully involve the public in decision-making in a more targeted way. Deliberative events are designed to offer those who will be affected by policy the time and space to consider issues from an informed perspective, prompting them to draw on multiple evidence sources and input from diverse experts. Whilst important political differences in view can remain following such events, they typically generate considerable consensus over the broad direction that policy should take. The kind of evidence this approach generates can complement evidence of ‘what works’ to demonstrate the democratic case for taking action.

• Stakeholder Participation: Chater and Lowenstein’s paper draws attention to the ways in which business have contested systemic reforms via lobbying. Whilst the power of corporate lobbying is undoubtedly an ongoing concern, in my experience there are also many within businesses looking to the government to produce regulation that will drive their industry in directions that they do not feel they are able to take alone due to commercial and competitive pressures. Facilitated and evidence-based discussions between business leaders and policymakers offer a transparent and constructive way to navigate the design intelligent regulation that raises standards across the board.

• Policymaker Participation: The involvement of policymakers in this kind of participatory work brings them into direct contact with those for or around whom they are shaping policy. It can also connect them to colleagues in different departments who may be working in related policy areas, forging relationships and creating space for collective action. From this perspective, the research process is itself an intervention, with the potential to break policymakers out of the usual organisational constraints that shape their actions.

Driving through systemic reform will remain challenging. However, given the challenges faced by behavioural policy making as it shifts its focus to systemic reform, there is likely to be benefit in scaling up approaches that more directly involve the participation of all parties. Making this happen will require an acknowledgement of the limits of a purely behavioural approach and an appetite for greater collaboration between those doing ‘behavioural science’ and those experienced in participatory methods.

The field of behavioural science is evolving, blurring the boundaries of what now constitutes practice. To make the most of its potential, we need an open conversation about both the limitations and opportunities on offer to those involved in shaping policy.

An evolution in practice

Behavioural science. Behavioural insight. Behaviour change. Whatever you choose to call it, the discipline’s star is undoubtedly on the rise. Since David Cameron established the original ‘Nudge Unit’ in 2010, behavioural teams have sprung up across central governmental departments. Keen to get in on the action, research agencies have also developed and grown their own behavioural teams, including Kantar Public’s Behavioural Practice, where until recently I oversaw the qualitative aspects of the offer. The field is thriving.

During this period, practice has undergone an evolution – and with good reason. The most pressing behavioural challenges we face as a society – including those relating to Net Zero and life-long learning – involve novel behaviours, taking place in fluid environments, in which individual behaviours are nested within complex systems that are in turn shaped by the actions of state and industrial actors. As an increasing number of policymakers have looked to the discipline to inform their work, it has therefore shifted its focus from the development of individual-level nudges aimed at driving incremental change to something much broader.

This is a welcome development, enabling a richer understanding of the full range of drivers and barriers to behaviour – and underpinning the creation of potential solutions to these. Across the last few years, just some of these developments include the greater use of:

• Diagnostic tools, often leveraging qualitative research, to understand and unpick the contextual factors shaping the behaviour of individuals and other influencing actors

• Systems thinking to map the relationships between actors, interdependencies between factors and entry points for intervention at different levels

• Participatory and co-creative approaches to developing and engaging around practical solutions, particularly where these rely on delivery by industry

Blurred boundaries

For those working in the field, this evolution can feel exhilarating, creating a spirit of creativity and innovation. One aspect of behavioural work that has remained constant is the way that it has incorporated research more closely into policymaking – and recent shifts have opened up this influence to a wider range of approaches. Arguably, the practice has become a bit of a ‘magpie’ discipline, borrowing the best from other fields, including ethnography, organisational research, service design and co-creation, and tying them together into a process aimed at creating more human-centred policy.

At the same time, these changes have also blurred the boundaries of what exactly we mean by behavioural science, or insights, or just good old behaviour change. This uncertainty around the name of the discipline is, in my experience, mirrored in a wider lack of consensus around what actually constitutes behavioural work in the present day. Undoubtedly, there is some freedom in this for practitioners, allowing us to draw on whatever approach might work best to understand how to influence behaviour in any given context. However, it also creates a mystique that can sometimes lead to unhelpful expectations from clients who are less familiar with practice and for whom some of the more traditional associations of the discipline still loom large.

This is particularly the case when thinking of some of the more challenging behavioural issues outlined above. I think that most practitioners will have experience of a client looking to behavioural science to provide a quick fix to what is in reality a complex policy issue requiring a similarly complex solution. In other instances, impacts on expectations are more subtle, but there can still be a pull towards arcane technical explanations drawing on seemingly scientific models, at the expense of a nuanced understanding of how to operate with the particular contexts shaping behaviour. Another common issue is a focus on producing quantified evidence to drive forward a policy agenda before a proper understanding has been developed of how best to intervene, let alone whether that response is amenable to experimental measurement.

A call to the industry

Despite these misunderstandings, I can’t help but wonder whether as a community we are also sometimes complicit in maintaining the discipline’s mystique? Cynically, this could read as an effort to shore up our own reputation as experts. More prosaically though, it could just reflect our own uncertainty.

So I’ve started this blog as my contribution towards starting a more nuanced conversation around how the application of theory and practice from across the social sciences can help better understand and positively shape human behaviour.

In my view, the direction of the field as currently practiced presents opportunities for a more contextually sensitive and relational approach to policymaking, integrating a deeper understanding of how individual behaviour is shaped by the wider systems in which we all live and act. To make the most of this opportunity though, it is also incumbent on us as practitioners to be more open about some of the limitations of the approach as it has traditionally been practiced.

In the spirit of conversation, I would like to hear from others. How does the above resonate with your experiences? How should we define the boundaries of behavioural practice – and is that even necessary? And perhaps most urgently, can we at least agree on a name for what we do?!

_{Photo by Towfiqu barbhuiya on Unsplash}