In this chapter we explore three approaches to ensuring that an effective intervention does lead to impact: they are scaling, dissemination, and knowledge translation. Each pathway can increase your impact - i.e., desired behaviour and societal change - but approach this goal from different directions and with emphasis on different activities and outputs. We will introduce you to the three approaches before deep diving into when and how to apply each approach.

Taking your offering to market requires a clear message that resonates with the audience. Your message is meaningful or meaningless: either your message aligns with the dominant cultural narrative and is accepted relatively easy, or your message must alter the cultural narrative before it gains widespread acceptance. Progressive ideas shift the dominant narrative, often at great cost to the messenger. Martin Luther King, like Moses, did not live to enter into the Promised Land. What makes a message convincing? What is a narrative? What makes it dominant? How does a message gain cultural acceptance? How does one shift or disrupt a cultural narrative? We will attempt to answer these questions by drawing on a number of diverse ideas and integrating them into a practical model.

On the whole, however, these behavioral interventions have been somewhat underwhelming, exposing an inherent brittleness that comes from three common “errors of projection” in current behavioral design methodology: projected stability, which insufficiently plans for the fact that interventions often function within inherently unstable systems; projected persistence, which neglects to account for changes in those system conditions over time; and projected value, which assumes that definitions of success are universally shared across contexts. Borrowing from strategic design and futures thinking, a new proposed strategic foresight model—behavioral planning—can help practitioners better address these system-level, anticipatory, and contextual weaknesses by more systematically identifying potential forces that may impact behavioral interventions before they have been implemented. Behavioral planning will help designers more effectively elicit signals indicating the emergence of forces that may deform behavioral interventions in emergent COVID-19 contexts, and promote “roughly right” directional solutions at earlier stages in solution development to better address system shifts.

The SMART acronym (e.g., Specific, Measurable, Achievable, Realistic, Timebound) is a highly prominent strategy for setting physical activity goals. While it is intuitive, and its practical value has been recognised, the scientific underpinnings of the SMART acronym are less clear. Therefore, we aimed to narratively review and critically examine the scientific underpinnings of the SMART acronym and its application in physical activity promotion. Specifically, our review suggests that the SMART acronym: is not based on scientific theory; is not consistent with empirical evidence; does not consider what type of goal is set; is not applied consistently; is lacking detailed guidance; has redundancy in its criteria; is not being used as originally intended; and has a risk of potentially harmful effects. These issues are likely leading to sub-optimal outcomes, confusion, and inconsistency. Recommendations are provided to guide the field towards better practice and, ultimately, more effective goal setting interventions to help individuals become physically active.

Method:Three participatory workshops were held with the independent Welsh residential decarbonisation advisory group(‘the Advisory Group’)to (1)maprelationships betweenactors, behavioursand influences onbehaviourwithin thehome retrofitsystem,(2)provide training in the Behaviour Change Wheel framework(3)use these to developpolicy recommendationsfor interventions. Recommendations were analysed usingthe COM-B (capability, opportunity, motivation) model of behaviourtoassesswhether they addressed these factors. Results:Twobehavioural systems mapswere produced,representing privately rented and owner-occupied housing tenures. The main causal pathways and feedback loops in each map are described.

In 2010, Colombia's defense minister contacted an ad agency to create an idea to demobilize FARC members, the oldest guerrilla army in Latin America. The agency, after spending over a year talking to nearly 100 of its members, learned two main things (1). -First, guerrilla members are ordinary men and women and not only guerrillas, a fact which is often forgotten after 60 years at war. -Secondly, they are more likely to demobilize during Christmas as it is a sensitive and emotional period. Based on these insights, they had a clever idea to put a Christmas tree in strategic walking paths in the middle of the jungle that would light up when someone passed by with a message promoting demobilization. The results? Three hundred thirty-one people who demobilized named this idea as one of the reasons to do so. Over the years, several campaigns from the same agency were quite successful, and overall, they were named in over 800 demobilizations. Causality, of course, cannot be established. Nevertheless, any measurable, non-violent efforts like this one are praised. Next time you think you have a difficult-to-reach customer, maybe think again!

CUBES (to Change behavior, Understand Barriers, Enablers, and Stages of change) is a comprehensive framework for analyzing behavior developed by Surgo Ventures. As described in the video with Peter Smittenaar below, CUBES builds on evidence-based behavioral models that are widely used across sectors and includes drivers that show evidence of changing behavior. It illustrates how adopting a new behavior is a process of stages; at each stage, people are influenced by internal and environmental factors (see Figure 1). The CUBES framework articulates three critical components of behavior change: The path toward a target behavior comprises distinct stages of change, progressing from knowledge to intention, action, repetition, and finally, habit. Perceptual and contextual drivers can act as enablers or barriers that influence each individual, shaping their progression through each stage of change. Influencers in the form of family and friends, community, and society can affect these drivers, either directly or via media channels.

A pressing goal in global development and other sectors is often to understand what drives people’s behaviors, and how to influence them. Yet designing behavior change interventions is often an unsystematic process, hobbled by insufficient understanding of contextual and perceptual behavioral drivers and a narrow focus on limited research methods to assess them. We propose a toolkit (CUBES) of two solutions to help programs arrive at more effective interventions. First, we introduce a novel framework of behavior, which is a practical tool for programs to structure potential drivers and match corresponding interventions. This evidence-based framework was developed through extensive cross-sectoral literature research and refined through application in large-scale global development programs. Second, we propose a set of descriptive, experimental, and simulation approaches that can enhance and expand the methods commonly used in global development. Since not all methods are equally suited to capture the different types of drivers of behavior, we present a decision aid for method selection. We recommend that existing commonly used methods, such as observations and surveys, use CUBES as a scaffold and incorporate validated measures of specific types of drivers in order to comprehensively test all the potential components of a target behavior. We also recommend under-used methods from sectors such as market research, experimental psychology, and decision science, which programs can use to extend their toolkit and test the importance and impact of key enablers and barriers. The CUBES toolkit enables programs across sectors to streamline the process of conceptualizing, designing, and optimizing interventions, and ultimately to change behaviors and achieve targeted outcomes.

The issue is: We try to solve every single box in the problem tree. If people don’t know about something, then we solve it by raising awareness. If people don’t care about something, then we solve it by getting them to care more. If people are doing illegal behaviors because of a lack of enforcement, then we solve it by increasing enforcement. We go through the whole set of problem tree causes in this manner, writing objectives with a one-to-one match per problem. Not only does this result in a long list of objectives, which will quickly overwhelm us, it also traps us into solving behavioral problems using logic-based approaches.

Want to build better habits? By answering these questions you will in less than 10 minutes have created your own personalized habit building plan backed by the best from behavioral science. The finished plan will be sent straight to your email.

Less is more As we look for ways to build memories and cement pre-disposition in consumers’ minds, coherence and clarity remain significant enablers across touchpoints and over time. Using Kantar’s Link database, we investigated the percentage of people who play back each of the key messages in ads that have 1, 2 or 3+ messages. One message in an ad has much more impact than multiple One message in an ad has much more impact than multiple Source: Kantar Link database, US TV ads The findings were a clear plea for simplicity. Too many messages can dilute communication as our brains can only really think about 3-4 things at once. So, in essence, the more messages an ad attempts to communicate, the lower the likelihood any single message will be communicated strongly. And although the results are somewhat varied by type of ad - TV or static - and by market, there was consensus that to successfully communicate a product benefit, we need to keep it simple and avoid too many messages. This is advertising 101 really, but with the rise of digital, it got lost in some places.

That’s why we’ve developed an evidence-based approach to identifying and prioritising the most suitable behaviour(s) to address a problem: The Impact-Likelihood Matrix (ILM), developed by our very own Sarah Kneebone. By undertaking a rigorous investigation of the literature and audience research, our technique ensures that the behaviour(s) you choose to target for your intervention or policy will have the highest likelihood of driving the change you are seeking.

Prioritizing work into a roadmap can be daunting for UX practitioners. Prioritization methods base these important decisions on objective, relevant criteria instead of subjective opinions. This article outlines 5 methods for prioritizing work into a UX roadmap: Impact–effort matrix Feasibility, desirability, and viability scorecard RICE method MoSCoW analysis Kano model These prioritization methods can be used to prioritize a variety of “items,” ranging from research questions, user segments, and features to ideas, and tasks.

If you write briefs as part of your job, read & bookmark this. So much that’s NB & useful, from truly interrogating the objective, to making sure the different sections line up, to writing your proposition as a headline, to the brief being a dynamic doc open to improvement.

We present a theoretical model to clarify the underlying mechanisms that drive individual decision making and responses to behavioral interventions, such as nudges. The model provides a theoretical framework that comprehensively structures the individual decision-making process applicable to a wide range of choice situations. We also identify the mechanisms behind the effectiveness of behavioral interventions—in particular, nudges—based on this structured decision-making process. Hence, the model can be used to predict under which circumstances, and in which choice situations, a nudge is likely to be effective.

To date, much of the discussion of behaviorally informed approaches has emphasized “nudges,” that is, interventions designed to steer people in a particular direction while preserving their freedom of choice. Yet behavioral science also provides support for a distinct kind of nonfiscal and noncoercive intervention, namely, “boosts.” The objective of boosts is to foster people’s competence to make their own choices—that is, to exercise their own agency. Building on this distinction, we further elaborate on how boosts are conceptually distinct from nudges: The two kinds of interventions differ with respect to (a) their immediate intervention targets, (b) their roots in different research programs, (c) the causal pathways through which they affect behavior, (d) their assumptions about human cognitive architecture, (e) the reversibility of their effects, (f) their programmatic ambitions, and (g) their normative implications.

In their maturity, the fields of experience strategy and behavior change design are moving past the casual flirtations of two complementary knowledge domains into a full fledged partnership: when we marry the design of behavioral interventions and the design of experiences, there’s a special power in combining the myriad frameworks from both domains. This becomes especially effective when the goal is not just to identify pain points in an existing experience journey or illustrate an ideal future one — but to make actionable recommendations that will help clients make the leap from actual to ideal.

Achieving sustained behavior change takes a long time. I mean, hell, we’re still running ads about buckling seat-belts and most states made it a law 35 years ago! Beyond achieving behavior change, seeing the positive impact of said change on species, habitats and ecosystems can take even longer. So how can we balance these longer term goals with the need to show more immediate outcomes?

I propose a four-stage model below that balances an understanding that each part is essential with the need to break it down into units of work that can be spread across internal teams and external vendors when necessary. But be warned: each handoff increases the potential for loss, particularly when there is an incomplete understanding of the adjoining stages. A tightly integrated process managed by people who understand the end-to-end process will always have the greatest likelihood of creating meaningful behavior change; that we can name the parts should not detract from the need for a whole. Behavioral Strategy: the defining of a desired behavioral outcome, with population, motivation, limitations, behavior, and measurement all clearly demarcated. Plain version: figuring out what “works” and “worth doing” mean in behavioral terms by collaborating with stakeholders. Behavioral Insights: the discovery of observations about the pressures that create current behaviors, both quantitative and qualitative. Plain version: figure out why people would want to do the behavior and why they aren’t already by talking to them individually and observing their behavior at scale. Behavioral Design: the design of proposed interventions, based on behavioral insights, that may create the pre-defined behavioral outcome. Plain version: design products, processes, etc. to make the behavior more likely. Behavioral Impact Evaluation: the piloting (often but not always using randomized controlled trials) of behavioral interventions to evaluate to what extent they modify the existing rates of the pre-defined behavioral outcomes. Plain version: figure out whether the products, processes, etc. actually make the behavior more likely. Behavioral Science: combining all four of those processes. Plain version: behavior as an outcome, science as a process.

To solve problems and suggest solutions on behalf of others is to have power. As a result, we behavioral scientists have a heightened responsibility: Being in this privileged position requires recognizing when and where assumptions about “what good looks like” might creep in. When we design interventions—even just determining what options are available, or what the default choice should be—we shape other peoples’ experiences in ways we may not always fully appreciate. And our decisions to address certain problems while leaving others aside implicitly declares what challenges, and audiences, we think are worthy of receiving attention.

One of the most effective approaches I have learned is called SCIPAB, a technique developed by Steve Mandel and now spread by the company he founded, Mandel Communications. I was lucky enough to be trained in SCIPAB by Mandel Communications as part of a more general “presentation skills“ training. I don’t want to steal their thunder (or their business!), but I do want to share some of the insights that I carry with me and use regularly. SCIPAB is an acronym, which stands for the phases of a story: Situation Complication Implication Proposal1 Action Benefit

In order for the software that supports collaboration and automation in production workflows to interoperate, common data models and schemas for data exchange are needed. MovieLabs and its member studios developed it’s Ontology for Media Creation (OMC) to improve communication about workflows between people, organizations, and software. The OMC can serve as the underpinnings for that by providing consistent naming and definitions of terms, as well as ways to express how various concepts and components relate to one another in production workflows.

We argue that the reason so little progress has been made against obesity and type 2 diabetes is because the field has been laboring, quite literally, in the sense intended by philosopher of science Thomas Kuhn, under the wrong paradigm. This energy-in-energy-out conception of weight regulation, we argue, is fatally, tragically flawed: Obesity is not an energy balance disorder, but a hormonal or constitutional disorder, a dysregulation of fat storage and metabolism, a disorder of fuel-partitioning. Because these hormonal responses are dominated by the insulin signaling system, which in turn responds primarily (although not entirely) to the carbohydrate content of the diet, this thinking is now known as the carbohydrate-insulin model. Its implications are simple and profound: People don’t get fat because they eat too much, consuming more calories than they expend, but because the carbohydrates in their diets — both the quantity of carbohydrates and their quality — establish a hormonal milieu that fosters the accumulation of excess fat.

Developed by the Right Question Institute, the Question Formulation Technique, or QFT, is a structured method for generating and improving questions. It distills sophisticated forms of divergent, convergent, and metacognitive thinking into a deceptively simple, accessible, and reproducible technique. The QFT builds the skill of asking questions, an essential — yet often overlooked — lifelong learning skill that allows people to think critically, feel greater power and self-efficacy, and become more confident and ready to participate in civic life.

Companies should experiment with interactive social media content types, include relevant calls to action in posts, and avoid posting too frequently.

The framework comprises 6 key stages. Each building on the insights of the previous and each with its own objectives, tools and resources: 1. What - are the target behaviours? 2. Who - should we focus our resource on? 3. Why - do/don’t those people manifest the target behaviours? 4. How - can we empower people to change? 5. So What? To what extent were our interventions effective? 6. What Now? How do we apply our learnings at scale?