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.
This toolkit outlines broad concepts of branding, post design, and post management. It also provides details, suggestions, and tips on how to create an account, gain a following, increase engagement, and more on both Facebook and Instagram. . Lastly, it details the process of using paid Facebook and Instagram advertisements for research purposes (i.e., recruiting participants).
From 10 to 25 is a collaborative storytelling game about the period of life we call adolescence. Players take on the role of a young person making their way through adolescence. Players combine the experiences life has dealt them with relationships and resources available in their community to tell a story about growing up. The game builds understanding of what adolescence is and what young people need to thrive.
Results show 9–17 interviews or 4–8 focus group discussions reached saturation.
The Meta-Analysis Learning Information Center (MALIC) believes in equitably providing cutting-edge and up-to-date techniques in meta-analysis to researchers in the social sciences, particularly those in education and STEM education.
Controlling for expected value, we found that a policy combining a high probability of inspection with a low severity of fines (HILS) was more effective than an economically equivalent policy that combined a low probability of inspection with a high severity of fines (LIHS). The advantage of prioritizing inspection frequency over punishment severity (HILS over LIHS) was greater for participants who, in the absence of enforcement, started out with a higher violation rate. Consistent with studies of decisions from experience, frequent enforcement with small fines was more effective than rare severe fines even when we announced the severity of the fine in advance to boost deterrence.
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.
How many interviews are enough depends on when you reach saturation, which, in turn, depends on your research goals and the people you’re studying. To avoid doing more interviews than you need, start small and analyze as you go, so you can stop once you’re no longer learning anything new.
awful example of landing page!
Narrative capture is when an industry, company, or group changes the common narrative for their benefit, even if that just means changing the status quo. What are our baseline expectations? What is acceptable behavior? What is the way we measure fairness? What should we complain about? As expected, narrative capture is different. Here are some of its forms.
“...Public health’s attempts at being apolitical push it further toward irrelevance. In truth, public health is inescapably political, not least because it has to make decisions in the face of rapidly evolving and contested evidence.“
Then our hero enters, and decides to coordinate and plan a persuasion campaign to get the rule changed. Here’s how I think this went down. He in advance arranges for various sources to give him a signal boost when the time comes, in various ways. He designs the message for a format that will have maximum reach and be maximally persuasive. This takes the form of an easy to tell physical story, that he pretends to have only discovered now. Since all actual public discourse now takes place on Twitter, it takes the form of a Twitter thread, which I will reproduce here in full.
For HCI survey research broadly, we recommend using a question similar to the first question in ’s measure (as quoted in ) – “Are you…?” with three response options: “man,” “woman,” “something else: specify [text box]” – and allowing respondents to choose multiple options. This question will not identify all trans participants , but is inclusive to non-binary and trans people and will identify gender at a level necessary for most HCI research. To reduce trolling, we recommend providing the fill-in-theblank text box as a second step only for those respondents who choose the “something else” option.
I love it that one of my students suggested we change the default “Other (please specify“) option to “Not Listed (please specify)“ in a demographic survey. Explicitly *not* “othering“ participants while still asking for the info we want. Any implied failure is on us, not them.
There are surely many ways in which our beliefs can be quite nuanced. We examined the different ‘styles’ of belief we come up against in a variety of the work we do and observed a number of ways these styles appear: Suspension of disbelief: We know not to look too closely at something – we think that overall it is a good thing (e.g. recycling) but aware of possible discrepancies (e.g. being poorly disposed of) that may or may not lead us to question our positive beliefs. We are aware of the possible conflicts but this does not make our belief in the value of recycling any less valid. There are a great many beliefs that we have that could be challenged yet they serve us sufficiently well that we do not need to interrogate them too closely (political representation, eating meat) Inconsistent beliefs: Linked to this, we may hold two conflicting beliefs at the same time. We may know that wild fires are a natural phenomenon that predates climate change; but also that the fires we see in many areas today are of a much greater intensity and frequency. Exactly which is responsible cannot really be picked out, we can only really see the patterns emerging at a more macro-level, so it is not unreasonably to either hold both as true for even consider that the fire you have experience is a normal wild fire. Off-loading beliefs to others: Much of the time our beliefs about how things work is not something that we each individually work out, but we rely on a community of knowledge to work on our behalf. How many of us can be sure that our beliefs are correct about how vaccines work or indeed even how a zipper work. If we are questioned, then we recognise that our belief about how something works is tenuous but we have a good enough sense of it that allows us to function. Unformed beliefs: Sometimes we have not quite worked out what our beliefs are about something, which means that we may well move about in those beliefs or in the strength to which we hold onto them. The vaccination example outlined earlier is a good case in point. Not sure fully believe it but ‘there is something in it’ beliefs: Recent work we have been doing on Conspiracy Theories suggests that people may consider something is believable (e.g. Princess Diana’s death in a car crash was not accidental) but at the same time, in a different question then say they ‘do not fully believe it but there is something in it’. So what might seem like a belief is actually something much more akin to a questioning stance.
“We’re basically creating an MCU-style universe of characters on TikTok,” says Benjamin. “Some succeed, some fail — it’s the TV pilot season model where we only invest in those that get traction and audiences love.”
CONCLUSIONS Debunking strategies that repeat vaccination myths do not appear to be inferior to strategies that do not repeat myths.
Purposeful ads that are executed well are more effective than ads that do not show a company is committed to wider social benefits, according to the research, which was commissioned by the Institute of Practitioners in Advertising. Successful purposeful ads also scored more highly both when looking at how far they improve market share and the extent to which they build brands in the long term, the study found. Meanwhile, less successful purposeful ads, which account for almost half of purposeful ads in the study, have the opposite result. They scored far lower than campaigns with no wider social message.
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.
Much of the discussion of behaviourally informed approaches has focused on ‘nudges’; that is, non-fiscal and non-regulatory interventions that steer (nudge) people in a specific direction while preserving choice. Less attention has been paid to boosts, an alternative evidence-based class of non-fiscal and non-regulatory intervention. The goal of boosts is to make it easier for people to exercise their own agency in making choices. For instance, when people are at risk of making poor health, medical or financial choices, the policy-maker – rather than steering behaviour through nudging – can take action to foster or boost individuals’ own decision-making competences.
This chapter goes beyond classic nudges in introducing public policy practitioners and researchers worldwide to a wide range of behavioural change interventions like boosts, thinks, and nudge pluses. These policy tools, much like their classic nudge counterpart, are libertarian, internality targeting and behaviourally informed policies that lie at the origin of the behavioural policy cube as originally conceived by Oliver. This chapter undertakes a review of these instruments, in systematically and holistically comparing them. Nudge pluses are truly hybrid nudge-think strategies, in that they combine the best features of the reflexive nudges and the more deliberative boosts (or, think) strategies. Going forward, the chapter prescribes the consideration of a wider policy toolkit in directing interventions to tackle societal problems and hopes to break the false synonymity of behavioural based policies with nudge-type interventions only
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.
we propose an integrative approach that combines three complementary paths: (1) putting the “social” back into health organizations’ culture by inserting more “social” content into the internal organizational discourse through consultation with experts from different fields, including those who diverge from the scientific consensus. (2) Using strategies to enable health organizations to respond to the public on social networks, based on health communications research and studies on emerging infectious disease (EID) communication. (3) Engaging the public on social media based on the participatory approach, which considers the public as a partner that understands science and can work with the organizations to develop an open and innovative pandemic realm by using crowdsourcing to solve complex global health problems.
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?
Schwartz has spent much of his career emphasising the shared, universal nature of values and in one paper with Anat Bardi, he demonstrates that Benevolence, Universalism and Self-direction values are consistently rated most important to most people across different cultures. The answers he has just given map pretty neatly onto Self-direction and Benevolence (see Figure 1). Figure 1: Value structure across 68 countries – Public Interest Research Centre (2011) based on Schwartz (1992) The Schwartz model shows that values have neighbours and opposites, that values close together (e.g. Humble, Honest) tend to have similar importance to people, that values far away (e.g. Equality, Social Power) act more like a seesaw – as one rises in importance, the other falls. When you add to this that values connect to behaviour (that Universalism and Benevolence are associated with cooperation, sustainable behaviour, civic engagement and acceptance of diversity – that Achievement and Power are most emphatically not), and that values can be engaged, you have more than a model: you have an imperative for all the activists and campaigners scrabbling around for the messages and tactics that are going to change the world.
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.