Low self esteem is the best predictor

You may be wondering: If users want personalization, then what’s the problem? The problem is that personalization is a bit like walking a tightrope. A very thin line separates the “good” kind of personalization from the creepy kind. “I like it because it’s so similar to me” can easily become “I don’t like it because it’s eerily similar to me.” “This is relevant to me and saves me time and effort” can easily become “The algorithm is stereotyping me and that’s not cool.” This switch from good to bad is where user psychology comes in. Understanding the real reason why personalization works can help us understand why it does not work sometimes.

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.

identity-focused campaign

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.

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.

CONCLUSIONS Debunking strategies that repeat vaccination myths do not appear to be inferior to strategies that do not repeat myths.

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.

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.

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.

“This research shows that the reward system has an important function in helping behavior and if we want to increase the likelihood of pro-social behavior, we must reinforce a sense of belonging more than a sense of empathy.

chatbot conversational “game“

Good intro to behavioral design - plus parts 2&3 also helpful for toolbox plus process

I - Intended Behavior N - Non-targeted Audiences C - Compensatory Behaviors A - Additional Behaviors S - Signalling E - Emotional Impact

It’s not just about really liking a product (although you definitely want users to really like your product). With the right design elements, your users might embark on a meaningful bond with your technology, where they feel engaged in an ongoing, two-way relationship with an entity that understands something important about them, yet is recognizably non–human. This is a true emotional attachment that supplies at least some of the benefits of a human-to-human relationship. This type of connection can help your users engage more deeply and for a longer period of time with your product. And that should ultimately help them get closer to their behavior change goals.

The following is from Dr. Bucher’s forthcoming book, Engaged: Designing for Behavior Change. I chose this section because it touches upon a PeopleScience theme: being successful and effective behavioral practitioners while also, and primarily, being good.

This is an excerpt from Amy Bucher’s book Engaged: Designing for Behavior Change. In this chapter, you’ll learn how to structure [users’ meaningful choices on their behavior-change journey] so that it’s easier for people to select good options that ultimately support their goals.

Key Takeaways When customers want to leave, don’t ask why. Shift their attention to why they committed to your product or service in the first place. Instead citing reasons for leaving back to you, they’ll need to recall all the benefits they could lose if they chose to leave. Don’t underestimate the power of opportunity and impact of the environment on behaviors. Keep in mind that preparation meets opportunity. Do your people have all the tools to commit to change? Do they understand and know what to do each step of the way? If not, they are unlikely to change their behavior. Allow people who will be using the new solution to co-create it. This way, implementing change will be much easier. It’s easier to toss aside talking points someone else has created, but not those you came up with - they seem much more valuable thanks to the IKEA effect.

Ultimately, the greatest threat to controlling the pandemic is the failure of people to get tested as soon as they have symptoms, and to provide their contacts and self-isolate. Providing adequate support for isolation is critical to all of these. And so, by deprioritising the case for support, blaming the public fuels the pandemic. The government’s psychological assumptions have, in fact, squandered the greatest asset we have for dealing with a crisis: a community that is mobilised and unified in mutual aid. When an inquiry is eventually held about the UK’s response to COVID-19, it is essential that we give full attention to the psychological and behavioural dimensions of failure as much as the decisions and policies implemented. Only by exposing the way in which the government came to accept and rely upon the wrong model of human behaviour can we begin to build policies that work.

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?

Key Terms in this Chapter Behavioural Policy Cube: The policy cube encapsulates three core features of the ‘libertarian paternalism’ framework; namely if an intervention or policy tool is informed by the standard axiomatic assumptions of rational man theory or by insights from behavioural theories, if it is internality or externality targeting, and if it is regulatory or libertarian in nature (Oliver, 2017b). Nudge: A nudge is any aspect of the choice architecture that alters people's behavior in a predictable way without forbidding any options or significantly changing their economic incentives (Thaler & Sunstein, 2009). Boost: A boost improves the competency of a decision-maker by enriching his or her repertoire of skills and decision tools and/or by restructuring the environment such that existing skills and tools can be more effectively applied (Grüne-Yanoff & Hertwig, 2016). Think: A think is a schooling strategy that involves large-scale deliberations to enable citizens to own the process of behavioural reforms. These often include citizen forums and large-scale behavioural therapies. Nudge Plus: Nudge plus refers to an intervention that has a reflective strategy embedded into the design of the nudge. It can be delivered either as a one-part device in which the classic nudge and the reflective plus are intrinsically combined, or as a two-part device whereby the classic nudge is extrinsically combined with a deliberative instrument that prompts individual reflection on the nudge. (Banerjee & John, 2020).

The five psychobehavioral segments of Americans Surgo identified from its survey are: 1. The “Enthusiasts” (40% of the U.S. population). Every person in this group said they would get the vaccine as soon as it is made available to them. There are no barriers to vaccination 1 for people in this group—in fact, the key challenge will be ensuring vaccine supply meets their demand before they lose enthusiasm, as we’re seeing now as people struggle to sign up. 2. The “Watchful” (20% of the U.S. population). For this segment, social norms are important: Before they get the shot themselves, people in this segment first need to see that others in their peer group or community are getting vaccinated and having safe, positive experiences. 3. The “Cost-Anxious” (14% of the U.S. population). For this segment, time and costs are the primary barriers to getting the vaccine. Every member of this group reports having delayed seeking care for their health in the past due to the expense. The irony: Only 28% of people in this group lack health insurance, indicating that their concerns about costs override having insurance to cover them. 4. The “System Distrusters” (9% of the U.S. population). This group primarily believes that people of their own race are not treated fairly by the health system. Members of this group are likely to belong to, but are not exclusively, communities of color. There are multiple, complicated barriers for this segment, but most of them are related to trust in and access to a health system that has an inequitable history. 5. The “Conspiracy Believers” (17% of the population). This segment has perceived barriers around COVID-19 vaccination that Surgo believes are simply too hard to shift in the short term. It includes people who don't believe in vaccines in general, but the primary barrier for people in this group is their very specific and deeply-held beliefs around COVID-19. Every person in this group believes in at least one conspiracy theory: ○ 84% believe that COVID-19 is exploited by government to control people ○ 65% believe COVID-19 was caused by a ring of people who secretly manipulate world events ○ 36% believe microchips are implanted with the COVID-19 vaccine The three most persuadable psychobehavioral segments Surgo recommends prioritizing are the “Watchful”, “Cost-Anxious” and “System Distrusters” for maximum benefit. Each segment has specific barriers to overcome:...