Deliberate ignorance is a potential barrier for information interventions aiming to reduce meat consumption and needs to be considered in future interventions and research. Self-efficacy exercises are a promising approach to reduce deliberate ignorance and should be further explored.

In this short follow up post we explain how and why we combine systems thinking and behavioural approaches. We start by introducing the concepts of ‘systems’ and ‘systems thinking’ before explaining why Systems thinking is useful to combine with a behavioural approach.

The Wheel of Progress® is a framework created by Eckhart Boehme and Peter Rochel leveraging jobs-to-be-done principles and methods to evaluate why customers “hire“ a given product or service to accomplish a Customer Job. Jobs to be done It provides a canvas to be used when conducting consumer research to evaluate the journey a customer takes from first thought to use of the solution (consumption/job satisfaction). In addition, it enables one to evaluate the four forces of progress at play (push, pull, habits, anxieties) in regards to 'switching behavior'. Finally, one is able to evaluate constraints (internal, external, time-based) that impact the customer journey.

Long-term behaviour change is essential to many societal and personal challenges, ranging from maintaining sustainable lifestyles to adherence to medical treatment. However, prior research has generally focused on interventions dealing with bounded, present-tense, and discretely measurable behaviour change problems, evaluated via relatively short-term trials. This has led to a skewed prioritisation of behaviour change techniques and left a critical gap in design guidance. Hence, there is an urgent need to (i) examine how behaviour change techniques can be abstractly prioritised and (ii) related to contextual, embodied interventions during long-term behavioural design. We address this need using a Delphi survey method with 12 international experts on behavioural intervention complemented by a reanalysis of over 100 real-world cases. This provides the basis for examining how experts prioritise the Behaviour Change Technique Taxonomy (BCTT) for the long-term, as well as how this corresponds to real-world long-term interventions. Based on this we provide essential, and as a first, guidance for long-term behavioural design as well as contributing to wider research on how to deal with the demands of long-term behaviour change.

If a tree falls in a forest and no one is around to hear it, does it make a sound? According to digital storyteller, Brian Clark it doesn't! In this DIY conversation, Brian Clark applies the philosophical concept of phenomenology to art in the digital age.

BEHAVIOURAL DESIGN TOOLS. Need a sidekick in your Behaviour Thinking journey? We’ve got you covered with tools and theories. Let’s go.

Info, research, questionnaires/scales, info on application to specific topics

this study presents a measurement instrument for evaluating susceptibility to seven social influence principles, namely social learning, social comparison, social norms, social facilitation, social cooperation, social competition, and social recognition. Each principle is represented by a construct containing six theory-driven items, both positively and negatively framed. Further, the study introduces a social influence research model that describes how the seven social influence constructs are correlated and impact each other.

To this end, we acknowledge the extant criticisms of social marketing – for being unethical (Laczniak & Michie, Citation1979), lacking reflexivity (Tadajewski & Brownlie, Citation2008), being power agnostic (Brace-Govan, Citation2015), being neoliberally oriented (Moor, Citation2011), being culturally insensitive and imperialist (Pfeiffer, Citation2004), being pseudo-participatory (Tadajewski et al., Citation2014) and for responsibilising the individual (Crawshaw, Citation2012). Accordingly, we recognise that social marketing needs the resources and repertoires available to appropriately respond to the current challenges and to critique. We argue that key pillars to this response are the adoption of a more critical research agenda (Gordon, Citation2018), a broader theoretical base, and a commitment to careful reflexivity, each of which are commitments of CSM. This special section of the Journal of Marketing Management on ‘Critical Social Marketing: Towards Emancipation’ provides the space to grapple with extant and emergent critique within the contextual challenges of our time, and to collectively contribute to the development of CSM and its future agenda.

(ie, Towards a Unified Theory of Behavior - all the biases boil down to the same underlying thing) One of the essential insights from psychological research is that people’s information processing is often biased. By now, a number of different biases have been identified and empirically demonstrated. Unfortunately, however, these biases have often been examined in separate lines of research, thereby precluding the recognition of shared principles. Here we argue that several—so far mostly unrelated—biases (e.g., bias blind spot, hostile media bias, egocentric/ethnocentric bias, outcome bias) can be traced back to the combination of a fundamental prior belief and humans’ tendency toward belief-consistent information processing. What varies between different biases is essentially the specific belief that guides information processing. More importantly, we propose that different biases even share the same underlying belief and differ only in the specific outcome of information processing that is assessed (i.e., the dependent variable), thus tapping into different manifestations of the same latent information processing. In other words, we propose for discussion a model that suffices to explain several different biases. We thereby suggest a more parsimonious approach compared with current theoretical explanations of these biases. We also generate novel hypotheses that follow directly from the integrative nature of our perspective.

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Here is an interesting way to visualize how to design for behavior using the COM-B Model and the Behavior Change Wheel If you don't know the Behavior Change Wheel, it is a framework developed by Susan Michie, Robert West and colleagues at UCL It is comprised of 19 different behavior change frameworks. At the center sits The COM-B Model: COM-B is used to look for the barriers or enablers to a behavior Capability (both physical and psychological) Opportuntity (both physical and social) Motivation (both reflective and automatic) It is a powerful way to analyze what may be stopping your customers or employees or even yourself of making the choices you already wanted to do. Outside the COM-B model (center of the wheel) sit the Intervention Types - which can include Education, Incentivization, and Training. As for the example here used in diabetes prevention design: The wheel has been filled with interventions and ways to deliver the intervention in this example. (I may have done it a bit different, but still a good representation) It looks at the Patient level - to Increase the patient's awareness of pre-diabetes It looks at Provider's Level - Improve communication skills, and teachable moments at diagnosis It looks at System Level - Invitation by physicians as well as social marketing. This of course is a small example of how the model could help you go from challenge to outcome.

Implementing new practices requires changes in the behaviour of relevant actors, and this is facilitated by understanding of the determinants of current and desired behaviours. The Theoretical Domains Framework (TDF) was developed by a collaboration of behavioural scientists and implementation researchers who identified theories relevant to implementation and grouped constructs from these theories into domains. The collaboration aimed to provide a comprehensive, theory-informed approach to identify determinants of behaviour. The first version was published in 2005, and a subsequent version following a validation exercise was published in 2012. This guide offers practical guidance for those who wish to apply the TDF to assess implementation problems and support intervention design. It presents a brief rationale for using a theoretical approach to investigate and address implementation problems, summarises the TDF and its development, and describes how to apply the TDF to achieve implementation objectives. Examples from the implementation research literature are presented to illustrate relevant methods and practical considerations.

Robert Meza Miro boards compiled in one place

M. Bilal Akbar1 · Elizabeth Barnes

Chapter 1|7 pages Introduction Abstract Size: 0.09 MB Chapter 2|38 pages Sequential Techniques Of Social Influence Abstract Size: 0.20 MB Chapter 3|19 pages Techniques Involving Egotistic and Self-Presentation Mechanisms Abstract Size: 0.13 MB Chapter 4|34 pages The Role of Wording the Request Abstract Size: 0.19 MB Chapter 5|34 pages Interaction Dynamics and the Surprise Factor Abstract Size: 0.36 MB Chapter 6|26 pages Techniques of Social Influence Using Mood and Emotion Abstract Size: 0.17 MB Chapter 7|10 pages A Few More Issues and Final Remarks Abstract

Developed over several years, the BehaviourWorks Method is a tried and tested approach to changing behaviours. Consisting of three primary phases - Exploration, Deep Dive and Application - The Method can be used in full, or in parts, to gather evidence on the behaviour change approach that is most likely to work.

BC patterns are a collection of reoccurring solutions used in Behavioural Design to change people’s behaviour. They are patterns that designers, change makers and problem solvers can consider when solving people problems and designing behaviour change.

We have identified 30 “elements of value”—fundamental attributes in their most essential and discrete forms. These elements fall into four categories: functional, emotional, life changing, and social impact. Some elements are more inwardly focused, primarily addressing consumers’ personal needs.

Why people do or do not change their beliefs has been a long-standing puzzle. Sometimes people hold onto false beliefs despite ample contradictory evidence; sometimes they change their beliefs without sufficient reason. Here, we propose that the utility of a belief is derived from the potential outcomes associated with holding it. Outcomes can be internal (e.g., positive/negative feelings) or external (e.g., material gain/loss), and only some are dependent on belief accuracy. Belief change can then be understood as an economic transaction in which the multidimensional utility of the old belief is compared against that of the new belief. Change will occur when potential outcomes alter across attributes, for example because of changing environments or when certain outcomes are made more or less salient.

TRA has added a layer of thinking to the well-established habit loop – can we think beyond push notifications for cues and think beyond a discount as a reward? We analysed five different habit models and over 60 case studies in order to understand the breadth and depth of cues and rewards. Our framework takes these learnings and provides a thorough checklist for the cue, the behaviour and reward for strengthening habits. When you’re working on strengthening a one-time behaviour into a routine habit, consider the various options for each stage.

The COM-B model of behaviour is widely used to identify what needs to change in order for a behaviour change intervention to be effective. It identifies three factors that need to be present for any behaviour to occur: capability, opportunity and motivation. These factors interact over time so that behaviour can be seen as part of a dynamic system with positive and negative feedback loops. Motivation is a core part of the model and the PRIME Theory of motivation provides a framework for understanding how reflective thought processes (Planning and Evaluation processes) and emotional and habitual processes (Motive and Impulse/inhibition processes) interact at every moment leading to behaviour (Responses) at that moment.

Bonus talks Why You Forget Everything And What to Do About It w/ Bec Weeks – https://youtu.be/VoDlOmHbaWE The Sneaky Things That Keep Good Habits From Sticking w/ Jessica Malone – https://youtu.be/oCwMXY7u73A Nicolas Fieulaine from NFÉtudes – https://youtu.be/E-XNZUGvVT0 ––– Timestamps 0:00 Event Intro 6:53 The Science of Habit Change with David Neal 38:10 The Science of Mindfulness with Dr. Clare Purvis 53:03 Creatures of Context with David Perrott 1:21:05 Time Smart: How to Reclaim Your Time and Live a Happier Life with Ashley Whillans 2:05:36 The Invisibility of Habit with Wendy Wood 2:34:19 Digital Behavior Change in Health with Jennifer La Guardia & Aline Holzwarth 2:59:11 Better Decision Making at Work: 5 Core Heuristics (& How to Manage Them) with Scott Young, BVA Nudge Unit 3:22:32 All the small things - How behavioral science can help you unlock success in love and at work with Logan Ury & Liz Fosslien 4:09:53 How to apply behavioral insights to cyber security training with Harriet Rowthron from BestAtDigital 4:22:44 Making Meaning When Life Stinks with Yael Schonbrun 4:54:47 The Power of Identity with Dominic Packer 5:30:40 The Untapped Science of Less with Leidy Klotz 5:55:10 Day Wrap-Up with Samuel Salzer & Peter Judodihardjo

Having a standardized unit of measure for risk would be helpful for our personal calculations, but it could also become a core part of the way the media or public health authorities talk about threats like epidemic disease, or even seasonal flu. Post-COVID—if we ever get there—I suspect I will still be interested to know if the flu risk starts to climb in New York, even by a few micromorts—I wouldn’t radically change my plans, but I might put on a mask in the subway for a few weeks. For the past seventy years, every single local news broadcast has been telling you what the temperature is going to be tomorrow, and the chance of precipitation. Why shouldn’t they also include genuinely life-or-death odds?

A difficulty for investigating the accuracy of everyday risks perception has been the lack of an obvious objective framework on which to compare subjective responses. This difficulty stands in contrast to other fields of risk research. For example, risk perception in health contexts, uses the probability of death or ill health (e.g. as compiled by disease data registries) as the objective comparator [2, 3, 27]; and in financial fields, losses and gains in gambling tasks can be used as an objective comparator [28, 29]. In the current study the concept of MicroMorts is introduced as an objective risk framework to investigate the accuracy of everyday risk perception. We have around a one in a million chance of dying from an accident or incident every day, and this acute risk is quantified as one MicroMort [30, 31]. That is, MicroMorts are units that index acute risk (i.e. sudden death): one MicroMort is a one-in-a-million chance of death. We increase our risk through our choices of activities, for example, skydiving has a MicroMort value of 10, walking 27 miles has a MicroMort value of one, and giving birth has a MicroMort value of 120 (i.e. 10, 1 or 120 chance in a million chance of dying respectively) [31]. MicroMorts enable us to compare the acute risk of death from various activities, for example, a general anaesthetic and a sky-dive both carry the same acute risk of death, 10 MicroMorts (10 in one million people will die as a result of doing either). This MicroMort framework is being increasingly being used to index health risks and provide a framework for risk communication, including patient consent [31–33]

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.

The presentarticle reviews the debate and research on nudges byfocusing on three main dimensions: (1) the exact defi-nition of nudges; (2) the justification of nudge policies,with a focus on “libertarian paternalism”; and (3) theeffectiveness of nudges, both over time and in compari-son with standard policies.

Excellent contrast with Embrace Life of gain vs loss framing!

Which theory of behavior change can help you plan a health communication intervention for a large audience? There is no single right answer, but some theories will fit your needs better than others. The purpose of this tool is to rank-order some commonly used theories by their degree of fit with your behavior change challenge.

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.