The development of effective interventions for COVID-19 vaccination has proven challenging given the unique and evolving determinants of that behavior. A tailored intervention to drive vaccination uptake through machine learning-enabled personalization of behavior change messages unexpectedly yielded a high volume of real-time short message service (SMS) feedback from recipients. A qualitative analysis of those replies contributes to a better understanding of the barriers to COVID-19 vaccination and demographic variations in determinants, supporting design improvements for vaccination interventions. Objective: The purpose of this study was to examine unsolicited replies to a text message intervention for COVID-19 vaccination to understand the types of barriers experienced and any relationships between recipient demographics, intervention content, and reply type. Method: We categorized SMS replies into 22 overall themes. Interrater agreement was very good (all κpooled . 0.62). Chi-square analyses were used to understand demographic variations in reply types and which messaging types were most related to reply types. Results: In total, 10,948 people receiving intervention text messages sent 17,090 replies. Most frequent reply types were “already vaccinated” (31.1%), attempts to unsubscribe (25.4%), and “will not get vaccinated” (12.7%). Within “already vaccinated” and “will not get vaccinated” replies, significant differences were observed in the demographics of those replying against expected base rates, all p . .001. Of those stating they would not vaccinate, 34% of the replies involved mis-/disinformation, suggesting that a determinant of vaccination involves nonvalidated COVID-19 beliefs. Conclusions: Insights from unsolicited replies can enhance our ability to identify appropriate intervention techniques to influence COVID-19 vaccination behaviors.

The first objective was to provide an overview of all activities that were employed during the course of a research project to develop a relapse prevention intervention for interdisciplinary pain treatment programs. The second objective was to examine how co-design may contribute to stakeholder involvement, generation of relevant insights and ideas, and incorporation of stakeholder input into the intervention design.

Thinking Styles are the archetypes that you would base characters on, like characters in TV episodes. (Try writing your scenarios like TV episodes, with constant characters.) Characters think, react, and made decisions based on their thinking style archetype. BUT they also switch thinking styles depending on context. For example, if you take a flight as a single traveler versus bringing a young child along–you’ll probably change your thinking style for that flight, including getting to the gate, boarding, and deplaning.

If you have ever been tasked with influencing a behaviour, you will know that it is critical to understand that behaviour in context. You need to understand the issues faced by the people affected. At BIT, we refer to the process of understanding behaviour in context as Exploring. Exploring is about discovering what people do and crucially why.

The JTBD Canvas 2.0 is a tool to help you scope out your JTBD landscape prior to conducting field research. It frames your field of inquiry and scopes of your innovation effort. Jobs to be done

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.

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

This is a map of subcultures within an organization (it's called a fitness landscape). It's built from stories told by the people in the organization. What can you do with it? Understand where the culture(s) are and request changes by saying I want “More stories like these...“ and “Fewer like those...“ Dave Snowden and The Cynefin Company (formerly Cognitive Edge) are offering impactful ways to visualize culture, and communicate direction in a manner that is customized to where each subculture is now and where their next best step is. Watch this video until 48:48 for more on the science and method (Link at 44:33) https://lnkd.in/emuAzp6E Stories collected using The Cynefin Co's Sensemaker tool.

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.

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.

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.

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.

This website offers practical tools helping relief and development practitioners understand and tackle the barriers that prevent people from following the desired behaviours.

The Food Security and Nutrition Network Behavior Bank features results from Barrier Analysis and Doer/NonDoer Studies conducted by food security and other practitioners globally. (Click here for a description of Barrier Analysis.) You can browse the database by country, region, and behavior studied to look for results for a particular area/behavior, or to look for patterns of barrier and enablers for a particular behavior or set of behaviors.

Workplace behavior change interventions, or workplace nudges, are strategies used to encourage people to act in their own self-interest. These interventions can be made possible with the help of digital technology, such as mobile applications or email, as well as choice architecture design in the physical environments of the workplace, such as posters, objects or furniture arrangement. To this end, we are going to focus on walking, napping, and eating. First, we will examine general workplace wellness programs - what other researchers have tried, how employees reacted to the programs, and their impact. Then, we will go into further detail about interventions related to our three focus areas.

Summary: Exposure to a stimulus influences behavior in subsequent, possibly unrelated tasks. This is called priming; priming effects abound in usability and web design.

Breakthrough ACTION has distilled guidance on social and behavior change (SBC) monitoring methods into a collection of technical notes. Each note provides an overview of a monitoring method that may be used for SBC programs along with a description of when to use the method and its strengths and weaknesses.

We applied a Hidden Markov Model* (see Figure 1) to examine how and why behaviours did or did not change. The longitudinal repeated measure design meant we knew about food waste behaviour at two points (the amount of food wasted before and after the program), changes in the amount of food wasted reported over time for each household (more or less food wasted) and other factors (e.g. self-efficacy). By using a new method we could extend our understanding beyond the overall effect (households in the Waste Not Want Not program group wasted less food after participating when compared to the control group).

To ensure these partnerships are beneficial to all involved—companies, employees, customers, and researchers­—behavioral scientists need a set of ethical standards for conducting research in companies. To address this need, we created The Behavioral Scientist’s Ethics Checklist. In the checklist, we outline six key principles and questions that behavioral scientists and companies should ask themselves before beginning their research. To illustrate how each principle operates in practice, we provide mini case studies highlighting the challenges other researchers and companies have faced.

Innovative solutions based on how people act and make decisions in the real world are often buried in academic journals. The Behavioral Evidence Hub (B-Hub) brings them into the light of day. On the B-Hub you’ll find strategies proven to amplify the impact of programs, products, and services—and improve lives. Projects + checklists

We demonstrate that the mere-measurement effect occurs because asking an intention question is not perceived as a persuasion attempt. In experiments 1 and 2, we show that when persuasive intent is attributed to an intention question, consumers adjust their behavior as long as they have sufficient cognitive capacity to permit conscious correction. In experiment 3 we demonstrate that this finding holds with product choice and consumption, and we find that persuasionknowledge mediates the effects. In experiment 4, we show that when respondents are educated that an intention question is a persuasive attempt, the behavioral impact of those questions is attenuated.

•Despite its sequential nature, healthcare seeking is often analysed as single event. •We demonstrate the value of sequential healthcare data analysis. •Descriptive analysis exposes otherwise neglected behavioural patterns. •Sequence-insensitive indicators can be inconsistent and misleading. •Sequence-sensitive evaluation hints at adverse behaviours of wealthy patients.

Typically, cascades are based on HIV treatment moni-toring data, which focus on getting people living with HIVto a point of viral suppression. HIV prevention cascadesfocus on the steps required to prevent HIV infection andsuccessfully implement HIV prevention programs. Preven-tion cascades include demand-side interventions that focuson increasing awareness, acceptability and uptake of pre-vention interventions, supply-side interventions that makeprevention interventions more accessible and available, andadherence interventions thatsupport ongoing adoption andcompliance with prevention behaviours or products...

behavioural scientists are as biased as anyone