The complete guide to applying behavioral science to organizational change

Reading time: 18 minutes

During three decades of working with organizations during change, I’ve seen leaders double down on the same tactics to try to improve change adoption. Yet, traditional change management that relies on communications to create awareness and training to build skills repeatedly misses the mark because it fails to address how people actually make decisions.

Evidence-based methods from behavior science can close the adoption challenge organizations going through transformational change experience. Leaders who understand these principles deliver better results, and build their own confidence in beginning to untangle the chaos that leading change can feel like.

I first got exposed to behavior science during four innovative digital healthcare product launches. My client, a large insurance company, wanted to build on the evidence base for self-supported patient care that leveraged digital health devices. This was innovative at the time, nearly 10 years ago.

Our product team learned so much about humans and how they interact with health-based technology, as well as how it changed behavior with their primary care provider team.

For the first time, I learned about:

• Choice architecture, and what influences humans in making decisions
• Emotional states and how they influence behaviors
• Habit formation that makes behavior adoption more likely, and stickier
• The role of immediate positive reinforcement in building confidence and sustaining behavior change

So, afterwards when I returned to consulting work supporting enterprise technology-based transformation efforts, it was only natural that I applied those new skills.

I tested a new method for organizational change using a behavior science approach.

I wrote it down in my book, Inspired by Fear: Becoming a Courageous Change Leader.

I designed and successfully delivered a leadership development program around the core behaviors.

Now, I want to share some of those insights with you, too. This complete guide shows you how to apply behavior science to organizational change to consistently deliver 90 percent + adoption rates.

Why traditional change management ignores reality

Does this story sound familiar to you:

You built the business case. You secured executive sponsorship. You hired consultants. You created detailed project plans. You invested in training. You communicated constantly.

And six months in, adoption is stuck at 35 percent.

Your new system sits underutilized. People are using workarounds to bypass the new process. Your carefully planned transformation is quietly failing—not because people don't understand it, but because they're not actually changing their behavior.

This is the story of so many transformations: We design change initiatives as if humans are rational decision-makers who simply need information and motivation to change their behavior.

News flash: They're not.

The field of behavioral science has spent the last 50 years proving this. Yet most change management approaches ignore how humans make decisions and form habits.

Let's start with an uncomfortable truth: organizational change management was built in the 1980s and 90s. This was long before we had the scientific evidence about how humans make decisions.

Most OCM frameworks (Kotter's 8 Steps, ADKAR) emerged from practitioner observation and management theory. They codified what seemed to work, based on three core assumptions about human behavior. It was a good start for an emerging field of professional practice.

But researchers in behavioral economics and psychology have been proving those early assumptions wrong.

The foundational work of Daniel Kahneman and Amos Tversky in the 1970s showed how cognitive biases systematically shape decision-making. Decades of subsequent research have confirmed that humans are predictably irrational. Kahneman won the Nobel Prize in Economics in 2002. Thaler won it in 2017. The evidence is clear.

Yet most organizations and OCM practitioners are still using those outdated assumptions because they’re familiar, rather than principles that behavioral science has proven. Let’s compare three key differences between traditional OCM and a behavior science approach.

False assumption #1: People make rational decisions

Traditional OCM assumes (1980s-90s thinking):

• Provide information → People understand benefits → They change behavior
• "Build the case for change"
• "Communicate the vision"

Behavioral science reality (1970s-present evidence):

• People make decisions based on cognitive shortcuts, emotions, and context
• The rational brain justifies decisions that the emotional brain has already made
• Information rarely changes behavior (you know you should exercise more—why don't you?)

What this means for transformation:

When you explain the benefits of your new CRM, people nod in agreement. They genuinely believe it's better. Then they go back to their desk and use their old spreadsheet because:

• It's automatic (habit)
• It's easier (less friction)
• Everyone else is still using it (social proof)
• The new system costs them time in the form of more data entry for benefits later (present bias)

Their behavior has nothing to do with understanding. It has everything to do with how their brains make decisions in the moment.

Traditional OCM built its methodology before we understood this.

False Assumption #2: Resistance is about generic "Fear of change"

Traditional OCM assumes:

• People resist because of vague emotional states: "fear of change," "change fatigue," "lack of trust"
• Solution: Generic reassurance, artificially positive communication
• "Manage resistance" by providing labor-intensive handholding

Behavioral science reality (neuroscience and emotional intelligence research):

• “Fear of change" isn't specific enough to be actionable
• Newer research from neuroanatomist Jill Bolte Taylor and neuroscientist Lisa Feldman Barrett shows emotions are constructed responses to specific perceived threats or opportunities
• Different emotional states drive different behaviors. In the emotional category of fear, consider that uncertainty makes people hesitate, anxiety causes erratic action, threat to identity triggers defensiveness
• Effective intervention design requires diagnosing which emotion is present and what's triggering it (and often, simply allowing the emotion to dissipate by responding, rather than reacting to it)

What this means for transformation:

When your sales team "resists" the new opportunity management process, you need to dig deeper. Which emotion is present, and how is it influencing behaviors? Is it universal to the team or is one person impacting the entire group?

Is it uncertainty? (Don't know how it will affect their success)

• Behavior: Hesitation, waiting to see what others do
• Intervention: Provide concrete examples, early wins, peer models

Is it anxiety? (Worried about looking incompetent or losing status)

• Behavior: Erratic adoption, defensive reactions, blame-shifting
• Intervention: Create safe practice environments (i.e. open hours testing/training), normalize learning curve to alleviate anxiety about performance expectations

Is it threat to identity? (Challenges their self-image as successful seller)

• Behavior: Active resistance, justification of old methods
• Intervention: Reframe as evolution of expertise, not replacement

Is it loss aversion? (Clear immediate costs: 15 minutes per opportunity)

• Behavior: Avoidance, procrastination, shortcuts
• Intervention: Reduce friction, make benefits more immediate

Yes, fear is involved—but which type of fear, what's triggering it, and how is it impacting behaviors for individuals or the group? Is it important enough to address based on how you’ve prioritized influence within the impacted stakeholder group?

Traditional OCM often stops at "fear of change." Behavioral science and emotional intelligence help you diagnose the specific emotional response and its behavioral signature—so you can prioritize influencing actions and design targeted interventions. (There’s also a predictable sequence of types of fear reactions that transformation leaders experience. You can read about how to shift out of each state in my book, Inspired by Fear: Becoming a Courageous Change Leader).

False Assumption #3: Training changes behavior

Traditional OCM assumes (training industry standard):

• Knowledge gap → Training → Behavior change
• "Provide training and documentation support"

Behavioral science reality (habit formation research):

• Knowing what to do ≠ doing it
• Training creates knowledge, not habits
• Behavior change requires changing the decision environment, not just the person

What this means for transformation:

After your comprehensive training program, people score 90% on the knowledge assessment. Then they return to their workflow and:

• Forget within 48 hours (forgetting curve)
• Can't apply abstract training to specific situations (transfer problem)
• Face friction, competing priorities, and old habits (environmental barriers)

Your training was perfect. Your environment design was absent.

Traditional OCM was built before we understood how habits form and change.

(P.S. This doesn’t mean you shouldn’t provide training …it means you need to understand where it fits in your change design).

The bottom line:

Organizational change management (OCM) frameworks were created 30-40 years ago based on what we had at the time, practitioner best practices, and management theory.

Behavioral science has spent the last 50 years producing rigorous experimental evidence about how humans make decisions and change behavior.

We now have newer, better science.

What behavioral science tells us about change

Let’s review three core principles from behavior science and apply them to real-world transformation efforts.

Core principle #1: Context shapes behavior more than motivation

The research:

In a famous study, researchers wanted to increase healthy eating in a cafeteria. They tried two approaches:

Approach A: Educational campaign about nutrition (traditional change management)

• Result: 12 percent increase in healthy choices

Approach B: Move healthy food to eye level, unhealthy food to the bottom shelf (choice architecture)

• Result: 87 percent increase in healthy choices

Same people. Same motivation. Different context. Radically different behavior.

What this means for you:

Stop asking "How do I motivate people to use the new system?"

Start asking, "How do I make the new system the easiest option at the moment of decision?"

The sales rep at 4 p.m. on Friday with three deals closing doesn't need motivation. They need the new process to be faster than the old one.

Core principle #2: Humans are predictably irrational

The research:

Daniel Kahneman won the Nobel Prize in Economics for proving that humans consistently violate the assumptions of rational decision-making. We exhibit predictable cognitive biases:

Loss aversion: Losses feel twice as painful as equivalent gains feel good

• Implication: People focus on what they'll lose in the change (time, status, familiarity) more than what they'll gain

Present bias: We overvalue immediate costs and undervalue future benefits

• Implication: The effort required today feels more significant than the benefits six months from now

Status quo bias: We prefer things to stay the same, even when change is beneficial

• Implication: The current state is the default; change requires overcoming inertia and apathy

Social proof: We look to others to determine appropriate behavior

• Implication: If nobody's using the new system, it signals "this isn't really required"; if there aren’t consequences for NOT using the new system, status quo bias prevails

What this means for you

These aren't personality flaws to overcome. They're design constraints to work within.

When you design your transformation, you're designing for how humans process decisions. In most transformation efforts, it’s also not one single decision either, but rather a series of smaller choices:

• I’m choosing to explore the idea → I’m choosing to experiment and practice some of the new behaviors → I’m choosing to fully adopt and build a habit around a series of new behaviors

Core principle #3: Behavior is habitual, not intentional

The research:

Neuroscience shows that 40 to 50 percent of our daily behaviors are habits. Automatic responses triggered by context cues, not conscious decisions. (If you’ve ever driven home from work and not remembered any part of your drive, this is an example of a habit).

Habits form through repetition in consistent contexts:

• Cue (context trigger) → Routine (behavior) → Reward (reinforcement) → Habit (automation)

Breaking a habit requires more than intention. It requires changing the cue, the routine, or the reward.

What this means for you:

Your transformation is asking people to break habits formed over years and build new ones.

The manager who's been running Monday meetings the same way for five years isn't choosing the old format over your new one. They're literally not thinking about it. It's automatic, which makes it easier. And, let’s face it, we all want easier. We all want less cognitive load.

Changing behavior means changing the environmental cues and reward structures that drive habits. Many of these routines are baked into enterprise-level technology through best practice business process flows. When you choose the out-of-the-box configuration rather than customizing technology to match your existing business process, what you are really asking of your people is that they learn a new routine and rebuild habits.

The behavioral science framework for organizational change

Now that you understand key principles about how humans make decisions, let’s go a little deeper into the how.

5 steps to designing change using behavior science

This is the process I use during transformational change efforts. It works because it starts with behaviors and focuses on those most critical to business success.

STEP 1: Identify goals in the form of specific people and specific behaviors (Not vague outcomes)

Traditional approach: "We need everyone to use the new CRM"

Behavioral approach: "We need sales reps to log opportunity details within 24 hours of first contact"

The difference:

Usage, AKA “adoption," is an outcome. "Logging opportunity details within 24 hours" is a behavior.

You can't measure or influence an outcome directly. You can only change the specific behaviors that produce it.

For your transformation, ask:

1. What specific behaviors do we need people to start doing?
2. What specific behaviors do we need people to stop doing?
3. When and where do these behaviors need to happen?

Example: Digital/AI transformation

Vague: "Adopt digital tools"

Specific roles and target behaviors:

• Start: Sales reps need to start using the AI assistant to get answers to basic contract questions
• Stop: Sales reps need to stop calling the service center team for basic contract questions
• Stop: Service center reps need to stop taking calls resulting in ServiceNow tickets

Each behavior is observable, measurable, and happens in a specific context. You could quickly measure whether sales reps are changing their behaviors based on ServiceNow ticket volumes.

STEP 2: Identify people development needs and diagnose behavioral barriers (Not knowledge gaps)

Traditional approach: "People don't understand the benefits" → More training

Behavioral approach: "What’s making the new behavior harder than the old one?" → Diagnose the behavioral barriers and development needs

The COM-B Model:

Behavior happens when three conditions are met:

C - Capability: Can they physically/mentally do it?

• Do they have the skills?
• Is it within their cognitive capacity in the moment?

O - Opportunity: Does the environment enable it?

• Is it easy to do?
• Are there competing demands?
• What are the cues and triggers?

M - Motivation: Do they want to do it?

• What are the immediate rewards/costs?
• What do they believe about it?
• What do they see others doing?

Most transformation barriers are Opportunity and Motivation problems, not Capability problems.

How to do this:

For each target behavior, diagnose:

Capability barriers:

• Do they know how? (Knowledge gap)
• Can they do it under time pressure? (Cognitive load)
• Do they have the tools/access? (Resource gap)

Opportunity barriers:

• Is the new behavior harder than the old one? (Friction)
• How significant are the competing priorities? (Attention scarcity)
• What environmental cues trigger the old behavior? (Context)

Motivation barriers:

• What do they lose by changing? (Loss aversion)
• What's the immediate cost versus the delayed benefit? (Present bias)
• What are others doing? (Social proof)
• How does it affect their identity/status? (Self-image)

Example: Low CRM adoption

Traditional diagnosis: "Sales team doesn't see the value" → More training

Behavioral diagnosis:

• Capability: They know how to use it (training was effective)
• Opportunity barriers:
• New system requires 15 minutes per opportunity versus two minutes in the old way (friction)
• They're entering data at the end of the day when cognitively depleted (timing)
• The old way of individual spreadsheets is still accessible (competing option)
• Motivation barriers:
• Compensation based on deals closed, not data quality (misaligned incentives)
• Sales manager doesn't check CRM data (no accountability)
• Top performers still use the old system (negative social proof)

Now you have actual problems to solve, not a vague "resistance" to address.

STEP 3: Design behavioral interventions using choice architecture

Traditional approach: Communicate, train, hope for compliance

Behavioral approach: Redesign the decision environment to make the right behavior easier

Choice architecture: The six principles

Choice architecture is the design of contexts in which people make decisions. Six principles consistently change behavior:

1. Defaults

Principle: People stick with pre-selected options

Why it works: Changing the default requires active effort; most people take the path of least resistance

Transformation application:

• Make the new system the default (turn off the old system, you would be surprised by how many transformation efforts fail to do this step)
• Make current elections the default

Client example:

• Before: Active enrollment in health plan elections
• After: Default to current year elections unless participants wish to change

Result: Reduced exception-based administrative change requests following open enrollment

2. Simplification

Principle: Reduce cognitive load and friction

Why it works: Complex behaviors get postponed or avoided

Transformation application:

• Break complex processes into smaller steps
• Remove unnecessary fields/steps
• Provide templates and shortcuts

Client example:

• Before: Login to a new system and enter all details of this year’s annual cost center budget, enter next year’s requested budget, and forecasted changes to salaries, the largest cost driver in most budgets
• After: Login and validate the current year cost center data. Complete the forecasting and people planning actions in separate sessions.

Result: Time of initial interaction reduced from multiple hours to less than 10 minutes

3. Salience

Principle: Make the desired behavior visible and attention-grabbing

Why it works: We act on what we notice; invisible options get ignored

Transformation application:

• Visual reminders at point of decision
• Highlight new process in workflow
• Make status visible to others

Client example:

• Before: Paper approvals for paid time off requests circulated between employee-manager-HR
• After: Notification alert message pushed via email to the manager to approve PTO request. The status is viewable by the employee and HR.

Result: Approval time reduced from 8 days to 2 day by removing HR from the process; employees know when a request has been approved and the system automatically prompts managers. This did require employees and managers to build new habits around a common task, requesting time off.

4. Feedback

Principle: Provide immediate information about behavior and its consequences

Why it works: Delayed feedback doesn't shape behavior; immediate feedback does

Transformation application:

• Real-time dashboards showing behavior
• Instant confirmation when the correct behavior is completed
• Progress tracking

Client example:

• Before: No operational tracking of calls received and the accuracy of answers provided by the internal client services team
• After: Daily dashboard showing each call center representative’s completion rate with call times and customer satisfaction. Tracking of open tickets to resolve issues.

Result: Established and met internal service level agreements to return answers within two business days

5. Incentives

Principle: Align rewards with desired behavior

Why it works: People do what gets rewarded, avoid what gets penalized

Transformation application:

• Tie recognition to new behaviors
• Remove rewards for old behaviors
• Make consequences immediate, not distant

Client example:

• Before: HR prints payslip copies for employees upon request
• After: Employee accesses payslip directly using the mobile app

Result: 90 percent+ adoption of new employee self-service mobile app within 30 days

6. Social proof

Principle: Show others performing the desired behavior

Why it works: We look to peers to determine appropriate behavior

Transformation application:

• Make usage visible
• Highlight early adopters
• Use change agents systematically in dispersed organizations
• Create peer comparison

Client example:

• Before: External consultant leads system demos for complex tasks with minimal uptake on basic behaviors (using a default report)
• After: Team members deliver system demos; peers help one another in a learning environment

Result: Competitive effect drove 90 percent+ early access to system

How to apply choice architecture:

For each behavioral barrier you diagnosed, select interventions:

You don't need all six. You need the right ones for your specific barriers. The changecapable^TM Leadership Program helps you identify the right interventions for the most critical behaviors.

STEP 4: Test and iterate (Rapid experimentation)

Traditional approach: Design entire program, launch to everyone, measure after six months

Behavioral approach: Small tests, fast feedback, iterate before scaling

How to do this:

1. Select a pilot group (50-100 people, size will vary but should represent impacted stakeholders. These are often your project team members plus additional business representatives, such as a change network for dispersed and decentralized organizations.)
2. Implement one intervention at a time (so you know what works)
3. Measure behavior change within 2 weeks (not satisfaction, actual behavior—this is ideal for technology-focused changes)
4. Iterate based on results (adjust, add, remove interventions)
5. Scale what works (roll out proven interventions)

Example: Testing new reporting and analytics

Week 1-2: Test default + simplification

• Auto-create template financial reports
• Reduce variations to simplify requests
• Result: 60 percent adoption (baseline was 15 percent)

Week 3-4: Add feedback

• Change reporting request variables
• Result: 67 percent adoption

Week 5-6: Add social proof

• Share reporting results amongst peers
• Result: 89 percent adoption

Week 7-8: Add incentives

• Tie comp to completions
• Result: 94 percent adoption, sustained

Total time to 94 percent adoption: 8 weeks (versus more than six months with traditional approach)

STEP 5: Measure behavior change (Not satisfaction)

Traditional approach: Post-implementation survey asking "Are you satisfied with the new system?"

Behavioral approach: Direct measurement of target behaviors

What to measure:

Behavior metrics (Leading indicators):

• Frequency: How often is behavior happening?
• Consistency: Is it happening across all people/teams?
• Quality: Is it being done correctly?
• Speed: How quickly after trigger/c

Example metrics:

• % of opportunities logged within 24 hours of first contact
• % of sales reps with 100% data completion
• Average time from opportunity creation to first update
• % of managers reviewing CRM data weekly

Outcome metrics (Lagging indicators):

• Forecast accuracy (improved by better data)
• Sales cycle length (reduced by better pipeline visibility)
• Win rate (improved by data-driven decisions)

Satisfaction tells you how people feel. Behavior tells you what they're doing.

You can have 90 percent satisfaction and 30 percent behavior change. Or 60 percent satisfaction and 90 percent behavior change.

Behavior change drives business results. Satisfaction doesn't.

Common mistakes in applying behavioral science during organizational change

I've seen smart leaders misapply behavioral science. Here's what to avoid:

Mistake #1: Treating it as tactical activities rather than systematic design

What I see: "Let's add some gamification and rewards to increase engagement!"

The problem: Nudges without diagnosis are random. You're guessing at solutions without understanding the problem.

What to do instead: Follow the five-step process. Diagnose first, design second.

Mistake #2: Focusing only on motivation

What I see: "We need to get people excited about this change! Let’s do a team-building exercise."

The problem: Motivation is only one-third of behavior (COM-B model). If the environment makes behavior hard (Opportunity) or people lack skills (Capability), motivation won't help. Motivation is finite, too, and varies over time.

What to do instead: Fix friction and capability gaps before trying to increase motivation.

Mistake #3: Using behavioral science to manipulate

What I see: "How can we trick people into adopting this?"

The problem: Manipulation erodes trust. When people feel tricked, this creates long-term change resistance, which often heads underground.

What to do instead: Remove barriers to beneficial behavior. Ethical test: "Would I be comfortable if people knew exactly how I'm influencing their decision?" Nudges are always designed to help people make a choice in their best interest.

Mistake #4: Ignoring the political reality

What I see: "The behavioral science says we should do X, so let's do it."

The problem: Organizations are political systems. Culture and power dynamics are real, and influence intervention design. Behavioral interventions that threaten power or status will face resistance regardless of their effectiveness.

What to do instead: Design interventions that work with the political system, not against it. Get powerful stakeholders involved in design and influencing others. If your organization is highly relationship-oriented or has employees with long tenure, the political reality and power dynamics will significantly influence intervention design.

Mistake #5: Doubling down on training when adoption is low

What I see: "Our post-training survey scores were 4.5 out of 5!”

The problem: Satisfaction ≠ behavior change. People can love the training and not change their behavior. They can appreciate the instructor-led sessions without using the system.

What to do instead: Measure actual behavior: frequency, consistency, quality. Diagnose the barriers before deciding that more training is the right answer.

Building your behavioral science capability

You don't need a PhD in behavioral economics to apply this. You need:

1. A different diagnostic mindset

Stop asking: "Why don't people want to change?"

Start asking:

• What specific behavior are we trying to change?
• What's making the new behavior harder than the old one?
• What environmental factors are driving current behavior?

2. Comfort with experimentation

Traditional mindset: Design the perfect (linear) program, launch to everyone

Behavioral mindset: Design a hypothesis, run an experiment with a small group such as your project team or a change agent network, iterate based on results

You'll learn more from a failed experiment than from a "successful" launch that doesn't change behavior.

3. Behavior observation skills

Most transformation leaders spend time in:

• Strategy meetings
• Stakeholder presentations
• Project status reviews (understanding deeply where the technology build is off track)

Behavioral science leaders also spend time:

• Watching people do the work (user testing rounds are great opportunities to do this!)
• Observing where friction occurs
• Asking "What happened right before they made that choice?"

You can't diagnose behavioral barriers from a GANTT chart.

4. Willingness to challenge assumptions

When someone says: "People just don't want to change."

You ask: "What specific behavior are they not doing, and what barrier is preventing it?"

When someone says: "We need more training."

You ask: "Is this a knowledge problem or a behavior problem?"

When someone says: "We need better communication"

You ask: "What decision do we want people to make differently, and what information would change that decision?"

The bottom line: Design change for real humans

Seventy percent of transformations fail. Not because of bad strategy. Not because of poor technology. Not because people "resist change."

They fail because we design them for rational actors in frictionless environments where we hope for the best, rather than designing for reality.

Behavioral science gives you a different approach:

• Diagnose behavioral barriers, not attitude problems
• Design decision environments, not just communication plans
• Measure behavior change, not satisfaction scores
• Iterate based on results, not assumptions

This isn't theory. It's a methodology that consistently drives 80-90 percent adoption in complex transformations.

The question isn't whether behavioral science works. The evidence is overwhelming.

The question is: Are you willing to design change for humans as they are, not as you wish they would be?

Join me in making transformational change easier, faster, and better. It’s possible! I’ve seen it.

Resources

Download: The behavioral sciences toolkit for change leaders

Get immediate access to four experiments that help you try out four of the behavioral science frameworks in your change.

✓ COM-B diagnostic template – Learn to identify capability, opportunity, and motivation barriers

✓ Choice architecture design canvas – Apply the six principles to your transformation

✓ Behavioral barrier worksheet – Move from symptoms to root causes

✓ Behavior measurement dashboard – Track what matters (not satisfaction scores)

Download the toolkit →

Take it further: Develop your change leadership capability

Reading about behavioral science is different from applying it in complex political environments with real constraints.

If you're leading transformations and want to systematically develop your capability to diagnose behavioral barriers and design effective interventions, explore The changecapable™ Leadership Program.

It's a 16-week group cohort where you:

• Apply behavioral science to your real transformation
• Learn by doing (not just discussing theory)
• Live learning sessions in the changecapable™ method
• Get expert mentorship, plus social support and accountability from peers
• Access behavioral science concepts in short video lessons, tools and guided experiments in a client portal that you have access to for one year

Learn about The changecapable™ Leadership Program and join the waitlist for the Fall 2026 cohort →

My favorite books on this topic:

• Thinking, Fast and Slow by Daniel Kahneman – Foundation of behavioral economics
• Nudge by Richard Thaler and Cass Sunstein – Choice architecture principles
• Influence by Robert Cialdini – Psychology of persuasion

• Good Habits, Bad Habits: The Science of Making Positive Changes That Stick by Wendy Wood – How humans form habits
• Whole Brain Living: The Anatomy of Choice and the Four Characters That Drive Our Life by Jill Bolte Taylor – How humans make choices
• How Emotions Are Made: The Secret Life of the Brain by Lisa Feldman Barrett – The science of emotions

External references and research

Foundational behavioral science research:

• Kahneman, D., & Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263-291. Access via JSTOR
• Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press. Publisher link
• Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. Publisher link

Choice architecture and Nudge Theory:

• Behavioral Insights Team – Case studies and practical applications of behavioral science in organizations and government. bi.team
• Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W.W. Norton & Company. Publisher link
• Johnson, E. J., et al. (2012). "Beyond nudges: Tools of a choice architecture." Marketing Letters, 23(2), 487-504. SpringerLink

Habit formation and behavior change:

• Duhigg, C. (2012). The Power of Habit: Why We Do What We Do in Life and Business. Random House. Publisher link
• Wood, W., & Neal, D. T. (2007). "A new look at habits and the habit-goal interface." Psychological Review, 114(4), 843-863. APA PsycNet
• Clear, J. (2018). Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones. Avery. Publisher link

Cognitive biases:

• Tversky, A., & Kahneman, D. (1974). "Judgment under Uncertainty: Heuristics and Biases." Science, 185(4157), 1124-1131. JSTOR
• Ariely, D. (2008). Predictably Irrational: The Hidden Forces That Shape Our Decisions. HarperCollins. Publisher link
• Behavioral Economics Guide – Comprehensive overview of cognitive biases and their applications. BehavioralEconomics.com

Organizational change and transformation success rates:

• OECD Behavioral Insights – Reports on applying behavioral science in organizational contexts. OECD.org
• McKinsey & Company (2015). "Why do most transformations fail? A conversation with Harry Robinson." McKinsey.com
• BCG (2020). "Flipping the Odds of Digital Transformation Success." BCG.com

Social proof and influence:

• Cialdini, R. B. (2006). Influence: The Psychology of Persuasion (Revised Edition). Harper Business. Publisher link

About Kris Jennings

Kris Jennings has spent three decades curiously exploring human behavior during organizational change. After watching well-resourced initiatives fail due to low adoption, she wanted to understand why humans don't behave the way change management assumes they will.

During national digital health product launches nearly 10 years ago, she was exposed to behavioral science concepts, which inspired her to experiment with new ways to approach transformations. She shares lessons in her book Inspired by Fear: Becoming a Courageous Change Leader. Out of her experience, Kris developed the changecapable™ method that consistently achieves 90 percent+ adoption rates in complex transformations.

Through The changecapable™ Leadership Program, Kris helps change leaders apply her proven method to their most critical change initiatives.

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