Fogg Behavior Model (B = MAT)

Canonical
FoggWendelAgentic UX

Confidence

92%

Cognitive Load

Medium

Evidence

production validated

Impact

feature

Ethical Guardrail

Agents must never exploit low-ability users by pushing high-motivation prompts that create guilt or anxiety.

Design Intent

Behavior does not happen by accident. It only occurs when three specific elements come together at the exact same moment. The Fogg Behavior Model (B = MAT) is the simplest and most practical diagnostic tool in behavioral design. It states that Behavior (B) = Motivation (M) + Ability (A) + Trigger (T). If any one of the three is missing or too weak, the behavior will not occur. Designers and AI agents do not need complex psychology. They need a clear 3-part lens to instantly diagnose why users drop off and then intervene with precision instead of guesswork. When used correctly, B = MAT turns vague 'users aren't engaging' complaints into exact, fixable problems.

Psychology Principle

Behavior (B) happens only when Motivation (M), Ability (A), and a Trigger (T) converge at the exact same moment.

Description

BJ Fogg's simplest and most practical diagnostic tool: if a behavior isn't happening, one of the three elements is missing or too weak.

When to use

Any time you need a user to perform an action -- onboarding, habit features, checkout, AI suggestions, etc.

Example

Duolingo daily lesson: high motivation (streaks), tiny ability (30-second lesson), perfectly timed push notification.

Autonomy Compatibility

SuggestConfirm

Behavioral Objective

Users consistently perform the target action when the opportunity arises.

  • Users feel the action is easy
  • Users respond to prompts positively
  • Reduced reliance on willpower

Target Actor

role

Everyday user

environment

Distracted, multitasking, attention-scarce

emotional baseline

Variable motivation, cognitive fatigue

ai familiarity

medium

risk tolerance

medium

Execution Model

1

ability

User must be able to perform the action with almost no effort.

User starts the action but abandons midway because it feels hard.

2

motivation

User must want to perform the action right now.

User sees the prompt but feels 'I don't care right now.'

3

trigger

User must be prompted at the exact moment when motivation and ability are high.

User never sees the prompt or sees it at the wrong time.

Failure Modes

High motivation but zero ability creates guilt

Simplify the action first using Action Structuring

feature

Strong trigger with low motivation leads to prompt fatigue

Strengthen motivation before increasing trigger frequency

micro

Over-reliance on motivation causes burnout

Anchor to Tiny Habits and existing routines

feature

Weak or poorly timed triggers cause missed opportunities

Use Contextual Triggers tied to user routines

micro

All three elements present but behavior still fails

Diagnose hidden environmental blockers

feature

Agent Decision Protocol

Triggers

  • User ignores repeated prompts
  • Action completion rate suddenly drops
  • Users report 'I keep meaning to do this but never do'

Escalation Strategy

L1: Diagnose which of the three B=MAT elements is weakest via behavioral_signals

L2: Nudge -- adjust trigger timing, add motivation cue, or simplify action

L3: Restructure -- apply Tiny Habits or Action Structuring to reduce ability gap

L4: Constrain -- limit prompt frequency to prevent fatigue, lock to high-ability moments

L5: Yield -- flag for human behavioral designer review

Example

User opens Duolingo notification but closes it -> Ability failure -> retrieve Action Structuring -> propose 15-second micro-lesson instead of full exercise.

Behavioral KPIs

Primary

  • Action completion rate when prompted
  • Median time from trigger to completion
  • Prompt dismissal rate

Risk

  • User-reported frustration after failed attempts
  • Drop-off in habit-forming features

Trust

  • Positive sentiment toward prompts
  • Autonomy Dial usage when agent suggests actions

Behavioral Signals

motivation_failure

prompt_viewed=true AND action_started=false AND dwell_time < 3s

prompt_dismissed=true AND repeat_dismiss_count > 2

ability_failure

action_started=true AND action_completed=false AND time_in_action > 60s

action_abandoned_midway=true

trigger_failure

prompt_delivered=true AND prompt_viewed=false

time_since_last_action > 48h AND prompt_sent=false

Decay Monitoring

Revalidate when

  • New user segments with different motivation baselines onboard
  • Platform notification policies change
  • Product introduces major new features that alter ability

Decay signals

  • Rising prompt dismissal rates
  • Increased user complaints about spam or guilt trips
  • Drop in long-term retention despite short-term engagement spikes

Pattern Relationships

Related Patterns

Canonical Implementation

Duolingo lesson flow: Motivation (streaks) + Ability (tiny lesson) + Prompt (notification)

Telemetry Hooks

prompt_receivedaction_startedaction_completedprompt_dismissed

Tags

fogg-corefoundationaldiagnosticagent-ready