Why I don’t use the Fogg Behavior Model

As a behavioural science consultant, I often come across models that try to explain what drives our behaviours and makes them stick. One model that's been popular for years in app development, product design, and persuasive technology is the Fogg Behaviour Model (FBM). Its big draw is its simplicity, focusing on three main elements: motivation, ability, and triggers. This straightforward approach makes FBM a go-to choice for encouraging specific actions, like engaging with an app or completing a task.

After taking a close look at FBM, I've decided not to use it in my work. Here are the main reasons why:

1. FBM is incomplete and simplistic, focusing too much on triggers and immediate behaviour without considering the consequences that shape and sustain behaviour over time.

2. FBM is an inferior model for long-term behaviour change, as it overlooks internal states and motivational shifts, which are crucial for sustaining or generalising behaviour.

3. FBM is outdated compared to modern behavioural science, relying on outdated principles that don’t reflect current scientific thinking about behaviour.

In this article, I'll explore how FBM stacks up against more comprehensive frameworks based on behavioural psychology and learning theory. If you're interested in seeing how the models compare, there are summary tables at the end of the article.

N.B. This is just my perspective—everyone is welcome to do their own analysis based on publicly available materials. Critique of ideas is a crucial part of scientific discourse, and my aim here is to share my own evaluation. Scientific models can evolve, and discussion helps refine them.

TL;DR - If you're designing apps or other persuasive technology, FBM may well work for you - that's not the kind of work I do, so there's nothing FBM can do that COM-B/BCW can't do much more robustly and comprehensively

Explanation of the Fogg Behaviour Model (FBM)

The Fogg Behaviour Model (FBM) is built on the idea that behaviour occurs when three elements align: motivation, ability, and a prompt.

1. Motivation: The individual needs enough motivation to perform the behaviour, but it interacts with Ability: high ability (easy behaviour) can compensate for lower motivation, and high motivation can drive behaviour even if ability is low (behaviour is difficult).

2. Ability: The behaviour must be simple enough for the individual to perform. The required motivation level depends on the behaviour's difficulty: difficult behaviour requires higher motivation and easy behaviour can occur with lower motivation.

3. Prompt: A prompt is something that cues the behaviour at the right moment. Prompts can be external (e.g. a notification, reminder, or suggestion or internal (e.g. a thought, feeling, or urge).

FBM suggests that behaviour happens when these three components—motivation, ability, and a prompt—converge. The model allows for a trade-off between motivation and ability:

• Low ability (difficult behaviour) can be compensated for by high motivation.

• Low motivation can still lead to action if ability is high (behaviour is easy)

• For example, a fitness app notification (external prompt) might remind a user to work out. Even if the user has low motivation, the simplicity of the action (like doing a short, easy exercise) may make the behaviour more likely.

The combination of internal and external prompts makes FBM adaptable for explaining behaviour initiation in various contexts, from digital interactions to everyday life. However, while FBM explains behaviour initiation, it leaves open the question of what maintains behaviour after it has been triggered.

Moreover, although FBM acknowledges both internal and external prompts, it doesn't sufficiently address the difference between the two when it comes to influencing behaviour. External prompts, like notifications or reminders, can be designed and controlled, making them a useful tool in behaviour design. In contrast, internal prompts, such as thoughts or feelings, are much harder to design for, making them less practical for consistent behaviour change interventions.

Note: I would have preferred to reproduce the visual here for easy reference, but unfortunately Dr Fogg claims copyright over the diagram (something that is highly unusual for scientific research in general). You can see the diagram through this Google search.

The ABC Model and the Importance of Antecedents

To fully understand the limitations of the Fogg Behaviour Model (FBM), it’s helpful to compare it with a widely established framework in behavioural psychology: the ABC model. The ABC model stands for Antecedent, Behaviour, Consequence, and provides a more comprehensive explanation of how behaviours are initiated, maintained, and either strengthened or weakened over time.

• Antecedent: The events or circumstances that occur before the behaviour. These can include internal or external stimuli that influence whether the behaviour occurs.

• Behaviour: The actual action taken by the individual in response to the antecedent.

• Consequence: What happens after the behaviour, which shapes whether the behaviour will be repeated in the future. Consequences can include reinforcement (increasing the likelihood of repetition) or punishment (decreasing the likelihood of repetition).

Unlike FBM, which focuses primarily on antecedents (via motivation, ability, and prompts), the ABC model provides a broader framework by also accounting for consequences—especially reinforcement—that play a crucial role in determining whether behaviour will persist or fade over time.

Different Types of Antecedents in the ABC Model

In behavioural psychology and learning theory, antecedents are not limited to simple triggers or prompts. There are several types of antecedents that can influence whether behaviour will occur, many of which FBM overlooks.

Discriminative Stimuli (SDs): These are specific cues or signals that indicate whether a behaviour is likely to result in reinforcement. For example, a green light at a traffic stop signals that driving forward is appropriate, whereas a red light signals it is not. FBM doesn’t account for discriminative stimuli, which are critical in determining when a behaviour will lead to a positive outcome. In contrast, SDs help explain how behaviours are influenced by specific cues within a given context, which FBM’s more simplified prompts fail to capture.

Establishing Operations (EOs) and Motivating Operations (MOs): These antecedents temporarily alter the value of a reinforcer or change the probability of a behaviour occurring. For example, if a person is hungry (an EO), food becomes a more powerful reinforcer, and they are more likely to engage in behaviours that lead to food. FBM treats motivation as static and doesn’t recognise how internal states (like hunger, fatigue, or stress) fluctuate and influence behaviour. These shifts in internal motivation, often driven by MOs, are critical for understanding why a person might engage in a behaviour at one moment but not at another.

Setting Events: These are broader contextual or environmental factors that influence behaviour indirectly. A setting event could be something like the weather, time of day, or the person’s emotional state, which impacts how they respond to other antecedents. For example, a person might be less likely to exercise if they’re tired or if it’s raining. FBM doesn’t address the role of setting events in shaping behaviour. While it focuses on immediate prompts, setting events can heavily influence whether a person is receptive to those prompts. Without accounting for these broader antecedents, FBM oversimplifies the conditions that lead to behaviour.

Real-world behaviour is shaped by a complex interaction of discriminative stimuli, motivating operations, and setting events - these factors are essential for explaining when and why behaviour happens. By reducing antecedents to prompts alone, FBM overlooks the contextual factors that behavioural psychology considers critical for understanding and shaping behaviour.

The problem with FBM’s approach to Motivation and Prompts

Motivation and prompts are essential components of FBM, but the model treats them in an overly static and simplistic manner. FBM focuses on aligning motivation and prompts to trigger behaviour but neglects the fluctuating nature of motivation and the complexity of antecedents.

Static view of motivation: FBM assumes motivation is consistent—either someone is motivated enough to perform the behaviour or they aren’t. However, behavioural psychology understands motivation as something that fluctuates based on internal states like mood, stress, energy levels, or hunger. These internal factors, known as motivating operations (MOs), can increase or decrease the likelihood of behaviour. By overlooking how these internal states alter motivation, FBM remains incomplete.

Simplified view of prompts: While FBM acknowledges both internal and external prompts, it oversimplifies how prompts interact with behaviour. In behavioural psychology, antecedents include more than just cues; they encompass discriminative stimuli (SDs), which signal whether behaviour will lead to reinforcement, and setting events, which are contextual factors that indirectly influence behaviour. FBM doesn’t consider the complexity of when and why prompts are effective, focusing only on their presence rather than their interaction with broader contextual factors.

Example: A fitness app may send a push notification (an external prompt) to encourage exercise, assuming that if the user is motivated, they’ll act. However, the user’s internal state—such as stress or tiredness—may reduce their motivation at that moment, even if the prompt is well-timed. Additionally, setting events like a rainy day or a bad mood can make a normally effective prompt fail. FBM’s failure to account for these nuances leaves it underdeveloped compared to more complete models like the ABC model.

Ability: Why behaviour isn’t always simple

FBM’s ability component emphasizes making the behaviour easy enough to perform. According to Fogg, if a behaviour is too hard—whether it requires too much time, money, physical or mental effort, or deviates from social norms—the individual is less likely to engage in it, even if motivation is high. FBM outlines six simplicity factors:

• Time

• Money

• Physical Effort

• Mental Effort

• Social Deviance (doing something socially inappropriate)

• Non-Routine (whether the behaviour fits into a person’s habits)

While this emphasis on simplicity is useful for explaining behaviour initiation, it overlooks the longer-term process of building skills and overcoming task difficulty.

Task Difficulty vs. Simplicity: FBM’s focus on simplicity is about removing barriers to make the behaviour easier in the moment. However, in animal training, task difficulty is addressed through shaping—a technique that gradually builds a more complex behaviour by reinforcing successive approximations of the desired behaviour. Instead of trying to make the entire behaviour simple from the outset, trainers break down complex behaviours into smaller, manageable steps that can be mastered over time.

Skill Building Over Time: FBM doesn’t consider the role of skill-building and learning in behaviour change. In animal training, for example, trainers use shaping and task analysis to help animals build up their ability over time. A complex behaviour, like agility training, is taught step by step, with each small success being reinforced. This gradual building of ability contrasts with FBM’s static focus on making behaviours simple from the start.

Example: A dog learning to retrieve might find the full sequence difficult at first (low ability), but with practice and reinforcement (such as receiving treats for partial steps like approaching or picking up the item), the dog gradually learns the entire behaviour. FBM’s focus on simplicity doesn’t capture how abilities are built through shaping and reinforcement schedules that support gradual skill development

FBM Ignores the Importance of Consequences

One of the key shortcomings of FBM is its lack of focus on consequences, which are essential for maintaining behaviour over time. In animal training, consequences are critical in shaping behaviour, and reinforcement is used to either increase or decrease the likelihood of a behaviour occurring again.

No structured approach to consequences: While FBM implicitly suggests that positive outcomes (like ease of use or a satisfying result) can lead to behaviour repetition, it does not deeply analyse or systematise this concept. In contrast, animal training relies heavily on reinforcement. Positive reinforcement (like rewards or praise) increases the likelihood of a behaviour being repeated, while negative reinforcement (removing an aversive stimulus) also encourages repetition. FBM, without addressing consequences, leaves a critical gap in understanding why behaviours persist.

Consequences drive long-term behaviour change: The ABC model (Antecedent, Behaviour, Consequence) emphasizes that what happens after the behaviour is crucial for determining whether it will continue. Positive reinforcement strengthens behaviour, while punishment or lack of reinforcement weakens it. FBM, however, focuses entirely on what happens before the behaviour (motivation, ability, and prompts), ignoring the role of consequences. Without reinforcement, behaviours typically fade over time, making FBM less useful for creating lasting behaviour change.

Key Difference: FBM focuses on setting up the environment to increase the likelihood of behaviour occurring but doesn’t have a formal mechanism for using consequences to maintain or decrease behaviour. Animal training rigorously uses consequences—primarily through reinforcement—to shape behaviour, ensuring that behaviours are either strengthened or weakened based on their outcomes.

Example: A dog may respond to a prompt to sit, and the behaviour may occur once. However, if there is no reinforcing consequence (such as receiving a treat or praise), the likelihood of repeating the behaviour drops significantly. In contrast, animal trainers ensure that every successful behaviour is reinforced, increasing the chances of repetition. FBM explains the initial action but doesn’t address what encourages the behaviour to continue.

I realise this is a lot of theory to digest, so I've made a quick comparison table as reference:

Aspect	Fogg Behavior Model (FBM)	Behavioural Psychology/Animal Training
Trigger/Prompt	Environmental cues or internal states prompt behaviour.	Antecedents include environmental conditions, internal states, and cues like discriminative stimuli.
Motivation	Driven by pleasure/pain, hope/fear, or social acceptance/rejection.	Motivation fluctuates based on factors like motivating operations (internal states that affect behaviour).
Ability	Focuses on making behaviour easy by reducing time, effort, and resources.	Uses task analysis, shaping, and reinforcement to gradually build more complex behaviours over time.
Consequences	Not a central focus; assumes that reinforced behaviours will be repeated.	Central focus; reinforcement and punishment are used to increase or decrease the likelihood of behaviours being repeated.

If you want to dive deeper into reinforcement and operant conditioning, I've written about it before:

• Lessons from dog training and parenting for pandemic behaviour change (accessible intro to learning theory)

• Beyond rewards: navigating the complexities of reinforcement in behaviour change (zooming in to reinforcement)

Recap: Understanding the Limitations of FBM

The Fogg Behaviour Model excels at initiating simple, immediate actions—like prompting someone to click a button or complete a short task. However, it falls short when it comes to complex, long-term behaviour change. FBM’s focus on motivation, ability, and prompts is too limited for behaviours that require ongoing reinforcement, skill development, and adaptation across different contexts. Real-world behaviour change is rarely a one-step process, and without the ability to generalise learned behaviours to new situations, FBM lacks the depth needed for sustaining change over time.

Additionally, FBM does not account for feedback loops, a critical element in behavioural psychology and animal training. Timely feedback—whether positive or corrective—is essential for adjusting behaviour and ensuring it is reinforced. FBM’s reliance on prompts without a built-in feedback mechanism creates a gap in how we understand behaviour maintenance and improvement over time.

Finally, FBM’s one-size-fits-all approach overlooks the importance of individual differences. Both in animal training and human behaviour change, strategies are personalised based on unique needs, learning styles, and prior experiences. FBM doesn’t offer a way to customise interventions, making it less adaptable for diverse learners or more complex behavioural contexts.

Conclusion: why FBM is not a fit with my professional practice

FBM offers a simplified view of behaviour by focusing on initiating actions in the moment. However, modern learning theory shows that behaviour is shaped by the consequences that follow. Without accounting for reinforcement, FBM leaves out the critical mechanisms needed for long-term behaviour maintenance. This is a major limitation when it comes to sustaining behaviour over time.

Modern behaviour models, like neo- and radical behaviourism, demonstrate that behaviour is influenced not just by external triggers, but also by internal factors such as motivation and emotions. FBM’s narrow focus on external prompts provides a limited and less effective approach for creating lasting behaviour change. By not addressing how behaviours are maintained, FBM doesn’t stand up to more modern models that integrate both external and internal processes.

FBM is built on older behaviourist principles that focus solely on observable triggers and ignore internal states. However, modern behaviourism has evolved to incorporate both external triggers and internal processes like cognition and emotion. Choosing FBM today means relying on a model that hasn’t kept pace with advances in behavioural science. As a result, while FBM may still work for short-term, simple behaviour initiation, it is insufficient for the complex, long-term behaviour change needed in many of the projects that I work on.

For those arguing for FBM’s simplicity, here’s the thing: a basic, everyday knife might work for cutting vegetables at home, but it’s not enough for a professional chef to do their job well. On the flip side, a professional-grade knife is far too sharp and precise for casual use in the kitchen—it could even be dangerous in the hands of someone who’s not trained to use it properly. The same goes for behaviour change—FBM’s simplicity might work in certain contexts, but it’s too blunt a tool for the complex, real-world scenarios, where precision and adaptability are essential. And, in the wrong hands, oversimplified models like FBM can lead to misuse or unintended consequences.

As a consultant and behavioural science practitioner, I prefer to base my work on up-to-date, evidence-based models and comprehensive frameworks that reflect the latest advances in behavioural science. While FBM may be effective in certain narrow contexts, it lacks the depth and adaptability required to create sustained behavioural impact. I believe that my clients deserve approaches that are scientifically grounded, tailored to their unique needs, and designed to support long-term behaviour change.

However, my concerns with FBM do not end in the substance of the model - I also have ethical concerns that make it difficult to consider integrating it into my practice.

Ethical concerns with FBM

• Oversimplification risks misuse: FBM's focus on triggers and prompts can lead to behaviour manipulation without considering long-term welfare. This raises ethical concerns, especially in contexts like consumer manipulation or persuasive technology, where people may be nudged toward behaviours that aren’t in their best interests.

• Lack of emphasis on consequences: By not fully addressing consequences, FBM ignores how certain behaviours might lead to undesired outcomes. Ethical models consider both the immediate impact of behaviour and its long-term consequences on a person's well-being.

• Absence of personalised intervention: FBM's one-size-fits-all approach lacks personalisation, potentially failing to respect individual needs, preferences, and contexts. More ethical, tailored behaviour-change approaches adapt to individual differences, ensuring interventions are client-centred and appropriate for the person’s specific circumstances.

• Risk of habit formation without accountability: FBM is designed to trigger behaviours but lacks accountability for whether those behaviours are beneficial or harmful. Ethical models ensure that behaviour-change techniques lead to positive, long-term outcomes rather than just achieving short-term goals.

• Persuasive technology and autonomy: FBM’s emphasis on creating triggers and external prompts can undermine individual autonomy by pushing people toward actions they wouldn’t necessarily choose on their own. Ethical practices in behavioural science focus on enhancing autonomy and empowering individuals to make decisions that are in their best interests.

Finally, concerns about transparency and attribution

A key principle of ethical science and practice is the transparent attribution of ideas and the recognition of prior work. While Fogg presents FBM as his own model, many of its core concepts—such as the three-part framework—are clearly rooted in existing behavioural science literature. By claiming ownership of a model that heavily draws from established work without proper referencing, and by associating his own name so closely with the framework, it raises questions about the integrity and originality of the model.

In scientific practice, the importance of proper attribution cannot be overstated. It ensures that ideas are built on solid foundations and that credit is appropriately shared among researchers. Transparency and accurate attribution are essential for maintaining the integrity of scientific work and fostering a collaborative research environment. Many people have different opinions about what is an appropriate level of referencing - in my view, would it seem reasonable to expect higher standards of referencing from those using the letters PhD after their name than what would be acceptable for first-year undergraduate students in psychology.

Ultimately, everyone has their preferred models and approaches. For me, I’ll continue to rely on evidence-based, adaptable frameworks that align with the latest research and ensure ethical, long-term behaviour change for my clients.

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Table 1: Comparison of Behaviorism vs. Behavioral Psychology

Aspect	Behaviourism	Behavioural Psychology
Core Focus	Studies observable behaviors only. Internal mental states are ignored.	Studies both observable behavior and internal mental processes.
Primary Methods	Focuses on stimulus-response relationships (e.g., reinforcement and punishment).	Uses operant conditioning and integrates cognitive processes like thoughts, expectations, and motivations.
Key Figures	John B. Watson, B.F. Skinner	B.F. Skinner, Albert Bandura, Aaron Beck
View on Internal States	Disregards internal processes like thoughts and emotions.	Considers internal states (thoughts, feelings) as part of the behavior-shaping process.
Approach to Learning	Learning occurs purely through environmental stimuli and consequences.	Learning is influenced by reinforcement but also by cognitive processes like perception and problem-solving.
Key Applications	Used in animal training, operant conditioning studies, and habit formation.	Used in behavioral therapy (e.g., CBT), learning theory, and educational psychology.
Limitations	Criticized for ignoring cognition, thoughts, and emotions.	Broader scope but sometimes viewed as too complex for purely behavioral analysis.

Table 2: Classical Behaviourism, Neo-Behaviourism, and Radical Behaviourism

Aspect	Classical Behaviorism	Neo-Behaviorism	Radical Behaviorism
Core Focus	Focuses on observable behavior and the stimulus-response relationship. Disregards internal processes.	Combines observable behavior with recognition of hypothetical constructs (e.g., drives, cognitive maps).	Expands to include private events (thoughts, feelings) as behaviors themselves but still focuses on environmental determinants.
Key Figures	John B. Watson, B.F. Skinner	Clark Hull, Edward Tolman	B.F. Skinner
Role of Cognition	Rejects cognition and other internal processes as irrelevant to behavior.	Accepts internal constructs as valid for understanding behavior, but only if they can be operationally defined.	Views cognition and emotions as behaviors subject to environmental control, not causes of behavior.
Approach to Learning	Behavior is learned through conditioning (classical and operant).	Behavior is learned through conditioning but influenced by drives and expectations.	Behavior is learned through reinforcement and punishment, including internal behaviors like thinking.
View on Mental States	Mental states are irrelevant or unobservable and thus ignored.	Mental states are relevant as they affect observable behavior.	Mental states are considered behaviors themselves, to be studied through the same lens as observable behavior.
Use of Reinforcement	Reinforcement and punishment drive behavior change.	Reinforcement is used but influenced by hypothetical internal variables.	Reinforcement applies to all behavior, including thoughts and emotions.
Applications	Animal training, habit formation, simple learning models.	Learning theory, problem-solving models, cognitive maps.	Behavior analysis, self-management strategies, therapeutic applications.
Limitations	Ignores internal processes, emotions, and cognition.	Acknowledges cognition but is often criticized for vague operational definitions.	Criticized for treating internal events like thoughts and feelings as mere behaviors without exploring their underlying complexity.