Behavior Change and Habit Formation: Habits, Routines, and Sustainable Action

Last Updated June 21, 2026

Behavior Change and Habit Formation appears here not as a motivational slogan, but as a serious field of behavioral science. It asks how repeated actions become automatic, how contexts cue behavior, how rewards and feedback strengthen routines, how effort and friction block change, how goals become practices, and how environments can be redesigned to make better action more possible. It connects individual psychology with social systems, institutional design, and public problem-solving.

The field matters because many important goals depend on sustained action rather than one-time decisions. Health, education, savings, climate behavior, civic participation, organizational reform, technology use, learning, and personal development all depend on routines that persist over time. A person may want change, intend change, understand change, and still return to older patterns when cues, environments, incentives, stress, identity, or social context remain unchanged. Behavior change research helps explain this gap between intention and action.

GitHub Repository

The companion repository for this knowledge series should be created as behavior-change-habit-formation-code under the Content-Catalyst-LLC GitHub organization. It should use article-level folders under articles/, shared reusable methods under _shared/, and documented synthetic-data workflows so the pillar can support conceptual articles, behavioral simulations, habit-loop models, intervention-planning tools, measurement templates, and reproducible demonstrations of behavior change over time.

Behavior Change as a Core Behavioral Science

Behavior change occupies a central place within behavioral science because it asks how action begins, stabilizes, weakens, and transforms over time. It studies the gap between intention and practice, the difference between knowing and doing, and the conditions under which repeated behavior becomes durable. People often experience change as an individual struggle of discipline, but behavior-change science shows that action is shaped by cues, routines, feedback, motivation, opportunity, friction, social support, identity, and environmental design.

This field does not deny the importance of agency. Rather, it makes agency more realistic. A person’s ability to change depends partly on goals and motivation, but also on whether the surrounding context supports the intended behavior. The same person may succeed in one environment and fail in another because cues, social expectations, convenience, time, tools, stress, and feedback differ. Behavior change therefore belongs to psychology, but also to systems thinking, design, public policy, health, sustainability, and institutional practice.

Behavior change is also distinct from simple persuasion. Persuasion may alter belief, attitude, or intention, but behavior change requires translation into action. A campaign may convince people that exercise, savings, vaccination, study, civic participation, or energy conservation matters, but sustained behavior depends on routines, access, timing, reinforcement, effort, and maintenance. This series therefore focuses on how change becomes embodied in practice.

Habit Formation as a Science of Repeated Action

Habit formation is the study of how repeated behavior becomes increasingly automatic in stable contexts. Habits allow people to act without constant deliberation. They conserve cognitive effort, stabilize daily life, and make routine behavior efficient. Yet the same automaticity that makes habits useful can also make unwanted patterns difficult to change. Once a cue reliably activates a routine, conscious intention may not be enough to interrupt it.

A habit is not merely something a person does often. It is a learned association between context and action. A particular place, time, emotional state, object, social situation, or sequence of events can become a cue for behavior. Over time, repetition strengthens the link between cue and response. Rewards, relief, convenience, identity, and social reinforcement may further stabilize the routine.

Habit formation therefore helps explain why behavior change often requires environmental change. To build a new habit, people may need stable cues, lower friction, immediate feedback, repeated practice, and a clear connection between action and reward. To disrupt an old habit, they may need to change cues, interrupt routines, introduce alternative responses, redesign environments, or alter the reinforcements that keep the pattern alive.

Behavior Change as Environment and Systems Design

Behavior change is often misrepresented as an individual project of willpower. A more serious account treats behavior as part of an environment. People act within homes, schools, workplaces, platforms, agencies, cities, markets, and communities. These environments arrange cues, defaults, tools, expectations, incentives, and obstacles. They make some actions convenient, visible, rewarded, and socially normal while making others costly, confusing, hidden, or unsupported.

This is why behavior change belongs to systems design. A health program that requires repeated appointments, complex forms, and unclear feedback is not merely asking for motivation; it is designing friction. A sustainability program that relies on moral appeal while leaving high-consumption defaults intact is not fully designed for behavior. An educational system that asks for persistence but gives little timely feedback or social support may produce failure that looks individual but is partly institutional.

Behavior-change design asks how systems can support better action without coercion or manipulation. It examines how to align cues, tools, routines, feedback, norms, identity, and infrastructure so that intended behavior becomes easier to perform and easier to maintain. In this sense, behavior change is not simply about changing people. It is about changing the conditions under which people act.

What Behavior Change and Habit Formation Studies

Behavior Change and Habit Formation studies the mechanisms that make behavior more or less likely over time. At the individual level, it examines motivation, intention, self-regulation, emotion, identity, attention, capability, skill, goal setting, planning, memory, automaticity, and habit strength. At the environmental level, it examines cues, friction, convenience, tools, defaults, prompts, rewards, feedback, and the design of action contexts.

At the social level, the field studies support, accountability, role models, shared routines, peer behavior, social norms, identity, cooperation, and cultural expectations. At the institutional level, it studies program design, forms, eligibility processes, service access, workplace routines, educational structures, public communication, platform design, and policies that make certain behaviors easier or harder.

The field also studies change over time. It asks how behavior begins, how it is repeated, how it becomes automatic, how it is interrupted, how relapse happens, how maintenance works, and how interventions can be evaluated. This time dimension is essential. A behavior that changes for one day, one week, or one trial may not become a stable pattern. Durable change requires attention to maintenance, context stability, identity, social reinforcement, and long-term fit.

What This Pillar Covers

This pillar covers the major domains through which behavior change and habit formation are studied. It includes habit loops, cues, triggers, routines, rewards, automaticity, friction, convenience, implementation intentions, goal setting, self-control, commitment devices, feedback, progress, relapse, maintenance, behavior-change models, COM-B, the Behavior Change Wheel, the Transtheoretical Model, the Fogg Behavior Model, social cognitive theory, self-determination theory, ecological models of behavior change, and the ethics of intervention design.

It also covers applied behavior change in health, sustainability, education, finance, organizations, public policy, civic life, and technology. These areas differ in context, but they share a common problem: how to support action that aligns with longer-term goals, public welfare, personal agency, or collective sustainability. In each case, behavior depends not only on information, but on whether the desired action is accessible, cued, reinforced, socially supported, and institutionally possible.

The series also treats behavior change as an evidence-based practice. Behavioral claims require measurement. Interventions require evaluation. Habit models require attention to context and time. A serious behavior-change framework must distinguish initial adoption from long-term maintenance, self-report from observed action, individual motivation from structural support, and ethical support from manipulation.

Mathematics, Computation, and Modeling in Behavior Change

Mathematics and computation help behavior-change research make assumptions explicit. A simplified behavioral probability model can represent action as a function of motivation, capability, opportunity, friction, cue strength, reinforcement, and social support:

where:

In this simplified model, \(M_i\) represents motivation, \(C_i\) capability, \(O_i\) opportunity, \(F_i\) friction, \(Q_i\) cue strength, \(R_i\) reinforcement, and \(S_i\) social support. The parameters \(\theta\) describe how strongly each condition influences behavior.

Habit strength can be represented as a dynamic process:

where \(H_t\) is habit strength at time \(t\), \(Q_t\) cue consistency, \(R_t\) reinforcement, \(D_t\) disruption, \(\alpha\) learning rate, and \(\delta\) disruption sensitivity.

Maintenance can be modeled by distinguishing initial adoption from continued practice:

where \(A_t\) represents adoption pressure or support, and \(L_t\) represents lapse pressure from stress, friction, disruption, competing goals, or loss of motivation.

These formulations do not reduce behavior change to formulas. They clarify a central insight: sustained action depends on dynamic relationships among motivation, capability, opportunity, cue stability, reinforcement, environment, and maintenance. Computation is especially useful when behavior-change systems are heterogeneous, long-term, context-sensitive, and difficult to interpret through verbal explanation alone.

R supports longitudinal behavior analysis, intervention evaluation, survival analysis, mixed models, causal inference, and visualization. Python supports simulation, behavioral analytics, habit-tracking data workflows, agent-based models, intervention planning tools, and platform-behavior analysis. SQL supports behavior logs, intervention records, treatment assignments, baseline measures, and reproducible data provenance. Other languages can support typed records, interactive tools, audits, simulations, and scalable behavioral systems where appropriate.

Major Domains of Behavior Change and Habit Formation

Behavior Change and Habit Formation includes several major domains. Habit architecture studies cues, routines, rewards, context stability, automaticity, repetition, disruption, and the redesign of behavioral environments. Self-regulation studies goals, planning, self-control, delayed gratification, commitment devices, implementation intentions, emotional regulation, and the gap between intention and action.

Behavior-change models provide structured ways to diagnose and design interventions. COM-B analyzes capability, opportunity, and motivation. The Behavior Change Wheel links behavioral diagnosis to intervention functions and policy categories. The Transtheoretical Model describes stages of change. The Fogg Behavior Model emphasizes motivation, ability, and prompts. Social Cognitive Theory emphasizes self-efficacy, observational learning, and reciprocal causation. Self-Determination Theory emphasizes autonomy, competence, and relatedness.

Applied behavior change studies how these mechanisms operate in health, sustainability, education, finance, organizations, public policy, technology use, and civic behavior. Measurement and evaluation study how behavior is observed, tracked, compared, and interpreted over time. Ethics studies when behavior-change design supports agency and when it becomes manipulation, blame, surveillance, or institutional control.

Why Behavior Change and Habit Formation Matters

Behavior change matters because many important problems are not solved by information alone. People may know that exercise is beneficial, debt is risky, energy conservation matters, studying improves outcomes, or public health guidance is important, but action still depends on time, stress, cues, convenience, social support, identity, feedback, and environmental design. Awareness may create intention, but habits and systems shape practice.

Habit formation matters because repeated behavior shapes life at scale. Daily routines influence health, learning, consumption, work, relationships, attention, savings, civic participation, and environmental impact. Small behaviors repeated over time can produce large cumulative effects. Likewise, small barriers repeated over time can produce lasting exclusion, inequality, or disengagement.

The field also matters because institutions routinely ask people to change without redesigning the conditions of action. Schools ask students to persist. Agencies ask citizens to complete forms. Employers ask workers to adopt new practices. Health systems ask patients to follow routines. Sustainability programs ask households to conserve. Behavior-change science asks whether these systems actually support the behavior they request.

Behavior Change and Human Agency

Behavior-change science changes how agency is understood. It shows that agency is not simply the possession of willpower. Agency is exercised within environments that cue, support, frustrate, reward, obscure, or burden action. A person can be responsible and still be constrained. A person can be motivated and still fail when the action is difficult, unsupported, unclear, socially costly, or repeatedly disrupted.

This perspective avoids two errors. It avoids blaming individuals for every behavioral outcome, especially when structural conditions make action difficult. It also avoids treating people as passive objects to be engineered. Human agency matters, but it is most meaningful when environments support informed, voluntary, and sustainable action.

A serious behavior-change framework should therefore aim to increase capacity rather than merely steer behavior. It should make better action easier, clearer, more supported, and more aligned with people’s values. Ethical behavior-change design helps people act on commitments they can endorse, rather than exploiting their vulnerabilities for institutional or commercial goals.

Behavior Change and Habit Formation Pillar Map

The map below organizes the Behavior Change and Habit Formation knowledge series into conceptual domains, moving from foundations of change toward habit architecture, motivation, self-regulation, behavior-change models, applied contexts, measurement, maintenance, technology, sustainability, and ethics. The article titles below are intentionally left unlinked until their individual pages exist.

Measurement, Maintenance, and Behavioral Practice

One of the most important distinctions in behavior-change practice is the difference between initial change and maintenance. A person may start exercising, save money, reduce energy use, attend class, complete a form, or use a new tool for a short period. The harder question is whether the behavior persists when motivation declines, contexts shift, stress increases, reminders disappear, or older routines return.

Measurement must therefore capture time. A behavior-change intervention should define the target behavior, baseline, frequency, context, duration, maintenance period, expected mechanism, and criteria for success. It should distinguish self-reported intention from observed action, short-term adoption from durable practice, and individual-level change from system-level support.

Behavioral practice should also evaluate failure. A program may fail because the target behavior was poorly defined, the intervention did not address real barriers, the context changed, social support was absent, friction remained high, feedback was delayed, or the intervention relied too heavily on motivation. Serious behavior-change work treats failure as evidence about systems, not simply as evidence of weak individual will.

Behavior Change, Technology, and the Modern World

Technology has made behavior change more measurable, more personalized, and more ethically complicated. Apps, platforms, sensors, wearables, reminders, dashboards, recommendation systems, and AI tools can support behavior change by providing feedback, reducing friction, tracking progress, prompting timely action, and making routines visible. They can also exploit habit systems through variable rewards, notifications, social comparison, streak pressure, friction asymmetry, and attention capture.

Digital behavior change is therefore not automatically good or bad. It depends on design purpose, transparency, autonomy, data use, consent, and alignment with user welfare. A tool that helps people remember medication, study consistently, reduce energy use, or build healthy routines can support agency. A platform that captures attention, manipulates defaults, or makes exit difficult can undermine agency.

A mature behavior-change science must therefore study technology as a behavioral environment. It should ask how digital systems cue behavior, how they reward repetition, how they shape attention, how they define success, how they use data, and how they respect human dignity. The future of habit formation will increasingly depend on ethical technology design.

Behavior Change, Sustainability, and Public Systems

Behavior change is central to sustainability because ecological transitions depend partly on routines: energy use, transportation, consumption, food, waste, water use, repair, reuse, conservation, and civic participation. Yet sustainable behavior cannot be reduced to individual virtue. It depends on infrastructure, affordability, access, social norms, policy design, feedback, trust, and institutional support.

A household may want to conserve energy but lack feedback, efficient housing, or affordable upgrades. A commuter may support low-carbon transport but lack safe, reliable options. A consumer may prefer sustainable products but face cost barriers, confusing information, or weak defaults. Behavior-change science helps explain why sustainable intention often fails when systems remain misaligned.

For Sustainable Catalyst, behavior change should therefore be framed as systems-supported agency. The goal is not to blame individuals for unsustainable systems, nor to manipulate people into compliance. The goal is to understand how public systems, institutions, technologies, communities, and environments can make sustainable behavior more possible, visible, rewarding, fair, and durable.

R Section: Simulating Habit Strength and Behavioral Maintenance

For analytical readers, R is useful for modeling repeated behavior, habit strength, intervention exposure, maintenance, and relapse. The example below simulates a synthetic behavior-change process in which cue consistency, reinforcement, friction, and disruption affect habit strength over time. It is not real behavioral data. It is a reproducible scaffold for thinking clearly about how behavior can strengthen or weaken across repeated contexts.

# Synthetic habit formation model in R
# Educational example only.
# This script simulates habit strength over time under different
# cue, reinforcement, friction, and disruption conditions.

# install.packages(c("tidyverse", "scales"))

library(tidyverse)
library(scales)

set.seed(4141)

n_people <- 900
n_days <- 90

people <- tibble(
  person_id = 1:n_people,
  motivation = runif(n_people, 0.35, 0.95),
  capability = runif(n_people, 0.35, 0.95),
  opportunity = runif(n_people, 0.30, 0.95),
  cue_consistency = runif(n_people, 0.25, 0.95),
  reinforcement = runif(n_people, 0.20, 0.90),
  social_support = runif(n_people, 0.10, 0.85),
  baseline_friction = runif(n_people, 0.05, 0.70),
  disruption_sensitivity = runif(n_people, 0.05, 0.45)
)

simulate_person <- function(row) {
  habit_strength <- 0.05
  records <- vector("list", n_days)

  for (day in 1:n_days) {
    disruption <- rbinom(1, 1, prob = 0.08)
    daily_friction <- row$baseline_friction + disruption * row$disruption_sensitivity

    behavior_score <-
      -1.40 +
      1.20 * row$motivation +
      1.10 * row$capability +
      1.05 * row$opportunity +
      1.25 * row$cue_consistency +
      0.90 * row$reinforcement +
      0.80 * row$social_support +
      1.50 * habit_strength -
      1.75 * daily_friction

    probability_behavior <- 1 / (1 + exp(-behavior_score))
    performed_behavior <- rbinom(1, 1, probability_behavior)

    habit_strength <-
      habit_strength +
      0.08 * row$cue_consistency * row$reinforcement * performed_behavior * (1 - habit_strength) -
      0.05 * disruption * habit_strength

    habit_strength <- max(0, min(1, habit_strength))

    records[[day]] <- tibble(
      person_id = row$person_id,
      day = day,
      disruption = disruption,
      friction = daily_friction,
      probability_behavior = probability_behavior,
      performed_behavior = performed_behavior,
      habit_strength = habit_strength
    )
  }

  bind_rows(records)
}

simulation <- people |>
  split(.$person_id) |>
  map_dfr(~ simulate_person(.x[1, ]))

summary_by_day <- simulation |>
  group_by(day) |>
  summarise(
    mean_behavior_rate = mean(performed_behavior),
    mean_habit_strength = mean(habit_strength),
    mean_friction = mean(friction),
    .groups = "drop"
  )

print(summary_by_day |> tail(10))

ggplot(summary_by_day, aes(x = day, y = mean_habit_strength)) +
  geom_line() +
  labs(
    title = "Synthetic Habit Strength Over Time",
    x = "Day",
    y = "Mean habit strength"
  ) +
  scale_y_continuous(labels = percent_format()) +
  theme_minimal()

ggplot(summary_by_day, aes(x = day, y = mean_behavior_rate)) +
  geom_line() +
  labs(
    title = "Synthetic Behavior Rate Over Time",
    x = "Day",
    y = "Mean behavior rate"
  ) +
  scale_y_continuous(labels = percent_format()) +
  theme_minimal()

This workflow models a core behavior-change intuition: repeated action becomes more durable when cues are stable, reinforcement is present, friction is manageable, and disruption is limited. In real research, such models require empirical grounding, careful measurement, attention to context, and ethical interpretation.

Python Section: Modeling Friction, Cues, and Behavior Change Trajectories

Python is useful for simulating alternative behavior-change environments. The example below compares three synthetic regimes: a high-friction environment, a supportive cue-rich environment, and a disrupted environment. The goal is not to produce real predictions, but to show how environmental conditions can shape behavior trajectories even when people begin with similar motivation.

# Synthetic behavior change simulation in Python
# Educational example only.
# This script compares behavior trajectories across decision environments.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

rng = np.random.default_rng(4141)

n_people = 1200
n_days = 100

def simulate_regime(regime_name, cue_boost, friction_level, disruption_rate):
    records = []

    motivation = rng.uniform(0.40, 0.95, n_people)
    capability = rng.uniform(0.35, 0.95, n_people)
    opportunity = rng.uniform(0.35, 0.95, n_people)
    reinforcement = rng.uniform(0.25, 0.90, n_people)
    social_support = rng.uniform(0.10, 0.85, n_people)

    cue_consistency = np.clip(rng.uniform(0.25, 0.75, n_people) + cue_boost, 0, 1)
    habit_strength = np.full(n_people, 0.05)

    for day in range(1, n_days + 1):
        disruption = rng.binomial(1, disruption_rate, n_people)
        friction = np.clip(
            friction_level + rng.normal(0.0, 0.08, n_people) + 0.25 * disruption,
            0,
            1
        )

        z = (
            -1.30
            + 1.10 * motivation
            + 1.00 * capability
            + 1.00 * opportunity
            + 1.25 * cue_consistency
            + 0.90 * reinforcement
            + 0.70 * social_support
            + 1.55 * habit_strength
            - 1.80 * friction
        )

        probability_behavior = 1 / (1 + np.exp(-z))
        performed_behavior = rng.binomial(1, probability_behavior)

        habit_strength = (
            habit_strength
            + 0.075 * cue_consistency * reinforcement * performed_behavior * (1 - habit_strength)
            - 0.055 * disruption * habit_strength
        )

        habit_strength = np.clip(habit_strength, 0, 1)

        records.append(pd.DataFrame({
            "regime": regime_name,
            "day": day,
            "behavior_rate": performed_behavior.mean(),
            "mean_habit_strength": habit_strength.mean(),
            "mean_friction": friction.mean(),
            "disruption_rate": disruption.mean()
        }, index=[0]))

    return pd.concat(records, ignore_index=True)

regimes = [
    simulate_regime("high_friction_environment", cue_boost=0.00, friction_level=0.62, disruption_rate=0.08),
    simulate_regime("supportive_cue_rich_environment", cue_boost=0.22, friction_level=0.28, disruption_rate=0.04),
    simulate_regime("disrupted_environment", cue_boost=0.10, friction_level=0.42, disruption_rate=0.20)
]

results = pd.concat(regimes, ignore_index=True)

final_summary = results.groupby("regime").tail(1)[[
    "regime",
    "behavior_rate",
    "mean_habit_strength",
    "mean_friction",
    "disruption_rate"
]]

print(final_summary.sort_values("mean_habit_strength", ascending=False))

plt.figure(figsize=(10, 6))
for regime in results["regime"].unique():
    subset = results[results["regime"] == regime]
    plt.plot(subset["day"], subset["mean_habit_strength"], label=regime)

plt.xlabel("Day")
plt.ylabel("Mean habit strength")
plt.title("Synthetic Habit Strength Across Behavior Change Environments")
plt.legend()
plt.tight_layout()
plt.show()

plt.figure(figsize=(10, 6))
for regime in results["regime"].unique():
    subset = results[results["regime"] == regime]
    plt.plot(subset["day"], subset["behavior_rate"], label=regime)

plt.xlabel("Day")
plt.ylabel("Behavior rate")
plt.title("Synthetic Behavior Rate Across Environments")
plt.legend()
plt.tight_layout()
plt.show()

results.to_csv("behavior_change_habit_formation_simulation.csv", index=False)

For analysts and practitioners, the key lesson is that behavior change cannot be understood only through motivation. Supportive cues, reduced friction, reinforcement, opportunity, and lower disruption can change the trajectory of action over time. The model is synthetic, but it clarifies why behavior-change design must consider environments as well as intentions.

Interpretive Limits and Behavior Change Cautions

Behavior-change science is powerful, but it can be misused when it becomes a tool for blaming individuals or manipulating behavior. A failed behavior may reflect weak motivation, but it may also reflect poverty, stress, limited time, inaccessible infrastructure, confusing systems, social risk, poor design, or institutional neglect. Serious behavior-change analysis must avoid treating every structural problem as an individual habit problem.

Behavior-change design also raises ethical questions. Interventions can support agency when they reduce friction, clarify choices, provide feedback, increase access, and help people act on their own commitments. They become ethically troubling when they obscure intent, exploit vulnerability, create dependency, shame people, extract attention, or serve institutional goals at the expense of human welfare.

The field is strongest when it combines behavioral realism with respect for autonomy. It should not pretend that people are perfectly rational, endlessly disciplined, or unaffected by environment. It should also not treat people as programmable objects. The best behavior-change design supports capacity, dignity, informed choice, and durable action within fairer systems.

Behavior Change in a Wider Intellectual Context

Behavior Change and Habit Formation belongs to a long intellectual history of inquiry into habit, character, learning, will, discipline, environment, and social practice. Philosophers have studied virtue, weakness of will, self-command, and the formation of character. Psychologists have studied conditioning, motivation, cognition, self-regulation, and social learning. Sociologists and anthropologists have studied routines, norms, institutions, rituals, and practices.

Modern behavior-change science brings these traditions into practical contact with public policy, health systems, sustainability, education, organizations, technology, and design. It asks how values become practices, how environments shape action, how routines stabilize life, and how change can be supported without reducing people to targets of influence.

For Sustainable Catalyst, this field is especially important because sustainable transformation requires more than ideas. It requires habits, systems, routines, infrastructures, institutions, and ethical support for durable action. Behavior Change and Habit Formation provides one of the most practical bridges between knowledge and action.

Related Reading

• Behavioral Science & Behavioral Psychology
• Behavioral Economics
• Choice Architecture and Nudging
• Motivation, Reinforcement, and Learning
• Social Norms and Behavioral Influence
• Behavioral Public Policy
• Behavioral Research Methods
• Ethics of Behavioral Intervention
• Psychology
• Cognitive Psychology
• Social Psychology
• Decision Science
• Sustainable Development
• Systems Thinking

Further Reading

• Ajzen, I. (1991) ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes, 50(2), pp. 179–211.
• Bandura, A. (1977) Social Learning Theory. Englewood Cliffs, NJ: Prentice-Hall.
• Bandura, A. (1986) Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.
• Deci, E.L. and Ryan, R.M. (1985) Intrinsic Motivation and Self-Determination in Human Behavior. New York: Plenum.
• Fogg, B.J. (2020) Tiny Habits: The Small Changes That Change Everything. Boston: Houghton Mifflin Harcourt.
• Gollwitzer, P.M. (1999) ‘Implementation intentions: Strong effects of simple plans’, American Psychologist, 54(7), pp. 493–503.
• Lally, P. et al. (2010) ‘How are habits formed: Modelling habit formation in the real world’, European Journal of Social Psychology, 40(6), pp. 998–1009.
• Michie, S., van Stralen, M.M. and West, R. (2011) ‘The behaviour change wheel: A new method for characterising and designing behaviour change interventions’, Implementation Science, 6, article 42.
• Prochaska, J.O. and DiClemente, C.C. (1983) ‘Stages and processes of self-change of smoking: Toward an integrative model of change’, Journal of Consulting and Clinical Psychology, 51(3), pp. 390–395.
• Wood, W. and Neal, D.T. (2007) ‘A new look at habits and the habit-goal interface’, Psychological Review, 114(4), pp. 843–863.

References

• Ajzen, I. (1991) ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes, 50(2), pp. 179–211. Available at: https://doi.org/10.1016/0749-5978(91)90020-T (Accessed: 21 June 2026).
• Bandura, A. (1977) Social Learning Theory. Englewood Cliffs, NJ: Prentice-Hall.
• Bandura, A. (1986) Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.
• Deci, E.L. and Ryan, R.M. (1985) Intrinsic Motivation and Self-Determination in Human Behavior. New York: Plenum.
• Fogg, B.J. (2020) Tiny Habits: The Small Changes That Change Everything. Boston: Houghton Mifflin Harcourt.
• Gollwitzer, P.M. (1999) ‘Implementation intentions: Strong effects of simple plans’, American Psychologist, 54(7), pp. 493–503. Available at: https://doi.org/10.1037/0003-066X.54.7.493 (Accessed: 21 June 2026).
• Lally, P. et al. (2010) ‘How are habits formed: Modelling habit formation in the real world’, European Journal of Social Psychology, 40(6), pp. 998–1009. Available at: https://doi.org/10.1002/ejsp.674 (Accessed: 21 June 2026).
• Michie, S., van Stralen, M.M. and West, R. (2011) ‘The behaviour change wheel: A new method for characterising and designing behaviour change interventions’, Implementation Science, 6, article 42. Available at: https://doi.org/10.1186/1748-5908-6-42 (Accessed: 21 June 2026).
• Prochaska, J.O. and DiClemente, C.C. (1983) ‘Stages and processes of self-change of smoking: Toward an integrative model of change’, Journal of Consulting and Clinical Psychology, 51(3), pp. 390–395. Available at: https://doi.org/10.1037/0022-006X.51.3.390 (Accessed: 21 June 2026).
• Ryan, R.M. and Deci, E.L. (2000) ‘Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being’, American Psychologist, 55(1), pp. 68–78. Available at: https://doi.org/10.1037/0003-066X.55.1.68 (Accessed: 21 June 2026).
• Skinner, B.F. (1953) Science and Human Behavior. New York: Macmillan.
• Wood, W. and Neal, D.T. (2007) ‘A new look at habits and the habit-goal interface’, Psychological Review, 114(4), pp. 843–863. Available at: https://doi.org/10.1037/0033-295X.114.4.843 (Accessed: 21 June 2026).

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