Explanation
What it is
A conceptual model is a structured representation that captures the key components of a system and how they relate.
It translates abstract theories into visual or schematic forms, helping to clarify ideas and expose relationships between variables, processes, or entities.
When to use it
- When clarifying the logic of a complex system or process
- When translating theoretical knowledge into an actionable framework
- When aligning interdisciplinary teams around a shared understanding of how something works
Why it matters
Conceptual models bridge the gap between theory and application.
By simplifying complexity into coherent relationships, they make abstract ideas communicable, testable, and adaptable.
This improves collaboration, sharpens research design, and ensures that solutions remain anchored in a clearly defined structure rather than intuition or assumption.
Reference
Definitions
Conceptual Model
A simplified, often visual representation of a system that shows the main elements and their relationships, used to explain, explore, or predict behaviour.
Theory
A set of principles that explains phenomena and predicts outcomes; broader and more abstract than a conceptual model.
Framework
An organising structure for ideas or actions, often derived from multiple conceptual models or theories.
System Map
A diagram that visualises how components of a system interact dynamically, often used within design or systems thinking.
Infographics*
A visual communication tool that combines data, imagery, and minimal text to make information quickly understandable.
*Unlike conceptual models (which are analytical), infographics are primarily explanatory and persuasive.
Canonical Sources
- Miles & Huberman, Qualitative Data Analysis, 1994
Classic treatment of conceptual modelling in research design. - Norman, D. A., The Design of Everyday Things, 2013
Applies conceptual models to human–computer interaction. - Kaplan, A., The Conduct of Inquiry, 1964
Distinguishes between theory, model, and concept in scientific reasoning. - Sowa, J. F., Conceptual Structures: Information Processing in Mind and Machine, 1984
Formal logic approach to conceptual representation.
Notes & Caveats
- Conceptual models are not theories; they describe structure but do not claim causal explanation.
- They can be empirically informed or purely hypothetical depending on stage of research.
- In design practice, models should balance rigour with legibility (over-complexity defeats their purpose).
- Terminology varies across disciplines (e.g., “mental models,” “conceptual frameworks,” “logic models”); always clarify context of use.
How-To
Objective
To construct a clear, fit-for-purpose conceptual model that communicates how elements of a system relate, supporting analysis, design, or decision-making.
Steps
- Define the purpose
Clarify what question or phenomenon the model will illuminate. - Identify core elements
List the variables, actors, or processes central to the system. - Establish relationships
Determine how the elements influence, depend on, or connect with one another. - Select a structure
Choose the most effective representation (e.g., flow diagram, Venn, feedback loop, causal map). - Visualise and test
Create a schematic version and test whether it accurately communicates the intended logic. - Refine collaboratively
Share with stakeholders or peers to expose blind spots or misinterpretations. - Validate against theory or data
Ensure alignment with evidence or theoretical grounding. - Document assumptions
Record simplifications and boundary conditions to maintain interpretive integrity.
Tips
- Keep the model simple enough to be understood at a glance, yet detailed enough to reveal system logic.
- Use consistent notation and avoid decorative elements that obscure meaning.
- Iteration improves clarity — each redraw should simplify or strengthen comprehension.
Pitfalls
Overcomplicating the model
Focus on essential elements only — complexity hides insight.
Mistaking it for theory
Use it to visualise, not to predict or explain cause-and-effect.
Ignoring user interpretation
Test legibility with intended audiences early.
Skipping validation
Compare model structure against data or established frameworks.
Acceptance criteria
- The model clearly depicts all key elements and their relationships.
- Stakeholders can articulate the system logic using the model as reference.
- Model assumptions, limits, and data sources are documented.
- The visual artefact (e.g., diagram, canvas, or flowchart) is version-controlled and stored for reuse.
Tutorial
Scenario
A UX research team is designing a digital wellbeing app.
Before wireframing, they need to clarify how psychological, behavioural, and interface components interact.
To do this, they decide to construct a conceptual model that illustrates the user’s journey from awareness to sustained habit change.
Walkthrough
Decision Point
How can the team visualise the relationships between user motivation, app features, and behavioural outcomes without jumping prematurely into design artefacts?
Input/Output
Input
Existing literature on habit formation, user interview data, stakeholder objectives.
Output
A system-level conceptual model showing causal relationships between triggers, actions, rewards, and feedback loops.
Action
- Define the purpose
The team agrees to model the habit formation loop, focusing on motivation, action, and reinforcement rather than the entire product lifecycle. - Identify core elements
They list the psychological and design variables involved: triggers, cues, feedback types, emotional states, and rewards. - Establish relationships
Lines are drawn to show dependencies and feedback loops (e.g., positive feedback strengthens future engagement). - Select a structure
They choose a loop diagram to represent cyclical reinforcement and place it within a broader system map of app use. - Visualise and test
The model is sketched in Miro and reviewed to see if all stakeholders can follow the logic unaided. - Refine collaboratively
Low-impact variables are removed, and unclear labels rewritten in plain language for cross-disciplinary understanding. - Validate against theory or data
Behavioural scientists compare the model’s logic to empirical research on habit loops (e.g., cue–routine–reward). - Document assumptions
Boundaries and simplifications are noted: the model focuses on individual behaviour, not group dynamics.
Error Handling
Issue
The first version overemphasised app features and underrepresented user motivation.
Resolution
The team rebalanced the model by foregrounding internal psychological states and demoting interface mechanics to secondary layers.
Closure
The validated conceptual model becomes part of the design system’s behavioural foundations, ensuring later design decisions remain aligned to the core psychological logic.
Result
- Before
- Team discussions on user behaviour were fragmented, with designers focusing on interface triggers and researchers on abstract psychology.
- After
- A unified, evidence-informed conceptual model bridged theory and design.
- Everyone could articulate how motivation, cues, and feedback interact within the app’s habit loop, reducing iteration cycles and misaligned assumptions.
- Artefact Snapshot
- Name: Digital Wellbeing Habit Loop Model
- Location: /DesignSystem/Foundations/BehaviouralModels_v3.miro
- Linked References:
Research repository → “Behavioural Insights / Habit Formation Study”
Variations
- Policy context
Replace user triggers with policy levers and social incentives to show how institutional interventions reinforce or weaken public behaviours. - Data-driven scenario
Integrate telemetry or usage data to quantify loop strength; evolve the conceptual model into a causal or system-dynamics map for simulation. - Onboarding or education
Simplify the model into a one-page explainer infographic highlighting key feedback loops, supporting team induction or stakeholder presentations. - Cross-disciplinary collaboration
Use a shared digital whiteboard template so behavioural scientists, designers, and PMs can iterate together in real time, keeping terminology aligned.