A substantive framework is a context-specific structure that organises the main concepts, categories, relationships, processes, or indicators relevant to a defined empirical subject. It may be developed from research data, existing literature, expert knowledge, or a combination of evidence. Because the term is used differently across disciplines, researchers should always explain precisely what it means in their study.
Introduction
Researchers use frameworks to organise complex ideas, guide inquiry, connect evidence, and communicate how a phenomenon is understood. However, the phrase substantive framework can be confusing because it does not refer to one universally standardised methodological tool.
In grounded theory and related qualitative research, a substantive framework commonly represents concepts and relationships developed for a particular empirical area, population, setting, or period. It may visually summarise a substantive theory or an emerging explanatory account.
In planning, policy, measurement, and applied research, the phrase may instead describe the substantive content that a framework should address—for example, the dimensions of sustainable urban development or the indicators used to represent economic well-being.
This article explains both uses. It shows how a substantive framework differs from conceptual, theoretical, analytical, and formal frameworks; how to develop one; how to evaluate it; and how it can be used responsibly with digital research tools and artificial intelligence.
Key Takeaways
- A substantive framework is bounded by a particular phenomenon, setting, population, or domain.
- It normally identifies important concepts or categories and explains how they relate.
- In grounded theory, it may represent a substantive theory developed from empirical data.
- In other fields, it may organise the content, dimensions, goals, or indicators relevant to a domain.
- A framework is not automatically a theory; theory requires a sufficiently developed explanatory account.
- Researchers should define the term explicitly because its meaning varies across disciplines.
What Is a Substantive Framework?
A substantive framework is an organised account of the important elements of a specific subject and the relationships among those elements. Its purpose is to make a bounded empirical phenomenon more understandable, researchable, measurable, or actionable.
The word substantive signals that the framework concerns the actual content or subject matter under investigation. It is usually tied to a defined substantive area such as:
- How first-generation doctoral students respond to supervisory feedback.
- How older adults experience gambling.
- How nurses navigate career progression.
- How organisations adopt Agile working practices.
- How safety is understood within a particular national sport system.
- Which sustainability priorities matter in desert-region planning.
The word framework indicates an organised structure. That structure may include concepts, categories, dimensions, processes, conditions, outcomes, indicators, or propositions.
Working definition
Definition: A substantive framework is a context-bound structure of concepts, categories, relationships, processes, or indicators that describes, interprets, explains, or guides inquiry into a particular empirical phenomenon or domain.
This definition is deliberately broad. It reflects the different ways the phrase is used without suggesting that every substantive framework has the same origin or methodological status.
Why Does “Substantive Framework” Have More Than One Meaning?
Research terminology evolves across disciplines. The same expression may acquire different meanings in sociology, nursing, education, policy, planning, management, or statistics.
Two uses are particularly important.
1. An empirically derived, context-specific framework
In grounded theory, researchers collect and analyse data iteratively to develop concepts and explain relationships or processes. The resulting account is usually limited to a substantive area rather than intended to explain all instances of a phenomenon everywhere.
Glaser and Strauss (1967) distinguished substantive theory, developed for a particular empirical area, from formal theory, developed at a higher level of abstraction. Contemporary grounded-theory traditions differ philosophically and procedurally, but they continue to emphasise systematic engagement with data, comparison, memo-writing, category development, and theoretical integration (Charmaz, 2024; Corbin & Strauss, 2015).
A diagram showing the core category, related categories, conditions, actions, and consequences may be called a substantive framework. In this usage, the framework is a representation of the study’s context-specific explanatory findings.
2. A framework specifying the substantive content of a domain
In policy, planning, evaluation, and measurement, substantive may refer to what should be addressed, rather than how an activity should be performed.
A substantive sustainability framework, for example, might identify environmental, economic, social, and governance priorities. A procedural framework would instead describe the steps through which planning, consultation, implementation, and evaluation occur.
Similarly, in quantitative research, a pre-existing framework of concepts and indicators may state what constitutes a multidimensional phenomenon such as well-being. Statistical methods can then examine how well the observed indicators represent the proposed construct.
Why the distinction matters
A framework derived inductively from interviews has a different evidential basis from a framework assembled from legislation, literature, indicators, or expert consensus. Both may be described as substantive, but they should not be reported as though they were produced by the same method.
Researchers should state:
- What substantive means in the study.
- Where the framework came from.
- What evidence supports it.
- Whether it is descriptive, interpretive, explanatory, evaluative, or predictive.
- Which contexts it is and is not intended to cover.
Essential Components of a Substantive Framework
Not every framework requires every component. However, a well-developed substantive framework normally contains several of the following elements.
| Component | Purpose | Example |
|---|---|---|
| Phenomenon | States what the framework addresses | Responding to doctoral-supervisor feedback |
| Context | Defines the setting and conditions | Research-intensive UK universities |
| Population or unit | Identifies whom or what the framework concerns | First-generation doctoral students |
| Concepts | Names important abstract ideas | Feedback clarity, confidence, belonging |
| Categories or dimensions | Groups related concepts | Interpreting, coping, seeking support |
| Relationships | Shows how components influence or connect with one another | Clear feedback improves action planning |
| Conditions | Identifies circumstances that shape the process | Supervisor accessibility and disciplinary norms |
| Actions or mechanisms | Explains what participants or systems do | Clarifying, reframing, revising |
| Outcomes | Describes consequences | Improved revision, delay, withdrawal, or confidence |
| Core process | Integrates the framework around a central idea | Converting critique into actionable revision |
| Propositions | Expresses tentative explanatory statements | Support networks reduce the effects of ambiguous feedback |
| Boundary conditions | States where the framework may not apply | Not designed for professional doctorates or laboratory-only supervision |
| Evidence links | Connects each element to data or literature | Quotations, cases, documents, or citations |
| Visual representation | Communicates the structure efficiently | Process diagram or concept map |
A decorative diagram without a clear relationship to evidence is not a strong substantive framework. The reader should be able to trace why each component is present and how its proposed relationship was established.
Substantive Framework Compared With Related Terms
Substantive framework vs conceptual framework
A conceptual framework is generally a researcher-crafted structure or argument that connects relevant concepts, literature, theories, context, questions, and methodological decisions. It may guide an entire study from its design onward (Kulesa et al., 2024; Varpio et al., 2020).
A substantive framework is more explicitly centred on the content or explanatory structure of a bounded empirical subject. It may be one part of the study’s broader conceptual framework or may be the principal product of an inductive study.
The terms can overlap. A context-specific conceptual framework developed from empirical findings may reasonably be described as substantive. The researcher should explain the intended distinction rather than assuming that readers will interpret the label uniformly.
Substantive framework vs theoretical framework
A theoretical framework applies one or more existing theories to frame a research problem, guide data collection, define variables, or support interpretation.
A substantive framework may:
- Be informed by an existing theory.
- Combine ideas from several theories.
- Emerge primarily from empirical data.
- Be developed before, during, or after data collection.
- Remain less abstract and less general than the theory used to interpret it.
For example, a study may use social identity theory as its theoretical framework while developing a substantive framework explaining how international doctoral students negotiate belonging within one university system.
Substantive framework vs substantive theory
These terms should not automatically be treated as synonyms.
A framework identifies and organises relevant components. It may show relationships without fully explaining why those relationships occur.
A theory provides a more developed explanation. It normally connects abstract concepts through propositions or an integrated account that explains variation, process, conditions, or consequences.
A substantive framework may represent a substantive theory, support theory development, or remain a preliminary conceptual product. Calling every category diagram a theory overstates the analytical achievement.
Substantive framework vs analytical framework
An analytical framework specifies how researchers will examine data. It may consist of coding categories, evaluative criteria, variables, questions, or interpretive dimensions.
The analytical framework is the tool used to analyse evidence. The substantive framework is usually the resulting or guiding account of the subject matter.
The two can interact. Researchers may begin with an analytical framework based on prior literature, revise it during analysis, and eventually produce a substantive framework of findings.
Substantive framework vs logic model
A logic model commonly links resources, activities, outputs, short-term outcomes, and long-term outcomes. It is often used for programme design and evaluation.
A substantive framework may include a process or outcome sequence, but it does not have to follow the input–activity–output–outcome structure.
Substantive framework vs formal framework
A substantive framework is bounded by a specific empirical area. A formal framework is more abstract and aims to apply across several substantive settings.
For example:
- A framework of how Canadian high-performance-sport stakeholders interpret safety is substantive.
- A framework intended to explain safety construction across countries, sports, age groups, institutions, and levels of participation would be more formal.
Moving from a substantive to a formal framework requires more than removing place names from a diagram. It requires comparison across sufficiently varied contexts and evidence that the higher-level concepts retain explanatory value.
Comparison Table
| Feature | Substantive framework | Conceptual framework | Theoretical framework | Analytical framework | Substantive theory |
|---|---|---|---|---|---|
| Main purpose | Organise or explain a bounded subject | Align concepts, literature, context, and design | Apply established theory | Guide analysis of evidence | Explain a bounded empirical phenomenon |
| Typical source | Data, literature, expertise, or mixed evidence | Researcher synthesis | Existing theory or theories | Theory, research questions, criteria, or prior evidence | Systematic empirical theorising |
| Context-specific | Usually | Often | Not necessarily | Depends on design | Yes |
| Must include causal explanation | No | No | May contain theoretical explanation | No | Usually requires an integrated explanatory account |
| Can guide data collection | Yes | Yes | Yes | Yes | Usually emerges or develops through analysis |
| Can be a study output | Frequently | Sometimes | Less commonly | Rarely | Yes |
| Usually visual | Often, but not required | Often | Sometimes | Sometimes | May be represented visually |
When Should a Researcher Use a Substantive Framework?
A substantive framework is useful when the research problem is:
- Strongly dependent on a particular context.
- Concerned with a process, experience, system, or set of relationships.
- Insufficiently explained by an existing general theory.
- Intended to produce an applied model for practice or policy.
- Focused on defining the content or dimensions of a domain.
- Seeking to connect qualitative categories in an integrated account.
- Measuring a multidimensional construct represented by established domains or indicators.
It may be unsuitable when:
- The researcher only needs to apply an established theory.
- The study is purely descriptive and does not integrate its findings.
- There is insufficient evidence to justify relationships among components.
- The term is being added merely to make a simple diagram sound more theoretical.
- The intended model is actually a programme logic model, coding framework, or measurement model and should be named accordingly.
How to Develop a Substantive Framework
The procedure depends on whether the framework is primarily data-derived or assembled from existing knowledge. The following steps can accommodate both pathways.
Step 1: Define the substantive area
State the phenomenon, population, setting, time period, and level of analysis.
A vague topic such as “student success” is too broad. A more defensible substantive area might be:
How first-generation master’s students at urban UK universities sustain engagement during dissertation supervision.
This statement establishes the subject and begins to define the framework’s boundaries.
Step 2: Clarify the framework’s purpose
Decide what the framework should do.
Will it:
- Describe the components of a phenomenon?
- Explain a process?
- Identify conditions and consequences?
- Guide intervention design?
- Organise indicators?
- Support future hypothesis development?
- Provide criteria for policy or practice?
The framework’s purpose determines the type of evidence and relationships required.
Step 3: State the methodological and philosophical position
Researchers should explain whether the framework is:
- Inductively developed.
- Deductively adapted.
- Abductively refined through movement between data and theory.
- Constructivist and interpretive.
- Postpositivist and intended for later testing.
- Pragmatically developed for applied decision-making.
Grounded-theory traditions are not interchangeable. A researcher following constructivist grounded theory should not describe the framework as objectively “discovered” without acknowledging the researcher’s interpretive role.
Step 4: Review relevant literature without allowing it to predetermine the findings
A literature review helps researchers understand:
- Existing definitions.
- Competing explanations.
- Previously identified constructs.
- Common methodological weaknesses.
- Relevant contextual conditions.
- Areas requiring further evidence.
In highly inductive research, existing concepts can be treated as sensitising ideas rather than compulsory coding categories. Researchers should remain willing to revise or reject them when they do not fit the data.
Step 5: Collect or assemble appropriate evidence
Evidence may include:
- Interviews.
- Focus groups.
- Observations.
- Documents.
- Digital records.
- Survey data.
- Existing datasets.
- Policy documents.
- Systematic or scoping reviews.
- Expert panels or Delphi studies.
- Stakeholder workshops.
- Mixed qualitative and quantitative evidence.
The evidence should match the framework’s intended claims. Expert consensus can identify priorities, but it does not automatically explain participants’ lived processes. Interviews can illuminate experience, but they may not establish population prevalence.
Step 6: Develop concepts and categories
For qualitative data-derived frameworks, researchers normally move from detailed observations or codes toward increasingly focused and abstract categories.
Useful questions include:
- What is happening here?
- What problem are participants attempting to resolve?
- What actions or strategies do they use?
- Under what conditions does the process change?
- What are the consequences?
- Which incidents are similar or different?
- Which category explains the greatest amount of variation?
Constant comparison involves comparing incident with incident, incident with category, category with category, and emerging interpretations with new data.
For literature- or expert-derived frameworks, researchers may instead extract, compare, consolidate, and define dimensions before seeking empirical or expert refinement.
Step 7: Identify relationships and processes
A list of themes is not yet an integrated framework.
Researchers should determine whether the relationships are:
- Sequential: A tends to occur before B.
- Conditional: B occurs when condition C is present.
- Reciprocal: A and B influence one another.
- Hierarchical: Several subcategories form a broader dimension.
- Mediated: A influences C through B.
- Contextual: The relationship changes across settings.
- Tensional: Two competing forces shape the outcome.
- Recursive: The outcome feeds back into an earlier stage.
Relationships should be supported by evidence rather than inferred solely because they make the diagram look complete.
Step 8: Develop the central organising idea
Strong substantive frameworks often have a core process or integrative statement.
For example:
Students sustain dissertation engagement by converting ambiguous supervisory feedback into manageable revision decisions through clarification, peer interpretation, and iterative action.
The central idea should connect the major categories without becoming so broad that it explains everything and therefore nothing.
Step 9: Specify boundaries and exceptions
State where the framework applies and identify important variation.
Ask:
- Which participants or cases are not represented?
- What negative or deviant cases challenge the pattern?
- What institutional or cultural conditions may alter it?
- Which proposed relationships remain tentative?
- What evidence would be needed for wider application?
Boundary statements increase rather than decrease credibility because they prevent unjustified generalisation.
Step 10: Create and revise the visual representation
Begin with a simple diagram showing:
- Context or conditions.
- Central process or phenomenon.
- Major categories.
- Relationships or arrows.
- Outcomes.
- Feedback loops where justified.
- Boundary notes.
Test several layouts. A circle implies recurrence; a left-to-right sequence implies progression; two-way arrows imply reciprocal influence. Visual grammar should accurately reflect the evidence.
Step 11: Evaluate the framework
Possible strategies include:
- Returning to raw data.
- Searching for negative cases.
- Comparing across participants, sites, or time periods.
- Discussing interpretations within the research team.
- Keeping analytic memos and a decision trail.
- Seeking stakeholder reflection.
- Obtaining expert critique.
- Comparing the framework with existing literature.
- Testing selected constructs or relationships in later research.
- Applying the framework to a new sample or setting.
Participant feedback can be useful, but agreement from participants is not the sole test of a framework’s quality. Participants and researchers may legitimately interpret the same social process differently.
Step 12: Report the evidential pathway
Readers should be able to understand:
- How the concepts were generated.
- How categories were defined.
- How relationships were established.
- How the visual model changed.
- How conflicting evidence was handled.
- Which researchers made interpretive decisions.
- What software or AI tools were used.
- What limitations remain.
Worked Example of a Substantive Framework
Consider a hypothetical qualitative study examining how first-generation doctoral students respond to critical supervisory feedback.
Research question
How do first-generation doctoral students at research-intensive universities interpret and act on critical written feedback from supervisors?
Data
The researcher conducts repeated interviews with 24 doctoral students, reviews anonymised feedback documents, and keeps analytic memos throughout concurrent data collection and analysis.
Initial concepts
Early coding identifies concepts such as:
- Unclear expectations.
- Fear of appearing incompetent.
- Translating academic language.
- Seeking peer interpretation.
- Delaying revision.
- Breaking feedback into tasks.
- Confirming priorities with supervisors.
- Rebuilding confidence.
Higher-level categories
The concepts are integrated into five categories:
- Interpreting the criticism
- Protecting academic identity
- Mobilising informal support
- Converting comments into revision tasks
- Re-entering supervisory dialogue
Core process
The central process is named:
Turning critique into actionable revision
Proposed relationships
- Ambiguous feedback increases uncertainty and threat to academic identity.
- Peer support helps students translate disciplinary language.
- Supervisor accessibility affects whether students seek clarification.
- Task decomposition converts emotional and conceptual uncertainty into action.
- Successful revision increases confidence and makes future dialogue more direct.
- Repeatedly unresolved ambiguity can produce avoidance and delay.
Boundary statement
The framework is intended to explain experiences among first-generation doctoral students in the participating research-intensive universities. It may not adequately represent students in professional doctorates, laboratory teams with daily supervision, or institutions with different supervisory structures.
Text-based representation
Institutional and supervisory conditions
↓
Interpretation of critical feedback
↔ Protection of academic identity
↓
Peer and informal sense-making
↓
Translation into manageable revision tasks
↓
Revision and renewed supervisory dialogue
↺
Growing confidence or continuing avoidance
This is a substantive framework because it organises a process within a clearly bounded empirical context. It would become a stronger substantive theory if the relationships, variation, mechanisms, and central explanatory process were developed and supported sufficiently through systematic analysis.
How Substantive Frameworks Are Used in Different Research Designs
Qualitative research
In qualitative research, a substantive framework may:
- Summarise an emergent process.
- Integrate themes and categories.
- Explain how context shapes action.
- Identify conditions, strategies, and consequences.
- Provide an applied model for practitioners.
- Form the main contribution of a grounded-theory study.
Grounded theory is especially associated with substantive theory development, but using grounded-theory coding techniques does not automatically mean that a study has produced a grounded theory. Researchers need theoretical integration, not merely several themes.
Quantitative research
A substantive framework can define the content of a quantitative model.
For example, a framework may propose that economic well-being is represented by material deprivation, income security, housing stability, and financial resilience. Researchers can then:
- Operationalise the constructs.
- Select or design indicators.
- Test a measurement model.
- Examine reliability and validity.
- Use confirmatory factor analysis or structural equation modelling.
- Compare the framework across groups or periods.
The substantive framework is not the statistical technique. It provides the domain-informed rationale for what should be measured and why. The statistical model examines whether the data are consistent with the proposed measurement or relational structure.
Mixed-methods research
In mixed-methods studies, a substantive framework can support integration by:
- Guiding qualitative and quantitative questions.
- Connecting interview categories with survey constructs.
- Explaining unexpected statistical results.
- Identifying variables for a later quantitative phase.
- Comparing lived experiences with institutional indicators.
- Organising joint displays.
- Refining a preliminary framework through multiple forms of evidence.
Researchers should show where integration occurred. Conducting one interview study and one survey without connecting their findings does not create an integrated substantive framework.
Evaluation and policy research
A substantive framework may define:
- What outcomes matter.
- Which values or standards should guide assessment.
- What dimensions of effectiveness should be examined.
- Which indicators should be monitored.
- Which contextual differences require adaptation.
In this setting, researchers should distinguish substantive criteria from the procedures used to implement or evaluate them.
How to Evaluate the Quality of a Substantive Framework
A useful framework should be assessed against criteria appropriate to its purpose.
Contextual fit
Do the concepts and relationships reflect the evidence from the substantive area?
Coherence
Do the components form an integrated account, or are they merely an unrelated list?
Explanatory usefulness
Does the framework help readers understand how or why the phenomenon occurs?
A descriptive framework may not need full causal explanation, but it should still clarify the structure of the subject.
Parsimony
Does the framework explain the phenomenon without unnecessary categories, arrows, or duplicated concepts?
Parsimony does not mean oversimplification. Important complexity should not be removed merely to produce a visually neat model.
Sensitivity to variation
Does the framework account for differences, exceptions, contradictory cases, and alternative pathways?
Evidential traceability
Can readers connect the framework’s components to data, literature, stakeholder input, or other evidence?
Transparency
Are the analytical decisions, revisions, researcher role, software use, and limitations reported?
Boundedness
Does the framework state the contexts in which it is expected to apply?
Practical or analytical usefulness
Can the framework guide additional research, interpretation, measurement, policy, or practice?
Modifiability
Can the framework be refined as new evidence becomes available?
A substantive framework should not be treated as permanently complete when it was developed from a limited context.
Advantages of a Substantive Framework
A substantive framework can:
- Make complex findings easier to understand.
- Preserve contextual detail that broad theories may overlook.
- Connect individual concepts into an integrated account.
- Reveal processes, mechanisms, and conditions.
- Support practical recommendations.
- Identify constructs for later measurement.
- Generate hypotheses for subsequent studies.
- Help compare similar cases or settings.
- Communicate a study’s original contribution.
- Provide a foundation for developing a more formal framework or theory.
Limitations
Limited transferability
A framework developed in one institution, community, profession, or country may not function in the same way elsewhere.
Dependence on the evidence base
Weak sampling, narrow data, incomplete literature, or unrepresentative expert input can produce a weak framework.
Researcher interpretation
Qualitative frameworks reflect analytic decisions. Researchers should practise reflexivity rather than presenting the framework as though it appeared independently of interpretation.
Risk of premature closure
Once a diagram has been produced, researchers may start forcing later evidence into it instead of revising the framework.
Visual oversimplification
Arrows and boxes can conceal uncertainty, contradiction, power relations, and temporal change.
Terminological confusion
Readers may interpret the phrase differently unless it is defined explicitly.
Risk of overclaiming theory
A framework that classifies concepts may be valuable without meeting the stronger requirements of a substantive theory.
Common Mistakes
Using the label without defining it
A sentence such as “the study developed a substantive framework” is incomplete. State what the term means in the methodological context.
Treating themes as a framework
Three themes placed in adjacent boxes do not automatically form a framework. Explain their relationships.
Calling every framework a theory
A theory requires an integrated explanatory contribution, not simply categorisation.
Ignoring existing knowledge
Even inductive researchers should eventually situate their findings within relevant literature. The goal is not to pretend that no previous knowledge exists.
Forcing data into a preferred model
An attractive existing theory or diagram should not override contradictory evidence.
Omitting negative cases
Exceptions often reveal boundary conditions or alternative pathways that improve the framework.
Claiming universal applicability
A substantive framework should not be described as universal when its evidence comes from one setting or a narrow sample.
Confusing substantive and procedural dimensions
A framework describing what should be achieved is different from a framework describing the steps through which it should be achieved.
Creating an unreadable diagram
Too many arrows, colours, shapes, and labels can make the framework less informative. Each visual element should have a defined meaning.
Allowing software or AI to determine the interpretation
Software can organise, retrieve, compare, and visualise evidence. It cannot assume responsibility for the methodological and interpretive claims made by the researcher.
Digital Research Tools and Artificial Intelligence
Computer-assisted qualitative data-analysis software
Programs such as MAXQDA, NVivo, and ATLAS.ti can support:
- Data organisation.
- Coding and recoding.
- Retrieval of coded segments.
- Memo-writing.
- Case comparison.
- Code co-occurrence exploration.
- Diagramming.
- Audit-trail maintenance.
- Team coding and review.
These tools do not perform a methodology by themselves. A researcher can use grounded-theory software functions without conducting grounded theory, just as a spreadsheet does not determine the validity of a statistical design.
Responsible uses of generative AI
Generative AI may assist with low-risk or exploratory tasks such as:
- Suggesting alternative labels for a researcher-developed category.
- Checking whether a written description is internally clear.
- Converting a researcher-approved framework into alternative text layouts.
- Producing questions that help researchers search for negative cases.
- Summarising non-confidential methodological notes.
- Comparing two versions of a framework description.
- Supporting code documentation when every suggestion is verified.
AI outputs should be treated as provisional prompts for human examination, not as findings.
Major risks
Recent methodological discussions identify several concerns:
- Confidential or identifiable data may be exposed through external systems.
- Model outputs may be difficult to reproduce.
- Generated categories may reflect hidden training-data patterns.
- Unusual or contradictory cases may be normalised or omitted.
- Fluent explanations may not be grounded in the actual dataset.
- Automated clustering can obscure researcher reflexivity.
- The model may invent quotations, sources, relationships, or rationales.
- Undisclosed assistance weakens methodological transparency.
Nguyen and Welch (2026) argue that qualitative interpretation depends on human meaning-making and warn against uncritical substitution of language-model output for analysis. Jones (2025) recommends detailed disclosure of AI’s role in the research team, participant interaction, study design, data practices, and analysis.
Minimum safeguards
Researchers using AI should:
- Follow institutional, funder, ethics-board, data-protection, and journal policies.
- Avoid uploading identifiable or confidential participant data to an unapproved service.
- Record the tool, model or version where available, date, purpose, prompts, and settings.
- Preserve original data and human-generated analytic records.
- Verify every AI-assisted output against the evidence.
- Keep interpretive authority and final decisions with accountable researchers.
- Report AI use transparently.
- Examine whether the tool changes sampling, coding, saturation judgments, or the framework itself.
- Document rejected as well as accepted suggestions where analytically important.
- Avoid listing an AI system as an author.
Research-integrity guidance emphasises legality, ethics, the integrity of the research record, publication practice, accountability, and preservation of critical thinking when AI is used in research (UK Research Integrity Office, 2025).
How to Present a Substantive Framework in a Thesis or Article
A framework section should usually include the following.
1. Terminological definition
Explain exactly how the study uses substantive framework.
Example:
In this study, a substantive framework refers to the context-specific configuration of categories and relationships developed from participants’ accounts of transitioning into remote clinical work. It is presented as an interpretive framework rather than a universally generalisable theory.
2. Purpose
State whether the framework describes, explains, evaluates, measures, or guides action.
3. Evidence base
Identify the data, literature, expert input, or mixed evidence used.
4. Development process
Explain coding, comparison, synthesis, modelling, consensus procedures, or statistical testing.
5. Components and relationships
Define every category and explain each important connection.
6. Visual model
Provide a readable figure with a complete caption and legend.
7. Boundary conditions
State the population, setting, assumptions, and exceptions.
8. Quality procedures
Describe reflexivity, negative-case analysis, team review, stakeholder reflection, triangulation, model testing, or other relevant strategies.
9. Uses and limitations
Explain how the framework may inform future research or practice without overstating its applicability.
Reusable Substantive Framework Template
Framework title:
Use a descriptive title linked to the core phenomenon.
Definition:
“In this study, substantive framework means…”
Purpose:
Describe, interpret, explain, evaluate, measure, or guide.
Substantive area:
State the phenomenon, context, population, and time boundary.
Evidence base:
List data sources, literature, stakeholder groups, datasets, or expert input.
Methodological approach:
State how evidence was collected and analysed.
Core concept or process:
Name the central organising idea.
Major categories or dimensions:
Define each one clearly.
Relationships:
Explain sequential, conditional, reciprocal, hierarchical, or other connections.
Conditions and mechanisms:
Identify what changes how the process operates.
Outcomes:
State likely or observed consequences.
Propositions:
Write concise, evidence-supported explanatory statements where appropriate.
Boundary conditions:
State where the framework should and should not be applied.
Negative cases or exceptions:
Explain important variation.
Visual representation:
Add the framework diagram and legend.
Quality and validation:
Describe the checks used.
Intended uses:
Research, practice, measurement, policy, education, or future theory development.
Limitations:
State evidential, methodological, contextual, and ethical constraints.
Conclusion
A substantive framework organises or explains the important elements of a bounded empirical subject. It may emerge from qualitative analysis, represent a context-specific substantive theory, define the content of a policy or planning domain, or provide a construct-and-indicator structure for quantitative research.
Its value depends less on the label than on methodological clarity. Researchers must identify how the framework was developed, define its components, justify its relationships, acknowledge exceptions, and state its boundaries. A strong substantive framework makes a specific phenomenon more understandable without pretending that one context provides a universal explanation.
