The System Lens

CHAT research often begins differently from more traditional approaches. Rather than starting with a theory or hypothesis, most studies begin with a situation that feels complex and not easy to explain through individual behaviour alone. E’87Engeström, Y. (1987)Learning by Expanding. Helsinki: Orienta-Konsultit.

You might notice that a digital tool is available but not widely used, that a policy has been introduced but outcomes are uneven, or that participants describe frustration even when resources are present. CHAT offers a way to look more carefully at how different elements of a situation are connected.

From individual explanation to system thinking

Instead of asking “Why is this person not performing as expected?”, begin to ask “What is happening in the system that shapes this activity?” Individual actions are understood as part of a wider set of relationships — situated within tools, rules, communities, and shared goals.

For example, rather than asking why a nurse is not completing records on time, you might ask how documentation rules, digital tools, shift structures, and team expectations interact to make timely completion difficult. The focus shifts from identifying individual failure to examining how the system produces particular patterns of action.

Nesting systems — the “Russian Doll”

Begin by identifying a micro-level system (e.g., a nursing team conducting a ward round), while recognising it exists within a larger context (hospital policy, NHS frameworks, national digitisation programmes). If an element does not directly interact with your Object, place it in the macro-system or leave it out. This prevents the most common structural error in CHAT theses: trying to include everything at once.

Starting a CHAT study can feel uncertain at first. Clarity develops gradually as you explore your context, engage with data, and revisit your initial assumptions.

Next: The Activity System Model →

The starting point for a CHAT thesis is always a situation that feels structured and complex — one that resists explanation at the level of the individual.

Use this chapter when you have identified your situation and need to represent it as a system for the first time.

Figure 2 — Engeström's Activity System Triangle (1987)

The six-element activity system model. Each element mediates the relationship between subject and object. Contradictions arise within and between elements, old systems and new systems, and neighboring activity systems.

What the Lines Mean: The Web of Mediation

The lines in the triangle are not just borders; they are paths of influence. They show that no two elements in a system interact directly — there is always something "in the middle" shaping that interaction.

While every node can influence every other node, we usually start by looking at these primary paths of mediation:

• Tools mediate Subject ↔ Object: We don't just "think" an object into existence; we use physical or symbolic tools (language, software, hammers) to work on it.
• Rules mediate Subject ↔ Community: Your relationship with the people around you is governed by "how things are done here" — schedules, laws, or unwritten social norms.
• Division of Labour mediates Community ↔ Object: How the community works on the object is determined by who does what, who has the power, and how tasks are shared.

The "Everything Affects Everything" Rule

In a living system, a change in one node sends ripples through all the others. For example:

• A new Tool (like AI) might conflict with existing Rules (assessment policies).
• A shift in the Division of Labour (a new manager) might change the Community dynamics.
• The Object itself might evolve, forcing the Subject to find new Tools.

Contradictions & Outcomes

Each node and line is a potential source of contradiction. These tensions — for example, when a tool doesn't fit the rules — are where learning and systemic change occur. The Outcome represents the long-term transformation that results from the activity.

The Core Elements of an Activity System

An activity system is usually described through six connected elements. You do not need to memorise them immediately. It is often more helpful to understand them gradually, in relation to your own research.

CHAT literature can feel dense because authors vary in emphasis and vocabulary. The key is to read for structure, not definition. Different scholars — Engeström, Vygotsky, Leontiev, Bligh, Virkkunen — emphasise different parts of the system. Understanding these emphases helps you position your own theoretical choices. B’17Bligh, B. & Flood, M. (2017)“Activity Theory in Empirical Higher Education Research.” Tertiary Education and Management, 23(2).

The reading problem in CHAT studies

Most graduate students approach the CHAT literature with one of two problems. The first is reading too broadly too early — trying to master the full theoretical landscape before beginning fieldwork, and finding that the landscape is vast, contested, and internally diverse enough to be disorienting. The second is reading too narrowly too late — citing only Engeström (1987) and using the framework as a labelling device rather than an analytical one. Both produce weak literature reviews: the first produces a survey that lacks a focused argument; the second produces a thin theoretical justification that examiners will probe.

The solution to both is purposeful reading — reading in response to specific analytical questions generated by your data and your emerging system map, rather than reading to achieve comprehensive coverage or satisfy a citation quota.

What to look for when reading

Every CHAT text is making decisions about the system: what to include, what to name, how to connect the elements, and how to explain change. Reading analytically means noticing those decisions rather than absorbing the conclusions. The following three questions work across any CHAT text:

• What is the unit of analysis? Does the author treat the individual, the interaction, or the activity system as the primary focus? If they claim to use CHAT but analyse individual perceptions, there is a tension worth noting — and potentially worth naming in your review as a limitation of that study.
• How are relationships between elements constructed? Does the author explain how tools shape rules, or how division of labour affects the object? Or does the text list elements without connecting them? The strength of a CHAT analysis lies in its relational claims, not in correctly labelling its nodes. A text that identifies six elements but does not explain how they interact is doing CHAT description, not CHAT analysis.
• How is change or development explained? Does the author trace contradictions to their historical origins? If the system appears as a static snapshot — a description of how things are, without an account of how they got that way — the analysis is incomplete in CHAT terms, and your own study can distinguish itself by doing the historical work the cited study did not.

A worked reading example

The following passage is from Engeström’s 2001 paper “Expansive Learning at Work” — one of the most widely cited CHAT texts and a standard reference in most CHAT theses. Reading it analytically, rather than receptively, looks like this.

A receptive reading notes: the object can change. That is true and worth knowing.

An analytical reading asks all three questions:

Unit of analysis: Engeström is not describing an individual’s goal. He is describing a collective, historically moving purpose — something the system is oriented toward, not the same as what any individual intends. When you map the object, you are not asking what participants are trying to achieve personally. You are identifying what the system as a whole is pointed toward — and noting where different participants’ understandings diverge. That divergence is data.

Relational claim: The object and the outcome are not the same thing. The object is the ongoing motive; the outcome is what is produced at a specific moment. In Amara’s study, the outcome of the EPR system (a completed, timestamped record) is not the same as the object of nursing (safe, responsive patient care). The system produces one while participants are oriented toward the other. That is a structural feature of the system — a secondary contradiction between tool and object.

Connection to your own system: In NorthCare, the object is contested — nurses and managers hold different versions, and the EPR embeds a third. Engeström’s claim gives Amara a theoretical warrant for treating that contest as analytically significant. She is not looking for the “correct” object; she is analysing what the multiplicity of object-versions reveals about the system’s contradictions. The literature has given her permission to treat the problem as structural.

Reading different scholars differently

Not all CHAT scholarship should be read the same way. The foundational texts — Vygotsky, Leontiev, Engeström (1987) — establish the conceptual architecture. Read them for the concepts, not for empirical findings. The empirical CHAT literature should be read for how the concepts are applied and adapted in settings like yours. Methodological texts — Virkkunen and Newnham, Bligh and Flood — should be read for research design decisions. These three types serve different purposes and belong in different parts of your thesis.

When reading empirical CHAT studies, add a fourth question: what does this study do that my study is building on, departing from, or doing differently? Your literature review should position your study in relation to existing work, not merely cite it. A study that found a secondary contradiction between tools and rules in a school setting is relevant to an NHS study, but the relevance needs to be articulated: similar contradiction type, different institutional context, different historical trajectory. That articulation is what makes a review an argument rather than an annotated bibliography.

Reading against the grain

Some of the most productive literature reading involves noticing what a CHAT text does not do — and using that absence to define your own contribution. A study that identifies contradictions but does not trace their historical origins gives you a gap to fill. A study that uses the Change Laboratory but compresses it into two sessions gives you a methodological contrast to articulate. A study that focuses on subject and object nodes but neglects division of labour gives you an analytical dimension to foreground. Reading for absence is not a dismissive critical exercise — it is a way of locating the space your study will occupy.

The lineage problem: Vygotsky, Leontiev, Engeström

Most CHAT theses spend several pages tracing the intellectual lineage from Vygotsky through Leontiev to Engeström. This is not wrong, but it is often longer than it needs to be and less analytically purposeful than it could be. A useful discipline: each step in the lineage should do analytical work, not just establish credentials.

Vygotsky matters because mediation is foundational to understanding why your tools matter analytically, not just descriptively. Leontiev matters because the distinction between activity, action, and operation explains why participants can describe their individual actions without ever naming the structural object those actions serve. Engeström matters because the six-node model and the concept of contradictions provide the analytical vocabulary your study depends on. Two or three paragraphs, each ending with a sentence that connects the concept to your own study, is more effective than two pages of intellectual history. If a paragraph about Vygotsky could be removed without affecting your analytical argument, it belongs in a footnote.

Building your reading list strategically

Build your reading list in three tiers. The first tier is non-negotiable: Engeström (1987), Vygotsky (1978), Leontiev (1978), and Engeström (2001). These four texts are the foundation. If you have not read them, your theoretical framework is not yet established.

The second tier is field-specific: two or three empirical CHAT studies in a setting similar to yours, and one or two methodological texts relevant to your design. These allow you to position your study within existing scholarship and demonstrate that you know the field.

The third tier is responsive: texts you read because your data raises a specific question the first two tiers do not answer. This tier grows throughout the study and cannot be fully planned in advance. If you find yourself identifying something that looks like a quaternary contradiction, you go back and read Engeström on boundary crossing. If your participants’ accounts raise questions about professional identity, you read Edwards on relational agency. Let the data tell you what else you need.

From reading to writing: your CHAT literature review

Reading CHAT literature and writing your CHAT literature review are connected but distinct tasks. The reading guidance above is about orientation — knowing what to look for, how to read analytically, and how to build a purposeful list. What follows addresses the writing task: how to translate that reading into a literature review that does the analytical work a CHAT thesis requires.

The literature review in a CHAT thesis has a specific job that is different from literature reviews in other qualitative traditions. It is not primarily a survey of what others have found. It is an argument for why the activity system model — and the specific concepts you are drawing on — is the right analytical apparatus for your research problem. Every section of your literature review should be traceable to a decision you have made about your own study.

Structure: two layers, not one chapter

Most CHAT theses need two distinct layers of literature work, which may appear in the same chapter or in separate chapters depending on your institution’s conventions. The first layer is the theoretical framework: CHAT itself, its conceptual vocabulary, and how you are positioning your use of it. The second layer is the substantive context: the literature about the setting, the phenomenon, or the policy you are studying. Both layers are necessary. A CHAT thesis with a strong theoretical chapter but no engagement with the substantive literature leaves the reader unable to assess the significance of the findings. A thesis with rich substantive literature but thin theoretical framing leaves the reader uncertain whether the researcher actually understands the framework they are using.

What your CHAT literature review must establish

• Your unit of analysis — why the activity system, rather than the individual, the dyad, or the institution, is the appropriate focus for your specific research problem.
• Your key concepts and their provenance — which elements of the activity system model you are drawing on most heavily, and from which scholars. If you are using Engeström’s contradiction typology, cite it. If you are using Edwards’s relational agency, establish why. Do not use concepts without citing the source that gives them their analytical meaning.
• Your positioning within CHAT scholarship — CHAT is not monolithic. Engeström, Bligh, and Virkkunen emphasise intervention and development. Edwards emphasises professional practice and relational agency. Daniels emphasises Vygotskian foundations. Your literature review should show where your study sits within these emphases and why - the tradition is internally diverse, and choosing your emphasis is itself a positioning decision.
• Why CHAT rather than an alternative — this does not require an exhaustive comparison of every qualitative approach. It requires a focused argument for why your specific research problem requires the kind of systemic, historically grounded, relational explanation that CHAT provides — and why description of individual perceptions or thematic patterns would be insufficient. Chapter 6 provides a methodology justification template for your methods chapter; this argument, made at the level of the literature, belongs here.

You cannot justify your use of CHAT without having read the four foundational texts. But you do not need to have read everything before you begin fieldwork. Read the first tier. Then let your data tell you what else you need to read.

← Activity System ModelNext: From Situation to Research Question →

The literature is most useful when read as a set of choices about how to construct and analyse a system — read to learn how others made those choices, identify where they fell short, and then make your own.

The two types of gap

Research questions in CHAT theses emerge from two connected gaps, which are often confused because they are usually found at the same moment — when you have read enough to know what the literature assumes, and observed enough to know that your situation does not fit those assumptions.

The practice gap is the tension in the activity system itself: the contradiction between what the system is designed to do and what it actually produces. You can see this gap in your data. Nurses defer documentation. The EPR generates end-of-shift clusters. Care and record-keeping make incompatible demands at the same moments. The practice gap is what you observe. It is not yet a research question.

The literature gap is the space between what existing scholarship explains and what your practice gap requires explaining. The literature on EPR implementation explains adoption failure through user factors: training, resistance, compliance. Your practice gap shows that even compliant, trained nurses defer documentation under predictable structural conditions. Existing accounts cannot explain that — because they do not look at the relationship between the Tool and the temporal Rules of the activity system. That is the literature gap.

The research question lives at the intersection of both gaps. It names what you observed (practice gap) and positions it against what the literature has not yet explained (literature gap). A research question that describes only the practice gap is a problem statement. A research question that describes only the literature gap is a literature review objective. The CHAT research question does both.

What CHAT research questions look like

CHAT research questions have a specific structure that distinguishes them from descriptive qualitative questions. They ask about how a system produces an outcome, not whether an outcome occurs or what people think about it. They name the unit of analysis (the activity system or a relationship between its elements), and they imply a relational, structural explanation rather than an individual or perceptual one.

Three structural features distinguish a strong CHAT research question from a weak one. It specifies the activity system or system element under analysis. It asks a question that a structural, systemic answer can resolve — not one that only individual testimony can address. And it implies the kind of explanation the study will produce: a claim about how system elements interact, mediate, or contradict, not a theme or a category.

Main questions and sub-questions

Most CHAT theses have one main research question and two or three sub-questions. The main question states the system-level claim you are investigating. The sub-questions break it into the analytical moves your data needs to make: mapping the system, identifying the contradictions, tracing their development, and examining their consequences. The sub-questions are how your chapters are organised. The main question is what your thesis answers.

A common mistake is writing sub-questions that correspond to chapters rather than to analytical moves. “What does the literature say about EPR implementation?” is not a sub-question; it is a chapter task. Sub-questions should all be answerable from your data. If a sub-question requires only reading, it is not a research sub-question — it is a literature review objective, and it belongs in your research rationale, not your question set.

How research questions actually emerge

Research questions in CHAT studies rarely arrive fully formed at the proposal stage. The first version is almost always too broad, too descriptive, or too individual-focused. This is normal. What matters is understanding the mechanism by which they sharpen: a question sharpens when the literature gap becomes specific.

Amara’s question sharpened not from more reading but from one specific moment of reading: the point at which she found that behavioural accounts of EPR adoption did not predict end-of-shift clustering, because they looked at individuals rather than system structures. At that moment the literature gap became nameable. The question followed from it.

The practical implication is this: do not expect your final research question to be in your proposal. Expect it to be in your analysis chapters. Work backwards from it to your methodology justification. This is not a failure of planning; it is how analytical research works. The viva question “how did your research questions emerge?” has a correct answer for a CHAT study: they emerged from the intersection of your data and your reading, and you will show the examiner exactly where.

Template for drafting your research question

What goes wrong

The question describes rather than explains. “What are nurses’ experiences of EPR implementation?” is a phenomenological question. It asks for accounts and perceptions. A CHAT question asks how a system structure produces an outcome — an answer that requires structural evidence, not only testimony.

The question is answerable without CHAT. If thematic analysis could answer your research question, it is not yet a CHAT research question. The question should name a system element, a relationship between elements, or a structural mechanism that CHAT’s analytical vocabulary is specifically equipped to illuminate.

The question is unanswerable from data. “How should healthcare organisations implement EPR systems?” is a policy recommendation, not a research question. It cannot be answered by your data; it can only be implied by your findings. Move it to your implications section.

There are too many sub-questions. More than four sub-questions usually indicates that the main question has not done enough analytical work. Each sub-question is a data chapter. Four sub-questions is four empirical chapters, which is too many for most doctoral theses. Three is the target.

You do not need a finalised research question before beginning fieldwork. You need a working question that is specific enough to guide your data collection decisions. The final form of the question will emerge from your analysis. What you must have is the literature gap: if you cannot name what existing accounts cannot explain, you cannot justify your study.

← Reading & Literature ReviewNext: Mediation and Tools →

Tools are not simply instruments people use. In CHAT, tools shape what is possible within the system itself. They carry historical traces of prior practice and constrain or enable action in specific ways. V’78Vygotsky, L.S. (1978)Mind in Society. Harvard University Press.

Tools include

• Technology and digital platforms
• Language and discourse
• Institutional rules and policies
• Cultural practices and conventions

Analysing mediation means asking: what does this tool make possible, and what does it foreclose? How does it carry the history of the system into present practice? A tool that appears neutral is rarely so — it embeds assumptions about how work should be done, by whom, and at what pace.

You cannot identify contradictions in your system until you have analysed what each tool makes possible and what it forecloses. For every tool in your system, ask: who designed this, with what model of practice in mind, and how does that model fit — or not fit — the activity as it actually occurs? That mismatch, if it exists, is your first candidate for a secondary contradiction.

← From Situation to Research QuestionNext: Contradictions →

Analysing mediation means asking not just what a tool does, but what assumptions about practice it carries — and whose assumptions those are.

Contradictions* emerge as recurring breakdowns, mismatches between intention and practice, or misalignment between system elements. They are the engine of development and change — the reason systems do not stay still. E’87Engeström, Y. (1987)Learning by Expanding. Ch. 2.

The contradictions Amara identified are illustrated below.

Four types of contradiction

• Primary — within a single element (e.g., a tool serving two conflicting purposes)
• Secondary — between two elements (e.g., Tool requires time; Rules limit time)
• Tertiary — between an existing system and a more advanced form of activity
• Quaternary — between the central system and a neighbouring activity system

Contradictions are not always stated directly. Look for patterns in your data: repeated delays, differences between expectations and practice, participant frustration. Statements like “We are expected to use the platform, but there is not enough time” point toward underlying systemic tensions.

Think of your context. Identify one contradiction using the template below.

You cannot write your findings chapter until each contradiction is supported by evidence from at least two data sources. If you can only find one piece of evidence for a tension, return to your data. Either the contradiction is not yet fully visible, or it is an anomaly rather than a structural feature. Structural contradictions recur. They leave multiple traces.

This paragraph is not the end of the analysis. It is the smallest complete version of your findings — the rest of the chapter builds from it.

CHAT provides a conceptual lens, informs how research is designed and interpreted, and supports explanation of complex systems. It does not replace your methodology — it informs and guides it. Always distinguish: CHAT provides the lens; your methods provide tools for collecting data; your methodology explains how and why these fit together. B’17Bligh, B. & Flood, M. (2017)“Activity Theory in Empirical Higher Education Research.” Tertiary Education and Management, 23(2).

CHAT vs other qualitative approaches

The most common challenge in a CHAT methodology chapter is justifying the framework choice against alternatives a supervisor or examiner might reasonably propose. The table below positions CHAT against three adjacent qualitative approaches — not to argue for its superiority in the abstract, but to sharpen the case for why your specific research problem requires a systemic and historically grounded explanation. For guidance on writing the CHAT literature review — including structure, what to establish, and a positioning template — see Chapter 3.

You cannot write your methodology chapter until you can answer this question in one sentence: “I am using CHAT rather than [alternative] because my research problem requires explanation of [specific systemic feature] rather than description of [what the alternative would produce].” If you cannot complete that sentence, your justification is not yet strong enough.

← ContradictionsNext: Research Instruments →

Your justification for using CHAT should explain not just what the framework is, but why your specific research problem requires a systemic and relational explanation that other approaches cannot provide.

Principles of CHAT-aligned instrument design

Before designing any instrument, identify which elements of the activity system each question is intended to illuminate. A question that does not serve the system map — that would produce data you could not place anywhere in your analytical framework — does not belong in your instrument. This is not restrictive; it is clarifying. It forces you to make your analytical intentions explicit before you enter the field.

The six system elements provide a natural organising structure. Most CHAT studies need data on: the tools participants use and how those tools shape their practice (mediation); the rules that formally and informally govern activity; the community involved and the different roles within it (division of labour); and the object — what participants understand the activity to be for, and whether that understanding is shared or contested. Historical data — what the activity looked like before any significant change — is a seventh category that many CHAT studies require.

Semi-structured interview protocol: NorthCare EPR study

The following protocol was used by Amara across her forty-two semi-structured interviews with nursing staff, ward managers, and IT support personnel at NorthCare. Each question is annotated with the system element it is designed to illuminate.

Adapting this protocol for your own study

Replace every reference to the EPR system, nursing, and NorthCare with the equivalents from your own research context. The structural logic of the protocol — opening on object, moving through tools and rules, tracing division of labour and community, seeking historical comparison, closing on contradiction and development — is transferable to any CHAT study. What is not transferable is the specific content. Your instrument must be grounded in your system, not borrowed from this one.

Survey instrument: NorthCare EPR implementation study

Amara supplements her interviews and observations with a short survey distributed to all registered nursing staff across three wards at NorthCare (n = 74; response rate 68%). The survey is not designed to produce statistical findings. Its purpose is to map the distribution of the tensions identified in the interviews — to establish whether the contradictions Amara has observed are concentrated in particular roles or shifts, or are systemic across the workforce.

In CHAT research, surveys are most useful not as standalone instruments but as triangulation tools. They can confirm that a pattern observed in interview and ethnographic data is not an artefact of who you happened to speak to. They cannot, on their own, identify contradictions or explain their structural origins — that analytical work requires qualitative depth.

When to use a survey — and when not to

A survey is not a required component of a CHAT study. Many strong CHAT theses use no survey at all. Before deciding to include one, ask a specific question: what would a survey tell me that my interviews and observations cannot? If the answer is “the distribution of a pattern I have already identified qualitatively,” a survey may add value. If the answer is “I am not sure,” it probably will not.

Surveys are most useful in CHAT research when your setting involves a large number of participants across whom you cannot conduct interviews — and when you want to test whether a contradiction you have identified in interview and observation data is concentrated in a particular system position (a specific role, shift pattern, or site) or is distributed across the system more broadly. They are least useful when your study is already analytically rich from interviews and observation, when your participant group is small enough that everyone can be interviewed, or when the questions you need to ask are too contextual and relational to translate into a survey format.

It is also worth being honest about what surveys cannot do in a CHAT study. They cannot identify contradictions — they can only confirm the distribution of patterns that qualitative analysis has already surfaced. They cannot explain why a tension exists — only how widely it is felt. And they can produce a false impression of quantitative rigour in a study that is fundamentally interpretive. If you include a survey, be explicit in your methodology chapter about its role: it is a triangulation instrument, not an independent source of findings.

Using survey data in a CHAT analysis

The value of Amara’s survey lies not in its means but in its distributions. When she breaks down responses to C2 (“I sometimes complete EPR records after my shift has officially ended”) by shift pattern, she finds that the pattern is significantly more pronounced among rotating and night-shift nurses than among day-shift staff. This is analytically significant: it suggests the Tool–Rules contradiction is not uniformly distributed across the system, but is particularly acute at specific positions within the division of labour. That finding generates new interview questions, deepens the contradiction analysis, and ultimately strengthens the argument that the tensions are structural rather than individual.

Survey data in a CHAT study should always be interpreted through the analytical framework rather than presented as findings in their own right. The question is not “what percentage of nurses agree?” but “what does the distribution of agreement tell us about how this contradiction is structured within the activity system?”

← MethodologyNext: Ethics and Reflexivity →

Every question in a CHAT instrument should be traceable to a system element, a potential contradiction, or a historical comparison — if it is not, it does not belong in the instrument.

Ethics in CHAT research is not a box to tick before fieldwork begins. It is a continuous analytical and relational responsibility that runs through every stage of the study — from the way you frame your research questions, to the mirror data you select, to the interpretations you publish. Because CHAT seeks to unearth structural contradictions, your findings may inadvertently point toward systemic failures, management gaps, or professional tensions. That representation carries consequences.

The Dual Role: Analyst and Facilitator

In CHAT, you are rarely a "fly on the wall." You are often a facilitator—bringing participants together to look at "mirror data" (evidence of their own work). This dual position is not a problem to resolve — it is a condition to work with transparently.

• Institutional Diplomacy: Framing findings as structural contradictions (gaps in the system) rather than individual failings (staff or management incompetence).
• Managing Disagreement: Treat a participant’s rejection of your system map as **primary data**. A disagreement reveals a gap between perceived reality and systemic pressure.
• Positional Reflexivity: Acknowledging how your own history (e.g., as a former clinician or an academic outsider) influences which tensions you "notice" and which you overlook.
• Power Dynamics: Recognizing that the Change Laboratory is a social space where hierarchy still exists, even if you try to "flatten" it.

You cannot complete your methodology chapter without a reflexivity section that accounts for your position within the system. This is an analytical requirement. Your system model is a construction; your contradictions are interpretations. Name the history and assumptions that led you to them.

← Research InstrumentsNext: From Raw Data to Activity System →

Reflexivity in CHAT means making your interpretive choices visible. The system map is always a product of the researcher's lens; ethical research involves explaining why that lens was used.

Chapter 9 explains how qualitative data (interviews, surveys, and observations) are systematically coded and reconstructed into an activity system model using CHAT concepts. This is a researcher-led analytical procedure for constructing explanatory models of activity systems. It does not involve participant modelling, triangulation workshops, or Change Laboratory intervention.

Data analysis in a CHAT study is not a single event. It is an iterative movement between data and framework, in which each pass deepens both your understanding of the data and your construction of the system. There is no shortcut through this process, but there is a sequence that makes it manageable.

Step 1 — Read before you code

Before applying any analytical framework to your data, read all of it once without coding. This sounds obvious and is frequently skipped. Reading without coding allows you to form an overall sense of the territory — what participants are concerned about, what keeps recurring, where the unexpected moments are. The patterns you notice in this first reading will orient your later analytical decisions in ways that are difficult to reproduce if you begin coding immediately.

Keep a running memo as you read. Note recurring words, repeated frustrations, moments where a participant says something that surprises you, and moments where different participants seem to be describing the same situation from different positions. These memos are data — file them with your transcripts, not in a separate document that will be lost.

Step 2 — Map data onto system elements

Return to your transcripts and, for each passage, ask: which element of the activity system is this participant describing? Not every passage maps to an element — some is contextual, some is narrative, some is biographical. But a significant proportion of interview data in a CHAT study will be describing, directly or indirectly, one of the six nodes. Mark those passages and note the element.

Use a simple coding system: T for tool, R for rules, C for community, DL for division of labour, S for subject, O for object. Do not force codes — if a passage does not clearly describe a system element, mark it as context or leave it uncoded for now.

If you find yourself assigning multiple elements to most passages, you are overcoding. Most data points should primarily describe one element, even if they gesture toward others.

The codes that matter most at this stage are those where the same element is being described differently by different participants. Those divergences are where contradictions begin to appear.

Step 3 — Build the system map from coded data

Once you have coded a substantial portion of your data — not all of it, but enough to have covered each system element multiple times — begin to build your system map.

Your system map at this stage should look like six lists — one under each element — built directly from your coded data.

This map is not a diagram yet. It is a working document: a list under each heading of what your data says about that element.

This step often reveals gaps. You may have rich data on tools and rules but thin data on division of labour or community. Those gaps are a signal to return to the field — to ask a question you have not yet asked, or to seek out a participant whose position in the system you have not yet accessed.

It also often reveals convergences that complicate your initial coding. A passage you coded as describing a tool may, on reflection, be describing a rule — because the tool has become so embedded in practice that participants experience it as a mandate rather than an instrument. Note these ambiguities. They are analytically significant.

Step 4 — Identify contradiction candidates

A contradiction candidate is a pattern in your data where two system elements appear to be pulling in different directions. At this stage, it is a hypothesis, not a finding. You are asking: does this data suggest a structural tension between these elements? You are not yet claiming it as a contradiction.

The following signals are reliable indicators of contradiction candidates in interview and observation data:

• Recurring workarounds — if multiple participants describe the same informal solution to the same problem, there is probably a structural tension that the workaround is compensating for
• Complaints that participants cannot explain — “it just doesn’t work” or “it’s always been a problem” are invitations to dig for the structural cause
• Divergent accounts of the same situation — when a manager and a frontline worker describe the same system very differently, the divergence often reflects different positions within the division of labour and different relationships to the object
• Moments of visible frustration or resignation in observation data — a participant who abandons a system or switches tools is showing you something that interview data alone would not reveal
• Breakdowns in sequence — when work cannot proceed in the expected order (e.g., documentation happening after action), this often signals tension between elements

Step 5 — Test each contradiction against the data

For each named contradiction, ask: what evidence do I have, from how many data sources, that this tension is structural rather than incidental?

A contradiction that appears in one interview is an observation. A contradiction that appears across interviews, observations, and documents is a structural feature of the system.

The threshold for naming a contradiction is evidence from at least two independent data sources showing the same pattern in different forms.

Repetition within a single interview is not multiple evidence. A pattern must appear across participants, data types, or moments in practice to count as structural.

Also ask: could this be explained without CHAT? If the tension can be fully explained by saying “the software is poorly designed” or “the manager made a bad decision,” it is not yet a structural analysis. A CHAT explanation must identify the system elements in tension and explain why the structure of the activity produces the pattern — not why an individual or a product caused it.

Step 6 — Revise the system map

Once you have named your contradiction candidates and tested them against the data, return to your system map and revise it.

The contradictions you have identified will have revealed new things about the system elements: the object may be more contested than your first map suggested; the division of labour may be more fragmented; a tool you initially listed as secondary may be central.

This is your second-version system map. Date it. It will not be your last.

The movement between Step 3 and Step 6 — map, identify candidates, test, revise map — is the core analytical cycle of CHAT research. Most studies go through this loop three to five times before the system map stabilises enough to write from.

Early stability is usually a sign of shallow analysis, not completion.

You cannot write a findings chapter until you have completed at least one full cycle of Steps 1–6 — read, code, map, identify candidates, test, revise. A findings chapter written from a first-pass system map will show its seams. The analytical depth that examiners look for is produced by the revision cycles, not the initial mapping.

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The distance between your first system map and your final one is not a sign of early error — it is the evidence of your analytical work.

Why the system must be historicised

A CHAT analysis that only describes the present system is incomplete. Activity systems are not created from scratch — they are modified. What nurses do now is intelligible only in relation to what they did before. Their workarounds are not irrational responses to a new tool; they are the residue of a practice system that worked, pressed into service against a replacement that does not quite fit.

One nurse in Amara’s study puts it precisely: “The handover sheet told you everything in one glance. The EPR tells you everything in forty clicks.” This is not a comment about technology. It is a description of two different epistemic structures — two different theories of what clinical knowledge is, how it should be organised, and who it is for. The handover sheet was designed by nurses, for nurses, over decades of accumulated clinical practice. The EPR was designed by software architects, for administrators, with compliance and audit as the organising logic. When Engeström writes of tertiary contradictions — tensions between an existing system and a more advanced form of activity — this is precisely what he means. Not simply a new tool, but a new theory of work, introduced into a system whose participants have a different theory, equally developed and equally valid. E’87Engeström, Y. (1987)Learning by Expanding. Ch. 4.

Constructing multiple system maps

The analytical work of this chapter is to build at least three maps and hold them in relation to each other. A single diagram cannot do this work. Multiple diagrams, compared deliberately, are what make historical explanation possible.

Map 1 — the past system. At NorthCare before EPR: tools were the handover sheet, the physical observation chart, and verbal communication. Rules were organised around shift handover as the primary documentation moment — retrospective, collective, and time-bounded. The division of labour distributed documentation responsibility across the team, with senior nurses synthesising information for handover. The object — safe patient care — was served by a system whose rhythms matched the rhythms of acute nursing work.

Map 2 — the designed system. The EPR implementation assumed that real-time individual documentation at the bedside would improve care quality and create auditable records. The design logic imagined nursing work as a series of discrete, documentable events occurring at a pace that permitted contemporaneous recording. This is a coherent model of nursing practice — it simply does not describe nursing practice as it occurs on a busy medical admissions ward.

Map 3 — the actual system. What Amara observes is an improvised hybrid: nurses using the handover sheet as their primary clinical tool and the EPR as a compliance instrument, entering data in bulk at the end of shifts from memory and from paper notes. The division of labour has fragmented — every registered nurse now carries individual documentation responsibility — while the workload has increased. The object is increasingly split: nurses are simultaneously oriented toward patient care and toward record completion, and the two are in structural competition for the same finite time.

From description to explanation

The analytical move from Map 2 to Map 3 is where explanation begins. It is not enough to say that the system is not working as designed. You must explain why — in terms of the structural relationships between elements, not in terms of individual failure.

Descriptive: “Nurses are using workarounds because the EPR is difficult to use.”

Analytical: “The workarounds nurses have developed are a structural response to a tertiary contradiction between the EPR system’s embedded theory of nursing work — discrete, individual, contemporaneous documentation — and the established activity system’s theory of nursing work — collective, synthesised, retrospective handover. The workarounds are not resistance to the tool; they are the activity system defending its object against a tool that threatens it.”

Notice what the analytical version does. It names the contradiction type. It characterises each system’s theory of work. It locates the workarounds not as a problem to be solved but as evidence of a structural tension to be explained. And it grounds the explanation in the relationship between system elements rather than in the behaviour of individuals.

Common mistakes in system building

• Overloaded diagram — use multiple maps rather than one large diagram that tries to contain everything
• Listing instead of analysing — the diagram is not the analysis; the analysis is the written account of how the elements relate and why the tensions persist
• Static model — a diagram that does not change between your first chapter and your last is a sign that your analysis has not deepened
• Individual blame — if your explanation ends with a person rather than a structural relationship, return to the system

You cannot write a historically grounded CHAT analysis until you have built your past-system map. This requires data about what came before — documents, accounts, historical records, participant memories. If you have not collected this data, return to your fieldwork. The present system is explained by the past system it displaced. Without that comparison, your analysis is synchronic. CHAT demands diachronic explanation.

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The most analytically powerful moment in a CHAT study is when you can show not just that a tension exists, but where it comes from historically and why it persists structurally.

The organising principle of a CHAT findings chapter

The single most consequential decision in writing a CHAT findings chapter is what you organise it around. Most students, arriving at the writing stage after months of fieldwork, organise their data the way it arrived: by participant, by site, by interview question, or by theme. All of these are natural organising principles. None of them is a CHAT organising principle.

A CHAT findings chapter is organised around contradictions. Each section takes one contradiction as its subject, presents the evidence that establishes it as a structural feature of the system, explains the relational mechanism that produces it, and connects it to the historical development of the activity. The participant data does not disappear — it becomes evidence rather than content. The difference between those two things is the difference between a thematic analysis and a CHAT analysis.

The analytical chain

Every section of a CHAT findings chapter follows the same chain, whether the contradiction is primary, secondary, tertiary, or quaternary:

Contradiction → Evidence → System analysis → Claim

The contradiction is named and typed. The evidence is presented from at least two independent data sources. The system analysis explains how the structure of the activity produces the tension — not why an individual made a choice, but why the configuration of elements makes the pattern inevitable. The claim is the analytical conclusion: what this contradiction reveals about the system that could not have been seen without CHAT.

The discussion chapter then takes each claim and extends it:

Claim → Literature positioning → Theoretical repositioning → Contribution

The claim from the findings chapter becomes the starting point of the discussion. It is placed in relation to existing literature: what does this finding confirm, challenge, extend, or qualify? The theoretical repositioning names what the finding adds to CHAT scholarship specifically — how it develops a concept, extends a typology, or demonstrates a relationship the existing literature has not yet described. The contribution is the explicit statement of what your study adds to the field.

Structuring a findings chapter across multiple contradictions

Most CHAT studies identify two to four major contradictions. The findings chapter needs to present each one, but not as a list — as an argument. The contradictions should build on each other: each one deepens the analysis, extends the system map, or reveals something the previous contradiction could not. The order matters.

A useful sequence is to move from the most locally visible contradiction outward toward the most structurally embedded one. In the NorthCare study, Amara begins with the secondary Tool–Rules contradiction (the most immediately observable tension in daily nursing practice), moves to the secondary Tool–Object contradiction (which requires understanding how the EPR embeds a different theory of nursing than nurses hold), and ends with the tertiary contradiction between the old and new activity systems (which requires the historical comparison and explains why the first two contradictions persist despite goodwill on all sides). Each contradiction deepens what the reader understands about the system.

Connecting passages between sections do important analytical work that students often underestimate. A transition sentence like “This secondary contradiction does not exist in isolation — it is sustained by a deeper tension in the system’s historical development” is not filler. It tells the reader that the contradictions are related, that the system is coherent, and that the analysis is moving somewhere. Without it, the findings chapter can feel like a series of separate analytical exercises rather than a connected account of a system under tension.

A worked example: from contradiction to claim

The following shows how the analytical chain works in practice, using the Tool–Rules contradiction from the NorthCare study.

Moving from findings to discussion

The discussion chapter begins where the findings chapter ends — with the claim. Its job is not to repeat the evidence but to place the claim in conversation with existing scholarship. Three moves are required.

The first move is literature positioning: what does this finding say in relation to what the field already knows? In the NorthCare study, the relevant literature includes implementation-focused studies that treat EPR adoption difficulties as behavioural (user resistance, training gaps, compliance failures). Amara’s finding challenges this framing directly: the tension she identifies is not behavioural but structural, and it would persist even with perfect compliance and ideal training.

The second move is theoretical repositioning: what does this finding contribute to CHAT scholarship specifically? Amara’s finding demonstrates that secondary contradictions between Tool and Rules are not simply organisational coordination problems — they involve the collision of different temporal logics embedded in different elements of the activity system. This extends Engeström’s typology by showing that Tool–Rules contradictions can be grounded in structural temporal misalignment, not just functional incompatibility.

The third move is contribution: what does your study add to the field that was not there before? The contribution must be stated explicitly. It is not enough to imply it. In Amara’s case: digital systems can generate secondary contradictions within activity systems when Tool temporality conflicts with Rule-based organisation of work — and addressing these contradictions requires system redesign at the level of temporal structure, not user-level intervention.

Template — Writing one findings section

Template — Writing one discussion section

Common writing problems and how to fix them

The system disappears mid-chapter. The most common writing problem in CHAT theses is beginning in clear analytical framing and drifting, section by section, into description. By the third section, the contradiction has become a theme, the evidence has become illustration, and the system has disappeared. The fix: before writing each section, write one sentence that states the analytical claim that section will make. Every paragraph should either evidence that claim, qualify it, or develop it. If a paragraph does neither, it does not belong in the section.

Data is quoted rather than analysed. Long interview extracts followed by restatements of what the participant said are not analysis. They are transcription. The test: if a paragraph could be written without CHAT by a researcher doing thematic analysis, it is not doing CHAT work. Every extract should be followed by a sentence that names the system element it describes and another that connects it to the contradiction being evidenced. The extract is the evidence; the analytical sentences are the argument.

The claim is implicit rather than stated. Many students reach the end of a well-evidenced section and stop without stating the claim. The evidence is there. The system analysis is there. But the reader is left to infer the conclusion. In a CHAT thesis, the claim must be stated explicitly. It is not enough to imply it. End each findings section with a sentence that begins “This contradiction demonstrates” or “This tension makes visible” — and finish it with something an examiner can push back on. If the claim feels safe, it is not yet strong enough.

The discussion repeats the findings. A common structural problem is a discussion chapter that restates the evidence before engaging with the literature. The discussion chapter does not need to re-establish what you found — it starts from what you claimed and asks what that claim means for the field. The transition from findings to discussion is a transition from evidence to argument, not a repetition.

What the examiner is looking for

Examiners reading a CHAT findings chapter are asking three questions, whether they articulate them or not. First: is this organised around system relationships or around something else? Second: does the analysis explain why the system produces these patterns, or does it describe what participants said? Third: is there a claim here that could only have been produced by this framework, and is it stated explicitly enough to engage with?

A findings chapter that answers all three questions affirmatively is a CHAT findings chapter. A findings chapter that answers the first two but leaves the third implicit is most of the way there — it needs one revision pass in which every section ending is sharpened into a stated claim. A findings chapter that answers only the first is still thematic analysis with CHAT labelling. The distinction is not always obvious from the inside. The template and the writing chain above are tools for making it visible.

For the discussion chapter, examiners are asking a related but distinct set of questions. Does the discussion begin with the claim or with a re-description of the findings? If the first paragraph of each section restates the evidence, the discussion has not yet started. Is the literature positioning genuine — does the finding actually challenge or extend the cited scholarship, or is the literature simply listed as context? Is the theoretical repositioning specific enough to be falsifiable? A sentence that says “this contributes to CHAT scholarship” without naming the concept being extended and the dimension being added is not a repositioning; it is a gesture toward one. And finally: is the contribution statable in a single sentence that could appear in the abstract? If it is not, the discussion chapter has not yet produced a contribution.

What distinguishes an excellent discussion chapter from an adequate one is not length or the number of references cited. It is specificity of engagement. An adequate discussion chapter places findings in relation to relevant literature and notes that they are consistent or inconsistent with it. An excellent discussion chapter explains why the finding is inconsistent, names the mechanism that existing literature has missed, and states what the field must now account for that it did not previously. Examiners are experienced enough to know which kind of chapter they are reading within the first paragraph of each section. The difference is not a matter of effort; it is a matter of structural understanding of what a discussion chapter is for.

You cannot write a CHAT findings chapter by organising your data around participant groups, themes, or interview questions. You must organise it around contradictions. If your section headings do not name system tensions, restructure before you write further. The organising principle is not a stylistic choice — it is what makes the analysis CHAT rather than thematic description with CHAT vocabulary added.

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In CHAT writing, the system is not the background to the argument — it is the argument. Every structural decision in your findings chapter, from section headings to transition sentences to the placement of evidence, should make the system more visible, not less.

These four (plus a fifth) patterns appear repeatedly in CHAT theses across disciplines and institutions. They are not signs of inadequate ability — they are signs of the genuine difficulty of the analytical work CHAT demands. Most supervisors have seen all of them. Most examiners have assessed theses that contain them. What distinguishes a strong CHAT thesis is not the absence of these tendencies early in the process, but the rigour with which they are recognised and corrected before submission.

Mistake 1 — Static system diagrams

The mistake: drawing your activity system triangle in your first chapter and reproducing it unchanged throughout the thesis. The diagram appears in the methodology, reappears in the findings, and is cited in the discussion — always the same, as if the analysis produced no surprises and the system revealed nothing not already known at the start.

Why it happens: drawing the diagram feels like completing a task. Once drawn, revising it feels like admitting the first version was wrong. In fact, revision is the evidence of analytical work. A system map identical in your final chapter to your first is a sign the analysis has not progressed — that you have described the system rather than investigated it.

The fix: date every version of your system map and keep them all. In your final thesis, show the development explicitly: “My initial map identified the EPR as the primary tool. After interview analysis, I revised this to show two tools in tension. After the two-triangle exercise, I identified a third tool — the informal handover sheet — that had been invisible in my original mapping.” That narrative of revision is not a confession of early error. It is the evidence of your analytical development, and it should be in your thesis, not hidden from it.

Mistake 2 — Forcing contradictions

The mistake: naming a tension as a contradiction without sufficient evidential grounding. A single interview excerpt, one observed incident, or a theoretical expectation that a tension should exist — none of these is sufficient. Yet students under pressure to produce findings often reach for contradiction labels before the evidence warrants them.

Why it happens: the CHAT framework creates an expectation of contradictions, and students feel pressure to find them. If the data is not obviously generating contradictions, the temptation is to impose them. Examiners detect forced contradictions quickly: they ask for the evidence and the student finds they have one example rather than a pattern.

The fix: for every named contradiction, require yourself to provide evidence from at least two independent data sources showing the same structural pattern. An interview extract and an observation that corroborates it. A document establishing the rule and an interview showing how the tool violates it. A pattern in timestamps confirming what nurses describe verbally. If you cannot produce two independent sources, the contradiction is a candidate, not a finding. Say so in your thesis and continue collecting data until the evidence is there — or the candidate is abandoned.

A second test: can the pattern be explained without CHAT? If the tension disappears when framed as “the software is poorly designed,” it is not yet a structural analysis. A structural contradiction must name the system elements in tension and explain why the structure of the activity — not an individual decision or a product flaw — produces the pattern.

Mistake 3 — Thematic instead of relational analysis

The mistake: structuring your findings chapter around themes, participant groups, or data sources rather than around the relationships between system elements and the contradictions those relationships produce. A chapter organised as “Nurse Perspectives,” “Manager Perspectives,” and “IT Support Perspectives” is a thematic analysis. A chapter organised as “The Tool–Rules Contradiction,” “The Tool–Object Contradiction,” and “The Tertiary Contradiction between Past and Present Practice Systems” is a CHAT analysis. The same data can produce either. The choice of organising principle is the analytical decision.

Why it happens: interview data arrives organised by participant. The natural temptation is to write from that organisation. Thematic analysis is also deeply familiar from prior training — many students have done it well before and find its logic reasserting itself under the pressure of writing up.

The fix: before drafting your findings chapter, write a list of your named contradictions with their evidential sources. That list is your chapter outline. Each section takes one contradiction as its subject, draws on data from multiple sources to evidence it, explains the relational mechanism that produces it, and connects it to the historical development of the system. If a piece of data does not serve any of your named contradictions, ask whether it belongs in the findings chapter at all — or in the background or literature review.

Mistake 4 — Losing system logic in writing

The mistake: beginning your findings chapter in clear CHAT framing — naming elements, constructing relationships, evidencing contradictions — and then allowing the prose to drift, section by section, into generic qualitative description. By the third or fourth section, the activity system has disappeared. Participants are being quoted at length. Themes are being reported. The CHAT framework has become decoration rather than architecture.

Why it happens: sustaining analytical framing across a long chapter is harder than it sounds. Writing is tiring, and the natural mode of qualitative writing — describing what participants said and did — reasserts itself when concentration lapses. The activity system that felt vivid during analysis can fade during writing, especially when the writing is going slowly.

The fix: before writing each section of your findings chapter, write one sentence that states the analytical claim the section will make: “This section demonstrates that the secondary contradiction between Tool and Rules is not uniformly distributed across the division of labour, but is concentrated in the position of rotating-shift nurses who have no documentation time built into their working pattern.” That sentence is your analytical compass. Every paragraph in the section should either evidence the claim, qualify it, or develop it. If a paragraph does neither, it does not belong in the section. The sentence keeps the system logic visible while you write, and it becomes the topic sentence of the section when the draft is done.

One further pattern: over-reliance on Engeström (1987)

A fifth pattern worth naming separately: using Engeström (1987) as the only theoretical reference, citing it for every conceptual claim and treating it as if the CHAT literature ended there. Engeström (1987) is essential, but it is thirty-eight years old. The field has developed substantially since then — through Engeström’s own later work, through Edwards on relational agency, through Virkkunen and Newnham on the Change Laboratory, through empirical researchers in healthcare, education, and professional practice who have applied, extended, and sometimes challenged the framework. A thesis that engages only with the 1987 text signals to examiners that the candidate has not read the field. It also misses conceptual tools — knotworking, formative intervention, double stimulation as a methodological principle — that a more current reading would make available. Read broadly within the tradition, then cite purposefully.

Before submitting your thesis, read your findings chapter and ask: could this have been written using thematic analysis? If yes — if you could remove every reference to the activity system and the argument would still hold — you have written a thematic analysis with CHAT terminology added. Return to the system. Restructure around the contradictions. Make the relational logic the spine of the chapter, not the decoration.

← Writing CHAT ResearchNext: Viva Strategy →

If your findings chapter could have been written using thematic analysis, it has not yet become a CHAT analysis — the system, not the theme, must be the organising principle.

The viva is not a test of memory. It is a discussion where you explain your reasoning, justify your choices, and reflect on your findings. Your central anchor throughout: what is your unit of analysis? In CHAT, this is the activity system — not the individual.

Defending CHAT — responses to common examiner questions

• “Why were nurses resistant?” — “Rather than individual attitudes, the study examined resistance as a systemic tension between new tools and existing rules within the activity system. What appeared as resistance was structurally produced.”
• “Why this approach?” — “CHAT was appropriate because the study aimed to explain how structural relationships within the activity system produced specific tensions — not to describe individual experience, but to explain its systemic origins.”
• “How does this go beyond description?” — “The study situates current tensions within the historical transition from paper-based to digital nursing practice, showing how established rules and the object of care continue to shape present activity in ways the EPR system’s design did not anticipate.”

Known limitations to acknowledge

• System construction is interpretive and researcher-dependent
• Contradictions are inferred from data, not directly observable as such
• Diagrams risk simplifying what are genuinely complex relational dynamics

If you feel unsure during a question, return to your core framework. If you lack a complete answer: “That is an area the study did not explore in depth, but it may relate to…” — this shows awareness of scope and openness to further development.

You cannot sit your viva until you can answer the question “Why not thematic analysis?” in two sentences, without notes, without hesitation. Practice that answer until it is automatic. Everything else in your viva defence rests on the credibility of that justification.

← Common Mistakes

A well-prepared viva candidate knows their system as clearly as their data — they can move between the two without losing the thread of either.

The Change Laboratory is a structured intervention method developed by Engeström in which researchers and participants work together to examine the contradictions in their activity system and begin to develop new forms of practice. V’13Virkkunen, J. & Newnham, D.S. (2013)The Change Laboratory. Sense Publishers. It is not a focus group, a consultation exercise, or a training session. It is a space in which the activity system itself becomes the object of collective analysis — where participants move from describing their experience to theorising its structural causes.

Selecting participants: who needs to be in the room

Participant selection for a Change Laboratory is not a sampling decision in the conventional research sense. You are not seeking representativeness. You are assembling a group that has the collective capacity to examine the activity system, analyse its contradictions, and — crucially — do something with what the analysis produces. That last requirement shapes who you invite.

The group needs to include people who experience the activity from different positions within it. In a workplace setting this means different roles, different levels of seniority, and — as discussed in the two-triangle section — both experienced staff who remember the old system and newer staff who know only the current one. This positional diversity is not about balance for its own sake. It is because the contradictions in an activity system are experienced differently depending on where you sit within it, and a group that only represents one position will produce a partial analysis.

But the most important selection criterion, and the one most often overlooked in PhD research, is this: at least one participant needs to have a credible route to management. This does not mean a manager must be in the room — managerial presence often inhibits the candour that the Change Laboratory requires. It means that someone in the group must have the standing, the relationships, and the confidence to take what the group produces and communicate it upward. Without this person, the Change Laboratory becomes a closed loop: participants develop a sophisticated understanding of their situation, the researcher produces a strong analysis, and nothing changes because there is no mechanism for the findings to reach the people with the authority to act on them.

In healthcare settings this might be a senior nurse, a ward education lead, or a union representative. In school settings it might be a department head or a teacher who chairs a working group. In any setting, it is someone who already has a legitimate reason to speak to management and who can frame the group’s proposals in terms that the institution can hear. Identify this person in your scoping conversations, before you finalise your participant group. Invite them explicitly, explain why their role matters, and make sure they understand from the outset that the later sessions will ask them to take something back.

What the Change Laboratory is designed to produce

The Change Laboratory produces two things simultaneously: data for the researcher, and expanded understanding for the participants. These are not separate outcomes. The discussion that generates your most analytically rich data is the same discussion in which nurses, teachers, or healthcare workers begin to see their situation differently. The researcher is not extracting insight from participants — insight is being produced collectively, in the room.

This is why the selection of mirror data is the most consequential decision you make in designing a Change Laboratory session. Mirror data is not illustrative — it is provocative. It should show participants something about their own practice that they cannot easily dismiss or explain away at an individual level. The two numbers Amara presents — twelve minutes versus thirty-eight minutes — are carefully chosen. They cannot be explained by any individual nurse working slowly. They can only be explained by something structural.

The Expansive Learning Cycle

Engeström describes the process of collective development through a Change Laboratory as an expansive learning cycle. E’01Engeström, Y. (2001)"Expansive Learning at Work." Journal of Education and Work, 14(1), 133–156. The cycle has seven stages, each of which represents a qualitatively different form of collective engagement with the activity system. They are not steps to be completed in sequence — they are zones that participants move through, return to, and sometimes inhabit simultaneously. What matters is the direction of travel: from accepting the current system as given, toward being able to imagine and act on a transformed one.

Opening the first session: problems first

Before mirror data, before activity system triangles, before any theoretical framework — start with the participants' own problems. Ask the group, in the very first session, to list every difficulty, frustration, or tension they notice in their daily work. Write them on the wall. Do not edit, prioritise, or comment. Just collect. This does something important: it establishes from the outset that the knowledge in the room belongs to the participants, not to the researcher. It also produces a raw list that you will return to in later sessions as a baseline — a record of what the group knew before the analysis began.

This problem-listing phase is distinct from the mirror data. The mirror data is selected by the researcher and introduced deliberately. The problem list is generated by the participants themselves, without prompting beyond the initial question. Together, they create the conditions for Stage 1.

Stage 1 — Questioning

Questioning is triggered when participants encounter something about their practice that they cannot accept as normal. It often begins with frustration or confusion — a sense that something is wrong without yet being able to name what. The researcher’s role at this stage is to create the conditions for questioning without directing its content. The problem-listing exercise and the mirror data work together: the participants’ own problems make the mirror data legible, and the mirror data gives structural weight to what would otherwise remain a list of complaints.

Stage 2 — Analysis: Present, Past, and Future

Analysis involves participants examining why the situation is as it is — tracing current tensions back to their historical and structural origins. This is the most intellectually demanding stage of the cycle, and it is where the activity system model earns its place as a practical thinking tool rather than a theoretical framework. When introduced at the right moment — after questioning has destabilised the assumption that current practice is simply normal — the model gives participants a vocabulary for naming what they are already noticing.

The sequence within the analysis stage matters. Begin with the present activity system — the one participants are living in now. Then move to the past system — what the activity looked like before the change that produced the current tensions. Only after both are clearly mapped does it become useful to look toward the future: what a different system might look like, and what it would require. This present → past → future sequence is not arbitrary. The present system is where participants’ frustration lives. The past system explains why the present feels wrong. The future system is where the energy released by that explanation can be directed. Compressing the sequence — jumping to the future before the past has been properly examined — produces proposals that are not grounded in structural understanding, and participants can feel that.

The two-triangle exercise — present and past systems mapped simultaneously — is the core analytical tool of this stage. It externalises the historical comparison that participants are already making internally, often without a language for it, and makes it available for collective scrutiny.

Two voices, two systems

In many workplace settings, this historical analysis is given particular texture by the presence of two distinct participant groups: those who remember the old system from experience — the “oldies”, to use an informal but useful term — and those who joined after the change and know only the current one. These two groups do not simply have different opinions about the present system. They carry different activity systems in their professional memory, and their accounts, placed alongside each other, construct the two triangles directly from participant knowledge.

Experienced staff can speak to what the tools were, what the rules demanded, how the division of labour was organised, and what the object of the activity felt like in practice. Newer staff can speak to the present system from the inside — often without the implicit comparison that longer-serving colleagues carry, but with a clarity about its current demands that those who knew the old system sometimes struggle to see freshly. Together, they build both triangles. The researcher’s role is to draw them out — to ask the questions that surface the comparison — and to represent what emerges in the two diagrams on the wall.

The analytical power of this moment should not be underestimated. When an experienced nurse describes how handover used to work and a newer colleague says “I didn’t know it used to be like that — that actually makes more sense,” something analytically significant has happened. The current system has been denaturalised. It is no longer simply how things are — it is one way of organising a system that was previously organised differently, and the comparison makes the structural choice visible. That visibility is the condition for everything that follows in the cycle.

Using the two triangles as a facilitation tool

The two-triangle approach works because it separates the historical comparison from the evaluative one. Participants are not being asked whether the old system was better. They are being asked what was different — a descriptive question, not a political one. Once the differences are named and diagrammed, the evaluative questions arise naturally from the comparison. The researcher does not need to prompt them. The structure of the activity system model, applied twice in parallel, does the analytical work.

A practical note on facilitation: allow the experienced staff to build the past triangle before newer staff comment on it. Allow newer staff to build the present triangle before experienced staff respond. The sequence matters. Premature commentary collapses the comparison before it can be fully constructed. Each group’s account deserves to be complete before it is placed in relation to the other.

Stage 3 — Modelling a New Solution

Modelling involves participants beginning to imagine an alternative — a different way of organising the activity that would resolve or reduce the contradictions they have identified. This stage is often tentative and partial. Participants do not produce a finished redesign of their system; they articulate a direction. The researcher’s role is to support the expression of that direction without steering it toward a predetermined conclusion. The model that emerges belongs to the participants. It is their theory of what a better system would look like.

Stage 4 — Examining and Testing the New Model

At this stage, the proposed model is subjected to scrutiny. Participants examine its implications, identify its limitations, and test it against known constraints. What would change? What would stay the same? Who would resist it, and why? What does it require that the current system does not have? This stage often produces the most analytically rich discussion, because it forces participants to articulate the structural features of the current system that their proposal would need to overcome.

Stages 5, 6, and 7 — Implementing, Reflecting, Consolidating

The later stages of the cycle move from analysis into action: new practices are tried in context (implementing), evaluated against the original contradictions (reflecting), and — if they work — stabilised into new forms of activity (consolidating). In many research contexts, including PhD studies, the researcher does not accompany participants through all of these stages. The timeline, access constraints, and scope of the study may mean that the research concludes during the modelling or examining phase. This is not a failure of the method. Reaching a point at which participants can articulate a grounded alternative, and at which the researcher can explain why the current system produces its contradictions, is a significant analytical achievement. Not every Change Laboratory produces a transformed activity system. All of them, done well, produce a deeper understanding of the one that exists.

Beyond the sessions: communicating findings to management

The expansive learning cycle does not end when the final session concludes. If the Change Laboratory has worked, participants leave Session 5 with a shared analysis of their situation and a set of modelled proposals for how it might be different. What happens to those proposals is not a separate, post-research question — it is part of the research design, and it needs to be planned from the start.

The participant with a route to management — identified during participant selection — is now the critical figure. Before the final session ends, the group should agree: what are the two or three concrete proposals we want to communicate? Who will communicate them, to whom, and in what form? The researcher’s role at this point is to support the preparation of that communication, not to make it on the group’s behalf. This is an important distinction. If the researcher presents findings to management directly, the Change Laboratory becomes a conventional consultancy exercise. If participants present their own analysis — using the language and the diagrams that emerged from the sessions — it is something qualitatively different: workers communicating a collectively produced understanding of their own activity system to the institution that governs it.

In practical terms, this means spending part of Session 5 preparing the communication. What are the key points? What evidence supports them? What is being asked of management — a decision, a resource, a conversation, a pilot? The group should produce something tangible: a one-page summary, a set of annotated diagrams, or a short presentation that the designated participant can take into their management conversation. The researcher can help draft or refine this, but the voice must be the participants’.

Testing, consolidating, and following up

Implementation is not guaranteed by the Change Laboratory process. Management may respond slowly, partially, or not at all. One or two proposals may be adopted; others may be declined; others may be absorbed into existing structures in a diluted form. This is normal and should be expected. What matters analytically is not whether the proposals are fully implemented, but what the response reveals about the activity system — about which contradictions the institution is willing to address and which it is structurally unable or unwilling to engage with.

Where implementation does occur — even partially — testing follows naturally. Does the change reduce the contradiction it was designed to address? Does it produce new tensions elsewhere in the system? Who benefits from it, and who does not? These questions are empirical, and if your research timeline allows, they are worth pursuing. A Change Laboratory that reaches implementation and produces data on its effects is a significantly stronger study than one that concludes at the modelling stage.

Consolidation — the point at which a new practice stabilises and becomes the new normal — typically takes months, sometimes longer. For most PhD studies, consolidation will not be observable within the research timeline. That is not a failure. It is an honest account of where the research ended, and what would need to happen for the cycle to complete. Your conclusions chapter can address this directly: what did the Change Laboratory produce, how far through the cycle did it progress, and what would the next stage require?

Follow-up sessions

If your research timeline and institutional access permit it, a follow-up session three to six months after the final Change Laboratory session is one of the most valuable things you can add to a PhD study. It serves three purposes: it allows you to observe what has changed and what has not in the activity system; it gives participants the opportunity to reflect on their own development through the process; and it produces data on whether the proposals generated in the modelling stage have moved toward implementation, stalled, or been absorbed into existing structures in modified form.

A follow-up session does not need to replicate the structure of the Change Laboratory sessions. It is closer to a structured reflection: what has changed since we last met? What has been attempted, what has been resisted, and what do we understand now that we did not understand then? Return to the problem list from Session 1 and ask participants to revisit it. Some problems will have been addressed; others will look different in the light of the analysis; others will be unchanged. The comparison between the Session 1 problem list and the follow-up reflection is a form of evidence about the impact of the process itself.

In a PhD context, a single follow-up session is realistic. Two follow-up sessions, at three and six months, is ideal but requires institutional access and participant willingness that not all settings can guarantee. Plan for one; design for the possibility of two; accept what your context makes possible.

Running the Change Laboratory online

Not every research context permits in-person sessions. Participants may be distributed across sites, shift patterns may make a common meeting time impossible to find, institutional access may be restricted, or — as the pandemic demonstrated — circumstances may change after the research has already begun. An online Change Laboratory is not a lesser version of an in-person one. It is a different version, with its own affordances and its own specific pitfalls. Understanding both before you begin is what makes the difference between an online Change Laboratory that works and one that produces thin data and frustrated participants.

Platform selection and setup

The platform matters less than how you use it, but some platforms are better suited to the Change Laboratory than others. The key requirements are: stable video with visible participant faces; a shared digital whiteboard that everyone can write on simultaneously; breakout room functionality for small-group work; and the ability to display and annotate documents together. The following platforms have all been used successfully in research contexts:

Setup procedures before the first session

Online sessions require more preparation than in-person ones, not less. The physical materials that would normally be on the table — printed triangles, sticky notes, marker pens — must be replaced by digital equivalents that participants can access and use without technical difficulty. This means testing everything in advance, not on the day.

Session protocols for online delivery

The protocols that make an in-person Change Laboratory work need to be explicitly re-established in an online environment, because the physical cues that normally reinforce them — everyone in the same room, materials visible, the researcher at the front — are absent.

• Cameras on by default — establish this as a norm from the first session, not as a rule but as an expectation: “It really helps the discussion if we can all see each other — can we keep cameras on where possible?” Participants who genuinely cannot keep their camera on (bandwidth, caring responsibilities in the background) should not be pressured. But a session where half the group is invisible significantly reduces the quality of discussion.
• Microphones muted except when speaking — reduces background noise that accumulates quickly in group video calls. Establish a hand-raise or thumbs-up convention for indicating you want to speak rather than the awkward interruptions that unmuted group calls produce.
• Name the facilitator and the scribe — in a large group, assigning one participant as scribe on the digital whiteboard frees others to speak without losing track of what is being said. Rotate the scribe role across sessions.
• Shorter sessions, same interval — ninety minutes in person becomes seventy-five minutes online. The cognitive load of video calls is higher than in-person conversation, and the analytical work of the Change Laboratory is already demanding. Do not try to compensate for reduced session time by packing more in. Do less, more carefully.
• Build in a structured break — for sessions over sixty minutes, a five-minute break at the halfway point is not a luxury. Participants who are on a work device, in a break room, or joining from home with other demands on their attention need to step away briefly. The break also serves as informal connection time, which replicates some of what refreshments provide in an in-person session.

Adapting the two-triangle exercise for online delivery

The two-triangle exercise is the most physically dependent element of the Change Laboratory and requires the most careful adaptation for online delivery. In person, participants stand around large paper on the wall, write directly on it, point to each other’s contributions, and physically organise sticky notes. Online, all of this must be replicated through a digital whiteboard, and the social dynamic that makes it work — the physical proximity, the shared material, the ability to reach across and add something — is more difficult to recreate.

The following approach has worked well in practice. Prepare two large triangle frames in Miro or Mural, labelled “Before” and “Now,” each with sticky note zones corresponding to the six system elements. Assign one colour of sticky note to experienced participants and another to newer ones. Ask participants to add their sticky notes simultaneously rather than sequentially — this preserves the energy of the in-person exercise and prevents the whiteboard from becoming dominated by whoever types fastest. After five minutes of simultaneous contribution, pause and read the board together: what patterns are visible? Where do the two triangles differ? Where do the differences produce tension?

Screen-share the whiteboard rather than asking participants to navigate to it themselves — this keeps the group’s attention on a single shared view. Use the pointer or annotation tools to draw attention to specific elements as you discuss them. At the end of the session, export the whiteboard as a PDF or image and share it with participants before the next session — this maintains continuity in a way that folded-away physical paper cannot.

Pitfalls of online Change Laboratory delivery

What online delivery cannot fully replicate

Honesty matters here. Some things that happen in an in-person Change Laboratory are genuinely difficult to replicate online, and your methodology chapter should acknowledge this if you ran sessions remotely.

The physical act of writing on large paper together — standing at a wall, reaching across each other, editing in real time — creates a kind of shared ownership of the emerging analysis that digital whiteboards approximate but do not fully reproduce. The informal moments — before and after the session, during refreshment breaks — are where participants process what they are noticing and sometimes say the most analytically significant things. The researcher’s ability to read the room — to notice a glance between two participants, to see who is leaning forward and who has pushed back in their chair — is significantly reduced on video. And the sense of collective presence in a shared physical space, which builds trust and candour over multiple sessions, is harder to establish online even when everything else is done well.

None of these limitations makes an online Change Laboratory invalid. They make it different, and that difference belongs in your analytical account of the method. If you ran sessions online, say so explicitly in your methodology chapter, describe the platform and tools you used, identify the limitations relative to in-person delivery, and explain how you attempted to mitigate them. That is not a weakness in your study. It is methodological transparency.

The researcher’s role: inside and outside simultaneously

The Change Laboratory places the researcher in an unusual position. You are simultaneously the analyst who has constructed the activity system model and identified the contradictions, and the facilitator who must not impose that analysis on participants. The mirror data you choose reflects your analytical judgements. The vocabulary you introduce shapes what participants can say. The questions you ask — and the ones you withhold — direct the conversation in ways that your reflexivity account must acknowledge. This dual position is not a problem to be resolved; it is a condition to be worked with honestly.

How many sessions? Setting a minimum

This is one of the most commonly underestimated aspects of Change Laboratory design, and it is worth addressing directly. Three sessions is not enough. It may feel like a manageable commitment for participants and a realistic scope for a PhD study, but it is insufficient to move through the cycle with genuine analytical depth. What typically happens in a three-session design is that Session 1 triggers Questioning, Session 2 reaches early Analysis, and Session 3 attempts Modelling before participants are ready for it. The result is a cycle that has been compressed rather than completed, and findings that reflect the limitations of the design rather than the complexity of the system.

The minimum for a modified Change Laboratory that can credibly claim to have engaged with the expansive learning cycle is five sessions. This is not an arbitrary figure. It reflects what each stage of the cycle actually requires:

• Session 1 — Introducing the mirror. Participants encounter the mirror data. Questioning begins. The session ends before it is resolved — participants need time to sit with the discomfort of what they have seen before they can analyse it.
• Session 2 — Historical analysis. The two-triangle exercise. Experienced and newer participants construct the past and present systems together. This session should not be rushed into the same meeting as Session 1. The gap between sessions — typically one to two weeks — allows participants to notice things in their daily work they would not have noticed before. Those observations are what Session 2 draws on.
• Session 3 — Deepening the analysis. The contradictions identified in Session 2 are named, evidenced, and interrogated. Participants are asked to bring examples from their own practice since the last session. The activity system model is used as a shared analytical vocabulary. This session moves from description toward structural explanation.
• Session 4 — Modelling. Only now, with a shared analytical vocabulary and a grounded understanding of the contradictions, are participants ready to propose alternative models of activity. The ideas that emerge at this stage are more structurally informed than anything that could have been produced in Session 3. They need to be.
• Session 5 — Examining and consolidating. The proposed models are tested against the known constraints of the system. Participants identify what would need to change for the models to work, who would need to be involved, and what the obstacles are. The session closes with a return to the mirror data from Session 1 — asking whether the group’s understanding of the situation has changed, and how.

For a full Change Laboratory aimed at reaching Implementation and Consolidation, six to eight sessions is a more realistic target, typically spread over three to four months. This allows participants to attempt new practices between sessions and to return with evidence of what changed and what resisted change.

The interval between sessions matters as much as the number of sessions. A week to ten days between sessions gives participants time to return to their work with new eyes, notice things they would previously have explained away, and arrive at the next session with fresh observations. Sessions held in rapid succession — say, within days of each other — compress the cycle in a way that defeats its purpose. The change laboratory works precisely because it is interwoven with the activity it is examining.

Practical logistics: what you actually need

The Change Laboratory is sometimes described in the literature in terms that make it sound more clinical and resource-intensive than it needs to be. In practice, a well-run session requires modest but specific physical provisions. Getting these right matters: a cramped room, no refreshments, and nowhere to put large paper will undermine a session before it begins. What follows is what Amara used, and what we recommend as a working baseline.

The room

The room should be separate from the normal working environment of participants — not the ward, not the office where line managers are present, not a space where participants feel observed by their institution. This matters for the quality of discussion. Participants need to feel that what they say in the room stays in the room, at least until they have decided together how it should be used. A meeting room, seminar room, or even a community space away from the site is preferable to a breakout area within the workplace.

The room needs to be large enough for participants to sit around a table together and to have wall space — or a floor space — for large paper. If wall space is not available, a long table or the floor works. What does not work is a room where everyone is seated in rows facing a screen. The Change Laboratory is a discussion, not a presentation. The physical arrangement should reflect that: circular or horseshoe seating, everyone able to see each other, the research materials visible and accessible to everyone in the room.

For five sessions with six to eight participants, the room should accommodate the group comfortably without feeling institutional. Community rooms, library seminar rooms, and hospital education centres have all worked well in practice. University seminar rooms, booked outside teaching hours, are usually adequate and often free to researchers.

Materials checklist

The following materials are needed across the five-session cycle. Some are used once; most recur throughout.

Refreshments and the social dimension

This may seem like a minor detail. It is not. Participants are giving you their time, often outside working hours or during a break. Tea, coffee, and something to eat are not a luxury — they are an acknowledgement of that generosity, and they materially affect the quality of discussion. A session that begins with participants waiting for a kettle to boil and helping themselves to biscuits is a session that begins with informal conversation, which loosens the room before the formal work begins. A session that begins with participants sitting in silence in a cold meeting room is a session that starts ten minutes late in atmosphere even if it starts on time on the clock.

Budget for refreshments at every session. In a UK context, a working figure is £5–10 per person per session for tea, coffee, milk, and a modest selection of biscuits or snacks. For five sessions with six participants, this is roughly £150–300 for the full Change Laboratory cycle. This should be costed into your research budget from the start and, where possible, covered by your institution rather than paid personally. Many universities have small research expenses funds that cover exactly this kind of cost; your supervisor or research administrator can advise.

Where institutional funding is not available, honest conversation with participants is better than cutting corners. In Amara’s study, the ward education budget covered refreshments for two sessions after the ward manager, who had heard about the first session from nursing staff, asked to be kept informed and offered practical support. That outcome was not planned — it emerged from the Change Laboratory process itself.

Participant time and institutional access

Securing five sessions with the same group of participants, over two to three months, requires more planning than three sessions. Be honest with participants and gatekeepers at the outset about what you are asking. A five-session commitment of ninety minutes each is seven and a half hours of participant time. That is significant, and asking for it requires a clear explanation of what participants will get from the process — not just what the research will produce.

The most effective framing is not “this will help my research” but “this is a structured process in which you will examine and analyse your own working situation, with support. The findings will be shared with you, and you will have the opportunity to contribute to how they are written up and used.” That framing is also, in a CHAT sense, more accurate: the Change Laboratory is as much for participants as it is for the researcher.

Schedule sessions at the same time and day each cycle where possible — this reduces the cognitive load of coordination and makes it easier for participants to protect the time. Early morning sessions (before shift changes), lunchtime sessions, or immediately after a shift ends tend to work better in healthcare settings than sessions in the middle of a working day. Ask participants what works for them in the scoping conversation, before booking anything.

Using a modified Change Laboratory in a PhD study

The full Change Laboratory as described by Engeström and Virkkunen involves multiple sessions over an extended period, often with institutional support, a dedicated physical space, and a research team. Many PhD researchers, working within time constraints and with limited institutional access, cannot implement the full model. A modified version is legitimate — but it must be modified thoughtfully, not minimally.

The minimum credible modified design for a PhD study is five sessions, as outlined above, spread over at least two to three months. Anything fewer than five sessions should be described in your methodology chapter honestly: as structured reflection sessions that engaged with the early stages of the expansive learning cycle, rather than as a Change Laboratory. That distinction matters, and examiners will notice if it is blurred.

What you cannot do — and what this handbook will not encourage — is run two or three sessions, call them a Change Laboratory, and claim to have traced the full expansive learning cycle. That is a misrepresentation of the method. It is also, practically, a weaker study than one that is honest about the scope of its intervention and rigorous about what that scope can and cannot support analytically.

You cannot introduce the activity system model to participants in a Change Laboratory session until they have already begun questioning their current practice. If you introduce the theoretical framework before participants are ready to use it as an analytical tool — before the mirror data has done its work — it will be received as a lecture rather than a thinking aid. Sequence matters. Mirror data first. Theory when the need for it has been felt.

The Change Laboratory is most powerful not when it produces a new system, but when it produces participants who can analyse the one they are in — and a researcher who has witnessed that analysis taking place.

1. Define your system boundary — identify subject(s), clarify the object, set boundaries. Refine as research develops.
2. Collect mirror data — observations, excerpts, documents. Select material showing breakdowns or contradictions in action.
3. Initial system mapping — map all six elements. This first model is provisional and will change.
4. Identify contradictions — use data to surface tensions within and between system elements. Ground each one in evidence.
5. Expand the analysis — compare planned vs actual systems; examine past, present, and future activity.
6. Work with participants (if applicable) — introduce mirror data, facilitate Change Laboratory or reflection sessions.
7. Trace system development — ask historical questions. Situate present findings within a trajectory of system change.
8. Write your analysis — organise sections around contradictions: tension → data → relationship → system link.
9. Refine your argument — revisit system boundaries, focus on key contradictions, remove material that does not serve the argument.
10. Prepare for the viva — clarify unit of analysis, write your elevator pitch, practise responses.
11. Final review — check clarity of system definition, consistency of terms, alignment between diagrams and writing.

Think of this workflow not as a checklist to complete but as a map of the research arc. Each stage builds on the one before it, and each produces something concrete — a named system boundary, a set of coded transcripts, an evidenced contradiction, a draft findings section — that the next stage depends on. The stages that feel most uncertain — identifying contradictions, tracing historical development, restructuring the findings chapter — are the stages where the most significant analytical work is done. Do not rush them.

This workflow is not strictly linear. Returning to earlier steps and revising your system model is a normal part of the process — not a sign of difficulty, but a sign of deepening analysis.

You cannot move to the next stage of this workflow until the current stage has produced something you can point to: a named system boundary, a provisional map, a named contradiction with evidence, a structured comparison of past and present systems. If you cannot produce the output, the stage is not complete. Return to it before moving forward.

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A CHAT thesis is built iteratively — the system you submit will look very different from the one you first sketched, and that development is the evidence of your analytical work.