Introduction – From Measurement to Prediction

Change initiatives often rely on the assumption that behavioural alignment can be secured through communication, leadership engagement, and formalised rollout structures. Resistance, when it arises, is typically addressed reactively — once it has become visible. This approach underestimates the temporal and situational variability of behavioural readiness.

Most assessments treat readiness as a static condition, measured through declarative surveys or general attitudinal metrics. However, these instruments offer limited diagnostic value. They are retrospective, coarse-grained, and often disconnected from the actual behavioural thresholds that determine whether individuals adapt, delay, or disengage.

Predictive behavioural analytics applies a different logic. It treats readiness not as a sentiment to be measured, but as a behavioural probability to be modelled. This perspective enables the identification of latent resistance patterns, emerging drift, and differential responsiveness to interventions—before these factors become operationally consequential.

This article presents five predictive models that support such anticipatory diagnostics. Each model targets a distinct aspect of behavioural readiness: from early resistance tendencies to time-based fatigue, conditional responsiveness, structural complexity, and uncertainty. Their combined application supports a shift from generalised planning to precision-guided intervention.

Redefining Change Readiness as a Predictive Concept

Change readiness is often defined in psychological terms: as a combination of beliefs, affective orientation, and motivation. Survey instruments ask whether individuals understand the intended change, whether they support it, and whether they feel equipped to contribute. These indicators describe an attitude. They do not account for the conditions under which people act — or refrain from acting.

For predictive purposes, a different understanding is required. Readiness refers here to the likelihood that a specific behavioural adjustment will occur under defined circumstances. This likelihood is not stable. It varies across organisational roles, team environments, and phases of implementation. It also fluctuates over time, depending on workload, perceived risk, and operational context.

Declared support at the outset does not ensure continuity. Conversely, employees may comply with behavioural expectations without voicing approval. Observable action cannot be inferred from declared intent. What matters is not how individuals position themselves verbally, but whether their behaviour aligns with what the situation requires.

Readiness, in this sense, is neither a general trait nor a stable opinion. It is a conditional behavioural disposition — situated, variable, and measurable only through its relationship to context and role-specific demands.

The Five Predictive Models

Behavioural readiness cannot be measured directly. It must be inferred — through models that estimate the likelihood of action under specific conditions. This likelihood is not fixed. It varies across roles, team settings, and organisational phases. It also shifts over time in response to workload, uncertainty, and perceived loss.

Declared support at the outset does not guarantee continuity. Nor does verbal alignment reliably indicate behavioural compliance. Readiness, for diagnostic purposes, must be understood as a conditional behavioural disposition: one that becomes analytically accessible only through its relationship to context, time, and role.

The five models presented here provide different ways of estimating this probability. Each captures a distinct aspect of behavioural readiness that cannot be identified through declarative instruments alone. Logistic regression detects early indicators of likely resistance. Time-series classification traces behavioural drift. Uplift modelling isolates the differential effect of interventions. Random forests identify interaction effects across multiple variables. Bayesian networks model conditional logic under uncertainty.

These models are not interchangeable. They address different diagnostic questions and operate under different assumptions. Their usefulness depends on the structure of available data, the phase of the change process, and the behavioural phenomena to be examined.

The following sections describe each model in turn — its purpose, technical logic, and the conditions under which its application is appropriate.

Logistic Regression

Logistic regression remains one of the most effective tools for identifying early behavioural risk in organisational settings. While its statistical foundation is simple, its diagnostic utility lies in the way it transforms qualitative hypotheses about behavioural resistance into quantitative likelihoods.

In the context of change management, logistic regression can be used to estimate the probability that individuals within specific roles or units will exhibit resistance to a planned intervention. To apply the model, a clearly defined binary outcome must be specified—such as engagement versus non-engagement, or acceptance versus refusal — together with a structured set of input variables. These variables may include prior participation in change processes, role tenure, proximity to decision making, workload exposure, or behavioural patterns observed in earlier phases of implementation.

What makes logistic regression particularly valuable is its interpretability. The relationship between each predictor and the outcome is expressed in terms of odds ratios, which allows decision makers to assess which factors contribute most to the likelihood of resistance. This makes the model suitable not only for data teams, but also for change leads who must communicate risk exposure to stakeholders or intervene early based on targeted indicators.

Data requirements

Logistic regression performs well under typical organisational data conditions. It does not require large volumes of data, but it does require inputs to be clearly structured and logically defined. Predictor variables must be either categorical (e.g. business unit) or numerically encoded (e.g. tenure in months), and the outcome variable must be binary and consistently recorded. Datasets drawn from previous change projects, workforce metadata, and basic HR or workflow systems are often sufficient. The model is sensitive to variable overlap and missing values, so data preparation remains essential. For organisations at an early or intermediate level of data maturity, logistic regression often provides the most accessible point of entry into predictive behavioural diagnostics.

In practice, logistic regression is best applied in the preparatory phase of change, where structured data is available and the primary goal is to detect behavioural segments with elevated risk profiles. It supports pre-emptive intervention strategies by making resistance patterns visible before they become operationally consequential.

While its assumptions limit its use in highly complex or non-linear environments, logistic regression offers a robust, transparent foundation for building an initial predictive readiness profile—especially when simplicity, speed, and communicability are essential.

Time-Series Classification

Change processes unfold over time, and so do behavioural responses. Initial engagement may decline gradually, even if no explicit resistance is voiced. Time-series classification models are designed to detect such patterns of behavioural drift—sequential changes that suggest fatigue, loss of clarity, or disconnection from the intervention’s intended direction.

Unlike static models, time-series classifiers treat behaviour not as a single observation, but as a sequence. These models classify sequences of data points into categories—such as stable engagement, increasing avoidance, or fluctuating compliance. Depending on the method used (e.g. LSTM, 1D convolutional networks, or classical techniques such as Dynamic Time Warping), the model learns to recognise subtle progression types that may precede disengagement or erosion of support.

In change diagnostics, this approach is particularly valuable when working with time-stamped behavioural data: participation over successive workshops, login activity on collaborative platforms, feedback response latency, or task completion patterns. These indicators rarely show sharp breaks. Drift is typically incremental, distributed, and easily missed in aggregate measures.

Data requirements

Time-series models require longitudinal, structured behavioural data—either from digital systems (learning platforms, workflow tools, communication logs) or from consistently logged interactions over time. Data must include a timestamp and be linked to identifiable behavioural actions (e.g. submissions, contributions, decisions, delays). The model’s utility increases with the frequency, regularity, and contextual richness of the sequences. For organisations with moderate to advanced data infrastructure, especially those with digital learning environments or enterprise collaboration systems, time-series classification offers significant diagnostic advantage. For those without such infrastructures, implementation is possible but more constrained and typically requires data aggregation over defined intervals.

Time-series classification is best applied in the active or rollout phase of change. It helps identify critical inflection points where attention wanes, participation drops, or message salience fades—long before formal feedback mechanisms reflect these shifts. Where logistic regression provides an early structural snapshot, time-series modelling adds temporal resolution and enables micro-interventions based on behavioural trajectory.

As a method, it demands more in terms of data structure and interpretation. But where applicable, it transforms change monitoring from retrospective reporting into real-time behavioural signal detection.

Uplift Modelling

Change interventions are rarely neutral in their effects. The same message, incentive or prompt may increase engagement in one group while having no measurable impact — or even the opposite effect — in another. Uplift modelling is designed to capture precisely this variance. It estimates the net behavioural effect of an intervention by comparing the probability of a given outcome with and without that intervention across different segments.

In contrast to conventional predictive models, which estimate the likelihood of an outcome regardless of intervention, uplift models isolate causal differences. They focus on the incremental effect — the behavioural change attributable to the intervention itself. This distinction is critical in environments where change resources are limited, and interventions must be targeted with maximum strategic precision.

Applied to change management, uplift modelling supports the identification of influenceable segments: individuals or groups whose behaviour is likely to shift positively as result of specific measures — such as tailored communication, local leadership engagement, or role-specific incentives. It also identifies groups with neutral or negative response patterns, enabling organisations to avoid unnecessary interventions or misallocation of attention.

Data requirements

Uplift modelling requires structured data that includes a clearly defined intervention flag (i.e. who was exposed to which message, tool, or change measure) and a binary behavioural outcome (e.g. participation, acceptance, or task execution). The model is most effective when based on experimental or quasi-experimental designs, such as A/B testing, staggered rollouts, or naturally occurring control–treatment divisions. Segment-level predictors can include demographic attributes, prior behavioural signals, or attitudinal baselines. The method is data-intensive and sensitive to imbalances between treatment groups, making it best suited to organisations with sufficient scale and analytical maturity to run comparative interventions under controlled or well-tracked conditions.

Uplift modelling is most valuable during the intervention phase, where decisions must be made about targeting, personalisation, and resource allocation. While logistic regression identifies likelihoods of resistance or acceptance in general, uplift modelling clarifies where interventions make a difference — and where they do not.

Its strength lies in distinguishing between likely responders and likely non-responders. This makes it a critical tool for designing scalable, evidence-based interventions that minimise waste and maximise behavioural return. Where time-series models identify erosion, uplift models help reverse it — by focusing action where it has the greatest behavioural leverage.

Random Forests

Organisations rarely display uniform behavioural patterns during change. Readiness can vary across locations, departments, leadership contexts, or legacy systems — even when the formal intervention is identical. In such settings, the interaction of multiple variables shapes behavioural response in ways that are often opaque to linear or single-factor models. Random forests are designed to detect these interactions. They aggregate multiple decision trees to identify how combinations of variables contribute to behavioural outcomes.

Unlike logistic regression, which estimates the marginal effect of each predictor independently, random forests examine how variables interact in practice. For example, tenure may increase the probability of compliance in one functional group but decrease it in another, depending on workload, role complexity, or leadership proximity. Random forests are particularly suited to such conditional patterns, where readiness is not determined by any single factor but emerges from a structural constellation.

In change diagnostics, this allows for nuanced profiling across diverse organisational units. The model can uncover previously unrecognised subgroups, for instance, project teams that comply when workload is low but resist under deadline pressure; or remote employees who engage more actively when line managers are present in key coordination meetings.

Data requirements

Random forests require structured, high-dimensional data with multiple predictors per individual or unit. Inputs may include demographic variables, role attributes, behavioural history, interaction counts, and contextual metadata. Unlike uplift modelling, no experimental design is needed. The model tolerates noise, handles missing values relatively well, and does not require prior knowledge of which variables are most relevant. However, interpretability is reduced compared to simpler models: although variable importance scores can be derived, the model does not provide clear coefficient-based explanations. For organisations with access to broad behavioural or HR-related datasets, but without formal experimentation or sequencing logic, random forests offer a robust intermediate solution for identifying readiness patterns.

This method is best applied in the exploratory or portfolio phase of change, where interventions affect a large and diverse population. It enables the identification of distributed risks, conditional behavioural clusters, and variable-specific patterns that guide segmentation, scenario planning, or the configuration of differentiated change tracks.

Where uplift models focus on causality and time-series models capture development, random forests reveal structural complexity. They help navigate the heterogeneity of behavioural response, especially in organisations where readiness is unevenly distributed and not well understood.

Bayesian Networks

In many change settings, behavioural responses do not follow direct lines of influence. Employees act not only based on role or instruction, but in relation to what others do, what they expect from leadership, and how they interpret local conditions. In such environments, readiness emerges from conditional dependencies, behavioural patterns that unfold under uncertainty and in interaction with other behavioural signals. Bayesian networks are designed to model such dependencies.

Technically, a Bayesian network is a probabilistic graphical model. It represents variables (such as role clarity, managerial consistency, perceived credibility, or prior success with change) as nodes in a graph, and the probabilistic dependencies between them as directional links. The network can then be used to compute how changes in one factor, such as communication timing or leadership visibility, affect the likelihood of a specific behavioural outcome, given the state of the surrounding conditions.

In change diagnostics, this makes Bayesian networks particularly useful in scenarios marked by distributed accountability, ambiguous authority, or interdependent roles. For example, in matrix organisations, whether an employee adapts to a new process may depend not only on task clarity, but on simultaneous signals from multiple reporting lines. In policy-driven change environments, readiness may hinge on the perceived alignment between formal governance and local practice. The model can be used to simulate how different configurations of conditions influence behavioural probabilities, thereby informing intervention design.

Data requirements

Bayesian networks require structured input variables that can be discretised into categorical states. While they do not demand large data volumes, they do require conceptual clarity: variables must be well-defined, and the relationships between them must be statistically identifiable or theoretically justifiable. The model can incorporate both observed data and expert priors, allowing for a combination of empirical evidence and domain knowledge. Bayesian networks are computationally more complex than other models and typically require advanced statistical support for parameter learning, structure discovery, and scenario simulation. They are best suited to organisations with high analytical maturity or access to specialised modelling resources.

Bayesian networks are most valuable in the design phase of complex change programmes, where multiple factors interact and intervention timing or messaging cannot be standardised. They are not diagnostic tools in a narrow sense, but architectural models: they help decision-makers understand which behavioural conditions must be aligned for readiness to emerge, and how sensitive behavioural outcomes are to changes in system structure.

Where random forests uncover unknown interaction effects, Bayesian networks make those interactions explainable. They provide a model of behavioural logic under uncertainty—allowing organisations to design interventions that are not only targeted, but condition-sensitive.

Why these Five Models?

Predictive behavioural analytics in change management must meet two conflicting requirements: it must accommodate the complexity of behavioural response, and it must remain interpretable enough to inform intervention. The five models presented here were not selected based on computational power or novelty. They were chosen because they reflect distinct diagnostic perspectives, each aligned to a specific behavioural dynamic that is critical in organisational transformation.

Logistic regression estimates resistance likelihood under clearly defined conditions. It is transparent, robust, and ideal where data maturity is limited but structural indicators are available. Time-series classification introduces temporal sensitivity, allowing organisations to detect early signs of drift in behavioural engagement. Uplift modelling isolates causal impact, identifying which interventions change behaviour—and in which segments. Random forests capture conditional heterogeneity, surfacing interaction effects across distributed readiness structures. Bayesian networks model interdependence under uncertainty, offering a logic map of behavioural dependencies in complex systems.

No single model suffices. Each addresses a type of behavioural uncertainty that is common in large-scale change:

• where action diverges from intention,
• where behaviour erodes gradually,
• where response to intervention varies,
• where readiness patterns resist simple explanation,
• and where causal pathways are conditional, not linear.

Together, the five models form a methodological architecture. Their value lies not in comparison, but in complementarity. They reflect a progression—not of sophistication per se, but of diagnostic perspective: from static probability to temporal progression, from general prediction to causal segmentation, from variable mapping to structural modelling.

For organisations navigating strategic change, the challenge is not simply to predict behaviour. It is to understand which models correspond to which questions, at which point in the change process, under which data conditions. The selection of models must follow the logic of readiness itself: conditional, context-sensitive, and distributed across time.

Selecting the Right Predictive Model

No predictive model is universally applicable. Each of the five approaches introduced above responds to a distinct type of behavioural uncertainty and requires different data conditions, levels of organisational maturity, and diagnostic intent. The selection of the appropriate model should therefore follow a clear logic: What behavioural pattern is to be anticipated? At what stage of the change process? And under which data constraints?

Logistic regression offers a transparent and accessible entry point. It is suited to early-phase diagnostics, where the aim is to identify segments with elevated risk of resistance or disengagement based on structural indicators. It is particularly effective when data is limited but well-structured, and where explanatory clarity is essential for internal communication.

Time-series classification addresses a different diagnostic need. It enables organisations to observe gradual drift in behavioural engagement during the rollout or active intervention phase. The model relies on timestamped sequences and is best deployed where digital behavioural traces are available and systematically logged.

Uplift modelling is most valuable when interventions are already underway, and the question is not who will comply, but who responds to what. It isolates the causal effect of change measures and supports targeted deployment of limited resources. This method assumes a higher level of data planning and experimental design capability.

Random forests help navigate complexity in organisations where readiness is uneven and shaped by multiple, interacting factors. The model detects non-linear patterns and interaction effects but is less interpretable than simpler approaches. It is well suited for exploratory diagnostics across large or diverse populations, particularly when the underlying structure of resistance or adaptation is unclear.

Bayesian networks serve a different function. They are not designed for prediction in the narrow sense, but for modelling conditional behavioural logic under uncertainty. They help clarify which variables must align to make readiness possible—and what happens when alignment fails. Their use requires conceptual clarity, modelling capacity, and a willingness to engage with indirect causality and system-level interaction.

The table below summarises these distinctions. It does not offer a hierarchy. It provides a decision structure: Which model answers which kind of question, with what kind of data, and for what diagnostic purpose?

The Five Predictive Models

Model	Diagnostic Purpose	Best Applied When	Required Data	Organi­sational Readiness Level
Logistic Regression	Likelihood of resistance or non-compliance.	Early-phase audits and risk segmentation.	Structured, categorical or numeric inputs; binary outcome.	Basic to moderate data maturity.
Time-Series Classification	Gradual behavioural drift over time.	During rollout and live-phase monitoring.	Timestamped behavioural sequences from digital systems.	Moderate to advanced.
Uplift Modelling	Segments responsive to specific interventions.	When tailoring communi­­cations, nudges or incentive design.	Experimental or quasi-experimental data; treatment–control logic.	Advanced; intervention-aware environments.
Random Forests	Complex interaction effects across diverse populations.	In hetero­geneous settings with unclear readiness patterns.	Wide structured datasets with multiple variables and moderate noise.	Intermediate to advanced.
Bayesian Networks	Conditional behavioural logic under uncertainty.	In complex, inter­­dependent or ambiguous change environments.	Well-defined variables; partial data; expert priors.	High analytical maturity; conceptual modelling capability.

From Prediction to Design

Prediction is not the endpoint. In change management, the purpose of behavioural modelling is not to categorise individuals or predict resistance for its own sake. The purpose is to design interventions that take behavioural probability seriously — by aligning timing, messaging, framing, and resource allocation to what the data indicates is likely to succeed.

Each model described above contributes to this design logic in a different way. Logistic regression supports early segmentation, enabling pre-emptive targeting before visible resistance forms. Time-series classification highlights inflection points during rollout, informing when reinforcement, recalibration, or reduction is required. Uplift modelling identifies which groups are likely to change their behaviour in response to specific measures (e.g. incentives) — guiding not just who to target, but with what. Random forests uncover structural patterns in readiness distribution, informing the configuration of parallel tracks, differential pacing, or layered intervention. Bayesian networks expose where behavioural dependencies must be resolved for change to gain traction, making it possible to simulate the effect of alignment or misalignment across conditions.

The implications are operational. Predictive outputs can shape:

• the framing logic of communication,
• the granularity of segmentation,
• the phasing of interventions,
• the escalation thresholds for managerial support,
• and the sequencing of change elements in complex environments.

Designing for readiness means using predictive insight to reframe what intervention is: not a one-size-fits-all rollout, but a context-sensitive response to anticipated behaviour. The models do not dictate action. They create the conditions for targeted design.

Conclusion

Organisational change is shaped by behaviour. Yet in many cases, behavioural response remains insufficiently understood at the point where decisions are made. Readiness is assumed rather than examined. Resistance becomes visible only when it affects implementation. Interventions are planned in general terms, not in response to specific behavioural probabilities.

Predictive behavioural modelling offers a different starting point. It does not replace judgement or experience. But it introduces a structure for assessing where behavioural risks are likely to occur, and under which conditions alignment is probable. Each model presented in this article contributes a specific diagnostic lens. Used appropriately, they make behavioural response less opaque and design decisions more situationally grounded.

Readiness is not a given. It must be examined in relation to role, timing, context, and exposure. Predictive modelling allows this examination to take place before interventions are deployed, and not only once behavioural dynamics have taken hold.