Potential for Business Development
Our thoughts shape how we feel, how we feel affects how we behave, and how we behave ultimately dictates how we perform.
But what's fundamentally wrong with this statement?
Here, firstly, there's no account for the wider social contexts that individuals' behaviours always take place in. In my recent blog, Networks that Shape Behaviour, I unpacked the webs of wider social-ecological levels that affect how we behave and perform.
Secondly, the only factor affecting our behaviour and ultimately performance here is our thoughts, while in reality, behaviours are underpinned by a range of so-called behavioural determinants (such as skills, behavioural regulation, attention, social influences, etc.). I've outlined the basics for understanding this phenomenon in my previous blog, Behaviour Change Pathways to Improving Business Outcomes.
In Matrices of Change: Making Organisational Change Management Visible, I then explored how desired outcomes can be translated into specific change objectives, behaviours, and the determinants that need to shift.
If behavioural determinants and networks are fundamental to shaping behaviours and outcomes, we are left with the question: which behavioural determinants should we target, and in whom?
To explore this, I use a hypothetical case study of advanced AI adoption in an upper mid-market organisation. For simplicity, we examine three stakeholder levels: leadership, managers and employees. The same logic could just as easily be applied in a smaller sports organisation or team, where the relevant layers might instead be club management, coaching staff and players. And adapt to many more layers.
Suppose our earlier behavioural diagnosis suggests that environmental opportunity, outcome expectations and perceived behavioural control, etc., are among the key determinants influencing AI adoption within the organisation. We then distribute a survey across the three stakeholder groups to assess where people currently sit on these determinants. Heavily simplified, the results might look something like this, with mean scores presented for each determinant and stakeholder group.
Behavioural Determinant | Leadership N=28 (Mean) | Managers N=64 (Mean) | Employees N=212 (Mean) |
Intention | 3.1 | 4.2 | 5.3 |
Perceived behavioural control | 3.4 | 4.4 | 5.6 |
Descriptive norms | 3.0 | 4.1 | 5.4 |
Injunctive norms | 3.2 | 4.3 | 5.5 |
Outcome expectations | 3.6 | 4.6 | 5.3 |
Autonomous motivation | 2.9 | 4.2 | 5.4 |
Controlled motivation | 3.2 | 4.4 | 5.5 |
Environmental opportunity | 2.8 | 4.8 | 6.0 |
Coping planning | 3.1 | 4.5 | 5.7 |
Behaviour | 3.0 | 5.0 | 6.2 |
When visualised (see Figure 1), differences between the three stakeholder groups become apparent. Leadership, managers and employees do not necessarily start from the same position across the behavioural determinants, meaning that the potential and barriers for change may differ substantially across organisational levels. See Keegan Knittle et al. (2019, September).
We assess these differences for each determinant using, for example, a one-way ANOVA, followed by appropriate post-hoc comparisons to identify where differences between stakeholder groups lie (Field, 2018).
Again, I revisit the EHPS 26 conference keynote by Stadler (2026, September): Looking at group means alone can hide substantial heterogeneity within each stakeholder level. Leadership, managers and employees are not homogeneous groups, meaning that individuals within each layer may differ considerably in their intentions, perceived control, social norms, motivation, opportunity and planning. Visualising the full distributions helps us see this variation more clearly (see Figure 3). And may be statistically modelled.
This is where a person-centred approach becomes particularly useful (Howard & Hoffman, 2018). Rather than assuming that organisational role determines readiness for change, methods such as cluster analysis or latent profile analysis may be used to identify naturally occurring subgroups of individuals who share similar determinant profiles across these dimensions.
If we treated managers as a single homogeneous group, we might prescribe the same intervention to both. In reality, manager A may benefit from changes to workflow and opportunity, while manager B may need capability-building, modelling and stronger social support.
Profiles may be organised through Rogers’ Diffusion of Innovations (1962), which offers a useful way of interpreting these differences (see Figure 4). Some people experiment with a new behaviour early, others wait until they see evidence that it works, while others may require stronger social proof, organisational support or clearer incentives before adopting it. Mapping person-centred behavioural determinant profiles onto Rogers’ adoption categories therefore gives us a practical lens for thinking about how readiness for change may differ across people and stakeholder groups over time.
Taken together, this approach moves organisational change away from generic assumptions about what people need and towards a more data-driven understanding of where potential for business change actually sits. By combining behavioural determinant profiling, stakeholder-level analysis, within-group heterogeneity and person-centred profiles, we can begin to see not only whether groups differ, but why they differ and what kinds of support may be most useful.
At Lasse Eini Co., we help organisations identify the behaviours, behavioural determinants, stakeholders and systems that shape performance and lead to sustainable change. Our work can include behavioural diagnosis, stakeholder mapping, survey design, determinant profiling, network analysis and the development of evidence-based interventions tailored to different groups within an organisation. Rather than delivering generic training, we aim to understand where the real barriers and opportunities sit, so that change efforts can be targeted where they are most likely to create meaningful and lasting value.
Get in touch to learn how this may look for your team — Book here.
References
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). SAGE
Publications.
Howard, M. C., & Hoffman, M. E. (2018). Variable-centered, person-centered, and person-specific approaches: Where theory meets the method. Organizational Research Methods, 21(4), 846–876. https://doi.org/10.1177/1094428117744021
Knittle, K., Peters, G.-J. Y., Heino, M., Tobias, R., & Hankonen, N. (2019, September). Potential for change (PΔ): New metrics for tailoring and predicting response to behavior change interventions [Conference presentation]. 33rd Annual Conference of the European Health Psychology Society, Dubrovnik, Croatia.
Rogers, E. M. (1962). Diffusion of innovations. Free Press of Glencoe.
Stadler, G. (2026, September). Understanding heterogeneity in health psychology [Keynote address]. 40th Annual Conference of the European Health Psychology Society, Pafos, Cyprus.
AI disclosure: Generative AI was used to support the visualisation of selected concepts presented in this article. The underlying ideas, interpretation, and final content are the author’s own.
Data disclosure. Values presented in this blog are illustrative and are used solely to demonstrate how behavioural determinant profiling could be applied within an organisation. They do not represent data collected from a specific organisation.
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