Good morning!

Before we begin, thank you to everyone who shared their feedback on the generative ai prompts included in this weekly newsletter. Most of you like the prompts—and some of you are even using them weekly! 🤓

This week's featured article builds on the Theory of Planned Behaviour (ToPB) introduced in last week’s edition, with a view on how the model applies to financial behaviours like borrowing, spending, and investing.

The goal of this edition is to show how base theoretical models can be moulded for your benefit and provide greater clarity on strategic change and product design initiatives.

The findings

🧬 The structural integrity of the original ToPB model remains true in the case of predicting financial behaviours.

✏️ Financial knowledge has a strong influence on attitudes toward the future, perceived norms, and perceived ease (or behavioural control) of financial behaviours, with knowledge most strongly predicting how easy the behaviour is thought to be.

👀 Perceived ease is the strongest predictor of financial behaviour—even more so than intentions—further reaffirming the well established role of friction (even if just perceived) in behaviour change.

You can find the ToPB model adaptation for Financial Behaviour below 👇🏻

Why it matters for you

• First principles in strategic decision making are only as strong as their alignment with the circumstances on the ground. Behavioural models can aid in clarity of trade-offs and prioritization, but enriching those models with enterprise data will create a resonate guiding framework for driving outcomes in your business. Should you focus on re-framing subjective norms or put your energy behind crafting an illusion of control to manufacture perceived ease? These decisions can be made more effectively with model reinforcement.

• The ToPB is a strong contender for baseline model evolution when trying to encourage premeditated behaviours.

What you can do about it

• Inspire your data teams to engineer factors within existing machine learning and segmentation models to align with the ToPB structure. Model accuracy and explainability will (likely) soar as a result.

• Build a tailored ToPB to help guide the prioritization of features and functionalities within your product roadmap. Decision making will be cleaner and constructive collaboration will rise.

• Leverage a tailored ToPB model to check the change assumptions within your strategic transformation business case, and refine program or product requirements as needed to better assess change estimates.

Explore the building a tailored Theory of Planned Bahaviour in your organization further using this prompt on favourite generative AI platform

• Explain a process by which my team can build a tailored theory of planned behaviour model in order to change ### behaviour. Outline the steps that would be required, the outcomes that we would expect, and what business, technology or data barriers we might run into along the way. For each of those barriers, suggest a plan for resolution.

Have a great week!

M

👋🏻 for those of you who are new here, you can find our previous posts in the Behavioural Strategy Briefing Archive here.

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