An Introduction to Behavioural Science

An Introduction to Behavioural Science


This article provides a short and accessible introduction to the interesting yet complex area of behavioural science. It covers three things:

  1. A discussion of what behavioural science is and why it exists

  2. A real-life example demonstrating one way in which human judgment and decision making does not conform to the theories long espoused by economists

  3. Recommendations on how the many “supposedly irrelevant factors1 typically ignored by economic theories should be explored further to develop more realistic, humanly relevant theories of judgment and decision making

What is behavioural science and why does it exist?

Put simply, behavioural science is the study of why we do what we do. Its aim is to understand as best as possible the (often complex and counter-intuitive) mechanisms by which we make the judgments and decisions which inform our behaviour.

There has been much interest in behavioural science over recent years, particularly from the market research and marketing industries. The key reason for this surge in interest was the highly significant – yet not entirely unexpected – realisation that a series of long held assumptions made by economists about human judgment and decision making in fact contain clear inaccuracies and inconsistencies. There has since been great interest, from both public and private entities, in the development of more realistic theories which can not only be used to better understand people but also, crucially, to influence them more effectively.

Expected Utility Theory

The particular economic theory that contributed significantly to the emergence of behavioural science is Expected Utility Theory (EUT)2. EUT was developed by the economist, Oskar Morgenstern, and the mathematician, John van Neumann, and includes five pronouncements about how humans make judgments and decisions. However, through experiments by behavioural scientists it has been shown that there are many situations in which one or more of these five do not hold.

As an illustration, one of the five key statements of EUT is that humans make the same judgments regardless of how information is presented. In technical speak this is known as the invariance norm2, and is often illustrated by the example of a bunch of bananas: the notion is that a £1 bunch holds equivalent appeal to an identical bunch reduced from £2 to £1.

Expected Utility Theory - bananas example

The results of numerous experiments have been published in journals by behavioural science academics, demonstrating the violation of the invariance norm. However, with academic research typically being conducted among samples of university students (and often relatively small numbers of such students), it is regularly subjected to criticism from the “outside world”. Therefore, to demonstrate in this article the violation of the invariance norm, a large and nationally representative sample of adults (n = 2,000) is used.

2. The BBC Licence Fee

In a study carried out for Prospect magazine by YouGov3, an online research sample of 2,000 adults was asked about the extent to which they believe the BBC TV licence fee represents good value for money. The proportion of the sample reporting that it represented good value came in at 39%.

However, when a different, yet still nationally representative sample of 2,000 adults was asked the same question – but in a very subtly different way – the proportion saying it represented good value fell by 12% to just 27%.

How did this apparent significant shift in perception occur? It was demonstrated simply by directly preceding the value question with the statement “the cost of the licence fee is £145.50 a year”.

This finding clearly demonstrates we do make potentially different judgments depending on how information is presented to us.

Finally, to further prove the point, one more question variation was used. This time, by first informing a different sample of 2,000 adults “the cost of a Sky package is £55.75 a month”, the proportion now judging the BBC licence fee to be good value increased by 16% to 43%!


Further Reading
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3. Developing More Realistic Models of Judgment and Decision Making

While the finding above is revealing, it only represents the tip of the iceberg with regards the many varied, and often not obvious factors from within the behavioural science literature which have been shown to influence human judgment and decision making. This varied – and constantly growing – group has been aptly labelled “supposedly irrelevant factors1 by the 2017 Nobel-prize winner Richard Thaler.

There is insufficient scope in this article to describe in detail or even summarise the myriad of “supposedly irrelevant factors”, however, what are being referred to are proven influencing factors such as social proof (the often surprising and significant impact that others’ behaviour has on our own), ease (the way in which, to save on cognitive effort, we often take the path of least resistance), and of course emotions (these are very likely to play a role in b2b as well as consumer decisions).

While quantitative metrics have been developed to measure the impacts of some of the above (for example, at B2B International we often use the Harvard Business Review’s Customer Effort Score4 in recognition of the importance of cognitive ease on customer choice), there remain significant challenges in easily measuring others. Also, in addition to developing survey questions or methods for easily understanding the impacts in isolation of supposedly irrelevant factors, there are two further, related challenges which should be tackled.

The first challenge – which has yet to be addressed by academics or by market researchers – is to understand the relative importance of such factors versus each other in judgment and decision making. For example, does social proof over-ride cognitive ease, and if so how does this work and in which situations?

Second, it is equally important that we as an industry develop methods to understand the importance of such factors relative to the more functional factors associated with products and services – the latter being factors on which market research has, to date, typically focused the majority of its efforts.


This article has introduced behavioural science and provided an illustration of one way in which human judgment and decision making does not conform to the theories long espoused by economists. However, it is only by understanding “supposedly irrelevant factors”, how they interact with each other, and how they interact with the more functional factors associated with products and services, that we can build the most realistic, humanly relevant theories of judgment and decision making.


1 Thaler, R. (2015). Misbehaving: The making of behavioural economics. London: Allen Lane.
2 Morgenstern, O., & Von Neumann, J. (1980). Theory of Games and Economic Behavior. NJ: Princeton University Press.
3 Prospect / YouGov, November 2011.
4 Harvard Business Review, July – August 2010. Stop trying to delight your customers.

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