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Brunel Lecture CentreIt’s nearly a year since I started my PhD, (and coming up to three years since this blog was launched). Last week I had my end-of-year review, and, while I don’t often post about the minutiae of being a research student on the blog, I know that at least a few of you are in a similar position, or thinking of doing it one day.

Certainly when I was deciding whether a not a PhD was the ‘right’ thing to do after a couple of years of pretty diverse peripatetic freelancing, the efforts of other bloggers – especially this article by Tom Coates (and the appended comments) – and Rich Watts’ blog, were very helpful and gave me some great, and sometimes sobering, insights. More recently, these posts by the polymathic Nicolas Nova and Julian Bleecker have given well-justified discourse on moving on from academia, even more pertinent because of their design/art-technology emphasis. (The ‘disciplinarity boundaries’ issue, which vexes me so much, has been addressed in this context by Julian more than once; Roberto Greco has a comprehensive review of more thinking on this issue, too).

Anyway, here’s (mildly edited to remove some commercial and personal information) the report I prepared, rather hurriedly, on what’s been accomplished in the first year, and what’s still to come:

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This is a kind of exploration of some ideas I worked on a while ago as part of my research, and have only just come back to, in order to tidy them up a bit. I’m putting it online as a way – perhaps – to get some comments/criticism, and also to enable me to refer to it, if necessary, in future blog posts. If I’m honest, classifications and taxonomies fatigue me quite a lot; coming up with ideas and making and testing them is a lot more fun. But sometimes they’re useful. I hope this one is.

If we think about how products are used, it’s clear that changes can result from the products themselves changing, users changing their behaviour, or a combination of both.

At the University of Bath, Ed Elias, Elies Dekoninck and Steve Culley [1] have captured these possibilities with a 2 × 2 matrix (Figure 1), in which ‘new products’ and ‘old products’ are compared with ‘new user behaviour’ and ‘old user behaviour’.

Diagram by Ed Elias

Along these lines, it’s possible to consider technology change (via design) and attitude change (via education) as two routes to achieve overall behaviour change. Especially in the sustainable design field, the emphasis is often on one strategy or the other, even though the routes are by no means mutually exclusive, as the ‘Design for New User Behaviour’ title implies in the matrix.

Loughborough’s Debra Lilley, Vicky Lofthouse and Tracy Bhamra [2] describe three ‘solutions to limit socially and environmentally undesirable behaviours’: Educational intervention – which corresponds closely to attitude change; Technological intervention – corresponding to technology change; and Product-led intervention – closely aligned with Elias et al’s Design for New User Behaviour.

Further consideration of the possibilities in this area, and how to represent them, led me to the development of a ‘Behaviour Change Barometer’. This diagram attempts to illustrate somewhat more nuanced ‘cases’ of behaviour change, and which factors are present or absent in each case. It ought to be applicable to many kinds of behaviour change with products, not just environmentally-related ones; equally, read ‘products/services/systems’ for ‘products’ to allow wider applicability. The barometer metaphor is stretched slightly, but it seemed appropriate given that the diagram’s mapping change.

A Behaviour Change Barometer. Diagram by Dan Lockton

Table to accompany Behaviour Change Barometer. Diagram by Dan LocktonThe same information is presented in tabular form here: in essence, there are six variables involved, with the possibility space divided into quadrants.

The focus of my research is on the intersection of technology change and attitude change (Quadrant 3): the design of products (and systems) which, through new product behaviour, change user behaviour. Quadrant 3 will be discussed last here – before that, it’s useful to run through the other quadrants briefly.

Quadrant 1 Status Quo Diagram by Dan LocktonQuadrant 1: Status quo

In the first quadrant, no overall behaviour change results.

It makes sense to describe case 1b first – this is the absolute ‘no change’ case, where there is no change in the actual functions of the products (they might be new products, but they don’t do anything different to the old products), people use them in the same way they did before, and users have no understanding or mindfulness of the issues around behaviour change.

Case 1a describes situations where the products’ functions have been changed, but users make no use of this, and have no understanding or mindfulness of the issues involved (e.g. a washing machine offers a new ‘eco’ mode alongside the other settings, but a user doesn’t use it). Therefore no overall behaviour change results, despite product improvement.

In 1c, users have an understanding of the issues, and may be mindful of their behaviour and its impacts, but nevertheless don’t change what they do, and continue to use products in the same way as before – e.g. someone who knows that leaving a television on standby wastes electricity, but doesn’t act on this understanding. Again, no overall behaviour change results, despite improved user understanding.

This quadrant encompasses much current behaviour with energy-using consumer products – improved education and improved technology have raised awareness of environmental issues, and allowed products to be operated more efficiently, but if users don’t act accordingly, there will be no overall change in behaviour.

Quadrant 2 New user behaviour with existing products. Diagram by Dan LocktonQuadrant 2: New user behaviour with existing products

Educating users about the implications of their behaviour is generally done with the intention that users will follow through and actually change the way they use products (if they don’t change, this is 1c as described above). If this is successful – e.g. a campaign to persuade people to keep their car tyres inflated correctly to save fuel – then new user behaviour occurs with existing products, and no design or engineering changes are needed to the products. Overall, there is a change in behaviour.

The scope of this quadrant corresponds closely with much current government policy of using social marketing, public education campaigns and so on – employing persuasion and rhetoric to drive attitude change as a foundation for behaviour change. There are many ways that this quadrant could be subdivided into behavioural cases, but from the point of view of the current study, this won’t be explored further here.

Quadrant 4 Existing user behaviour with new product behaviour. Diagram by Dan LocktonQuadrant 4: Existing user behaviour with new product behaviour

Where new products themselves behave differently in use, yet allow users to maintain their existing behaviours, overall behaviour change results without users necessarily needing to understand the issues involved. No persuasion occurs. For example, compact fluorescent lightbulbs, from the user’s point of view, do not require any different user behaviour to tungsten filament bulbs, but in operation they always result in new product behaviour. A refrigerator door which automatically closes itself if left ajar does not, again, require the user to do anything different, but the product itself behaves differently to accommodate existing user behaviour.

This quadrant would include the major proportion of ‘eco-products’ available, most of which are designed to allow the user to change routines and behaviours as little as possible; there are many possible ways the category can be subdivided further according to various other factors.

Quadrant 3 New user behaviour with new product behaviour. Diagram by Dan LocktonQuadrant 3: New user behaviour with new product behaviour

In the cases described by this quadrant, both product behaviour and user behaviour change, resulting in an overall behaviour change. The behaviour change can be driven entirely by functional changes to the product, or by mindful user understanding, or by both, but the products are designed to lead to this. This is Design with Intent.

These are products that persuade, guide or force – influence – users to change the way they interact with them. A common factor is that there is a perceived affordance change with the product: it somehow indicates that a change in behaviour is needed (compared with quadrant 4 where there is no such indication). This quadrant is where my research is focused.

In case 3a, the perceived affordance change does not reflect actual functional change to the product, yet it influences users to change their behaviour. For example, a washing machine which gives users an ‘estimated cost’ for each mode still embodies all the same functions as one which doesn’t – the user can choose to ignore the recommendation, but is influenced to choose the most economical mode, and thus a change in product behaviour is likely to result from the change in user behaviour. This is where much of the Persuasive Technology research seems to fit.

3c is the case where a user need not think about the issues involved, but will still behave differently due to functional changes to the product – e.g. a washing machine which automatically determines the most efficient settings for a particular load, and silently carries them out, doesn’t require the user to understand what’s going on, but does end up changing the user’s behaviour (removing inefficient decisions) and thus the product behaviour changes too. These products have the potential to be complex, especially where automation is required, but need not be. Something as simple as removing an option from a menu changes the user’s behaviour (prevents him or her choosing it) but doesn’t require the user to think about it.

Finally, returning to the centre of the quadrant, 3b describes cases where user understanding, alongside functional changes to the product and perceived affordance change, lead to user and product behaviour change in practice: these are the real core of what this study is about and where, I hope, I’ll be able to make advances in understanding useful to designers and anyone else working in the field of influencing user behaviour. These are interesting products, potentially involving lots of factors and effects but not necessarily complex in themselves.

[1] Elias, E W A, Dekoninck, E A, Culley, S J. The Potential for Domestic Energy Savings through Assessing User Behaviour and Changes in Design. EcoDesign2007, 5th International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Tokyo, 2007
[2] Lilley, D, Lofthouse, V, Bhamra, T. Towards Instinctive Sustainable Product Use. 2nd International Conference: Sustainability Creating the Culture, Aberdeen, 2005. Available here [PDF].

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Part 1 | Part 2 | Part 3 | Part 4 | Part 5 (coming soon)

Continued from part 3

This series is looking at what design techniques/mechanisms are applicable to guiding a user to follow a process or path, performing actions in a specified sequence. The techniques that seem to apply with this ‘target behaviour’ fall roughly into three ‘approaches’, which if anything describe the mindset a designer might have in approaching the ‘problem’: i.e., the techniques suggested may well apply more than one at a time to many designed solutions, but each reflects a particular way of looking at the problem. In this post, I’m going to examine what I’ve called the Persuasive Interface approach, which draws heavily from the work of BJ Fogg, though applied specifically to this particularly target behaviour.

As noted before, a lot of this may seem obvious – and it is obvious: we encounter these kinds of design techniques in products and systems every day, but that’s part of the point of this bit of the research: understanding what’s out there already.

Persuasive Interface approach

The design of the interface (however loosely defined) of a product or system can be an important factor encouraging users to follow a process or path in a specified sequence. Interfaces can use a number of psychological persuasion mechanisms (outlined by B J Fogg) – a ‘human factors’ approach – in conjunction with the technical capabilities of the interface itself. Some mechanisms applicable to this behaviour, then, are – as well as the Interface capabilities themselves – Tunnelling, Suggestion (kairos), Self-monitoring and Operant conditioning.

Interface capabilities
What I mean by this – there is probably a better term for it waiting to be coined – is the choice of degree/type/format of information or feedback that an interface can provide a user. Clearly, an interface with few capabilities for actually providing the user with feedback, or worse, inappropriate feedback capabilities (e.g. a car speedometer only telling you your mean speed for the journey, rather than the instantaneous velocity), has a different (probably much worse) chance for affecting users’ behaviour. (Which is why having the electricity meter in a cupboard, and looking at it four times a year, is not very persuasive in energy-saving terms.)

Careful selection of what information, feedback and control capabilities are designed into a system, from a technical point of view, can have a major effect on user behaviour. To some extent, the addition of an interface to a system which did not previously have one may drive behaviour change in itself. Technical decisions about the types of interaction possible between an interface and the underlying system or product, and between the user and the interface – the capabilities of the interface – determine how the user experience will work: if a system is not designed with a function for monitoring progress through a sequence of operations, for example, then the possibility of indicating this via an interface is not possible, or far more difficult. Providing the infrastructure for a meaningful and useful interface for a system is a design decision which can shape or even determine the system’s use characteristics.

Self-monitoring
Self-monitoring, as defined by BJ Fogg, is an interface design mechanism which explicitly links feedback of information to the user’s actions: the user can monitor his or her behaviour and the effect that this has on the system’s state. As applied to helping a user follow a process or path in sequence, it makes sense for the self-monitoring to involve real-time feedback – so that the ‘correct’ next step can immediately be taken if the feedback indicates that this is what should happen – but in other contexts, ‘summary’ monitoring may also be useful, such as giving the user a report of his or her behaviour and its efficacy over a certain period.

Even giving a user the ability to self-monitor where previously there was none can help change behaviour: for example, providing a home electricity meter in an immediately visible position is likely to be more persuasive at inspiring energy saving – by increasing awareness of consumption – than having the meter hidden away.

LinkedIn: Self-monitoringExample: LinkedIn‘s ‘Profile Completeness’ indicator allows users to monitor their ‘progress’, driving them to follow a specified sequence of actions

Tunnelling
Tunnelling is a ‘guided persuasion’ mechanism outlined by Fogg, in which a user ‘agrees’ to be routed through a sequence of pre-specified actions or events:

When you enter a tunnel, you give up a certain level of self-determination. By entering the tunnel, you are exposed to information and activities you may not have seen or engaged in otherwise. Both of these provide opportunities for persuasion.

Applying this mechanism involves treating the user as a captive audience: presenting only the ‘correct’ sequence of actions, step by step, with any user choices being limited, and the commitment to following the process being a motivator to accept the advice or opinions presented. Fogg uses the example of people voluntarily hiring personal trainers to guide them through fitness programmes. Some software wizards provide an interface analogy, where the intention is not merely to simplify a process, but additionally to shape the user’s choices.

Wizard: tunnellingExample: This software wizard helps the user ‘tunnel’ through a file conversion process in the right order.

Suggestion (kairos)
Suggestion (kairos) involves suggesting a behaviour to a user at the ‘opportune’ moment, i.e. when that behaviour would be the most efficient or otherwise most desirable step to take (either from the user’s point of view, or that of another entity). In the context of helping a user follow a process or path in a specified sequence, this is very easily implemented: the system can simply ‘cue’ the desired next step in the sequence by alerting or reminding the user, whether that comes through indicators on the interface itself, or some other kind of alert.

Suggestions can also help steer users away from incorrect behaviour next time they use the system, even if it’s too late this time; when presented at the point where a mistake or incorrect step is obvious, advice on what to do next time may be more easily recalled. The key to this mechanism is that the suggestion is timed or triggered at the right point in the sequence, so that its effect is most persuasive. This may imply a system which monitors the user’s behaviour and responds accordingly via the interface, or it might be realised by an interface designed so that, by helping the user keep track of where he or she is in a sequence of operations, the suggestions only appear or are visible at the right point.

Volvo gearchange light
Example: This Gearchange Indicator light, fitted to certain Volvo models, suggests the most efficient moment to change gear, based on measurement of engine RPM and throttle position. Thanks to Mac MacFarlane for the image.

Operant conditioning
Controversial, certainly, but reinforcing target behaviours through rewards or punishment may be applicable where we want the user to perform a (perhaps complex) behavioural sequence repeatedly, so that it becomes habit, or successive iterations approximate the intended sequence. But it is unlikely to be effective in encouraging users to follow one-off sequences, where actual direction (e.g. suggestion, tunnelling) is far more useful. In general, punishing users for mistakes is an undesirable way of designing.

In part 5, we’ll review the approaches we’ve looked at, and see how one might actually go about choosing among them to design a new product or system with this particular target behaviour.

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EDIT: I’ve now added the audio! Thanks everyone for the suggestions on how best to do it; the audio is hosted on this site rather than the Internet Archive as the buffering seemed to stall a bit too much. Let me know if you have any problems.

I’ve put my presentation from Persuasive 2008 on SlideShare, – because of the visual style it really needs to be listened to, or viewed alongside the text (below, or in the comments when viewing it on the SlideShare site). Alternatively, just download it [PPT, 11.6 Mb] – it comes with the notes.

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Something which came out of the seminar at Brunel earlier this week (thanks to everyone who came along) was the idea that any method of selecting ways to design products that aim to shape or guide users’ behaviour really must incorporate some evaluation of users’ actual goals in using the product – users’ intent – alongside that of the designer/planner. This seems obvious, but I hadn’t explicitly thought of it before as a prerequisite for the actual selection method (instead, I’d assumed these kinds of issues could be shaken out during the design process, based on designers’ experience and judgement, and then in user testing). In retrospect it really does need to be considered much earlier in the process, while actually choosing which approaches are going to be explored. (Given how long I’ve spent reading about bad design and poor usability, you’d think I’d have twigged this earlier.)

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Image from uselog.com

TU Delft’s Renee Wever and Jasper van Kuijk (who runs the insightful Uselog product usability blog), together with NTNU’s Casper Boks, have produced a very interesting paper, ‘User-Centred Design for Sustainable Behaviour’ [PDF, 400 kb] for the International Journal of Sustainable Engineering (indeed, probably in the same edition as my own paper addressing many similar ideas.)

It’s great to find more people investigating this same area of using design to guide more sustainable user behaviour, both from the point of view of validation (i.e. I’m not barking up completely the wrong tree) and because it helps add additional perspectives and research to the pot. Wever, van Kuijk and Boks’ classification of different strategies may be useful, too, in helping me structure my own taxonomy:

We provide a typology of four user-centered design strategies for inducing sustainable behavior.

* Functionality matching: adapt a product better to the actual use by consumers and thereby try to minimize negative side effects;
* Eco-feedback: the user is presented with specific information on the impact of his or her current behavior, and it is left to the user to relate this information to his or her own behaviour, and adapt this behaviour, or not;
* Scripting: creating obstacles for unsustainable use, or making sustainable behaviour so easy, it is performed almost without thinking about it;
* Forced functionality: making products adapt automatically to changing circumstances, or to design-in strong obstacles to prevent unsustainable behaviour.

That’s a simpler and possibly clearer way of dividing it up than the designer-centric approach I’ve been taking (e.g. see this series of posts), though my method aims to apply to all using-design-to-shape-behaviour problems, including, but going beyond, ecodesign.

I’m heartened to read this in the paper:

An overview of the available design strategies is missing, as is a clear approach for choosing the right strategy for a given product.

That’s very much part of what I’m trying to achieve.

I’ll certainly keep an eye on what the guys from Delft and NTNU do next!

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