How to give people feedback on their eco-behaviour


Many of us assume that if we just give people the information they need, they will act in the right way.

This assumes that households and businesses are run by micro-resource managers who make decisions about how to act after looking at the costs and benefits of all their options.

To change behaviour all we need to do then is provide things like smart meters and everything will be better, won’t it?

It turns out that isn’t the case because there are a number of problems we face.

For starters, not everyone understands the language of energy or water as shown on a display or report.

It’s hard to visualise 140 litres of water, for example.

It may be easier to grasp when it is translated into something that people can relate to – for example a one litre bottle for every child in a small primary school or the amount of water that goes into making one unit of product.

Even when people understand it, they may not feel they have any choice about using energy.

No one wants cold tea – and we would rather wear clean clothes. That means using the kettle and washing machine.

We can’t turn off energy if we have to do things – we have no control really.

A related problem is focusing on visible things and forgetting the stuff running all the time in the background.

For example, using the kettle will turn displays red with a power spike – but it only lasts a small time.

Or many organisations focus on replacing lights, because that is a very visible way of showing that they are reducing energy – but miss out on larger savings.

Then there are traffic light systems – which have been shown to improve behaviour by getting people to try and stay in the green.

At the same time, they make it ok to do things as long as the display is green – consuming more power overall over time.

A major stumbling block is simply what we want. We’d like a bigger telly, even though the old one works.

Christmas is about shopping – and we’re not going to deprive the kids of stuff even if it’s mostly plastic.

All these problems and more mean that getting people to change behaviour is a bit of a challenge.

So, how should we look at solutions? We can take some lessons from the designers of better eco-feedback solutions.

First, we need to provide better information.

For information to work, it needs to be easy to understand, attract attention, have a social component and be provided at the right time – the EAST framework.

An example is making sure there are labels that say when and who something must happen – like a sticker on light switches saying the last person out should turn off the lights.

Then there are goals.

Companies that commit to targets like Science Based Targets will give people the responsibility and permission to take action.

Government targets can ripple through economies and cause changes in behaviour in order to comply.

There is some evidence that comparisons work – people like being on top of league tables, for example.

After a certain point, however, it may not be possible to eke out further savings, and so comparisons become less important as people feel they are impossible to achieve.

Incentives and rewards, even small ones, can affect behaviour.

Simply having a star after the names of people can change how the group views and engage with the activities required – either to get their own star or keep one they already have.

For eco-feedback to work, it needs to follow the Goldilocks principle.

Not too much, not too little, but just right.

What is a smart contract?


We hear a lot about smart contracts these days.

But what exactly is one, how do we create it and when can we use it?

The term was invented by Nick Szabo, who applied his interests in computer science, law and cryptography to create a cyberspace equivalent of gold – and came up with the idea of decentralized digital currency.

The point of a currency, however, is that it acts as a store of value and a medium of exchange – but when do we actually need it?

Nick, in his original article, says a contract is really a set of promises.

The things that go into a contract have evolved over time – from a contract of marriage to a contract of employment, from a contract of sale to a contract of lease.

All these structures say that the people involved promise to do certain things as set out in the contract.

While the concept of a contract can encompass a blood oath sworn at midnight under a lightning blasted oak tree, normal contracts have four characteristics:

  1. The participants can observe how each party is performing on the contract.
  2. An arbitrator can verify that the contract is performing as set out or is being breached.
  3. The only people involved are the people that need to be involved – their rights are secure and private.
  4. The contract can be enforced – for which we need acceptance of the contract by the powers that be.

A smart contract takes these concepts and recreates them in the digital world.

The difference is that parties enter into a digital agreement instead of signing a paper contract.

This has been done several times before – every payment with a Visa debit card, Ebay purchase with Paypal and software contract we sign without reading are all digital contracts.

The idea of smart contracts when linked with Blockchain technology adds a few ideas to standard paper contract.

The first is that contracts can be expressed in code using an algorithm.

Common algorithms may be if-then or when-do.

For example, if you pay me X, then I will give you an hour of work.

Or when a shipment of copper is delivered in two months time I will pay you the price then that we agree now.

So this means that we need to identify the real life elements that the contract talks about like money and stuff.

For example, payments might be done using a decentralized digital currency like Bitcoin or Ethereum while each hour of work or shipment of copper is tagged with a digital identifier.

So, a second part of a smart contract is the ability to give things a digital ID.

Then there is the question of where to store the smart contract – and that’s where the idea of a ledger comes in – especially a blockchain based distributed ledger.

This keeps the information related to the contract secure by design.

Smart contracts are the way things are going to go – paper agreements have little place in the future.

Many other part of a contract – the promises we make, the reliance on the state to enforce agreements, and the lawyers that create and interpret them – are all still likely be around.

The key, however, is going to be to get the contract algorithm right.

How to think about waste in knowledge work


A principle of lean thinking is to remove waste – anything that stops us from producing our product or service for a paying customer.

This is called muda in Japan and seven ways to create waste are especially important to manufacturing organisations.

They can also be used to think about knowledge work and if we are doing it effectively or not.

Take transport for example. Are we moving work between locations unnecessarily?

This can be as simple as splitting a process up between teams, so that one team focuses on just one part and the other team on another.

This is a good way of creating silos – where people focus on their bit and forget the overall system and waiting customer.

Having a small group of multi-skilled individuals close together that can handle the entire process from starting the project to delivering to the customer reduces this type of transport and waste.

What might inventory look like in knowledge work?

Could it be the collection of reports, analyses, studies, comments, meeting notes and so on that accompany the simplest of projects or decisions?

Is all that really necessary?

A lean organisation can design and test a product with a customer and iterate to a finished version in the time it takes for another to come up with specifications.

Motion simply adds heat to a process. The best knowledge work gets done when people sit down and work on a single task for a stretch of time.

Flapping between projects, having to check in all the time with managers and getting interrupted break the flow of work, raise stress levels and increase the total time needed to do the job.

Then there is the time we spend waiting. Meetings are places where we wait – where interminable discussions happen to decide what to do.

We spend so much time in meetings that there is little time left over to do any of the work or actions that come out of them.

Or, we often do too much – over-processing the work we need to do.

Modern computer systems are so powerful that we can do almost anything on them – which means we spend ages selecting fonts and sizes and colours and logos and header placement instead of creating content or analysis.

User interfaces that try and make things simple don’t help.

A quote from an old newsgroup says that graphical user interfaces (GUIs) make simple things simple and complex things impossible.

Closely related to over-processing is overproduction.

There is little point creating work-in-progress that has to go through a bottleneck.

It makes more sense to work to the capacity of the bottleneck and spend the rest of the time working on a different project or improving the performance of the bottleneck.

And finally, there are defects.

Defects in knowledge work often result from not understanding the end result well enough and rushing to create something too quickly.

Information degrades quickly it is passed along links in the chain of communication.

For example a customer speaks to a sales person who speaks to an operational manager who asks an analyst to do some work who then creates something.

It is almost a certainty that the something that is created is not what the customer had in mind.

The problem is that knowledge work is not as visible as manufacturing – we don’t see piles of inventory piling up.

We may only see a cluttered desk and be aware of late projects.

We’ll also see the effect of waste in rising stress levels among colleagues.

And all that’s just a waste.

What are people really looking for from us?


Theodore Levitt, a Harvard economist and professor, said People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.

With larger purchases come greater expectations of performance, reliability and longevity.

An approach companies use to deliver these is servitization.

Servitization is a change in mindset from selling a product to selling a Product-Service System.

Professor Andy Neely from the University of Cambridge is a leading researcher in this area and says that the term has been around since the 1980s.

The classic example here is Rolls Royce.

It no longer sells jet engines. Instead, it sells flying hours.

Rolls Royce now delivers reliability – done through planned and predictive maintenance by analysing data from monitoring systems in the field.

Making a move from scheduled repairs to targeted repairs based on performance data enables fleet operators, from the military to councils, to improve things from combat readiness to bus times.

The way we get media has changed with this approach.

Netflix offers media as a service and Amazon offers ebooks.

Servitization has quietly transformed the software business.

It’s hard to buy a software product any more. It’s all software as a service (SAAS) now.

This is not always a good thing.

Netflix has lots of rubbish on it. The good stuff still needs to be paid for.

We get a million books with an Amazon Unlimited subscription – but the good authors still set their own prices and get committed readers.

Servitization in these cases gives the companies a revenue stream and a customer convenience – but the pressures of controlling costs means that the performance is not superior to just buying a really good collection of books and movies.

And software as a service really all-too-often means self-service software.

So, it’s still a product sell rather than a service when we think about it.

According to Professor Neely, servitization is something of a paradox.

Many firms offer product-service systems, but it makes less money and carries higher risks than we first think.

The key thing is a change in mindset – in how we connect a product, service and performance.

This shift starts with the customer going from wanting to own a product to being happy with a service.

How to think about mining data for value


Why should we try and use data better?

The short answer is because we can make money or save money using the insights we get from analysing data.

But a more useful answer might be because it helps us see more clearly.

That’s very Zen.

Zen teaches that we normally see the world through a haze – a fog made up of the assumptions, preconceptions and fixed ideas – that is created by our desires.

For example, many people are so involved in the way they do something that they fail to see that the something they do is no longer relevant. Innovation simply passes them by.

Or, if we really want something to happen or believe that an approach is right, we become blind to anything that contradicts that point of view.

The way to see clearly is to look at the world as it is.

This is pragmatic advice, but how do we go about doing it?

The Cross Industry Standard Process for Data Mining (CRISP-DM) is now over 20 years old – but still useful.

There is data everywhere these days, leaking out of sensors, meters, surveys, analysis and social media.

We could analyse everything, but we should probably start with an understanding of the business.

What is special about this particular industry, what is the competition doing, how do customers act and what would the business like to do?

We can then stick our heads in the fog and try to understand the data that’s available – going back to the business to ask questions or get some more.

Is it clean or patchy? Do we have lots of numbers or is it full of words? Is it in an open format or do we need to get it out of databases or proprietary formats?

For example, the way we treat energy metering data is different from twitter comment mining or newsfeed monitoring

A huge amount of work then often goes into data preparation.

The data needs to be bashed and cut and manipulated into a form that we can work with.

All too often, the data preparation takes so long and ends up in another messy form that there is little time left to do any modelling.

But – getting to the right kind of data structures can make the task of modelling much easier.

Models help us create and test theories.

Do we think there is a relationship between variables or a robust way to identify problems?

Then its time for evaluation.

We could do an infinite number of analyses, but we need to focus on the ones that are aligned with what the business needs to know.

Are we providing useful insights that can be used to make the business more effective?

If so, then we can head towards deployment.

There is no point doing analysis once and then forgetting about it.

The law of entropy – things decay over time – means that without management things will simply drift and become worse again.

Ideally, we’d automate the analysis and reports and let people know when things are going wrong so that they can get involved and fix things quickly.

The CRISP-DM is a process we can follow to create a data mining project.

The real value comes from the understanding we get when we see what is actually going on.

How can we solve messy real-world problems?


What do we do when we have a situation – perhaps something has gone wrong?

We probably rely on experience – either fix things ourselves or get an expert in to solve the problem.

Most solvable problems like this tend to be hard problems, in the sense that they have clean edges.

For example, the tap is broken, a flood has disrupted shipments and we need an alternative or the printer is jammed and we have to get this proposal to the couriers in the next fifty minutes.

Many other problems are less well defined – how do we reduce our environmental impact while still growing the business, how can we develop people’s skills, what information systems do we need to invest in or what changes do we need to make now to stay relevant?

As a species, we have been phenomenally successful at solving hard problems.

For example, many of us live safer, longer and healthier lives as a result of the medical and technological breakthroughs of the last couple of centuries.

This success has been accompanied by an unexpected problem.

Many people are concerned about the growth in population as we head towards 8 billion people.

They point out that it’s unsustainable and we’ll run out of food to feed everyone and really it would be much better if everyone in all these developing countries stopped making so many babies.

But is the cause of population growth really more babies?

It’s not, reallyit’s not that we breed like rabbits – it’s just that we’ve stopped dying like flies.

The real solution to population growth might be somewhere else.

It turns out that richer people have fewer children.

So, if we want to manage population, a better way might be to help everyone get richer – so instead of aid should we focus on trade?

We normally think that charities or the UN handle big problems like that but maybe it’s business that will solve it in the end.

The thing is that we don’t know. And with many real-world problems we don’t know what solution is going to work – or for that matter what the real problem is.

That’s where a learning cycle comes in.

Hard problem solvers like to say give me your requirements, I’ll build a system and you’ll be happy.

That works sometimes – but all too often it doesn’t.

That’s because most real-world problems are context specific – they depend on the ideas and assumptions of the people involved as well as the situation and environment.

So, instead of solving a problem we may need to work our way to a solution.

In a learning cycle, we have a theory about what might work and that leads to ideas we can put into practice.

We can learn from the results and that leads to modifying our theory and ideas.

It’s not rocket science – but it’s easy to fall into the trap of thinking a new system or platform will solve our problems.

Real-world problems, however, are usually solved after going round the learning cycle loop several times.

Reference: Information, Systems and Information Systems – Peter Checkland and Sue Holwell

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