Why Evidence Plays a Key Role in Scaling Innovation

Prove it to me!

Why Evidence Plays a Key Role in Scaling Innovation

GUEST POST from John Bessant

A good idea will sell itself, right? Unfortunately not — Emerson was spectacularly wrong when he suggested that all you needed to do was build that better mousetrap to have the world beating a path to your door.

History is full of examples of innovations that, whilst being good and proven solutions, more than just a gleam in their inventor’s eye, stubbornly refused to scale. They failed to have impact on a widespread basis.

Think about Earl Tupper and his alchemical miracle, creating an award-winning product out of the unpromising raw material of black sludge waste from oil refining. Tupperware eventually made it as an innovation which scaled but it was only after Brownie Wise teamed up with him and pioneered the social marketing which brought the product into the homes of key influencers.

Or Toshiba, investing close to a billion dollars in the technology underpinning what they saw as the next generation of high quality DVD recording, only to fall in the final straight as the market opted instead for Sony’s Blu Ray system. This was a fascinating echo of the story which Sony had encountered decades earlier when its Betamax video tape format lost the battle to the VHS standard, despite having many technical advantages over its rival.

Or Better Place, an ambitious green start-up that offered to make the world more sustainable by introducing battery swap technology for electromobility. Despite raising a huge amount of venture finance and gaining the backing of world leaders and CEOs prepared to set up factories the vision fell apart after three years.

These are not the failures of foolish and unprepared entrepreneurs; they all had much to offer and had proven their technologies worked. But they each stumbled over one or other of the many rocks strewn in the way of those trying to make the journey to scale. There are plenty of them in the world of commercial innovation — and in the field of social change, innovations designed to have an impact and change the world, it’s even more difficult.

Evidence and scale

One of the challenges is around the role of evidence. At its simplest we adopt new things because we see some benefit in them, they make our lives easier, more comfortable or better in some way. That’s what gives rise to the S-curve shape which you can find associated with any innovation — it isn’t a case of all or nothing, adoption takes time. And one of the key influences on that is the role of evidence.

For early adopters it’s a matter of being convinced enough by data or demonstrations that the innovation has real advantages to offer — they’re looking for hard and measurable facts to underpin their decision. But as we move along the road diffusion becomes more of a social process as well.

The more we see others getting benefit, the more we’re prepared to take the risk. Shaping our perceptions of new things so that we adopt them sooner is a huge part of what advertising does and it plays on our desire for evidence. Being persuaded — by facts, figures, demonstrations or simple observation accelerates the process.

Think of Washington Carver’s famous attempts to get rural farmers in the southern USA (a sceptical breed) to adopt new strains and methods. Simply giving them the hard facts wasn’t enough — his success came when he could show that the crops in his demonstration fields grew higher or thicker than those around. Seeing is believing — and it reminds us that evidence comes in many forms and can be communicated in different ways.

It’s also a matter of who is offering us the evidence — can we trust it, can we believe it? The advertising industry has played this tune for a long time, persuading us about the virtues of better toothpaste or headache pills by invoking the (eminently trustworthy) authority of medical practitioners. We also listen to key influencers, opinion leaders whose perceptions we trust — and we’re much more likely to adopt something if it is recommended by ‘people like us’.

All of these factors help shape the familiar S-curve pattern which we see over the life of innovations whereby adoption accelerates after the initial first wave. There’s a kind of snowball effect with the accumulation of evidence (especially the experience of satisfied adopters) driving up the pace of adoption. (Or not — negative evidence or word of it can quickly stop adoption in its tracks).

So if we are concerned with trying to scale our innovation it’s worth looking a little more closely at the role evidence plays, at the monitoring and evaluation processes which build that evidence base, and at how evidence is communicated. We could do a lot worse than break our review down into some key question areas — the who, what, when and how of scaling evidence.


Who needs evidence? Well, self-evidently (!) adopters, as we’ve seen, it’s a key part of the innovation decision process. But we often use proxies — opinion leaders — to influence our decisions — whether it’s the Jones’s we try to keep up with or our favourite social media influencer. Adoption is based on trusting others judgment and we assume they have reviewed the ‘evidence’ in coming to their decision.

Beyond that there’s another group — investors. Whether it is donors funding social innovation or government promoting a new technology or individual investors in a crowdfunding campaign those investors are looking for evidence to shape their behaviour. Is the innovation worth doing — is there evidence of demand and potential impact? Is there evidence downstream of actual impact, and along the way are the trends in the right direction? And afterwards, was the investment worthwhile, was it done well, could it have been done better, what have we learned? All questions which require evidence.

And then there’s the innovators themselves, the teams growing and scaling their innovation. Their core approach in coming up with their original solution will have been based on prototyping and experimenting, pivoting as they learn from the market what works and what doesn’t. And that experimental learning cycle doesn’t stop once the solution is established. If anything the journey to scale requires even more of this pivoting and adaptation to suit different contexts and situations on the scaling journey. Once again what the team needs is evidence.

In the field of social innovation there are other stakeholders to consider, all of whom have influence on whether or not an innovation can scale. Research on innovation scaling in the humanitarian sector suggests that there are many different players involved, each of whom have different evidence needs, as shown here.

(Source: ‘Building evidence for scaling’, ALNAP Response Innovation Lab, 2020)


So what kind of evidence do we need? And, in a world increasingly plagued by fake news and unreliable facts ,what constitutes ‘good’ evidence? There isn’t one size fits all, different players (as we’ve just seen) look for different kinds of evidence.

During a recent webinar Lydia Tanner of The Research People showed a helpful graphic which underlines this point; evidence is very much a matter of horses for courses.

(Source: ‘Building evidence for scaling’, ALNAP Response Innovation Lab, 2020)

Of course we’re looking for evidence of impact, of relative advantage. But in the field of social innovation where donors and funders may be asking the question there’s also a need to provide evidence that the problem is important and the ‘right’ one to address, and that the solution has real value to end-users. Is there real advantage to the solution, is it compatible with the context into which it will operate?

And on the left had side of the diagram there are considerations of how well the solution is delivered. This involves reflecting and collecting data on the innovation process itself and how well it is working , alongside the nature and experience of the solutions being offered.

There’s also something important about the quality and reliability of the evidence we assemble. Clearly our aim should be to provide proof, facts which can be verified — not for nothing does the healthcare sector place so much weight on randomised control trials as a gold standard to help determine whether a new medicine is effective or not. RCTs are all about assembling an evidence base of reliable and robust data. The trouble is that getting at good evidence is difficult, not least because there are so many kinds of information we can assemble as ‘evidence’ — not least those vanity metrics which tell us that ‘20,000 people can’t be wrong…..’!


And then there’s the when question. When should we start to assemble our evidence base and when does it have most impact? The simple answer is ‘always’ — throughout the innovation and scaling process.

At the start of the scaling journey we want evidence to reassure us that there is potential demand, that our innovation will be solving a big enough and important enough problem and that what we have developed represents a robust solution which is capable of being scaled. Without this to back up our claims we’re unlikely to get very far in trying to convince others to buy into or support our solutions.

During the process it’s all about pivoting, using evidence of success and failure to help shape and adapt our innovation to suit different contexts. In the social innovation field the ‘market’ may involve a number of different players but the principle is the same. We can use the different kinds of evidence outlined above to help us get a better fit between innovation and context. Which will increase our chances of successfully scaling it.

A simple example might be the case of Netflix. Early on in their innovation journey Netflix realised that their entertainment supply model based on shipping DVDs by post was not the way to go; whilst their model worked there was increasing evidence that people were turning to online streaming of music and the same was likely to happen to video as high speed internet bandwidth became available. So they pivoted to a streaming approach, learning with the newly-emerging market while at the same time maintaining their video-by-post approach.

(And contrary to popular myth Blockbuster didn’t simply plough on with its old bricks and mortar solution using shops as rental hubs. They saw the evidence of Netflix ‘s successful new online model and developed their own solutions to emulate it. But their wider value network had too much invested in the original model and was reluctant to let go. So in spite of the evidence they couldn’t change their business model with the resulting collapse of their operations).

And at the end of the process there’s an opportunity for collecting a different kind of evidence, around learning. If we succeeded, why and what can we do more of next time? And if we failed, what can we change? Smart organizations concerned with learning to repeat the innovation trick develop ‘routines’ — embedded patterns of behaviour which become ‘the way we do things’ around innovation. These routines find their way into polices , procedures and processes — but not by accident. There’s a need for post-project reviews, set down meetings and other devices to capture learning. The trouble is , particularly with projects which have not gone so well, that there’s a tendency to cover up and disguise things — obscuring the evidence we need so badly to help us improve things next time.


Which brings us to the how? How do we set up robust and flexible monitoring and evaluation so we can collect the different kinds of evidence needed to by different stakeholders? What frameworks and tools are available? What different approaches might be needed under different circumstances? Not surprisingly there is no simple answer to this but a clear need to put an evidence strategy in place at the outset of the innovation scaling journey. Since evidence will play such a key role we need to allow time and resources and develop or bring in expertise to work on this aspect of our project in parallel with rolling out our solution.

And we need to think hard about how we communicate the evidence we acquire to a variety of different audiences. How do we build on good evidence to tell the innovation story? Adoption of innovation is a social process which is accelerated or retarded by more than facts; it depends on perceptions and on social influence. That’s a lesson which comes through repeatedly in the work of Everett Rogers, the ‘godfather’ of innovation diffusion research and it continues to play a key role according to current research findings. It’s also clear from the experience of would-be innovators trying to scale their solutions

There was nothing wrong with Earl Tupper’s product innovation except that no-one was particularly interested in buying it. That all changed when he switched his marketing from in store sales to doorstep selling and through that to the in-home party. Brownie Wise was one of the early demonstrators and quickly proved her facility at persuading home-makers to adopt the product. Her sales pitch was essentially around changing the way in which the core evidence — that the product worked and was a viable better food storage solution than traditional glass jars — was communicated and perceived.

She had great attention-grabbing skills — for example one of her ‘party tricks’ involved filling a Tupperware bowl with tomato soup and throwing it across the room where it landed, seal still intact and without spilling and staining the carpet. But she accompanied these tricks with a much more powerful approach which was to engage the party hostesses as sales agents. Their ‘source credibility’ — the degree of trust and respect in which they were held by their peers — meant that they were powerful opinion leaders, able to accelerate adoption across social networks. These days we’d call them ‘influencers’ but whatever the label the way in which they could amplify the positive perception of evidence played a key role on the successful scaling.

So what lessons can we take from this? First we should remind ourselves that scaling innovation is not automatic it’s a long and difficult Journey — and one in which evidence makes a difference. Evidence is what drives and accelerates (or retards) that S-curve around adoption.

But we need to consider an evidence strategy — it’s not just that we need evidence but we need to think about who’s it for and their different needs, what form it can take that will be convincing, how are we going to communicate the story, etc.?

Innovation is what what’s helped us as a species to survive and grow in what is still a very hostile, turbulent and uncertain world. But that innovation process hasn’t been a matter of simply adopting every new idea because it’s new. That’s a very dangerous approach, not least because many innovations may take us in the wrong direction. We’re actually quite cautious about adoption; we’re not risk averse but we’ve evolved to be careful about the risks we take. Having credible evidence occupies a place centre stage in that adoption decision. Which means that if we’re serious about scaling our innovation then we need to take the evidence question seriously.

You can find my podcast here and my videos here

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Image credit: Dall-E via Bing

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About John Bessant

Originally a chemical engineer, John Bessant has been active in the field of research and consultancy in technology and innovation management for over 40 years. He is Emeritus Professor of Innovation and Entrepreneurship at the University of Exeter and also has visiting appointments at the universities of Stavanger, Norway and Erlangen-Nuremburg, Germany. Author of over 30 books and 200 articles, you can find out more here: www.johnbessant.org

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