Innovation Is Not a Light Bulb Moment — It’s an Engineering Discipline with David Cropley

What if innovation were not a mysterious creative gift but a measurable engineering discipline? That is exactly the argument Professor David Cropley of the University of Adelaide makes — and he has the diagnostic framework to back it up.

In this episode of Arkaro Insights, Mark Blackwell talks with David about why most organisations are failing at innovation, where precisely the failure occurs, and what leaders can do about it.

David introduces the Innovation Phase Model, a 42-cell diagnostic matrix that maps seven stages of innovation against six psychosocial dimensions. Administered as a survey across teams and organisations, it pinpoints exactly where the roadblocks are — and consistently reveals the same pattern: most organisations are competent at implementation but struggle badly at the front end, where problems need to be identified and ideas generated before anything can be built.

They also discuss why AI is being deployed the wrong way in most organisations — treated as a product innovation when it is fundamentally a process innovation — and David shares the mathematical proof that large language models are capped at roughly average human creativity. Useful, but not the innovation engine many believe it to be.

The Smith Corona story brings it home: a world-class innovator that went bankrupt because it could only think incrementally when disruption arrived.

In this episode:

• The Innovation Phase Model and the 42-cell diagnostic
• Why structure determines behaviour, and most people are rewarded for exploiting not exploring
• AI as a process innovation being treated as a product — and why that matters
• The mathematical ceiling on large language model creativity
• Three things a new CEO or VP should do first to build a genuine innovation culture

Guest: Professor David Cropley, University of Adelaide Author of The Psychology of Innovation in Organizations (Cambridge University Press) 

LinkedIn: linkedin.com/in/davidcropley

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Transcript

Innovation Myths And Failure Rates

Mark Blackwell 0:50

Hello everyone, welcome to the Arkaro Insights Podcast. This is the show where we help business executives thrive in a complex adaptive world. And today we're going to talk about innovation. Now, a lot of people, when they think about innovation, think it's like light bulb mom, that spark of creativity that comes out of nowhere. But our guest today is going to challenge that, I believe. Anybody, an engineering expert, believes that it's a measurable engineering discipline. And it's one of those disciplines that many uh businesses are currently failing. Professor David Cropley is a world-renowned expert in engineering innovation. And we'll be talking about some of the reasons why innovation doesn't work, why we have new product launches with a 75%, 80% failure rate, and what can be done about that. We might even just touch on AI during the da during the conversation. David, welcome to the show. Thanks very much, Mark. It's great to be with you and and with your audience. Thank you. Well, yeah, I'm looking forward to this conversation, and I'm looking forward to some challenging discussions. As I've been reading up about you, I think that may happen during our discussions. Hope so. So let's think about uh, you know, innovation. It's something that all companies say, yeah, we love innovation, we want to do innovation, we're here to innovate, but the reality is different. Tell me, can you unpack that statement a bit more?

David Cropley 2:24

Well, the view and and my experience of this from the research I've done and from trying to work with a number of companies on this is that innovation, of course, is risky and it represents change, and it's a it's fundamental human characteristic that we don't like change. And we see examples of that all the time around us in different ways. We've probably all experienced it ourselves individually, that change is uncomfortable, it's messy, it's annoying, it's expensive, and so on. And I think the same principles apply for organizations. And and so the very nature of innovation is finding new solutions to problems, finding new ways to do things. That means change, that means discomfort. And, you know, and some organizations are good at that. They have a a culture or even a history of working with that. But I think the problem is a lot of organizations don't like that. And and they won't necessarily admit to that, because of course it's, you know, it's it's people expect you to say that you're about innovation and we're an innovative company and this sort of thing. And there's a real sort of um uh, you know, there's these two opposing forces, the desire and the recognition that we need to change, we need to be involved in innovation, but the the sort of counter force that this makes a lot of people and a lot of our organizations very uncomfortable. It's a fundamental sort of mismatch between these two opposing forces. It's very difficult to resolve.

Mark Blackwell 3:53

Yeah, I indeed I spent my life in small family firms and uh in a large corporation. And one of the things Osterwelder talks about is explore versus exploit. Explore is like innovation, and exploit is short-term incremental gains of a pretty mature innovation, whatever it be. And the sad reality in the corporate world is that most people get promoted because of their exploit activities, because it's got high certainty, pretty short-term results, which means that they can move up to the next term on the rat ladder. If you commit to being an exploit, uh explore person, you're on a longer term between notches on the ladder with a lower chance of hitting them. So we're almost baked into the system.

The Innovation Phase Model Explained

David Cropley 4:40

Exactly. I mean, that's that's one of the elements of what I just described as this sort of general discomfort with change and so on, is because, you know, people, individual people also, as you've just described, you know, we've all figured out exactly, you know, what do you need to do to get promoted? What's the best way to get ahead in this organization or in in life in general? And you know, people are good at at sort of figuring out those pathways. A colleague of mine, one of his favorite sayings used to be, structure determines behavior. And, you know, if you know that if the structure of this organization is that if you do this, this, and this, you'll get promoted, you'll be successful, then no surprise that people do that. It's the same in universities. Professors and and academics have all figured out the things that get you promoted and the things that don't get you promoted. And so it's no surprise that people quickly work out the best pathway to achieve their goals, promotion, increased salary, satisfaction, and so but the reality is if an organization doesn't innovate, it's gonna die.

Mark Blackwell 5:42

So let's unpack some of the tools that you've developed to help guide people to stop thinking that innovation is some mystic spirit, but is something that can be measured and managed. First of all, you've got a framework. Can you tell me what that is?

David Cropley 6:00

Well, so the the the framework that you're referring to is not something entirely new. What I and some colleagues did many years ago is we took existing sort of concepts of the stages of innovation or invention, and you know, based on our research and thinking we we added some some extra detail to that. But it's a thing we we call the innovation phase model. And uh so that looks at at a set of core stages that individuals or organizations have to work through in order to innovate. So I'm talking about things like first understanding that there is a problem that needs to be solved, and then stages like generating new ideas, and then evaluating those ideas, implementing them, and so on and so on. And we we developed what we thought was a more, a slightly more detailed model that better reflects reality. And another feature of it is that because uh the work I was doing and the people I was working with at the time were psychologists, and uh was putting a sort of psychological spin on what happens in each of those stages. So what results from that is is a stereo series in our model of seven steps of innovation. But then by applying these different psychological factors, you end up with a kind of two-dimensional matrix of, if you like, seven stages across the top and six psychosocial dimensions kind of on the on the y-axis. And the that seven by six matrix gives you a much more detailed sort of roadmap of what things need to happen at what stage in order to maximize the the chances of successful innovation. And again, where where the the things I'm talking about are personal properties of the individuals, for example, you know, willingness to take risk and things like that, and are also the qualities of the ideas you generate and even the organizational climate. So we we came up with this basically the six by seven matrix that that tries to describe how to be successfully innovate according to these organizational and psychological factors.

Mark Blackwell 8:09

And do you do this by a survey? Do you do this by watching people? How does it work?

David Cropley 8:14

Yep. So so what then happened was we thought, okay, we've we've got that's a descriptive model. That's just a here in the ideal world is, you know, if if when you're in the stage of idea generation, we should all be thinking divergently and we should work in an organization with a culture that's very supportive and open and so on. But but that's fine as a sort of static description of an ideal world. But we realize that then with that six by seven matrix, we've also basically we've got the basis of creating an instrument to uh like a survey instrument to assess an organization to see how it matches up to our theoretical ideal. And that was quite exciting because I mean, for any academic, at least in my case, it takes it from theory to applying that in the real world. So from our six by seven matrix, we created a questionnaire, a set of questions that that tap into this ideal model and the way it works then. So we we we give it to an organization, it could be the whole organization, or it could be a division or a team within that organization. And the questions are all asking people to, they're all preceded by the STEM in this organization. So every question is saying things like, in this organization, people are allowed to take risks and try new things. And it's questions like that, they're all based on our theoretical model. And the idea is that that when you get, let's say, 50 people in a team or a or a part of an organization to answer those questions, then you build up a picture of how those people think about the organization and the conditions in the organization that we can match up to our theoretical ideal. So, for example, again to use that same example, if we said, if one question is in this organization, people are free to take risks and try new things. If everybody surveyed says no, they're not, then clearly, you know, you can say, well, if you're trying to innovate and if you're at this particular stage of the innovation process, you may run into difficulties because at certain stages of the innovation process, people have got to take risks and you've got to be given a bit of support and space. And if your people are telling you, we can't do that here, then then it's gonna be no surprise that you run into a roadblock. Now, where it where it got more interesting was with our matrix, because there are 42 sort of individual cells in that matrix, it means it's not just these generic sort of you need to be more open or you need to support your employees more. We could drill down to basically to 42 different things, and we could tell an organization how well aligned they are to the ideal for all 42 of those things. There's lots of innovation surveys that are very kind of high level. This one was really drilling down to say what exactly is going on, and and that allows us to also relate that to different stages of the process. Basically, in to cut a long story short, we could really pinpoint where the roadblocks are and also where the things, things are going well in the organization and paint this much more detailed picture to sort of diagnose an organization's readiness for innovation. That was the underlying idea.

Mark Blackwell 11:36

Well, I've been involved in these sorts of surveys before, whether the surveys or interviews, so I'm I'm have a natural interest and experience in how it works. I mean, obvious questions come to mind is how do you make sure that people don't put bias or sort of give a rosy flavour to the story that doesn't exist?

David Cropley 11:56

How did you think that's a real problem in in all sort of social sciences research, this idea of socially desirable answers. And the kinds of things you do to try and make sure that that doesn't happen is firstly, I mean, we didn't we didn't give any clues. So we didn't say, you know, these questions are asking you about, you know, whether or not you think this place is good for innovation. It was presented, no, not not sort of disguised, but we we didn't sort of explain in advance the purpose of the questions. And and uh we also the questions were then randomized so people didn't sort of notice a pattern that, oh, these are all questions about generating ideas, or these are all questions about the organizational climate. They're all randomly mixed around, so people aren't getting into a sort of a run of questions. And of course, you can never prevent and and some clever people might look at the questions and think, I can see what the good answer is here, or the bad answer. But by and large, I mean, we you know, you you you do what you can to prevent that. You also rely on people's kind of goodwill that you know they're they're trying to help find an answer. In we did, I don't know, maybe a dozen of these with with samples of you know between one and two hundred people. And um, you know, by and large, I mean, we we seem to get honest answers. So there are also statistical techniques that also help you to see if there are kind of you know patterns that shouldn't be there and so on. So by and large, we we we seem to be getting good data from this, and we were also getting answers that that didn't paint a uniformly rosy picture. So there were in every company or every team that we uh surveyed, there were always very good things, but also very bad things. So it seemed like people people were being honest about this, and you know, and if something was good, they said it was good, if something was bad, they said it was bad.

Mark Blackwell 13:51

I suppose one more thing that came to mind from doing this is you know, the goal isn't necessarily to score five out of five on a light card scale on every metric. Have you sort of normalized this by having a sample size can where you can show where the biggest gap or to help prioritize what are the most value-adding gap closures or something like that?

David Cropley 14:12

So the the the way we would this is with with our model, um, the the the we we created with all these 42 cells, we had four questions per cell, so a total of 168 questions. But the the model defines the the ideal answer for each phase of innovation or for each of these psychosocial dimensions. So the model basically says in the perfect world, these are the answers you would get, and and that organization would sort of score 100%. But of course, no, no, the no organization achieves that model. So the sort of the baseline for this was set by the theory that there's a theoretical perfect organization. And then any time a question or a response to a question from an individual deviates from the ideal, that's where you you sort of begin to see, you know, the the weaknesses and problems emerging. So we we set this kind of theoretical, this is what's perfect. And then as groups of answers were less than perfect, that's where we begin to see where the strengths and weaknesses of the organization lie. And of course, by doing it with, you know, a hundred people, then you you also begin to see, you know, areas where 90% of people are saying this is good, or only you know, 20% of people are saying this is good. So by having a reasonably large sample of individuals, that's that's the other way that you know you you begin to paint a reliable statistical picture of this.

Mark Blackwell 15:42

So you've got this great data from the grid of 42. What's your experience of going back to the organization and giving the results?

David Cropley 15:50

It was prior to the podcast, you know, you and I were exchanging some emails, and I explained that one of the difficulties of this is that it's somebody finally said to me, you know, the problem is this is like marriage counseling, and you're selling marriage counseling. And of course, nobody wants to be told everything that's wrong in their marriage or or in something else. So it's it's a hard sell. But it was very interesting because we usually, before doing this with a with an organization or a team, we always sat down with the manager of that team or the general manager of the organization and said, What do you want to know from this? And also, what do you think we're going to see in this survey? And it was always very interesting because every single time when we then went back and presented the results, we often got the reaction from managers saying, Aha, I thought it was that. You know, I thought we had a problem in that phase or in that particular dimension. And so there was a it was very interesting, like this this reaction of they they already knew in their heart, you know, what was strong and what was weak. And I think what a lot of managers found valuable in this was not suddenly having this revealed to them, like something that they didn't know, but it was confirming what they already thought and knew. And of course, that gave them then some tools and some some ammunition to say, okay, I I thought there was a problem in this area. Now I know there is a problem. Now we can we can really work on fixing that.

Mark Blackwell 17:17

So I guess from our very first discussion, one of the common findings is perhaps that leaders are c creating an exploit culture, not an explore culture. And it's the leaders themselves that are a key part of the issue.

David Cropley 17:31

Aaron Powell That sounds a bit pejorative. I mean, uh what we found in most of these, and these were you know engineering organizations and even a team of um firms that that made stuff, but also you know, teams of uh a group of sort of geotechnical engineers whose job was searching for new gas deposits out in the Australian Outback and even several times local government organizations. And um, really across the board with most organizations, even organizations that maybe saw themselves as a bit more innovative and researchy, they they tended to be good at the implementation. So the exploitation phase. I'd say it's the implementation phase. It sounds nicer than the exploitation phase, but but I mean, you're right. It's the the phase of actually, you know, once you know what it is you're trying to make or build or fix, then a lot of organizations were pretty good at that. And you know, they've evolved processes, but they know how to do the things that they're meant to do. Pretty much across all of the organizations, where they struggled more was the inventive, explorative or the innovative front end. It was how do you come up with the ideas in the first place? Because we once we've got a good idea, we know we can build it and implement it, but it's it's getting those solutions in the first place that was where most organizations struggled a bit more. So I mean it's it's exactly what you described, but I mean, just using slightly different terminology.

Mark Blackwell 18:58

One of the other things about innovation that is the myths that try to dispel is it's not a creative light bulb moment, it's a process. The other one is that innovation in the workplace is really all about new product development. But I've challenged that in as much as there's innovation required in our business processes and the way we get things done as much as anything else. Notably, I'm reading a lot of LinkedIn posts at the moment, which I make think like a very valid point, is if you're bringing an AI tool into a workplace, it's not a bolt-on. That's 10%. The 90% is thinking through how does this change our business model, our value proposition, the way we get things done, all of which is incredibly innovative. What do you think about and have you applied this tool to those sorts of opportunities?

David Cropley 19:49

No, no, not not yet, and but it's a a good point. But I I agree with you exactly. Most people, as you talk about innovation and things like that, people kind of can't help but think of widgets and you know and and tangible objects. But as you say, I mean, innovation could be product innovation, but it can also be process, it can be system innovation, it can be service innovation. But I think that there's a sort of natural human tendency to think more in terms of tangible products. And and sometimes that's kind of hard. I mean, especially engineers like that. Engineer, you know when even with engineering students, when I say we're gonna do a little exercise on, you know, coming up with some creative idea, they they just want to make something tangible and and then and play with it. And it's hard sometimes to to say, you know, let's think about more intangible ideas. That we can also be innovative there. So I think you're right. And when coming back to AI and so on, I I wonder if a problem that a lot of organizations will have is they'll treat it as if it's a product innovation when, as you have described, it's really a process innovation, let's say. And and because they treat it as the wrong kind of innovation, they're they're probably going to go about it in completely the wrong way. And and I suspect then, applying sort of lessons learned from engineering, that that you know, we're going to begin to see problems of time, cost, and quality. So, you know, we're going to see projects that take far longer than they were supposed to, that cost far more than they're supposed to, and don't do what they're supposed to do. And I think you're you've hit the nail on the head. It's it's not because their their intent is wrong, it's because they're fundamentally misconceiving what kind of innovation AI represents for them. And they'll they'll treat it as a bolt-on product thing when in fact it's a process innovation, and and they'll they'll misdesign the whole thing that they're trying to do because they're they're they're categorizing it incorrectly.

Mark Blackwell 21:50

Well, the way I think about it, like they're missing the whole front end of innovation. They're just saying, oh, well, we've been given this new shiny toy, we're going to add it because it looks good, without sitting down defining the problem, working out what the value propositions they think about from an internal business model, prototyping, and so forth. I'd like to see more innovators applying their thinking to bring it in to the world rather than just dumping it on people who haven't had the experience. Because the reality in organizations, most people who know anything about innovation, I'm afraid to say, are restricted either to the RD department or the new business development department. Yeah. And they live in that lovely world when everyone else carries on exploiting.

David Cropley 22:30

So that's a big, I'm sure, frantic pressure, you know, to we've got to do something with this AI, quick do something useful with it. And so, of course, if you're under time pressure and going back to the whole innovation and creativity thing, I mean we Know that to be innovative, there's a sort of Goldilocks zone of having enough time, sufficient resources, and so on. But if you if you cut those out of the picture because there's a perceived need to hurry, then you can see the train wreck from a mile away. Um if you if you think about these processes a little bit.

Mark Blackwell 23:09

Listeners, if you're in any doubt about the need to think about your problem first, just check out the podcast we had recently with uh Roni Reiter-Palmon. And she's got the science behind that on the need to invest time thinking about the problem. So that's right.

AI Hype And Limits Of LLMs

David Cropley 23:22

I mean, that's Ronnie's whole thing is the you know problem finding and problem identification and the whole that whole front, the front of the front end of innovation.

Mark Blackwell 23:32

Yeah. But I guess there's some people out there saying, well, anyway, David and Mark, AI is going to fix this for us anyway. And so your your discussion's just a bit academic. The way AI is moving on, they'll it'll taken over this by 12 months. What's your response, David, to that?

David Cropley 23:48

I mean, I you know, I I post a lot of things on LinkedIn, and and sometimes, you know, you you have to be quite blunt on on LinkedIn to get through. So, you know, I I sometimes, I occasionally post and just to remind people, you know, I'm not anti-AI or anything. My background is an engineer. I I think these things are fantastic. But I I like to just think, you know, just just cool your jets, slow down a bit, and let's just take a deep breath before we jump in. Because as we've just been describing, if you get too carried away too quickly, you're going to make mistakes because you haven't, as you said, you haven't identified the the actual problem you're trying to solve. You're just solving a symptom of another problem. And in terms of, you know, my my day job, so to speak, is teaching systems engineering. And there it's all about investing heavily in the front end of stakeholder identification, needs identification, translating that into technical requirements and so on. And the whole reason for that is because for the last you know years, organizations keep relearning the lesson that when you don't do those things, you end up spending billions of dollars on white elephants. So we've got to we've got to sort of send that message of just slow down. But my my view, I mean, with AI is, you know, there there are like probably most technologies, there are fantastically useful things about it. I think people are getting maybe a bit overexcited with with like AI in the form of large language models, because it, unlike a lot of technologies, it it has this sort of human-like seductive element to it, you know, which which I don't know, uh, you know, cranes and levers didn't have a thousand years ago. But, you know, so so AI, AI is sort of, it sells itself very well because, you know, it's got this human-like quality to it, but it's no different from any other technology. So, you know, we we need to understand it, we need to slow down and learn how to make use of it, how to exploit it. And I mean, I I go on a lot on on places like LinkedIn about, you know, weaknesses of large language models with respect to things like creativity. And uh and people for the last three and a half years, people have been saying, oh, just wait six months and you'll see. And every six months, you know, the things they thought would would improve haven't improved. Other things have, but you know, we we'd we've just got to sort of take a deep breath and and not get carried away, I think is the the the main message I'd say to people. But but I also believe, at least with respect to creativity and and that core element of innovation, language models as they are now have hit a ceiling. And that if they're going to to be able to be more creative and come up with brilliant new ideas, it's going to require a different technology. That that might be a very major upgrade to the way that large language models work, but it's not just more data, yeah, but it's more likely going to be a different technology altogether. You know, there are easy to understand technical reasons why large language models produce average outputs, which which look pretty good and so on, but there's at the same time, there are uh very straightforward reasons why they they can't just invent things out of nothing. And so we've got to be kind of careful to understand what they can do for us in innovation processes.

Mark Blackwell 27:15

As an engineer, you've classically tried to model this mathematically. I was reading. Can you tell me this?

David Cropley 27:23

Well, I I thought so I I published a paper recently describing this ceiling on creativity, and it was kind of the perfect scenario for me because I come from an engineering background, but I've worked for 25 years with psychologists in the field of creativity. And so, you know, I always feel like I've I've been a bit of an outsider in the field of creativity research, but this was one time I thought I can really bring something new and a bit different to this psychological view of creativity. Uh, and that was saying, you know, I mean, that there are like we understand the mathematics of how large language models work. We also understand how, you know, very clearly we have very clear definitions of creativity. And if we just sort of marry those two things together, you know, it's it's possible to basically calculate, and it's not a not a difficult calculation. We can show sort of mathematically that if if you take the agreed way that we define creativity and just apply the sort of mathematics of large language models to that, there's a very clear sort of upper limit on the creativity, and it's it's not very high. So on the spectrum of sort of naught to one from no creativity up to maximum creativity, I've shown that large language models only sit at about 0.25, which is not useless. And 0.25 sounds below the middle, but it's a kind of creativity is a sort of nonlinear thing. So 0.25 corresponds pretty much to the human average. But being able to show this mathematically and say, you know, we we there's a clear answer here. Large language models can produce average human creativity, that's quite useful, but just don't get carried away with it and don't assume that it can produce, you know, genius-level creativity when it can't. But but average creativity is still a useful thing, the same as, you know, a um I mean a car that only goes 20 miles an hour can still be a useful vehicle. It doesn't need to go 100 miles an hour. So there's still usefulness in average creativity as long as we know what it's producing and and we sort of work out how to incorporate that in what we do.

Mark Blackwell 29:31

Dave, thanks very much. One final question just to wrap up. So if you were CEO of a mid-sized company or a VPGM in a corporation, you've just been appointed, and you realize that the company has been living off old products for too long or old processes for too long, whether it be new product development or internal process, and innovation in its broadest sense needs to be done, what would be the first three things to come to mind to think about?

David Cropley 29:59

That's a that's a tough question. Sorry. I think I mean if I don't know, I'll I'll I'll start talking and see if we we come out with any better. I think uh I mean but based on what I've learned about creativity and innovation over the last 25 years is is you know that well that that you can't just um subcontract that off to to a like a you know a small team, like a Lockheed Martin Skunkworks type thing or whatever. If you just you know give and say they're the innovation team, go off and be innovative, that ultimately won't work. It's so it's the the the one lesson is if we're going to be innovative, it's gotta be a pervasive culture in the whole organization. And the second point that follows on from that is, and therefore you've got to be prepared to spend a bit on that because it's not gonna come for free. So we're it's got to be pervasive part of the culture, it's gonna require some money, some time and effort. The third thing, maybe this is is more of a sort of, and here's, you know, here's why this is a good thing, rather than something I'd change tomorrow, is, you know, I've said this in various talks and things before, that um, like, you know, it's easy to say, oh, it's costing us money and and wasting time, but when the change comes that it's going to kill you, then that's when you'll be glad that you invested and that it became part of the company culture. The problem is that nobody knows when those changes are coming, so it's very easy to ignore it. And if I can go talk about the example of Smith Corona for one minute, just that I think illustrates this perfectly. Smith Corona made typewriters. You may have even used one like me, you know, in your undergraduate days. And um, Smith Corona started making typewriters in the late 1800s, and they were the world's best typewriter manufacturer. They were innovative, they constantly introduced new things. They they went from mechanical to electric typewriters, they introduced the ball head, you know, instead of the keys and self-correcting typewriters and so on. They were great at making typewriters. And then in 1981, I mean almost overnight, a change occurred, and that of course was the introduction of the PC by IBM. And within months, Smith Corona was was in trouble. Interesting thing about that that goes back to the points I made is that they were they were somewhat innovative, but they still weren't innovative enough because their solution was to try and make better typewriters. So that they were still thinking only in terms of a of an incremental innovation mindset. So, but but not in a in a disruptive innovation mindset. So they just said, let's make better typewriters and that'll win our customers back. They went bankrupt, they they got reconstituted and tried again, but made the same mistake and ultimately went out of business. Although they they do exist, they vestiges of Smith Corona still exist in the the uh those label makers and so on. That tape, that was one of their sort of technologies. But so the the third point is first point was it this has got to be across all our culture. The second point is you've got to be prepared to spend some time and effort on it. The third point is to understand that it's that it's a mix of both incremental but also disruptive innovation. You've got to have some proportion of that effort, is not just doing things better, faster, and cheaper, being incremental, but a proportion of it also has to be disrupted. Because one day something will come down the pipeline, a new government regulation, another piece of technology, a global crisis that will either wipe you out or be your moment in the sun. But you've got to be ready for that with both incremental and disruptive innovation. So a little bit of that, really, the really new ideas are important as well.

Mark Blackwell 33:43

David, thank you very much. Sorry for springing that on you, but it was a great response. It may be an easier one. Been a great, fascinating podcast, and I'm sure our listeners are gonna want to know more about the frameworks, the and diagnostics, uh, and maybe your books. Tell can you tell us where they can find resources to learn about it?

David Cropley 34:00

Yeah, well, the the the so uh with my my colleagues, so my father is is a colleague who worked on a lot of this with me. He was a professor of psychology and a creativity researcher for many years, and then we we sort of started collaborating about 25 years ago. So he and I wrote uh the one particular book that's relevant to this discussion and the model and so on is a book we wrote called The Psychology of Innovation in Organizations. It's it's published by Cambridge University Press, but people will find it on Amazon. That's the easiest place to go. Um as far as the the instrument and so on is concerned, the the best thing is to contact me. If if a person saw the podcast and thought I'd like to do this with my team, contact me. You can find me, of course, on LinkedIn or at Adelaide University. You know, of course, all that's all public sort of websites and emails and and information. I I can I can send you those details as well to share uh on the podcast. But I'm very happy if somebody has a serious inquiry and I can I can share more detailed examples and sort of sample reports of how we present this information and so on. But but I'm yeah, I'm happy as as a sort of university academic trying to engage with industry, I'm very happy for people to contact me directly.

Mark Blackwell 35:13

Thank you, David. That's great. Really enjoyed the show. Thank you very much. And listeners, hope you enjoyed it too. There's plenty more to come. So tune in to the next episode of Arkaro Insights. Be sure to subscribe. Thank you. Thank you very much.