And so I know what I want. I mean, I sort of know what I want regarding some aspects of my life. That business I want to put together in connection to smart cities is a centre of experimental research focused on studying human behaviour in the interaction with smart technologies. The business idea behind is to supply experimental knowledge, and the tools for discovering new experimental knowledge, to businesses which would like to accelerate their innovation and to smoothen their relations with big institutional customers. The metric of utility I have come up with is the possible reduction of payment lag in those customer relations. This is what I have come up with so far, and you can find it in my last update in French: Ça semble tenir le coup. Ça promet .
The business concept I am entertaining for this experimental centre is based on sort of an educated guess, which I have formulated, over years, as I have been studying different business cases: when something lasts longer than it technically should last, someone in that process is learning something by trial and error. When an IT company, like Asseco Polska, whose financials I highlighted in that last update in French, used to receive payment for its services after 80 days, on average, in 2011, and this time extended to 101 days in 2016, the process of interaction with the average customer must have changed. Of course, some of that extension is due to changes in the financial liquidity in customers themselves. Still, as I try to reconstruct the interaction between an IT provider and an institutional customer, there is that component of tailoring the solutions supplied to the exact needs and characteristics of the latter. The supplier learns by doing, and that learning frequently manifests itself as an additional lag in payment: the customer withholds a part or the total of the sum due to the supplier until the latter removes from the solution provided what the customer sees as flawed.
This phase of a contract is quite tricky. It is crucial to separate true necessity of improvement in the solutions provided, on the one hand, from tactical withholding of payment, on the other hand. Said necessity of improvement seems to follow a common pattern, well-known from the management literature: engineers think in terms of blueprint (coding in the case of programmers), whilst the end-users of a technology think in terms of the most basic utility. That basic utility has to include fool-proof safeguards: if clicking the wrong link on the screen blocks an entire accounting system, there is something wrong.
You would say ‘No, these things don’t happen anymore. Digital solutions are past that phase’. Well, recently, I attended a presentation, by a fellow scientist, about how he and his team had improved some utilities in the SAP system. For the mildly initiated: SAP Business One, marketed by SAP SE, Germany, is one of the most advanced ERP-class systems in the world, maybe even the most advanced one. So, those fellow scientists from Krakow (Poland) did a simple thing: they took some parts of the SAP interface and they submitted it to behavioural testing, where users were being observed with an eye-tracking device. After the tests, the interface has been modified so as to optimize its functionality for the eye-movements of the users. Result: a series of operations which took the users 6 minutes before the modification, dropped to 1,5 minutes. Yes, this is a fourfold rise in productivity, and it was just one tiny piece of the total functionality available in SAP Business One.
My home university, the Andrzej Frycz Modrzewski Krakow University (Krakow, Poland), prides itself to rely on 100% their own technology. Library, grading, syllabuses, e learning platform: all that is genuinely made for the university by an appointed team of IT engineers, without any Google-made prosthesis. Still, man, sometimes you wish it was Google-powered somehow. It takes years to optimize each piece of integrated software so as to make it really functional. A lot of this optimizing relies on communication, which, of course, is what communication usually is: imperfect. I imagine the same thing happens in hundreds of cases, when an integrated technology (it does not have to be exclusively digital, you know) is being implemented in a large social structure. It is only after starting the implementation that engineers face the real users’ behaviour, and this is when the real tailoring starts.
Summing it up, partly, here is my idea: providers of complex technologies, when they have a big institutional client, go through that tricky phase in the contract, when the technology is basically in place, the invoice has been issued on the buyer, but the latter is still no satisfied and withholds the payment, and his dissatisfaction is well-founded, as the true utility of the technology installed is only taking shape, and it brings some surprises. The experimental centre I want to create would run experiments useful in minimizing the trial-and-error component in such situations. I would like to create a body of experimental knowledge about human behaviour in interaction with smart technologies, and experimental tools for developing more of such knowledge, so as to shorten the payment lag that the suppliers of smart technologies experience in their relations with big institutional customers.
Now, I am advancing a behavioural hypothesis: any large social structure, when absorbing a new technology, behaves like a mean institutional customer, and it pays the full bill only after it is fully satisfied with said technology. For those of you who want solid theory, here comes a piece: Robertson, T. S. (1967). The process of innovation and the diffusion of innovation. The Journal of Marketing, 14-19. The idea is the following: when you are a supplier of advanced technologies and you are selling your solutions on kind of a retail basis, to many small customers, they all make sort of a community in the sense that their behaviour regarding your technology is coordinated through socially recurrent, partly ritualized patterns. In that community, you will find the early innovators, the early adopters, the sceptical late adopters, and the fence-sitters. You can fully cash your returns on investment only when all those customers have absorbed your technology. If you are launching your technology in a form adapted to the tastes of the early innovators, it is going to bump against the wall of different tastes observable in more conservative customers. You need to modify your technology so as to make it easier to swallow conservatively. Modifications take time, and you need much of that time just in order to figure out the right modifications to make.
I think I can start generalising those ideas in the form of a model, regarding the interaction between the suppliers of technologies and their customers. Thus, the mission of my experimental centre will be to reduce the cost of innovation, or CI, incurred by the suppliers of smart technologies for smart cities. The cost of innovation is made of two component costs, namely: the cost CIN of ongoing invention in the strictly spoken new technologies (i.e. those not marketed yet), and the cost CIM of improvement/modification in the already marketed technologies. Both CIN and CIM contain two further parts: the cost of engineering work strictly spoken (blueprinting the technology), which I am going to symbolize with ‘B’ in subscript, and the cost of discovering behavioural patterns in the end-users of the technology in question, and I symbolize this one with ‘D’ in subscript.
Right, I am an economist, and so I need writing some formulae, so here is what I am thinking in mathematical terms:
[CI = CIN + CIM = CIN;B + CIN;D + CIM;B + CIM;D] => [CI = CIB + CID]
The last part of that complex logical implication, or ‘=> [CI = CIB + CID]’ means that with the assumptions given above, the total cost of innovation is, in other words, the sum of what we spend on the strictly spoken blueprinting (CIB), on the one hand, and the expense on patterning the users’ behaviour (CID).
The mission of my experimental centre will be to shave off the CID. Next question: how the hell do I want to achieve that? Next piece of theory coming, then. The process of innovation, i.e. the process of blueprinting a technology, and optimizing it as user-friendly, can be represented as a series of interactions involving a prototyped technology, and a user testing it. Each prototype is an educated guess about the optimal utility for the users. Each interaction of a prototype with the users who are testing it can be a success (correct guess) or a failure (go and figure it out again, anew). In the process of innovation, we have n interactions, with p successes and q = n – p failures. The total CID cost comprises both the expenses connected to successful trials, and to the failed ones. Now, I start sailing uncharted waters, at least uncharted for me, as I am asking myself: how can I reduce the total cost of that process and/or increase its efficiency?
In my previous update in English, the one entitled ‘And so I ventured myself into the realm of what people think they can do’, I started developing on that idea that p successes and q = n – p failures can happen as many distinct sequences, technically equiprobable, but actually very different in their outcomes. My most personal intuition, which comes from my academic teaching, my sport practice, and my experience with the Wim Hof’s method of breathing, all that tells me that the sequence which works the best for learning is the following: ‘serious ass-kicking, technically a failure >>> small success >>> small success … (a whole series of small successes) >>> another ass-kicking (another failure) >>> and I loop on a series of small successes’ etc. My nervous system learns new skills through a sequence of successful attempts, not through a sequence of failures. Failures just teach me what not to do, but I need those small successes in order learn what to do, or form the right pattern of doing things. Still, failures keep me sharp and alert, they prevent me from becoming too self-enclosed, or, I should rather say, failures can keep me in that blessed frame of mind under the condition of being properly taken in.
Applying this general wisdom to experimenting with new technologies implies that I can calibrate successes and failures into different sizes, sort of “big success <> small success”, “big failure <> small failure”. I imagine an experimental sequence, where engineers start with confronting an advanced prototype with testing from the part of users, and I arrange this particular step so as to make failure as probable and as deep as possible. I select the testing users, and I design the conditions of testing so as to put every possible hurdle and trap on this path. I want the engineers to bounce against something they are very likely to perceive as a brick wall. This is the initial ass-kicking. The next step consists in studying that failure very minutely, so as to own it completely. This phase of the experimental sequence brings two major learnings. Firstly, it shows what happens to our technology in a really unfriendly environment. This is the equivalent of those IKEA armchairs being crushed by a mechanical press thousands of times a day. Secondly, it teaches the engineers how they behave, as human beings, in the presence of a major failure. Behavioural study of the engineers is as important at this stage as that of the users.
In Stage 2, engineers modify the prototype on the grounds of the ass-kicking received in Stage 1. This time, it is important to break down the sequence ‘modification >> testing’ in very small steps, so as to make successful guessing of the users’ needs as probable as possible. In this stage, engineers learn to interact with users on a current basis, and, hopefully, create a series of successful – i.e. fully functional from the users’ point of view – modifications.
Now, the big question is: how to alternate Stage 1 and Stage 2. In other words, when is it the right moment to expose ourselves to the functional outcomes of every possible flaw in our technology? I have to ruminate it a bit.
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