**2.2 If you do not know where you're going…**

…any road will take you there. Why do the solutions offered to customers occasionally make jumps? Is digital 'better' than physical? Is service 'better' than product? According to the next big finding of the TRIZ research, they are indeed ultimately better because the top of the new s-curve sits further up the y-axis of **Figure 2** than the top of the previous curve. The y-axis, as discussed earlier, could be any of a host of different parameters. It could also be defined to include *all* of the parameters that customers might be interested in. TRIZ calls this integrated parameter, 'Ideality'. More generally, it is known as 'value'. Whichever label is used, the meaning effectively becomes the same: ideality or value is the sum of all the positive things customers want, divided by the sum of all the negative things they do not. Examination of the 2% of successful innovation attempts through this ideality lens reveals a very clear direction of success: over time, customers expect the positives to increase and the negatives to decrease. Hopefully, this should not be a great surprise to anyone. The directionality concept becomes interesting, however, when the idea of an *ultimate* destination is brought into play. Theoretically at least, the end point might be seen as the point where customers receive all the positives they want and all the negatives have disappeared. The ideal solution, in other words delivers 'free, perfect and now' to all customers. Although simple to say, many organisations have profound difficulty with the statement's underlying implications. Not least of which is, if customers expect 'free', how does the provider make the money required to stay in business? For enterprises operating in the physical world, the answer is that 'free' will likely not happen for a long time. For those operating in the digital space, however, because it is so much easier to change and evolve solutions, it happens much faster. To the extent that e-service organisations like Google and Facebook effectively already operate as 'free, perfect and now' businesses. No doubt there will be deep philosophical arguments about some of the other implications of the 'allthe-benefits-none-of-the-negatives' evolutionary destination as the ideality concept becomes more widely known. One of the implications becoming most visible relate to the moral and ethical aspects of mankind's 'direction': from an e-service perspective, the easiest of the free, perfect and now destination elements to achieve is 'now'. Innovations that provide customers with instant gratification of their perceived needs has resulted in a growing awareness that the increased convenience can too easily arrive at the expense of meaning [11]. Fast food makes for convenient fuel. Fast food ordered on an app makes the job even easier. But, as can be seen in the slow-food movement, and the rise in home-cooking through Covid-19 pandemic triggered lockdowns, the preparation and consumption of food is a highly social and highly meaningful act. Ultimately, if the ideality destination principle is interpreted in its pure form, this kind of convenience-versus-meaning contradiction merely means that we will not achieve a true Ideal Final Result (IFR) solution until it has been solved. This is a topic that will be explored in more detail in Section 3. In the meantime, the discussion here about emotion-related issues and moral and ethical debate takes us to the next cluster of first principles emerging from the study of the 2%...

#### **2.3 If you do not know where you are**

…in the same way we need a compass to point innovators in the direction of future success, **Figure 1** suggests that the most common reason for failure in the e-service domain is that the project team does not know where it is starting from. Projects get launched, and the team jumps off a cliff ('Crosses The Threshold' in Hero's Journey terms) with a mistaken understanding of where their customers are.

#### *Systematic e-Service Innovation DOI: http://dx.doi.org/10.5772/intechopen.96463*

The heart of the problem here, from a first principle perspective, is that humans have two brains. A fast brain and a slow one [12]. The fast (limbic) brain makes near instant, emotion-based decisions about what a person wants, and the slow (prefrontal cortex) one rationalises those decisions. The fast brain provides the 'real' reasons a person wants something; the slow brain provides the 'good' reasons. Both of these need to be present if the customer is going to make a decision to hire our novel e-service solution. By far the easiest of the two for providers to deal with are the rationalisable, 'good' reasons. These are all the things that get written into the service offering descriptions and pricing information on the website. All the information, in fact, that the myriad competitors will also have on their website. Which in turn why there are so many e-service price comparison sites. Unfortunately, few if any of these offerings has anything to say about the information the customer's fast brain is looking for. There's a frequently used saying in China: 'when all else is equal, we buy from our friends. When all else is unequal, we still buy from our friends'. Friendship, in other words, very easily trumps the tangible offerings made by most e-service providers. The problem this gives innovators, unfortunately, is that amorphous concepts like 'friendship' are very difficult to measure. The same goes for a host of other emotion-related parameters such as trust, empathy, anxiety or confidence. But just because a parameter is difficult to measure, does not excuse a choice to go and measure something simpler instead. The next important question then becomes, what were the first-principle 'real' reason parameters the 2% focused their attention. The answer is shown in **Figure 4**. On the left hand side are the four parameters that form the 'decision-making' foundations of the limbic brain [13]. On the right-hand side are the six parameters that form the equivalent core of the human moral decision making process [14].

Having recognised the fundamental nature of these ten parameters comes the recognition that a good way to help ensure an e-service innovation attempt ends up in the 2% category is to find ways to measure each of them. This, in fact, has been the rationale and focus of PanSensic since it's inception fifteen years ago [15].

So much for measuring the fast-brain/real-reason information required to inform innovation projects. This might be the more difficult of the two types of measurement required, but it is also safe to say that only a small proportion of innovation attempts get the easier part right either. It may indeed be easier to formulate a specification describing the tangible parameters that will motivate customers to hire a provider's service solution, but unless the search incorporates contradiction-finding, then the heart of a potential innovation opportunity will have been missed. What usually happens here is customers are surveyed to establish what attributes they want, and then, finally, how much they are prepared to pay for them. One of the benefits of shifting to digital services is that it becomes very easy

**Figure 4.** *First-principle human emotion and morality drivers.*

to conduct experiments that will help innovators to establish price elasticity. This is something readily observable on many online retail websites in the form of occasional 'personalised' special offers, or, more generally, prices that are made highly dynamic. Dynamic pricing in this sense may be called an innovation, but its an innovation more for the provider than the customer. And, moreover, such models completely fail to identify the main customer innovation opportunities. In complex systems, it is not so much the attributes of a system that drive purchase so much as the relationship *between* those attributes. The moment a provider attempts to deal with such relationships as optimisation opportunities, the innovation opportunity is effectively discarded. Customer might *expect* to have to make trade-offs between, say, price and quality, or efficiency and effectiveness, or long-term versus shortterm, but each time providers encourage such behaviour, innovation opportunity is removed. Again the real task here is to look at these kinds of trade-off from a contradiction solving perspective. Customers do not fundamentally wish to choose between quality and price, they want high quality *and* low price. Hence deploying measurement methods that, first, identify these kinds of underlying contradiction, and, second, are able to prioritise them, in effect becomes the only way – from the tangible side of the story – to identify the genuine innovation opportunities. After a decade or so of working on this problem, the most reliable method of revealing contradictions involves two measurements: a) which attributes make customers express positive emotions, and, b) what are they frustrated about? Necessity may be the mother of invention, but frustration, it turns out, is the mother of innovation.
