Information is used all over the place. It is vital to all activity, and it holds particular value when it comes to decision making and problem solving. Most activities seek to make information work for them, by getting it to work as best as possible for their needs. Information is used to help people organise and process information they have. It is also used to help people understand the information they have so that they can make decisions about it.

So what kind of information does a business need? Well, information provides context for other data, it provides information for analysis and problem solving and it gives full context to statistics. For instance, a single customer’s sale in a restaurant is very detailed information-it becomes information when the company is able to accurately identify which dish is the most popular or least popular.

In this context we are talking about something like customer satisfaction surveys which allow researchers to track customer satisfaction over time, identify patterns, draw conclusions and attribute reduction. Similarly, another important example is researching product attributes by using a design model. This way, the search for a better product attribute can be constrained according to a research objective, which in turn provides information about which attributes are most desired.

In fact, the current literature on human preferences focuses on studying descriptive and contextual variables. This way of thinking has led to a set of specialised research fields, of which one of the most prominent is Covid-19. The basic idea behind Covid-19 is that men and women differ in how they perceive and interpret information, and how they form general thoughts. By observing two people, and asking them a series of questions about a range of factors, such as colour, sex, product brand names, price and so on, the researchers then build up a profile of the individual’s preferences. The same process can be applied to rating the strength of personal beliefs.

In a recent study co-authored by Christopher Stringham and academics Antonio Criado, Floris Vrethagen and Wimbledon Phillips, the researchers constructed an experiment in which they used two robots, each equipped with unique specification sets, and asked the participants to rate the robots on a five-point scale. The results showed that the robots were indeed different in how they formed general impressions of the user, with the male robot being perceived as stronger and more aggressive than the female robot. The same research, performed with a similar design, also showed that the two robots had different opinions about the same physical problem: a common task, such as lifting a cup, was solved efficiently only with one robot (the one equipped with a strong arm) while the other robot was deemed inefficient because it could not lift a cup with its arm. This study therefore demonstrates the potential value of designing an artificial intelligent system, which can be trained to form unbiased opinions about complex problems, using a face-to-face model methodology.

However, this research does not demonstrate that the abstract conceptualization approach can be applied in designing products or services, and future work may still need to be done on whether or not abstractions are useful in this context. Instead, it is probably best to consider using concrete analogies in product or service design, which are more directly applicable and which provide greater meaning to customers. This is because people generally accept abstract arguments when they are explained in a concrete fashion, rather than instantly accepting an abstract idea presented in an overly-technical or ‘vague’ manner. Moreover, abstract arguments, when supported by concrete examples, can better explain complicated issues than purely hypothetical ones.