Re-creating the engagement in managerial learning

Introduction

Towards Organizational Knowledge edited by Krogh et al. (2013), a Festschrift honouring the lifework of Ikujiro Nonaka, dealt with the ecology of knowledge creation. In chapter 11, Konno who together with Nonaka in 1998 introduced the concept of ba—the shared context formed among individuals through interaction in pursuit of knowledge—points out that we are now in need of knowledge creation management as a follow-up concept to knowledge management. The idea behind knowledge creation management is to connect “social innovation, business model generation and a new paradigm software development method” (Konno, 2013, p. 205). According to Konno (2013, p. 206), knowledge creation management can be considered an area that will transform capitalism and society. A paradigm shift in “the traditional theories of management” is required, whereby business organizations move away from the analytical model of management to a more creative one (Konno, 2013, p. 206). In the same chapter Konno (2013) takes Peter Drucker’s ideas and questions about post capitalism (Drucker, 1993) as the starting point for his own answers. Drucker (1993, p. 177) states:

We need an economic theory that puts knowledge into the centre of the wealth-producing process. Such a theory alone can explain the present economy. It alone can explain economic growth. It alone can explain innovation. It alone can explain how the Japanese economy works, and above all, why it works.

While bureaucratic rationalization and mass production techniques were capable of producing economic value in the initial stages of capitalism, Drucker (1993) argues that in today’s economy new factors have come into play and hence new means/techniques for creating value have appeared such as flexible networking and collaboration among knowledge workers. Konno (2013, p. 204) suggests that Drucker (1993) was “sounding a warning to our society and that we should seek such an organizational evolution beyond the industrial society.” Furthermore, Konno (2013, p. 204) argues that knowledge creation theory is one answer to Drucker’s request for a new economic theory, and suggests that we make the transition from knowledge management to knowledge creation management and that we consequently require a new management model.

Paul Mason (2016) also discusses Drucker (1993), though from a different perspective. He concentrates on two questions posed by Drucker (1993). First: “How do we improve the productivity of knowledge?” (Mason, 2016, p. 273). Second: “Who is the social archetype of post capitalism?” (Mason, 2016, p. 274). Dealing with the first question, Mason (2016) argues that humanity came up with a better solution than Drucker’s: the network, where the “focus [is] on ‘connection’ and the modular use of information as the key to productivity” (p. 274). Regarding the second question, Mason (2016) wonders whether it could be Drucker’s “the universal educated person” (p. 275).

This paper adopts a semiotic and, ultimately, generalized model-theoretic approach. Its purpose is to reintroduce Umberto Eco’s semiotic approach to knowledge transfer and knowledge creation, called “narrative fallacy” (Eco, 2011), into managerial thinking and learning to support the ecology of knowledge creation within the grammar of knowledge creation management for prudent capitalism. It will implicitly link Drucker’s (1993) suggestions with Paul Mason’s (2016) equally interesting thoughts on post capitalism and show what we can learn from Umberto Eco (2011) to further develop their ideas.

In Confessions of a Young Novelist (2011), Umberto Eco says that when defending his doctoral dissertation, he was accused (though in a friendly way) of a sort of narrative fallacy because he had presented his research in the style of a “whodunit” narrative as if it were a detective novel. Instead, he should have presented his conclusions. However, this criticism inspired Umberto Eco with the idea that “Every scientific book should be ... the report of a quest for some Holy Grail” (Eco, 2011, p. 7).

In this paper, we will argue that Eco is not only right in certain contexts but that his experience sheds light on the idea that when trying to apply knowledge properly (i.e. considering the limits of the application of knowledge) we have to concentrate on understanding how that knowledge came about (cf. the discussion below on the crash of Air France flight 447 and how prior learning on the coming about and credibility of the data could have helped). We will establish whether it is possible to reduce knowledge to rules and practices (economic or otherwise) so they can be used alongside universal common sense, whether we need to understand any exceptions to these rules/practices as well, and how the background knowledge for the use of the rules/practices could be enriched in practice. Our arguments will be supported by diagrams and real-life cases/examples.

As indicated above, we will also outline a model-theoretic scheme for producing analyses, explanations and actions in the context of reconceiving organizations from the perspective of knowledge and innovation ecosystems. This will be based on an examination of how knowledge comes about in both an explanatory and empirically descriptive and/or operative manner, or action guiding, way, and how the two approaches can be appropriately linked.

The narrative fallacy

Our point is illustrated by the Air France flight 447 disaster described by Oliver, Calvard, and Potočnik (2017). The crash was caused by the malfunctioning of the pitot tubes, which had become clogged up with ice crystals during the high-altitude flight. Pitot tubes convey information on the plane’s altitude and airspeed to the autopilot. Because the tubes became clogged, the autopilot turned off automatically and the pilots had to revert to manual control. This is where a lack of background knowledge and a lack of knowledge about the limits of rules and systems becomes relevant. The presence of the ice crystals in the pitot tubes caused the system to show the wrong altitude values, suggesting the altitude was lower than it in fact was. The pilots subsequently wrongly decided to gain altitude, which caused them to lose speed. When the pitot tubes eventually became clear of the ice crystals, they began showing the correct values. However, by this point the plane had slowed by 90 knots, and the pilots did not believe the readings. It is likely that the pilots were operating according to rules that stipulated they should take certain actions depending on altitude and airspeed. Had the pilots known the limits of this rule (if the tubes malfunction, an altitude lower than the real altitude may be displayed), they would not have made the fatal decision to climb, which led to the loss of speed. Moreover, had they known that the tubes would eventually clear as the ice crystals warmed up and melted, they would have known the speed readings telling them they were going too slowly were accurate, and would not have disregarded them. One of the things the pilots lacked that would have helped them in this situation was background knowledge of the technical side of how the system works. But since they lacked this knowledge, they were unable to handle the situation, and the plane crashed leaving no survivors.

Let’s return to the case of Umberto Eco (2011, p. 7), accused of a sort of narrative fallacy in his doctoral examination because he had presented his dissertation as if it were a detective novel instead of only presenting the conclusions.

[A] mature scholar, when setting out to do some research inevitably proceeds by trial and error, making and rejecting different hypotheses; but at the end of the inquiry, all those attempts should have been digested and the scholar should present only the conclusions. (Eco, 2011, p. 7)

This is obviously considered to be the appropriate academic approach and behaviour. Discussing the results in the proper way (especially when explaining their validity) should therefore also be a means of transferring/conveying some “knowledge”. However, discussing the results is only the cognitive or generalizable part of what knowledge amounts to, and cognition is only one side of the coin. The other side relates to what should we do with the knowledge, or how should we apply it. When we build up (or convey) just a kind of explanatory understanding, it does not help us to apply knowledge properly especially in new situations.

Umberto Eco’s (2011, p. 7) suggestion that “every scientific book must be a sort of whodunit—the report of a quest for some Holy Grail” is quite fascinating because it involves and supports learning engagement in both research and creating/constructing meaning and the application of knowledge. It can also be interpreted as signifying that we have to be able to fully understand the meaning of a case. Put differently, we have to make an effort to properly understand the core of problem. Maybe this has lain unforgotten since the times of the enlightenment. We need to provide the knowledge in a digestible way but the recipient is also tasked with having to make sense of the information conveyed or build up its meaning. This latter requires them to learn things, to be open to new insights and to the creation of a new epistemic resolution level and even be tolerant. These principles do not only apply only to managerial learning but also to learning in general. The practical problem is of course identifying and selecting the right background knowledge (to create meaning), without exerting pressure, but simply offering it up for discussion instead. We must steer a pathway between the (Socratic) ignorance of blind evolution and the arrogance of pretending to know—like stubborn parents making decisions on behalf of their children (thinking that they—like the senior management of a company—know what is good for their children/employees).

Some theory is needed here to explain the issue. Why do we think Eco is right to reject the accusation that he committed a narrative fallacy or was right not to convey only the conclusions of his research? What could be wrong with following the classical procedure? Are there any examples? Yes, but as you perhaps know, practitioners do not like to be reminded of the roots of their knowledge and theoreticians are not fond of keeping their feet on the ground. However, there is a good example of misusing fiction: the prisoners’ dilemma (Tucker, 1950). It does not even describe reality in its simplest form and we forget that originally it was invented to illustrate the ideas of game theory (Crocker, 2009). The only way to transform this fiction into reality would probably be for it to be enacted on stage. Nonetheless, it has been descriptively misunderstood, as being literally action guiding. A rational analysis indicates that the prisoners’ dilemma only accords with the theory if the prisoners defect. If one wants to do the rational thing, one “has to” defect, according to Crocker (2009), who sarcastically states “but at least you would have done the rational thing”, though you would be worse off. We prefer to think of this action as the faulty projection (operationalization) of theory onto reality. However, this example nicely corresponds to our understanding that an explanation may be valuable in one realm but in need of interpretation in other areas. We therefore need a constructive dialogue involving both expertise and lay knowledge, based upon the perhaps wrong assumption of a universally valid common sense, in which the aim is to understand the limits of application stemming from the simplifications around which our theories are built. The dialogue should include engagement or a willingness to strive to understand the limits of the classifications/categorizations we use (or depend on) to orientate ourselves in the world. The general rule is that the conclusions (of our research) should be conveyed as knowledge (about the world or appropriate parts of reality), but that they represent only a small part of the things that are can or must be used to provide guidance for our actions.

The prisoners’ dilemma can explain economic success in specific situations. However, when it is used in a practical/action-guiding way, it leads to the justification of short-sighted actions that rely upon explanations of economic success in which the maximization of individual profit is taken for granted (i.e. accepted as a sort of universal law of nature). Hence it lacks the corrective power of real life consequences, values and the possibility of innovative changes, obtained due to a better understanding of how our theoretical and explanatory theories come or came about. This is where the missing link in the evolution and application of scientific thinking can be found.

In our attempt to draw upon Umberto Eco’s thinking on the practical side of life, we can consider the relation between heart (emotions), brain (reason) and hands (the idea of manufacturing the world or at least tiny parts of it) outlined above. If we start with the heart, intuitively our values become important and we may reflect on or analyse the explorations which lead to (generalizable) results. In the next step (consciously or not) we may intuitively become aware of structures and concentrate either on reproducing a thing in the world or our understanding of the world (by means of argumentation). Meaning cannot just be created by conveying an explanatory argument. The third step involves applying our knowledge to new problems or situations (depending on the classifications or categorizations we tend to choose). It can also be called the analysis of replacement, because we can project our structural insights onto new cases and see the outcome as the operationalization of those structures. We now have the practical means to overcome the shortcomings of the narrative fallacy, where it is too close to stories about individual or personal experiences, and contains nothing else. However, we can also see that the narrative fallacy helps us to understand the cognitive part of our conclusions in an open and more practical way, which supports the pathway to creativity and beyond. The crude theory behind this triangle of heart, brain and hands (which could be used to help select the appropriate means of conveying meaning) contains a significant amount of model-theoretic knowledge and can be understood as a summary of the ideas underlying the geo example (see Figure 2) and can be projected in Figure 3.

Our geo example demonstrates the limitations of explanations. The shortest airborne distance between the two destinations shown on the flat projection differs from that in the spherical model due to the distortion of the surface. If we try to measure the distance on the flat projection, we will obtain the correct findings in this setting. But the spherical model offers a more accurate method of measuring distance, since it better reflects the reality. If we apply our three-step model, the limitations of the flat projection represent the heart, while the comparison with the globe is the brain and the need for correction is the hands and the associated expertise/experience.

Language-Information-Reality (LIR) is a means of analysing the action and argumentation (Gatarik & Born, 2015) in Figure 3 and this can be demonstrated using the Beham GmbH case referred to above. The lack of an adequate budget (represented as P in Figure 3) is—representationally speaking—summarised as the initial situation (S). The difficulty was identified by applying knowledge of the accountant’s rules/routines (shown at K). Because the CEO had insufficient knowledge in this area (folk knowledge in F), the tax adviser was invited to contribute his expertise/expert knowledge (intuition E), so the desirable result (R) could be achieved. This can be formulated as equation H (hypotheses/background knowledge) = {E, F, K}; S → R, that is, intuition, folk knowledge and rules/routines create the background knowledge or hypothesis which can be used in the argumentation on the transition from situation (S) to result (R). Producing a practical, real-life solution (Q^*) required a new routine (K) to be established. In the language of our model, to solve the undesirable situation (P ==> Q) we implement rules (K) using the lay knowledge (F). The first solution proposed is that in future new rules/routines (K) should be established.

However, the company did not accept the proposed solution. Instead they introduced a management team that could provide the necessary meta-knowledge (explanations M). Our model can therefore be expanded to H = {E, F, K, M}; S → R. The additional segment enabled the extended participation of the other components and led to improvements in the communication process. The solution adopted by Beham GmbH is an example of not obeying the rules of explanation and complying with the principles of narrative fallacy—not just giving it validity but understanding it.

Operationalizing engagement

Let us now look at operationalizing engagement in conveying knowledge. First, we must return to our meta-scheme (see Figure 3) to obtain guidance on the decisions we make when selecting certain learning environments. To do so we consider the triangle (see Figure 1) illustrating the procedure as briefly described in the example of a successful HRO implementation (see above):

Figure 1

Transferring expertise.

Figure 2

Geo example.

Figure 3

LIR (Language-Information-Reality) model-theoretic scheme.

We start with the “analysis of exploration”—analysing things that worked well to understand how acceptable results were arrived at.

The next step in applying the triangle is a sort of “abstraction”. We consider the background knowledge of the people to be expertise, which—technically speaking—could be understood to lead us to identification and normalization of the structures we think they could explain and reproduce some results under investigation. However, this also should be able to reproduce the acceptance of those results in a group of people living or working together. Thus, we do not only reproduce the results/problem solutions (“best practices”) but also the acceptance of the results as “results” by way of persuasion (i.e. if argumentation as such is not enough!). Bluntly speaking, good results do not come about by chance but we have to identify the causality of the matter.

Re-interpreting (in the sense of operationalizing or materializing the procedure or reviewing it) is the last step, which can be called “analysis of replacement”.

This approach involves looking at the assumptions that are key to explaining the classifications or categorizations of (parts of) reality within our own epistemic possibilities and “gifts” within the resolution level/means of distinction (seeing important differences on the one hand and equivalences on the other), which are available in our own maps or representation of the system. To understand this, we suggest looking at some of Ellen Langer’s (2016) experiments, in which reflective learning (she calls it “sideways learning”) is essential to explain the occurrence and replicability of successful problem solutions (“best practices”) in practice.

Conclusions and recommendations for further research

Imagine a man with a pebble in his fist, palm downwards. He opens his fist and (so) the pebble falls to the ground. He repeats the process with his palm upwards. The pebble remains in his hand.

If you were only presented with the result of the first experiment, you would also expect the pebble to fall out in the second one. If we want to be able to understand the core issue, we need to know more than just the conclusions. We can therefore say that a derived conclusion does not necessary generate knowledge.

The following three propositions build upon some of the ideas that are central to our approach:

1. In order to look at the formalizations or digitalisations of the routines, rules or heuristics that help us to reproduce observable and acceptable results in real life (especially in the current era), we need to consider the background knowledge in use. It can of course be explained but that presupposes trust and engagement on both sides. The problem is whether it is beneficial to presume the existence of a more or less static and universal common sense—the idea that everything can be fully explained in words and then used with no special background knowledge at all. In this case, we could be said to believe in or assume the existence of blind (or guided by God) evolution, which is enough for the progress of mankind, and we definitely do not need any kind of real endeavour on the side of the users. We never need to change anything about our own assumptions or ideologies. The Enlightenment has achieved its aim and a third or a pragmatic enlightenment in the Hilary Putnam (2004) sense, for example, is out of the question.

2. In contrast to this bit of irony we think that in managerial learning, but also in other fields, it is essential to consider the idea that in the practice of building up knowledge, success does not necessarily stem from strictly obeying rules (procedures aimed at achieving parameter values set as the characteristics of success). Instead, we think that we need to experience the limits of the application of rules and to understand the fuzziness of our concepts, and this may be achieved by considering the preconditions for the application of those rules. In daily life, however, we also have to take into account the desire for trust. We must learn to place our “trust in the rules” and in our peers (and also perhaps life) so that we can educate ourselves on the limits of the rules in an “open” way. This is the idea that unsaturated concepts, also found in Dummett (2000), are indefinitely extensible.

3. Finally, we need our reading or projection of theories to be reflected in reality, where both cognitive and emotional social learning as a means of achieving the corrective power of intuition or reflection are essential and should be considered in the way that Antonio R. Damasio’s (2003) brain research on emotions is. This means that “teaching” the skills to optimize the production of the right parameter values (considered key to resolving all problem situations) is not the same as teaching or building on the skills or means to solve problems.

   Empirical research is needed to confirm the conclusions of this paper. It should focus on how best practices (successful managerial problem solutions) and knowledge can be incorporated into practice, and test whether a detailed narrative approach yields better results and if so in which contexts.