3. Knowledge management: explicit and implicit knowledge

KM implies the acquisition, development, transfer, storage and use of knowledge in a company [12–15]. A condition for knowledge generation is generic information like scientific theories and models. The information is based on data defined as simple facts or events etc., which are not systematised for a specific context. Thereby, the information forms a structured, meaningful summary of explicit data points in order to take a conclusion or prediction. In particular, knowledge represents the generic skill to connect and use recent information with previously collected information in several new application fields. As a consequence, the knowledge carriers evolve a new understanding or subjective perception of the actual situation, which generates a new knowledge basis [13, 16]. Here, knowledge can generally differ in explicit and implicit one [17].

Figure 1. Degree of tacitness and the steps to objectivisation.

Explicit knowledge is communicable and thus not exclusively available to the person who possesses and uses it. It declares the relevant know-what [18, 19]. Knowledge concerning company-specific cause-and-effect dependencies can furthermore be drawn on the subjectbound, intuitive experience-based knowledge of competent employees and managers. This as 'tacit' specified implicit knowledge is difficult or impossible to verbalise as well as to formalise in contrast to explicit knowledge [18]. It is understood as individual specific know-how.

According to Ambrosini and Bowman [20], knowledge can be graded in relation to the degree of tacitness as shown in Figure 1.

Between the explicit knowledge (A) and deep-rooted tacit knowledge (D), which cannot generally be revealed, the communicable knowledge (B and C) have to be specified. One specification comprises the implicit knowledge (B), which can be appropriately articulated and revealed. But, this knowledge becomes less obvious over time, because the knowledge carriers have not been mentally concerned with it and no third party has demanded for it. Additionally, there exists tacit knowledge (C), which can be articulated only incompletely. Although it is possible to get access to this knowledge, it is not describable by general language use [20]. The implicit knowledge of type B and C is of special importance for a company in order to discover the performance-relevant SFs and develop their hypothetical causal relationships in a map.

The experts´ tacit knowledge has to be externalised by applying adequate elicitation techniques in the context of KM [15, 21, 22]. For this purpose, three groups are basically distinguished in the literature under the term 'knowledge elicitation techniques': observations and interviews, process tracing and conceptual techniques [23]. It generally cannot be defined that one method is more appropriate than another. The choice of a technique for extracting performance-relevant knowledge should be taken case specifically. However, for the development of causal hypotheses, there exists the experience that interview techniques as most commonly applied methods generate more information on company-specific connections than other approaches [22–24].

Subsequently, the externalised implicit knowledge of the performance-related SFs is causally systematised into a more generic and easily comprehensible form by formulating a causal map [25, 26]. Depending on the chosen mapping method, the causal relationships base on purely subjective judgments. This subjective knowledge stands for the relationship of an individual to its environment. Thus, it is not objective. Subjectivity can be seen as an error source in the current subject, although it offers an epistemic value [27]. Increasing comparability and transparency of individual subjective evaluations about causal relationships generate a degree of intersubjectivity [28]. Thus, intersubjectivity is achievable, if only more than one individual can clearly comprehend the formulation and structuring process of causal relations among SFs.

However, strategic forecasts about the future performance developments are only possible to a certain extent or cannot even be performed by application of subjectively and intersubjectively based maps. (Intersections of subjective maps would deliver intersubjectively based ones.) But, only a statistical validation of the causal map generates an objective understanding of the causal relationships, which thus are directly empirically verifiable [5, 29]. As a consequence, valid predication of the performance generation can be given. Initially, the subjective mapping methods are considered more closely in the following section.
