The Class of Problems and Meta-Requirements 4.0 Introduction
4.7 Individual Knowledge and Competence
artefact-based view allowing for the separation of knowledge and competence. The elements of design are then to be synthesized into objects.
Requirements to the methodology of visualization and valuation of the objects are being
identified in order to secure objectivity, recognition and content and prevent local interpretations of knowledge and competence. The visualized and valued objects are to be represented in
distributed stocks by technology.
The objects, when allocated by managerial decisions to tasks in operations, are activated by the connection between knowledge and competence through working individuals making the objects flow into innovation, new products, patents and routines in operations. In flows new knowledge and competences may be produced requiring periodically/ real-time update of stocks.
The requirements for auditable elements and processes have been identified in the financially based guidelines for intangibles to produce understandable outcomes, which makes sense as exchangeable mechanisms for intermediaries between the financial and intellectual capitals.
The challenge now is to fabricate knowledge and competence objects that meet these requirements through an application of the identified elements of design.
Figure 7 Bloom’s cognitive taxonomy
Bloom operates in three dimensions establishing relations between the dichotomized notions of complexity–simplicity and known–unknown, which correspond to the research question’s concerns for visualization of competence, because these notions are considered as organizational context variables and, therefore, contenders to enter into a visualization and valuation
methodology (C. Argyris, 1996; C. Argyris, 2004a; C. Argyris, 2004b; C. Argyris, 1977). The x-axis represents a continuum from the known to the unknown, which, in operations, also may characterize processes from routines to development and innovation (ibid.). The y-axis
represents the continuum from a simple to a complex context, so every dot defined by this area constitutes an organizational context to address actively in operations when allocating to teams and when planning and managing. The steps indicate the ability of individuals to mobilize relevant knowledge in relation to the defined context. Bloom denominates the Step 1
“Knowledge” on a scale, which increases the individual’s capability to perform in a context of enhanced insecurity, because the context grows unknown and increasingly complex. The scale describes five steps of performance, which, in accordance with Argyris’ thoughts from the mid-1970s about single and double loops, also define precise distinctions in creativity and
performance in operations.
The Australian, J. Biggs, has also taken an interest in ordering the outcome of teaching (Biggs, Collins, & Edward, 1982). His taxonomy, reproduced below, “Structure of Observed Learning Outcome” also deals with five levels, but he elaborates more than Bloom on the competencies of every step:
Complexity
Simplicity
1
2
3
4
5
Steps:
1 Know 2 Understand 3 Apply (in simple new contexts) 4 Synthesis (Apply in more complex contexts)
5 Estimate
The known
The unknown
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1. Level 2. Level 3. Level 4. Level 5. Level The person can:
only deliver piecemeal information
The person can:
identify, rewrite apply simple procedures
The person can:
list, describe and combine
The person can:
compare, contrast, explain causalities, analyze, relate, and apply
The person can:
theorize, generalize, create hypotheses, and put into perspective but speak only
single parts
speaks more perspectives
speak and integrate more aspects into a whole
and move from the specific to the abstract level but is not able to
integrate these in a whole
Figure 8 J. Biggs’ competence taxonomy (Biggs et al., 1982).
The two taxonomies are rather similar, but Bloom includes the notion of known–unknown, which is interesting in dispersed unstructured organizational contexts, because the demand for intellectual capacity differs along processes of operations based on routines, rules, innovation and shared processes. Therefore, some processes like routines can be categorized as “known”
and related knowledge as known. In unstructured processes projects requiring fast change and radical development can be categorized as “unknown.” Employees work more or less based on routines, with known processes, in known settings, and are often mediated by hierarchy, while processes in “development” and “projects” are often iterative in new settings and with new colleagues, and have another relation to hierarchy (Nonaka, 1994). Therefore, Bloom’s multi-dimensional taxonomy adds to its relevance. Due to similarities and relevant supplementary elements in the two taxonomies, this author has merged them into one, distinguishing human capacity to activate knowledge in organizational settings in two different types of competencies:
“creative competencies” and “performance competencies”. The descriptive elements “known–
unknown” and “simplicity–complexity” supply and clarify the two types of competencies using generic constructs to describe the outcome/input of competencies.
4.7.2 Dimensions in Competence
Studies of management technologies, MTs, show that an important feature for success in mobilization is simplicity (Busco, Quattrone, & Riccaboni, 2007; Qu & Cooper, 2011; Sarker, Sarker, & Sidorova, 2006). Even though concerns may be complex and the complexity is made calculable and standardized, MTs still have to stay simple in order to get distributed.
However, at least two reasons for the differentiation into creative/ performance competence seem to complicate this insight. The first is the possibility to manage quite different agendas of qualities of competencies at different levels of taxonomy, because the elements are then
combinable in various ways; and second, because the choices offer ex ante tools to adjust and communicate quite complex combinability of the respective types of collaboration in operations.
Therefore, the assumption is that it is relevant and still worth it to identify two simple, but important distinctions between the various individual competencies that activate knowledge in operations.
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In the figure below, by merging the orders (Biggs et al., 1982; Bloom, 1970), the notions of
“known/certainty” and “unknown/uncertainty” describe simple, general contextual prerequisites that are assumed to correspond with future organizational contexts and with the general notions of “structured”/“unstructured” as illustrated :
Organizational contexts for various requirements for competences Organizational
context Knowledge &Competence Management
Known Unknown
Structured: Routine based in known processes, known colleagues, certainty in processes, known outcome, known relations to other processes (within companies)
Competence is restricted to deliver piecemeal information, to identify, rewrite, apply simple procedures, and list, describe, and combine, but speak only single parts, and is not able to integrate these in a whole
Daily operations still frame uncertainty.
Incremental change might require high performance, but not high creative competence
Unstructured: Development &
projects
Unknown, interventions (a project), or organizational development.
Innovation: incremental/radical change (between companies)
With known targets, known new contexts for products, and predicted economy, known conditional framework competence can be planned and described in terms of requirement for creativity and performance
Independent of certainty and known conditions. Competence is able to compare, contrast, explain, and construct causalities; to analyze, relate, and apply; to theorize, generalize, create hypothesis, and put into perspective; to speak and integrate more aspects into a whole;
and move from the specific to the abstract level and back again Figure 9 The merged taxonomies of Biggs and Bloom (Biggs et al., 1982; Bloom, 1970) in relation to contexts of operations, by the author.
The table relates the framework of the merged taxonomies to the descriptions of operations, using the notion of structured–unstructured, because the distinction between
routines/development is generally addressed in theory and practice (merged). The above table differs between the categories of “known–unknown,” so when booking, reserving or allocating resources across locations, the distinction between a single and a double loop perspective (C.
Argyris, 1996; C. Argyris, 2004a) serves as an identifying notion for the ex ante choice of capability to create radical change in an unknown context, because the level 5 in both
taxonomies as seen as corresponding to the double loop perspective. The potential combinations of competencies in are embedded in the table. The distinctions in the above model will serve as elements of design for the concretization of IC calculating devices.
For Argyris and Schoen (C. Argyris & Schoen, 1978) learning involves the detection and correction of errors so that given or chosen goals, values, plans, and rules are operationalized rather than questioned, which is known as single-loop learning. Single-loop learning is like a thermostat (Argyris’ example) that indicates when it is too cold or too hot and turns the heat up or down. The thermostat can perform this task, because it can receive information (the
temperature of the room) and take corrective action. When the error, detected and corrected,
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permits the organization to carry on its present policies or achieve its present objectives, then that error-and-correction process is single-loop learning. When something goes wrong, they suggest, a general port of call is to look for another strategy—a different perception of the nature of the problem—that will address the governing variables. An alternative response is then to question those variables themselves and subject them to critical scrutiny, described as double-loop learning, which requires different individual competence capacity. This may then lead to a change in the governing variables and, therefore, a shift in the way in which strategies and consequences are framed. Double-loop learning occurs when errors are detected and corrected in ways that involve the modification of an organization’s underlying norms, policies, or objectives. The thermostat is discarded and another world view is applied.
Single-loop actions are used when goals, values, frameworks, and, to a significant extent, strategies are taken for granted. Double-loop action, in contrast, involves questioning the role of both the framing and operations, which underlies actual goals and strategies. The former
involves following routines and pre-set plans; the result is predictable and less risky for both the individual and the organization, thereby facilitating greater control. The latter is critical,
creative, and reflexive, and the result is not predictable. Reflections here are more fundamental:
the basic assumptions behind ideas or policies are confronted, hypotheses are tested, and
thoughts are disrupted, like methods of critical research (M. Alvesson, 2000; C. Argyris, 1982).
Finally, the latter may address the art of planning for innovation.
A focus in Chris Argyris’ research has been to explore how organizations may increase their capacity for double-loop learning. He argues that double-loop learning is necessary if
practitioners and organizations are to make informed decisions in rapidly changing and often uncertain contexts (C. Argyris & Schoen, 1974; C. Argyris, 1982; C. Argyris, 1990). Newer literature even discards the sense of strategies, because the business contexts are seen to change faster than the production and deployment of strategies and thus arguing for the establishment of awareness and resources for unlimited disruptive organizational behavior (McGrath, 2013). For an organization to exert control over intellectual competencies across locations, it needs the ability to plan and predict disruptive development. IC control is a potential key, because
organizational uncertainty assumingly can be reduced and handled within the shared framing of metrics denominating the allocation of relevant competencies at level 5, which is the human capacity to execute double loop processes. Thus, theoretically, it becomes possible ex ante to describe the application of knowledge objects in a creative and performative way to address defined organizational contexts.
By the merger of the rankings in the two taxonomies, design elements for objects of competences technically enable the construction of calculative devices of competence addressing an array of various combinations of competence for allocations in various organizational situations in and between companies.
The ranking of competences use dimensions of knowledge like “information” and “knowledge”
and knowledge is described as the object of the competences: knowledge is identified, recognized, spoken about, listed, combined, detailed, compared, contrasted, argued, related,
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applied, integrated, theorized, generalized, put into perspectives, moved from specific to abstract levels and estimated. The systematization expresses dimensions in the objects of knowledge, which will be examined and ordered in the next section.
4.7.3 Dimensions in Knowledge Objects
Biggs’ and Bloom’s (Biggs, 1982; Bloom, 1970) taxonomies rank and apply knowledge while ordering the competences in dimensions of superficial knowledge (information) to deep
knowledge (estimations). The argument is that estimations, comparisons or abstractions cannot take place without the ownership of objects of deep knowledge. Implicit the rankings assume that knowledge objects which have properties able to establish estimations or comparisons are available and thus apply qualitative dimensions of knowledge in the model ranking
competences. The ownership of knowledge objects may with one person be of many or few objects of knowledge. It is therefore relevant to insert a quantitative dimension for individual knowledge ownerships when documenting an individual stock of knowledge. This indicates requirements for dimensions of quality and quantity at the individual level when measuring knowledge and competence illustrated in the model below.
Qualitative Knowledge Dimensions
The figure 10’s dimensions in knowledge enable horizontal and vertical exchange of demand /supply for knowledge in the search and allocation processes to identify remote quality in the IC objects offered at a distance. Consequently, the structure for the explicit, academic knowledge classification is envisaged as an endless capacity to add new/ ascending levels of detailed knowledge objects along the two dimensions, because the global academic body of knowledge grows at an exponential rate (Bontis, 2001) and firms apply various subsets of academic
Vertical Knowledge Level of Educations and Experiences
Horizontal Knowledge Number and combination of knowledge objects
Unspecialized Specialized
5
4
3
2
Deep general knowledge in one K-object
Superficial general knowledge
Specialist: deep knowledge in many K-objects
Superficial knowledge in many
Figure 10 Proposal for qualitative knowledge dimensions, by the author.
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knowledge. Experience-based, tacit knowledge is classified in labor market categories12 which evolve, so entities must constantly be able to register and allocate new knowledge that provides capacity to entities, whether it is public/private companies, borderless, scientific networks, or free agents.
Combinations are possible between the qualitative and quantitative dimensions of knowledge.
The number of knowledge objects is countless and adding 2 types of competences to objects of knowledge multiply the combinations even more. These highly combinatory value
representation principles frames the concrete construction of the further design of an IC measurement unit.
Having now explained why the main take in IC literature is moved from an organizational to an individual level of analysis and having furthermore theorized the objectification of knowledge by firstly disentangling knowledge from competence, by secondly theoretically explaining the objectification of knowledge and competence and its relation to the notions of IC stocks and flows in an accounting logic, constructs, bit and pieces for the conceptualization of HC/IC coordination have been prepared. This is studied next.
4.8 Conceptual Development of Management of HC Assets in Stocks and Flows