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108 Seiten, Note: 1,0
List of Figures
List of Tables
List of Appendices
List of Abbreviations
1.3 Methods and Overview
2 Towards an understanding for Maturity Modelling and Knowledge Maturing
2.2 Knowledge Management
2.3 Knowledge Maturing
2.4 Conceptual Modelling
2.4.1 Systems as Subjects of Modelling
2.4.2 From original to model
2.4.3 Capability Models
2.4.4 Maturity Models
2.4.5 Process models
3 A Study Design for the Analysis of Maturity Models
3.1 Research Approach
3.2 Content Analysis
3.2.1 Determination of the Research Material
3.2.2 Analysis Question
4 Results of the Model Comparison
4.1 Similarities between Maturity Models
4.2 Differences between Maturity Models
5 Transition to Maturity Modelling
5.1 Recommendations concerning the Conception of Maturity Models
5.2 Recommendations concerning the Construction of Maturity Models
6 Perspective on maturity modelling and knowledge maturing
6.1 Summary of the results of this work
6.2 Further challenges
Figure 1 – General approach of this work
Figure 2 – The relation of data, information and knowledge
Figure 3 – The concept of semiotics
Figure 4 – The knowledge maturing process
Figure 5 – The generic system
Figure 6 – Isomorphous and homomorphous models
Figure 7 – How the model reflects the original system
Figure 8 – Relation between scale level, quality and quantity
Figure 9 – The process of qualitative content analysis
Figure 10 – The adapted communication model of the study
Figure 11 – Structuring Content Analysis
Figure 12 – The initial codes for the empirical study
Figure 13 – The three steps of the coding process
Figure 14 – Meaning of maturing (bar chart)
Figure 15 – Direction of change (bar chart)
Figure 16 – Maturing objects (bar chart)
Figure 17 – Model complementation (bar chart)
Figure 18 –Relation between levels (bar chart)
Figure 19 – Level approach (bar chart)
Figure 20 – Number of triggers (bar chart)
Figure 21 – Number of trigger levels (bar chart)
Figure 22 – Way of goal assessing (bar chart)
Figure 23 – Model use (bar chart)
Figure 24 – Foundation of maturity models (bar chart)
Figure 25 – Certification of Maturing Elements (bar chart)
Figure 26 – Meaning of maturing (bar chart)
Figure 27 – Conceptual mother model (bar chart)
Figure 28 – Model users (bar chart)
Figure 29 – Number of stages (bar chart)
Figure 30 – Number of stages (bar chart)
Figure 31 – Content of the stage description (bar chart)
Figure 32 – Level skipping (bar chart)
Figure 33 - Parallel maturing (bar chart)
Figure 34 – Assessment data derivation (bar chart)
Figure 35 – Tool support (bar chart)
Table 1 – Number of models found in each model category
Table 2 – Sample of models for the analysis
Table 3 – Literary sources of the observed models
Table 4 – Randomised model selection
Table 5 – Similarities among maturity models
Table 6 – Differences among maturity models
Table 7 – Hypotheses about maturity modelling
Table 8 – Model diversification
Table 9 – Recommendations to maturity modelling
Appendix A – Research Process
Appendix B – List of models
Appendix C – Summarisation Rules
Appendix D – Explication Rules
Appendix E – Structuring Rules
Appendix F – Development of the Codes
Appendix G – Code Description
Appendix H – Coding Table
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This thesis is dedicated to the field of knowledge maturing and especially to the analysis of maturity models to obtain implications on the core topic of knowledge management. The first chapter explains the main motivation factors of the topic (section 1.1), defines the objectives of this work (section 1.2) and gives an overview of the applied methods and the structure (section 1.3).
Although the efforts around the exploration of the process of knowledge maturing are very recent (e.g. Maier & Schmidt, 2007; Schmidt, 2005), the process behind them is not. Knowledge maturing, a form of knowledge transfer in organisations (Argote & Ingram, 2000, p. 151pp), has probably been exercised since the beginning of organisational work. Maier and Schmidt (2007, p. 3) describe this maturing process after having analysed several practical cases.
Knowledge emerges on an individual level in form of an informal idea. The individual expresses the idea to other individuals and the knowledge gets shared by a group. As the idea wins favour and matures, it might get picked up by a higher organisational level (e.g. a department) and gets formalised. From now on the former idea is anchored within the organisation. It is passed on within the organisation through training efforts in form of ad-hoc trainings at first and later maybe in form of standardised trainings.
This example however shows – as most people would likely be familiar with a similar situation – that knowledge and knowledge maturing are essential elements within each organisation.
In 2007, the European Union initiated an integrated research project called MATURE-IP to build a bridge between everyday work and, what we call, future trends. The project’s goals are getting a better understanding of the process of knowledge maturing within organisations on the one hand side and developing tools or services to support this maturing process on the other side (MATURE-IP-Consortium, 2007)1. The Mature-IP- Consortium considers this as a necessary step to support the agility of organisations, which is for them "[…] the critical success factor for competitiveness in a world characterised by an accelerative rate of change" (MATURE-IP-Consortium, 2007). Besides, a change of learning and working habits2 makes it necessary to explore the learning and maturing process within organisations.
In order to get a better understanding of knowledge maturing, the existing knowledge maturity model will be rebuilt as a first step of MATURE-IP. Therefore, two sets of information are needed. First, it needs a lot of inputs about how learning and maturing takes place in everyday organisational practice. This information will be provided by an ethnographical study performed at different companies all over Europe. Second, it requires input information on the act of maturity modelling itself. Although literature comes up with a great amount of different maturity models, there have only been weak efforts to explore the process of maturity modelling itself. Ahlemann, Schroeder and Deuteberg were the first to come up with a definition proposal for maturity models in 2005 (Ahlemann, Schroeder, & Teuteberg, 2005). However, this was just the beginning, as there exists no meta-maturity model, which is needed to create new well-founded maturity models, in literature yet. To make a first step in this direction this work tries to derive a catalogue of relevant model criteria from existing maturity models. The aim of this catalogue is to provide a basic concept for the creation of new maturity models – particularly the revised knowledge maturity model of MATURE-IP.
While the importance of knowledge maturing has been dealt with in the previous section, this subchapter should give a survey of the objectives of this work. Both, the research question of this thesis and the working questions for the individual sections will be outlined.
As described in section 1.1 (on page 1), the analysis of real world maturing within the MATURE-IP project should bring out a general conceptual maturity model of the knowledge maturing process. This model will be the main item of all further project efforts, as it allows to describe, analyse, simulate, optimise and document phenomena that are significant for the development of information systems (Gaitanides, 2004, p. 1214).
Although there are many different maturity models available within scientific and non- scientific literature, there is no consensus about how a meta-maturity model should look like. Ahlemann et al. (2005, p. 15p) tried to overcome this problem by suggesting how a project management meta-maturity model could look like. However, there is no general implication on maturity models existing yet.
Hence, this work addresses the following research question: Which characteristics of existing maturity models are relevant for maturity modelling? It thus tries to figure out:
- Which maturity model domains can be identified?3
- Which maturity models are the most diversified ones within these domains?4
- Which characteristic patterns can be observed by comparing these maturity models?5
- Which model properties can be deduced?6
- How do the observed characteristics affect maturity modelling?7
The answers to these questions will be a first step for a better understanding of maturity models. The recommendations of this work can be used as an input for the creation of new maturity models.
By concluding the introduction, this section points out the methods that are used to answer the working questions, mentioned above in section 1.2, and the general research question of this thesis. It further gives an overview of the chosen approach to apply these methods. Figure 1 depicts the general approach and demonstrates how it is related to the chapters of this work.
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Figure 1 – General approach of this work
The first chapter (Ch. 1) introduces the work to the reader. It points out its relevance, the main objectives and the general approach as well as the working and the research questions of this thesis.
The second chapter (Ch. 2) provides an insight into the subject matters of this work. It introduces the reader to the concept of knowledge, knowledge management, conceptual modelling and the research project MATURE-IP. This chapter is also called foundation because it lays the cornerstone for the further understanding of this work.
Chapter three presents the study design of the empirical part and therefore explains the basics of qualitative content analysis and how this method is used within this work (Ch. 3.1). It describes which maturity models are collected in the areas of personal maturing, object maturing and the maturing of social systems and how the observed sample of models is drawn from the basic population of maturity models and then analysed (Ch. 3.2). This chapter answers the working questions one and two through literature research.
Chapter four (Ch. 4) comprises the detailed description of the results of the qualitative analyses and shows up potential coherences between the observed data. Thus, it answers working question three by interpreting the results of the study.
The last chapter of this work, chapter five (Ch. 5), leads the results of the chapters three and four back to the domain of knowledge maturing. It therefore answers working question four and five, as it reduces the list of patterns found in chapter four to a condensed list of properties that are relevant to knowledge maturing. This conclusion will be done through literature research.
The foundation provides the basic theoretical concepts concerning the empirical part of this work. Thus, this chapter will give an overview of the basics in the concept of knowledge, knowledge management and knowledge maturing as well as the basics in conceptual modelling. Furthermore, it will introduce the reader to the Mature-IP research project of the European Union, which tries to scrutinise the knowledge maturing process and which this work participates in.
These days, the importance of knowledge and knowledge related concepts (e.g. knowledge management) in business and economics is beyond doubt. More precisely, knowledge is nowadays treated as the key resource organisations have to deal with in order to operate profitable on the market.
Compared to other concepts, this way of thinking is very recent. Its origins can be traced back to the 1980s, in which the market based view was the most diffused explanation for business activities. Michael E. Porter, one of the main representatives of the market based view and therefore a trend-setting person in business, perceived the organisation’s success as a determinant of its ability to place itself on the market (Porter, 1980, p. 4pp, 1985, p. 4pp). This view, however, had many critics arguing that an exclusive focus on the organisations environment is only effective to some extent. They missed the internal perspective on the organisation. Hence, this discussion resulted in the development of a new perspective on organisational behaviour, the resource based view (Wernerfelt, 1984). The basic idea of Wernerfelt’s concept was that a firm "wants […] to create a situation where its own resource position directly or indirectly makes it more difficult for others to catch up" (Wernerfelt, 1984, p. 173). During the 1990s many authors within the resource based view specifically focused on one resource that Wernerfelt had only indirectly mentioned8 in his first article on the resource based view – knowledge. Although producer experience, as Wernerfelt called it, and knowledge, as it was defined by the representatives of the knowledge based view, were very similar, the latter went one step further. They declared knowledge as the superior key resource that organisations have to deal with (e.g. Tang, 2005, p. 41pp; Zahn, Foschiani, & Tilebein, 2000, p. 251pp).
The view of knowledge as a production factor is wide spread nowadays. Several authors contributed to this way of thinking. For example, Douglass C. North, recipient of the Nobel Prize in economics, affiliated this current of thought through the following statement: "The speed of economic change is a function of the rate of learning […]"9.
But the rise of knowledge does not only affect organisations. The social system is also subject to a likewise change. Peter Drucker, an American Professor for social science, explained in his article about the social transformation in the twentieth century (1994) that knowledge workers "in many if not most developed societies […] will be the largest single population and work-force group." Although the term knowledge worker today is frowned upon because many critics have adduced that on the one hand no one can abscond from doing knowledge work in any form and on the other hand there can be no clear cut drawn between normal work and knowledge work, Drucker’s basic idea remains the same. Knowledge work is getting increasingly important in modern societies.
However, the importance of knowledge can not only be traced back to the fact that it guides organisational work. Knowledge is also considered to be the product of organisational work. Terms that can often be read in product descriptions of service companies, like knowledge service, knowledge-intensive- , information-intensive- (Glazer, 1999, p. 60pp) or knowledge-based products (Davis & Botkin, 1994, p. 165pp), indicate that knowledge has already captured business.
In this connection, regarding the introduction of this section and the developments that have been instanced, one thing can clearly be stated: The importance of knowledge is obvious and cannot seriously be questioned. However, this brings up a new problem to the discussion. If knowledge is really that important, how can it then be defined?
In philosophic circles, the attempt to find a definition for the term knowledge can be traced back to the ancient world. Plato, for instance, gave a profound definition in his work Theätet (1981, 201d-206b). For him, knowledge was "[…] the true, justified opinion." However, the way of thinking changed within the last thousands of years and new definitions emerged. Today, the English language, for example, knows eight different meanings (Merriam-Webster, 2008a), three synonyms and eleven related words (Merriam-Webster, 2008b) of the noun knowledge. But which definition of knowledge is appropriate to be used in this work?
Before answering this question it has to be explained how the concept of knowledge relates to other cognate disciplines, like information? Figure 2 gives a first insight into the relations between signs, data, information and knowledge.
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Figure 2 – The relation of data, information and knowledge10
Signs are the smallest peaces within the field of semiotics11. They are the basis for all higher concepts (data, information and knowledge). Depending on the context a user works in, he/she can chose from a certain set of signs.
From a computer science based perspective, data are (machine-) readable and editable – commonly digital – representations of signals. Signals themselves are physical perceptible matters of fact (DIN, 1988). In semiotics, data is classified in the concept of sigmatics, which already contains the concept of syntactic. Data are therefore potential information (Berthel, 1975, p. 1860pp).
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Figure 3 – The concept of semiotics12
Figure 3 shows the connection of the four semiotic dimensions: syntactic, sigmatics, semantics and pragmatics. As mentioned above, data are ranked among the concept of sigmatics. Therefore, they consist of signs which can be related to each other by rules (syntactic) and to the named element itself.
Example 1: An example for data could be a series of numbers, e.g. 45600789 as it could be found in a database table.
Information is compiled of data by introducing the context that they are used in. This means that the raw data are enriched by a semantic. Miller (1978, p. 15) relates to information as a dual concept. For him, information is both, an abstract model of a real occurrence and a real occurrence itself.
Example 2: The set of numbers given in Example 1 has no meaning for us as long as we do not know that it is, for instance, a telephone number. This information brings in the context that enriches the data for us.
Knowledge, as it is defined by Maier (2007, p. 76), comprises "[…] all cognitive expectations – observations that have been meaningfully organised, accumulated and embedded in a context through experience, communication, or inference – that an individual or organisational actor uses to interpret situations and to generate activities, behaviour and solutions no matter whether these expectancies are rational or used intentionally." This means, that the information – in Miller’s (1978, p. 15) sense the real occurrence or, as Maier calls it, the situation – has to be enriched with a pragmatic component. As information can be seen as a neutral status of the individual's reality, knowledge is the ability to use this information.
Example 3: To have the information about someone's telephone number is not very useful as long as we do not know what to do with it. To be able to benefit from any information, we will have to be able to use it or know how to use it. If we do so, the information turns into knowledge.
Knowledge management attracted much interest in both, business practice and literature in the last twenty years. Many authors lived up to the discussion around knowledge management with their contributions and studies, many organisations built up new knowledge management units, whose only objective was to improve the knowledge flow within the organisation, and consultancies specialised in this new field of business strategy. In summary, business was highly affected by knowledge management. The further subsections will illustrate three foci of knowledge management that are worth mentioning because they play an important role when it comes to knowledge maturing and later to the study design. Regarding knowledge transfer, the first topic, it is important to understand what happens between the single steps of the knowledge maturing model. The second topic, knowledge work, describes the work field where knowledge maturing tries to join in. The last topic describes the elements that knowledge can be attached to. These elements are called knowledge media.
The concept of knowledge transfer is essential to this work because it can be understood as one facet of knowledge management. The term transfer is commonly used within cognitive psychology to express social processes. Singley and Anderson (1989, p. 1), for example, use the term transfer on an individual level as "[…] how knowledge acquired in one situation […] applies to another." Although knowledge transfer definitely includes the transfer of knowledge elements on the individual level, it includes transfers on higher levels too, e.g. between departments or even between larger organisational units.
Therefore, Argote and Ingram (2000, p. 151) give a more diversified definition of transfer. For them, knowledge transfer is the process of one unit influencing another unit by its experiences. Units can be persons, groups, departments or divisions of organisations.
Knowledge work is worth to be explained here because it is the subject matter within the knowledge management. The term knowledge worker was introduced by Peter Drucker (1994). These days, many countries are what Drucker calls knowledge societies. Thomas Davenport (2005, p. 5p) estimates that approximately one third13 of the workforce nowadays are knowledge workers who have a "[…] high degree of expertise, education, or experience, and the primary purpose of their jobs involves the creation, distribution, or application of knowledge."
In order to characterise a knowledge worker and to be able to separate them from non- knowledge workers, Paul Dorsey (2007, p. section 2) retrieves seven information skills.
- Retrieving information: In face of the "[…] large information environment which helps to create knowledge […]," Dorsey points out that the main objective of the concept of information retrieval of a knowledge worker can only by satisfying, not optimizing. Therefore, the knowledge worker has to know where to acquire that information.
- Evaluating information: The problem that every knowledge worker has to face is well known as information overload (e.g. Schenk, 1987). From Dorsey's point of view, a knowledge worker’s ability to evaluate information has to focus "[…] on both the quality and relevance of information."
- Organizing information: The organisation of information is one of the key attempts of personal knowledge management. Frand and Hixon (1999, paragraph 2) for example reduce personal knowledge management exclusively on the organisation and integration of information. For them, this approach provides a "[…] strategy for transforming what might be random pieces of information into something that can be systematically applied […]".
- Collaborating around information: Given the array of collaboration technologies, the great challenge is to identify those technologies that can support the process of working smarter. The time which is spent in more face-to-face meetings (even if they are arranged in a web-based context, e.g. a chat, a video conference or a blog) needs to be devoted to a higher value of the activities, which emerge from them.
- Analyzing information: Avery et al. (2001, paragraph 16) describe the analysis of information as "[…] fundamental to the process of converting information into knowledge." For them, the human element is the only one that is able to frame the models beneath information. No electronic tool is able to fix this task. The skill of analysing information therefore addresses the ability to extract meaning out of data (Dorsey, 2007).
- Presenting information: The number of technologies suitable for the presentation of information increases rapidly. As it is nevertheless important to present information effectively to an audience, according to Dorsey it is essential for knowledge workers "[…] to become familiar with the work of the communication specialists, the graphic designers and the editors."
- Securing information: As the rate of shared information increases, knowledge workers have to become aware of the trade between the creation and operation of information sharing relations with other persons and/or organisations and the security aspects of these sharing processes.
Closing the bracket to Peter Druckers essay on the social transformation within the western world, the relevance of knowledge work within our society has increased since the release of the text and cannot seriously be questioned. As manufacture production rises while manufacture deployment diminishes, Drucker speaks of an industrial paradox (Drucker, 1999) that the developed world has to deal with. The specialization on knowledge work seems to be the only way out of this paradox.
Finally, one further aspect of knowledge management is worth to be highlighted within this foundation. It is the medium on which knowledge can reside. Maier (2007, p. 80p) identifies three media which carry knowledge in every work practice: objects, persons and social systems.
Knowledge that is connected to an object is independent from any knowledge holder. This does not mean that it is explicit knowledge because the connection of knowledge and object does not necessarily mean that this knowledge has been documented (Maier, 2007, p. 81). Object related knowledge can be found in the form of e.g. documents, products, services or even ICT infrastructure.
Knowledge that is connected to a person is individual knowledge and therefore probably the most well-known form of knowledge. This form of knowledge is, among others, reflected in skills or experiences and has to be distinguished from knowledge connected to social systems. The latter stands for collective knowledge that represents an inter-personal body of knowledge, shared by the individuals engaged into the collective (Maier, 2007, p. 80). This form of knowledge can be observed as e.g. routines, structures or processes.
The empirical part of this work will take on this trisection, as it will subdivide the basic set of maturity models into models that describe personal maturing, models that describe object maturing and models that describe the maturing of social systems.
The concept of knowledge transfer has already been discussed in the last section (section 2.2, page 10) and it has been pointed out that it is one facet of knowledge maturing. Now, the concept of knowledge maturing itself will be investigated.
In 1973, Richard Nolan, an American Professor for information technology, released an investigation on the growth of computer budgets of companies (Nolan, 1973, p. 401pp) where he described this growth process as a sequence of four certain steps a company proceeds through while increasing its computer budget. Therefore he used findings of the economical stage theory, which famous scientists like Simon Kuznets (e.g., 1966) contributed to. Nolan’s stage model was the beginning of what we call maturity modelling today. Over the years a dozens of different maturity models emerged in scientific and non-scientific literature for a variety of branches, all describing development processes of different maturing elements.
As mentioned in the introduction of this thesis, the efforts concerning the exploration of knowledge maturing are very recent. It seizes the idea of a "knowledge flow" (Maier & Schmidt, 2007, p. 2), which connects different learning parts within an organisation. This conception partly coincides with what we became acquainted with as knowledge transfer within organisations (section 2.2, page 10). Maier and Schmidt understand this "knowledge flow" as a "[…] metaphor for interconnected individual learning processes where knowledge is passed on and reconstructed and enriched by the individuals involved."
Within this maturing process the passed on knowledge elements can run through different phases of maturity, which are characterised by various identifiable information artefacts. Figure 4 shows the structure of the knowledge maturing process: the phases, the identifiable information artefacts and a short phase description.
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Figure 4 – The knowledge maturing process14
As Figure 4 demonstrates, the knowledge maturing process consists of five consecutive maturing phases, each one characterised by specific information artefacts.
- Emergence of ideas: New ideas emerge in highly informal settings. There is no specific vocabulary used to express these ideas. Personal notes are the only real discoverable artefacts during this stage.
- Distribution in communities: The ideas which have been generated through individuals are passed on to other individuals; thus, they are shared. Therefore, a common language is needed in order to enable this communication. Typical artefacts of this phase can be Web 2.0 application entries like forums, wikis, blogs and others.
- Formalization: This phase brings the first formal structuring to the still unstructured ideas. Hence, the knowledge is brought into a more formal form, e.g. a project report.
- Ad-hoc Training: As the documents of the previous phase are not very well suited to guide training efforts, this phase provides training documents that can be used to communicate the knowledge in an ad-hoc manner to other parties, e.g. through a presentation.
- Formal Training: The previously mentioned training efforts are only suited to cover ad-hoc training requirements. In some cases, however, it might be necessary to provide a much stronger guidance. University courses, for example, use text books with knowledge of a very high degree of maturity to teach this knowledge to novices. Formal training covers a broad range of subject areas.
During this whole process of maturing, the characteristics of knowledge elements within the dimensions hardness, interconnectedness, commitment and teachability might change while they are passed through the organisation. As already mentioned in the introduction of this work, the above described process takes place in the daily business of nearly every organisation. The awareness of this process makes it easier to integrate ICT infrastructures, provide learning support and improve workflows.
The term model can be seen from many different perspectives. From an etymologic one, a model can be an ideal, an example, a prototype, a form, an abstract or even a person as subject of art. These very general meanings of the term model are – in fact – not very useful in terms of scientific research. Hence, this section should give an insight into modelling from a scientific perspective and will briefly look into the systems theory as a basic theory for conceptual modelling.
As the model domain terminology is closely related to the terminology of systems theory, this section should deliver a very brief insight into what models consist of - systems.
The German sociologist Niklas Luhmann was the founder of the modern Systems Theory. In 1984 (Luhmann, 1984), he released his first book on the topic, which was revolutionary for the fields applied social studies and sociology and constructed an absolute new systems term.
Luhmann (1984, p. 35) begins with his definition of a system by arguing that the most essential element in systems theory is the boundary between the system and its environment (later also called non-system). He further argues that boundary maintenance for a system means self maintenance. However, the existence of boarders implies the existence of something behind these boarders (Luhmann, 1984, p. 52). This means that every system element has the chance to exceed the boundary and leave the ancestral system.
The differentiation between system and environment further forces the observer to differentiate between the whole and a partition of the whole. Luhmann (1984, p. 37) therefore establishes the term system differentiation. For him, system differentiation is nothing else but the repetition of the process of system formation in a system. Due to that every system can be divided into as many subsystems as necessary to understand the system or to reduce the complexity of the system.
Luhmann further discovers that systems are not only a set of relations between system elements, but have a clearly specified relations structure defined by certain regularities. He calls this conditioning of the system. The British psychiatrist, neuroscientist and mathematician William Ross Ashby (2004, p. 103p)15 introduces the term organisation to describe this phenomenon. Thus, the structure of relations within the system follows certain rules and cannot emerge randomly.
As the rules, under which relations between system elements are created, are purpose driven, each system is a purpose- or function-related construct (it is created to serve a certain function). Therefore, a certain set of relations between system elements is selected (conditioning). This selection places and qualifies the elements although they could have been set into other relations. This process of selecting and conditioning explains why it is possible to create different systems with only few system elements. Luhmann describes this circumstance with the term complexity. For him, complexity emerges through the attempt to reduce complexity through selective conditioning (Luhmann, 1984, p. 47).
Another important fact in terms of systems theory is autopoesis. Luhmann (1984, p. 57pp) describes systems as self organizing constructs that reproduce themselves, a singularity that can not only be observed within social systems, but also in biological organisms. Similar to a system, the organism defies its collapse by reproducing its elements and relations.
However, one of the greatest challenges in systems theory is the observation and documentation of an existing system. As each system is built upon a large number of assumptions that even a system element (in this sense a person) is not able to express explicitly (Luhmann, 1984, p. 57pp), the system can only be described through observation and through concluding onto the assumptions that may stand behind the observed artefacts. Thereby, the observer has to take into account that every system, including the observing system, has a blind spot – thus, something that it cannot see.
The aim of this section was to provide a very brief insight into systems theory. The results of these considerations can be combined to a complete picture of a system, as shown in Figure 5.
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Figure 5 – The generic system16
In the 1960s, the German philosopher Klaus Dieter Wüstneck (1963, p. 1504pp) tried to find a general scientific definition for the term model. For him, a model is a system, which represents another (original) system by using certain properties that both have in common. The model is thereby set up by a third system that either wants to allow or facilitate the understanding of the original system or wants to replace the original system by the model. Although this definition provides a first insight into modelling theory, it is still very general and theoretical.
The German cyberneticist Herbert Stachowiak (1973) built on Wüstneck’s theory and introduced a more specific definition of the term model which had been approved by a broader scientific community. Stachowiak therefore used three new attributes to characterise models:
- Attribute of reproduction17: A model is always a reproduction of a natural or a synthetic original system which can be a model itself (Stachowiak, 1973, p. 131).
- Attribute of abbreviation18: A model does not reproduce all attributes of the original system. Which attributes are modelled depends on the model creator or the model user and the kind of attributes they are interested in (Stachowiak, 1973, p. 132). If all model’s relations can be traced back to the original system and the original system’s relations are fully reflected in the model, the model is called an isomorphous model. Otherwise the model is called homomorphous. This model type just reflects selected relations of the original system. Figure 6 shows two different models of the same original system (OS). As the isomorphous model (i) represents all entities and relations of the original system, the homomorphous model (h) only represents some exclusive entities and relations. In the latter case, the structure of the original system cannot be reproduced by only knowing this model because of the missing of the entity (h.c) and the relations (h2) and (p).
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Figure 6 – Isomorphous and homomorphous models
- Attribute of appliance19: Each model is used by the model creator or the model user for a certain period of time to serve a certain purpose – it is used in a certain context. The allocation of the model to its original system is therefore always affected by the following questions (Stachowiak, 1973, p. 132pp): For whom is the model? What is the model for? Why has the model been created? This means, that models are not only models of something but also models for something in a certain period of time. Hence, in order to understand a model, one has to know the context in which the model has been created.
Following the attribute of abbreviation, each model does only reproduce some attributes of the original system. Thus, the model creator or model user picks out those attributes that he/she wants to be modelled20 and leaves out the obsolete ones21. He/she generates a core model22 out of these attributes, which consists of all attributes that can be directly ascribed back to the original system, and adds some attributes that are not represented in the original system23 to make the model applicable. This relation between the original system and the model is shown in Figure 7.
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Figure 7 – How the model reflects the original system24
The term capability model is very popular because many auditing frameworks use capability models for performance measurements. However, neither scientific nor non- scientific literature has come up with a practical definition yet. Beyond this, the term capability model is often wrongly used as a synonym for maturity model. Motzel (2004, p. 2) tried to overcome this problem and described capability models as "[…] models and methods for the evaluation of individual, organisational and social competencies […]"25. Although his definition produces a first relief, he does not trace the outline between models and methods and how they are related to each other.
Similar problems can be observed by looking to other definition approaches. SEI26 (2007a, p. i), for example, defines its Capability Maturity Model Integration (CMMI) as "[…] collections of best practices that help organizations improve their processes." This definition is in fact not very precisely and uses circumlocutions which mainly aim at the utility and the objectives of the model but do not define what a capability model is.
Ahlemann, Schroeder and Teuteberg (2005, p. 13p) took this initial situation and tried to find a universal definition for the term capability model. As a foundation for their definition they observed that each capability model reflects an original system in certain aspects. The reflected parts of the reality are called the set of capability objects and can be distinguished into several object classes. Within each object class different kinds of quality attributes can be defined. According to these initial considerations, Ahlemann, Schroeder and Teuteberg deduce the following definition: A capability model evaluates if and how far a competence object achieves the qualitative requirements that have been universally set up for a class of competence objects. Therefore, an assessor uses information ascertainment and analysis methods to gain information from information suppliers. The output of this process is made available for the purpose of all model stakeholders.
Ahlemann, Schroeder and Teutebergs definition proposal for the term capability model will be the cornerstone for the following discussion about maturity models, where we will find a similar situation as we did in respect of capability models.
Maturity models can be understood as a very special form of capability models. However, the term maturity model faces a similar problem as the term capability model. Although this expression is widely used, there is no standardised definition within scientific discussion and literature. PMI27 (2003, p. 5), for instance, defines its project management maturity model as "[…] a conceptual framework, with consistent parts, that defines maturity in the area of interest – in this case, organisational project management." Although this definition of the term maturity model is much more differentiated than SEI’s definition of a capability model (see 2.4.3, page 21), it does not mention how maturity is defined and what this definition can be used for.
Ahlemann, Schroeder and Teuteberg (2005, p. 13p) give a more profound definition of maturity models. They understand a maturity model as an instance of a capability model, whose qualitative requirements are expressed through degrees of maturity which are brought into a sequential progression. This means that they build on each other.
1 For further details see section 2.5 (page 22).
2 A delphi survey of the German government for example showed that 55% out of 9392 interviewed education experts think that a clear separation of work and learning will completely disappear until 2020 (Ehrenthal, Krekel, & Ulrich, 2004, p. 1). Studies from other countries show similar results.
3 Working question 1
4 Working question 2
5 Working question 3
6 Working question 4
7 Working question 5
8 Wernerfelt (1984, p. 174) identified four competitive resources in his article on the resource based view. These were machine capacity, customer loyalty, producer experience and technological leas.
9 Douglas C. North, in his lecture to the occasion of being awarded the Nobel Price in Economics (Stockholm, 9 December 1993).
10 According to Choo, Detlor & Turnbull (2000) and Krcmar (2005, p. 14)
11 The study of sign processes
12 According to Berthel (1975, p. 1869)
13 Davenport estimates that 28% of the workforces are knowledge workers.
14 According to Maier and Schmidt (2007, p. 3)
15 The original text of Ross Ashby was published in 1962 and was republished in 2004.
16 Own depiction on the basis of Luhmann (1984, p. 35pp)
17 In the original text described as "Abbildungsmerkmal".
18 In the original text described as "Verkürzungsmerkmal".
19 In the original text described as "Pragmatisches Merkmal".
20 In the original text described as "Abbildungsvorbereich".
21 In the original text described as "Präterierte Attribute".
22 In the original text described as "Abbildungsnachbereich".
23 In the orginal text described as "Abundante Attribute".
24 Based on Stachowiak (1973, p. 157)
25 Original text in German; literally translated by the author.
26 SEI, the Software Engineering Institute of the Carnegie Mellon University provides CMM and CMMI, two popular capability models.
27 PMI, the Project Management Institute provides OPM3, a project management maturity model.
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