Reusable Conceptual Models – Requirements Based on the Design ...

Reusable Conceptual Models – Requirements Based on the Design Science Research Paradigm Jan vom Brocke, Christian Buddendick European Research Center for Information Systems (ERCIS) University of Muenster Leonardo-Campus 3 48149 Muenster – Germany {jan.vom.brocke; christian.buddendick}@ercis.de

Abstract. Conceptual modeling is one major topic in information systems research and becomes even more important with the arising of new software engineering principles like model driven architecture (MDA) or serviceoriented architectures (SOA). Research on conceptual modeling is characterized by a dilemma: Empirical research confirms that in practice conceptual modeling is often perceived as difficult and not done well. The application of reusable conceptual models is a promising approach to support model designers. At the same time, the IS research community claims for a sounder theoretical base for conceptual modeling. The design science research paradigm delivers a framework to fortify the theoretical foundation of research on conceptual models. We provide insights on how to achieve both, relevance and rigor, in conceptual modeling by identifying requirements for reusable conceptual models on the basis of the design science research paradigm.

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Introduction

Since 1970, conceptual models have been one of the key research topics in the field of information systems (IS) (Wand et al. 2002). Nevertheless, this

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research stream has been languished in the past years. Recently, the interest in conceptual modeling has been revitalized again. Actual trends in IT as service-oriented architectures (SOA) and model driven architectures (MDA) (Klepp et al. 2003; Thomas 2004) emphasize the importance of research in conceptual modeling. Conceptual models, representations of a specific domain (Batani et al. 1992), serve as an important step in the software development process. The method of conceptual modeling can serve a large variety of purposes (Wand et al. 2002). Examples for typical purposes are the facilitation of communication between users and software developers, the support of software

developers

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understanding

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domain,

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documentation of requirements for further design processes (Kung et al. 1986). Against the background of the documentation of requirements for further design processes, it appears to be obvious that reusability is a central subject of conceptual modeling. In order to achieve reusability, conceptual models have to fulfill certain requirements. These requirements have to be derived from a sound theoretical foundation (Weber 2003). Considering the empirical observation about conceptual modeling in practice not being done satisfyingly (Batra et al. 1995; Goldstein et al. 1990; Hitchman 1995; Maier 1996; Prietula et al. 1991), a strong theoretical base is not sufficient on its own. When deriving requirements for reusable conceptual models, the practitioner’s call for relevance has also be taken into account.

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With this paper we deliver answers to the question of an adequate paradigm for reusable conceptual modeling, taking into account rigor as well as relevance. Furthermore requirements are identified that have to be met by reusable conceptual models based on a suitable paradigm. We then make suggestions on how to meet these requirements in practical modeling projects. In the following section we look at the state of the art in IS research and identify an adequate research paradigm for reusable conceptual models. Then, more insights on reusable conceptual modeling is delivered (section 3) before we derive requirements for reusable conceptual modeling in section 4. In this section, we also deliver suggestions on how to meet certain requirements. We close with a brief conclusion and potential further research questions.

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About the Search for a Theoretical Foundation for Research on Reusable Conceptual Models

2.1

IS Research Between Relevance and Rigor

The field of IS research is characterized by a high degree of diversity in two ways, diversity on opinions (1) on relevant research topics and (2) on rigor research methods (Benbasat et al. 1996; Robey 1996; Vessey et al. 2002). The vital discussion about research topics aims at the achievement of relevance in IS research by defining the core of IS discipline. Different approaches for defining the IS core are applied, empirical research on published articles in

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leading IS journals either by one journal over a certain period of time (Banker et al. 2004; Swanson et al. 1987), or by a sample of journals as well as surveys and interviews of faculty deans (Watson et al. 2000) and practitioners (Grover et al. 1989; Pearson et al. 2005). The IT artifact and its immediate nomological-net is a common definition of the core of IS research (Benbasat et al. 2003; Weber 1987). IT artifact is used in terms of “bundles of material and cultural properties packaged in a socially recognizable form such as hardware and software” (Orlikowski et al. 2001). Research in the field of IS should be limited only on these topics. These rigid boundaries of IS discipline are questioned by a number of researchers. They accept the IT artifact as central in IS research, yet they argue either that the boundaries should be fluid or that the core should be enlarged by adding additional topics to the focus (e.g. Alter 2003; Whinston et al. 2004). Despite the definition of the adequate limits of topics covered in IS research, Wand and Weber argue that conceptual modeling has been at the core of IS research for a long time and will proceed to be at the core in future times (Wand et al. 1995; Weber 2003). In contrast to the debate on how relevance can be achieved in IS research, a second stream focuses on the achievement of rigor in IS research (Lee 1999). Within this field, alternative methodologies are examined on their fit for IS research. The ongoing debate can be ascribed to a discussion on paradigms, the positivistic paradigm on the one hand and the interpretive paradigm on the other hand (Chen et al. 2004). Weber delivers an introduction and comparison

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of these two paradigms (Weber 2004). The critical paradigm is sometimes referred to as a third possible position when conducting IS research (Brooke 2002a; Brooke 2002b; Orlikowski et al. 1991). These paradigms differ in terms of their underlying ontological and epistemological foundations. Furthermore, these paradigms call for different research methodologies respectively methods as means for rigor research. In the past, the positivism paradigm has been dominating in IS research. Recently, a shift towards an increasing importance of interpretivist research can be observed. Due to this fact a large body of work can be found on adequate methodologies and methods for interpretive IS research. When discussing relevance and rigor in IS research, one should consider, that both issues cannot be separated from each other. Robey and Markus argue that there is a widely accepted inverse relationship between academic rigor and practical relevance (i.e., the greater the rigor, the less the relevance to the community of practitioners) (Robey et al. 1998). IS research has to address both, relevance and rigor at the same time, as well as balance work against these issues (Appelgate 1999). Therefore, an adequate research paradigm for IS should address both questions at the same time. In the following paragraph, we will discuss the suitability of the design science research paradigm as a framework for IS research.

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2.2

Design Science as a Framework for IS Research

The framework of design science is founded on the “Science of Artificial” by Simon, a paradigm that has been applied in a large variety of scientific fields (e. g. architecture science, engineering science). Simon addresses artifacts of any kind in general, e. g. buildings. Recently, this paradigm has been adopted also in Management Science (van Aken 2004). As described in the paragraph above, the field of IS addresses phenomena of artificial nature, centrally, the IT artifact and its immediate nomological-net. Consequently, Simon’s work has been adopted for IS research in order to examine the artificial phenomena in IS research. As every theory borrowed from another scientific field, the special requirements of IS have to be considered in the adoption process (Weber 2003). Based on Simon’s findings, a research framework for IS has been introduced and is referred to as “design science research” (March et al. 1995). The significance of this approach is confirmed by a multitude of international research works which reflect this paradigm (e.g. Gavish et al. 1998; Markus et al. 2002; van der Aalst et al. 2003a). Design science research can be distinguished from both natural (behavior) science and routine design (Hevner et al. 2004). Natural science research aims at the revelation of truth by the explanation of phenomena, discovery, and justification (Kaplan 1964). Design science research, on the contrary, aims at utility. Utility means the fulfillment of identified business needs by an artifact.

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Routine design is the application of existing knowledge (artifacts) to a specific problem. In contrast, design science research addresses business needs in either innovative or unique ways, or it delivers either more effective or efficient solutions for a solved business need. Fig. 1 displays the framework for design science research processes.

Fig. 1. Design Science Research Cycle A typical design science research cycle consists of six distinctive stages (see fig. 1). Stage one includes the identification of business needs and the derivation of requirements. Based on the requirements, an artifact is to be built in the next stage that should deliver utility. In the consecutive stage, the artifact has to be represented and documented, including also the documentation of constraints. In order to analyze the utility, an evaluation has

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to be conducted. Therefore, both evaluation criteria and evaluation methods have to be selected. Consequently, the artifact has to be evaluated applying selected evaluation criteria and methods. The final stage of a design science research process encloses the communication. The result can either be a knowledge basis enlarging useful artifact or requirements for a new iteration of the design science research cycle. This design cycle is embedded in the environment and the existing knowledge base. The environment, technologies, and organizations define the problem space (Simon 1996). The knowledge base consists of artifacts and theories (Hevner et al. 2004). Theories are “a statement or group of statements about how some part of the world works—frequently explaining relations among phenomena” (Vogt 1993; p. 232). In design science, four types of artifacts are differentiated: constructs, models, methods, and instances (Hevner et al. 2004). Constructs, such as languages, provide the vocabulary and symbols in which problems and solutions are defined and communicated (Schön 1983). Models are the results of a design process that serves to condensate people’s perceptions about a matter (Gupta et al. 2001; Rolland et al. 1992). Methods are composites comprising a process as well as the constructs to describe the results of its application (Brinkkemper 1996; Nuseibeh et al. 1996). Instantiations are either intellectual or software tools aimed at improving the process of information system development (Hevner et al. 2004).

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Based on this framework, Hevner et al. propose seven guidelines for researchers in the field of IS which should be addressed when conducting design science research (Hevner et al. 2004). These guidelines, illustrated in fig. 2, define requirements for IS research processes and address both, relevance and rigor. Design Science Research Guidelines Guideline

Description

The output of design science research should be an IT artifact (Construct, model, method, instantiation) Research should aim for a technology-based solution to an important Problem Relevance and relevant business need Utility, quality and efficency of artifact should be demonstrated by use Design Evaluation of rigor evaluation methods Research Output should have a significant contribution to the Research Contribution knowledge base Research should rely on rigor methods for the conctruction and Research Rigor evaluation of the IT artifact Existing means and fundamental laws of the phenomena environment Design as a Search Process should be considered in the design science research process Outcomes should be communicated properly for technology- as well as Communication of Research management oriented audiences Design as an Artifact

Issue adressed by Guideline Relevance Relevance Relevance and Rigor Relevance Rigor Rigor Relevance

Fig. 2. Guidelines for Design Science Research In the following section, an introduction to preliminary work in the field of conceptual modeling is given as well as to the design process of reusable conceptual models. Later on we will discuss how design science research can be used as a framework for research on reusable conceptual models.

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Reusable Conceptual Models

3.1

Overview of Some Existing Research

Conceptual models are representations of an application domain used to capture the important features to be incorporated into a specific information system (Batani et al. 1992; Bodart et al. 2001). This comprises mainly the

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application of the principle of abstraction (Frank 1999). The design of conceptual models is an essential task in every software development life cycle, for instance to detect and correct errors in early stages of the life cycle (Hungerford et al. 2004; Wand et al. 2002). The arise of new design principles for software development e. g. the MDA, will even enforce the importance of this task (Klepp et al. 2003; Thomas 2004). Research on conceptual models has already produced a remarkable body of work in different fields. Research work covers mainly the development of languages and grammars (e.g. Chen 1976; Coad et al. 1997), comparisons of different grammars (e.g. Green et al. 2000; e.g. Wand et al. 1993), methods (e.g. Basu et al. 2000; e.g. Parsons 1996), and case tools (e.g. Scheer et al. 2000). Due to an increasing demand for conceptual models which address similar design problems to a certain extent, the development of reusable conceptual models is subject to research. The principle of reuse is widely spread in the development of information systems (Jones 1984; Mili et al. 1995). Common examples for the application of this principle are patterns (Coad et al. 1997; Gamma et al. 1995) and components (Szyperski 1998). Applying the principle of reuse in the development of information systems enhances its efficiency and the effectiveness. Approved elements of information systems are adopted in new developments. Doing so, designers are able to focus on the new and innovative system elements and do not have to waste resources on reconstructing reliable and tested basic system elements. Consequently, the

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overall design task is facilitated because designers are not in need of domain specific knowledge about the inner structure and functions of the system elements they reuse. Instead, they need knowledge in the following fields: meta-information about artifacts that can possibly be reused, information about the whereabouts of these artifacts can be found and knowledge about methods of integration of these artifacts. Applying the principle of reuse in software engineering leads to a shift of necessary competences of software designers from domain specific knowledge towards method specific knowledge of IT-artifacts. With this, the division of labor between software designers can be enhanced and, consequently, economies of scale can be realized. In contrast to the increasing importance of reuse in software engineering, the application of this principle has been, so far, inadequately broached as an issue in conceptual modeling. Predominantly, this subject has been discussed in German literature under the topic of “reference modeling” so far. A detailed discussion on this topic can be found at vom Brocke (vom Brocke 2003). As a result, a reference model is referred to as a special conceptional model that serves to be reused in the design process of other business process models (vom Brocke 2003). Fettke and Loos give a short introduction and an overview of designed reference models (Fettke et al. 2003). Further examples of domain specific reference models are given by Becker and Schütte as well as Scheer (Becker et al. 2004b; Scheer 1994). In international research

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literature, only a small amount of work dealing with this issue can be found (e.g. van der Aalst et al. 2003b). Recently, a trend towards the internationalization of this topic has emerged (e.g. Fettke et al. 2006; Kindler et al. 2005). In the following section, a deeper examination of the design process of reusable conceptual models is given. 3.2

Design Process of Reusable Conceptual Models

The essential idea is to provide conceptual models as a kind of reference in order to raise both, efficiency and effectiveness of modeling processes. Fig. 3 displays the design process of reusable conceptual models and their application.

Fig. 3. Design Process of Reusable Conceptual Models

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Basically, two relevant design processes can be differentiated. First, the design process of conceptual models aiming at building a reusable conceptual model (design for reuse) and the design of an application conceptual model on the basis of a reusable conceptual model (design by reuse). The difference between the two processes is the intended purpose of the construction result. When designing “for reuse”, the conceptual model should serve as a reference for the design of another conceptual model. In case of design by reuse, a conceptual model is constructed that should be applied in the design process of another IT artifact. In order to emphasize these two purposes, we define the construction result of design for reuse as reference conceptual models and the result of design by reuse as application conceptual models. In both processes, two kinds of stakeholders are important. Designers and users work together in order to build a conceptual model. Both roles can be fulfilled by the same person, but it is also possible that more than two people are involved in the process (vom Brocke et al. 2004a). According to transaction cost theory, distributed research communities are preferably organizational settings for conceptual modeling (vom Brocke et al. 2004b). A prospective user of a model defines the specific requirements for the construction as a customer with respect to a certain model purpose. A designer is in charge of building the model as a supplier according to the user’s needs regarding methodological feasibility. Due to different mental

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models of both roles involved, coordination is needed for requirements and results. Based on the requirements identified and the surrounding environment (technology and organization), designers search the knowledge base for adequate artifacts (e.g. metamodels (Brinkkemper et al. 1999; Kruchten 2003)) and theories to be applied in the design process. In order to get a sounder foundation in this stage findings of domain engineering (Gomaa 1995; Kang et al. 1998; Mili et al. 2002) can be applied. All activities of the design process result in a reference conceptual model intended to be reused in a design process of an application conceptual model. Between the design of a reference conceptual model and its application there might be a time-lag. For this time-period, the reference conceptual model will be stored in the knowledge base in order to be reused in further design processes. The design “by reuse” process is bases on an existing reference conceptual model of the knowledge base. In more detail, re-use is conducted by taking parts of one or more original models, adapting and extending them in the resulting model. That way, both the efficiency and the effectiveness of the modeling process may be fostered. The effectiveness is a measure for the customer’s satisfaction due to the appropriateness of the model’s quality. The efficiency, additionally, takes into account the ratio of in- and output coming along with the process. The result of design by reuse processes are application

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conceptual models intended to be used in development processes of ITartifacts e.g. instances of software. Empirical research has discovered shortcomings of conceptual modeling in practice (Batra et al. 1995; Hitchman 1995; Maier 1996). Practitioners perceive conceptual modeling as difficult (Wand et al. 2002). In contrast to conceptual modeling without reuse of reference models, this concept assists designers of application models by providing reliable bricks as a foundation. Therefore, the entire design process can be facilitated, and the quality of conceptual modeling can be enhanced. According to empirical studies, designers who reuse artifacts in general will (1) omit more necessary additions, (2) propagate more errors in the artifact, and (3) include more extraneous functionality than designers who do not reuse artifacts (Parsons et al. 2004). In order to gain more insights about reuse oriented conceptual modeling, a firmer theoretical background as a foundation is desirable (Wand et al. 2002). In the following section we will therefore examine how reusable conceptual models can be designed on the basis of the design science research framework in order to achieve relevance and rigor in reference conceptual models. Therefore, we will examine how reusable conceptual modeling can be conducted against the background of the design science research cycle and its guidelines.

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4

Applying the Framework of Design Science Research on Reusable Conceptual Models

4.1

Requirements Based on the Design Science Research Cycle

Based on the design science research cycle, essential parameters for design processes of reference conceptual models can be identified. These parameters and their relationships are displayed by an entity-relationship-diagram (Chen 1976) in Fig. 4.

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Fig. 4. Entity Relationship Diagram for Reference Conceptual Models Based on a perceived, as yet unsolved business need, requirements for reference conceptual models can be derived. When deriving the requirements,

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environmental factors have to be taken into account. These factors also define the evaluation criteria for the model. A central requirement is to ensure the reusability of the content of the reference model in a specific application environment. Based on the elements of the knowledge base, designers have to decide whether the design task can be solved by routine design or design science research. If design science research has to be applied, elements of the knowledge base will either be used or adapted. Furthermore, new artifacts can be constructed in order to complete the reference conceptual model. Of critical importance for the construction result are the documentation of the result (RCM) itself and the design process including lists of applied elements of the knowledge base. The entire reference conceptual model and its documentation have to be evaluated in the next stage of the design cycle. The evaluation should be based on reliable evaluation methods of the knowledge base and use the criteria defined at the beginning of the research cycle. Reusability is one criterion that is evaluated in this stage of the process. The evaluation result determines whether the research process led to the intended result, or if a new design cycle has to be conducted. In order to design a rigor and relevant reference conceptual model, these stages of the design cycle have to be conducted in an adequate manner. In the following section, we will examine which requirements for reusable conceptual models can be derived on the basis of the design science research guidelines.

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4.2

Requirements Based on the Guidelines

According to Hevner et al., seven distinctive guidelines should be taken into account when conducting design science research (Hevner et al. 2004). In the following section, we will discuss the implication of every guideline for the design science research process of reusable conceptual models. Guideline 1: Design as an Artifact: According to this guideline, the outcome of a design process should be an artifact. IT artifacts are “bundles of material and cultural properties packaged in a socially recognizable form such as hardware and software” (Orlikowski et al. 2001). Reference conceptual models build an important foundation for the development of information systems (Wand et al. 2002), therefore, they can serve as an IT artifact. In order to become an IT artifact the reference conceptual model must be represented in a socially recognizable manner. Hence, reliable grammars should be used for the representation of the model. Examples of suitable grammars are software diagrams in general (e.g. Hungerford et al. 2004), EPCs (Keller et al. 1992), ERDs (Chen 1976), or UML-Diagrams (Rumbaugh et al. 2004). Representing a reference conceptual model with such a grammar enables designers in search for reusable models to identify adequate IT artifacts.

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Guideline 2: Problem Relevance: The outcome of a design science research process must be relevant to the user community and, therefore, deliver utility in terms of solving a still unsolved important problem. Consequently, reference conceptual models must solve a still unsolved problem in the domain they are designed for. Unsolved problems can be of two kinds: Either the reference model solves an important and not yet solved problem or it enhances an already existing solution by adding reusability to the formerly designed model. In order to safeguard relevance, potential users of the reference model should be involved in the design process as early as possible (vom Brocke 2003). Domain specific virtual communities or expert networks are organizational settings that support this subject. Also the adoption of findings in the field of domain engineering can support relevance (Gomaa 1995; Kang et al. 1998; Mili et al. 2002). Guideline 3: Design Evaluation: The utility, quality, and efficacy of a design artifact must be rigorously demonstrated via well-executed evaluation methods. Reference conceptual models can be evaluated in different manners. In order to achieve rigor of the artifact, they can be evaluated in terms of ontology, grammar, and methods used in the design process (e.g. Becker et al. 2004b; Green et al. 2000; Opdahl et al. 2002; Wand et al. 1998). Beneath the evaluation of rigor, reference conceptual models also have to be evaluated in terms of relevance. A design

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accompanying evaluation can be achieved by involving the potential users in the design process (vom Brocke 2003). An evaluation by users appears to be most effective compared to other evaluation settings in term of quality (Shanks et al. 2004). If the reference model is designed for an anonymous market, other evaluation methods can be applied e.g. scenarios and informed arguments or a laboratory test. The results of the evaluation should be documented and announced to potential customers in order to demonstrate rigor and relevance. Guideline 4: Research Contribution: Effective design-science research has to provide a clear contribution to solve a relevant business need with respect to the existing environment. The design of a reference model should, therefore, be based on a reliable body of research in the specific domain. Expert interviews with potential users can help to get a deeper understanding of the problem situation addressed by the conceptual model. In order to achieve reusability for a certain number of problem situations, designers have to reflect whether the identified problem is of specific nature in one situation or if this problem can occur in more than one situation. The contribution can also be demonstrated via positive evaluation reports on the relevance of the topic. A major challenge in designing a reference conceptual model is to manage the trade-off between generality and specificity of the model. Guideline 5:Research Rigor:

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This guideline addresses the way research is conducted. According to this guideline, rigor artifacts should be used in the construction process as well as in the evaluation of the reusable conceptual models. The domain of conceptual modeling offers reliable artifacts to be used in evaluation and construction. Examples of reliable artifacts for design processes are grammars like ERD (Chen 1976) and UML-diagrams (Rumbaugh et al. 2004), procedure models for the application of certain grammars (Kruchten 2003) and case tools (Scheer et al. 2000). Reliable methods for evaluation are also provided in the conceptual modeling domain (e.g. Green et al. 2000; Hungerford et al. 2004; Opdahl et al. 2002; Pfeiffer et al. 2005; van der Aalst 1999; Wand et al. 1999; Wand et al. 1993). Rigor can also be achieved by adoption of design principles of other research disciplines. Engineering discipline provides established principles for construction processes (Pahl et al. 1996). Promising principles that could be adopted are optionality, configuration, instantiation, aggregation, specialization, and analogy (e.g. Becker et al. 2004a; Bodart et al. 2001; Brocke 2003; Brocke et al. 2004; Conradi et al. 1998; Meinhardt et al. 2006). When designing a reusable conceptual model it is not sufficient only to use these artifacts in design and evaluation. Additionally designers have to document which artifacts have been used and adopted in their construction.

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Guideline 6: Design as a Search Process: Research based on the design science research paradigm requires iterative design cycles in order to find the best solution. When designing reference conceptual models, a high level of interaction and communication between designers and potential users offers the opportunity to generate iterative suggestion cycles (vom Brocke 2003). Therefore, potential users should be involved in the design process as early as possible. Virtual communities as well as knowledge management platforms can facilitate the interaction and communication between stakeholders of the model (Klein 1993). They also provide the opportunity to discuss research results on a broader base in early stages of the cycle without having completed all necessary tasks e.g. documentation of the research process. By this, granular research results can be evaluated by the relevant community without communication of the entire problem solution and all existing means and ends. Guideline 7: Communication of Research: All research outcomes should be presented properly to a technical as well as management audience. In the domain of reusable conceptual models, this leads to the recommendation of representing research outcomes redundantly in different grammars. Formal or Semi-Formal grammars like mathematics or ERD´s are especially suitable for technical oriented audiences, while natural and non-formal language is preferred by a management audience (Wand et al. 1993). Both aspects should be integrated in the presentation of the research

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result. The research results displayed in an ERD should also be described in addition by natural language in order to satisfy both audiences. Designers of reusable conceptual models should address all of the above discussed guidelines as well as the requirements based on the design science research cycle. The consideration of these requirements puts research work on reusable conceptual models on a sounder theoretical base. This theoretical base does not only contribute to the enhancement of rigor in research but also to the relevance from a practitioner’s point of view. Meeting these requirements, therefore, facilitates the achievement of the benefits of reusable conceptual models.

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Conclusion

Research on conceptual modeling is caught in a trade-off between relevance and rigor. Insufficient relevance is shown by the empirical observations, that conceptual modeling in practice is not done well and perceived as difficult. Insufficient rigor is demonstrated by the absence of a common theoretical foundation for conceptual modeling. The principle of reusing artifacts is widely accepted and applied in software engineering. This principle can be applied to conceptual modeling by designing reusable conceptual models. These models can facilitate the design of application conceptual models and support practitioners in their modeling tasks. The paradigm of design science

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research offers an approved theoretical foundation for design processes of IT artifacts. Against the background of design science research, we identified requirements to be met by reusable conceptual models. These requirements cover the design process as well as the outcome of this process. We delivered findings on how these requirements can be met when designing reusable conceptual models. Beyond our findings some opportunities for further research can be identified. Further insights on methods and tools that support design science research processes belong to theses opportunities. Equally, research on adequate artifacts for the design and evaluation of reusable conceptual models represent an opportunity for further research. Empirical research on the question whether application conceptual modeling by reuse is also perceived difficult from a practitioner’s point of view, may deliver precise starting points for suggestions on how to enhance reusable conceptual modeling.

Acknowledgment This research is based on work done within the research cluster "Internet Economy" at the University of Muenster. The authors wish to thank the German Federal Ministry of Education and Research (BMBF) for financial support (grant number 01AK704).

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