Within the information systems field, reference models have been known for many years. A reference model is a conceptual framework and may be used as a blueprint for information systems development. Despite the relevance of reference model quality, little research has been undertaken on their systematical evaluation. In this paper, we propose an approach to evaluate reference models based on the Bunge-Wand-Weber ontology. We demonstrate that this application is possible and useful for model evaluation and several other areas. The main idea of our approach is the ontological normalization of a reference model. An ontological normalization is comparable with the normalization of a database schema but considers the structure of reality and not technical aspects. The ontological normalization of a reference model consists of four steps: (1) Developing a transformation mapping, (2) Identifying ontological modeling deficiencies, (3) Transforming the reference model, and (4) Assessing the results. Although our approach is based on sound theory, we argue that this approach is not inherently superior to other approaches of reference model evaluation.

Up to now there is neither data available on how many errors can be expected in process model collections, nor is it understood why errors are introduced. In this article, we provide empirical evidence for these questions based on the SAP reference model. This model collection contains about 600 process models expressed as Eventdriven Process Chains (EPCs). We translated these EPCs into YAWL models, and analyzed them using the verification tool WofYAWL. We discovered that at least 34 of these EPCs contain errors. Moreover, we used logistic regression to show that

Today, XML is primarily regarded as a syntax for exchanging structured data, and therefore the question of how to develop well-designed XML models has not been studied extensively. As applications are increasingly penetrated by XML technologies, and because query and programming languages provide native XML support, it would be beneficial to use these features to work with well-designed XML models. In order to better focus on XML-oriented technologies in systems engineering and programming languages, an XML modeling language should be used, which is more focused on modeling and structure than typical XML schema languages. In this paper, we examine the current state of the art in XML schema languages and XML modeling, and present a list of requirements for a XML conceptual modeling language. 1

This paper studies the role that ontologies can play in establishing conceptual data models during the process of information systems development. A mapping algorithm has been proposed and embedded in a special purpose Transformation-Engine to generate a conceptual data model from a given domain ontology. In addition, this paper focuses on applying the proposed approach to a bioinformatics context as the nature of biological data is considered a barrier in representing biological conceptual data models. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. The results of this evaluation indicate that the quality of the generated conceptual data models can reflect the problem domain entities and the associations between them. The results are encouraging and support the potential role that this approach can play in providing a suitable starting point for conceptual data model development. 1.

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 service-oriented 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. 1

The challenge of defining the domain and core concepts of the IS field is a perennial topic at major IS conferences. This paper describes the architecture of Sysperanto, a model-based ontology of the IS field. Sysperanto is being developed as part of an ongoing effort to create methods that typical business professionals can use to analyze systems and system-related projects for themselves at whatever level of depth is appropriate. The name Sysperanto is meant as a metaphor combining generality (covering the IS field), vocabulary (identification of terms), and structure (internally consistent organization) to create an ontology more powerful and useful than a list of keywords or propositions. Sysperanto’s architecture provides an organizing framework for codifying the disparate and inconsistent propositions, methods, and findings that constitute the current state of IS knowledge and, in combination, form a major obstacle to knowledge accumulation and use in the IS field. Instead of yet another discussion of whether the IS field lacks a conceptual core and what might be the consequences of such a shortcoming, this paper proposes an architecture and preliminary details of a plausible set of core concepts for the IS field. It starts by summarizing Sysperanto’s

Business process modeling is heavily applied in practice, but important quality issues have not been addressed thoroughly by research. A notorious problem is the low level of modeling competence that many casual modelers in process documentation projects have. Existing approaches towards model quality might be of potential benefit, but they suffer from at least one of the following problems. On the one hand, to be applicable for novices and non-experts in practice. On the other hand, there are collections of pragmatic hints that lack a sound research foundation. In this paper, we analyze existing research on relationships between model structure on the one hand and error probability and understanding on the other hand. As a synthesis we propose a set of seven process modeling guidelines (7PMG). Each of these guidelines builds on strong empirical insights, yet they are formulated to be intuitive to practitioners. Furthermore, we analyze how the guidelines are prioritized by industry experts. In this regard, the seven guidelines have the potential to serve as an important tool of knowledge transfer from academia into modeling practice.

Despite the large body of knowledge on formal analysis techniques for process models, in particular Petri nets, there has been a notable gap of empirical research into verification. In this paper we compare the few studies that report results from applying verification techniques to real-world process model collections. For this comparison we are particularly interested in the different approaches, their computational performance, and the number of errors found. Our comparison reveals that most of the samples have error rates of 10 % to 20%. Some of the studies have established a connection between error probability and process model metrics, as well as between model understanding and both metrics and modeling competence of the model reader. Based on these results, we discuss implications and directions for future research.

In this paper we outline a new approach for constructing design theories in IS research by formulating theoretical statements as conceptual models. In recent history the philosophical and methodological aspects of design research have been in the focus of many research groups in IS research. We argue that applying conceptual models in the theory-building process enables design researchers to express hypotheses and underlying assumptions more accurately. Furthermore, our approach allows researchers to specify empirically refutable statements. We ground our work in the concept of IS design theories as proposed by Walls et al. (1992) and extend their idea twofold: first, based on an analysis of the output types of design research as proposed by March & Smith (1995) we incorporate conceptual modeling to formulate theory statements. Second, to facilitate developing concise testable theory statements, we apply the idea of patterns as proposed by Alexander (1973). Overall, we propose a detailed framework that integrates conceptual modeling in the process of theorizing in design-oriented IS research. Thus, we present an important step towards building “own ” theories of IS research.

In this paper we develop and discuss a special-purpose computational ontology (similar to the notion of conceptual grammar) termed GRITIKA (acronym for