Marta Indulska

Modeling languages for business processes and business rules: A representational analysis

Process modeling and rule modeling languages are both used to document organizational policies and procedures. To date, their synergies and overlap are under-researched. Understanding the relationship between the two modeling types would allow organizations to maximize synergies, avoid content duplication, and thus reduce their overall modeling effort. In this paper, we use the Bunge-Wand-Weber (BWW) representation theory to compare the representation capabilities of process and rule modeling languages. We perform a representational analysis of four rule modeling specifications: The Simple Rule Markup Language (SRML), the Semantic Web Rules Language (SWRL), the Production Rule Representation (PRR), and the Semantics of Business Vocabulary and Business Rules (SBVR) specification. We compare their BWW representation capabilities with those of four popular conceptual process modeling languages. In our analysis, we focus on the aspects of maximum ontological completeness and minimum ontological overlap. The outcome of this study shows that no single language is internally complete with respect to the BWW representation model. We also show that a combination of two languages, in particular SRML and BPMN, appears to be better suited for combined process and rule modeling than any of these modeling languages used independently.

Business Process Modeling: Current Issues and Future Challenges

Business process modeling has undoubtedly emerged as a popular and relevant practice in Information Systems. Despite being an actively researched field, anecdotal evidence and experiences suggest that the focus of the research community is not always well aligned with the needs of industry. The main aim of this paper is, accordingly, to explore the current issues and the future challenges in business process modeling, as perceived by three key stakeholder groups (academics, practitioners, and tool vendors). We present the results of a global Delphi study with these three groups of stakeholders, and discuss the findings and their implications for research and practice. Our findings suggest that the critical areas of concern are standardization of modeling approaches, identification of the value proposition of business process modeling, and model-driven process execution. These areas are also expected to persist as business process modeling roadblocks in the future.

A study of the evolution of the representational capabilities of process modeling grammars

A plethora of process modeling techniques has been proposed over the years. One way of evaluating and comparing the scope and completeness of techniques is by way of representational analysis. The purpose of this paper is to examine how process modeling techniques have developed over the last four decades. The basis of the comparison is the Bunge-Wand-Weber representation model, a benchmark used for the analysis of grammars that purport to model the real world and the interactions within it. This paper presents a comparison of representational analyses of several popular process modeling techniques and has two main outcomes. First, it provides insights, within the boundaries of a representational analysis, into the extent to which process modeling techniques have developed over time. Second, the findings also indicate areas in which the underlying theory seems to be over-engineered or lacking in specialization.

Do ontological de ficiencies in modeling grammars matter

Conceptual modeling grammars are a fundamental means for specifying information systems requirements. However, the actual usage of these grammars is only poorly understood. In particular, little is known about how properties of these grammars inform usage beliefs such as usefulness and ease of use. In this paper, we use an ontological theory to describe conceptual modeling grammars in terms of their ontological deficiencies, and formulate two propositions in regard to how these ontological deficiencies influence primary usage beliefs. Using BPMN as an example modeling grammar, we surveyed 528 modeling practitioners to test the theorized relationships. Our results show that users of conceptual modeling grammars perceive ontological deficiencies to exist, and that these deficiency perceptions are negatively associated with usefulness and ease of use of these grammars. With our research, we provide empirical evidence in support of the predictions of the ontological theory of modeling grammar expressiveness, and we identify previously unexplored links between conceptual modeling grammars and grammar usage beliefs. This work implies for practice a much closer coupling of the act of (re-)designing modeling grammars with usage-related success metrics.

Business Process Modeling: Perceived Benefits

The process-centered design of organizations and information systems is globally seen as an appropriate response to the increased economic pressure on organizations. At the methodological core of process-centered management is process modeling. However, business process modeling in large initiatives can be a time-consuming and costly exercise, making it potentially difficult to convince executive management of its benefits. To date, and despite substantial interest and research in the area of process modeling, the understanding of the actual benefits of process modeling in academia and practice is limited. To address this gap, this paper explores the perception of benefits derived from process modeling initiatives, as reported through a global Delphi study. The study incorporates the views of three groups of stakeholders --- academics, practitioners and vendors. Our findings lead to the first identification and ranking of 19 unique benefits associated with process modeling. The study in particular found that process modeling benefits vary significantly between practitioners and academics. We argue that the variations may point to a disconnect between research projects and practical demands.

A Reference Methodology for Conducting Ontological Analyses

The ontological analysis of conceptual modelling techniques is of increasing popularity. Related research did not only explore the ontological deficiencies of classical techniques such as ER or UML, but also business process modelling techniques such as ARIS or even Web services standards such as BPEL4WS. While the selected ontologies are reasonably mature, it is the actual process of an ontological analysis that still lacks rigor. The current procedure leaves significant room for individual interpretations and is one reason for criticism of the entire ontological analysis. This paper proposes a procedural model for the ontological analysis based on the use of meta models, the involvement of more than one coder and metrics. This model is explained with examples from various ontological analyses.

Emerging challenges in information systems research for regulatory compliance management

Managing regulatory compliance is increasingly challenging and costly for organizations world-wide. While such efforts are often supported by information technology (IT) and information systems (IS) tools, there is evidence that the current solutions are inadequate and do not fully address the needs of organizations. Often such discrepancy stems from a lack of alignment between the needs of the industry and the focus of academic research efforts. In this paper, we present the results of an empirical study that investigates challenges in managing regulatory compliance, derived from expert professionals in the Australian compliance industry. The results provide insights into problematic areas within the compliance management domain, as related to regulatees, regulations and IT compliance management solutions. By relating the identified challenges to existing activity in IS research, this exploratory paper highlights the inadequacy of current research and presents the first industry-relevant compliance management research agenda for IS researchers.

Improving the quality of process reference models: A quality function deployment-based approach

Little academic work exists on managing reference model development and measuring reference model quality, yet there is a clear need for higher quality reference models. We address this gap by developing a quality management and measurement instrument. The foundation for the instrument is the well-known Quality Function Deployment (QFD) approach. The QFD-based approach incorporates prior research on reference model requirements and development approaches. Initial evaluation of the instrument is carried out with a case study of a logistic reference process. The case study reveals that the instrument is a valuable tool for the management and estimation of reference model quality.

Business Process and Business Rule Modeling: A Representational Analysis

Process modeling and rule modeling languages are both used to document organizational policies and procedures. However, little work has been done to understand their synergies and overlap. Understanding the relationship between the two modeling types would allow organizations to maximize synergies and reduce their modeling effort. In this paper we use the well-established Bunge-Wand-Weber (BWW) representation theory to compare the representation capabilities of both types of languages. We perform a representational analysis of two rule modeling languages, viz., SRML and SBVR. We compare their representation capabilities with those of four popular conceptual business process modeling languages, and focus on the aspects of maximum ontological completeness and minimum ontological overlap. The outcome of this study shows that no single language is internally complete with respect to the BWW representation model and that a combination of two languages, viz. SRML and BPMN, is better suited for process modeling than any single modeling language.

Quantitative approaches to content analysis: identifying conceptual drift across publication outlets

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.