X-IM Framework to Overcome Semantic Heterogeneity Across XBRL Filings

Semantic heterogeneity in XBRL precludes the full automation of the business reporting pipeline, a key motivation for the SEC’s XBRL mandate. To mitigate this problem, several approaches leveraging Semantic Web technologies have emerged. While some approaches are promising, their mapping accuracy in resolving semantic heterogeneity must be improved to realize the promised benefits of XBRL. Considering this limitation and following the design science research methodology (DSRM), we develop a novel framework, XBRL indexing-based mapping (X-IM), which takes advantage of the representational model of representation theory to map heterogeneous XBRL elements across diverse XBRL filings. The application of representation theory to the design process informs the use of XBRL label linkbases as a repository of regularities constitutive of the relationships between financial item names and the concepts they describe along a set of equivalent financial terms of interest to investors. The instantiated design artifact is thoroughly evaluated using standard information retrieval metrics. Our experiments show that X-IM significantly outperforms existing methods

Construct redundancy in process modelling grammars: Improving the explanatory power of ontological analysis

Conceptual modelling supports developers and users of information systems in areas of documentation, analysis or system redesign. The ongoing interest in the modelling of business processes has led to a variety of different grammars, raising the question of the quality of these grammars for modelling. An established way of evaluating the quality of a modelling grammar is by means of an ontological analysis, which can determine the extent to which grammars contain construct deficit, overload, excess or redundancy. While several studies have shown the relevance of most of these criteria, predictions about construct redundancy have yielded inconsistent results in the past, with some studies suggesting that redundancy may even be beneficial for modelling in practice. In this paper we seek to contribute to clarifying the concept of construct redundancy by introducing a revision to the ontological analysis method. Based on the concept of inheritance we propose an approach that distinguishes between specialized and distinct construct redundancy. We demonstrate the potential explanatory power of the revised method by reviewing and clarifying previous results found in the literature

Does construct overload truly overload the performance? - An experimental study of experienced data modeler.

학위논문 (박사)-- 서울대학교 대학원 경영대학 경영학과, 2017. 8. 박진수.A principal activity in information systems development involves building a conceptual model of domain that an information system is intended to support. Such models are created using a conceptual-modeling grammar fundamental means to specifying information systems requirement. However, the actual usage of grammar is poorly understood and some issues regarding conceptual grammar such as construct overload still remain unsolved. With regard to construct overload in conceptual modeling, past studies have had some deficiencies in research methods and even have presented contradicting results. In this paper, we experimented to test whether construct overload enables conceptual models users to understand a domain more efficiently. To acquire a more complete and accurate understanding of construct overload, our study focused on three major pointsthe evaluation of conceptual modeling grammar semantics, research participants and domain familiarity. This papers key contribution is that it is one of the first studies to investigate practitioners aspects of construct overload employing different degrees of domain familiarity by investigating the cognitive processes of practitioner. In addition, this research reconciles conflicting outcomes by examining practical directions for model variation. The result of study will broaden the perspective on usability in the context of the conceptual model and may serve as an ontological guidance to construct overload when modelers create a conceptual model.1. Introduction 2 2. Theory and Related Work 5 2.1. Theory 6 Theory of Ontological Clarity 7 Feynman-Tufte Principle 7 Mayers Cognitive Theory of Multimedia Learning 8 Information Processing Theory 8 Theory of Visual Attention 9 2.2. Related Work 9 Ontological Clarity 13 Domain Familiarity 13 3. Proposition Development 14 4. Research Method 18 4.1. Design and Measures 19 4.2. Materials 20 Personal Profile and Training Materials 20 Conceptual Models 21 Understanding Task Materials 27 4.3. Participants 31 4.4. Procedures 32 4.5. Results 33 Data Scoring 33 Quantitative Data Analysis 33 5. Cognitive Process Tracing Study 36 5.1. Design and Measures 36 5.2. Materials 37 5.3. Participants 38 5.4. Procedures 38 5.5. Coding Scheme 39 5.6. Analysis of Protocol Data 40 5.7. Analysis of Eye-tracking Data 45 Scan Path 48 Focus and Heat Map 52 Quantitative Data Analysis of Key Performance Indicators 56 6. Discussion 63 6.1. Conclusion 63 6.2. Implication 63 6.3. Limitations and Future Research Directions 65 Reference 66 Appendix A 73 Summary of Information Processing Coding Typology 73 Appendix B 75 Glossary of Eye Tracking Technique 75 Focus Map of Unfamiliar Domain (Waste Processing System) 75Docto

Reflections on Replications

We are immensely pleased to see a publication dedicated to replications in the IS field (Dennis and Valacich, 2014). We believe it is long overdue and will serve a useful and path breaking function in the Information Systems discipline and, perhaps, in the larger academic community (Berthon, et al., 2002). Toward that end, we share our particular view of replication: why it is important and how various types of replications may co-exist. We also offer some thoughts about ways that researchers might go about this type of research that will add value to their own work and more effectively add value to the body of knowledge that represents the IS discipline

Diversity in IS research : a fictive metaphor analysis

In striving to understand Information Systems phenomena Information Systems researchers frequently draw on a seemingly endless array of different disciplines to inform their studies. This act has drawn both the ire and admiration of those within the field as well as those outside its porous boundaries. On the one hand Information Systems researchers are berated for being chaotic and schizophrenic in their combined research endeavour - for producing a collective output that shows neither rhyme nor reason. On the other hand they are praised for being intellectually open and democratic in their approach. These reactions draw their strength from the many issues that stem from diversity in Information Systems research. These reactions are stimulated in part by the assertion that research in the Information Systems discipline is diverse. Despite this assertion not much is known or understood about diversity in Information Systems research. This thesis addresses this critical oversight by making research diversity the prime focus. The contributions it makes to current understandings of research diversity in Information Systems are philosophical, theoretical and empirical. Philosophically, this thesis relies on the novel approach of fictism - a blend of positivism and interpretivism. Theoretically, it explores diversity through the alternative lens of concepts. Empirically it examines the conceptual diversity of three key Information Systems concepts: organisations, technology and people. Grounded in Lakoff and Johnson's (1980) work with metaphors, the results show that Information Systems research may not be as diverse as was initially thought. Of the three primary views of key Information Systems concepts - machine, organism and culture - the study finds a distinct bias toward conceptualising these concepts as machines. This bias, one that exists at the very core of the Information Systems research endeavour, has important implications not only for individual researchers but the broader Information Systems community alike

On the specification of part-whole relations in conceptual modelling: An empirical study

Part-whole relation (PWR), as a fundamental element to model the real world, has long been concerned by researchers and practitioners. Theoretical work has been undertaken to develop the classifications of distinct types of part-whole relations (PWRs) and their properties in an attempt to clarify their semantics. There is no empirical evaluation, however, that supports whether it is necessary to specify distinct types of PWRs and their properties in conceptual modeling. In this light, this study first designs a specific representation of PWRs through a systematic review of the literature, and then by employing the theory of ontological clarity and the theory of cognitive fit, empirically compares its performance with that of conventional representation of PWRs. The findings are expected to enrich the growing body of knowledge that supports the usefulness of ontological and cognitive theories, and provide empirical evidence regarding how to model the PWRs for practitioners

How semantics and pragmatics interact in understanding conceptual models

Underlying the design of any information system is an explicit or implicit conceptual model of the domain that the system supports. Because of the importance of such models, researchers and practitioners have long focused on how best to construct them. Past research on constructing conceptual models has generally focused on their semantics (their meaning), to discover how to convey meaning more clearly and completely, or their pragmatics (the importance of context in model creation and use), to discover how best to create or use a model in a given situation. We join these literatures by showing how semantics and pragmatics interact. Specifically, we carried out an experiment to examine how the importance of clear semantics in conceptual models-operationalized in terms of ontological clarity-varies depending on the pragmatics of readers' knowledge of the domain shown in the model. Our results show that the benefit of ontological clarity on understanding is concave downward (follows an inverted-U) as a function of readers' prior domain knowledge. The benefit is greatest when readers have moderate knowledge of the domain shown in the model. When readers have high or low domain knowledge, ontological clarity has no apparent benefit. Our study extends the theory of ontological clarity and emphasizes the need to construct conceptual models with readers' knowledge in mind