theory and empirical test

Successful information systems (IS) development requires the under- standing of the real world domain in which the IS is situated in and of which it is a representation. Developing such an understanding is the role of systems analy- sis, the first major step in IS development. Conceptual models developed during systems analysis are used to support understanding of and communication about the real world domain. Recent years have seen the emergence of the object-oriented approach in general and UML special cally for IS design and implementation. However, no generally accepted modelling language has been proposed for use during IS analysis. This study will examine the suitability of UML as a conceptual modelling lan- guage. This study comprises two parts. The first part studies UML from an ontological perspective, attaches real- world semantics and derives ontologically grounded rules for applying UML to conceptual modelling. It is argued that by following these rules, modellers will improve the performance of the resultant models. In a second step, the derived rules and proposed advantages must be empirically supported. An experimental study is designed for this purpose

Applying Cognitive Principles of Similarity to Data Integration – The Case of SIAM

Increasingly, modern system design is concerned with the integration of legacy systems and data. Consequently, data integration is an important step in many system design projects and also a prerequisite to data warehousing, data mining, and analytics. The central step in data integration is the identification of similar elements in multiple data sources. In this paper, we describe an application of principles of similarity based in cognitive psychology, specifically the theory of Similarity as Interactive Activation and Mapping (SIAM) to the problem of database schema matching. In a field that has been dominated by a multitude of ad-hoc algorithms, cognitive principles can establish an appropriate theoretical basis. The results of this paper show initial success in matching applications and point towards future research

Structural Equation Models in IS Theory and Measurement

Structural Equation Modeling (SEM) is taking on an increasingly important role in the examination of theory and measurement issues in information systems. Despite its popular use and the many software packages that exist for the researcher, SEM is a complex technique that provides may pitfalls for the unaware researcher. This workshop provides a gentle introduction to SEM from basic principles. It also covers some of the advanced issues that the researcher might encounter, not only from a statistical but also, and more importantly, from an ontological and epistemological perspective. The workshop is targeted at PhD students and IS researchers new to SEM and little to no statistical knowledge. Demonstrations and hands-on examples make use of the open source R system for statistical computing, which will be provided to all participants

Dimensions of psychosis: Elucidating the subclinical spectrum using neuroimaging markers

Psychosis unifies a collective of disorders characterised by symptom dimensions (Gaebel & Zielasek, 2015). Purposefully delimited clinical descriptors of schizophrenia spectrum and psychotic disorders (American Psychiatric Association, 2013) impose challenges on the identification of aetiological and clinically meaningful predictors. The disassembly of psychiatric diagnoses into their elementary symptom dimensions has helped formulate psychosis phenotypes fitted on a psychosis continuum (Verdoux & van Os, 2002). Aetiological models of psychosis may be studied through schizotypy and transient psychotic experiences (Barrantes-Vidal et al., 2015; Nelson, Fusar-Poli, & Yung, 2012), collectively termed subclinical psychosis phenotypes. The dimensional psychometric structures of these phenotypes varying in temporal stability (Linscott & van Os, 2013; Mason et al., 1995; Stefanis et al., 2002), and their implications might be further consolidated when paired with neuroimaging parameters (Siever & Davis, 2004). Three neuroimaging studies aimed to examine the relationship between subclinical psychotic phenotypes and neurobiology. Surface and volume-based morphometric (VBM) methods were implemented to examine the variety of cortical and subcortical signatures of different phenotype dimensions. Study 1 investigated whether cortical surface gyrification -a maker of genetic and developmental influences on cortical morphology (Docherty et al., 2015; Haukvik et al., 2012)- is associated with dimensional psychosis prone phenomena (Konings, Bak, Hanssen, van Os, & Krabbendam, 2006; Stefanis et al., 2002). Early cortical organisation contributes to cognitive capacities in later life (Gautam et al., 2015; Gregory et al., 2016; Papini et al., 2020). Given that cognitive deficits are present in psychosis prone and clinical samples to varying extents (Hou et al., 2016; Siddi et al., 2017), Study 1 also explored the mediating role of cognition (both as a general measure and intelligence quotient) as a psychosis endophenotype in the relationship between regional gyrification and PLE distress. Study 2 and Study 3 used VBM to investigate structural brain correlates for psychotic-like experiences (PLE) and trait psychosis phenotypes (schizotypy). Different PLE facets (quantity and distress severity) (Hanssen, Bak, et al., 2005; Ising et al., 2012) were used to estimate whole-brain grey matter volume, followed by interaction models in subsequent prefrontal regions of interest (Study 2). The medial temporal lobe includes the hippocampal subfields, which are regions of interest in psychosis pathophysiology (Lieberman et al., 2018; Mathew et al., 2014; Schobel et al., 2013). Based on a previous study in schizoytypy (Sahakyan et al., 2020), Study 3 examined the relationship between schizotypal trait dimensions (Mason et al., 1995) and PLE, and their interactions, and hippocampal subfields and the amygdala. The results of Study 1 showed that psychometrically assessed PLE were associated with reduced gyrification in parietal and temporal regions, indicating that psychosis proneness correlates with neurodevelopmental factors (Fonville et al., 2019; Liu et al., 2016). A lack of mediating pathways between regional gyrification and PLE suggested that cognition effects may emerge in larger samples (Mollon et al., 2016) and/or increasingly psychosis pone phenotypes. Elaborating on the distinction between PLE quantity versus distress, Study 2 showed that PLE load, but not distress severity, were associated with volume increases in prefrontal and occipitotemporal regions. At increased distress severity for perceptual abnormalities, PLE were associated with regional volume reductions of the superior frontal gyrus. Study 3 showed differential relationships between schizotypy dimensions and volumes of the MTL that are involved in the pathophysiology of schizophrenia. PLE per se did not associate with amygdala or hippocampal subfield volumes, but a positive association between the hippocampal subiculum and PLE was moderated by positive schizotypy. Study 3 underscored the enhanced usefulness of schizotypy as an endophenotype in psychosis research when its multidimensional organisation (Grant, 2015; Vollema & van den Bosch, 1995) is respected. The results support the use of psychosis symptom dimensions, showing different (positive and negative) neuroanatomical associations. While case-control studies in schizophrenia show consistent volume reductions of the prefrontal and temporal cortices (Haijma et al., 2013; Honea, Crow, Passingham, & Mackay, 2005), these findings contribute to more heterogeneous volumetric relationships in nonclinical individuals. Reduced regional cortical gyrification proposes a continuous distribution of neurodevelopmental impacts. Distress severity and schizotypy occasioned modulatory effects in prefrontal and hippocampal subfield volumes, respectively. Collectively, these three cross-sectional studies extend previous research suggesting that dimensional phenotypes show neuroanatomical variation supportive of a psychosis continuum possibly characterised by an underlying non-linearity (Bartholomeusz et al., 2017; Binbay et al., 2012; Johns & van Os, 2001)

Ontology-driven conceptual modeling: A'systematic literature mapping and review

All rights reserved. Ontology-driven conceptual modeling (ODCM) is still a relatively new research domain in the field of information systems and there is still much discussion on how the research in ODCM should be performed and what the focus of this research should be. Therefore, this article aims to critically survey the existing literature in order to assess the kind of research that has been performed over the years, analyze the nature of the research contributions and establish its current state of the art by positioning, evaluating and interpreting relevant research to date that is related to ODCM. To understand and identify any gaps and research opportunities, our literature study is composed of both a systematic mapping study and a systematic review study. The mapping study aims at structuring and classifying the area that is being investigated in order to give a general overview of the research that has been performed in the field. A review study on the other hand is a more thorough and rigorous inquiry and provides recommendations based on the strength of the found evidence. Our results indicate that there are several research gaps that should be addressed and we further composed several research opportunities that are possible areas for future research

Bayesian Structural Equation Models for Cumulative Theory Building in Information Systems―A Brief Tutorial Using BUGS and R

Structural equation models (SEM) are frequently used in information systems (IS) to analyze and test theoretical propositions. As IS researchers frequently reuse measurement instruments and adapt or extend theories, they frequently re-estimate regression relationships in their SEM that have been examined in previous studies. We advocate the use of Bayesian estimation of structural equation models as an aid to cumulative theory building; Bayesian statistics offer a statistically sound way to incorporate prior knowledge into SEM estimation, allowing researchers to keep a “running tally” of the best estimates of model parameters. This tutorial on the application of Bayesian principles to SEM estimation discusses when and why the use of Bayesian estimation should be considered by IS researchers, presents an illustrative example using best practices, and makes recommendations to guide IS researchers in the application of Bayesian SEM

A Systems Model of IS Success Using Agent-Based Simulation

Measuring the value realized from information systems (IS) and understanding the factors which influence success are critical to organizations. DeLone and McLean’s IS success model is one of the most well-known theories in IS literature; however, the model has been primarily examined from a variance perspective and this offers an opportunity to explore ways to improve its explanatory capability. This study presents an agent-based simulation model of the IS success model based on complex adaptive systems theory. Principles from the unified theory of acceptance and use of technology, social learning theory, and expectation disconfirmation theory are incorporated in the model to capture individual behavior and interactions, feedback loops and emergent effects. The model is under development in the context of a hospital surge management system with the goal of extending the IS success model and to improve understanding of IS success in a complex digital ecosystem. The next steps are to calibrate the model and to conduct multiple case studies