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

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

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

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

Comparing Out-of-Sample Predictive Ability of PLS, Covariance, and Regression Models

Partial Least Squares Path Modelling (PLSPM) is a popular technique for estimating structural equation models in the social sciences, and is frequently presented as an alternative to covariance-based analysis as being especially suited for predictive modeling. While existing research on PLSPM has focused on its use in causal-explanatory modeling, this paper follows two recent papers at ICIS 2012 and 2013 in examining how PLSPM performs when used for predictive purposes. Additionally, as a predictive technique, we compare PLSPM to traditional regression methods that are widely used for predictive modelling in other disciplines. Specifically, we employ out-of-sample k-fold cross-validation to compare PLSPM to covariance-SEM and a range of a-theoretical regression techniques in a simulation study. Our results show that PLSPM offers advantages over covariance-SEM and other prediction methods

Enhancing Learning Outcomes through Experiential Learning: Using Open-Source Systems to Teach Enterprise Systems and Business Process Management

Enterprise systems and business process management are the two key information technologies to integrate the functions of a modern business into a coherent and efficient system. While the benefits of these systems are easy to describe, students, especially those without business experience, have difficulty appreciating how these systems are used to improve the efficiency of business operations. This paper reports on a project to provide experiential learning to beginning business students. We focus on open-source enterprise and process management systems to investigate whether the benefits can be provided even by small institutions and without a large investment into commercial systems. The results of experimental studies are provided and suggest that hands-on learning on open-source systems can lead to improved learning outcomes. The main contribution is the demonstration that educators need not shy away from experiential learning when faced with the obstacles that large-scale commercial enterprise systems may present, but can instead choose a “bottom-up” approach of easily integrating enterprise systems into the curriculum to benefit student learning