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AIS Newsletter |
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IS SECTION / AMERICAN ACCOUNTING ASSOCIATION. |
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In the Literature : Research on Conceptual Modeling and Data Warehousing |
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Research on Conceptual Modeling
Pnina Soffer and Irit Hadar, 2007. Applying Ontology-based Rules to Conceptual Modeling: a Reflection on Modeling Decision Making, European Journal of Information Systems, Volume 16, pp. 599-611.
The authors state that conceptual modeling represents a domain independently of implementation considerations for purposes of understanding the problem at hand and communicating about it. However, different people may construct different models given the same domain. Variations among correct models, while known and familiar in practice, have hardly been investigated in the literature. Their roots are in the decisions made during the modeling process, where modelers face the need to map reality into modeling constructs. This paper reports an empirical study whose aim is to explore model variations and in particular to examine possible directions for reducing them. Specifically, the study uses a multi-method research paradigm to examine the effect of applying ontology-based modeling rules on modeling decisions as reflected in resulting model variations. The findings of the study provide insights into the variations phenomenon, as well as to the application of ontology-based modeling rules. .
Research on Data Warehousing
K. Ramamurthy, Arun Sen, and Atish P. Sinha, 2008. An Empirical Investigation of the Key Determinants of Data Warehouse Adoption, Decision Support Systems, Volume 44 Issue 4, pp. 817-841, March.
The authors state that Data warehousing (DW) has emerged as one of the most powerful decision support technologies during the last decade. However, despite the fact that it has been around for some time, DW has experienced limited spread/use and relatively high failure rates. Treating DW as a major IT infrastructural innovation, the authors propose a comprehensive research model – grounded in IT adoption and organizational theories – that examines the impact of various organizational and technological (innovation) factors on DW adoption. Seven factors – five organizational and two technological – are tested in the model. The study employed rigorous measurement scales of the research variables to develop a survey instrument and targeted 2500 organizations in both manufacturing and services segments within two major states in the United States. A total of 196 firms (276 executives), of which nearly 55% were adopters, responded to the survey. The results from a logistic regression model, initially conceptualizing a direct effect of each of the seven variables on adoption, indicate that five of the seven variables (three organizational factors – commitment, size, and absorptive capacity – and two innovation characteristics — relative advantage and low complexity) are key determinants of DW adoption. Although scope for DW and preexisting data environment within the organization were favorable for adopter firms, they did not emerge as key determinants. However, the study provided an opportunity to explore a more complex set of relationships. This alternative structural model (using LISREL) provides a much richer explanation of the relationships among the antecedent variables and with adoption, the dependent variable. The study, especially the revised conceptualization, contributes to existing research by proposing and empirically testing a fairly comprehensive model of organizational adoption of an information technology (IT) innovation. |
