Research Perspectives: Design Theory Indeterminacy: What Is it, How Can it Be Reduced, and Why Did the Polar Bear Drown?

Design science research strives to be practical and relevant. Yet few researchers have examined the extent to which practitioners can meaningfully utilize theoretical knowledge produced by design science research in solving concrete real-world problems. Are design theories developed by scientists readily amenable to application by practitioners? Does the application of a theory by practitioners always lead to the outcomes predicted (by the scientists)? We examine a particularly difficult challenge—ensuring that the development and deployment of an IT artifact by practitioners based on a design theory result in appropriate changes in the environment predicted by the design theory. As we show in our paper, a gulf exists between theoretical propositions and concrete issues faced in practice—a challenge we refer to as design theory indeterminacy. Design theory indeterminacy might result in considerable ambiguity when implementing a design theory in practice and reduce the potential relevance of information systems knowledge. In this paper, we articulate the problem of design theory indeterminacy, examine factors that contribute to it, and suggest fruitful directions for future research to help reduce it

A Survey of Cognitive Theories to Support Data Integration

Business intelligence applications are being increasingly used to facilitate managerial insight and maintain competitiveness.These applications rely on the availability of integrated data from multiple data sources, making database integration anincreasingly important task. A central step in the process of data integration is schema matching, the identification of similarelements in the two databases. While a number of approaches have been proposed, the majority of schema matchingtechniques are based on ad-hoc heuristics, instead of an established theoretical foundation. The absence of a theoreticalfoundation makes it difficult to explain and improve schema matching process. This research surveys current cognitivetheories of similarity and demonstrates their application to the problem of schema matching. Better integration techniqueswill benefit business intelligence applications and can thereby contribute to business value

Artifact Sampling: Using Multiple Information Technology Artifacts to Increase Research Rigor

Researchers in many scientific disciplines routinely conceptualize information technologies (IT) as antecedents or outcomes in theoretical models. The ongoing theorizing of IT leads to a novel methodological challenge termed instantiation validity (IV). In this paper, we contribute to research on remediating IV challenges by proposing and advocating the methodological practice of artifact sampling, whereby multiple artifacts are sampled from the population of all possible artifacts (the instantiation space). Artifact sampling extends the practice of employing multiple research subjects or survey respondents, routinely used in social sciences, into the IT artifact design space. Artifact sampling is an important methodological practice that stands to increase the rigor of research dealing with software artifacts. As it is currently not being adequately undertaken in the aforementioned research, many studies may result in biased or unjustified conclusions

Data Collection Interfaces in Online Communities: The Impact of Data Structuredness and Nature of Shared Content on Perceived Information Quality

The growth of online communities has resulted in an increased availability of user-generated content (UGC). Given the varied sources of UGC, the quality of information it provides is a growing challenge. While many aspects of UGC have been studied, the role of data structures in gathering UGC and nature of to-be-shared content has yet to receive attention. UGC is created in online platforms with varying degrees of data structure, ranging from unstructured to highly-structured formats. These platforms are often designed without regard to how the structure of the input format impacts the quality of outcome. In this study, we investigate the impact of the degree of data structure on the perceived quality of information from the novel perspective of data creators. We also propose and evaluate a novel moderating effect due to the nature of content online users wish to share. The preliminary findings support our claims of the importance of these factors for information quality. We conclude the paper with directions for future research and expected contributions for theory and practice

System: A core conceptual modeling construct for capturing complexity

[EN] The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggest-ing the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of "system"should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research.We wish to thank the editor-in-chief, Carson Woo, and three anonymous reviewers for their exceptionally insightful and developmental comments. The substantial improvements that resulted from their feedback were much deeper than we usually experience in journal review processes. We wish to thank the participants of www.nlnature.com (now inactive) who contributed their sightings from 2010 to 2022. We also thank Jeffrey Parsons and Yolanda Wiersma - the co -investigators of NLNature. We are grateful to the late Mario Bunge and to Ron Weber with whom we discussed ontological ideas that inspired this paper. We also want to thank the participants and reviewers of AIS SIGSAND and ER Conference for their comments and feedback on earlier versions of this paper. This research was supported by McIntire School of Commerce, University of Virginia, J. Mack Robinson College of Business, Georgia State University, United States, and by VRAIN Research Institute of the Universitat Politecnica de Valencia and the Generalitat Valenciana, Spain under the CoMoDiD project (CIPROM/2021/023) .Lukyanenko, R.; Storey, VC.; Pastor López, O. (2022). System: A core conceptual modeling construct for capturing complexity. Data & Knowledge Engineering. 141:1-29. https://doi.org/10.1016/j.datak.2022.10206212914