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 <i>semantics</i> (their meaning), to discover how to convey meaning more clearly and completely, or their <i>pragmatics</i> (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-<i>U</i>) 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.
Transactive memory is an effective mechanism for locating and coordinating expertise in small groups and has been shown to hold numerous benefits for groups and organizations. To extend transactive memory beyond the scope of small groups, researchers have proposed the use of information technology (IT). This paper provides an integrated discussion of our knowledge from three studies concerning IT support in transactive memory in organizations. Focusing on meta-memory, which is at the heart of transactive memory systems, we examine what meta-memory is maintained by members of transactive memory systems, whether providing this meta-memory in a technology-mediated environment can lead to transactive memory development, whether IT can realistically provide this meta-memory, and whether different requirements exist for different users and in different stages of transactive memory development. We discuss the implications of these studies to both research and practice.
Organizations often provide workers with knowledge management systems to help them obtain knowledge they need. A significant constraint on the effectiveness of such systems is that they assume workers know what knowledge they need (they know what they don't know) when, in fact, they often do not know what knowledge they need (they don't know what they don't know). A way to overcome this problem is to use visual ontologies to help users learn relevant concepts and relationships in the knowledge domain, enabling them to search the knowledge base in a more educated manner. However, no guidelines exist for designing such ontologies. To fill this gap, we draw on theories of philosophical ontology and cognition to propose guidelines for designing visual ontologies for knowledge identification.We conducted three experiments to compare the effectiveness of guided ontologies, visual ontologies that followed our guidelines, to unguided ontologies, visual ontologies that violated our guidelines. We found that subjects performed considerably better with the guided ontologies, and that subjects could perceive the benefits of using guided ontologies, at least in some circumstances. On the basis of these results, we conclude that the way visual ontologies are presented makes a difference in knowledge identification and that theories of philosophical ontology and cognition can guide the construction of more effective visual representations. Furthermore, we propose that the principles we used to create the guided visual ontologies can be generalized for other cases where visual models are used to inform users about application domains.
Organizing phenomena into classes is a pervasive human activity. The ability to classify phenomena encountered in daily life in useful ways is essential to human survival and adaptation. Not surprisingly, then, classification-oriented activities are widespread in the information systems field. Classes or entity types play a central role in conceptual modeling for information systems requirements analysis, as well as in the design of databases and object-oriented software. Furthermore, classification is the primary task in applications such as data mining and the development of domain ontologies to support information sharing in semantic web applications. However, despite the pervasiveness of classification, little research has proposed well-grounded guidelines for identifying, evaluating, and choosing classes when modeling a domain or designing information systems artifacts. In this paper, we adopt the cognitive notions of inference and economy to derive a set of principles to guide effective and efficient classification. We present a model for characterizing what may be considered useful classes in a given context based on the inferences that can be drawn from membership in a class. This foundation is then used to suggest practical design rules for evaluating and refining potential classes. We illustrate the use of the rules by showing that applying them to a previously published example yields meaningful changes. We then present an evaluation by a panel of experts who compared the published and revised models. The evaluation shows that following the rules leads to semantically clearer models that are preferred by experts. The paper concludes by outlining possible future research directions.
Within the information systems field, the task of conceptual modeling involves building a representation of selected phenomena in some domain. High-quality conceptual- modeling work is important because it facilitates early detection and correction of system development errors. It also plays an increasingly important role in activities like business process reengineering and documentation of best-practice data and process models in enterprise resource planning systems. Yet little research has been undertaken on many aspects of conceptual modeling. In this paper, we propose a framework to motivate research that addresses the following fundamental question: How can we model the world to better facilitate our developing, implementing, using, and maintaining more valuable information systems? The framework comprises four elements: conceptual-modeling grammars, conceptual-modeling methods, conceptual-modeling scripts, and conceptual-modeling contexts. We provide examples of the types of research that have already been undertaken on each element and illustrate research opportunities that exist.