A study on uncertain dynamic disaster management tasks, knowledge sharing, and task performance

Each disaster presents itself with a unique set of characteristics that are hard to determine a priori. Thus disaster management tasks are inherently uncertain, requiring knowledge sharing and quick decision making that involves coordination across different levels and collaborators. While there has been an increasing interest among both researchers and practitioners in utilizing knowledge management to improve disaster management, little research has been reported about how to assess the dynamic nature of disaster management tasks, and what kinds of knowledge sharing are appropriate for different dimensions of task uncertainty characteristics. ^ Using combinations of qualitative and quantitative methods, this research study developed the dimensions and their corresponding measures of the uncertain dynamic characteristics of disaster management tasks and tested the relationships between the various dimensions of uncertain dynamic disaster management tasks and task performance through the moderating and mediating effects of knowledge sharing. ^ Furthermore, this research work conceptualized and assessed task uncertainty along three dimensions: novelty, unanalyzability, and significance; knowledge sharing along two dimensions: knowledge sharing purposes and knowledge sharing mechanisms; and task performance along two dimensions: task effectiveness and task efficiency. Analysis results of survey data collected from Miami-Dade County emergency managers suggested that knowledge sharing purposes and knowledge sharing mechanisms moderate and mediate uncertain dynamic disaster management task and task performance. Implications for research and practice as well directions for future research are discussed.^

Comparing novice designer performance using a rule -based approach versus a pattern -based approach in conceptual data modeling tasks

Conceptual database design is an unusually difficult and error-prone task for novice designers. This study examined how two training approaches---rule-based and pattern-based---might improve performance on database design tasks. A rule-based approach prescribes a sequence of rules for modeling conceptual constructs, and the action to be taken at various stages while developing a conceptual model. A pattern-based approach presents data modeling structures that occur frequently in practice, and prescribes guidelines on how to recognize and use these structures. This study describes the conceptual framework, experimental design, and results of a laboratory experiment that employed novice designers to compare the effectiveness of the two training approaches (between-subjects) at three levels of task complexity (within subjects). Results indicate an interaction effect between treatment and task complexity. The rule-based approach was significantly better in the low-complexity and the high-complexity cases; there was no statistical difference in the medium-complexity case. Designer performance fell significantly as complexity increased. Overall, though the rule-based approach was not significantly superior to the pattern-based approach in all instances, it out-performed the pattern-based approach at two out of three complexity levels. The primary contributions of the study are (1) the operationalization of the complexity construct to a degree not addressed in previous studies; (2) the development of a pattern-based instructional approach to database design; and (3) the finding that the effectiveness of a particular training approach may depend on the complexity of the task.