Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Exploration and Exploitation in the Presence of Network Externalities
Management Science
Exploration vs. Exploitation: An Empirical Test of the Ambidexterity Hypothesis
Organization Science
Journal of Management Information Systems
The effect of knowledge sharing model
Expert Systems with Applications: An International Journal
On the classification performance of TAN and general Bayesian networks
Knowledge-Based Systems
Reasoning about bayesian network classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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As market competition grows fierce, how to maintain competitiveness in the market emerges a crucial issue for all the organizations. Aware of this urgent fact, companies have been seeking best way of managing their creativity at competitive levels. However, most of existing approaches currently discussed in literature were limited to narrative and elusive statements from which creativity management strategists could not extract set of concrete action rules. To overcome this pitfall, this study proposes a novel approach to creativity management by adopting General Bayesian Network (GBN). Especially, based on the findings from literature that balance of exploration and exploitation leads to sustainable management of creativity, we built a research model including the five variables affecting individual creativity such as exploration, exploitation, task complexity, bureaucratic culture, and supportive culture. To induce a set of causal relationships among the individual creativity and the five variables, GBN was applied to 227 valid questionnaire sample data. Through the what-if and goal-seeking simulations, promising empirical results were obtained, which shed robust and meaningful platform for further studies in this exciting field.