Computational and mathematical organization theory: perspective and directions
Computational & Mathematical Organization Theory
Computational & Mathematical Organization Theory
Organizational response: the cost performance tradeoff
Management Science
The innovator's dilemma: when new technologies cause great firms to fail
The innovator's dilemma: when new technologies cause great firms to fail
Active Nonlinear Tests (Ants) of Complex Simulation Models
Management Science
Design Rules: The Power of Modularity Volume 1
Design Rules: The Power of Modularity Volume 1
Distribution of Knowledge, Group Network Structure, and Group Performance
Management Science
An industry-level knowledge management model-a study of information-related industry in Taiwan
Information and Management
Computational & Mathematical Organization Theory
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This paper provides an early attempt at operationalizing and testing the concept of knowledge strategy. Using a computer-simulated product development process, we compare the performance of generalist and specialist knowledge management strategies under conditions of market turbulence. The generalist knowledge strategy emphasizes breadth of knowledge in product development teams, while the specialist strategy focuses on depth of knowledge. Our generalist and specialist strategies are grounded in Eastern and Western perspectives of knowledge management, respectively. A primary difference between these two approaches is the strong emphasis on cross-learning, or learning across team members, in the Eastern perspective relative to the Western perspective. As such, we examine the performance implications of different modes of cross-learning for teams utilizing the generalist knowledge strategy. Specifically, we examine three modes of cross-learning, which are reflected in the use of three learning decision rules: (1) averaging, (2) majority, and (3) hot hand. A learning decision rule indicates how decision-makers learn from their fellow team members. Under the first rule, the decision-maker adopts an average of the beliefs held by fellow team members. Under the second rule, if a majority of fellow team members agree on a particular solution, then the decision-maker adopts the beliefs held by the majority. Under the third rule, the decision-maker learns from the team member whose beliefs have been consistent with market desires most recently. Surprisingly, we find that specialist strategies outperform generalist strategies under conditions of low and high market turbulence. We also find that cross-learning can be beneficial or detrimental, contingent upon the mode of learning. Generalist teams utilizing the averaging decision rule perform significantly worse, while generalist teams utilizing the hot hand decision rule perform significantly better.