Action Sequence Matching of Team Managers

Traditionally, team managers are analysed and compared based on human perception with data collected from surveys and questionnaires. These methods normally have low efficiency especially in dynamic and complex environments. In order to improve the accuracy and stability of manager analysis in management science, in this paper, we propose a novel manager representation method which is general and flexible enough to cover most types of managers. For manager analysis, we introduce the first manager matching algorithm that calculates the global similarity between managers. The proposed matching algorithm not only returns robust and stable manager similarities, but also details the matched parts among managerial action sequences. With this, the proposed methods provide more research possibilities in management science.

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