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음성 센서를 활용한 팀 의사결정 과정에서 팀원의 영향력 연구
홍운기 ( Woonki Hong ),우한균 ( Han-gyun Woo ),정윤혁 ( Yoonhyuk Jung ) 한국생산성학회 2021 生産性論集 Vol.35 No.3
Organizational scientists have started a new journey to re-examine the previous findings of behavioral patterns and actions of employees with the lens of new technology and analytic methods. Following this, the current paper introduced and developed a verbal activity detection sensor badge, named Human Matrix for predicting team member influence in the team decision-making process. Based on the previous studies, this paper hypothesized that team members’ expertise level and members’ familiarity with others will be positively related to the members’ perceived and actual influence on team decision-making outcomes. Further, this paper hypothesized that the duration of members’ speaking time is positively related to the members’ perceived and actual influence, whereas the frequency of members’ short response to other members is negatively related to the members’ perceived and actual influence in team decision-making process. Using a survival simulation experiment, we collected verbal activities (i.e., duration of speaking time and frequency of short responses) using the Human Matrix badges from 35 teams with 142 student members. We found that team members’ expertise levels was positively related to the members’ actual influence in team decision-making. Also, our results indicated that members’ familiarity with others showed a significant positive relationship with the members’ perceived influence in team decision-making. Further, we found that the duration of members’ speaking time and the frequency of members’ short response to other members could predict both the members’ perceived and actual influence in the team decision-making process. This paper developed and validated the verbal sensor technology (i.e., Human Matrix) by testing the member influence in team decision-making from the survival simulation experiment. Our findings demonstrated that the Human Matrix or a sensor technology in general might help organizational scholars to capture real-time interaction patterns for investigating team behaviors. We discuss the implication of our results for the research.
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