Human-AI Collaborative Management: Measuring Effectiveness in Hybrid Decision-Making Teams

Abstract

This study examines the measurement of effectiveness in human-AI collaborative management within hybrid decision-making teams. Using publicly available datasets including chess decision-making data (n=100), CoAuthor collaborative writing interactions (n=63), and Amazon Mechanical Turk team performance data (n=125), we employed mixed-methods analysis to identify key performance indicators and factors contributing to optimal human-AI collaboration. Our findings reveal that team efficacy is significantly influenced by decision-making style, AI transparency, and task allocation strategies. Autocratic decision-making styles negatively impact team effectiveness (OR=1.85), while collaborative approaches show improved performance outcomes (OR=3.48). The research contributes to organizational behavior theory by establishing a framework for measuring human-AI collaborative effectiveness and identifying critical success factors for hybrid decision-making teams. Implications for management practice include the need for adaptive leadership styles, transparency-enhanced AI systems, and structured collaboration protocols in human-AI teams.