Exploring the Investment Construct for Quantifying AI Utilization

ObjectiveWe explored the <i>Investment</i> construct to quantify human utilization of AI and examined respect, trust, workload, and self-confidence as factors influencing utilization within the <i>Investment</i> Framework.BackgroundThe <i>Investment</i> construct is a new approach proposed to quantify human utilization of AI-enabled agents based on the measurable opportunity costs incurred by humans. An experimental study was conducted to operationalize the construct and examine its relationships with influencing factors.MethodThirty participants performed a maintenance task while an AI assisted them. Participants were split into three groups with different allocated completion times to simulate different task-load levels, under the assumption that a lower task load would lead to higher <i>Investment</i>. We collected typing data as the objective measure of <i>Investment</i>, and responses to a six-item custom scale as the subjective measure. Additionally, we collected data for the influencing factors listed above.ResultsThere was a significant difference between groups in terms of both <i>Investment</i> measures, and <i>Investment</i> was higher with a lower task load, as expected. Furthermore, these two <i>Investment</i> measures correlated. We also revealed a correlation between respect and the subjective measure of <i>Investment</i>. No other significant correlations were revealed.ConclusionThis study presents the first empirical evidence of AI utilization conceptualized through incurred opportunity costs within the <i>Investment</i> Framework, explored using two independent measures and influencing factors.ApplicationThe <i>Investment</i> Framework can guide the design of effective human AI interaction by helping to balance human effort and AI benefit, prevent underutilization, and enhance both team performance and AI improvement.