Matching Mind and Method: Augmented Decision-Making with Digital Companions based on Regulatory Mode Theory

Abstract

Digital companions shall augment complex human processes like extensive decision-making. However, their acceptance may depend upon their ability to adapt to individuals' psychological states and preferred decision strategies. Regulatory Mode Theory divides human self-regulation into assessment (i.e., making comparisons) and locomotion (i.e., movement from state to state). These regulatory modes are more or less compatible with different decision strategies. In an experimental study (N=81, 2x2-between-subjects design) we explored whether digital companions can gain higher acceptance by considering these compatibilities. Participants were confronted with a decision task. The assisting digital companion first induced a regulatory mode (assessment vs. locomotion) and subsequently presented information according to one of two decision strategies (full evaluation vs. progressive elimination). We show that a fit between regulatory mode and decision strategy (assessment/full evaluation or locomotion/progressive elimination) leads to a favorable evaluation of decisions and the digital companion. No differences regarding decision accuracy and speed were observed.