Paper Number

ECIS2026-2145

Paper Type

SP

Abstract

Organizations increasingly use intelligent systems for high-stakes strategic decision-making (SDM). Current research on AI-supported conflict techniques has focused predominantly on Devil's Advocate (DA), where an AI assistant critiques the human's initial ideas, while neglecting Dialectical Inquiry (DI), where it provides alternatives and synthesizes a resolution. This research addresses this gap by comparing them. Adopting a sociotechnical systems perspective and integrating constructive conflict research with Cognitive Load Theory, this research investigates how different interventions influence SDM. Study 1 tests tool-shaping interventions by comparing three AI bot prototype conditions (Information-only, DA, DI) against a control treatment. Study 2 tests mind-shaping interventions through user strategy training. Both studies examine benefit (information elaboration) and cost (cognitive load) pathways, with cognitive flexibility as moderator. This research contributes by empirically comparing DA and DI in AI contexts, revealing benefit-cost trade-offs in human-AI collaboration, contrasting tool-shaping and mind-shaping pathways, and identifying cognitive flexibility as a boundary condition.

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Jun 14th, 12:00 AM

Shaping The Tool Or Shaping The Mind: An Investigation Of Dual Pathways In Human-AI Strategic Decision-Making

Organizations increasingly use intelligent systems for high-stakes strategic decision-making (SDM). Current research on AI-supported conflict techniques has focused predominantly on Devil's Advocate (DA), where an AI assistant critiques the human's initial ideas, while neglecting Dialectical Inquiry (DI), where it provides alternatives and synthesizes a resolution. This research addresses this gap by comparing them. Adopting a sociotechnical systems perspective and integrating constructive conflict research with Cognitive Load Theory, this research investigates how different interventions influence SDM. Study 1 tests tool-shaping interventions by comparing three AI bot prototype conditions (Information-only, DA, DI) against a control treatment. Study 2 tests mind-shaping interventions through user strategy training. Both studies examine benefit (information elaboration) and cost (cognitive load) pathways, with cognitive flexibility as moderator. This research contributes by empirically comparing DA and DI in AI contexts, revealing benefit-cost trade-offs in human-AI collaboration, contrasting tool-shaping and mind-shaping pathways, and identifying cognitive flexibility as a boundary condition.

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