AI Technical Competence and Algorithmic Trust in Strategic Decision Making
TL;DR
Results show that AI Technical Competence (AITC) significantly predicts Algorithmic Trust, which fully mediates the link to Strategic Decision Quality, and Crucially, Task Complexity exerts a “dampening effect,” weakening the impact of trust on quality in hyper-complex scenarios.
Abstract
The rapid global proliferation of AI has created an “AI paradox” where technical adoption fails to yield superior strategic outcomes. Grounded in Organizational Information Processing Theory (OIPT), this study investigates the human-centric “bridge” between capability and decision quality. The authors frame AI as an information-processing capacity and Algorithmic Trust as the essential cognitive processor. Using a sample of 365 managers in China—a global digital laboratory—they employed PLS-SEM to test a moderated-mediation model. Results show that AI Technical Competence (AITC) significantly predicts Algorithmic Trust, which fully mediates the link to Strategic Decision Quality. Crucially, Task Complexity exerts a “dampening effect,” weakening the impact of trust on quality in hyper-complex scenarios. This research contributes to JGIM by shifting focus from “what” AI can do to “how” managers trust it. Practitioners are urged to move toward hybrid-sequential workflows rather than full delegation to navigate the complexities of the global digital economy.