From Use to Dependency: An Algorithmic Dependency Framework for Digital Work Environments

Abstract

Purpose – The increasing use of artificial intelligence (AI) systems in digital work environments suggests that interactions with these systems extend beyond instrumental use. The purpose of this study is to develop the Algorithmic Dependence Framework to explain the evolving nature of human–AI interaction. Methodology – This study adopts a qualitative research design based on publicly available posts reflecting users' professional experiences with AI systems on digital platforms. The data were analyzed using the Gioia methodology.

Findings – The findings indicate that algorithmic dependence is shaped by four interrelated dimensions: cognitive delegation, algorithmic authority, emotional reliance, and algorithmic privacy. These dimensions reflect users' tendencies to delegate cognitive tasks to AI, accept algorithmic outputs without critical evaluation, develop emotional reliance on AI systems, and disclose personal or sensitive information to these systems.

Discussion – The proposed Algorithmic Dependence Framework extends beyond traditional technology adoption perspectives by conceptualizing human–AI interaction through cognitive, epistemic, emotional, and privacy-related dimensions. The findings suggest that AI systems are evolving from decision-support tools into informal decision partners in digital work environments, with important implications for employee autonomy, critical thinking, data governance, and the quality of organizational decision-making.