Paper Type
ERF
Abstract
Predictive analytics systems are widely expected to enhance supply chain agility, yet empirical outcomes remain inconsistent. Drawing on Sociotechnical Systems (STS) theory, this paper proposes a model in which supply chain agility depends on the joint optimization of two subsystems: organizational assimilation of predictive analytics (technical) and algorithmic reliance (social). We argue that organizational assimilation positively influences supply chain agility (H1), and that algorithmic reliance — the degree to which managers enact algorithmic recommendations with minimal override — strengthens this relationship (H2). Additionally, we introduce outcome feedback latency as a boundary condition, proposing that delayed performance feedback undermines sustained algorithmic reliance (H3). We test these hypotheses using a cross-sectional survey of supply chain managers via Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings will contribute to theory on human-algorithm interaction and offer practical guidance for organizations seeking to realize agility from analytics investments.
Paper Number
1624
Recommended Citation
NAYEEM, ZANNATUN; DEHGHAN, NARIMAN; and Yang, Yanxia, "Translating Predictive Analytics into Supply Chain Agility: The Behavioral Role of Algorithmic Reliance" (2026). AMCIS 2026 Proceedings. 3.
https://aisel.aisnet.org/amcis2026/agile/agile/3
Translating Predictive Analytics into Supply Chain Agility: The Behavioral Role of Algorithmic Reliance
Predictive analytics systems are widely expected to enhance supply chain agility, yet empirical outcomes remain inconsistent. Drawing on Sociotechnical Systems (STS) theory, this paper proposes a model in which supply chain agility depends on the joint optimization of two subsystems: organizational assimilation of predictive analytics (technical) and algorithmic reliance (social). We argue that organizational assimilation positively influences supply chain agility (H1), and that algorithmic reliance — the degree to which managers enact algorithmic recommendations with minimal override — strengthens this relationship (H2). Additionally, we introduce outcome feedback latency as a boundary condition, proposing that delayed performance feedback undermines sustained algorithmic reliance (H3). We test these hypotheses using a cross-sectional survey of supply chain managers via Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings will contribute to theory on human-algorithm interaction and offer practical guidance for organizations seeking to realize agility from analytics investments.
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