<![CDATA[The increasing use of artificial intelligence and algorithmic systems has transformed managerial decision-making by providing predictions, evaluations, and recommendations as inputs to organizational decisions. However, the way managers respond to algorithmic information remains insufficiently understood, particularly regarding the behavioral mechanism linking algorithmic information with managerial interpretation. This study aims to examine the effects of Algorithmic Information on Reliance on Algorithmic Recommendation and Managerial Interpretation, as well as to test the mediating role of Reliance on Algorithmic Recommendation. Drawing on Bounded Rationality and Behavioral Theory of the Firm, the study proposes that algorithmic information influences managerial interpretation both directly and indirectly through reliance on algorithmic recommendations. A quantitative approach was employed using survey data from 150 respondents, with the proposed relationships tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that Algorithmic Information has a significant positive effect on Managerial Interpretation (? = 0.519, p < 0.001) and Reliance on Algorithmic Recommendation (? = 0.687, p < 0.001). Reliance on Algorithmic Recommendation also has a significant positive effect on Managerial Interpretation (? = 0.382, p < 0.001). Furthermore, the indirect effect of Algorithmic Information on Managerial Interpretation through Reliance on Algorithmic Recommendation is significant (? = 0.263, p < 0.001), indicating complementary mediation. These findings demonstrate that managerial interaction with algorithmic systems involves both direct informational and behavioral pathways. The study contributes to human–AI decision-making literature by positioning reliance on algorithmic recommendations as a behavioral mechanism connecting algorithmic information with managerial interpretation.]]>
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