Journal article
Marion Korosec-Serfaty, Bogdan Negoita, Sylvain Sénécal, Pierre-Majorique Léger
In press, Information Systems Frontiers
In press, Information Systems Frontiers
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APA
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Korosec-Serfaty, M., Negoita, B., Sénécal, S., & Léger, P.-M. Artificial Intelligence Facets and System-Level Properties as Drivers of Use: A Critical Review, Research Framework and Agenda. Information Systems Frontiers.
Chicago/Turabian
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Korosec-Serfaty, Marion, Bogdan Negoita, Sylvain Sénécal, and Pierre-Majorique Léger. “Artificial Intelligence Facets and System-Level Properties as Drivers of Use: A Critical Review, Research Framework and Agenda.” Edited by In press. Information Systems Frontiers (n.d.).
MLA
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Korosec-Serfaty, Marion, et al. “Artificial Intelligence Facets and System-Level Properties as Drivers of Use: A Critical Review, Research Framework and Agenda.” Information Systems Frontiers, edited by In press.
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@article{korosec-serfaty-a,
title = {Artificial Intelligence Facets and System-Level Properties as Drivers of Use: A Critical Review, Research Framework and Agenda},
journal = {Information Systems Frontiers},
author = {Korosec-Serfaty, Marion and Negoita, Bogdan and Sénécal, Sylvain and Léger, Pierre-Majorique},
editor = {press, In}
}
Abstract
Artificial intelligence (AI) introduces a new paradigm that challenges established information systems (IS) frameworks, prompting a reassessment of IS use in AI-driven systems. To address this, we conducted a three-iteration multi-approach review of 52 articles centered on AI facets and system-level properties as drivers of AI use. Our review identifies and classifies AI’s facets (anthropomorphism, autonomy, inscrutability, and learning) and system-level properties (explainability, transparency, and reliability). We propose working definitions to resolve conceptual issues. Building on these findings, we inductively develop a framework that positions AI use as a process in which AI facets disrupt or enable user interactions, while system-level properties mediate these effects, individually or in combination. Together, these elements shape established and emerging forms of AI use and outcomes. Leveraging our framework and iterations insights, we critically assess the extant understanding and propose a research agenda with actionable paths to support future IS research addressing AI use complexities.