Author ORCID Identifier
Bethany Niese: 0000-0002-1766-6218
Reza Vaezi: 0000-0001-8618-4274
Saurabh Gupta: 0000-0002-5622-341X
Mike Gallivan: 0009-0000-5711-0559
Abstract
This study explores the factors influencing perceived decision quality in contexts where advanced technologies (e.g., AI tools) support decision-making. Drawing on attribution theory and task-technology fit (TTF), the research presents a model that integrates internal (self-efficacy, intolerance for ambiguity) and external (TTF) factors to understand how individuals assess the quality of decisions. Survey data from business decision-makers reveal that TTF has the strongest effect on perceived decision quality, surpassing the impact of decision-making and technology self-efficacy. Additionally, intolerance for ambiguity negatively affects decision-making confidence more than technology-related self-efficacy. These findings highlight the importance of aligning decision support tools with user needs and supporting psychological readiness in complex decision environments. The study contributes to theory by connecting cognitive and technological constructs within a unified framework and offers practical implications for managers and designers aiming to improve decision outcomes and user satisfaction in AI-augmented settings.
Recommended Citation
Niese, B., Vaezi, R., Gupta, S., & Gallivan, M. J. (In press). Explaining Decision Quality: The Role of Self-Efficacy, Ambiguity, and Task-Technology Fit for Decision Making in the Age of AI. Communications of the Association for Information Systems, 59, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol59/iss1/55
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