Paper Type

ERF

Abstract

Artificial intelligence (AI) systems increasingly support descriptive, predictive, prescriptive, and generative decision making. While trust is recognized as a critical determinant of AI adoption, prior findings remain fragmented due to inconsistent conceptualizations of trust and variations in AI typologies. This study analyzes how different AI applications are associated with distinct conceptualizations of trust (i.e., dispositional, institutional, and interpersonal), and their respective antecedents. This investigation is conducted through a systematic literature review methodology of IS research and practitioner publications, and a semantic text similarity analysis. The study contributes by offering a structured synthesis of trust research in AI to address conflicting findings and by proposing a framework and research agenda to guide future IS scholarship and practice. This study will also help technical experts, such as AI providers, designers, IT and business managers, implement technical and managerial solutions to mitigate risks and foster trust, in line with current institutional demands.

Paper Number

1515

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Aug 15th, 12:00 AM

Conceptualizing Trust in AI Adoption: A Systematic Literature Review

Artificial intelligence (AI) systems increasingly support descriptive, predictive, prescriptive, and generative decision making. While trust is recognized as a critical determinant of AI adoption, prior findings remain fragmented due to inconsistent conceptualizations of trust and variations in AI typologies. This study analyzes how different AI applications are associated with distinct conceptualizations of trust (i.e., dispositional, institutional, and interpersonal), and their respective antecedents. This investigation is conducted through a systematic literature review methodology of IS research and practitioner publications, and a semantic text similarity analysis. The study contributes by offering a structured synthesis of trust research in AI to address conflicting findings and by proposing a framework and research agenda to guide future IS scholarship and practice. This study will also help technical experts, such as AI providers, designers, IT and business managers, implement technical and managerial solutions to mitigate risks and foster trust, in line with current institutional demands.