Abstract

Artificial intelligence (AI) is increasingly embedded within organizational processes, transforming how employees and users interact with technology in hybrid human–AI environments. Prior research shows that AI increasingly augments rather than replaces human capabilities across service, leadership, teamwork, learning, and decision-making settings (Gnewuch et al., 2024). Studies further demonstrate that perceptions of AI significantly influence collaboration, engagement, and adoption outcomes (Seeber et al., 2020). Existing literature has largely focused on AI role framing, such as whether AI is perceived as a tool or teammate, alongside related concepts including social presence, trust, and anthropomorphism. Despite these advances, limited attention has been given to the role of perceived information quality in strengthening collaborative human–AI systems. This gap is important because users primarily engage with AI systems through information exchange and continuously evaluate AI-generated outputs based on accuracy, reliability, relevance, usefulness, and contextual appropriateness. While prior studies acknowledge trust and perceived usefulness, they do not adequately explain how perceptions of information quality shape collaborative engagement with AI systems. This study proposes that perceived information quality functions as a foundational mechanism that enhances collaborative human–AI systems. Specifically, when users perceive AI-generated information as accurate, relevant, reliable, and contextually meaningful, they are more likely to collaborate effectively with AI systems, rely on AI-supported recommendations, and develop positive attitudes toward hybrid intelligence environments. Consequently, higher perceived information quality strengthens user satisfaction with AI interactions and increases intention to continue using AI-enabled systems. The study further argues that perceived information quality influences users’ communication behavior with AI. Higher information quality perceptions may encourage users to engage in more open, collaborative, and detailed interactions with AI systems, while lower information quality perceptions may increase skepticism and reduce engagement. This paper contributes to the human–AI collaboration literature by shifting attention from anthropomorphic framing and social presence toward an information-centric perspective. The proposed framework positions perceived information quality as a key driver of collaborative AI effectiveness, satisfaction, and continued AI use. Practically, the findings may help organizations design AI systems that improve not only operational efficiency but also the perceived quality and usefulness of AI-generated information, thereby strengthening sustainable and human-centered AI adoption.

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