关键词: 人机信任, 工具信任, 价值信任, 人机关系, 动力机制

Abstract: With the rapid development of artificial intelligence (AI) technology, human-AI relationships have become increasingly prevalent and consequential in organizations. Human trust in AI lies at the core of human-AI relationships and is critical to the effectiveness of human-AI interactions. Key challenges in research on human trust in AI include how to conceptualize trust, understand dynamic patterns of human-AI relationships, and achieve complementary advantages through human-AI interactions.
This research addresses these issues by focusing on the dyadic interaction between humans and AI to explore the dynamic processes of human trust in AI over time in the workplace. Specifically, our research is structured around three progressive studies—theoretical reconceptualization, dynamic patterns, and impact effects—to systematically answer the fundamental questions of what human trust in AI is, how it develops, and how it can be effectively leveraged. First, drawing on the perspective of technological ethics, this study conceptualizes human trust in AI as a two-dimensional construct comprising instrumental trust and value trust, and further develops a corresponding measurement scale. Second, adopting a dynamic development perspective, the study explores the temporal characteristics and dynamic patterns of human trust in AI, thereby opening the “black box” of trust dynamics in human-AI relationships. Finally, from the perspective of human-AI collaboration, the study investigates the effect of different forms of human trust in AI on employee creativity, offering a nuanced understanding of human-AI relationship development and providing insights into how trust in AI shapes employees’ core competencies in the digital intelligence era.
Our work contributes to the literature on human trust in AI in three ways. First, we advance a conceptual reconstruction of human trust in AI by proposing an instrumental-value trust framework. Moving beyond the cognitive-affective trust framework, and grounded in the classical definition of trust as well as the established analytical distinction between trust beliefs and trust intentions, we propose a dual-dimensional structural model of human trust in AI based on the distinct value orientations embedded in trust beliefs: instrumental trust and value trust. This framework unpacks the dual logic underlying human acceptance of AI, namely, technological reliance and value consensus, and overcomes the theoretical and measurement limitations of prior research that directly transplanted the categories of cognitive trust and affective trust from interpersonal trust studies into the human-AI context, thereby offering a more contextually grounded and explanatorily powerful theoretical framework for human trust in AI.
Second, we uncover the dynamic patterns of human trust in AI. Adopting the information processing perspective and integrating three core elements of trust dynamics—structure, intensity, and time—we delineate the nonlinear developmental trajectories of human trust in AI. Specifically, we identify the threshold effects associated with the development of instrumental trust and value trust, and clarify the relative importance of different trust cues throughout the trust development process. These findings address the limitations of existing dynamic research on human trust in AI, which has largely focused on variations in trust intensity alone, and further specify the temporal boundaries of the effects of different trust cues.
Finally, we elucidate the mechanisms through which human trust in AI influences employees’ creativity. From the perspective of human-AI collaboration, we explore how instrumental trust and value trust differentially shape modes of human-AI collaboration and, in turn, differentially enhance individual creativity. We further identify the contextual roles of individual mindsets and algorithmic management in this process. By doing so, we not only extend the general proposition that “human trust in AI promotes AI acceptance and use” into the more nuanced insight that “different forms of human trust in AI foster differentiated patterns of AI use”, but also provide concrete pathways for cultivating creativity through human-AI collaboration, alleviate concerns regarding cognitive degradation resulting from overreliance on AI, and offer a dialectical perspective on the development of human-AI relationships.

Key words: human trust in AI, instrumental trust, value trust, human-AI relationship, dynamics