PDF(1339 KB)
PDF(1339 KB)
PDF(1339 KB)
AI反噬下科技成果转化中双系统机制研究
The dual-system mechanism in sci-tech achievement transformation under AI counter-productivity
AI过度依赖与技术异化可引发“反噬”,反向制约科技成果转化,但其作用机理尚不清晰。本文基于双系统视角,构建“技术-社会双助推”框架,通过情景实验、问卷调查与纵向案例,探究AI反噬对转化的影响及社会性助推的缓冲作用。研究发现:AI反噬负向影响团队知识决断力;交互记忆系统(TMS)在其中起中介作用,且AI反噬沿专业化、可信度与协调性三维度序贯侵蚀TMS;团队心理安全感(TPS)正向调节AI反噬对TMS的负面影响,高TPS下间接效应被削弱,并通过仪式化对话、包容性信任与清晰化授权激活社会性助推的免疫机制。研究揭示了AI反噬的团队认知传导路径,拓展了技术负面效应理论,为构建技术-社会双元协同的转化风险管理提供了实践导向。
Over-reliance on AI and technological alienation may induce "AI counter-productivity", which reversely constrains the transformation of scientific and technological achievements, yet its underlying mechanism remains unclear. From a dual-system perspective, this study constructs a "techno-social dual nudge" framework and employs scenario experiments, questionnaires, and a longitudinal case study to examine the impact of AI counter-productivity on transformation and its mechanisms. Findings reveal that AI counter-productivity negatively affects team knowledge decisiveness; the transactive memory system (TMS) mediates this relationship, and AI counter-productivity sequentially corrodes TMS along its three dimensions of specialization, credibility, and coordination; team psychological safety (TPS) positively moderates the negative impact of AI counter-productivity on TMS, with the indirect effect being weakened under high TPS, and activates the immune mechanism of the social boosting system through ritualized dialogue, inclusive trust, and clarified authorization. This study reveals the team cognitive transmission path of AI counter-productivity, extends the theory on negative effects of technology, and provides practical guidance for constructing techno-social dual-system synergy in transformation risk management.
人工智能反噬 / 交互记忆系统 / 团队心理安全感 / 知识决断力 / 技术-社会双助推系统
AI counter-productivity / transactive memory system / team psychological safety / knowledge decisiveness / socio-technical dual nudge system
| [1] |
|
| [2] |
\n The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise and the reduction of opportunities for skill acquisition due to AI-driven decision support systems. Anchoring the discussion in the core medical competencies outlined by the\n Federation of Royal Colleges of Physicians of the UK-Practical Assessment of Clinical Examination Skills\n (PACES-MRCPUK), the systematic review identifies key vulnerabilities in physical examination, differential diagnosis, clinical judgment, and physician-patient communication. The narrative review explores broader themes related to Human–AI Interaction and the Impact of AI on Human Skills in Organizations. In response to concerns about the\n Second Singularity\n -a scenario in which decision-making autonomy is increasingly ceded to AI, weakening human oversight-this review advocates for a research agenda that prioritizes longitudinal studies, real-time monitoring of AI’s impact, and the development of frameworks to mitigate skill erosion, ensuring the preservation of professional autonomy and the safeguarding of the irreplaceable elements of human judgment in medicine and beyond.\n
|
| [3] |
Employees in many contemporary organizations work with flexible routines and flexible technologies. When those employees find that they are unable to achieve their goals in the current environment, how do they decide whether they should change the composition of their routines or the materiality of the technologies with which they work? The perspective advanced in this paper suggests that the answer to this question depends on how human and material agencies—the basic building blocks common to both routines and technologies—are imbricated. Imbrication of human and material agencies creates infrastructure in the form of routines and technologies that people use to carry out their work. Routine or technological infrastructure used at any given moment is the result of previous imbrications of human and material agencies. People draw on this infrastructure to construct a perception that a technology either constrains their ability to achieve their goals, or that the technology affords the possibility of achieving new goals. The case of a computer simulation technology for automotive design used to illustrate this framework suggests that perceptions of constraint lead people to change their technologies while perceptions of affordance lead people to change their routines. This imbrication metaphor is used to suggest how a human agency approach to technology can usefully incorporate notions of material agency into its explanations of organizational change.
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
赵秉文, 雷宏振, 余昊. 人工智能如何重塑决策模式:基于人机权力关系的系统性文献综述[J]. 科学学研究, 2026, 44(1):29-40.
|
| [8] |
芮正云, 马喜芳, 史清华. AI 多多益善吗:AI 能力非线性创新效应研究[J]. 科学学研究, 2026, 44(1):51-60.
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
孙元, 牟赛雅, 苏芳, 等. 异质性逆境事件下组织韧性形成机制的单案例研究[J]. 南开管理评论, 2025, 28(11):64-75.
|
| [20] |
Knowledge is a broad and abstract notion that has defined epistemological debate in western philosophy since the classical Greek era. In the past few years, however, there has been a growing interest in treating knowledge as a significant organizational resource. Consistent with the interest in organizational knowledge and knowledge management (KM), IS researchers have begun promoting a class of information systems, referred to as knowledge management systems (KMS). The objective of KMS is to support creation, transfer, and application of knowledge in organizations. Knowledge and knowledge management are complex and multi-faceted concepts. Thus, effective development and implementation of KMS requires a foundation in several rich literatures.
|
/
| 〈 |
|
〉 |
