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中文题名:

 人工智能面试降低组织吸引力——非人化的中介作用    

姓名:

 钟羚升    

保密级别:

 公开    

论文语种:

 chi    

学科代码:

 045400    

学科专业:

 应用心理    

学生类型:

 硕士    

学位:

 应用心理硕士    

学位类型:

 专业学位    

学位年度:

 2024    

校区:

 北京校区培养    

学院:

 心理学部    

研究方向:

 社会心理学    

第一导师姓名:

 周阳    

第一导师单位:

 心理学部    

提交日期:

 2024-06-18    

答辩日期:

 2024-05-22    

外文题名:

 AI INTERVIEWS DECREASE ORGANIZATIONAL ATTRACTIVENESS —— THE MEDIATING ROLE OF DEHUMANIZATION    

中文关键词:

 AI面试 ; 非人化感知 ; 组织吸引力 ; 凸显公平    

外文关键词:

 AI Interview ; Dehumanization Perception ; Organizational Attraction ; Highlighting Fairness    

中文摘要:

在全球经济快速变迁的背景下,众多组织纷纷转向人工智能(AI)技术以提高人力资源管理的效率,尤其是在招聘过程中。AI面试通过算法优化,能够实现对职位需求与应聘者能力的高效匹配。然而,尽管AI面试拥有灵活便捷、降本增效等诸多优势,但其应用也可能伴随着一些问题和风险,包括AI面试可能引发应聘者的担忧、降低面试接受度和继续求职的意愿,甚至可能对采用AI面试的组织产生负面影响。那么,AI面试究竟如何造成应聘者对组织的负面反应?又该如何有效缓解这一负面影响呢?本研究围绕这些问题进行探讨,并提出在AI面试降低组织吸引力的影响中,非人化起中介作用;且进一步提出,组织通过凸显公平,可以缓解AI面试对组织吸引力带来的负面影响。

本研究通过三个研究来检验以上假设,研究1探讨了在接到面试通知的情境下,不同面试形式(人工面试 vs AI面试)对组织吸引力的影响,以及非人化在其中的中介作用。结果表明,相比于人工面试,人们在接到AI面试通知后所报告的组织吸引力更低,非人化在该影响机制中起中介作用。研究2考察了正式进入面试流程的情境,发现相比于人工面试,人们在AI面试中所报告的组织吸引力更低,而非人化在该影响机制中同样起中介作用。研究3发现凸显公平可以通过减少人们的非人化感知扭转其对AI面试的负面反应。

本研究探讨了不同情境下,面试形式对组织吸引力的影响,以及非人化在其中的中介机制;与此同时,还发现了缓解AI面试对组织吸引力负面影响的方法,即组织发出声明,在AI面试中凸显公平。研究结果在理论上丰富了非人化理论中的被非人化效应,并且将该理论拓展到人机交互领域中;此外,以一种新颖的角度解释了社会交换理论,将组织向应聘者传递的公平信息,视为一种作为交换的社会资源,从而有助于缓和AI面试(相较于人工面试)所产生的非人化感知,进而提升组织吸引力。在实践上,本研究的发现提示组织在应用AI技术时,需重点关注对人性的考量;同时,也为组织应用AI技术提供了一个简便易行的方式,即凸显AI技术的公平性,以缓解应聘者对组织的负面反应。

外文摘要:

In the backdrop of rapid global economic transformation, numerous organizations are turning to artificial intelligence (AI) technologies to enhance efficiency in human resource management, particularly within the recruitment process. AI interviews, optimized through algorithms, facilitate efficient alignment between job requirements and candidate capabilities. However, despite the manifold advantages such as flexibility, cost reduction, and enhanced efficiency associated with AI interviews, their application may engender certain challenges and risks. These include candidate apprehension, decreased interview acceptance rates, reduced willingness to continue pursuing job opportunities, and potentially adverse effects on organizations employing AI interview methods. Consequently, an inquiry arises concerning how AI interviews precipitate negative reactions from candidates towards organizations. Moreover, what strategies are effective in mitigating these negative impacts?

This study delves into these queries, proposing that dehumanization plays an intermediary role in diminishing organizational attractiveness in the context of AI interviews. Furthermore, it suggests that organizations can alleviate the negative impact of AI interviews on organizational attractiveness by emphasizing fairness. This research examines these hypotheses through three studies. Study 1 explores the impact of different interview formats (human vs. AI interviews) on organizational attractiveness upon receiving interview notifications and the mediating role of dehumanization therein. The results indicate that compared to human interviews, organizational attractiveness reported after receiving AI interview notifications is lower, with dehumanization mediating this effect. Study 2 investigates the context of formally entering the interview process, revealing that organizational attractiveness reported in AI interviews is lower than in human interviews, with dehumanization similarly acting as a mediator. Study 3 discovers that highlighting fairness can mitigate negative reactions towards AI interviews by reducing perceived dehumanization.

This study examines the influence of interview formats on organizational attractiveness across diverse contexts and elucidates the mediating role of dehumanization therein. Additionally, it identifies methods to alleviate the negative impact of AI interviews on organizational attractiveness, emphasizing fairness in AI interview processes. The findings contribute theoretically by enriching dehumanization theory and extending it into the human-computer interaction domain. Moreover, they offer a novel perspective on social exchange theory, interpreting fairness information conveyed by organizations to candidates as a form of social resource exchange, thereby aiding in ameliorating dehumanization perceptions arising from AI interviews compared to human interviews, consequently enhancing organizational attractiveness. Practically, this study's findings underscore the importance of considering human factors when deploying AI technologies and provide a straightforward approach for organizations to emphasize the fairness of AI technologies to mitigate negative candidate reactions.

参考文献总数:

 150    

馆藏号:

 硕045400/24073    

开放日期:

 2025-06-24    

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