The Effect of Feedback Valence and Feedback Source on Employee Feedback Adoption : A Study Based on Face Theory

doi:10.16719/j.cnki.1671-6981.20240517

Abstract

Abstract: Negative feedback from managers is often resisted by employees compared to positive feedback. In work situations, the negative feedback given by leaders will upset employees, making them focus on their gaining or losing face rather than whether the feedback itself is reliable. But negative feedback can help employees correct their mistakes and adjust their work status in time, which has an irreplaceable and important role. Therefore, it is important to explore how to provide feedback to employees in an appropriate way. Previous research on facilitating feedback adoption has primarily explored (1) how to accurately and rationally deliver the content of feedback and (2) how to do so by adjusting the order and timing of multiple feedback pushes. However, according to the Face theory in China, as the lowly person in the power relationship, employees instinctively feel nervous and uneasy when they receive negative feedback, and may even see the evaluation as a denial of their abilities. The impact of the gap caused by this hierarchical relationship cannot be resolved by handling the feedback itself. However, few studies have explored how to balance negative feedback from the perspective of feedback sources. We think the AI systems in work situations may help, as studies have found that employees feel less face loss from negative advice given by AI and rarely resist it. Based on the traditional face theory in China, this study establishes a mechanism for the impact of leadership feedback on employee feedback adoption.
Study 1 used a situational experiment to simulate a work scenario in which employees receive performance feedback to initially explore the effects of positive and negative feedback from AI systems and human leaders on employee feedback adoption. Study 1 adopts a 2 × 2 between-groups design, with the two variables manipulated being the valence of feedback (positive feedback/negative feedback) and the subject of the feedback (AI system/ human leader), comprising four experimental scenarios. Subjects would randomly fill in one of the four contexts, and the number of pushers was same in all contexts. Two hundred and twenty questionnaires were randomly distributed with the help of Credamo, a widely-used online questionnaire platform in China, and the subjects were required to be working employees. A total of 208 valid questionnaires were returned. The ANOVA results showed a significant interaction effect between feedback valence and feedback source. Relative to AI systems, positive feedback from human leaders yielded a higher willingness to adopt. Negative feedback from AI systems can obtain a higher willingness to adopt relative to human leaders.
Study 2 used a situational experiment to simulate a workplace scenario in which employees receive performance feedback to explore in depth the impact of positive and negative feedback from AI systems and human leaders on employee feedback adoption through the mediating mechanism of face. Study 2 targeted 300 questionnaires to current employees through the Seeing Numbers platform, with no crossover between the sample and Study 1. At the time of distribution, the subjects were randomly divided into four groups based on the valence of feedback (positive feedback/negative feedback) and the source of feedback (AI system/ human leader), and 75 questionnaires were distributed to each group, and a total of 287 valid questionnaires were returned in the end. Finally, it was found that when a human leader provided positive feedback, the positive influence on feedback adoption through face gaining was stronger, and when he provided negative feedback, the negative influence on feedback adoption through face loss was stronger.
Study 3 was a two-period follow-up experiment with a group of delivery workers. It was designed to explore the mechanism in real work scenarios. A total of 307 valid questionnaires were collected. The results of data analysis showed that the hypotheses were supported, which indicated the high reliability of our study.
The findings of this study inspire us to promote cooperation between humans and AI to fully utilize each other's strengths rather than focusing on achieving complete automation (i.e. replacing humans with AI). This study reveals the mechanism of how AI systems and real human leaders work together to improve feedback adoption, which enriches the research in the field of human-computer cooperation and serves as a guide for the proper use of AI systems.

Key words: face, feedback valence, feedback adoption, artificial intelligence

摘要： 人工智能嵌入工作场所并成为员工反馈来源的趋势愈发明显,然而其与真人领导在反馈过程中各自扮演何种角色亟待研究。基于面子理论,探究了反馈效价（正面反馈和负面反馈）及来源对员工反馈采纳的影响机制。研究1发现正面反馈由真人领导提供时更容易被员工采纳;负面反馈由人工智能系统提供时更容易被员工采纳。研究2和3发现由真人领导提供偏正面的反馈时通过面子获得对反馈采纳的正向影响更强,由其提供偏负面的反馈时通过面子损失对反馈采纳的负向影响更强。本研究揭示了人工智能和真人配合提高反馈采纳的机制,对于澄清人工智能在工作场所中的角色具有指导作用。

关键词: 面子, 反馈效价, 反馈采纳, 人工智能

Zhao Chen, Qu Beijia, Zhou Jinlai, Lin Chen. The Effect of Feedback Valence and Feedback Source on Employee Feedback Adoption : A Study Based on Face Theory[J]. Journal of Psychological Science, 2024, 47(5): 1184-1193.

赵晨, 渠蓓佳, 周锦来, 林晨. 白脸红脸谁来唱?——正负反馈及来源对员工反馈采纳的影响机制^*[J]. 心理科学, 2024, 47(5): 1184-1193.

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