Supervisor‐Directed Emotional Labor as Upward Influence: An Emotions‐as‐Social‐Information Perspective

心理学 监督人 社会心理学 能力(人力资源) 印象管理 意外事故 情感劳动 透视图(图形) 管理 计算机科学 语言学 哲学 人工智能 经济
作者
Hong Deng,Frank Walter,Yanjun Guan
出处
期刊:Journal of Organizational Behavior [Wiley]
卷期号:41 (4): 384-402 被引量:32
标识
DOI:10.1002/job.2424
摘要

Summary To access organizational resources, subordinates often strive to influence supervisors' impressions. Moreover, subordinates' interactions with supervisors are known to be ripe with emotions. Nevertheless, research on upward impression management has rarely examined how subordinates' emotion regulation in supervisor interactions may shape their tangible outcomes. The present study introduces subordinates' emotional labor toward supervisors as a novel means of upward influence. Building on the emotions‐as‐social‐information model, we propose that supervisor‐directed emotional labor indirectly relates with supervisory reward recommendations by shaping supervisors' liking and perceived competence of subordinates. Moreover, we cast supervisors' epistemic motivation as a boundary condition for these indirect relations. We tested these notions using time‐lagged data from 377 subordinates and 91 supervisors. When supervisors' epistemic motivation was higher (but not lower), (1) supervisor‐directed surface acting related negatively with supervisors' liking and perceived competence of subordinates and (2) supervisor‐directed deep acting related positively with supervisors' liking of subordinates. Liking and perceived competence, in turn, related positively with supervisors' willingness to recommend subordinates for organizational rewards. These findings highlight supervisor‐directed emotional labor as an upward impression management strategy with both beneficial (deep acting) and detrimental (surface acting) implications, and they illustrate important mechanisms and a key contingency factor for these consequences.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
moai完成签到,获得积分10
刚刚
刚刚
1234567890发布了新的文献求助10
1秒前
充电宝应助ruiheng采纳,获得10
1秒前
TTT0530发布了新的文献求助10
1秒前
Yu发布了新的文献求助10
1秒前
xiaxiao应助oucedv采纳,获得80
2秒前
3秒前
3秒前
4秒前
善学以致用应助wusuowei采纳,获得10
4秒前
4秒前
heolmes完成签到,获得积分10
5秒前
5秒前
婷婷完成签到,获得积分10
5秒前
Jorna完成签到,获得积分10
5秒前
晚霞不晚发布了新的文献求助10
6秒前
隐形的乐枫完成签到,获得积分10
6秒前
7秒前
toda_erica完成签到,获得积分10
8秒前
8秒前
爆米花应助轩仔采纳,获得10
8秒前
第一张完成签到,获得积分10
9秒前
Akim应助宇圆少女科研版采纳,获得10
9秒前
糊涂的服饰完成签到,获得积分10
9秒前
Marayoung发布了新的文献求助10
9秒前
10秒前
10秒前
10秒前
大个应助科研通管家采纳,获得10
11秒前
Jasper应助科研通管家采纳,获得10
11秒前
上官若男应助科研通管家采纳,获得10
11秒前
李健应助科研通管家采纳,获得10
11秒前
wyj0815应助科研通管家采纳,获得10
11秒前
11秒前
情怀应助科研通管家采纳,获得10
11秒前
隐形曼青应助科研通管家采纳,获得10
11秒前
AAAAA应助科研通管家采纳,获得10
11秒前
orixero应助科研通管家采纳,获得10
11秒前
科研通AI5应助科研通管家采纳,获得10
11秒前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
Treatise on Ocular Drug Delivery 200
studies in large plastic flow and fructure 200
New Syntheses with Carbon Monoxide 200
Quanterion Automated Databook NPRD-2023 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3834697
求助须知:如何正确求助?哪些是违规求助? 3377202
关于积分的说明 10497023
捐赠科研通 3096605
什么是DOI,文献DOI怎么找? 1705084
邀请新用户注册赠送积分活动 820451
科研通“疑难数据库(出版商)”最低求助积分说明 772054