Literature DB >> 30730166

Gender and the evaluation of humor at work.

Jonathan B Evans1, Jerel E Slaughter1, Aleksander P J Ellis1, Jessi M Rivin2.   

Abstract

Although research has added to our understanding of the positive and negative effects of the use of humor at work, scholars have paid little attention to characteristics of the humor source. We argue that this is an important oversight, particularly in terms of gender. Guided by parallel-constraint-satisfaction theory (PCST), we propose that gender plays an important role in understanding when using humor at work can have costs for the humor source. Humor has the potential to be interpreted as either a functional or disruptive work behavior. Based on PCST, we argue that gender stereotypes constrain the interpretation of observed humor such that humor expressed by males is likely to be interpreted as more functional and less disruptive compared with humor expressed by females. As a result, humorous males are ascribed higher status compared with nonhumorous males, while humorous females are ascribed lower status compared with nonhumorous females. These differences have implications for subsequent performance evaluations and assessments of leadership capability. Results from an experiment with 216 participants provides support for the moderated mediation model. Theoretical and practical implications are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Entities:  

Mesh:

Year:  2019        PMID: 30730166     DOI: 10.1037/apl0000395

Source DB:  PubMed          Journal:  J Appl Psychol        ISSN: 0021-9010


  2 in total

1.  Stereotyping in the digital age: Male language is "ingenious", female language is "beautiful" - and popular.

Authors:  Tabea Meier; Ryan L Boyd; Matthias R Mehl; Anne Milek; James W Pennebaker; Mike Martin; Markus Wolf; Andrea B Horn
Journal:  PLoS One       Date:  2020-12-16       Impact factor: 3.240

2.  Assessment of Gender-Based Linguistic Differences in Physician Trainee Evaluations of Medical Faculty Using Automated Text Mining.

Authors:  Janae K Heath; Gary E Weissman; Caitlin B Clancy; Haochang Shou; John T Farrar; C Jessica Dine
Journal:  JAMA Netw Open       Date:  2019-05-03
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.