The impact of impression management on writing tendencies: Investigating faking susceptibility of open-ended personality assessments.

IF 6.7 1区 心理学 Q1 MANAGEMENT
Andrew B Speer,Takudzwa A Chawota,Cort Rudolph,Amani Qaqish
{"title":"The impact of impression management on writing tendencies: Investigating faking susceptibility of open-ended personality assessments.","authors":"Andrew B Speer,Takudzwa A Chawota,Cort Rudolph,Amani Qaqish","doi":"10.1037/apl0001397","DOIUrl":null,"url":null,"abstract":"Applicant faking on self-report personality measures has long been a concern. One method that might be more resistant to faking is open-ended personality assessments. Guided by a new framework of open-ended faking behavior, we investigated how faking emerges in open-ended personality assessments. Additionally, we examined an alternative form of faking: artificial intelligence (AI)-generated responses. Results showed that elaborate responding (i.e., effortful crafting of narratives, Cohen's d = .29) and selective information management (i.e., promoting positive information while withholding negative information, d = .72) were higher in faked than honest conditions and that individuals with higher trait levels were more capable of producing elaborate responses and providing favorable information under motivated conditions. When faking, scores increased more on Likert self-reports (d¯ = .65) than on open-ended personality scores (d¯ = .26) derived via natural language processing, though when trained to predict human ratings rather than honest Likert scores natural language processing scores were more susceptible to faking (d¯ = .63). Training sample configuration also influenced psychometric properties, such that training on a mixed sample of honest and motivated responses yielded the best results. Finally, AI-generated responses (fully synthetic and human-AI hybrid) received scores that varied considerably across models, though they typically were higher than human scores. However, a customized algorithm accurately differentiated human from AI responses with near-perfect accuracy (across seven large language models and for humans using AI), with only a 1% false positive rate. Collectively, these findings suggest that open-ended personality assessments, when paired with natural language processing scoring, have potential to offer a scalable, more faking-resistant approach to personality assessment. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":15135,"journal":{"name":"Journal of Applied Psychology","volume":"30 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/apl0001397","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

Abstract

Applicant faking on self-report personality measures has long been a concern. One method that might be more resistant to faking is open-ended personality assessments. Guided by a new framework of open-ended faking behavior, we investigated how faking emerges in open-ended personality assessments. Additionally, we examined an alternative form of faking: artificial intelligence (AI)-generated responses. Results showed that elaborate responding (i.e., effortful crafting of narratives, Cohen's d = .29) and selective information management (i.e., promoting positive information while withholding negative information, d = .72) were higher in faked than honest conditions and that individuals with higher trait levels were more capable of producing elaborate responses and providing favorable information under motivated conditions. When faking, scores increased more on Likert self-reports (d¯ = .65) than on open-ended personality scores (d¯ = .26) derived via natural language processing, though when trained to predict human ratings rather than honest Likert scores natural language processing scores were more susceptible to faking (d¯ = .63). Training sample configuration also influenced psychometric properties, such that training on a mixed sample of honest and motivated responses yielded the best results. Finally, AI-generated responses (fully synthetic and human-AI hybrid) received scores that varied considerably across models, though they typically were higher than human scores. However, a customized algorithm accurately differentiated human from AI responses with near-perfect accuracy (across seven large language models and for humans using AI), with only a 1% false positive rate. Collectively, these findings suggest that open-ended personality assessments, when paired with natural language processing scoring, have potential to offer a scalable, more faking-resistant approach to personality assessment. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
印象管理对写作倾向的影响:开放式人格评估的伪造敏感性调查。
应聘者在自我报告人格测试中作假一直是人们关注的问题。一种可能更不容易作假的方法是开放式人格评估。在开放式假装行为的新框架的指导下,我们研究了在开放式人格评估中假装是如何出现的。此外,我们还研究了另一种伪造形式:人工智能(AI)生成的回应。结果显示,在虚假条件下,精心的回答(即,努力制作叙述,科恩的d = 0.29)和选择性信息管理(即,促进积极信息而隐瞒消极信息,d = 0.72)比诚实条件下更高,并且具有较高特质水平的个体更有能力在动机条件下产生精心的回答和提供有利的信息。作假时,李克特自我报告的分数(d¯= 0.65)比通过自然语言处理得出的开放式人格分数(d¯= 0.26)提高得更多,尽管当被训练来预测人类评分而不是诚实的李克特分数时,自然语言处理的分数更容易作假(d¯= 0.63)。训练样本的配置也会影响心理测量的特性,例如,在诚实和积极反应的混合样本上进行训练产生了最好的结果。最后,人工智能生成的反应(完全合成和人类-人工智能混合)在不同模型中获得的分数差异很大,尽管它们通常高于人类的分数。然而,一种定制的算法以近乎完美的准确率(在七种大型语言模型和使用人工智能的人类中)准确地区分了人类和人工智能的反应,假阳性率仅为1%。总的来说,这些发现表明,开放式人格评估,当与自然语言处理评分相结合时,有可能提供一种可扩展的、更抗伪造的人格评估方法。(PsycInfo数据库记录(c) 2026 APA,版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
17.60
自引率
6.10%
发文量
175
期刊介绍: The Journal of Applied Psychology® focuses on publishing original investigations that contribute new knowledge and understanding to fields of applied psychology (excluding clinical and applied experimental or human factors, which are better suited for other APA journals). The journal primarily considers empirical and theoretical investigations that enhance understanding of cognitive, motivational, affective, and behavioral psychological phenomena in work and organizational settings. These phenomena can occur at individual, group, organizational, or cultural levels, and in various work settings such as business, education, training, health, service, government, or military institutions. The journal welcomes submissions from both public and private sector organizations, for-profit or nonprofit. It publishes several types of articles, including: 1.Rigorously conducted empirical investigations that expand conceptual understanding (original investigations or meta-analyses). 2.Theory development articles and integrative conceptual reviews that synthesize literature and generate new theories on psychological phenomena to stimulate novel research. 3.Rigorously conducted qualitative research on phenomena that are challenging to capture with quantitative methods or require inductive theory building.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书