A Person-Centered Approach to Understanding Perceived Deception in Job Advertisement Text

Regardless of industry or job type, most organizations aim to recruit large qualified applicant pools via job advertisements or postings. With little control over those individuals that choose to apply and those that do not, organizations and their recruiters are likely to do what they can to increase their applicant pool. This allows for more options in potential hires during the selection process. In order to control the applicant pool as much as possible, recruiters can try and influence potential applicants through the posted job advertisement. Therefore, it is reasonable to assume that many recruiters will write a slightly inflated or overly positive view of the job in order to appeal to more applicants. However, individuals job searching may perceive this attempt as misleading or deceptive. In order to understand perceived deception in job advertisements and what features of their text elicits an overall negative attitude towards the advertisement, this study proposes a mainly exploratory approach to discover if there is a homogenous higher-level construct of perceived deceptiveness or if there is a more person-centered approach via latent profile analysis (LPA) to explain what applicants perceived as deceptive. After the nature of perceived deceptiveness is better understood, this study aims to utilize natural language processing (NLP) topic modeling to find common deceptive topics within different dimensions of the job posting such as, pay, benefits, qualifications, etc. With the limited empirical guidance provided to practitioners, the proposed study can help facilitate research on best practices in job advertisement writing to gain qualified and quality candidates. In turn, those candidates will tend to maintain positive attitudes towards the job and organization, which can persist even after being hired.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Psychology

Department dc:contributor.department

Psychology

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Ristow, Teresa Lauren

Chair dc:contributor.committeechair

• Hernandez, Jorge Ivan

Committee members dc:contributor.committeemember

• Hauenstein, Neil M. A.
• Calderwood, Charles
• Diana, Rachel A.

• Recruiting
• Job Advertisement
• Perception
• Deception
• Natural Language Processing
• Latent Profile Analysis

Statement dc:rights

• Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37161

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/114998