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Record W2953688754 · doi:10.1037/apl0000431

Faking to fit in: Applicants’ response strategies to match organizational culture.

2019· article· en· W2953688754 on OpenAlex
Nicolas Roulin, Franciska Krings

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Psychology · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployer Branding and e-HRM
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsPsychologyPsycINFOOrganizational culturePersonalitySocial psychologyPropositionBig Five personality traitsPersonnel selectionPublic relationsManagementMEDLINE

Abstract

fetched live from OpenAlex

We examine applicant faking as an adaptive response to the specific environment that applicants are confronted with. More specifically, we propose that applicants fake by adapting their responses to the culture of the hiring organization so that they display the personality profile that best matches the organization's culture. In other words, they fake in a targeted manner, to increase their person-organization (P-O) fit. We tested this proposition in 6 studies, including experiments and surveys, and focused on competitiveness and innovativeness as 2 central elements of organizational culture. Results confirm that applicants infer an ideal personality profile from elements of organizational culture and then adapt their responses on personality inventories accordingly. Faking to increase P-O fit was present, albeit slightly weaker, when accounting for the fact that applicants choose organizations that fit their values. Overall, this research highlights the adaptive component of faking and underlines that it should not be considered a behavior that only dishonest individuals show. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.296
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it