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Record W4384662029 · doi:10.22215/etd/2023-15519

Personality as a Predictor Of Job Performance in an All-Remote Workforce: A Study of Workers Within the Canada Pension Centre for the Federal Public Service

2023· dissertation· en· W4384662029 on OpenAlexafffundabout
Sandra A. I. Wright

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsCarleton University
FundersGovernment of CanadaAustralian Government
KeywordsConscientiousnessJob performanceJob designOpenness to experienceJob attitudePsychologyPersonalityContextual performanceAgreeablenessBusinessJob analysisPublic serviceExtraversion and introversionApplied psychologyJob satisfactionPublic relationsBig Five personality traitsSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Prior research has demonstrated that the Big Five dimensions of personality (Extraversion, Neuroticism, Conscientiousness, Openness, and Agreeableness) can significantly predict overall job performance in traditional office settings where employees work in person (Barrick & Mount, 1991).The arrival of the recent pandemic has required many organizations to rethink their service delivery models in response to public health threats and safety measures.Transitioning to remote work was a popular organizational response.The pandemic has likely changed service delivery models for good, with some organizations considering permanently shutting down in-person office space as a cost-saving measure.Remote work has also been viewed as a hiring tool as more employees are showing an interest in working from home.However, despite this interest, remote work may not be suitable for all employees.Unlike previous research, this study investigates the relationship between the Big Five dimensions of personality and eight job performance criteria (job knowledge, organizational skills, efficiency, persistent effort, cooperation, organizational conscientiousness, interpersonal and relational skills) in an all-remote work environment.The research consists of 201 responses from employees in the Canada Pension Centre who transitioned to fully remote work as a response to public safety measures.It centres around a survey designed to collect personality and job performance data.Semistructured interviews were conducted with members of the management team to bolster research findings.Conclusions from this research confirmed, as with prior research where employees were situated in offices, a positive relationship exists between personality and job performance in fully remote work environments.As with previous iii research in this area, findings demonstrated that Conscientiousness and Openness to new experiences are associated with higher organizational outcomes in a remote work environment.The study also concluded that employees with access to outdoor spaces, such as yards or balconies, reported higher overall job performance.This research combines past research on personality, job performance, and remote work.It examines the relationship between personality and job performance in a fully remote workforce, specifically the use of personality to predict job performance measured in terms of task and contextual productivity.The findings add to the current literature on personality, job performance, remote work, and the research on the effects of the pandemic on organizations.Results reaffirm a one-size-fits-all approach to organizational design may not improve overall organizational outcomes.Organizations can use these findings to aid in recruitment and productivity decisions and designing the workplace of the future.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.441

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.364
Teacher spread0.276 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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