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Record W1480882406

Do individual attachment styles in the workplace affect a follower’s job satisfaction, engagement and commitment?

2014· article· en· W1480882406 on OpenAlexfundno aff
Jillian Ryan

Bibliographic record

VenueeSource (Dublin Business School) · 2014
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersLakehead University
KeywordsAffect (linguistics)Social psychologyJob satisfactionPsychologyOrganizational commitmentEmployee engagementPublic relationsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The aim of this study was to investigate if individual attachment styles of followers within the leader-follower relationship were associated with levels of job satisfaction, employee engagement and organisational commitment. A cross sectional sample of 70 employees ranging from entry level to middle management were included in the study. Participants completed an online self report survey covering demographic items, followed by 4 existing questionnaires covering attachment, job satisfaction, employee engagement and commitment in the workplace. Results supported a negative relationship between anxious attachment and job satisfaction (R (65) = -0.295, p< 0.5). A positive relationship was found between anxious attachment and continuance commitment (R (59) = 0.383, p < .01) and avoidant attachment and normative commitment (R (59) = 0.269, p < .05), which although was contrary to the supposed hypotheses with respect to commitment, serves to add to the growing literature regarding the application of Attachment Theory to the workplace. Findings suggest further research is required, however indicate that addressing attachment styles in the workplace could have a beneficial outcome for the organisation and the individual.

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)0.004
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.354
Teacher spread0.317 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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Same venueeSource (Dublin Business School)Same topicAttachment and Relationship DynamicsFrench-language works237,207