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The role of dispositions, entry stressors, and behavioral plasticity theory in predicting newcomers' adjustment to work

2000· article· en· W4243519435 on OpenAlexaff
Alan M. Saks, Blake E. Ashforth

Bibliographic record

VenueJournal of Organizational Behavior · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyStressorRole conflictAmbiguitySocial psychologyDevelopmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

This longitudinal field study examined the combined effects of dispositions, entry stressors, and behavioral plasticity theory in predicting newcomers' adjustment to work after four and 10 months of organizational entry. Recent graduates completed a questionnaire prior to entry that measured two dispositions (negative affectivity and general self-efficacy), and a questionnaire four months after entry that measured four entry stressors (role conflict, role ambiguity, role overload, and unmet expectations). Measures of work adjustment were taken after four and 10 months. Based on behavioral plasticity theory, it was expected that the effects of the entry stressors would be most negative for the adjustment of newcomers with low general self-efficacy. Limited support was found for behavioral plasticity theory. Those interactions that were significant indicated that increasing levels of role conflict were associated with lower organizational commitment and identification for newcomers with low general self-efficacy. The results also provided weak support for a dispositional theory of work adjustment. The dispositions only predicted three of seven adjustment variables at four and 10 months. However, the entry stressors significantly predicted all seven adjustment measures. The results are discussed in terms of the predominant role played by the organizational setting in predicting newcomers' adjustment to work. Copyright © 2000 John Wiley & Sons, Ltd.

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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 teacher head, not a consensus.

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

Citations12
Published2000
Admission routes1
Has abstractyes

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