MétaCan
Menu
Back to cohort
Record W2151978773 · doi:10.1002/cjas.169

Organizational socialization and positive organizational behaviour: implications for theory, research, and practice

2010· article· en· W2151978773 on OpenAlexaffvenue
Alan M. Saks, Jamie A. Gruman

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsSocializationOptimismPsychologyPsychological resilienceProcess (computing)Organizational behaviorOrganizational learningOrganizational cultureSocial psychologyKnowledge managementPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Abstract The purpose of this paper is to advocate a shift in research and practice on organizational socialization towards one based on positive organizational behaviour (POB). First, we demonstrate how the prevailing perspectives of organizational socialization are based on a cognitive‐learning process that emphasizes information and knowledge acquisition. We then review the literature on POB and psychological capital (PsyCap) and argue that socialization processes should be designed to develop the PsyCap of newcomers. We offer a new approach to organizational socialization called socialization resources theory (SRT) and describe four broad socialization resources that can be used to develop newcomers' self‐efficacy, hope, optimism, and resilience. Finally, we discuss the implications of this approach for research and practice on organizational socialization. Copyright © 2010 ASAC. Published by 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 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)0.022
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0030.030
Scholarly communication0.0100.007
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.103
GPT teacher head0.368
Teacher spread0.264 · 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 designTheoretical or conceptual
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

Citations90
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l AdministrationSame topicJob Satisfaction and Organizational BehaviorFrench-language works237,207