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Record W2784786098 · doi:10.1108/cdi-12-2016-0214

Socialization resources theory and newcomers’ work engagement

2018· article· en· W2784786098 on OpenAlexaff
Alan M. Saks, Jamie A. Gruman

Bibliographic record

VenueCareer Development International · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsSocializationWork engagementOriginalityWork (physics)Industrial and organizational psychologyProactivityPublic relationsOrganizational behaviorPsychologySocial psychologyKnowledge managementCreativityPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Purpose Although work engagement has become an important topic in management, relatively little attention has been given to newcomers’ work engagement in the socialization literature. The purpose of this paper is to explain how newcomers’ work engagement can fluctuate during the first year of organizational entry and the role of organizational socialization in developing and maintaining high levels of newcomers’ work engagement. Design/methodology/approach A review of the socialization literature indicates that uncertainty reduction theory has been the basis of research on socialization tactics and newcomer information-seeking both of which function by providing newcomers with information to reduce uncertainty. Socialization resources theory is used to develop a new pathway to newcomer socialization which focuses on providing newcomers with resources during the first year of organizational entry and socialization. Findings The uncertainty reduction pathway to newcomer socialization is narrow and limited because it primarily focuses on minimizing and reducing the negative effects of job demands rather than on providing newcomers with resources that are necessary to facilitate work engagement and socialization. Practical implications Organizations can use newcomers’ work engagement maintenance curves to map and track fluctuations in newcomers’ work engagement during the first year of organizational entry and they can conduct an audit of socialization resources to determine what resources are required to develop and maintain high levels of newcomers’ work engagement. Originality/value This paper describes newcomer work engagement maintenance curves and explains how socialization resources can be used to develop and maintain high levels of newcomers’ work engagement. A model of a new pathway to newcomer socialization is developed in which socialization resources, personal resources, and job demands influence newcomers’ work engagement and socialization outcomes.

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)0.006
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0040.007
Scholarly communication0.0060.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.031
GPT teacher head0.248
Teacher spread0.218 · 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

Citations133
Published2018
Admission routes1
Has abstractyes

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