Socialization resources theory and newcomers’ work engagement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".