Getting newcomers engaged: the role of socialization tactics
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the relationship between socialization tactics and newcomer engagement and the mediating role of person‐job (PJ) and person‐organization (PO) fit perceptions, emotions, and self‐efficacy. Design/methodology/approach A survey was completed by 140 co‐op university students at the end of their work term. Findings Institutionalized socialization tactics were positively related to PJ and PO fit perceptions, emotions and self‐efficacy, but not newcomer engagement. Socialization tactics were indirectly related to newcomer engagement through PJ fit perceptions, emotions, and self‐efficacy. Research limitations/implications Socialization tactics might be too broad and general to predict newcomer engagement. Future research should measure more specific socialization practices and job resources. Practical implications Organizations that want to engage new hires should use social socialization tactics to create positive emotions, develop higher PJ fit perceptions, and strengthen newcomers' self‐efficacy beliefs. Social implications Organizations can contribute to the well being of individuals and society by designing socialization programs that will engage new hires. Originality/value This is the first study to examine relationships between socialization tactics and newcomer engagement and to study engagement as a socialization outcome.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".