Employment goal commitment moderates the impact of job search goal orientation on the job search process
Bibliographic record
Abstract
Purpose The purpose of this paper is to enhance the understanding of self-regulation during job search by integrating goal-orientation theory with a resource allocation framework. Design/methodology/approach The author surveyed job searching new labor market entrants at two time points and hypothesized that the effects of job seekers’ state goal orientations on indicators of self-regulation during the job search process (procrastination, anxiety, and guidance-seeking behaviors) depended on levels of employment goal commitment (EGC). Findings Results indicate that for job seekers with higher levels of EGC, a state learning-approach goal orientation (LGO) was beneficial for the job search process and a state performance-approach goal orientation (PGO) was detrimental. For job seekers with lower levels of EGC, a state LGO was detrimental to the search process, while a state PGO was beneficial. Research limitations/implications This research extends the understanding of state goal orientation in the context of job search. Future research may replicate these findings with different samples of employed and unemployed job seekers and extend this research with additional conceptualizations of resource limitations. Practical implications The present research suggests that the effectiveness of learning-approach goal-orientation training methods in the context of job search must be considered in light of individual differences in resource availability. In particular, individuals with lower resources available for job searching may benefit from interventions focusing on increasing state PGO. Originality/value The present results suggest that EGC is an important moderator of the impact of job search goal orientation on indicators of self-regulation during job search, and therefore present important boundary conditions regarding the role of state goal orientation in the job search process.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".