Happy, and they know it? The roles of positive affectivity, intrinsic motivation and network building on LinkedIn on employment predictions
Bibliographic record
Abstract
Purpose Drawing on trait activation theory, this study examines the influence of positive affectivity on employment predictions (e.g. the probability of obtaining an interview and being hired) via intrinsic motivation and network building on LinkedIn. Design/methodology/approach Multisource field data were collected from student job seekers ( n = 179) searching for an internship over two points with a six-month time separation between the first and second data collection. Findings Structural equation modeling (SEM) analyses revealed marginal support for the mediating roles of intrinsic motivation and network building in positive affectivity’s indirect effect on employment predictions about the probability of obtaining an interview and being hired. Research limitations/implications This study extends research on job search networking/selection by demonstrating the sequential process through which job seekers’ positive affectivity influences employment predictions, emphasizing the intermediary roles of intrinsic motivation and network building on LinkedIn. Practical implications Job seekers, recruiters and career counselors should consider network building on LinkedIn as a relevant expression of positive affectivity. Originality/value We apply trait activation theory as an overarching framework to examine how an affective between-person difference is expressed via intrinsic motivation and network building and is, at the same time, perceived and valued by employers on LinkedIn.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".