INTRAORGANIZATIONAL EMPLOYEE NAVIGATION AND SOCIALLY DERIVED OUTCOMES: CONCEPTUALIZATION, VALIDATION, AND EFFECTS ON OVERALL PERFORMANCE
Bibliographic record
Abstract
Intraorganizational employee navigation (IEN) is conceptualized as a means of better understanding how the organizational actor proactively works across their firm's internal environment in the execution of their jobs. Navigation is argued to be a precursor to the employee's overall performance through a class of mediating variables labeled “socially derived outcomes,” which are variables inside the organization that are bestowed upon the employee as a result of them first engaging in proactive behavior (e.g., IEN). Two studies are reported. Study I sees IEN psychometrically validated versus a range of existing proactive behaviors and individual traits (discriminant, nomological, and criterion‐related validity) with a heterogeneous sample of 704 employees. Study II then tests a model relating IEN to performance through six mediating “socially derived outcomes” by leveraging data from 2 Fortune 500 firms. The results of Study II show that IEN significantly impacts multiple measures of the employee's overall performance through mediating effects brought about by key socially derived outcomes, such as the employee's “manager alignment.” The contributions, broader implications, and limitations of the research are then put into context.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".