"Accounting for Endogeneity in Associations between HR Systems, Leadership, and Employee Attitudes"
Bibliographic record
Abstract
The purpose of this research was to make stronger causal inferences about the associations between HR systems, leadership, and employee attitudes in an observational field sample. We controlled for a number of potential sources of endogeneity with longitudinal employee survey data that was collected over a period of eight years. We first conducted propensity score matching to reduce the likelihood that selection or sorting effects influenced the results. We then compared lagged panel regression models that tested if HR systems and/or leadership likely had causal effects employee attitudes, if omitted third variables were a potential source of endogeneity, or if reverse causality (employee attitudes cause perceptions of HR systems or leadership) could explain the observed effects. The results showed that there was a reciprocal relationship between HR systems and person-organization fit perceptions, leadership caused perceptions of the organization’s employment reputation, and that person-organization fit caused perceptions of leadership. We discuss how causal inference affects theory development and provide suggestions about how scholars can improve research designs to make stronger causal inferences with observational field data.
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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 | 0.075 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".