Human resource analytics, creative problem-solving capabilities and firm performance: mediator moderator analysis using PLS-SEM
Bibliographic record
Abstract
Purpose Based on resource-based and dynamic capabilities theorizing, this study explores how human resource analytics (HRA) can improve human resource management (HRM) performance and organizational performance, with creative problem-solving capability (CPSC) as an underlying mediator for creating value from HRA. It also explores how data quality and HRA personnel expertise act as moderators in this relationship. Design/methodology/approach Hypotheses are tested in an empirical study including 191 firms using partial least square structural equation modeling technique. Findings The findings confirm the direct and indirect effect of HRA use and maturity on HRM and organizational performance, as well as the mediating role of CPSC. HRA personnel expertise was found to moderate the relationship between HRA and CPSC, data quality being an important factor. Originality/value The findings contribute to the sparse evidence of value creation from HRA use/maturity on HRM and organizational outcomes, providing a theoretical logic of resource-based view and dynamic capabilities view based on the underlying causal mechanism through which HRA creates value. The study identified complementary capabilities which when combined with HRA use/maturity and CPSC result in value creation.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".