How do human resources analytics create value for organizations? A qualitative investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Human resources analytics (HRA) can potentially create value and provide a competitive advantage; however, whether and how HRA creates this value has been sparsely explored in scholarly literature. Hence, the purpose of this study is to provide a process-oriented framework for value creation from HRA use by exploring the underlying mechanisms, complementary resources and outcomes. Design/methodology/approach The study used a qualitative research design as the research question was exploratory. A total of 26 in-depth expert interviews with different organizations were conducted. These interviews were transcribed and coded for emerging themes, which were placed in a temporal sequence of occurrence to derive a process understanding of value creation from HRA. Additionally, validation tests were conducted. Findings The thematic analysis using NVivo provided qualitative evidence of the value-creating potential of HRA. Further, it unraveled the process of value creation from HRA in the form of problem construction, insight generation, the buy-in of stakeholders and solution implementation. This process resulted in various human resource management (HRM) and organizational outcomes. The analysis also highlighted the significance of three complementary resources, namely data quality, analytical competency and business knowledge. Practical implications This study offers guidance for HR executives and business managers to assess the conditions under which HRA can add business value to organizations. Originality/value The paper is novel as this is among the first studies to provide evidence of value creation from HRA and identify the underlying mechanism, which has been highlighted as a gap in the literature. Based on resource-based theory and its complementarities perspective, the study makes a valuable contribution to the nascent HRA literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it