Taxonomy of business value underlying motivations for e-HRM adoption
Bibliographic record
Abstract
Purpose The purpose of this paper was to develop a taxonomy of organizations based on business value (BV) underlying electronic human resource management (e-HRM) adoption motivations. Design/methodology/approach A taxonomy was developed using cluster analysis of the online case stories of 146 firms. Results were validated using discriminant analysis. Differences in organization and environmental characteristics across clusters were examined. Findings Seven meaningful and distinct clusters were uncovered showing asymmetry in the consideration of strategic BV underlying the motivations of e-HRM adoption. Statistical tests revealed that the seven clusters have high internal validity. Statistically significant differences in organizational conditions were found among clusters. Research limitations/implications This research offers an empirically and conceptually grounded taxonomy of organizations that reveals strategic and nonstrategic BV that organizations actually put forward and the way they combine together to form different profiles. This research is based on secondary data, that is, data initially gathered for a distinct goal different from this research. Practical implications The developed taxonomy provides human resource (HR) managers, executives, researchers and consultants a useful way to describe and understand motivations underlying e-HRM adoption. The taxonomy may also facilitate valid and systematic assessment of e-HRM effectiveness. Originality/value This research moves the debate beyond normative arguments to a more analytic assessment of the actual practice of organizations regarding e-HRM adoption and expected BV.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".