eHRM adoption in emerging economies: The case of subsidiaries of multinational corporations in Indonesia
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
Abstract Electronic HRM (eHRM) is assumed to strengthen the position of HRM as a business partner by promising strategic benefits. Empirical support for this assumption, however, mostly comes from studies conducted in developed economies. Yet eHRM adoption in the emerging economy context remains poorly understood as is how eHRM can result in strategic benefits. We argue that the difference between an emerging economy compared to that of a developed economy affects the adoption of eHRM in multinational corporation (MNC) subsidiaries. In order to investigate which extrinsic factors of a firm in an emerging economy context play a role in the adoption of eHRM, we conducted semistructured interviews in 11 subsidiaries in Indonesia. We found that headquarters’ influence and the available resources have a strong influence on eHRM adoption in Indonesia. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 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