Managing upward and downward through informal networks in Jordan: The contested terrain of performance management
Bibliographic record
Abstract
Abstract This study explores how local managers, in practicing Human Resource management (HRM), may pursue their own interests that are out of line with the agendas of headquarters in multinational companies (MNCs). It is widely acknowledged that informal networks have an impact on HRM practices in emerging markets. While these networks are often regarded as beneficial for organizations in compensating for institutional shortfalls, they may also lead to corruption, nepotism, or other ethical transgressions. Indigenous scholarship on informal networks in emerging markets has highlighted how their impact occurs through a dynamic process; powerful placeholders deploy informal networks to entrench existing power and authority relations when managing people. Qualitative data were gathered through 43 in‐depth interviews and documentary evidence from MNCs operating in Jordan. MNCs are subject to both home and host country effects; we highlight how, in practicing HRM, country of domicile managers deploy the cultural scripts of wasta informal network to secure and enhance their own relative authority. HRM practices are repurposed by actors who secure and consolidate their power through wasta. They dispense patronage to insiders and marginalize outsiders; the latter includes not only more vulnerable local employees but also expatriates. This phenomenon becomes particularly evident during the performance appraisal process, which may serve as a basis for the differential treatment and rewards of employees. Consequently, this further dilutes the capacity of MNCs to implement—as adverse to espousing—centrally decided approaches to HRM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".