Building higher value-added firm practices in challenging contexts: Formal networks and talent management in Turkey
Bibliographic record
Abstract
Where do high-impact human resources management practices thrive, and how do they make a difference in environments with limited institutional support? This study delves into the realm of talent management (TM) in Turkey, where institutional coverage is incomplete and unstable. Drawing on survey data, we explore the conditions under which TM succeeds, supplementing previous research on internal networks by examining the impact of external networks that encompass the entire firm. We find that when firms have closer ties with customers, suppliers and competitors (and hence, the basis for formal network tie building), TM is more prevalent and more likely to be successful. While conventional wisdom in comparative institutional literature suggests that such dense ties might be less effective in emerging markets owing to the absence of advanced complementarities found in mature economies, our study challenges these assumptions. In the eyes of managers, TM is not merely a tool to overcome disadvantages; it is perceived as a source of opportunities. This prompts a critical question: what specific advantages does the emerging economy system confer on firms embracing TM? Our study seeks to unravel these dynamics and contribute to a deeper understanding of the interplay between institutional contexts and TM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".