Bibliographic record
Abstract
The theme of this article is value capture in the context of executive search. We build on literature in economic sociology and organisational theory that demonstrates that relationships contribute to creation of economic value and addresses how thus-created economic value is distributed amongst participants in transactions. In our context transactions consist of search firm commencing a concluding searches for appropriate candidates for executive vacancies at hiring firms. This research examines what kinds of pre-existing relationships with candidates enable search firms to complete searches quicker and thus capture more value. It derives hypotheses from literature on learning and socialisation in relationships, and tests them with a sample of 924 searches conducted by a global executive search firm between January 2005 and May 2009. We found evidence of learning in relationships that supports value capture (with delayed effect). Specifically, value captured due to relationship-based learning can be almost half greater than when there is absence of such learning. Our findings extend the literature on value creation and value capture through relationships and demonstrate that value may be captured in situations that are not strictly competitive. We discuss the limitations of our study and indicate how the combination of findings and limitations opens avenues for future research.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".