Investment dealers' ways of working: Integrating compensation with relational capital
Bibliographic record
Abstract
Abstract For the past 20 years, practitioner and academic research has highlighted that the performance of companies is linked to staff and management's ways of working, particularly in service‐oriented enterprises (A. P. Kakabadse, Savery, Kakabadse, & Lee‐Davies, 2006). Yet despite the monumental impact that financial institutions have on a nation's economy, few studies have examined the ways of working of investment dealers. In the finance literature, two distinct bodies of thought have endeavored to grasp the enigmatic nature of financial markets, investor behavior, and investment decision making. These are, on the one hand, the more traditional finance theories and, on the other, behavioral theories that examine, respectively, the quantitative and qualitative psychological attributes of individually driven investment decisions. Yet it appears that both areas do not meaningfully consider the impact of contextual dynamics on investment decisions (A. Kakabadse, 2000). In response, this article attempts to redress this imbalance by presenting emerging findings from 41 interviews with corporate finance specialists and managers employed in retail and institutional broker departments. Presented is an array of evidence highlighting that employees' ways of working are influenced by investment‐related demographics—namely, structure of compensation and the disciplined pursuit of relational capital. © 2007 Wiley Periodicals, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".