Do firm compensation strategies matter to all employees? The scope of firm compensation strategies in high technology firms
Bibliographic record
Abstract
This study evaluated the extent and nature of firm compensation strategies using employee compensation data from U.S. high-technology firms. I found that firm pay level strategies influenced the pay level for strategic employee groups (i.e., managers, R&D employees) to a greater extent than the pay level for other employee groups. My analyses also revealed that firms applied consistent pay level and pay mix strategies across different employee groups. “(Strategy is defined as) the determination of the basic long-term goals and objectives of an enterprise, and the adoption of courses of action and the allocation of resources necessary for carrying out these goals ” (Chandler, 1962: 13). As suggested by Chandler, resource allocation presents a key element of corporate strategy. Granted, the way firms manage employee compensation will form one important dimension of corporate strategy as it is directly associated with the allocation of compensation budget, which accounts for more than 50 percent of operating expenses in most firms (Milkovich & Newman, 2008). Reported relationships between compensation management and subsequent firm performance endorse the strategic nature of
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".