The effects of the chief technology officer and firm and industry R&D intensity on organizational performance
Bibliographic record
Abstract
Between 1993 and 2013 the number and power of CTOs increased; as indicated in the percentage of firms with CTOs, their increasing presence on boards, their compensation relative to their CEOs, and compensation relative to other highly compensated executives. Firms which pursue an aggressive technology strategy (powerful CTO, high R&D spending) in industries in which technology is a critical contingency have well above normal market adjusted returns while those which pursue that strategy in industries in which technology is not critical have well below normal returns. These results empirically confirm longstanding, untested assumptions in the field of technology management. Moreover, the effect of R&D expenditures on firm performance is contingent on the degree to which technology is a critical contingency in the industry and on the power of the firm's CTO. These findings may explain the mixed results of past studies of the effects of R&D expenditure on firm performance. A model which integrates its own insights with those of earlier work on CTOs, R&D expenditures, firm strategy, and firm power dynamics is presented and supported.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".