Bibliographic record
Abstract
Plenty of powerful and promising companies have failed in the history of Corporate America. While many factors may have contributed to this outcome, lack of innovation is often one of the factors highlighted. After all, innovation, which can be defined as the successful exploitation of new ideas (Nathan & Lee, 2013), has been found to be a strong predictor of a firm’s future returns. However, what drives the creation of successful corporate innovation is not yet fully understood. On this study, I want to focus on managerial characteristics and how such diversity attributes can drive innovation. Joshi & Jackson (2003) define diversity as “the distribution of personal attributes among interdependent members of a work unit.” The combination of these diversity attributes can generate ideas and fresh perspectives feeding the creativity process which in turn will result in innovation. The diversity attributes at the managerial level are critical as the top management team (TMT) has the ability to define the direction of the firm. The purpose of this study is to assess whether diverse managerial characteristics at the TMT level drives firm innovation. This research question is tested using a sample of S&P 500 firms over the period 2010-2017. Innovation is measured by patent filings and citations. The empirical results show diversity traits such as tenure, culture, education and political affiliation do positively influence innovation. Gender, age and job diversity were found not to be significant.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".