Corporate Innovation Strategy and Narrative Disclosures
Bibliographic record
Abstract
In this thesis, I examine how firms with different prioritizations of innovation strategy utilize narrative disclosures in their 10-K filings to communicate information about their innovation activities. I hypothesize and find that firms with a greater focus on exploratory innovations (versus exploitative innovations) disclose less narrative innovation information based on a cost-benefit tradeoff. Conducting a content analysis of the quality of narrative innovation disclosures, I find that exploration-focused firms tend to disclose fewer details but use a more positive tone in their disclosures compared to exploitation-focused firms. The tendency for exploration-focused firms to employ a more positive tone in narrative disclosures may be due to managerial overconfidence rather than management opportunism or firm performance. I also find that product market competition and technology spillover have opposite effects on narrative innovation disclosures due to their different proprietary cost implications. The negative relation between exploration-focused firms and narrative innovation disclosures is more pronounced when product market competition intensifies, while it becomes less pronounced when technology spillover becomes more prominent. Finally, I find that narrative innovation disclosures enhance investors’ understanding of innovative activities and reduce misvaluation and future stock price crash risk for exploration-focused firms. My thesis contributes to the disclosure and innovation literature with insights into how firms’ innovation strategies affect their narrative innovation disclosure decisions, which helps investors better evaluate corporate innovation strategy.
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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 | 0.004 | 0.050 |
| 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 | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".