Stacking the deck: the effects of top management backgrounds on investor decisions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Young firms going public are dependent upon the decisions of investors for a successful public offering. Yet convincing investors to invest is not easy, as young firms have limited track records and, thus, face challenges associated with gaining legitimacy in their respective industries. This paper examines ways in which select information about firms undertaking an initial public offering (IPO) can affect investor decisions. Building upon recent research on upper echelons and signaling theory, we propose that the composition of a firm's top management team can signal organizational legitimacy that in turn affects investor decisions. In the context of young firms undertaking an IPO, such signals are critical, especially when objective measures of firm quality are not easily available. We introduce a typology of signals of organizational legitimacy to elaborate on our hypotheses. Analyses of a comprehensive set of data on the career histories of the top management teams of young biotechnology firms show that investor decisions are affected by the extent to which a firm's top management team has employment affiliations with prominent downstream organizations (e.g., pharmaceutical companies), with a diverse range of organizations, and upon the role experience of one key member of the top management team—the Chief Scientific Officer. We assess and find that these effects are not mediated by the prestige of a firm's lead underwriter. We conclude with a discussion of the implications of our study for strategy research on upper echelons and organizational legitimacy. Copyright © 2006 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it