Bibliographic record
Abstract
Abstract We survey chief financial officers (CFOs) from 12 European countries regarding the determinants of going public and exchange listing decisions. Most CFOs identify enhanced visibility and financing for growth as the most important benefits of an IPO, but other motivations for IPOs differ significantly across firms, countries, and legal systems. We find strong support for the IPO theories that emphasise financial and strategic considerations, such as enhanced reputation and credibility, and financial flexibility as a major advantage of an IPO. At the same time, we find moderate support for theories that focus on exit strategy, balance of power with creditors, external monitoring, and merger and acquisition motivations. European CFOs' views on the major benefits of an IPO are generally similar to those of US managers as reported in Brau and Fawcett (2006) , but differ significantly on outside monitoring; outside monitoring is considered a major benefit by European CFOs but a major cost by US CFOs. Our evidence suggests that the decision to go public is a complex one, and cannot be explained by one single theory because firms seek multiple benefits in going public. These motivations are influenced by the firm's ownership structure, size and age as well as by the home country's institutional and regulatory environment.
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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 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".