Linking Key Performance Indicators to New International Venture Survival
Bibliographic record
Abstract
ABSTRACT Based on the four major challenges firms face in the early stage of their life cycle, we identify and use financial and non-financial performance measures to predict the survivability of new international ventures. We use a sample of 3,729 new manufacturing ventures from the Chinese Foreign Invested Enterprises Database. The study sample consists of wholly owned ventures of multi-national corporations (MNCs) and joint ventures between pairs comprising foreign and local investors in China. The results are consistent with the study's hypotheses. Using the Cox (1972) survival model, we find that employee training, employee productivity, accounts receivable collection period, export intensity, and sales growth are positively related to new venture survival. This study contributes to the existing business venturing and accounting literature in three ways. First, it fills the gap in the existing literature on bankruptcy prediction by focusing on firms in the early stage of their life cycle. Second, it uses survivability as a measure of business success. Survivability is a more comprehensive measure of firm performance than traditional financial measures during the start-up stage because during this stage firms tend to carry large losses that make financial measures inappropriate. Finally, this study has the potential to help new venture managers improve a firm's chances of success by using customized performance measures that fit its unique situation. JEL Classifications: D21; G32; M41.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".