Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Este trabajo analiza la evolución en el tiempo de los estudios sobre fracaso empresarial. Con carácter general, partimos de la revisión crítica realizada en la literatura previa, y aportamos un análisis de la evidencia empírica adicional, con especial atención a la obtenida durante la última década. Pero además, para subsanar algunas deficiencias detectadas en las revisiones anteriores, nos ocupamos de tres aspectos, que pueden considerarse la principal contribución de este trabajo: primero, analizamos la evolución en las últimas décadas del concepto de fracaso empresarial o fallido, detectando cierta evolución desde la identificación hacia la predicción; segundo, analizamos las variables empleadas en los modelos, aportando un estudio de los rasgos empresariales que se representan con las variables (frente al tradicional análisis de frecuencia de las propias variables individuales), siendo los resultados más acordes con los planteamientos y desarrollos teóricos clásicos sobre el fracaso empresarial; y, finalmente, destacamos los puntos fuertes y débiles de las metodologías que, por su reciente aparición, no habían sido analizadas o muy poco por revisiones anteriores: las técnicas de inteligencia artificial y el análisis envolvente de datos (DEA). Adicionalmente, integramos en la revisión el numeroso grupo de trabajos empíricos publicados en España sobre la cuestión, y que no aparecían en ninguna de las revisiones previas analizadas. This work analyzes the evolution of business failure literature. In it, we consider previous critical revisions, contributing with the analysis of additional empirical evidence, paying special attention to the last decade. In order to make up for some deficiencies detected in previous revisions, we deal with three aspects that can be considered the main contribution of this work. First, we analyze the business failure concept during the last decades, detecting, from identification to prediction, certain evolution. Second, we analyze the variables used in the different models, adding –to the traditional frequency analysis of the individual variables– a study of the business features proxied by the variables, obtaining rankings more in line with the classical theoretical approaches and developments on business failure. Finally, we illustrate the salient strengths and weaknesses of the recently, and scarcely analyzed methodologies, such as artificial intelligence techniques and data envelopment analyses (DEA). In addition, we incorporate a large group of empirical works on this matter published in Spain, missing in the previous revision works examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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