La mercadotecnia en las PYMES y su influencia en el crecimiento de utilidades
Bibliographic record
Abstract
Abstract. This research aims to identify the marketing factors that enable SMEs succeed relative to its earnings growth in order to enhance their business opportunities. The independent variables included in this study are Product Management, Customer Service and Price Management. Research in conducted in Monterrey and its metropolitan area of Nuevo Leon, Mexico. The methodology used is multiple linear regression. According to the results obtained with sample items, earnings growth is not a function of Product Management,Customer Service and Price Management, that is to say, the area of marketing activities used by the entrepreneur are not significant to achieve with ANOVA parametric test, positive growth in earnings.Keywords:earnings, marketing factors, owners-managers, SMEResumen. La presente investigación tiene como objetivo determinar los factoresmercadológicos que permiten a las PYMES tener éxito con relación a su crecimiento de utilidades con el fin de mejorar sus oportunidades de negocio. Las variables independientes contempladas en este estudio son Manejo del Producto, Servicio al Cliente y Manejo del Precio. La investigación es realizada en Monterrey y su área metropolitana del Estado de Nuevo León, México. La metodología utilizada es la regresión lineal múltiple. Acorde a los resultados obtenidos con los elementos muestrales, el crecimiento de utilidades no está enfunción del Manejo del Producto, Servicio al Cliente y Manejo del Precio; es decir, las actividades del área de mercadotecnia utilizadas por el empresario no son significativas para lograr, con la prueba paramétrica ANOVA, un crecimiento favorable en las utilidades.Palabras clave: factores mercadológicos, propietarios-administradores, PYMES, utilidades
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".