The influence of entrepreneurial orientation and family business’s resources and capabilities on marketing performances
Bibliographic record
Abstract
This paper uses data from the Successful Transgenerational Entrepreneurship Practices Project (STEP Project) to investigate the influence of entrepreneurial orientation (EO) and family business’s resources and capabilities towards marketing performance. Previous researches represent an evidence of a relationship between EO and firm performance. Nevertheless, there are limited studies to investigate both psychological and physical aspects of family business like EO and family business’s resources and capabilities to firm performance. The empirical analysis includes a STEP project data set of 28 countries (n = 1,008) in 4 regions of the world; Asia-Oceania (Australia, China, Hong Kong, India, Japan, Malaysia, Taiwan, Thailand), Europe (Belgium, France, Germany, Ireland, Italy, Netherlands, Spain, Sweden, Switzerland, UK, Russia), Latin America (Chile, Colombia, Dominican Republic, Peru, Puerto Rico, Venezuela), and North America (Canada, Mexico, USA) which was collected during 2013-2015. SEM is used to investigate the effect of EO and family business’s resources and capabilities on marketing performances. \nThe findings support the relationship of EO and family business’s resources and capabilities on entrepreneurial performances. The results of the study show that both EO and family business’s resources and capabilities affect positively to the entrepreneurial performance. This study provides insights to researchers, practitioners and managers on the significance of both entrepreneurial orientations and firms’ resources and capabilities for the survival and growth of family businesses.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".