Factors influencing sales performance in small and medium-sized enterprises
Bibliographic record
Abstract
This thesis examines factors that influence the sales performance of small and medium-sized enterprises (SMEs). A self-administered survey, based on the SME literature, was mailed to 500 small and medium businesses in Southwestern Ontario. A total of 243 usable responses were received, for a response rate of 49%. Regression and discriminant analysis were used to analyze the data. Support was found for 6 of the 16 hypotheses, which related gross sales to each of the following: number of employees, business dependency, bank financing, use of technology, gender, and age of the owner. The prediction rate of correctly classifying businesses into sales categories increased from 25% to almost 57% using the supported variables as predictors. The results provide an indication of what factors contribute to the sales performance of small and medium businesses in the sampled area. Implications for businesses, academics, and providers of assistance to businesses are discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".