Effects of Pricing Influences and Selling Characteristics on Plant Sales in the Green Industry
Bibliographic record
Abstract
Data from the 2004 National Nursery Survey conducted by the USDA-CSREES S-1021 Multistate Research Committee (referred to as the Green Industry Research Consortium) were used to evaluate the effect of pricing influences and selling characteristics on total gross firm sales and gross sales of several plant categories (trees, roses, shrubs/azaleas, herbaceous perennials, bedding plants, foliage, and potted flowering plants) for commercial nurseries and greenhouses. As expected, the firm's selling characteristics play a large role in whether a firm sells a specific plant category. Demand factors also play a role in affecting plant category sales with income, population, and race tending to be the only significant variables, except for the potted flowering plants category. In regard to sales, our results show that certain factors affecting pricing decisions play a critical role in both plant category sales and total sales. Furthermore, demand and business characteristics play a limited role as well, but not as big a role as selling characteristics. Of note is that firms with an increased percentage of sales through wholesale channels (of most plant categories and overall) result in increased sales. By understanding the nursery and greenhouse industry environment and how decisions affect overall and categorical sales, firms can implement strategies that capitalize on factors that have the potential to generate increased sales.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".