Determination of factors used to influence purchasing price: Enhancing profitability and competitive edge in discount retail chains
Bibliographic record
Abstract
Wintana Tadesse – Determination of factors used to influence purchasing price: Enhancing profitability and competitive edge in discount retail chains This study investigates the key determinants of purchasing prices in the retail sector, with a focus on Dollarama, a major Canadian discount retailer. Using Ordinary Least Squares (OLS) regression, the research analyzes the influence of four core operational variables: Gross Margin (GM), Minimum Order Quantity (MOQ), stock levels, and consumer demand—on purchasing price. Initial exploratory analysis using scatter plots indicated a strong positive relationship between GM and purchasing price, a negative relationship with demand, and weaker but noticeable trends for stock levels and MOQ. Diagnostic checks revealed violations of OLS assumptions related to linearity and constant variance. To address these issues, a logarithmic transformation was applied to both dependent and independent variables. Post-transformation, the model satisfied all key assumptions, enhancing the robustness and interpretability of the regression results. The refined analysis confirmed that GM, MOQ, and stock levels have a positive and statistically significant effect on purchasing prices, while demand shows a negative effect—suggesting that increased demand may be associated with supplier discounts or economies of scale in procurement. To capture more nuanced relationships, interaction terms (e.g., stockouts × MOQ, GM × demand) were introduced. These revealed that the effects of some variables are conditional on others, indicating that purchasing price is influenced by interdependent operational dynamics. However, the addition of interaction terms also increased multicollinearity, diminishing the individual significance of previously important predictors. Variance Inflation Factor (VIF) analysis is proposed as a next step to evaluate and address this issue. Furthermore, the study explores potential endogeneity by regressing current purchasing prices on lagged values of the independent variables. The significance of these lagged variables suggests that past operational conditions have a persistent impact on present pricing decisions. The findings hold practical implications for retail decision-makers. Understanding how GM targets, MOQ requirements, inventory levels, and demand trends influence purchasing prices enables retailers to develop more informed procurement strategies, negotiate better supplier terms, and optimize inventory management. These insights are especially critical for discount retailers where cost control and pricing efficiency directly impact profitability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".