Discriminant Analysis for Classifying Online Shoppers Based on Purchase Intentions
Bibliographic record
Abstract
The proliferation of internet shopping necessitates the identification of variables that motivate consumers to adopt this behavior. This study employs Discriminant Analysis, a robust multivariate technique, to classify online shoppers into distinct experience levels based on specific behavioral and preference predictors, thereby distinguishing potential prospects from suspects. Primary data were collected from 220 respondents and analyzed to formulate a discriminant equation using three established predictors: Frequency of purchase through the internet, Preferable mode of payment, and New product buying frequency. The dependent variable—Level of internet shopping experience—was categorized into Beginner, Intermediate, and Expert groups. The analysis successfully demonstrated that these variables collectively differentiate the shopping experience groups (Wilks’ Lambda was significant for both discriminant functions). The strongest predictor was found to be the Frequency of purchase through the internet. The resulting classification matrix yielded an overall prediction accuracy of 55.9%, which is statistically robust against chance level (33.3%). This study offers retailers a quantitative approach to market assessment, enabling them to reduce market risk by tailoring policies to customer intent and experience levels.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".