Shopping Behavior in the Context of the Digital Economy
Bibliographic record
Abstract
Consumers shop to meet their needs. When buying, they always compare and evaluate the available alternatives to the goods. The purchasing process involves various factors. These factors can also be described as attributes that can affect consumers during the purchasing process. Identifying important attributes can be really challenging for the digital economy and global markets. Most retailers do not have accurate knowledge of the attitudes and characteristics of their customers, which greatly affects purchasing processes. Combining accurate knowledge of the combination of attributes can increase revenue and improve retailers’ market position. The aim of this paper is to present the results of primary research, processed by reducing the number of attributes influencing purchasing behavior using factor analysis. The target group of the primary research was women who bought mostly online. The most important factors influencing women’s shopping behavior are traditional influences such as online payment for orders, diversity of delivery options, nicely crafted sites, and store reviews, but also the influences of social networks. Another important factor is the possibility of in-store purchases and payments for cash purchases. The results of this research will complement the view of women’s consumer behavior, thus creating the conditions for retailers to react to this target group.
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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 | 0.002 |
| 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) | 0.002 | 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".