Shopping Behavior in the Context of the Digital Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it