Bibliographic record
Abstract
As with ordinary in-store shopping, product characteristics affect an individual’s online purchase decisions. The variety of devices used to access the Internet also affects the probability of engaging in e-commerce. The objective of this study is to investigate e-commerce behaviour as it varies by kinds of products and devices, personal computers and mobile devices. Using national survey (2005-2012) data from Canada, we explore two broad factors: demographic factors and Internet-access factors that influence the probability of engaging in e-commerce in 15 product categories. Our study reveals that consumers behave differently according to product category and access device. We detect that, in general, perceived risk by consumers produces a negative effect on the likelihood of engaging in e-commerce, although the effect varies by category. Additionally, personal computers are found to cause more security concerns to consumers than do mobile devices. Simultaneously, having a mobile device can increase the odds of engaging in e-commerce more than having a personal computer does. Mobile users are more inclined to purchase online. In addition, demographic information is related to purchase probability in different degrees for each category. By identifying the key factors affecting the actual online purchase, our results may help small and medium-sized enterprises to determine their sales channels and establish their marketing strategies.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".