Determinants Of Retail Electronic Purchasing: A Multi-Period Investigation1
Bibliographic record
Abstract
Academic research as well as business literature recognizes the importance of electronic commerce and retail electronic purchasing (REP), i.e., the use of the Internet by individuals to purchase goods and services. This paper draws upon established theoretical areas of marketing and innovation adoption to identify the factors expected to influence REP. It empirically examines the effects of these factors, as well as the changes in these factors and in their effects over time, using publicly available multi-period data. The results indicate that REP is facilitated by the individual’s household income, educational level, Internet use and Internet search, as well as by perceptions of Internet security and the perceived quality of web vendors’ sales processes. We did find some surprising results: most notably, there was no association between REP and the perceived quality of web vendors’ post-sales activities. Moreover, there was no significant change over time in the perceived quality of web vendors’ sales and post-sales activities. However, Internet use, Internet search, and perceptions of Internet security, show an increasing trend.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".