Technology Usage and Online Sales: An Empirical Study
Bibliographic record
Abstract
Despite the widespread adoption of search and recommendation technologies on the Internet, empirical research that examines the effect of these technologies is scarce. How do online consumers use these technologies? Does consumers' technology usage have an effect on the sales to them or their purchasing patterns? This paper empirically measures consumers' usage of website technologies by analyzing server log data. We match technology usage data to sales data, controlling for consumers' historical purchasing behavior. Our unique data set allows us to reveal the relationship between technology usage and online sales. Our analyses show that consumers' information technology usage has a significant effect on the sales to them, but this effect varies for different technologies and across different products. In particular, the use of directed search has a positive effect on the sales of promoted products, whereas it has a negative effect on the sales of nonpromoted products. In contrast, the use of a recommendation system has a positive effect on the sales of both promoted and nonpromoted products. Surprisingly, the use of nondirected search has an insignificant effect on online sales.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".