The Impact of Artificial Intelligence on Online Retail Performance: An Empirical Investigation
Bibliographic record
Abstract
The advancement of Artificial Intelligence (AI) has significantly transformed the e-commerce landscape by enabling businesses to enhance operations, personalize customer experiences, and improve decision-making. Despite AI’s widespread adoption in online retailing, theoretical and empirical research assessing its impact on e-commerce performance remains limited. This study addresses this gap by leveraging the DeLone and McLean Information Systems (IS) Success Model as a conceptual framework to evaluate AI-driven e-commerce systems. By extending traditional IS success metrics, this research integrates AI-specific measures across system quality, information quality, and service quality. Using a structured dataset from Digital Commerce 360, complemented by web-scraped AI adoption data and user satisfaction scores from Trustpilot, this study applies Structural Equation Modeling (SEM) to analyze AI’s influence on platform use, user satisfaction, and net benefits. The findings provide a validated framework for measuring AI effectiveness in online retail, while offering insights for industry practitioners.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".