Customer Satisfaction Response to Artificial Intelligence Tools Usage During Online Shopping
Bibliographic record
Abstract
Artificial intelligence (AI) is a technology that enables products to be combined with new features and create innovative customer experiences . A lot of businesses have embraced various AI tools to offer customer care interactions. Research gaps arise from an unclear picture of how customers' experience with online shopping will be affected by the experience and usage of AI tools. This study aims to predict satisfied online shoppers based on their usage experience with AI tools, by leveraging data mining methods and machine learning techniques. Data was collected from India, China, and Canada in 2021 and 2022 by distributing online survey to online shoppers with exposure to AI tools. Five machine learning algorithms; decision tree, random forest, naïve bayes, gradient boosted tree and multilayer perceptron neural network techniques were applied and compared to predict satisfied shoppers using. Overall, all the models showed a prediction accuracy of more than 86.5% f-score value and random forest outperformed with 91.5% f-score value. The findings demonstrated that the online retail business can identify satisfied customers with 91.5% accuracy using machine learning. Business can derive such data-driven actionable knowledge from integrating machine learning into their operations, resulting in a more satisfied customer base and a more efficient and competitive business model.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".