Experiencing the AI emergence in Indian retail – Early adopters approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The usage of Artificial Intelligence (AI) technique under retail industry will bring glorious outcome and flourishing benefits for both the retailers and the distinguished customers. The multiple platforms of AI usage in retail arena are discussed under two different cluster classified as online and offline, based on the terms of execution of retail activity. The present research was conducted with the objectives of evaluating the contribution of quality, customer relationship management and big data in designing futuristic retail model and analyzing the intention of retailers and shoppers in experiencing the emergence of AI. Disproportionate multistage judgement sampling method was employed. A sample of 610 shoppers from four different capital regions of southern part of states in India was considered for the statistical analysis. Data was collected during the first quarter period of 2018. Descriptive research design was used to describe the intention of shoppers towards the emergence of AI in the Indian retail sector. The usages of AI technologies in online and offline retail are grouped separately and its effect on building the quality, customer relationship management and big data was evolved. Finally, its impact on the retailers intention and customers delight was studied through Structural Equation Modeling with testing of appropriate hypothesis.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it