Bibliographic record
Abstract
Retail industry is one of the largest industries in India. India is the third-most attractive retail market for global retailers among the 30 largest emerging markets, according to US consulting group AT Kearney’s report published in June 2010. The total retail sales in India will grow from US$ 395.96 billion in 2011 to US$ 785.12 billion by 2015, according to the BMI India Retail report for the third quarter of 2011. The branding of fast moving consumer goods has become an integral part of the lives of consumers. Consumers are literally confronted with hundreds of brands on a daily basis and are, therefore, spoilt for choice. The objective of conducting the research was to analyse the extent to which Indian retail store managers perceptions and consumers’ perceptions converge to promote brand equity in respect of fast moving consumer goods in retail chain stores at Bangalore. This was carried out by identifying the main variables like branding, packaging, pricing, promotions and quality. The study was based on the impact of these variables on the perception of consumers’ and Indian retail store managers. The study revealed that the consumers and store managers believe that consumer purchase depend on branding and the quality of the products and all other variables have a least impact.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".