FDI and Indian Retail Sector: An Analysis
Bibliographic record
Abstract
In the post-liberalisation period, changes in the consumer purchase behaviour are seen with growing liberalisation, rise in per capita income, GDP and explosion of brands. This rise in large base of consumers has been an attraction for big global retailers and major domestic corporate sector to invest in modern retail sector in India. This unprecedented rise in multiple brands has given Indian consumers a wider choice of products and ample opportunities to take advantage of in the present scenario. The retail industry is expected to grow at a rate of 14% by 2013. The first step towards allowing Foreign Direct Investment in Retail was taken in the year 2006. Subsequently the government of India has allowed 100% FDI in single brand retail to give consumers greater access to foreign brands, with the ongoing debate whether it should be allowed in multi-brand retail or not. With emergence of new ways like E-retailing, Indian retail sector is growing at a faster rate along with the employment potential. The retail landscape is showing a marked change, along with changes in the strategies of retailers towards the suppliers so as to get the best advantage. With the rapidly changing retail scene, India is soon going to be one of the fastest growing regions having great potential. The objective of the present paper is to analyse the impact of the present retail FDI policy on Indian consumers and economy using SWOT analysis. The analysis reveals that it will have a positive impact on the growth of Indian economy as a whole.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| 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) | 0.010 | 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".