EFFECTS OF CULTURAL HOLIDAYS ON ONLINE SHOPPING AND ADVERTISING: A COMPARATIVE STUDY BETWEEN USA AND INDIA
Bibliographic record
Abstract
ABSTRACT In cultural holidays, an exponential increase in sales in online platforms as compared to sales during the regular season is expected. This study aims to compare the effect of cultural holidays on online shopping and advertising in India and USA in 2023. According to Hofstede’s second dimension regarding Individualism and Collectivism, most of the US people take shopping decisions individually and whereas in India people will take suggestions from others and collectively shop online. Research questions are: (Q1) How do cultural holidays effect online shopping in USA as compared to India? (Q2) How do different cultural holidays affect Online Advertising in creating awareness about products In USA compared to India? The findings and conclusions for each question are: (Q1) Online shopping sales are high in last four months (last quarter) in both countries, when compared to other quarters. Because in both countries, most of the cultural festivals and holidays come under these four months therefore, online sales are at the highest numbers in these four months. And US is leading in percentage of sales in every other quarter when compared to India but in Quarter 1 India leads US in percentage. Because in India, people will celebrate Sankranthi festival. There will be a week of holidays for this festival. in which people would like to buy in online stores. Therefore, this paper can conclude that online shopping will be affected by cultural holidays. (Q2) In cultural holidays, these two states will run different campaigns according to respective countries’ holidays. Holiday-Themed campaigns will be run in both countries for bringing the awareness of products to people. Please provide the conclusions and areas for further study. Conclusions for the research questions are: (Q1) Online shopping is significantly affected by cultural holidays in both countries. People would like to shop online during these holidays. (Q2) In cultural holidays, both countries will have holidays-themed advertisements and both countries’ online websites have big discount sales in this period so that people will be attracted and shop online. Areas of further studies for each question are: (Q1) Future studies can focus on different geographical locations with different cultural contexts. (Q2) Future studies could focus on other marketing strategies which attract the customers most.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".