Impact Of Demographics On The Consumption Of Different Services Online In India
Bibliographic record
Abstract
The role of Internet is becoming inevitable to corporate and society. Across the world, governments and corporate are increasingly working towards the better utilization of the internet. The Internet which was initially perceived as a communication media is now metamorphosing into a powerful business media. The late 20 and early 21st century witnessed the entry and exit of the dot.com companies. The internet motivated many brick & mortar companies to use the Internet to sell products/services online and found negative outcomes as the companies failed to understand the internet buyer behavior and could not figure out the categories of services the Internet users intend to buy. In offline marketing, demographics plays a vital role in understanding buying behavior of consumers belong to different segments which would enable companies to develop products/services according to their specific requirements. Internet is a medium which does not offer this luxury to companies to know the profile of Internet users as it is an indirect medium. The companies would do well if they could find the demographic profile of Internet users which would help them devise strategies accordingly. Hence, the author conducted an extensive primary research in Bangalore, India (Silicon Valley of India) in order to identify the willingness of Internet users to buy different services over Internet. The paper aims at providing a specific focus to identify the impact of demographics in influencing Indian Internet users in consuming different services online. The outcomes would help the corporate world to understand the importance of demographics on online purchase which could be adopted and deployed for better use.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".