Challenges Faced in Digital Economy Due to Consumer Behavioral Changes
Bibliographic record
Abstract
Digitalization has been the need of the modern era but yet the most important fact lies in the behavioral aspect of the consumer, whether consumer is ready to accept or has acknowledged himself/herself with the digital instrument available in the economy whether in the retail or any other medium of financial transaction. The study identifies that only 2.02% of digital transaction is catered by India in the world Economy. Cashless economy is the need and has become the most vulnerable to the aspect of perception and attitude of the individuals who are still in a way of promoting liquid or cash mode of payment. This provide a major emphasis to the point supported by previous studies as well as market research done on the basis of survey method, that cashless economy is not yet prevalent within our very own nation as consumer behavior and its different components cater the major challenge for the esteemed cause and a serious initiative led by our own prime minister Narendra Modi. The money supply and demand chain have to be circulated in the economy as well as the individuals in such a way that traditional approach of consumer behavior should be transformed to the current demanding and modern digital economy. Thus, the finding of this research paper identifies the challenges of transforming or shifting the mode of payment to digitalization on the note of changes in consumer behavior and its components. This is a research based on primary data obtained by survey method on the basis of questionnaire as the key instrument over a sample size of 101 individuals and analyzed using the digital software Microsoft Excel 2019.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".