Entrepreneurship in India's Handicraft Industry with the Support of Digital Technology and Innovation During Natural Calamities
Bibliographic record
Abstract
This research aimed to identify the characteristics that either foster or stifle digital innovation and entrepreneurship amongst small businesses operating in the Handicraft industry during times of economic downturn. In the eyes of young Indian craft entrepreneurs, digital technology is essential for surviving the crisis and would help, for the most part, the artisanal and handmade goods market and the entrepreneurial spirit. Fifty owners of online handicraft businesses, all of whom held unique craft skills, were interviewed using a qualitative technique, and the researcher then utilized inductive (qualitative) content analysis to draw out common threads from the transcripts. The findings showed that the Pandemic's internal and external factors encourage the movement of handicraft businesses to digital platforms, fostering entrepreneurship and digital innovation. The respondents identified several obstacles, including a lack of available high-quality digital infrastructures, the spread of pandemics, market worries over digital platforms, and the lack of knowledge and IT skills required to run an online business. The article's findings contribute to the growing body of digital information on novel approaches to entrepreneurship and suggest avenues for carrying out quantitative research toward the end of creating aid programmes for proprietors of handmade goods enterprises during economic downturns. This could serve as a standard against which new policies and tactics for reviving the economy and expanding the handmade goods industry through technological and entrepreneurial ingenuity can be measured.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".