Modeling the Interface Among the Critical Barriers to Innovation Capability in Microenterprises
Bibliographic record
Abstract
The marketplace is predominantly a microbusiness-dominated phenomenon. Across the globe, the status of the tiniest form of business is different and dynamic. It has been widely argued that the tiniest forms of business do not get equal opportunities and the resources to come up with something new for the world. There are several barriers to the innovation capability of existing research, and they are all treated with equal importance. Authors opine that prioritization will advance our knowledge further. This article, based on primary data collected using focus group discussions, identifies 14 barriers to innovation capability and seeks to establish a causal (utilizing Decision-Making Trial and Evaluation Laboratory method) relationship among them in the context of microenterprises. Low involvement of Generation Z emerged as the most important barrier to innovation capability in Indian microenterprises, resulting in limited availability of resources, an orthodox approach, shallow lateral creativity, isolation from the mainstream, lack of market sense and foresight, one person shouldering all responsibility, and a highly unorganized sector. To the best of the researcher's knowledge, no study in the literature has attempted to identify barriers to innovation capability and establish causal relationships among them in the context of microenterprises in India.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".