An empirical study to identify and develop constructive model of e-supply chain risks based on Indian mechanical manufacturing industries
Bibliographic record
Abstract
Management towards control of risk issues appeared as an important area in E-supply chain for researchers in the present fast growing market.Extensive research has been accomplished in this area, but still there are several risks and uncertainties.The present research aims to identify and consolidate various e-supply risk factors for developing a constructive measurement model.The study also assesses the influence of risk factors over e-supply chain operation in Indian mechanical industries.Thus, after a thorough research and detailed discussion, 38 Risks factors are identified through literature to prepare the questionnaire.A questionnaire based survey is carried out for collecting the primary data from 148 experts of mechanical manufacturing industries located in the national capital region of India.The research methodology is combined with descriptive statistics and factor analysis.SPSS 21 software tools is used for investigating the reliability and Amos Graphics 21 software is used for the fitment validation of the theoretical construct.The results suggest that all risk issues create significant negative influence e-supply chain process.The results also show negative effects of risks over e-supply chain performance.The current research outcome develops a stochastic model based on the e-supply chain risk factors used for reducing risks in esupply chain operation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".