Barriers to effective value chain management in developing countries: new insights from the cotton industrial value chain
Bibliographic record
Abstract
A rigorous and extensive application of the value chain management (VCM) has become the vogue in modern day business practices and processes. However, due to the complex and multidimensional nature of value chains, achieving efficient and effective value chain management in real value chains remains a major conundrum for practitioners. Many unknown barriers continue to impede effective and efficient value chain management in developing countries’ industrial value chains. The purpose of this study was to find out the common barriers to effective value chain management in a developing country’s industrial value chains using evidence from the cotton industry in Zimbabwe. The analysis was based on survey data sets obtained from 157 purposively sampled experts from the cotton industry value chain in Zimbabwe. Exploratory factor analysis was used to find the barriers to effective value chain management. The results revealed both architectural and governance barriers to effective value chain management. The findings also presented major policy implications for industrial value chains in the developing countries and also indicated areas for further robust research founded on a broader data set from other developing countries’ industrial chains as a way of validating the findings of this study.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| 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".