E-Commerce Platforms and Their Impact on Fish Product Supply Chains in India: A Comprehensive Analysis
Bibliographic record
Abstract
This study examines the transformative impact of e-commerce platforms on India's fish product supply chains from 2018 to 2022. Using a mixed-methods approach, we analyze primary data from 450 stakeholders (fishermen, retailers, and consumers) across six coastal states and secondary data from government and industry reports. Key findings reveal a 27.4% reduction in post-harvest losses (from 38% to 27.6%) and a 22.1% increase in fishermen's net income due to e-commerce adoption. Our structural equation modeling shows that digital platform integration explains 41.3% of supply chain efficiency improvements (β = 0.643, p < 0.001). The study identifies three phases of e-commerce adoption: incubation (2018–19), acceleration (2020–21), and consolidation (2022). Regional disparities are evident, with Kerala achieving 34.2% adoption rates compared to West Bengal's 18.7%. The COVID-19 pandemic accelerated platform adoption by 3.4 times, highlighting the resilience of digital solutions. Policy recommendations include ₹8,200 crore investments in cold chain infrastructure and standardized quality certifications. This study underscores the potential of e-commerce to enhance sustainability and equity in India’s fisheries, aligning with SDG 14.b goals for small-scale fisheries access to markets. This research provides actionable insights for policymakers, platform developers, and fisheries stakeholders to optimize India's aquatic food systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".