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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".