Platform Economy and the Food Service Sector: Economic Analysis of Food Delivery Apps in Kollam District, Kerala
Bibliographic record
Abstract
Food delivery applications such as Swiggy and Zomato have significantly altered food consumption patterns in urban and semi-urban areas like Kollam, Kerala. This study explores the economic implications of these platforms on three major stakeholder groups: consumers, delivery partners, and food establishments including restaurants, hotels, and eateries. The objectives include examining shifts in consumer spending and youth consumption behaviour, increased adoption of digital payments, economic benefits and constraints faced by delivery workers, effects on the revenue and operations of food businesses, and environmental concerns due to packaging-related plastic waste. Primary data was collected from 130 respondents using structured questionnaires and interviews. The study found a rise in impulse spending, especially among younger consumers, driven by ease of access, app-based discounts, and digital transactions. Digital payment systems like UPI and mobile wallets have become the dominant modes of payment, contributing to a cashless local economy. Delivery partners enjoy income flexibility but face long working hours, job insecurity, and limited social protection. Restaurants benefit from higher visibility and order volumes but are negatively affected by high commission fees and stiff competition, impacting profitability. Additionally, increased use of single-use plastic packaging has raised environmental concerns. The study concludes that food delivery platforms are reshaping Kollam’s food economy, offering new opportunities while presenting economic and environmental challenges that warrant regulatory attention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".