Transformation of the Digital Payment Ecosystem in India: A Case Study of Paytm
Bibliographic record
Abstract
Paytm is a payment app in India providing e‐wallet services; it is also the most prominent mobile e‐commerce app in the world’s third‐largest economy. This article uses Paytm as a case study to better understand the global platform economy and its implications for social and economic inequities. We contextualize the emergence of Paytm by drawing attention to its relationship with India’s developing digital infrastructure and marginalized populations—many of whom are part of the platform’s user base. We use a political economy lens to investigate Paytm’s market structure, stakeholders, innovations, and beneficiaries. Our research is guided by the question: What resources, infrastructures, and policies have given rise to India’s digital payment ecosystem, and how have these contributed to economic and social inequities? Accordingly, we audited the international and Indian business press and Paytm’s corporate communications from 2016 to 2020. Our analysis points to the tensions between private and public interests in the larger platform ecosystem, dispelling notions of platforms as neutral arbiters of market transactions. We argue that Paytm is socially beneficial to the extent that it reduces transaction costs and makes digital payments more accessible for marginalized populations; it is detrimental to the time that it jeopardizes user data and privacy while suppressing competition in the platform economy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".