One App for Everything: A Multidisciplinary Review of Super Apps
Bibliographic record
Abstract
Super apps have become a defining feature of digital ecosystems and an integral part of everyday life for millions of users, yet scholarship on the topic remains fragmented across disciplines and regions. This paper provides a multidisciplinary, PRISMA-guided systematic review of 177 publications and maps the field through a structured bibliographic analysis and a qualitative synthesis of a subset of 126 papers from the social sciences and legal studies. Our results reveal significant imbalances in the literature: a pronounced focus on Asian markets and few super app case studies (i.e. WeChat, KakaoTalk, LINE), as well as uneven coverage across service domains. We outline the key factors shaping user adoption and continued use, and summarize how prevailing platform strategies, often built around closed ecosystems, raise questions about competition, data governance, and systemic resilience. Beyond commercial platforms, municipalities and agencies are beginning to assemble ‘local super apps’ that unify public-service access, signaling a parallel public sector trajectory. Our results serve as an accessible entry point to the super app literature and set out clear lines for future research, calling for stronger interdisciplinary designs, comparative work beyond Asia, better conceptualizations of super apps, and more robust evaluations of societal, regulatory, and welfare impacts.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".