Reverse innovation: a systematic literature review
Bibliographic record
Abstract
Abstract Purpose Interest in reverse innovation (RI) is increasing. According to the authors' review, more than 350 reliable sources (scientific publications, academic books and working papers) examine or at least discuss the concept. As RI gains popularity among academic authors, some discrepancies have started to appear. This wealth of publications could impact prior advancements related to understanding of the phenomenon. The purpose of this paper is to decrease fragmentation and focus on identifying and understanding RI. Design/methodology/approach A systematic review of RI was conducted. The review conformed to a rigorous set of core principles: it was systematic (organized according to a method designed to address the review questions), transparent (explicitly stated), reproducible and updatable, and synthesized (summarized the evidence relating to the review question). Findings This systematic review provides an improved theoretical and practical framework for the concept of RI. In terms of theory, the authors have demonstrated that the idea behind the concept is not entirely new. A consensus on the definition of RI is not reached in the literature, and descriptions in organizational theory contexts are sometimes misleading. The authors analyzed all the various definitions provided in the literature. From a practical point of view, the authors have explained the academic interest in RI in relation to organizational strategy, in particular the context in which strategies are adopted. The concept of RI has significant managerial implications, and the authors have proposed a conceptual framework to help managers understand and grasp the implications of RI. Finally, the authors have provided suggestions for future research on RI. Originality/value To the best of the authors' knowledge, this is the first exhaustive literature review on RI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.134 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".