Management of Frugal Innovation Projects: Research Approach and Perspectives
Bibliographic record
Abstract
Frugal innovation is a key lever to meet the needs of low-income consumers. Its real challenge is to introduce something new (innovate) to the market while minimizing (frugal) resource constraints. The phenomenon of innovation frugality has traditionally been studied in relation to emerging and developing countries. Today, several studies point out that frugal innovation is also practiced in advanced economies. Although the studies are of great interest to many, they have not been able to identify the concept or map the approach. Therefore, we do not know (i) how the frugal innovation approach developed during the period from 2010 to 2018 (ii) What is the mapping of the phenomenon of frugal innovation? (iii) What are the relevant areas of research during this period? And finally (iv) what are the areas and opportunities for future research? To answer these questions, we first conducted a systematic review of the literature by analyzing 106 articles published from 2010 to 2018. Second, we conducted an interview with 61 respondents made up of researchers, experts, and practitioners to validate the main research gaps identified and identify themes relevant for future research. The results of the research show that researchers affiliated with universities and institutes in the UK, India and both countries are in the majority and have been instrumental in research on frugal innovation management. Similarly, it appears from the above that several products resulting from frugal innovation come from India and China. In addition, our results indicate that articles on frugal innovation are published in a wide range of disciplines and journals. However, researchers, experts and practitioners perceive the concept of frugal innovation in different ways. As a result, studies on frugal innovation focus on sectors such as health, electricity, electronics, transport, finance, ICT, and energy. On the other hand, despite their great importance, the agriculture and education sectors are little explored. The results of the research call on actors to further explore the housing, agriculture, education, and energy sectors in emerging and developing countries.
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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 | 0.005 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".