Low-Cost Versus Frugal Innovation Building Blocks & the Fundamentals of Jugaad Business Modeling (Podstawy innowacji niskokosztowych w porownaniu z innowacjami oszczednosciowymi oraz fundamenty tworzenia modeli biznesowych typu jugaad)
Bibliographic record
Abstract
In this paper, we seek to present the first holistic and frugal business model ever designed to bridge the digital divide in Greenfield Africa. Based on an extensive implication in the Connecting Africa and OLPC Projects, Airtel Telecom Group (a Canadian company) partners decided to go one step further in their quest to revamp state owned telcos and postal service providers who were on the brink of collapse. Unlike low-cost models developed by well-known heavyweights like SFR, Ryanair, Blue Jet, Easyjet, Walmart, etc., a frugal and sustainable business model like the one discussed in this paper has a lot more to do with Jugaad Innovation frameworks which led to many affordable and responsible breakthroughs in healthcare, education, housing, broadband, transportation, alternative energies, etc.; therefore, our main goal is to (a) distinguish between frugal and costkilling models, (b) explain the characteristics of jugaad-based framework and philosophy versus revenue-driven business models, (c) explain the importance of sustainable and organic business models in developing markets versus imported plug & play recipes. At last, we will describe the fundamental building blocks of juggad innovation business models and their impact on growth perspectives for MNCs and entrepreneurs willing to break into greenfield markets.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".