Frugal Digital Transformation in SMEs: Low-cost solutions for high-impact outcomes
Bibliographic record
Abstract
Frugal Digital Transformation is a new, emerging paradigm of low-cost innovation. It integrates the concept of frugal innovation with the practices of digital transformation to allow for novel, yet affordable and impactful innovations in products, services, processes, platforms, and business models. This paper highlights frugal digital transformation in the context of three SMEs in the U.K., Canada, and India. The paper follows a qualitative research approach using the multiple case study method to explain how SMEs conduct frugal digital transformation. It sheds light on the underlying digital mechanisms and business processes that enable transformation. The research informs the current literatures on frugal innovation, digital responsibility, and digital transformation. It provides insights to practitioners about the benefits and pitfalls of this type of innovation. It also lays a foundation for future research in this domain.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".