Beyond Traditional IT-enabled Innovation: Exploring Frugal IT Capabilities
Bibliographic record
Abstract
Innovation programs in developed economies are centered on resource richness and abundance. As firms seek newer innovation paradigms to sustain competitive advantage, we suggest the use of “frugal innovation”, which originates in emerging economies and is rooted in low cost approaches, constrained resources, and flexible improvisation. Frugal innovation principles when viewed from the theoretical lens of IT capability, RBV, and other related literature, can provide a rich foundation for frugal IT innovation research. In this exploratory paper, we explain the concept of frugal innovation and its position within a number of existing innovation paradigms. This is a significant contribution considering the number of emerging innovation concepts. We also demonstrate the potential integration of frugal innovation principles with traditional IT-enabled innovation approaches – a concept we call “frugal IT innovation”. We then explore potential “frugal IT capabilities” that may enable frugal IT innovation and finally provide propositions to guide future research.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".