Bibliographic record
Abstract
In this conceptual paper, we examine IT-enabled frugal innovation from the lens of absorptive capacity theory (ACAP). The practice of frugal innovation in emerging economies is rooted in low cost approaches, constrained resources, and flexible improvisation. As frugal innovation is an emergent phenomenon, there is little theoretical development and empirical investigation with respect to the enabling role of IT. We address this gap by examining the conceptual underpinnings of frugal innovation and its antecedents, such as IT leveraging capability, dynamic knowledge capabilities, and organizational learning. We develop a research model and provide testable propositions. This paper contributes to ACAP literature by providing a look inside the âblack boxâ of the relationships between three different types of learning (according to ACAP) and their effects on the underlying dimensions of frugal innovation. Furthermore, based on our findings, implications for theory and practice are provided along with guidance for future empirical 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 | 0.005 | 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 | 0.000 | 0.002 |
| 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".