Trajectory Dynamics in Innovation: Developing and Transforming a Mobile Money Service Across Time and Place
Bibliographic record
Abstract
This paper examines how and why innovations are reshaped as they become implemented and used in locales that are distant and distinct from those where the innovation was initially developed. Drawing on an in-depth field study of the innovation process that produced a mobile money system for Kenya, we contribute an understanding of the particular dynamics that arise when an innovation trajectory interacts with local trajectories that constitute the local conditions and practices of specific places. We identify four distinct patterns of trajectory dynamics—separation, coordination, diversification, and integration—each of which has different implications for the innovation, its implementation, and consequences on the ground. Developing a model of trajectory dynamics in innovation, we theorize the processes through which innovations are transformed over time as they interact with multiple local trajectories and the specific innovation outcomes that are generated as a result. Such theorizing reconceptualizes traditional notions of innovation diffusion by explicating how and why innovations change in multiple and unexpected ways as they move to particular places and engage with local conditions and practices.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".