Trajectory Dynamics in Innovation: Developing and Transforming a Mobile Money Service Across Time and Place
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| 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 it