The Interplay of Core and Peripheral Actors in the Trajectory of an Accounting Innovation: Insights from Beyond Budgeting*
Bibliographic record
Abstract
ABSTRACT Previous studies on accounting innovations emphasize the key role played by innovators and other core actors in theorizing and popularizing such innovations. This paper extends this literature by drawing attention to the role of actors who occupy a more peripheral position within the innovation‐based field. We regard accounting innovations as strategic action fields, in which core and peripheral actors interact to shape the trajectory of the innovation. In contrast to core actors, peripheral actors only weakly identify with the innovation‐based field and often occupy a core position in some other industry, professional, and/or geographical field. Given their embeddedness in these other fields, they are likely to try to accommodate an innovation with existing practices. Such frame blending can be problematic for core actors who envisage a more radical frame shift. Using the case of Beyond Budgeting, we show how the interplay between core and peripheral actors shapes the trajectory of an innovation, in terms of the composition of the field and the framing tactics that dominate at different stages in the development of the field. Our paper advances a perspective on accounting innovations which highlights the variable nature of the innovation space, in terms of different actors entering and exiting this space over time, as well as the importance of considering the overlaps between an innovation‐based field and other (industry, professional, geographical) fields.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".