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Record W3004917385 · doi:10.1111/1911-3846.12593

The Interplay of Core and Peripheral Actors in the Trajectory of an Accounting Innovation: Insights from Beyond Budgeting*

2020· article· en· W3004917385 on OpenAlexvenueno aff
Sebastian D. Becker, Martin Messner, Utz Schäffer

Bibliographic record

VenueContemporary Accounting Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
FundersAustrian Science Fund
KeywordsEmbeddednessFraming (construction)Field (mathematics)Core (optical fiber)Position (finance)Space (punctuation)Frame (networking)Political scienceBusinessEconomic geographyAccountingSociologyEconomicsEngineeringSocial scienceComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.303
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2020
Admission routes1
Has abstractyes

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