Exposing organizational tensions with a non-traditional budgeting system
Bibliographic record
Abstract
Purpose The Beyond Budgeting movement has argued that traditional budgets failed to contribute to the management of tensions associated with the increasing complexity of business models. The literature has reported a range of budgeting practices developed to address these problems, which the authors refer to collectively as “non-traditional (NT) budgets.” The purpose of this paper is to consider how the design and use of a NT budgeting system facilitates the management of multiple organizational tensions. Design/methodology/approach The study reports the findings of an in-depth case study on three business units (BUs) of the French chemical giant SSB, a company that implemented a NT budget inspired by the Beyond Budgeting Round Table model. Findings The authors provide detailed empirical insights into the design and use of a NT budgeting system and analyze the manner in which the new system exposes organizational tensions across multiple axes. Research limitations/implications It is a limitation of the study that only three of SSB 21 BU’s which implemented the NT budget project were examined in depth. This limitation is mitigated to some extent by the review of audit reports in respect of the implementation of the NT budget in a total of 15 BU’s. Practical implications The study contributes a means of analyzing NT budgets in terms of the different types of organizational tensions generated, which should be of use to both researchers and practitioners in researching, designing, and evaluating NT budgets. Originality/value This study provides detailed empirical insights into the design and use of a NT budgeting system and evidence of the success of this system in exposing organizational tensions across multiple axes. The study illustrates how productive tensions can be generated through the analysis of discrepancies between alternative views of organizational performance.
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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 | 0.037 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".