Status and Challenges in Implementing Beyond Budgeting: Evidence from Sri Lanka
Bibliographic record
Abstract
Due to the weaknesses of Traditional Budgeting and Better Budgeting, budgeting moved to its third wave called Beyond Budgeting. Beyond Budgeting is an alternative, coherent management model that enables companies to manage performance through processes specifically tailored to suit today’s volatile market. Although, researchers have explained how organisations should move to Beyond Budgeting they have not discussed as to why some organisations are lagging behind in terms of Beyond Budgeting implementation. Therefore, this study intends to address and bridge the above research gap. Specifically, the study investigates how far the existing organizational set-ups support an advanced model called Beyond Budgeting and explores why can or cannot these organisations move to Beyond Budgeting. The study carries out a multiple case study approach because it provides an in-depth analysis of budgetary processes of four reputed Sri Lankan companies. Data was collected through semi-structured interviews and documentation reviews where data triangulation was used to validate the data. Based on the findings the study concluded that in the existing organizational set-ups, leadership principles of Beyond Budgeting were strongly present compared to process principles. It was also found that complications in setting rolling forecasts, bureaucracy, lack of virtues, dependency culture on budgets to evaluate performance, perceiving dynamic goals as too ambiguous to set and lack of competitor intelligence as main barriers of moving to Beyond Budgeting concept.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".