Revising the budgeting model: challenges of implementation at a university
Bibliographic record
Abstract
Purpose The purpose of this paper is to review the challenges of implementing a new activity-based budgeting model in a university setting. Design/methodology/approach The authors have conducted heuristic inquiry and content analysis to provide an in-depth examination and overview of the process of budget change at a not-for-profit institution. Findings Despite attempts to design a process where resource allocation is guided by principles of revenue generation, cost attribution, measures of quality and fit with strategic plan, overarching issues such as complexities of implementation and a lack of continuity of key personnel made it difficult to implement a new budgeting system. Research limitations/implications As it is a single case study, there may be some concerns regarding reliability and replicability. Subsequent work on a larger scale may mitigate some of these concerns. Practical implications The study demonstrates the challenges of implementing a new budgeting system where strategic choices may differ from revenue generating opportunities and when there has been significant turnover in personnel. The authors provide a perspective on how budgeting can be used to support an organization’s mission in addition to supporting revenue generating prospects, the empirics reinforce the implementation challenges and the need for continuity of key employees to implement change effectively. Originality/value The study suggests a new approach to incentive-based budgeting where resource allocation is informed by a number of activities (revenue generation, cost attribution, fit with strategic goals and quality of programs). It is not formula-driven and it stresses the importance of judgment to determine final resource allocation. Furthermore, the authors provide some support for the change management literature for implementing change in a complex organization.
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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 | 0.184 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.006 | 0.018 |
| 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".