Implementing responsibility centre management in a higher educational institution
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the design, development and implementation of responsibility centre management at a mid-sized Canadian university, within the context of decentralized decision-making. More specifically our study focused on the design, development and implementation of a revenue and cost allocation process known as transparent activity–based budgeting system (TABBS). Design/methodology/approach The authors conducted this study using a qualitative case study methodology, rooted in grounded theory, as the primary approach to collect and analyse data, and report the findings. Primary data were collected from ten participants using semi-structured interviews. Findings The main takeaways from our research are that (1) such systems take time to design, develop and implement, (2) consultation, communication and information sharing and model adjustment and refinement are important enabling mechanisms, (3) internal and external events posed significant challenges, (4) although such systems are often designed keeping in mind several intended outcomes, there exists the possibility of experiencing some unintended consequences and (5) the juxtaposition of the above has the potential to negatively or positively impact organizational performance. Originality/value The research demonstrates that the design, development and implementation of a complex resource allocation model is an important element of a responsibility-centred approach to planning and decision-making. It highlights the importance and contribution of enabling mechanisms as well as the challenges that large, complex organizations may confront when introducing change.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".