The relationship between management control systems and organisational agility: the case of a Canadian commercial bank
Bibliographic record
Abstract
Organizations are found to become more agile by optimizing processes and implementing new technology in response to changing market conditions, customer preferences, and competitive environments.However, there is little research on how organizations can design and use management control systems (MCSs) and mechanisms for increased agility.This research aims to explain how MCSs affect organizational agility in the banking sector.Methodologically, the qualitative case study forming the empirical part of this thesis builds on the analytical performance management frameworks by Ferreira andOtley (2009), and Pfister et al. (2009).The interview questions were derived from their research; however, they were modified and complemented with new ones considering the context of the case company, i.e., the Canadian commerce bank under study.The findings of this case study contribute to the previous management control research by providing a framework for explaining agility enhancement through the designing and use of management control systems.The findings show that in the case company ComBank, the level of agility differed between across its organisational units, while the hierarchical organisational structure served as a key control mechanism hindering organizational agility.There is a lot of room for improvement in the case company as regards to enhanced agility in many facets.Future research could continue analysing agility in banks by focussing on the effects of newly emerged technologies, such as machine learning, artificial intelligence, and the internet of things.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".