Explaining implementation difficulties associated with activity-based costing through system uses
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how and why the uses (enabling or controlling) of an activity-based costing system could cause difficulties in implementing such a cost system. Design/methodology/approach The authors conducted a case study in a French insurance company. Three successive research periods were undertaken: from March to August 2005, between October 2008 and June 2009, and in 2012. In total, 51 interviews were conducted during these periods. Other useful information was also collected through conversations, observation, and through the consultation of internal documents. Findings The results show that designing a cost system aimed at being simultaneously used in controlling and enabling ways can generate important difficulties. Furthermore, the results show that attempting to get around these difficulties could result in investing significant amounts of resources with no guarantee of success. Research limitations/implications Beyond the difficulties of extending the scope of application of case studies, the study was conducted in an organization involved in the insurance industry which could further limit its general applicability. Practical implications Based on the experience at Rassura, the authors argue that managers should be aware that designing and implementing a cost system that can simultaneously be used in both controlling and enabling ways is a very difficult, if not an insurmountable challenge. Originality/value The results highlight that one important characteristic of a cost system, how it is used, could explain, at least partially, implementation difficulties related to technical challenges, resistance to change and lack of resources.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".