Using Prototypes to Induce Experimentation and Knowledge Integration in the Development of Enabling Accounting Information*
Bibliographic record
Abstract
We investigate enabling performance measurement systems (PMS) in operations, i.e., systems that are perceived by employees as assisting with their daily work. We contrast enabling PMS with those that are primarily a control device by senior management. Previous research in accounting suggests that the development of enabling PMS requires room for experimentation by users, as well as cross-functional knowledge integration. How this can be done has received less attention. Building on the new product development literature, we investigate how prototypes can support experimentation and knowledge integration in the context of accounting. The empirical results are based on a longitudinal case study, using action research. We study the process of developing a performance measurement system for transportation costs. In this process, employees from various functions and departments had to cooperate as no single employee possessed sufficient knowledge about operations, accounting and information systems by oneself. The process consisted of several cycles of experimentation in which prototypes of the new accounting system were used as integrating devices. The prototypes were based on traditional accounting tools, such as variance analysis, and on information provided by the employees during the discussions. This process gave the employees many new insights and led to a new accounting system that was accepted by all of them. We identify several characteristics of effective experimentation and knowledge integration in accounting.
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".