Accounting for Flexibility and Efficiency: A Field Study of Management Control Systems in a Restaurant Chain*
Bibliographic record
Abstract
Abstract While some field studies have suggested that management control systems can be used simultaneously to make organizations more efficient and more flexible, the contingency literature has found it difficult to address this issue in the absence of a clear and comprehensive typology for analyzing more processual uses of management control systems. This paper distinguishes between enabling and coercive (Adler and Borys 1996) uses of management control systems. Coercive use refers to the stereotypical top‐down control approach that emphasizes centralization and preplanning. In contrast, enabling use seeks to put employees in a position to deal directly with the inevitable contingencies in their work. The design principles that underlie the enabling use of management control systems are repair, internal transparency, global transparency, and flexibility. Through a detailed analysis of a single‐case field study carried out over a two‐year period, we illustrate how management pursued the objectives of efficiency and flexibility by using management control systems in enabling ways. We suggest that the four design principles of enabling use can facilitate field studies of management control systems, but that they can also be used to define an enabling typology for contingency researchers to analyze the ways in which organizations simultaneously pursue efficiency and flexibility through their management control systems.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".