Construct Clarity in Management Accounting (With a Specific Application to Interactive Control Systems)
Bibliographic record
Abstract
ABSTRACT Construct clarity is associated with the process of taking imprecise notions and deriving crisp, agreed‐upon meanings within a scholarly community based on establishing the construct's constitutive theoretical properties and its range of applicability (Bisbe, Batista‐Foguet, and Chenhall, ). This article aims to provide an increased understanding of construct clarity by extending Bisbe et al.'s ( ) analysis in several significant ways in the context of using practice‐defined variables. Specifically, it describes and illustrates why construct clarity is essential to the empirical research enterprise and the development of strong theory and how it can assist in closing the research‐practice gap. In addition, the article elaborates on the elements of construct clarity beyond the definitional component and provides concrete methodological guidance for improving construct clarity through the illustrative use of examples. Further, three management accounting research programs (two historical and one contemporary) are examined. The results support Bisbe et al.'s ( ) assertion that the discipline lacks concern for this issue. They also indicate that the discipline has paid (and continues to pay) a significant price for this inattention. Finally, the process of improving constructs, including the translation of description and understanding into theoretical properties, is illustrated by conducting an analysis of the decision‐making process involved with managing strategic uncertainty and adapting strategy that is related to the interactive control systems construct introduced by Robert Simons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".