Exploring the tensions within and among management control elements from a paradox perspective
Bibliographic record
Abstract
This study examines the inner tensions within MC elements, as well as how these inner tensions influence the tensions among MC elements. To accomplish this, we draw on a qualitative case study of an international organisation, focusing on a newly acquired division and the new MC elements implanted during the integration process. By mobilising concepts from paradox theory and ideas concerning the interdependency of management control (MC) elements to analyse our data, we are able to explain that MC elements can embed various paradoxical tensions, which are interwoven, resulting in various cross-level competing or complementary effects among the MC elements. In this way, we provide a new theoretical explanation for the emergence of dynamic tensions among MC elements. Moreover, we determine that MC elements can render the opposing poles of a paradoxical tension recognisable and, therefore, manageable for actors. Thus, our results elucidate the role that MC elements can play in managing tensions within organisations.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".