Explaining the Unintended Consequences of Management Control Systems: Managerial Cognitions and Inertia in the Case of Nokia Mobile Phones*
Bibliographic record
Abstract
ABSTRACT Management control systems (MCS) have been known to produce unintended, dysfunctional consequences. However, relatively little is known about how MCS can contribute to the inertia and even decline of a firm. Our analysis in the abductive mode was triggered by a surprising case study observation that although Nokia Mobile Phones (NMP) certainly had many capabilities that could have facilitated a timely response to disruptive environmental change, this did not happen. In developing an explanation for this, we draw on the managerial cognitions literature, showing how the cognitions at NMP, developed in the era of organizational success, became embedded in its MCS. This embeddedness, in turn, intensified existing cognitions. As the cognitions became less accurate over time, the once effective MCS started to cause various inertial effects, such as suboptimal and slow decision‐making. We contribute to the literature on the dysfunctional consequences of MCS by theorizing how MCS can contribute to inertia via cognitions in two ways: first, by reinforcing prevailing cognitions and hence preventing management from realizing a need for change; and second, by moderating the impact cognitions have on actions by delaying actions based on renewed cognitions. Both ways may be fatal, especially in hyper‐competitive contexts.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".