Effectuation control: Modified management control system for sustainability in facing the uncertainty
Bibliographic record
Abstract
This study fills the research gap on the existence of joint control in management control systems—the object-oriented control framework (MCS-OOC)—by focusing on the interaction between results and action control, especially in companies that employ prospector strategies that were not considered in previous studies. This study aims to investigate the functioning of joint control by introducing a novel construct known as effectuation control, which forms effectuation MCS. Effectuation control is the synergistic, complementary, and simultaneous effects of a special relationship between action control and result controls. This study will contribute to the understanding of the dynamics of MCS or the control tightness of MCS-OOC. The Effectuation MCS model modifies the MCS-OOC model to account for uncertainty factors, thereby leveraging its capabilities to ensure the long-term sustainability of the company. In terms of methodology, this research will employ two initial models and two modified models, one for each of the prospector and non-prospector manufacturing companies. By comparing these four models and investigating several hypotheses using SEM-PLS, the results demonstrate that result control is no more significant toward existing capabilities when effectuation control is included in the model. Effectuation control significantly influences existing capabilities, whereas result control significantly influences new capabilities. In times of uncertainty and unpredictability, prospectors who implement a pay-for-performance system (result control) in conjunction with the implementation of sound policies, rules, procedures, and bureaucracy (action control) can leverage the company's existing capabilities and explore new ones, thereby enhancing its performance both now and in the future. Action control, a component of effectuation control, serves as a buffer against complex and confusing situations arising from high uncertainty, as every employee responds and refers to the same guidance, policies, rules, and procedures. On the other hand, result control serves as a buffer as well as a driving force, leveraging its capabilities to discover new capabilities amidst uncertainty with the aim of achieving breakthroughs, leading the market, and maintaining sustainability. This result is relevant only to prospectors, as they possess the ability to quickly adapt to uncertainty and seize opportunities presented by these changes, a trait that non-prospectors lack.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".