Fostering Enabling Perceptions of Management Controls during Post‐Acquisition Integration*
Bibliographic record
Abstract
ABSTRACT The purpose of this paper is to increase our understanding of how enabling perceptions of new management controls (MCs) can be fostered. Prior research suggests that employees are more likely to use new MCs if they perceive them as enabling. However, rapid implementation of new MCs due to circumstances such as mergers and acquisitions can leave employees feeling coerced into using them, making it difficult to foster enabling perceptions. Based on a case study where an acquirer faces pressure to impose rigid controls on an acquired firm, we suggest factors contributing to enabling perceptions. Using interviews, observation, and document analysis, we find that positive relationships and mutual trust between the acquirer and the acquiree facilitated enabling perceptions of the MCs. We show that managers at the acquirer actively fostered trust using trust‐building activities and communicated their intentions underlying the implementation of new MCs. Doing so helped employees rationalize the controls as tools to help them do their work tasks. We also find that positive relationships reinforced by regular meetings were a way of providing assistance to employees in dealing with rigid MCs. This study contributes to the literature on enabling controls by developing a processual framework that suggests how trust can foster enabling perceptions from the intentions behind the implementation of new MCs, to their development process and daily use. In doing so, the study further develops our understanding of the relationship between enabling control and trust and helps in understanding how rigid controls can be implemented without generating mistrust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".