The Efficiency of Mergers and Acquisitions in Malaysia Based Telecommunication Companies
Bibliographic record
Abstract
This paper analyses the technical efficiencies (TE) of total asset model, current asset model and expense model of the mergers and acquisition (M&A) activities in Malaysia based telecommunication companies. For the purpose of this research, a two-stage analysis has been used. For the first stage, a CCR model is used to calculate the technical efficiency of individual companies during the period 2005-2010 and the multiple regression analysis is adopted in the second stage to examine factors such as intensity of acquisitions, sequence of acquisitions and size which influence the efficiency of the companies. As a result of the regression analysis, there is an existence of significant relationship which is of negative value between sequence of acquisitions and TE in both total asset model and current asset model. Further findings also showed that there is a significant relationship between size and TE in the current asset model and in the total asset model but the former holds a positive relationship while the latter has a negative relationship. Based on the regression analysis, none of the said factors above could explain the variation in TE in the expense model. At the end of this research, it is recommended that a further study should be done on this area by including additional variables namely the existence of the government linked corporations (GLCs) among the telecommunication companies and role of regulatory as they might also affect companies which are involved in M&A.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".