Relative Comprehensiveness of Performance Measurement System: Organisational Ownership Structure and Size
Bibliographic record
Abstract
Change in the business environment has resulted in significant implication in the use of Management Control System (MCS) particularly Performance Measurement System (PMS). Strategic Performance Measurement System (SPMS) has been widely used by organisation to monitor the implementation, achievement and improvement of its plan objectives. Considerable prior research identified inconsistent findings in the relationship between PMS and organisational performance. In view of the fact that organisational culture would significantly being influenced by ownership structure, this research will further explore the comprehensiveness of PMS, the extent to which the systems provide information and integration with strategy and value chain, with different ownership structure. Data were gathered in two (2) phases; firstly, using the survey data administered to the 120 strategic business unit (SBU) managers of the manufacturing companies, members of the Federation of Malaysian Manufacturers (FMM). The second phase involves conducting semi-structured interviews with SBU managers of the 10 companies with foreign and local ownership structure. Findings from the research identified that more comprehensive PMS is being implemented by foreign owned companies rather than local own companies. The size of the companies may also influence the PMS comprehensiveness. The PMS implementation was also found to be influenced by the parent companies. Adequate information technology (IT) plays an important role for effective use of the PMS, provide added supports for performance assessment, communication and exchange of information within the organisation and inter-organisations worldwide. Findings provide significant insights into the organisational factors influence the PMS design.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".