An empirical study of performance measurement in manufacturing firms
Bibliographic record
Abstract
Purpose The recent performance measurement literature suggests that organizations should put more emphasis on non‐financial measures in their performance measurement systems, that organizations must use new performance measurement approaches such as the balanced scorecard and that measures should be aligned with contextual factors such as strategy and organizational structure. The purpose of this paper is to assess the extent to which organizations are following these prescriptions. Design/methodology/approach A survey of a sample of Canadian manufacturing firms was conducted. In the questionnaire, organizations had to indicate the extent to which they use 73 performance measures. They also had to respond to questions about determinants such as strategy, organizational structure and environmental uncertainty. More than 100 organizations responded to the survey. The response rate was 50.5 percent. Findings The results show that manufacturing firms continue to use financial performance measures. Despite the recommendations from experts and academics, the proportion of firms that implement a balanced scorecard or integrated performance measurement systems is low. Furthermore, organizations that use these approaches are not employing more extensively non‐financial measures than those which are applying traditional performance measurement approaches. This research project also shows that there are some significant relationships between the types of measures and contextual factors like strategy, decentralization and environmental uncertainty. This research finally demonstrates clearly that there is a need to develop a theory that explains how firms can use their performance measurement system to enhance their performance. Originality/value This paper provides information on performance measures used by organizations and their association with organizational determinants.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".