Contextual factors affecting the deployment of innovative performance measurement systems
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the association between strategy, structure and environmental uncertainty, and the design and the use of performance measurements systems. The paper provides empirical evidence on the contextual factors associated with the use of financial and non-financial measures, process and outcome measures and the deployment of innovative performance measurement systems in manufacturing business units. Design/methodology/approach A questionnaire was sent to a random sample of 200 Canadian manufacturing organizations. Respondents were asked to indicate to which extent they use different measures. They also had to mention if they had adopted an innovative performance measurement approach such as the balanced scorecard. The questionnaire also included questions to classify organizations as prospectors, defenders or analyzers and to measure the levels of decentralization and perceived environmental uncertainty. Findings The results show that there is a significant association between strategy, organizational structure and environmental uncertainty and the use of non-financial and process measures. They also indicate that there is an association between strategy and environmental uncertainty and the deployment of innovative performance measurement systems. Practical implications Since the 1990s, performance measurement has become an important issue for both academics and practitioners. The professional literature has suggested that managers should design innovative performance measurement systems such as balanced scorecards that include financial and non-financial measures and also process and outcome measures. This paper provides a better understanding of the factors that affect the implementation of innovative performance measurement systems. Originality/value The paper presents one of the few studies that provide a better understanding of the contingent factors that influence the design and the use of innovative performance measurement systems.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".