The use of performance information in strategic decision making in public organizations
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate how the importance of different components of strategic performance measurement systems (SPMS) and their deployment influence the use of performance information from the SPMS in making strategic decisions. Design/methodology/approach – Data were collected through a survey of 143 managers of Canadian public organizations. Findings – The findings indicate that two SPMS components, namely, the importance of non-financial performance measures and the use of operational efficiency measures, have significant positive associations with performance information use for strategy implementation and strategy assessment decisions. The extent to which SPMS models were used is found to be positively associated with performance information use for strategy implementation, but not for strategy assessment, decisions. Furthermore, the relationships between SPMS variables and strategic decision making are moderated by information systems/data limitations and management’s commitment to attaining strategic goals. Managerial skills acquired through training or experience with SPMS also contribute positively to such relationships. Research limitations/implications – The results are affected by limitations associated with the survey method used. Practical implications – The findings could be useful for supporting public policy, strategic decision making, public service improvement, operational efficiency, and effectiveness. Originality/value – The study contributes to public management and performance measurement literature by investigating multiple determinants of performance information use in a cross-section of Canadian public organizations.
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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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".