The use of performance information in strategic decision making in public organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it