Strategic performance measurement system, organizational learning and service strategic alignment
Bibliographic record
Abstract
Abstract Purpose Considering the significant contribution of service sector of the whole contribution of the economics, this study aims to investigate the impact of strategic performance measurement sytstem (SPMS) on sustainability strategic outcomes in the industry through organizational learning and service strategic alignment. Design/methodology/approach Using a survey study, 158 usable data were analysed using SmartPLS. Findings The results show that service strategic alignment and organizational learning mediate the relationship between SPMS and performance for product differentiation companies. For cost leadership companies, the results indicate that there is no mediation of service strategic alignment and organizational learning on the relationship between SPMS and performance. Research limitations/implications This study first provides evidence that SPMS improves performance through service strategic alignment and organizational learning for product differentiation companies in which innovation is crucial to thrive and succeed. Second, it introduces to the literature the characteristics of SPMS. Originality/value New insights of implementation of SPMS in improving companies' performance in Indonesian financial institutions are provided.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".