The Balanced Scorecard: Plant-Level Evidence of Relations Between the Four Perspectives
Bibliographic record
Abstract
We use structural equation modeling to evaluate patterns of relations in two models – a sequential model that links a series of non-financial and financial outcome measures in an ordered hierarchical manner according to the four perspectives described in the balanced scorecard (BSC) and a complex model that permits direct relations between outcome measures in lower-order perspectives with outcome measures in any higher-order perspective of the BSC hierarchy. We perform our analysis at the plant level using generic outcome measures from a nation-wide survey of business establishments in Canada. Our model specifications follow Bryant, Jones and Widener (2004) who conduct a firm-level analysis of companies included in the American Customer Satisfaction Index. Our plant-level results indicate that both the sequential and complex models fit the data well. The validity of the sequential model supports the premise of linking the outcome measures in a sequential chain ordered according to the BSC hierarchy. Direct relations observed in the complex model support the proposition that some relations between perspectives exist that are not permitted in the sequential model. We find evidence of two such types of relations for our outcome measures and data. Innovation and quality (internal business process perspective) link sequentially to profitability outcomes (financial perspective) through customer satisfaction and market share (customer perspective) but also link directly to productivity (financial). Computer usage (learning and growth perspective) links sequentially through innovation and quality (internal business process) to customer satisfaction (customer) but also links directly to return on sales and productivity (financial).
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".