An Exploration of Inclusion of Spirituality into the Balanced Scorecard (BSC) to Support Financial Performance: A Review
Bibliographic record
Abstract
The BSC has been well accepted as a suitable tool for assessing and monitoring financial performance oforganisations. Subsequently it has been embraced by organisations as financial and nonfinancial performanceindicators that help organizations align their initiatives with the organization's strategy. However, the question ofhow the BSC can be enhanced to incorporate non-financial performance is still being raised. One of theimportant non-financial prospectives is the Spirituality. The goal of this paper is to review the recent research onSpirituality as nonfinancial performance indicator for the BSC. In this paper an overview of the BSC and itsperspectives is given, providing a justification for the importance of non financial performance measures tooverall organizational performance. The novelty of this paper is that through an elaborate discussion, Spiritualityis proposed as a potential additional dimension that can enhance the BSC. Subsequently, the enhanced BSCwould incorporate spirituality as an important dimension that affects employees as well employers, who are thekey drivers of financial performance. Overall, the proposed dimension would strengthens the tool’s efficiencyand effectiveness.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".