An Empirical Study of BI-based Corporate Performance Management in North America and East Asia
Bibliographic record
Abstract
Managing corporate performance is an important yet challenging process. Recently, many enterprises have adopted businessintelligence (BI) tools to facilitate more effective corporate performance management. Based on a survey with 290organizations across North America and East Asia, this paper presents empirical evidence on the key benefits of and barriersto BI-based corporate performance management (CPM). The study reveals that the implementation of BI-based CPM facesmulti-dimensional challenges. Organizations in East Asia perceived higher CPM benefits as well as higher CPM barriers thantheir counterparts in North America. Cultural, economic and environmental differences between the two regions explain theseissues. The research findings offer important insights for multinational organizations that are planning or are in the process ofimplementing or reviewing their BI-based CPM, as well as for consulting companies that are assisting with CPMimplementation in different countries.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".