Factors affecting financial performance in companies based on big data analytics
Bibliographic record
Abstract
Strategy is a way to be able to find effective and efficient ways to achieve goals. There are three important strategies carried out by companies, namely strategic planning, strategic role, and strategic maneuvering. The interesting thing is that relatively not much research has been conducted regarding the measurement of financial performance which is studied from the perspective of corporate strategy that influences financial performance. This is also supported by technological developments that affect companies, so companies must have the right strategy so that the company's financial performance can improve. This study examines the effect of strategic planning, strategic roles, and strategic maneuvers on financial performance. This study uses resource-based view theory as a study in testing the influence model of strategic planning, strategic role, and strategic maneuvering on financial performance. The study is conducted in Indonesia with a sample consisting of owners, directors and managers who use big data analytics in the companies they run. The sampling technique used is convenient sampling. The analysis technique used in this study is multiple linear regression analysis with the independent variables namely strategic planning, strategic role, and strategic maneuvering and the dependent variable namely financial performance. The results of this study indicate that strategic planning had no significant effect on financial performance, but strategic role had a significant effect on financial performance. Furthermore, strategic maneuvering has a significant effect on financial performance.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".