Firm performance through quality project management aspects: Environmental dynamism and digital innovation approaches
Bibliographic record
Abstract
Indonesia's economic growth has been steady at 5% in recent years, supported by the development of the real sector, and market demand for property needs has also increased. According to the Central Bureau of Statistics, in 2022, the property industry sector absorbed 4,373,950 workers or 4.6% of the total workforce in Indonesia. It contributed significantly to the country's economic growth in the national GDP. This research will test whether, if companies can increase the value of their investments, their performance will improve, supported by the solution variables: Property Management, Quality Project Management, Digital Innovation, and the factor of Environmental Dynamism, to strengthen the statement of the impact of Value Investing on firm performance. This study employs SEM-PLS version 3 software to measure the variables used. The sample consists of the largest property companies in Indonesia listed on the IDX over five years (2018-2022) with the criteria of having more than 10 entities. The research results were obtained using a questionnaire (survey). An interesting finding from this research is that environmental dynamism has no moderating effect on value investing on company performance. Good environmental dynamism cannot increase or decrease value investing to improve company performance. The influence of investment value on company performance does not only depend on its intrinsic principles but is also influenced by environmental dynamics.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".