Enhancing company performance and profitability through agile practices: A comprehensive analysis of three key perspectives
Bibliographic record
Abstract
This research aims to reveal the role of three types of agility (employee agility, work method agility, and organizational agility) in improving company performance and profitability. In this research, a quantitative survey was carried out using a questionnaire adapted by the author based on learning agility and organizational agility theories. Five hundred and ninety-seven respondents from 25 companies, 13 sub-industries in Indonesia were taken as samples using the purposive sampling method. Data analysis was carried out using Smart PLS3. The research results show that the three dimensions of agile have a beneficial impact on the performance and profitability of the company. It was found that the impact of agile work approaches on corporate performance productivity and profitability was more significant than employee agility and organizational agility. These findings have implications for companies that implement agile work methods more optimally to improve company performance and profitability. Apart from that, companies also need to pay attention to the importance of developing employee skills and organizational flexibility amidst the swift transformations in the corporate landscape. This research contributes to management literature, especially in expanding understanding of the influence of agile dimensions on company performance and profitability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".