Entrepreneurial Orientation in Government-Owned Bank: Do They Improve Competitive Advantage?
Bibliographic record
Abstract
In Indonesia, housing finance is mainly raised from banks, with the government-owned housing bank (GOHB) BTN taking the largest share of the market. In constantly growing population need for new housing unit every year and increased number of competitors requires managers of government-owned housing bank to be able to develop their dynamic capabilities and adopt a more entrepreneurial orientation (EO). However, (GOHB) are typically being linked to organization that administratively influenced by government that impeding GOHB from being high performance organization driven by EO. Moreover, the dual goals of GOHB which are business and social goals makes the managers struggle to develop and adopt entrepreneurial orientation, since they have to set priorities and trade-off between those goals. The aims of this study is to investigate the role of entrepreneurial orientation (EO) in improving competitive advantage of government-owned housing bank, and fills a gap in the literature by linking entrepreneurial orientation to the theory of dynamic capabilities. This study explored the mediating effect of multi-dimensional EO which is: innovativeness, proactiveness, and risk taking in the relationship between dynamic capabilities (DC) and competitive advantage. The method of the study is a survey using area sampling and proportionate random sampling to collect data from 115 managers in 20 branches in island of Java, during the month of May to August, 2018 (cross-sectional method). The result shows a positive relationship between dynamic capabilities to innovativeness, proactiveness and risk taking. As expecting risk taking has no mediating effect to competitive advantage.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".