The role of asset management on project performance moderated by environmental dynamism on Indonesia's mining project
Bibliographic record
Abstract
In Indonesia, the success of coal extraction largely depends on the role of mining contractors. One of these successes is determined by proper asset management amidst uncertainty in the coal business, such as sustainability and digitalization issues. However, research that identifies the role of asset management in improving the performance of mining projects is still rare. Therefore, this research investigates the impact of Information technology capability, sustainability practices, and asset management on improving the performance of coal mining contractor projects in Indonesia. This research uses quantitative methods, with questionnaire data filled in by 128 project managers of mining contractor projects spread across Indonesia and analyzed by applying structural equation modeling (SEM). The findings indicate that Information technology capability and sustainability practices have a substantial and beneficial effect on improving asset management, which then enhances the performance of mining contractors. Interestingly, there is no direct impact of Information technology capability and sustainability practices on the operational performance of mining projects, so asset management is highly needed. The originality of this study is in the recognition of asset management as a valuable asset for firms that have integrated Information technologies & sustainability practices, as it promotes improved operational performance of projects, and there is still rarely any previous research that examines sustainability practices and asset management, especially mining projects. This research contributes to developing the resource-based view (RBV) theory and also contributes managerially to the practices that mining contractors must carry out when dealing with uncertain business situations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".