Company performance model of wholesale carrier service companies in Indonesia: Company capability, co-creation strategy, and external business environment
Bibliographic record
Abstract
The goal of the study was to assess how Indonesian ICT companies that provide wholesale carrier services can manage the external business environment and improve their performance with the help of their co-creation strategy and corporate skills. Using partial quadratic structural equation modeling (PLS-SEM) to evaluate the research hypothesis, the study concentrated on 45 Indonesian ICT companies offering wholesale carrier services. The outcomes demonstrated how the company's co-creation strategy and capabilities increased its effectiveness and greatly improved its capacity to negotiate the external business climate. According to these results, enhancing organizational capacities and putting co-creation techniques into practice should be Indonesian ICT enterprises' top priorities if they want to improve performance and successfully navigate the external business environment. Involving consumers, users, or other stakeholders in the design and development of digital goods or services is known as the co-creation strategy. It can happen in a lot of different ways, such hackathons, online platforms, workshops, or prototype sessions.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".