Linking of digital supply chains and digital transformation on the competitiveness of government companies in the supply chain 4.0 era
Bibliographic record
Abstract
In the ever-growing digital era, technology has changed various aspects of human life, including the way governments manage supply chains in procuring goods and services. Digital transformation has opened new opportunities to increase efficiency, transparency, and accountability in procurement in government companies. This research aims to investigate the relationship between digital supply chains and competitiveness and the relationship between digital transformation and the competitiveness of government companies. This type of research is quantitative through survey methods. The population of this research is employees of government logistics companies who are responsible for supply chain processes, have digital activities and implement Enterprise Resource Planning (ERP). The research questionnaire was designed using a Likert scale of 1 to 9, a scale of 1 indicating strongly disagree and a scale of 9 indicating strongly agree. Questionnaires were distributed via social media to 780 employees of government logistics companies related to supply chain processes, respondents were determined using a simple random sampling method. Of the 780 questionnaires that were returned, 570 were returned for analysis. Data analysis uses the partial least square-structural equation modelling (PLS-SEM) method with data analysis tools, namely SmartPLS 3.0. The data analysis stages are reliability, validity and hypothesis testing. The independent variables of this research are digital supply chain and digital transformation. The dependent variable is the company's competitiveness. The results of this research show that digital supply chains have a positive and significant relationship to competitiveness and digital transformation has a positive and significant relationship to competitiveness. By utilizing digital technology optimally, companies can obtain several extraordinary benefits. Among other things, companies will be able to expand markets and increase revenue more effectively. Apart from that, digital technology also makes it easier to monitor business activities, create structured financial reports, and reduce costs, especially in terms of marketing, logistics and shipping.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".