Developing models of environment uncertainty, incoterms on strategic alliance and competitive advantage
Bibliographic record
Abstract
This study aims to analyze the impact of environmental uncertainty and Incoterm on strategic alliances and competitive advantage in the Freight Forwarder Industry in Indonesia. The measuring method examines how variables, including environmental uncertainty, Incoterm, and strategic alliances, affect competitive advantage using SmartPLS technology and structural equation modeling (SEM) analysis. The questionnaire was created using a 5-point Likert scale. Through a simple random selection procedure, study participants selected freight forwarder companies. Validity, reliability, and significance tests—often along with hypothesis testing—are all included in the sequential data analysis. The study has identified the following relationships based on an evaluation of the data processing: Strategic alliances are positively impacted by environmental uncertainty, Incoterm is positively affected by environmental uncertainty, Incoterm is positively influenced by competitive advantage, and strategic alliances are positively affected by competitive advantage. The novelty of this study is the influence of environmental uncertainty and Incoterm on Strategic alliance and competitive advantage in the Freight Forwarder Industry in Indonesia. The theoretical implication supports previous theories on environmental uncertainty and Incoterm to increase competitive advantage through strategic alliances. The practical implication is for the management of a Freight forwarding company to scan the uncertainty of the logistics environment and implement the selection of the right Incoterm to encourage strategic alliances to encourage competitive advantage. This research makes a significant contribution to the advancement of scientific principles, particularly within the field of strategic management. Furthermore, it provides a valuable reference and comparative material for future studies focusing on the impact of environmental uncertainty and Incoterm on strategic alliances and competitive advantage within the freight forwarder industry in Indonesia.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".