Supply chain and digital marketing in increasing the acceleration of repositioning in the millennial generation and the implications for cooperative sustainability
Bibliographic record
Abstract
The aim of this research is to analyze the influence of supply chain and digital marketing in increasing the acceleration of repositioning in the Millennial Generation and its implications for the sustainability of cooperatives in the city of Palangka Raya where the study was executed. The sample in this study was 250 respondents consisting of the millennial generation (born 1981-1996) and Generation Z (born 1997-2012), using the Stratified random sampling technique. Data collected through questionnaires was then analyzed using SEM-PLS. The findings of the research and data analysis indicate that: Supply Chain and Digital Marketing directly have a positive and significant effect on the Acceleration of Repositioning in the Millennial Generation in Palangka Raya City; Apart from that, Supply Chain, Digital Marketing and Accelerated Repositioning to the Millennial Generation directly have a positive and significant impact on the Sustainability of Cooperatives in the City of Palangka Raya; Accelerating Repositioning in the Millennial Generation was able to partially mediate Supply Chain and Digital Marketing towards Cooperative Sustainability in Palangka Raya City, Central Kalimantan Province, Indonesia. So, it can be concluded that to improve Cooperative Sustainability among the Millennial Generation in Indonesia, important factors that must be improved include Supply Chain, Digital Marketing, and Accelerated Repositioning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".