Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
According to study, Indonesia e-commerce market was the 9th world largest in 2021 with a value of US$ 43 billion (Market Intelligence, 2022). Meanwhile, Indonesia's e-commerce sector increased 23% in 2021 with about 63 million new customers. Statista research study (2022) stated that Indonesian e-commerce users reached over 189 million (or about 65% of total population) by 2024. Additionally, it is forecasted the total e-commerce transactions will reach US$ 137.5 billion by 2025 that Indonesia will become the highest e-commerce in the Asia Pacific region representing 59% of the region. Besides, Indonesia's e-commerce revenue will increase from US$ 36.2 billion in 2022 to US$ 58.6 billion by 2027 (Financial Services Monitor Worldwide, 2023). Thus, the objective of this phenomenological qualitative/ exploratory research study is to explore and understand what major factors lead Indonesia's e-commerce grow rapidly and successfully that other countries may learn from them for further improvements and participation in such a huge and growing e-commerce market. JEL Codes: Keywords: online retailing, digital purchasing behaviors, brand trust, COO effects,e-commerce services, loyalty programs
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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 it