Post-pandemic analysis of millennial consumers’ purchasing behavior and expenditure for online food products
Bibliographic record
Abstract
\nThe millennial generation is the primary consumer group in Indonesia today, covering more than a quarter of the population. They are known for their familiarity with purchasing food products online. This research aims to understand purchasing behavior and the factors related to millennial consumers’ online food expenditure post-COVID-19 pandemic. Frequency analysis, ordinary least squares (OLS), and log-log regression were employed to explore purchasing behavior and factors pertaining to millennial consumers’ online food expenditure. A total of 182 millennial consumers from across Indonesia participated as respondents by filling out a self-administered questionnaire via Google Forms. The results disclosed that most respondents were late millennials with bachelor’s degrees who lived in urban areas and were within the middle-income group. Professionals dominated the occupation of the respondents, with monthly food expenditures below IDR 500,000. Their primary reason for purchasing food online was to save time, and they frequently did it 2-3 times per month, with fresh foods being the most commonly purchased item. Smartphones were the preferred device, and marketplaces were the most utilized platform. Regression analysis unveiled that higher purchase frequency and post-pandemic changes significantly raised online food expenditures among millennials.\n
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.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".