Determinants of customers’ intention to use online food delivery platforms in Thailand
Bibliographic record
Abstract
This paper aims to examine what factor relevancy influences consumers’ preference to use online food delivery platforms in Thailand. Further, it investigates consumers' behavior during the covid-19 pandemic. A questionnaire was used to examine a sample of 400 Thai consumers who were using online food delivery platforms. Collected data were analyzed using a statistical package program in 3 steps: Confirmatory factor analysis, path analysis, structural equation model analysis (SEM). Thai consumers increasingly used food delivery on online platforms during the covid-19 epidemic. The study also found that the platform's ease of use, the food delivery service fees, offers or privileges, and payment security on online food delivery platforms influenced consumers’ future use of online food delivery platforms. The food delivery service fees are the most influential factor in using online food delivery platforms. Future research can explore the role of using online food delivery platforms and their impact on the online food delivery platforms. Online food delivery platform providers should lower their service fees, always be implementing new promotions, provide various coupon codes, and create games so that consumers can win prizes. The online food delivery platform providers need to develop consumer trust, ensure that payments are secure, and enhance the ease of use of their platforms. This study contributes to the emerging literature related to online food delivery platforms by studying the relevancy factor that influences consumers’ preference to use online food delivery platforms, which are critical for the success of any online food delivery platform provider.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".