An empirical study of consumers’ attitudes toward using online food delivery services in Bangladesh
Bibliographic record
Abstract
The online food delivery service industry has the potential to be one of the biggest industries in Bangladesh and contribute significantly to the economy. Technological advancement has powered the potential expansion of online food delivery services in Bangladesh. While there has been some recent research on the shift in Bangladeshi consumers' food consumption habits, there still needs to be more research on the online food delivery service industry. Noticeably, online food delivery services are only available to people living in large cities or suburban areas in Bangladesh. Rural populations have limited access to these services. The reputation of the delivery services is sometimes adversely affected by inconsistent customer service, which includes problems with order accuracy, poor user interface, and lack of responsiveness. Therefore, this study analyzes the consumers' attitudes toward using online food delivery services in Bangladesh. This study revisited the theory of the technology acceptance model (TAM) theory by retaining user motivation. According to the TAM theory, user motivation is considered as perceived ease of use and perceived usefulness, whereas in this study, perceived usefulness has been simplified by two independent variables: various food choices and time-saving orientation. Thus, the three original constructs from the TAM theory included in this study are attitudes toward using the technology, perceived ease of use, and perceived usefulness. The core objective of the study is to determine the influence of perceived ease of use, various food choices, and time-saving orientation on the consumers' attitudes toward using online food delivery services in Bangladesh. Secondly, the study examines the moderating role of the 'online shopping experience' on the user's motivation in Bangladesh.,
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".