Behavioral Intention to Use Online for Shopping in Bangladesh: A Technology Acceptance Model Analysis
Bibliographic record
Abstract
Consumer behavior and the way businesses conduct their operations have changed due to the widespread usage of internet purchasing worldwide. Bangladesh’s reliance on online shopping presents both opportunities and difficulties. The relatively large marketplace is driving up demand for online shopping. On the contrary, the need for greater technological proficiency that underpins online purchasing presents a significant challenge for entrepreneurs, managers, and consumers. This paper employed TAM (Technology Acceptance Model) to explore and predict Bangladeshi customers’ online purchasing intentions. The data were collected from 322 online consumers in Dhaka and analyzed with SEM utilizing SMART PLS 3. The data analysis demonstrates a significant association between consumers’ buying intention and Perceived Usefulness (PU), Perceived Ease of Use (PEU), Perceived Enjoyment (PE), and Subjective Norms (SN). On the contrary, the data portrayed Perceived Risk (PR) as insignificant. However, our findings suggest that the TAM can still be used to explain the change in behavior associated with using a marketplace, particularly when buying online products or services. In addition, to give a more profound knowledge, various user characteristics according to generation group still need to be studied. Findings further suggest that this study has academic and industry ramifications regarding anticipating consumers’ online purchasing choices in the digital marketing community. The study concludes with a discussion of its limitations and future research directions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".