The Effect of Motivating and Inhibitory Factors on Using the Electronic Commerce by Adopting UTAUT2 and SQB Models
Bibliographic record
Abstract
E-commerce is the practice of using information technology to do business online.Developing nations have not yet completely adapted to the use of e-commerce, despite wealthy countries having embraced and utilized it.To provide a framework that illustrates the influencing elements (enabling factors and discouraging factors) of e-commerce, this research aims to analyze the key variables that influence e-commerce adoption from the viewpoint of the two-factor theory.The Unified Expanded Technology Acceptance Theory (UTAUT2) components and the Status Quo Bias Theory (SQB) factors were both incorporated into the suggested model in this research.487 individuals were given a questionnaire to complete to gather data.The study's findings demonstrated that behavioral intention is favorably and substantially influenced by performance expectations, price value, effort expectations, and confidence.Therefore, the present research concluded that it is important to look at the enabling and inhibiting elements that affect e-commerce intention and adoption.Additionally, the last portion will cover the future directions for the research.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".