Structural Relationships between Disruptive Attributes and Women Consumers’ Attitude when Using Mobile Retailing
Bibliographic record
Abstract
This study aims to examine the structural relationship between disruptive attributes and women consumers’ attitude when using Mobile Retailing. A total of 486 completed sets of structured self-administered questionnaires were analyzed using the purposive sampling technique. The sample for this study consisted of Malaysian women who have used Mobile Retailing in the past six months, including mobile retailers and members of women organizations in Malaysia. A Structural Equation Modeling (SEM) technique was used to evaluate the relationship among the hypothesized variables for this study via the Analysis of Moment Structure (AMOS) computer program version 21. Based on the SEM analysis, five significant results and two insignificant results were obtained in regard to the direct relationship between disruptive attribute factors and women consumers’ attitude when using Mobile Retailing. Specifically, reachability, ubiquity, personalization, connectivity, and convenience have a direct, positive relationship with women consumers’ attitude when using Mobile Retailing, whereas mobility and localization have no significant relationship with women consumers’ attitude when using Mobile Retailing. It is vital for retailers to entice female consumers to buy their products. The sellers are able to re-observe and alter their marketing approaches to specific target markets and earn a competitive advantage by recognizing their customers’ personal attitudes and subjective norms, all of which may affect their behavior. The direction for future research concludes this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".