Determining Antecedent of Re-Purchase Intention: The Role of Perceived Value and Consumer’s Interest Factor
Bibliographic record
Abstract
With the widespread use of smartphones, strategic marketing of smartphones has become the focus of related brands. Although creating brand loyalty is an important factor of global strategic marketing and re-purchase intention, little research investigated the antecedent of smartphone's brand loyalty and repurchase intention. The purposes of this study are to investigate what are the antecedent brand loyalty and re-purchase intention in smartphone marketing. In the light of the literature and for this purpose; the effects of perceived value factor (perceived ease of use, perceived irreplaceability), utilitarian factor (system quality), hedonic factor (visual design), and consumer’s interest factor (technology consciousness) on brand loyalty and repurchase intention were investigated in an integrated model. The results of the analysis show that smartphone's re-purchase intention is largely determined by brand loyalty, perceived ease of use, perceived irreplaceability, system quality, visual design, and technology consciousness. Moreover, analysis results demonstrate that perceived irreplaceability, system quality, and visual design affect brand loyalty.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.007 | 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".