Sustainable Customer Retention: A Study on Social Media Marketing Activities, Brand Equity and Smartphone Purchase Intention among Generation Y
Bibliographic record
Abstract
This study looked into the social media marketing activities (customization, electronic word of mouth, entertainment, information richness, interactions and trendiness) for a mobile brand that led to consumer-level brand equity, relationship equity and value equity. These consumer-level equities facilitate generating the intention to purchase a mobile brand. This study adopted a cross-sectional design and collected quantitative rata from a total of 288 respondents through an online survey. This study used partial least squares structural equation modelling to test the associations hypothesized. The outcomes indicated that electronic word of mouth, information richness and trendiness significantly influenced brand and relationship equities among the study sample. Meanwhile, customization, electronic word of mouth and information richness significantly influenced mobile value equity. Additionally, brand, relationship and value equities significantly influenced the intention to purchase a mobile brand. Brand equity of the mobile significantly mediated the relationship between trendiness and purchase intention. Similarly, relationship equity significantly mediated the correlations of information richness and trendiness with purchase intention. Customization and electronic word of mouth were significantly mediated by value equity for the intention to purchase a mobile brand. Apparently, brand and relationship equities emerged as the most significant contributors to purchasing a mobile brand.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".