The effect of electronic word of mouth communication on purchase intention and brand image: An applicant smartphone brands in North Cyprus
Bibliographic record
Abstract
This study aims to examine the effects of electronic word of mouth communication (eWOM) among consumers on purchase intention and brand image, specifically, Generation Y and Z groups in relation to smartphone brands. The study utilizes an empirical research model using data collected from 402 valid respondents among consumers who use smartphone brands in North Cyprus. The study uses structural equation modeling (SEM) to explore and conduct the analysis. The results confirm the significant effects of eWOM on purchase intention through brand image and the moderating role of product type among eWOM, purchase intentions and brand image. The study also recommends that firms and marketers must concentrate on online communication channels to affect consumers' intention toward purchasing brands and brand image. Moreover, the current study model suggests that future study can be extended in different context, countries (i.e. developed, emerging, developing), industries (i.e. banks, e-commerce, tourism) and different social media platforms sites (i.e. Facebook, Twitter).
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".