eWOM, eReferral and gender in the virtual community
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the influence of electronic referral (eReferral) marketing and electronic word-of-mouth (eWOM) on brand image and purchase intention, coupled with the moderating effect of gender in the relationship. Design/methodology/approach – Structural equation modeling was applied to examine the interplay between the proposed variables, using a random sample of 308 respondents in Cyprus. Findings – The empirical results suggest the following: eReferral does influence brand image, and the impact is significant with women only; eWOM influences brand image, and the impact is more significant with women than men; eWOM influences purchase intention, and the impact is the same for both genders; brand image influences purchase intention, and the impact is more significant with women than men. Research limitations/implications – Marketing managers can benefit from these competitive advantage tools. Brand image, awareness and sales volume can be increased by utilizing eWOM or eReferral, depending on the product and/or service functionality as well as gender. Originality/value – While there is a substantial research stream on eWOM, to the best of the authors’ knowledge no research has differentiated eReferral from eWOM. This paper provides useful insights regarding the two concepts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it