eWOM, eReferral and gender in the virtual community
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".