Exploring the Influence of Positive- and-Negative Electronic Word of Mouth on Online Consumer Behaviour and Customer Loyalty
Bibliographic record
Abstract
This study aims at investigating the effect of both positive and negative electronic word of mouth (eWOM) on online consumer buying behaviour, customer satisfaction, and customer loyalty. A conceptual model is developed and tested with the data collected from 652 respondents from a developing country. After checking the psychometric properties of the survey instrument, hierarchical regression was performed to test the hypothesized relationships. The results indicate that (a) positive eWOM has a positive effect and negative eWOM has a significant negative effect on online consumer buying behaviour, (b) online buying behaviour is positively associated with customer satisfaction, which, in turn, is related to customer loyalty, and (c) trust in information moderates the relationship between (a) positive eWOM and online buying behaviour, and (b) negative eWOM online buying behaviour. The findings also suggest that trust in the product increases the strength of the positive effect of online buying behaviour and customer satisfaction. The theoretical contribution of this article stems from highlighting the importance of trust in information and trust in products in strengthening the relationship between eWOM and online buying behaviour. The conceptual model developed and tested in this study provides valuable insights into the effects of both positive and negative eWOM on customer satisfaction and loyalty. The study recommends that e-retailers identify the most appropriate platforms where the potential buyers interact with others and exchange reviews and comments that may profoundly affect online buying behaviour.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".