Electronic word-of-mouth marketing on Amazon: Exploring how and to what extent Amazon reviews affect sales
Bibliographic record
Abstract
Consumers today base many of their decisions on peer referrals and online reviews. With the omnipresence of social media and online reviews, electronic word-of-mouth marketing (eWOM) has become a priority for many companies for both business growth and reputational management. The objective of this study is to examine the effectiveness of eWOM and its impact on sales. This study also seeks to help organizational leaders understand the significance of eWOM and its role in effective consumer and stakeholder relations, and in overall brand management. The researchers of this project explored eWOM by examining Amazon reviews from two different Kickstarter companies to determine which elements of online reviews impact product sales. By overlaying Amazon review data and sales figures from each Kickstarter company, researchers were able to determine the review factors that companies should focus on to increase their sales and grow their brands. The results of this study show that products with a high volume of positive reviews made by verified purchasers positively correlate to product sales. Keywords: electronic word-of-mouth marketing, Amazon, online reviews, Kickstarter, sales, reputational management, brand reputation, online reputation management
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".