The role of online product reviews on information adoption of new product development professionals
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the impact of features involving online product reviews (OPRs) on information adoption by new product developers (NPDs). Design/methodology/approach – In total, 143 OPRs on a specific product on Amazon.com were collected as the sample of this study. Using content analysis ratings and observed data in OPRs, the research model was analyzed with the partial least squares (PLS) method. Findings – Results suggest that helpfulness rating and the degree of referencing are positively associated with NPDs’ information adoption, while the extremeness of product rating is negatively associated. Moreover, title attractiveness mitigates the negative relationship between the extremeness of product rating and information adoption. Practical implications – The findings provide interesting insight for NPDs who visit e-commerce sites to learn through electronic word-of-mouth (eWOM) communication. OPRs with a higher degree of referencing, higher helpfulness rating, moderate level of product rating, and higher degree of title attractiveness are better adopted by NPDs. Social implications – This paper investigates the value of OPRs for a specific group of information users and suggests that information about products generated by anonymous consumers can be crucial. Originality/value – While extant studies have focussed on the impacts of OPRs on consumers’ purchasing intention and behavior, this paper is among the first attempts to investigate the impacts of OPRs on developers’ information adoption. Therefore, it contributes to the body of knowledge on knowledge transfer from consumers to business as well as the information adoption literature.
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 | 0.007 | 0.076 |
| 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 | 0.002 | 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".