Factors Influencing the Effectiveness of Third Party Online Organizational Reviews
Bibliographic record
Abstract
Online reviews of organizations through portals such as Glassdoor and Indeed are growing in popularity and have the potential to influence organizational attractiveness. This study examined how the various components of online organizational reviews affect helpfulness ratings (also referred to as information adoption). We examined the relationship between the valence of the reviews (i.e. the extent to which reviews vary from positive to negative attitudes) and information adoption. Overall organizational ratings, deviation from consensus in attitudes and employee status were also evaluated as moderators of this relationship. We assessed nearly 22,000 Glassdoor reviews across 450 companies and found that negatively valanced reviews generally received higher helpfulness ratings. This relationship was even more pronounced when negative reviews were provided by former employees and deviated from a consensus in attitudes. However, higher organizational reputational ratings were able to partially buffer these effects. These findings illustrate the impact that negative attitudes can have on information adoption and they further highlight the need for organizations to consider how their brand images are portrayed online.
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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 | 0.006 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".