Examining the characteristics and effectiveness of online employee reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Employee reviews on platforms like Glassdoor and Indeed significantly influence organizational attractiveness of millions of prospective applicants. To deepen our understanding of this phenomenon, we examined the effects of employee review characteristics on perceived helpfulness – a proximal indicator of adopting shared information. Specifically, we investigated the relationship between the sentiment of organic Glassdoor reviews (ranging from positive to negative attitudes) and their helpfulness ratings. Additionally, we explored the moderating roles of overall corporate ratings and employee status in shaping the impact of employee reviews. Employing automated text analysis with Latent Dirichlet Allocation (LDA) and Structural Topic Modeling, we further delved into employee review content to extract the topics discussed and how their attributes (e.g., the extent to which the topic is discussed) influence perceived helpfulness. Drawing insights from an extensive analysis of 24,687 Glassdoor reviews, our findings revealed that negative reviews of lower rated organizations tend to receive higher helpfulness ratings, particularly when provided by former employees. The topics identified through LDA encompassed both instrumental and symbolic aspects of organizations, with their extent of discussion uniquely interacting with sentiment. Our study sheds light on the profound impact of employee satisfaction on the perceived helpfulness of online reviews. By presenting a comprehensive analysis of online reviews, this research offers valuable insights for businesses to enhance their organizational attractiveness and better understand the dynamics of online reputation management. • Negative reviews are considered more helpful and are partially buffered by organizational ratings. • Former employees' reviews are considered more helpful to readers. • Computerized text analysis uncovers that employee reviewers discuss aspects of organizations' climates and provisions. • Employee reviewers who discuss several topics are more helpful. • Employee reviewers are less helpful when they negatively elaborate on a specific topic.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it