Understanding Employee Attrition Factors in the Information Technology Sector: A Text Analytics Perspective
Bibliographic record
Abstract
Recently online reviews on websites have provided a precious source of data for businesses. Some of these websites collect customers’ opinions about products and services provided by these businesses, some others such as Glassdoor and Indeed are the websites on which the employees write their opinions about their company. Although this data source is usually used by job seekers to find proper job opportunities, it has been used recently by researchers and business owners for discovering the employees’ satisfaction and dissatisfaction factors. In this study we collected 825129 comments people left on Glassdoor and Indeed about their current or previous companies in IT section. First, we have applied the Latent Dirichlet Allocation (LDA) topic modelling technique to find out the most cited attrition factors in the comments. We recognized that “Personal Development”, “Financial and Professional Development” and “Cultural Development” are the most frequent factors mentioned by employees in IT companies. Then, by applying a novel topic-based sentiment analysis technique we have tried to figure out the polarity of comments about each of three found factors. The trend of these factors in terms of their frequency and polarity were tracked through the time and also among different IT sections. The assessment of current and former employees’ comments showed that these two groups have some behavioral differences which can help IT companies to unravel the reasons of their recent high attrition rate. Some behavioural differences were also detected among different IT sections. Further analysis showed that there is a statistically significant correlation between the polarity of these factors and quantitative rating review of companies and also their market capitalization.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".