# Forecasting managerial turnover through e-mail based social network analysis

arXiv: https://arxiv.org/abs/2105.09208 · HTML: https://mlsanity.com/paper/2105.09208/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 65 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 1 of 1)

- [Measuring Team Creativity Through Longitudinal Social Signals](https://mlsanity.com/paper/1407.0440/) (2014)

## Built on by (most cited first, 8 of 8)

- [Measuring the impact of spammers on e-mail and Twitter networks](https://mlsanity.com/paper/2105.10256/) (2021)
- [It is rotating leaders who build the swarm: social network determinants of growth for healthcare virtual communities of practice](https://mlsanity.com/paper/2105.12659/) (2021)
- [Finding top performers through email patterns analysis](https://mlsanity.com/paper/2105.13025/) (2021)
- [Editorial introduction: The power of words and networks](https://mlsanity.com/paper/2105.11263/) (2021)
- [Look inside. Predicting stock prices by analysing an enterprise intranet social network and using word co-occurrence networks](https://mlsanity.com/paper/2105.11780/) (2021)
- [From words to connections: Word use similarity as an honest signal conducive to employees' digital communication](https://mlsanity.com/paper/2111.06133/) (2021)
- [The language and social behavior of innovators](https://mlsanity.com/paper/2209.09511/) (2022)
- [Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails](https://mlsanity.com/paper/2508.20328/) (2025)

## Abstract (arXiv)

In this study we propose a method based on e-mail social network analysis to compare the communication behavior of managers who voluntarily quit their job and managers who decide to stay. Collecting 18 months of e-mail, we analyzed the communication behavior of 866 managers, out of which 111 left a large global service company. We compared differences in communication patterns by computing social network metrics, such as betweenness and closeness centrality, and content analysis indicators, such as emotionality and complexity of the language used. To study the emergence of managers' disengagement, we made a distinction based on the period of e-mail data examined. We observed communications during months 5 and 4 before managers left, and found significant variations in both their network structure and use of language. Results indicate that on average managers who quit had lower closeness centrality and less engaged conversations. In addition, managers who chose to quit tended to shift their communication behavior starting from 5 months before leaving, by increasing their degree and closeness centrality, the complexity of their language, as well as their oscillations in betweenness centrality and the number of "nudges" they need to send to peers before getting an answer.