# E-Mail Network Patterns and Body Language Predict Risk-Taking Attitude
###### Abstract  
As the Enron scandal and [PERSON]'s pyramid scheme have shown, individuals' attitude towards ethical risks can have a huge impact on society at large. In this paper, we compare risk-taking attitudes assessed with the Domain-Specific Risk-Taking (DOSPERT) survey with individual e-mail networking patterns and body language measured with smartwatches. We find that e-mail communication signals such as network structure and dynamics, and content features as well as real-world behavioral signals measured through a smartwatch such as heart rate, acceleration, and mood state demonstrate a strong correlation with the individuals' risk-preference in the different domains of the DOSPERT survey. For instance, we found that people with higher degree centrality in the e-mail network show higher likelihood to take social risks, while using language expressing a \"you live only once\" attitude indicates lower willingness to take risks in some domains. Our results show that analyzing the human interaction in organizational networks provides valuable information for decision makers and managers to support an increase in ethical behavior of the organization's members. risk-preference; social network analysis; e-mail communication; body language +
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\"honest signals\" extracted from e-mail communication have successfully predicted personality characteristics, moral values, and \"tribal affiliations\" [18; 21; 22]. On the other hand, the rapid growth of sensor technology allows researchers to capture physical body signals through a variety of wearable sensors in many areas [23; 24; 25; 26; 27; 28; 29; 30]. Recently, a body sensing system called \"Happimeter\" was proposed to extract \"honest signals\" in terms of body movement, physiology, and environmental information, which have been shown to be useful for predicting happiness, moral values, and creativity [25; 31]. Leveraging well-established social networking and sensor technologies, and given their predictive power shown in many previous studies mentioned above, it would clearly be useful if we could utilize e-mail communication and body signals measured through sensors to predict the risk-taking attitude of an individual. In this work, we systematically evaluate individuals' risk-preference by combining SNA, text mining, body signals, and mood data, pursuing two different approaches documented in two empirical studies. In Study A, we investigate an e-mail communication network through the mailbox of an active participant, analyzing network structure, digital \"tribes\" features, and emotionality from word usage. In Study B, using the Happimeter system based on smartwatches [25], we collect individuals' daily records including body movement, physiology, as well as self-reported mood states. Based on these empirical data, we find strong correlations between social networking and behavioral variables and risk-preference, which is measured by the Domain-Specific Risk-Taking (DOSPERT) survey [32; 33] in five specific domains. Our regression results imply that properties obtained both from social networking and body signals can be good candidates to predict individual risk-preference. The overall theoretical framework for this study is shown in Figure 1.  
The main contributions of this paper include:  
1. [label=(0)]
2. Combining network theory, text mining, and body-sensing technology, we introduce a novel interdisciplinary research method for analyzing one's attitude of risk-taking. 3. We validate the utility of the proposed method through two different empirical studies, using e-mail archives and the Happimeter sensing system, respectively. A strong  
Figure 1: Theoretical framework. In this work we conduct two different case studies. On the one hand, a social network through e-mail archives is studied by analyzing structural metrics, virtual features, and emotionality.