We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people's geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies.
Abstract:This paper reexamines the reasons for continued usage of information systems (IS), methodologically replicating a study by Bhattacherjee (2001) that investigates IS continuance by means of the expectation-confirmation model. For this purpose, the original research model was adapted and examined in a different context: cloud service usage in Germany, focusing on Dropbox. The conditions in a cloud service context differ fundamentally from those in the original study (online banking), since use is free of charge (freemium business models), customers have a wide choice of providers with low switching costs, and positive network effects are presumably in effect. The empirical analysis of 321 responses from a cross-sectional study based on the research model of Bhattacherjee (2001) confirmed his results for a different sample group and in a different context: confirmation was a predictor of perceived usefulness, satisfaction was significantly influenced by confirmation and perceived usefulness, and satisfaction and perceived usefulness predicted continuance intention. Nevertheless, the path coefficients of satisfaction and perceived usefulness on continuance intention were measurably lower in our results than in the original study. The findings imply that although the model is generally confirmed, additional factors are likely to influence the intention to continue IS usage in this specific context.
Do employees with high ethical and moral values perform better? Comparing personality characteristics, moral values, and risk-taking behavior with individual and team performance has long been researched. Until now, these determinants of individual personality have been measured through surveys. However, individuals are notoriously bad at self-assessment. Combining machine learning (ML) with social network analysis (SNA) and natural language processing (NLP), this research draws on email conversations to predict the personal values of individuals. These values are then compared with the individual and team performance of employees. This prediction builds on a two-layered ML model. Building on features of social network structure, network dynamics, and network content derived from email conversations, we predict personality characteristics, moral values, and the risk-taking behavior of employees. In turn, we use these values to predict individual and team performance. Our results indicate that more conscientious and less extroverted team members increase the performance of their teams. Willingness to take social risks decreases the performance of innovation teams in a healthcare environment. Similarly, a focus on values such as power and self-enhancement increases the team performance of a global services provider. In sum, the contributions of this paper are twofold: it first introduces a novel approach to measuring personal values based on “honest signals” in emails. Second, these values are then used to build better teams by identifying ideal personality characteristics for a chosen task.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.