The aim of this study was to identify the most relevant positive and negative events, and their consequent emotional experiences, occurring during the adaptation to a new country in an expatriate mission. We opted to train an artificial neural network to explore the relation between events and emotions since there is increasing evidence of the nonlinear patterns characterizing the adaptation to a new country as well as regarding the superior performance of nonlinear methods for understanding the experience of emotions. We surveyed 99 expatriate workers who reported a total of 221 events and 2,467 associated emotions. Three judges categorized the events, reducing them into 11 categories. The neural network architecture grouped the events into 3 hidden layers, two of them leading to positive emotions and the other one leading exclusively to negative emotions. We found that events related to self-realization and recognition and to overall well-being were the most relevant to the experience of positive emotions while those related to security were the most important predictors of negative emotions. This study addresses a major gap in the expatriates' literature by relating specific occurrences with the phenomenological emotional experience. Practical implications are further discussed.

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Emotions,Humans,Motivation,Surveys,Questionnaires

• Psychology - Social Sciences
• Economics and Business - Social Sciences