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Purpose: The purpose of this paper is to investigate the role of the "Big Five" personality traits (extraversion, openness, agreeableness, conscientiousness and neuroticism) on the adoption of augmented reality (AR), with a particular focus on the role AR may play in interactive marketing. Design/methodology/approach: A quantitative-based approach was followed by a questionnaire survey, which was completed by 230 respondents comprising graduate and postgraduate students, using structural equation modelling. Findings: While the trait of openness was positively associated with the perceived ease of use of AR, the usefulness of AR and subjective norms, the trait of neuroticism was negatively associated with the perceived ease of use of AR. Extraversion was positively associated with subjective norms. Perceived ease of use of AR, the usefulness of AR and subjective norms were positively associated with attitudes toward AR. Practical implications: The data gathered will add a valuable contribution to the currently limited data available on empirical consumer behaviour research, particularly in relation to the adoption of AR for interactive marketing. Originality/value: The findings of this study will benefit academics working on the adoption of technology in rapidly developing fields such as automation and artificial intelligence; the study also contributes to the emerging interdisciplinary domain of psychology, information systems, marketing and human behaviour. [ABSTRACT FROM AUTHOR]

Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user’s social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey’s model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair. [ABSTRACT FROM AUTHOR]