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Abstract Engagement with social networking sites is influenced by the cognitive and learning processes which in turn is influenced by culture. This paper aims at unbundling the effect of culture on the use of social network sites and thus contributes to our understanding on the way cognitive and learning processes influence the engagement with social networks. The study of over 600 social networking users addresses how professional and leisure use of social networks differs across cultures, gender and other demographics. Firstly, by utilising ANOVA techniques we associate users' behaviour with nationality and furthermore through the use of an ordered logistics regression we delineate clusters of users by the purpose of social networking adoption and their cultural characteristics. Our study helps bridge the gap in literature on identifying how cultural traits, nationality and gender affect both business and leisure use of social networking. The implications of differences in user behaviour driven by nationality and gender warrant further need for applying learning analytics in social platforms to enhance user experience. Future directions of research on social networking in relation to cognition and culture are offered for discussion. Highlights • We unbundle the cognitive and learning processes that influence engagement with SNS. • We investigate how cultural traits, nationality and gender affect the use of SNS. • Utilising ANOVA techniques we associate individual behaviour based on a study of over 600 users. • Application of learning analytics in social platforms is needed to enhance experience. • Future directions of research in relation to cognition and culture are offered for discussion. [ABSTRACT FROM AUTHOR]

Organizations are increasingly flooded with large amounts of data on which they base their business decisions. It is thus crucial to visualize relevant data efficiently so that employees are able to extract relevant information most accurately and quickly. However, at present, information visualization research lacks a coherent and evidence based theoretical framework providing clear guidelines on how to design efficient visualizations. When it comes to the use of redundant elements in charts like commonly used data labels, cognitive fit theory and cognitive load theory make different predictions. Whereas the latter assumes that redundant labels generally reduce visualizations’ efficiency due to increased cognitive load, cognitive fit theory predicts increased efficiency for some task types that shall be solved by means of the visualization. In the online experiment presented in this paper, we investigated the effect of data labels in line and bar charts on users’ accuracy and speed in solving chart-related business tasks dependent on different task types. Our results reveal that users perceive charts with redundant labels as significantly more efficient and answer related questions significantly more accurate and faster, which we explain by the help of cognitive fit theory. We provide valuable insights into cognitive processing of charts and encourage graph designers to consider redundant elements as a possible means to increase efficiency under particular circumstances. [ABSTRACT FROM AUTHOR]