Computers in Human Behavior
ISSN: 0747-5632
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Datasets associated with articles published in Computers in Human Behavior
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Human–machine intellectual comparisons increasingly threaten the distinctiveness of humans. Drawing on social identity theory, we assume that people will manage the distinctiveness threat from human–machine comparisons using a social creativity strategy. On this basis, we investigate whether people compensate for the loss of human distinctiveness by valuing “alternative” human attributes. A preliminary study first distinguished various attributes of humanness into threatened and alternative dimensions; then, studies 1–3 found that participants primed with human–machine comparison, compared to controls, evaluated the alternative dimension as uniquely human (Study 1) and as superior to machines (Studies 2–3). Finally, Study 4 found that the perceived loss of distinctiveness in threatened dimensions led people to evaluate the alternative dimension as valuable for humanness. These findings suggest that people use social creativity to manage distinctiveness threat under human–machine comparison.
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This dataset presents 159 players’ computer log data when they play a Serious Game along with their characteristics data including problem-solving performance scores, demographic information, metacognition, and goal orientation measurements. A total of 85,194 log files were recorded during one-hour game play. The data here are related to the research paper entitled “The Impact of Learner Metacognition and Goal Orientation on Problem-Solving in a Serious Game Environment”. Tableau and R tools were used to analyse and visualize player log data. The data collected would provide insights on the relationship among individual player characteristics, in-game behavior and performance in the game.
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This is a simplified file containing the measures reported in the paper after computing composites.
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This is a data set for the study.
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This is the data for the study.
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Data file for Video gaming and working memory: a large-scale cross-sectional correlative study
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Raw data that were the basis for the analysis in our manusript "Content is King – But Who is the King of Kings? The Effect of Content Marketing, Sponsored Content & User-Generated Content on Brand Responses". Further information on how the analyses were run is found in the manusript.
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