Data for: Compensating for the Loss of Human Distinctiveness: The Use of Social Creativity under Human-Machine Comparisons

Published: 28 Sep 2019 | Version 1 | DOI: 10.17632/r4wfgkcyw8.1

Description of this data

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.

Experiment data files

This data is associated with the following publication:

Compensating for the loss of human distinctiveness: The use of social creativity under Human–Machine comparisons

Published in: Computers in Human Behavior

Latest version

  • Version 1


    Published: 2019-09-28

    DOI: 10.17632/r4wfgkcyw8.1

    Cite this dataset

    Cha, Young-Jae; Baek, Sojung; Ahn, Grace; Lee, Hyoungsuk; Lee, Boyun; Shin, Ji-eun; Jang, Dayk (2019), “Data for: Compensating for the Loss of Human Distinctiveness: The Use of Social Creativity under Human-Machine Comparisons”, Mendeley Data, v1


Views: 136
Downloads: 18


Social Psychology


CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?
You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.