Are we different? Analyzing the role of algorithmic curation and algorithmic literacy during online shopping from a gender differences perspective

Jing Liu, Dan Wu and Qingyue Guo

Behaviour and Information Technology, 2024, vol. 43, issue 10, 1990-2006

Abstract: In the traditional stereotype that continues to this day, female users are naturally considered to be more disadvantaged in technology. As algorithmic curation becomes more prevalent in daily life, do gender-specific users adapt differently? Research on gender differences in this new technological environment is needed to prevent bias from traditional stereotypes. To this end, this study conducted 40 semi-structured interviews (20 males, 20 females) to explore: (1) How do male and female users interact with algorithmic curation? (2) How does algorithmic literacy affect users’ interactions with algorithmic curation, and are there gender differences in this interaction? By applying qualitative content analysis, we found no significant gender differences overall, with female users showing slightly higher algorithmic literacy and better coping with algorithmic information curation. However, upon conducting specific comparisons, we still identified certain gender differences in the effects of algorithmic curation, which can offer insights for developing strategies to improve algorithmic literacy and stimulate algorithmic engagement among users of different genders.

Date: 2024
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DOI: 10.1080/0144929X.2023.2237602

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