Paper Type

Complete

Abstract

The underrepresentation of women in digital spaces reinforces gender bias and ultimately shapes how decisions are made (Smith & Rustagi, 2021). The internet gender gap has been calculated to have cost low- and lower-middle-income countries $1 trillion USD in GDP over the past decade (Horvát & González-Bailón, 2024). Crowd mobilization is a powerful mechanism that can challenge injustice and inequalities. We are using an exploratory case study to identify and describe components of the mobilization process of volunteers specifically targeting the gender gap on Wikipedia through events called WikiGap. We detect a range of digital mobilization strategies along the two axis of "recruitment" and "participation", and we describe the three archetypes "Native, "Hybrid" and "Beyond Native" of mobilization. This study contributes to our understanding of digital crowd mobilization strategies (Phang et al., 2015; Horvát & González-Bailón, 2024).

Paper Number

1708

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2025/papers/1708

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Aug 15th, 12:00 AM

Mobilizing for Gender Equality in Digital Spaces

The underrepresentation of women in digital spaces reinforces gender bias and ultimately shapes how decisions are made (Smith & Rustagi, 2021). The internet gender gap has been calculated to have cost low- and lower-middle-income countries $1 trillion USD in GDP over the past decade (Horvát & González-Bailón, 2024). Crowd mobilization is a powerful mechanism that can challenge injustice and inequalities. We are using an exploratory case study to identify and describe components of the mobilization process of volunteers specifically targeting the gender gap on Wikipedia through events called WikiGap. We detect a range of digital mobilization strategies along the two axis of "recruitment" and "participation", and we describe the three archetypes "Native, "Hybrid" and "Beyond Native" of mobilization. This study contributes to our understanding of digital crowd mobilization strategies (Phang et al., 2015; Horvát & González-Bailón, 2024).

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