Context: Research has shown gender differences in problem-solving, and gender biases in how software supports it. GenderMag has five problem-solving facets related to gender-inclusiveness: motivation for using software, information processing style, computer self-efficacy, attitude towards risk, and ways of learning new technology. Some facet values are more frequent in women, others in men. The role these facets may play when interacting with social goal models is unexplored. Objectives: We evaluated the impact of different levels of GenderMag facets on creating, modifying, understanding, and reviewing iStar 2.0 models. Methods: We performed a quasi-experiment and characterised 180 participants according to each GenderMag facet. Participants performed creation, modification, understanding, and reviewing tasks on iStar 2.0. We measured their accuracy, speed, and ease, using metrics of task success, time, and effort, collected with eye-tracking, EEG and EDA sensors, and participants’ feedback. Results: Although participants with facet levels frequently seen in women had lower speed when compared to those with facet levels more often observed in men, their accuracy was higher. There were also statistically significant differences in visual and mental effort, and stress. Overall, participants were able to create, modify, and understand the models reasonably well, but struggled when reviewing them. Conclusions: Participants with a comprehensive information processing style and a conservative attitude towards risk (characteristics frequently seen in female) solved the tasks with lower speed but higher accuracy. Participants with a selective information processing style (characteristic frequently seen in males) were able to better separate what was relevant from what was not. The complementarity of results suggests there is more gain in leveraging people’s diversity.
- iStar 2.0
- Social goal models
UN Sustainable Development Goals (SDGs)
- SDG 5 - Gender Equality