Context: Recent research has shown gender differences in problem-solving, and gender biases in how software supports it. GenderMag has 5 problem-solving facets related to gender-inclusiveness: motivation for using the 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 building social goal models is largely unexplored. Objectives: We evaluated the impact of different levels of GenderMag facets on creating and modifying iStar 2.0 models. Methods: We performed a quasi-experiment. We characterised 100 participants according to each GenderMag facet. Participants performed creation and modification 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 perceived performance and speed, their accuracy was higher. We also observed some statistically significant differences in visual effort, mental effort, and stress. Conclusions: Participants with a comprehensive information processing style and a more conservative attitude towards risk (characteristics more frequently seen in women) solved the tasks with a lower speed but higher accuracy.