Abby, Patricia, Patrick and Tim are identical in several ways: all have the same job, live in the same place, and all are equally comfortable with mathematics and with the technology they regularly use. Their differences are strictly derived from the gender research on five facets: their Motivations to use software, Information Processing Styles, Computer Self-Efficacy, Attitudes toward Risk, and style of Learning new technologies. Tim's facet values are those most frequently seen in males, Abby's facet values are those frequently seen in females that are the most different from Tim's, and the two (identical) Pats' facet values add coverage of a large fraction of females and males different from both Abby and Tim.
Abby represents a fraction of female users with backgrounds similar to hers. For data on females (and males) similar to and different from Abby, see the Footnotes.
Note: All gray-background portions are fundamental to Abby. In contrast, the white-background portions can be customized to match your software's target audience.
Abby has always liked music. When she is on her way to work in the mornings, she listens to music that spans a wide variety of styles. But when she arrives at work, she turns it off, and begins her day by scanning all her emails first to get an overall picture a before answering any of them. (This extra pass takes time but seems worth it.) Some nights she exercises or stretches, and sometimes she likes to play computer puzzle games like Sudoku. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]
Abby is generally comfortable using familiar technology, but she does not get a big kick out of obtaining the latest gadgets or learning how to use them f . She prefers to stay with the technologies for which she has already mastered the peculiarities [5, 28], because of the following facets:
Patricia represents a fraction of female users with backgrounds similar to hers. For data on females (and males) similar to and different from Patricia, see the Footnotes.
Note: All gray-background portions are fundamental to Patricia. In contrast, the white-background portions can be customized to match your software's target audience.
Pat loves public transportation and knows at least three routes to get there from home. When she arrives at work, she scans all her emails first to get an overall picture a before answering any of them. (This extra pass takes time but seems worth it.) Some evenings she plays computer puzzle games like Sudoku before bed. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]
Pat is generally comfortable using familiar technology, but she does not get a big kick out of obtaining the latest gadgets or learning how to use them f . She prefers to stay with the technologies for which she has already mastered the peculiarities [5, 28], because of the following facets:
Patrick represents a fraction of male users with backgrounds similar to his. For data on males (and females) similar to and different from Patrick, see the Footnotes.
Note: All gray-background portions are fundamental to Patrick. In contrast, the white-background portions can be customized to match your software's target audience.
Pat loves public transportation and knows at least three routes to get there from home. When he arrives at work, he scans all his emails first to get an overall picture a before answering any of them. (This extra pass takes time but seems worth it.) Some evenings he plays computer puzzle games like Sudoku before bed. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]
Pat is generally comfortable using familiar technology, but he does not get a big kick out of obtaining the latest gadgets or learning how to use them f . He prefers to stay with the technologies for which he has already mastered the peculiarities [5, 28], because of the following facets:
Tim represents a fraction of male users with backgrounds similar to his. For data on males (and females) similar to and different from Tim, see the Footnotes.
Note: All gray-background portions are fundamental to Tim. In contrast, the white-background portions can be customized to match your software's target audience.
Tim loves public transportation. He knows several routes to get there from home and he's always exploring ways to optimize his trips into the office. Work starts with email, which he answers one at a time, as soon as he reads them a . (Sometimes this backfires, if there is a second related message he hasn't read yet, but he doesn't mind sending a follow-up email.) Some nights he plays computer games with his online friends. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]
For Tim, technology is a source of fun, and he is always on the lookout for new computer software f . He likes to make sure he has the latest version of all software with all the new features [5, 28], because of the following facets:
a This is tied to information processing style e .
b GenderMag incorporates cognitive walkthroughs, and cognitive walkthroughs evaluate learnability by a new user .
c The stereotype of gender differences in mathematics performance has been debunked in recent years: controlling for stereotype threat shows no statistical differences between male and female math performance . To avoid evaluators inappropriately invoking that stereotype, we have made explicit that all four personas are good at math and enjoy math. The "numbers person" phrase is a verbatim quote from an interview with a female accountant .
Motivations: Research spanning over a decade has found that females tend (statistically) to be motivated to use technology for what it enables them to accomplish, whereas males' motivations sometimes come from their enjoyment of the technology for its own sake. This difference can affect which features of problem-solving software females vs. males choose to use.
Sources: [5, 6, 10, 20, 21, 24, 28, 37].
Sample data: Figure 1 shows data from a study in , which is one portion of the foundations of the Motivations facet values. In that study, about 2/3 of males and 1/3 of females were motivated by exploring next-generation technology, and this value for the Motivations facet is covered by Tim; about 1/5 of both males and females felt neutral about it (covered by the two Pats). The largest percentage of females and smallest percentage of males did not enjoy exploring next-generation technology (covered by Abby).
e Information processing style: To solve problems, people often need to process new information, and there is extensive research reporting gender differences here too. In essence, when problem-solving, females are more statistically likely to use comprehensive information processing styles-gathering fairly complete information before proceeding-whereas males are more statistically likely to use selective styles-following the first promising information, then potentially backtracking, in "depth first" order. Each of these styles has particular advantages, but either is at a disadvantage when not supported by the problem-solving software environment. Particularly relevant here are studies tying gender differences in information processing style to software-based tasks, such as with e-commerce web sites, software-based auditing, and sensemaking in spreadsheets. Sources: [7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37].
f Sources: [5, 28]. This also ties back to Motivations d .
g Computer self-efficacy: One specific form of confidence is self-efficacy: a person's confidence about succeeding given a specific task. Self-efficacy matters to problem solving because a person's self-efficacy influences their use of cognitive strategies, amount of effort put forth, level of persistence, and strategies for coping with obstacles. Empirical data have shown that females tend statistically to have lower computer self-efficacy than males, as one would expect given phenomena like stereotype threat, and non-inclusive work environments and education practices. Self-efficacy levels, in turn, affect people's behavior with technology, such as which features they choose to use and how willing they are to persist with hard-to-use features. Fortunately, features designed explicitly for diverse self-efficacy levels have been shown to be preferred by everyone. Sources: [1, 2, 3, 4, 5, 6, 15, 17, 19, 22, 27, 28, 32, 34, 38].
h Risk aversion: Studies have shown that females tend statistically to be more risk-averse than males , , surveyed in , and meta-analyzed in  -- in numerous decision-making domains, such as in ethical decisions, investment decisions, gambling decisions, health/safety decisions, career decisions, and others. In contrast, we have been unable to locate any study in any domain reporting males to be more risk-averse than females. Applying these findings on risk aversion to software usage suggests that risk aversion can impact females' decisions as to which feature sets to use. Sources: [10, 14, meta-analysis 12, survey 39]
i Tinkering: Research across age groups and professions reports females being statistically less likely to playfully experiment ("tinker") with features new to them, compared to males. However, when females do tinker, they are more likely to reflect more in the process and thereby sometimes profit from it more than males do. Further, some males tinker excessively. One effect of these differences in tinkering behaviors is their impact on which features of software females vs. males will elect to use, especially when a design choice underlying the software product is that users will learn new features by exploring and tinkering with them. Sources: [4, 5, 8, 11, 21, 36].
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This document prepared by Margaret Burnett, Amber Horvath, and Alannah Oleson.
Date of last update: Sept. 12, 2017