Abi, Pat, 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 men, Abi's facet values are those frequently seen in women that are the most different from Tim's, and Pat's facet values add coverage of a large fraction of people different from both Abi and Tim.
Abi represents a fraction of people with backgrounds similar to theirs. For gender data on people similar to and different from Abi, see the Footnotes.
Note: All gray-background portions are fundamental to Abi. In contrast, the white-background portions can be customized to match your software's target audience.
Abi has always liked music. On their way to work in the mornings, Abi listens to music that spans a wide variety of styles. But when Abi arrives at work, they turn it off, and begin their day by scanning all their emails first to get an overall picture a before answering any of them. (This extra pass takes time but seems worth it.) Some nights Abi exercises or stretches, and sometimes Abi likes to play computer puzzle games like Sudoku. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]
Abi is generally comfortable using familiar technology, but they do not get a big kick out of obtaining the latest gadgets or learning how to use them f . Abi prefers to stay with the technologies for which they have already mastered the peculiarities [5, 28], because of the following facets:
Pat represents a fraction of people with backgrounds similar to theirs. For gender data on people similar to and different from Pat, see the Footnotes.
Note: All gray-background portions are fundamental to Pat. 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 they arrive at work, Pat scans all their emails first to get an overall picture a before answering any of them. (This extra pass takes time but seems worth it.) Some evenings Pat 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 they do not get a big kick out of obtaining the latest gadgets or learning how to use them f . Pat prefers to stay with the technologies for which they have already mastered the peculiarities [5, 28], because of the following facets:
Tim represents a fraction of people with backgrounds similar to theirs. For gender data on people similar to and different from Abi, 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. They know several routes to get there from home and they're always exploring ways to optimize their trips into the office. Work starts with email, which Tim answers one at a time, as soon as they read them a . (Sometimes this backfires, if there is a second related message Tim hasn't read yet, but Tim doesn't mind sending a follow-up email.) Some nights Tim plays computer games with their online friends. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]
For Tim, technology is a source of fun, and they are always on the lookout for new computer software f . Tim likes to make sure to have 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 gender differences in math performance [ 16]. 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 woman accountant .
d Motivations: Research spanning over a decade has found that women tend (statistically) to be motivated to use technology for what it enables them to accomplish, whereas men's motivations sometimes come
from their enjoyment of the technology for its own sake. This difference can affect which features of problem-solving software different individuals choose to use. Sources: [5, 6,
10, 20, 21, 24, 28, 37].
Sample data: Figure 1 shows data from a study (two genders represented) in , which is one portion of the foundations of the Motivations facet values. In that study, about 2/3 of men and 1/3 of women were motivated by exploring next-generation technology, and this value for the Motivations facet is covered by Tim; about 1/5 of both men and women felt neutral about it (covered by the two Pats). The largest percentage of women and smallest percentage of men did not enjoy exploring next-generation technology (covered by Abi).
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, women are more statistically likely to use comprehensive information processing styles-gathering fairly complete information before proceeding-whereas men 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].
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 women tend statistically to have lower computer self-efficacy than men, as one would expect given phenomena like stereotype threat, and non-inclusive work environments and education practices. To date, we have been able to find self-efficacy data on only those two genders. 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 women tend statistically to be more risk-averse than men , , 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 men to be more risk-averse than women. Applying these findings on risk aversion to software usage suggests that risk aversion can impact women's decisions as to which feature sets to use. Reports are emerging on risk-aversion beyond those two genders, so we may be able to update this explanation soon. Sources: [10, 14, meta-analysis 12, survey 39]
i Tinkering: Research across age groups and professions reports women being statistically less likely to playfully experiment ("tinker") with features new to them, compared to men. However, when women do tinker, studies report that they are more likely to reflect more in the process and thereby sometimes profit from it more than men do. Further, some men tinker excessively. So far, data on this facet are available on only two genders. One effect of these differences in tinkering behaviors is their impact on which features of software women vs. men 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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