GenderMag Personas Foundations Document

GenderMag currently has four personas: Abby, Patricia, Patrick, and Tim. This document shows the foundations behind them.

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-Efficacies, Attitudes toward Risk, and Willingness to Tinker. 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 (Abigail) Jones Persona Foundations

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 Jones

When Abby drives to work in the mornings, she listens to her favorite music. She likes a variety of music, and adds to her music collection often. 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 nights she goes to yoga classes and plays computer puzzle games. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]

Background Knowledge and Skills

Motivations and Strategies

Attitude to Technology

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:




Pat (Patricia) Jones Persona Foundations

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.

Patricia Jones

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]

Background Knowledge and Skills

Motivations and Strategies

Attitude to Technology

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:




Pat (Patrick) Jones Persona Foundations

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.

Patrick Jones

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]

Background Knowledge and Skills

Motivations and Strategies

Attitude to Technology

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 (Timothy) Hopkins Persona Foundations

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 Hopkins

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.) Some nights he plays computer games with his online friends. [Sources: 7, 9, 18, 23, 25, 26, 29, 30, 31, 35, 37]

Background Knowledge and Skills

Motivations and Strategies

Attitude to Technology

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:




Footnotes

a This is tied to information processing style e .

b GenderMag incorporates cognitive walkthroughs, and cognitive walkthroughs evaluate learnability by a new user [40].

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 [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 female accountant [33].

Figure 1

d Motivation: 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 [5], which is one portion of the foundations of the Motivation 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 Motivation 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 Motivation 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 [10], [14], surveyed in [39], and meta-analyzed in [12] -- 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].




References

[1] Markus Appel, Nicole Kronberger and Joshua Aronson. 2011. Stereotype threat impairs ability building: Effects on test preparation among women in science and technology, European Journal of Social Psychology, 41(7), 904-913.

[2] Albert Bandura. 1986. Social Foundations of Thought and Action. Prentice Hall, Englewood Cliffs, NJ, USA.

[3] Laura Beckwith, Margaret Burnett, Susan Wiedenbeck, Curtis Cook, Shraddha Sorte, and Michelle Hasting. 2005. Effectiveness of end-user debugging software features: Are there gender issues? In Proceedings CHI, ACM, 869-878.

[4] Laura Beckwith, Cory Kissinger, Margaret Burnett, Susan Wiedenbeck, Joey Lawrance, Alan Blackwell, and Curtis Cook. 2006. Tinkering and gender in end-user programmers' debugging. In Proceedings CHI, ACM, 231-240.

[5] Margaret Burnett, Scott Fleming, Shamsi Iqbal, Gina Venolia, Vidya Rajaram, Umer Farooq, Valentina Grigoreanu, and Mary Czerwinski. 2010. Gender differences and programming environments: across programming populations. In Proceedings ACM Empirical Software Engineering and Measurement (ESEM), ACM.

[6] Margaret Burnett, Laura Beckwith, Susan Wiedenbeck, Scott Fleming, Jill Cao, Thomas Park, Valentina Grigoreanu, and Kyle Rector. 2011. Gender pluralism in problem-solving software. Interacting with Computers 23, 450-460.

[7] Patricia Cafferata and Alice Tybout. 1989. Gender Differences in Information Processing: A Selectivity Interpretation, Cognitive andAffective Responses to Advertising. Lexington Books.

[8] Jill Cao, Kyle Rector, Thomas Park, Scott Fleming, Margaret Burnett, and Susan Wiedenbeck. 2010a. A debugging perspective on end-user mashup programming. In Proceedings IEEE Visual Languages and Human-Centric Computing, IEEE, 149-156.

[9] Jill Cao, Irwin Kwan, Faezeh Bahmani, Margaret Burnett, Scott Fleming, Josh Jordahl, Amber Horvath, and Sherry Yang. 2013. End-user programmers in trouble: Can the Idea Garden help them to help themselves? In Proceedings Symposium on Visual Languages and Human-Centric Computing, IEEE.

[10] Justine Cassell. 2002. Genderizing HCI, In J. Jacko and A. Sears (eds), The Handbook of Human-Computer Interaction, Lawrence Erlbaum, 402-411.

[11] Shuo Chang, Vikas Kumar, Eric Gilbert, and Loren Terveen. 2009. Specialization, homophily, and gender in a social curation site: Findings from Pinterest. In Proceedings ACM Computer Supported Cooperative Work & Social Computing, ACM, 674-686.

[12] Gary Charness and Uri Gneezy. 2012. Strong Evidence for Gender Differences in Risk Taking. Journal of Economic Behavior & Organization 83, 1, (June 2012), 50-58.

[13] Constantinos Coursaris, Sarah Swierenga, and Ethan Watrall. 2008. An empirical investigation of color temperature and gender effects on web aesthetics. Journal of Usability Studies 3, 3, 103-117.

[14] Thomas Dohmen, Armin Falk, David Huffman, Uwe Sunde, Juergen Schupp and Gert G. Wagner. 2011. Individual risk attitudes: measurement, determinants, and behavioral consequences. Journal of the European Economic Association 9, 3, 522-550.

[15] Alan Durndell and Zsolt Haag. 2002. Computer self efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample. Computers in Human Behavior 18, 521-535.

[16] Nicole M. Else-Quest, Janet Shibley Hyde, Marcia C. Linn, 2010. Cross-national patterns of gender differences in mathematics: A meta-analysis, Psychological Bulletin 136(1), 103-127.

[17] Valentina Grigoreanu, Jill Cao, Todd Kulesza, Chris Bogart, Kyle Rector, Margaret Burnett, and Susan Wiedenbeck. 2008. Can feature design reduce the gender gap in end-user software development environments? In Proceedings Symposium on Visual Languages and Human-Centric Computing, IEEE, 149-156.

[18] Valentina Grigoreanu, Margaret Burnett, Susan Wiedenbeck, Jill Cao, Kyle Rector, and Irwin Kwan. 2012. End-user debugging strategies: A sensemaking perspective. Transactions on Computer-Human Interaction 19, 1, ACM.

[19] Kathleen Hartzel. 2003. How self-efficacy and gender issues affect software adoption and use. Communications ACM 46, ACM, 167-171.

[20] Jonas Hallstrom, Helene Elvstrand, and Kristina Hellberg. Gender and technology in free play in Swedish early childhood education. Int J. Technology and Design Education (2015), 25:137-149. DOI 10.1007/s10798-014-9274-z.

[21] Weimin Hou, Manpreet Kaur, Anita Komlodi, Wayne G. Lutters, Lee Boot, Shelia R. Cotten, Claudia Morrell, A. Ant Ozok, and Zeynep Tufekci. 2006. "Girls don't waste time": Pre-adolescent attitudes toward ICT. In Proceedings CHI Extended Abstracts, ACM, 875-880.

[22] Ann Hergatt Huffman, Jason Whetten, and William H. Huffman. 2013. Using technology in higher education: The influence of gender roles on technology self-efficacy. Computers in Human Behavior 29, 4, 1779-1786.

[23] William Jernigan, Amber Horvath, Michael Lee, Margaret Burnett, Taylor Cuilty, Sandeep Kuttal, Anicia Peters, Irwin Kwan, Faezeh Bahmani, and Andrew Ko. 2015. A principled evaluation for a principled Idea Garden. In Proceedings of the 2015 IEEE Symposium on Visual Languages and Human-Centric Computing, October 2015. 8 pages.

[24] Caitlin Kelleher. 2009. Barriers to programming engagement. Advances in Gender and Education 1, 5-10.

[25] Michael Lee and Andrew Ko. 2011. Personifying programming tool feedback improves novice programmers' learning. In Proceedings of the 7th International Workshop on Computing Education Research (ICER'11), 109-116. http://doi.acm.org/10.1145/2016911.2016934

[26] Michael Lee, Faezeh Bahmani, Irwin Kwan, Jilian Laferte, Polina Charters, Amber Horvath, Fanny Luor, Jill Cao, Catherine Law, Mihcael Bethwetherick, Sheridan Long, Margaret Burnett, and Andrew Ko. 2014. Principles of a debugging-first puzzle game for computing education. In Proceedings of the 2014 IEEE Symposium on Visual Languages and Human-Centric Computing (VLHCC'14), 57-64.

[27] Ewa Luger. 2014. A design for life: Recognizing the gendered politics affecting product design, In CHI Workshop: Perspectives on Gender and Product Design. https://www.sites.google.com/site/technologydesignperspectives/papers

[28] Jane Margolis and Allan Fisher. 2003. Unlocking the Clubhouse: Women in Computing. MIT Press.

[29] Joan Meyers-Levy and Durairaj Maheswaran. 1991. Exploring differences in males' and females' processing strategies. Journal Consumer Research 18, 63-70.

[30] Joan Meyers-Levy and Barbara Loken. 2015. Revisiting gender differences: What we know and what lies ahead. Journal of Consumer Psychology 25, 1, 129-149.

[31] Ed O'Donnell and Eric N. Johnson. 2001. Gender effects on processing effort during analytical procedures. International Journal of Auditing 5, 91-105.

[32] Anne O'Leary-Kelly, Bill Hardgrave, Vicki McKinney, and Darryl Wilson. 2004. The influence of professional identification on the retention of women and racial minorities in the IT workforce. NSF ITWF & ITR/EWF Principal Investigator Conference, 65-69.

[33] Quote (verbatim): "I'm a numbers person." In interview with female accountant, age about 50, about working with spreadsheets. Interview conducted by Margaret Burnett, June 27, 2007.

[34] Piazza Blog. 2015. STEM Confidence Gap. http://blog.piazza.com/stem-confidence-gap/

[35] Rene Riedl, Marco Hubert, and Peter Kenning. 2010. Are there neural gender differences in online trust? An fMRI study on the perceived trustworthiness of ebay offers. MIS Quarterly 34, 2, 397-428.

[36] Daniela Rosner and Jonathan Bean. 2009. Learning from IKEA hacking: I'm not one to decoupage a tabletop and call it a day. In Proceedings CHI, ACM, 419-422.

[37] Steven John Simon. 2001. The impact of culture and gender on web sites: An empirical study. The Data Base for Advances in Information Systems 32, 1, 18-37.

[38] Anil Singh, Vikram Bhadauria, Anurag Jain, and Anil Gurung. 2013. Role of gender, self-efficacy, anxiety and testing formats in learning spreadsheets. Computers in Human Behavior 29, 3, 739-746.

[39] Elke Weber, Ann-Renee Blais, and Nancy Betz. 2002. A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. Journal Behavior and Decision Making 15, 263-290.

[40] Cathleen Wharton, John Rieman, Clayton Lewis, and Peter Polson. 1994. The Cognitive Walkthrough method: A practitioner's guide. In J. Nielsen and R. L. Mack (Eds.) Usability Inspection Methods, John Wiley, New York. (Also available as Technical Report #CU-ICS-93-07, University of Colorado, Institute of Cognitive Science, at http://ics.colorado.edu/techpubs/pdf/93-07.pdf).


This document prepared by Margaret Burnett, Amber Horvath, and Alannah Oleson.
Contact: burnett@eecs.oregonstate.edu
Date of last update: Nov. 12, 2015