Effectiveness of end-user debugging software features: are there gender issues?

Although gender differences in a technological world are receiving significant research attention, much of the research and practice has aimed at how society and education can impact the successes and retention of female computer science professionals-but the possibility of gender issues within software has received almost no attention. If gender issues exist with some types of software features, it is possible that accommodating them by changing these features can increase effectiveness, but only if we know what these issues are. In this paper, we empirically investigate gender differences for end users in the context of debugging spreadsheets. Our results uncover significant gender differences in self-efficacy and feature acceptance, with females exhibiting lower self-efficacy and lower feature acceptance. The results also show that these differences can significantly reduce females' effectiveness.

[1]  G. Torkzadeh,et al.  Factorial Validity of a Computer Self-Efficacy Scale and the Impact of Computer Training , 1994 .

[2]  Deborah Compeau,et al.  Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study , 1999, MIS Q..

[3]  Margaret M. Burnett,et al.  Rewarding "Good" Behavior: End-User Debugging and Rewards , 2004, 2004 IEEE Symposium on Visual Languages - Human Centric Computing.

[4]  Margaret M. Burnett,et al.  Forms/3: A first-order visual language to explore the boundaries of the spreadsheet paradigm , 2001, Journal of Functional Programming.

[5]  T. Busch.  Gender Differences in Self-Efficacy and Attitudes toward Computers , 1995 .

[6]  Chuck Huff,et al.  Gender, software design, and occupational equity , 2002, SGCS.

[7]  William D. Schafer,et al.  Gender differences in risk taking: A meta-analysis. , 1999 .

[8]  Gregg Rothermel,et al.  Harnessing curiosity to increase correctness in end-user programming , 2003, CHI '03.

[9]  Deborah Compeau,et al.  Computer Self-Efficacy: Development of a Measure and Initial Test , 1995, MIS Q..

[10]  Tracy Camp,et al.  The incredible shrinking pipeline , 1997, CACM.

[11]  N. Jianakoplos,et al.  ARE WOMEN MORE RISK AVERSE , 1998 .

[12]  Viswanath Venkatesh,et al.  Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..

[13]  A. Bandura.  Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychological review.

[14]  G. Rothermel,et al.  An empirical study of fault localization for end-user programmers , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[15]  Allan Fisher,et al.  Undergraduate women in computer science: experience, motivation and culture , 1997, SIGCSE '97.

[16]  Gregg Rothermel,et al.  End-user software engineering , 2004, Commun. ACM.

[17]  Margaret M. Burnett,et al.  Impact of interruption style on end-user debugging , 2004, CHI.

[18]  Gregg Rothermel,et al.  A methodology for testing spreadsheets , 2001, TSEM.

[19]  A. Bandura.  Social Foundations of Thought and Action , 1986 .

[20]  Margaret M. Burnett,et al.  Gender: An Important Factor in End-User Programming Environments? , 2004, 2004 IEEE Symposium on Visual Languages - Human Centric Computing.

[21]  Alan F. Blackwell,et al.  First steps in programming: a rationale for attention investment models , 2002, Proceedings IEEE 2002 Symposia on Human Centric Computing Languages and Environments.

[22]  Thomas P. Van Dyke,et al.  Effects of training on Internet self-efficacy and computer user attitudes , 2002, Comput. Hum. Behav..

[23]  Desney S. Tan,et al.  Women take a wider view , 2002, CHI.

[24]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[25]  Carl Martin Allwood,et al.  Error Detection Processes in Statistical Problem Solving , 1984, Cogn. Sci..

[26]  Margaret Burnett,et al.  Interactive fault localization techniques to empower the debugging efforts of end-user programmers , 2004 .

[27]  Desney S. Tan,et al.  Women go with the (optical) flow , 2003, CHI '03.

[28]  Raymond R. Panko,et al.  What we know about spreadsheet errors , 1998 .

[29]  Kathleen S. Hartzel.  How self-efficacy and gender issues affect software adoption and use , 2003, CACM.

[30]  Allan Fisher,et al.  Caring about connections: gender and computing , 1999, IEEE Technol. Soc. Mag..

[31]  C. K. Mertz,et al.  Gender, race, and perceived risk: The 'white male' effect , 2000 .

[32]  C. Nass,et al.  Are Machines Gender Neutral? Gender‐Stereotypic Responses to Computers With Voices , 1997 .

[33]  Barbara A. Price,et al.  Factors That Affect College of Business Student Opinion of Teaching and Learning , 2002 .