Advertisement

Patterns of Gaming Preferences and Serious Game Effectiveness

  • Katelyn Procci
  • James Bohnsack
  • Clint Bowers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6774)

Abstract

According to the Technology Acceptance Model (TAM), important predictors of system use include application-specific self-efficacy, ease of use, and perceived usefulness. Current work with the TAM includes extending the assessment framework to domains such as serious games as well as how other typically under-researched factors, such as gender, affect technology use. The current work reports on how there are gender differences in both game playing behaviors as well as general game genre preferences, offers implications for serious game designers regarding the development of effective learning interventions based on these differences, and finally suggests avenues for future research in this area.

Keywords

gender differences serious games technology acceptance model user preferences 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bonanno, P., Kommers, P.A.M.: Gender Differences and Styles in the Use of Digital Games. Educ. Psychol. 25, 13–41 (2005)CrossRefGoogle Scholar
  2. 2.
    Davis, F.D., Bagozzi, P., Warshaw, P.R.: User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manage. Sci. 35, 982–1003 (1989)CrossRefGoogle Scholar
  3. 3.
    Davis, F.D.: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quart. 13, 319–340 (1989)CrossRefGoogle Scholar
  4. 4.
    Yi, Y.M., Hwang, Y.: Predicting the Use of Web-Based Information Systems: Self-Efficacy, Enjoyment, Learning Goal Orientation, and the Technology Acceptance Model. Int. J. Hum.-Comput. Int. 59, 431–449 (2003)CrossRefGoogle Scholar
  5. 5.
    Gefen, D., Straub, D.W.: Gender Differences in the Perception and Use of E-mail: An Extension to the Technology Acceptance Model. MIS Quart. 21, 389–400 (1997)CrossRefGoogle Scholar
  6. 6.
    Sun, H., Zhang, P.: The Role of Moderating Factors in User Technology Acceptance. Int. J. Hum-Comput. St. 64, 53–78 (2006)CrossRefGoogle Scholar
  7. 7.
    Evans, A.W.: Learning for the Next Generation: Predicting the Usage of Synthetic Learning. Doctoral dissertation, University of Central Florida (2009)Google Scholar
  8. 8.
    Sherry, J.L.: Flow and Media Enjoyment. Commun. Theor. 14, 328–347 (2004)CrossRefGoogle Scholar
  9. 9.
    Hartmann, T., Klimmt, C.: Gender and Computer Games: Exploring Females’ Dislikes. J. Comput-Media Commun. 11, 910–931 (2006)CrossRefGoogle Scholar
  10. 10.
    Glazer, S.: Video Games. CQ Res. 16, 937–960 (2006)Google Scholar
  11. 11.
    Steele, C.M., Aronson, J.: Stereotype Threat and the Intellectual Test-Performance of African-Americans. J. Pers. Soc. Psychol. 69, 797–811 (1995)CrossRefGoogle Scholar
  12. 12.
    Spencer, S.J., Steele, C.M., Quinn, D.M.: Stereotype Threat and Women’s Math Performance. J. Exp. Soc. Psychol. 35, 4–28 (1999)CrossRefGoogle Scholar
  13. 13.
    Aronson, J., Lustina, M.J., Good, C., Keough, K.: White Men Can’t Do Math: Necessary and Sufficient Factors in Stereotype Threat. J. Exp. Soc. Psychol. 35, 29–46 (1999)CrossRefGoogle Scholar
  14. 14.
    Dede, C., Ketelhut, D., Nelson, B.: Design-Based Research on Gender, Class, Race, and Ethnicity in a Multi-User Virtual Environment. Paper presentation. American Educational Research Association Annual Meeting (2004)Google Scholar
  15. 15.
    Turkay, S., Adinolf, S.: Free to be Me: A Survey Study on Customization with World of Warcraft and City of Heroes/Villains Players. Procedia - Soc. Behav. Sci. 2, 1840–1845 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Katelyn Procci
    • 1
  • James Bohnsack
    • 1
  • Clint Bowers
    • 1
  1. 1.Department of PsychologyUniversity of Central FloridaOrlandoUSA

Personalised recommendations