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Collective efficacy as a measure of community

Published:02 April 2005Publication History

ABSTRACT

As human-computer interaction increasingly focuses on mediated interactions among groups of individuals, there is a need to develop techniques for measurement and analysis of groups that have been scoped at the level of the group. Bandura's construct of perceived self-efficacy has been used to understand individual behavior as a function of domain-specific beliefs about personal capacities. The construct of collective efficacy extends self-efficacy to organizations and groups, referring to beliefs about collective capacities in specific domains. We describe the development and refinement of a collective efficacy scale, the factor analysis of the construct, and its external validation in path models of community-oriented attitudes, beliefs, and behaviors.

References

  1. Bandura, A. (1997). Self-efficacy: The exercise of control. NY: W.H. Freeman and Company.Google ScholarGoogle Scholar
  2. Bandura, A. (2005). Guide for creating self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents. Greenwich, CT: Information Age Publishing.Google ScholarGoogle Scholar
  3. Bellah, R., Madsen, R., Sullivan, W., Swindler, A. & Tipton, S. (1986). Habits of the heart: Individualism and commitment in American life. U. California Press.Google ScholarGoogle Scholar
  4. Bendig, A.W. (1962). The Pittsburgh scales of social extroversion, introversion and emotionality. The Journal of Psychology, 53, 199--209.Google ScholarGoogle ScholarCross RefCross Ref
  5. Carroll, J.M. & Reese, D.D. (2003). Community collective efficacy: Structure and consequences of perceived capacities in the Blacksburg Electronic Village. Proceedings of HICSS-36. New York: IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  6. Carroll, J.M. & Rosson, M.B. (1996). Developing the Blacksburg Electronic Village. CACM, 39(12), 69--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Carroll, J.M., Rosson, M.B., Kavanaugh, A., Dunlap, D.R., Schafer, W., Snook, J. & Isenhour, P. (2005). Social and civic participation in a community network. In R. Kraut, M. Brynin & S. Kiesler (Eds.) Domesticating information technologies. New York: Oxford University Press.Google ScholarGoogle Scholar
  8. Cohill, A. and Kavanaugh, A., Eds. (2000). Community Networks: Lessons from Blacksburg, Virginia. Norwood, MA: Artech House. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Ghinea, G. & Chen, S.Y. (2003). The impact of cognitive styles on perceptual distributed multimedia quality. British Journal of Educational Technology, 34, 393--406.Google ScholarGoogle ScholarCross RefCross Ref
  10. Goddard, R.D., Hoy, W.K. & Hoy, A.W. (2004). Collective efficacy beliefs: Theoretical developments, empirical evidence, and future directions. Educational Researcher, 33(3), 3--13.Google ScholarGoogle ScholarCross RefCross Ref
  11. Karsvall, A. (2002). Personality preferences in graphical interface design. Proceedings of NordiCHI (October 19-23), New York: ACM, pp. 217--218. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kavanaugh, A., Reese, D.D., Carroll, J.M., & Rosson, M.B. (2003). Weak Ties in Networked Communities. In M. Huysman, E. Wenger & V. Wulf (Eds.) Communities and Technologies. The Netherlands: Kluwer Academic Publishers, pp. 265--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kavanaugh, A., Carroll, J.M., Rosson, M.B., Reese, D.D. & Zin, T.T. (2005). Participating in civil society: The case of networked communities. Interacting with Computers, 17, 9--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Kavanaugh, A. & Patterson, S. (2001). The impact of community computer networking on community involvement and social capital. American Behavioral Scientist 45, 496--509.Google ScholarGoogle ScholarCross RefCross Ref
  15. Kraut, R., Scherlis, W., Mukhopadhyay, T., Manning, J. & Kiesler, S. (1996). The HomeNet field trial of residential Internet services. CACM, 39, 55--63. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Neale, D.C., Carroll, J.M., & Rosson, M.B. (2004). Evaluating computer-supported cooperative work: Models and frameworks. Proceedings of CSCW (Chicago, Nov. 8-10), New York: ACM, pp. 368--377. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Pedhazur, E.J. (1997). Multiple Regression in Behavioral Research. New York: Harcourt Brace.Google ScholarGoogle Scholar
  18. Putnam, R.D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon & Shuster.Google ScholarGoogle Scholar
  19. Ramalingam V. & Wiedenbeck S. (1998). Development and validation of scores on a computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19(4), 365--379.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Collective efficacy as a measure of community

      Recommendations

      Reviews

      Sharon Tettegah

      In light of the recent interest in group processes and Web-based asynchronous and synchronous forums, collective efficacy can be as important as individual efficacy. Bandura [1] argues that individual self-efficacy contains cognitive, motivational, affective, and selection processes involving an individual's belief about his or her capability of accomplishing a task in a specific situation. In this sense, the aforementioned attributes are necessary aspects of an individual's belief system related to judgments about the ability to accomplish a task. For example, an individual may be efficacious in driving an automobile, but not so efficacious in driving a van. According to Bandura [1], the more you believe you can accomplish a task, the more motivated you will be, and the harder you will try to complete the task. In this paper, the authors seek to broaden Bandura's theory of self-efficacy. The investigators developed a community collective efficacy (CCE) scale to measure group beliefs about a group's ability to accomplish a particular task. The CCE scale is specifically not about the beliefs that an individual may hold, but is about his or her ability to accomplish a task individually. The authors present some interesting results from their CCE scale, which is in development. The CCE is a 17-item scale that looks at a shared call to action, and reveals these factors: active cooperation, social services, and economic infrastructures. The goal of this research is to establish a better distribution of items on four factors: managing conflict, development, united action, and social services. As their study notes, "people higher on the CCE report stronger feelings of belonging and are activists in their communities compared to those with less feelings of belonging" (page 6). Another important aspect affecting high CCE scores is a person's level of education and Internet use. In understanding the aforementioned results, it is clear that individual efficacy plays an important role in community efficacy. It appears that CCE really depends on the self-efficacy of group leaders and individual group experiences. This scale holds a lot of promise for future investigations of Web-based community organizations. Online Computing Reviews Service

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      • Published in

        cover image ACM Conferences
        CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
        April 2005
        928 pages
        ISBN:1581139985
        DOI:10.1145/1054972

        Copyright © 2005 ACM

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        Publication History

        • Published: 2 April 2005

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