Facebook “friends”: Effects of social networking site intensity, social capital affinity, and flow on reported knowledge-gain

Valerie Barker, David M. Dozier, Amy Schmitz Weiss, Diane L. Borden


Using a subset of data from a survey of a representative sample of U.S. Internet users, 236 participants responded to questions regarding social networking site intensity, their experience of flow (concentrated engagement in/enjoyment of an activity), social capital affinity (sympathy marked by community of interest, and likeness based on weak ties) and perceived focused and incidental-knowledge gains from social networking sites. Social networking site intensity strongly predicted flow and social capital affinity, but the latter appeared to be a stronger predictor of perceived focused and incidental-knowledge gains from social networking sites.


: Social Network Site Intensity, Social Capital Affinity, Flow, Knowledge-gain

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Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.

Aldrich, C. (2009). Learning online with games, simulations, and virtual worlds. San Francisco, CA: Jossey-Bass.

Aydin S. (2012). A review of research on Facebook as an educational environment. Education Technology Research and Development, 60,1093–1106.

Barker, V. (2009). Older adolescents’ motivations for sse of SNS: The influence of gender, group identity and collective self-esteem. Cyberpsychology & Behavior, 12, 209-213.

Barker V. (2012). A generational comparison of social networking site use: The influence of age and social identity. International Journal of Aging and Human Development, 74, 163-187.

Barker, V, Dozier, D. M. Schmitz Weiss, A., & Borden, D. L. (2013, August). Harnessing Peer Potency: Predicting Positive Outcomes from Social Capital Affinity and Online Engagement With Participatory Websites. Paper presented at the meeting of the Association for Education in Journalism and Mass Communication, Washington, DC.

Bourdieu, P. (1985). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York, NY: Greenwood.

Black, S. (March, 2008). Getting into the flow. American School Board Journal, 40-47.

Brenner, J., & Smith, A. (2013). 72% of online adults are social networking site users social networking sites remain most popular among young adults, but other age groups continue to increase their engagement. Accessed September 3, 2013 from: http://pewinternet.org/~/media//Files/Reports/2013/PIP_Social_networking_sites_update.pdf

Caspi, A., & Blau, I. (2008). Social presence in online discussion groups: Testing three conceptions and their relations to perceived learning. Social Psychology of Education: An International Journal, 11(3), 323-346. doi:http://dx.doi.org/10.1007/s11218-008-9054-2

Castells, M. (2009). Communication power. New York, NY: Oxford University Press.

Chan, T. S., & Ahern, T.C. (1999). Targeting motivation – Adapting flow theory to instructional design. Journal of Educational Computing Research, 21(2), 151-163.

Chan, T. S., & Repman, J. (1999). Flow in web based instructional activity: An exploratory research project. International Journal of Educational Telecommunications, 5(3), 225-237.

Chang, Y-P. & Zhu, D-H. (2012). The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China. Computers in Human Behavior, 28, 995–1001.

Cheung, C. M.K., Chiu, P-Y. & Lee, M. K.O. (2011). Online social networks: Why do students use Facebook? Computers in Human Behavior, 27, 1337–1343.

Choi, B., & Baek, Y. (2011). Exploring factors of media characteristic influencing flow in learning through virtual worlds. Computers and Education, 57, 2382-2394.

Clarke, J., & Dede, C. (2005). Making learning meaningful. American Educational Research Association National Conference, Montreal.

Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass.

Custodero, L. A. (2002). Seeking challenge, finding skill: Flow experience and music education. Arts Education Policy Review, 103(3), 3-9.

Egbert, J. (2003). A study of flow theory in the foreign language classroom. The Modern Language Journal, 87(4), 499-518.

Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends”: Exploring the relationship between college students’ use of online social networks and social capital. Journal of Computer-Mediated Communication, 12, 1143–1168.

Ellison, N. B., Steinfield, C., Lampe, C, & Vitak, J. (2011). With a little help from my friends: How social network sites affect social capital processes. In Z. Papacharissi (Ed.), A networked self: Identity, community, and culture on social network sites, (pp. 124–145). Greenwich, CT: JAI Press.

Fenton. L. (2008). Adventure education and Csikszentmihalyi’s flow theory: A critical analysis of stress and optimal experience as learning tools. Research in Outdoor Education 9, 89-90.

Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior. Reading, MA: Addison-Wesley Publishing Company, Inc.

Ghani, J. A. (1995). Flow in human-computer interactions: Test of a model. In J. M. Carey (Ed.), Human factors in Information Systems: Emerging Theoretical Bases, (pp. 291-309). Norwood, NJ: Ablex Publishing Corporation.

Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380. doi: 10.1086/225469

Guo, Y.M., & Poole, M.S. (2008). Antecedents of flow in online shopping: A test of alternative models. Information Systems Journal, 18, 1-22.

Huang, L-Y, & Hsieh, Y-J. (2011). Predicting online game loyalty based on need gratification and experiential motives. Internet Research, 21, 581-598.

Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport and Exercise Psychology, 18, 17-35.

Jin, A-S. A. (2012). Toward integrative models of flow: Effects of performance, skill, challenge, playfulness and presence on flow in video games. Journal of Broadcasting and Electronic Media, 56, 169-186.

Junco, R., & Cole-Avent, G. A. (2008). An introduction to technologies commonly used. New Directions for Student Services, 124, 3–17.

Kiili, K. (2005). Content creation challenges and flow experience in educational games: The IT-Emperor case. The Internet and Higher Education, 8(3), 183-198.

Konradt, U., Filip, R., & Hoffmann, S. (2003). Flow experience and positive affect during hypermedia learning. British Journal of Educational Technology, 34(3), 309-327.

Mauri M., Cipresso P., Balgera A., Villamira M., Riva G. (2011). Why Is Facebook so successful? Psychophysiological measures describe a core flow state while using Facebook. Cyberpsychology, Behavior, and Social Networking, 14(12), 723-731.

Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In S. J. Lopez & C. R. Snyder (Eds.), Handbook of positive psychology (pp. 89–105). London: Oxford University Press.

O’Cass, A., & Carlson, J. (2010). Examining the effects of website-induced flow in professional sporting team websites. Internet Research, 20, 115-134.

Packiam-Alloway, T., & Alloway, R. G. (2012). The impact of engagement with social networking sites (SNSs) on cognitive skills. Computers in Human Behavior, 28, 1748–1754.

Papacharissi, Z. (Ed.) (2011). A networked self: Identity, community, and culture on social network sites. Greenwich, CT: JAI Press.

Pew Research Center (2010). Millennials, Media, and Information. Accessed October 23, 2012 from: http://pewresearch.org/pubs/1516/millennials-panel-two-millennials-media-information

Pew Research Center (2013). The Role of News on Facebook Common yet Incidental. Accessed October 29, 2013 from: http://www.journalism.org/files/2013/10/facebook_news_10-24-2013.pdf

Putnam, R. (2000). Bowling alone: The collapse and revival of American community. New York, NY: Simon & Schuster.

Rea, D. W. (2000). Optimal motivation for talent development. Journal for the Education of the Gifted, 23(2), 187-216.

Rossin, D., Ro, Y. K., Klein, B. D., & Guo, Y. M. (2009). The effects of flow on learning outcomes in an online information management course. Journal of Information Systems Education, 20(1), 87-98. Retrieved from http://search.proquest.com/docview/200167242?accountid=13758

Ryu, H., & Parsons, D. (2012). Risky business or sharing the load? – Social flow in collaborative mobile learning. Computers & Education, 58, 707–720.

Shernoff, D. J., Czikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18(2), 158-176.

Skadberg, Y. X., & Kimmel, J. R. (2004). Visitors’ flow experience while browsing a website: Its measurement, contributing factors and consequences. Computers in Human Behavior, 20, 403-422.

Smith, J. S. (2005). Flow theory and GIS: Is there a connection for learning? International Research in Geographical and Environmental Education, 14, 223-230.

Stephenson, W. (1967). The play theory of mass communication. Chicago: University of Chicago Press.

Steinfield, C., Ellison, N. B., & Lampe, C. (2008) Social capital, self-esteem, and use of online social network sites: A longitudinal analysis. Journal of Applied Developmental Psychology, 29, 434–445.

Tajfel, H., & Turner, J. C. (1986). The social identity of intergroup behavior. In S. Worchel & W. G. Austin (Eds.), Psychology of intergroup relations (pp. 7-24). Chicago: Nelson.

Taylor, R., King, F., & Nelson, G. (2012). Student learning through social media. Journal of Sociological Research, 3(2), 29-35. Retrieved from http://search.proquest.com/docview/1032658405?accountid=13758

Tewksbury, D., Weaver, A. J., & Maddex, B. D. (2001). Accidentally informed: Incidental news exposure on the World Wide Web. Journalism & Mass Communication Quarterly, 78, 533-554.

U. S. Census Bureau (2011). Age and sex composition: 2010. Accessed October 23, 2012 from: http://search.census.gov

Walther, J. B., Carr, C. T., Choi, S. S. W., DeAndrea, D. C., Kim, J., Tong, T. S., Van der Heide, B. (2011). Interaction of interpersonal, peer, and media influence sources online: A research agenda for technology convergence. In Z. Papacharissi (Ed.), A networked self: Identity, community, and culture on social network sites, (pp. 17-38). Greenwich, CT: JAI Press.

Walther, J. B., DeAndrea, D. C., Kim, J., & Anthony, J. (2010). The influence of online comments on perceptions of anti-marijuana public service announcements on Youtube. Human Communication Research, 36, 469–492.

Walther, J. B., Liang, Y-J., Ganster, T., Wohn, D. E., & Emington. (2012). Online reviews, helpful ratings, and consumer attitudes: An extension of congruity theory to multiple sources in Web 2.0. Journal of Computer-Mediated Communication, 18, 97-112.

Walther, J. B., Van der Heide, B., Hamel, L. M., & Shulman, H. C. (2009). Self-generated versus other-generated statements and impressions in computer-mediated communication. Communication Research, 36, 229–253.

Weber, R., Tamborini, R., Westcott-Baker, A., & Kantor, B. (2009). Theorizing flow and media enjoyment as cognitive synchronization of attentional and reward networks. Communication Theory, 19, 397-422.

Williams, D. (2006). On and off the ’Net: Scales for social capital in an online era. Journal of Computer-Mediated Communication, 11, 593–628.

Wright, K. (2012). Similarity, network convergence, and availability of emotional support as predictors of strong-tie/weak-tie support network preference on Facebook. Southern Communication Journal, 77, 389-402.

Zaman, M., Rajan, M. A., & Dai, Q. (2010). Experiencing flow with instant messaging and its facilitating role on creative behaviors. Computers in Human Behavior, 26, 1009–1018.

Zemsky, R., & Massy, W. F. (2004). Thwarted innovation: What happened to e-learning and why. Learning Alliance. (A Learning Alliance Report).

Zhang, Z. (2010). Feeling the sense of community in social networking usage. IEEE Transactions on Engineering Management, 57, 225-239.


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