User generated content is being recognized as part of the value proposition of e-commerce organizations. To make available fresh content on topics, one needs to understand how individuals consider existing contributions in their decision to contribute. This study develops and tests three hypotheses based on a survey and archival data of 235 contributors of reviews at Amazon.com. Results from a LISREL analysis indicate that those with greater self-perceived competence tend to submit content on topics with fewer existing contributions. On the other hand, those with higher social adjustive motive toward others in general tend to contribute on topics where there are many existing contributions. In contrast, people for whom the social adjustive motive toward specific others is salient are equally likely to contribute on topics with few and many existing contributions. These findings suggest ways for site administrators to ensure a balanced coverage across topics by addressing these individual and social factors.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Allen, G. A., Burk, D. L., & Davis, G. B. (2006). Academic data collection in electronic environments: defining the use of Internet resources. MIS Quarterly, 30(3), 599–610.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.
Armstrong, J. S., & Overton, T. S. (1977). Estimating non-response bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.
Bagozzi, R. P., & Dholakia, U. M. (2006). Open source software user communities: a study of participation in Linux user groups. Management Science, 52(7), 1099–1115.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.
Bandura, A. (1977). Social Learning Theory. Englewood Cliffs: Prentice-Hall.
Banjo, S. (2012). Firms take online reviews to heart. Wall Street Journal, July 30, 2012.
Bateman, P. J., Gray, P. H., & Butler, B. S. (2011). The impact of community commitment on participation in online communities. Information Systems Research, 22(4), 841–854.
Bock, G., Zmud, R. W., & Kim, Y. (2005). Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social psychological forces and organizational climate. MIS Quarterly, 29(1), 87–111.
Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley.
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43, 345–354.
Chi, M. T. H. (2006). Two approaches to the study of experts’ characteristics. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge Handbook of Expertise and Expert Performance (pp. 21–30). New York: Cambridge University Press.
Clary, E. G., & Orenstein, L. (1991). The amount and effectiveness of help: the relationship of motives and abilities to helping behavior. Personality and Social Psychology Bulletin, 17(1), 58–64.
Clary, E. G., Snyder, M., Ridge, R. D., Copeland, J., Stukas, A. A., Haugen, J., et al. (1998). Understanding and assessing the motivations of volunteers: a functional approach. Journal of Personality and Social Psychology, 74(6), 1516–1530.
Constant, D., Sproull, L., & Kiesler, S. (1996). The kindness of strangers: the usefulness of electronic weak ties for technical advice. Organization Science, 7(2), 119–135.
Cook, S. (2008). The contribution revolution: Letting volunteers build your business. Harvard Business Review, October, 60–69.
Couper, M. P. (2000). Web surveys: a review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.
Dellarocas, C., Gao, G., & Narayan, R. (2010). Are consumers more likely to contribute online reviews for hit or niche products? Journal of Management Information Systems, 27(2), 127–157.
Ericsson, K. A. (2006). An introduction to Cambridge Handbook of Expertise and Expert Performance: Its development, organization and content. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge Handbook of Expertise and Expert Performance (pp. 3–19). New York: Cambridge University Press.
Faraj, S., & Johnson, S. L. (2011). Network exchange patterns in online communities. Organization Science, 22(6), 1464–1480.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Fulk, J., Heino, R., Flanagin, A. J., Monge, P. R., & Bar, F. (2004). A test of the individual action model for organizational information commons. Organization Science, 15(5), 569–585.
Garcia, S. M., Weaver, K., Moskowitz, G. B., & Darley, J. M. (2002). Crowded minds: the implicit bystander effect. Journal of Personality and Social Psychology, 83, 843–853.
Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–70.
Kaplan, D. (1995). Statistical power in structural equation modeling. In R. H. Hoyle (Ed.), Structural Equation Modeling: Concepts, Issues and Applications (pp. 100–117). Thousand Oaks: Sage.
Kee, T. (2008) Majority of online shoppers check at least four reviews before buying. Online Media Daily (February 19). http://www.mediapost.com/publications/article/76727/majority-of-online-shoppers-check-at-least-four-re.html
Lakhani, K. R., & von Hippel, E. (2003). How open source software works: “free” user-to-user assistance. Research Policy, 32, 923–943.
Latane, B., & Darley, J. M. (1970). The Unresponsive Bystander: Why Doesn’t He Help? Englewood Cliffs: Prentice-Hall.
Ma, M., & Agarwal, R. (2007). Through a glass darkly: information technology design, identity verification, and knowledge contribution in online communities. Information Systems Research, 18(1), 42–67.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149.
Moon, J. Y., & Sproull, L. S. (2008). The role of feedback in managing the Internet-based volunteer task force. Information Systems Research, 19(4), 494–515.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–22.
Olivera, F., Goodman, P. S., & Tan, S. S. (2008). Contribution behaviors in distributed environments. MIS Quarterly, 32(1), 23–42.
Orlikowski, W., & Yates, J. (1994). Genre repertoire: the structuring of communicative practices in organizations. Administrative Science Quarterly, 39, 541–574.
Pantin, H. M., & Carver, C. S. (1982). Induced competence and the bystander effect. Journal of Applied Social Psychology, 12(2), 100–111.
Peddibhotla, N. (2011) An examination of early and late contributions at online contexts, AMCIS 2011 Proceedings—All Submissions. Paper 100, http://aisel.aisnet.org/amcis2011_submissions/100
Peddibhotla, N. B. (2012) Why different motives matter in sustaining online contributions, Electronic Commerce Research and Applications, http://dx.doi.org/10.1016/j.elerap.2012.11.001
Prentice, D. A., Miller, D. T., & Lightdale, J. R. (1994). Asymmetries in attachments to groups and to their members: distinguishing between common identity and common-bond groups. Personality and Social Psychology Bulletin, 20(5), 484–493.
Ren, Y., Kraut, R., & Kiesler, S. (2007). Applying common identity and bond theory to design of online communities. Organization Studies, 28(3), 377–408.
Segars, A. H. (1997). Assessing the unidimensionality of measurement: a paradigm and illustration within the context of information systems research. Omega, 25(1), 107–121.
Thorn, B. K., & Connolly, T. (1987). Discretionary data bases: a theory and some experimental findings. Communication Research, 14(5), 512–528.
Tirunillai, S., & Tellis, G. (2012). Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science, 31(2), 198–215.
Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1), 35–57.
Zmud, R. (1978). An empirical investigation of the dimensionality of the concept of information. Decision Sciences, 9(2), 187–195.
I thank the Senior Editor Dr. Hans-Dieter Zimmermann and the anonymous reviewers for their comments and suggestions that have helped in improving this paper. Feedback from the following was also useful while developing this research: Shawn Curley, Gordon Davis, Jungpil Hahn, Mark Snyder, Mani Subramani, Weidong Xia, and participants at the Friday workshop at the University of Minnesota, the 2009 Summer Institute of the Consortium for the Science of Socio-technical Systems at Blue Mountain Lake, New York and the 2011 Americas Conference on Information Systems in Detroit, Michigan. An earlier version of this paper was published in the Proceedings of the Americas Conference on Information Systems, Detroit, Michigan (Peddibhotla 2011). I gratefully acknowledge financial support from the Juran Center for Leadership in Quality, University of Minnesota and the President’s Opportunity Fund for Faculty and Staff Development at the SUNY Institute of Technology.
Responsible Editor: Hans-Dieter Zimmermann
About this article
Cite this article
Peddibhotla, N. How individuals choose topics to contribute at an online context. Electron Markets 23, 241–250 (2013). https://doi.org/10.1007/s12525-013-0125-7
- Online contexts
- User-generated content
- Product reviews
- Sequential position of contribution
- Bystander model