The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Social Dollars: The Economic Impact of Customer Participation in a Firm-Sponsored Online Customer Community
Open accessAbstract
Many firms operate customer communities online. This is motivated by the belief that customers who join the community become more engaged with the firm and/or its products, and as a result, increase their economic activity with the firm. We describe this potential economic benefit as “social dollars.” This paper contributes evidence for the existence and source of social dollars using data from a multichannel entertainment products retailer that launched a customer community online. We find a significant increase in customer expenditures attributable to customers joining the firm’s community. While self-selection is a concern with field data, we rule out multiple alternative explanations. Social dollars persist over the time period observed and arose primarily in the online channel. To assess the source of the social dollar, we hypothesize and test whether it is moderated by participation behaviors conceptually linked to common attributes of customer communities. Our results reveal that posters (versus lurkers) of community content and those with more (versus fewer) social ties in the community generated more (fewer) social dollars. We found a null effect for our measure of the informational advantage expected to accrue to products that differentially benefit from content posted by like-minded community members. This overall pattern of results suggests a stronger social than informational source of economic benefits for firm operators of customer communities. Several implications for firms considering investments in and/or managing online customer communities are discussed.
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Sample Selection Bias as a Specification Error
Heckman, J. J. (1979) · Econometrica · doi:10.2307/1912352 · Cited by 28,779
Centrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062
Social network sites: definition, history, and scholarship
boyd, D. M., & Ellison, N. B. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00393.x · Cited by 16,251
How Much Should We Trust Differences-in-Differences Estimates?
Bertrand, M., Duflo, E., & Mullainathan, S. (2004) · The Quarterly Journal of Economics · doi:10.1162/003355304772839588 · Cited by 10,641
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
Muniz, A. M., & O'Guinn, T. C. (2001) · Journal of Consumer Research · doi:10.1086/319618 · Cited by 3,998
Linear Mixed Models for Longitudinal Data
Verbeke, G. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2294-1_3 · Cited by 3,506
How Brand Community Practices Create Value
Schau, H. J., Muñiz, A. M., & Arnould, E. J. (2009) · Journal of Marketing · doi:10.1509/jmkg.73.5.30 · Cited by 2,444
Characterizing Selection Bias Using Experimental Data
Heckman, J., Ichimura, H., Smith, J., & Todd, P. (1998) · Econometrica · doi:10.2307/2999630 · Cited by 2,286
Inference with Difference-in-Differences and Other Panel Data
Donald, S. G., & Lang, K. (2007) · Review of Economics and Statistics · doi:10.1162/rest.89.2.221 · Cited by 1,324
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Metadata
- Authors
- Manchanda, P., Packard, G., & Pattabhiramaiah, A.
- Venue
- Marketing Science
- Published
- 2015-05
- Volume
- 34
- Issue
- 3
- Pages
- 367-387
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W3122176968
- Cited by
- 280
- References
- 11
- FWCI
- 101.88
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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