Social Dollars: The Economic Impact of Customer Participation in a Firm-Sponsored Online Customer Community
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".