Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,228

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content

Goh, Khim-Yong, Heng, Cheng-Suang, Lin, Zhijie·2013·Information Systems Research

Abstract

Despite the popular use of social media by consumers and marketers, empirical research investigating their economic values still lags. In this study, we integrate qualitative user-marketer interaction content data from a fan page brand community on Facebook and consumer transactions data to assemble a unique data set at the individual consumer level. We then quantify the impact of community contents from consumers (user-generated content, i.e., UGC) and marketers (marketer-generated content, i.e., MGC) on consumers' apparel purchase expenditures. A content analysis method was used to construct measures to capture the informative and persuasive nature of UGC and MGC while distinguishing between directed and undirected communication modes in the brand community. In our empirical analysis, we exploit differences across consumers' fan page joining decision and across timing differences in fan page joining dates for our model estimation and identification strategies. Importantly, we also control for potential self-selection biases and relevant factors such as pricing, promotion, social network attributes, consumer demographics, and unobserved heterogeneity. Our findings show that engagement in social media brand communities leads to a positive increase in purchase expenditures. Additional examinations of UGC and MGC impacts show evidence of social media contents affecting consumer purchase behavior through embedded information and persuasion. We also uncover the different roles played by UGC and MGC, which vary by the type of directed or undirected communication modes by consumers and the marketer. Specifically, the elasticities of demand with respect to UGC information richness are 0.006 (directed communication) and 3.140 (undirected communication), whereas those for MGC information richness are insignificant. Moreover, the UGC valence elasticity of demand is 0.180 (undirected communication), whereas that for MGC valence is 0.004 (directed communication). Overall, UGC exhibits a stronger impact than MGC on consumer purchase behavior. Our findings provide various implications for academic research and practice.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1287/isre.1120.0469

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 9 references

Cited by

See all 1,228

Metadata

Authors
Goh, K.-Y., Heng, C.-S., & Lin, Z.
Venue
Information Systems Research
Published
2013-03
Volume
24
Issue
1
Pages
88-107
ISSN
  • 1047-7047 (ISSN-L)
  • 1526-5536
OpenAlex
W3125774461
Cited by
1,228
References
9
FWCI
189.59
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content. https://app.cochat.ai/papers/10.1287/isre.1120.0469
The paper
Goh, K.-Y., Heng, C.-S., & Lin, Z. (2013). Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content. Information Systems Research. https://doi.org/10.1287/isre.1120.0469

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.