Cited by 181Digital Marketing and Social MediaConsumer Market Behavior and PricingDigital Platforms and Economics

Thumbs Up, Sales Up? The Contingent Effect of Facebook Likes on Sales Performance in Social Commerce

Lee, Kyunghee, Lee, Byungtae, Oh, Wonseok·2015·Journal of Management Information Systems

Abstract

In this study we investigate whether social reference systems, such as Facebook “likes” (FBLs), promote sales in social commerce, wherein adverse selection and quality uncertainty often severely damage consumer trust and impede efforts to achieve sustainable growth. We also examine the extent to which product characteristics (product uncertainty and product franchising) and deal characteristics (tipping points, discount rates, and deal durations) moderate the social selling stimulated by FBLs. On the basis of 1,327 samples collected from a major social commerce platform provider, we identify several interesting empirical regularities regarding the relationship between FBLs and social commerce sales. The findings suggest that FBLs drive traffic and increase sales. Information technology artifacts and social technologies, such as FBLs, can endow a consumer’s shopping experience with a socialization component and induce social selling in collective buying platforms. Nevertheless, significant variations occur across products and deals. For example, consumers who purchase experience goods more frequently depend on FBLs than do those who buy search goods. FBLs exert a far greater influence on the sales of goods from independent stores than those from franchise chains. Social commerce consumers are unaffected by heavy discount rates as they make purchase decisions, but they extensively rely on FBLs, particularly when purchasing products that have low tipping points. Our results sugg…

Integrity signals

Registry

Crossref · 10.1080/07421222.2015.1138372

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Likes and comments are significant predictors of companies' financial and social performance, and they positively influence sales.

    “likes and comments are significant predictors of companies' financial and social performance, and they positively influence sales (Lee, Lee, and Oh 2015;Yoon et al. 2018)”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Lee, K., Lee, B., & Oh, W.
Affiliations
Korea Advanced Institute of Science and Technology (KR)
Venue
Journal of Management Information Systems
Published
2015-10-02
Volume
32
Issue
4
Pages
109-143
ISSN
  • 0742-1222 (ISSN-L)
  • 1557-928X
OpenAlex
W2338945520
Cited by
181
References
3
Citations by year
2026: 13 · 2025: 18 · 2024: 21 · 2023: 28 · 2022: 24 · 2021: 13 · 2020: 29 · 2019: 15 · 2018: 12 · 2017: 6 · 2016: 2
FWCI
22.11
Type
journal article
Language
en
Open access
closed
Locations
Journal of Management Information Systems (publishedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
Keywords
Facebook likes, social commerce, sales performance, social reference, social selling, adverse selection, quality uncertainty, consumer trust, product uncertainty, experience goods, search goods, tipping points, discount rate

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