JOURNAL ARTICLE
Do "Likes" in a Brand Community Always Make You Buy More?
Published In: Information Systems Research (INFORMS), 2024, v. 35, n. 4. P. 1681 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Liang, Chen; Wu, Ji; Li, Xinxin 3 of 3
Abstract
This article investigates the impact of the Like feature—a one-click social plug-in enabling customer-to-customer (C2C) interactions—in online brand communities (OBCs) on users' purchase behavior across online and offline retail channels. Using proprietary data from a prominent Chinese apparel firm and a scenario-based online experiment, the study finds that, on average, adoption of the Like feature leads to a 4.1% decrease in order numbers and a 25.0% reduction in expenditure, with more pronounced declines in online purchases than offline. The effect varies over time and by user: initially, the feature increases community participation and online purchases for about two months, but subsequently induces negative purchase effects primarily due to unflattering social comparison when users receive few Likes. Conversely, users receiving more than two Likes experience positive effects on online purchases. The experiment further reveals that brand attachment mediates these effects and that users with higher social comparison orientation are more sensitive to the Like feature's influence. The findings highlight potential unintended negative consequences of the Like feature in OBCs and suggest managerial strategies to mitigate these effects, such as fostering supportive community environments and customizing user feedback.
Additional Information
- Source:Information Systems Research (INFORMS). 2024/12, Vol. 35, Issue 4, p1681
- Document Type:Article
- Subject Area:Marketing
- Publication Date:2024
- ISSN:1047-7047
- DOI:10.1287/isre.2022.0008
- Accession Number:181625009
- Copyright Statement:Copyright of Information Systems Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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