Journal article
What makes user-generated content more helpful on social media platforms? Insights from creator interactivity perspective
Information processing & management, Vol.60(2), p.103201
03/2023
DOI: 10.1016/j.ipm.2022.103201
Abstract
A growing number of enterprises begin to utilize user-generated content (UGC) to help build brand awareness and loyalty on social media platforms. Thus, it is important to investigate what makes UGC more helpful under the new social media environment. This study attempts to identify the influence mechanism of UGC helpfulness by examining the systematic impacts of argument quality and source reliability, especially considering the effects of creator interactivity. Using a dataset of product-related UGC in a popular social media app, our empirical study finds that the detailedness, readability, and objectivity of the content, as well as the social recognition and popularity of the creator, all have a significant impact on UGC helpfulness. Furthermore, the results indicate that creator interactivity plays a vital role in building UGC helpfulness by moderating other factors. This study contributes to both UGC and social media literature by proposing a comprehensive model to better understand UGC helpfulness. It also provides several practical insights for content creators to improve their online performances.
Details
- Title: Subtitle
- What makes user-generated content more helpful on social media platforms? Insights from creator interactivity perspective
- Creators
- Wei Zhuang - Shanghai University of Finance and EconomicsQingfeng Zeng - Shanghai University of Finance and EconomicsYu Zhang - Shanghai University of Finance and EconomicsChunmei Liu - Shanghai University of Finance and EconomicsWeiguo Fan - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Information processing & management, Vol.60(2), p.103201
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.ipm.2022.103201
- ISSN
- 0306-4573
- eISSN
- 1873-5371
- Language
- English
- Date published
- 03/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380515302771
Metrics
125 Record Views