In user-generated content (UGC) platforms, content generators (i.e., posters) account for only a minority of users. The majority of users lurk, participating in information diffusion only and making no direct contributions to the platforms (i.e., diffusers). In this paper, we study diffusers’ reposting behavior in a UGC platform and compare it with that of posters. We find that diffusers generally behave similarly to posters in reposting. Both groups repost more when seeing more posts and encountering popular posts. Interestingly, their reposting behavior diverges under information redundancy, i.e., when more popular posts are seen in a dense network. Under this condition, diffusers show a much higher propensity to repost, which is (partially) driven by their lesser need for uniqueness (NFU). Overall, this study suggests an exquisite way for platforms to activate their lurking users and it sheds light on their value in generating word-of-mouth and in facilitating information diffusion. It also provides useful guidelines for firms to approach the right type of lurking users (i.e., diffusers in a dense network) by using the right method of stimulation (i.e., offering popular albeit redundant information) during product diffusion online.
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Although the majority of the existing marketing literature studies customer communities, we study a more general phenomenon, and for two reasons, we do not distinguish between general-purpose communities and those with a specific purpose. First, even in customer communities, many discussions are not consumption related, focusing instead on topics such as the news, sports and feelings. Second, many campaigns are carried out in general-purpose communities such as Facebook; thus, it is more relevant to study user behavior in communities that are not directly related to consumption.
Other scholars have coined this phenomenon the “1% Rule”, i.e., in online environments, only 1% of users create content, while the other 99% only view without contributing any new content (Arthur 2006).
The underlying mechanism to be identified is only suggestive, as it is not directly measured and tested using the field data. Nor is the mechanism exhaustive, as the survey is specifically designed to understand the role of NFU.
Another reason to examine content popularity is for identification purposes. It is easy to conduct our analysis at the user level, i.e., to compare the reposting behavior between posters and diffusers situated in various local networks. However, such a comparison is unfair, as there is unobserved heterogeneity that may explain both the posting status and the sharing pattern. Our main identification uses the changes in content popularity in a panel structure, thus controlling for user-level unobserved heterogeneity.
One such factor is the Matthew effect, which usually depicts the phenomenon of “the rich get richer” in the diffusion of scientific studies (Merton 1968). Similar patterns are detected in the discipline of marketing (Stremersch et al. 2007, 2015; Uslay et al. 2008; Zamudio et al. 2013). One underlying mechanism for the Matthew effect is visibility. Li et al. (2016) find that the visibility of mobile apps determines their diffusion and download. A Matthew effect in word-of-mouth may result in bias from online word-of-mouth (Gao et al. 2015; Hu and Li 2011).
Our main data consist of weekly observations of the number of posts and reposts that the users have (i.e., no details on each post or repost by a user), which does not allow us to test the different theories for why popular posts are more likely to be reposted. We believe future research can follow this line of inquiry and offer more insights.
We would like to thank an anonymous referee for raising this point.
Qiushi-Baike actually means “encyclopedia of funny stories and embarrassing moments in life.”
We follow Nonnecke et al. (2006) in using the posting behavior in the three-month pre-period to label the posting status. In the robustness check section, we show that our main finding is robust to other definitions of posters.
We check for selection bias and run the model with all the valid users in the robustness check section.
It would be great to delve into the content of each post and extract any factors that matter in the diffusion process (e.g., its sentiment). The content data also provide opportunities to compare the applicability of our findings in marketing-related posts (i.e., posts that mention consumption or other marketing-related topics) and others. However, we do not have access to the content and cannot perform these analyses.
While it is arguable that the network structure may evolve during the six-week period, we calculate the correlation between the density and its lagged value to check the stability of the network. This correlation is 0.95, which indicates that the network structure is quite stable for these users during the six-week period.
The field data do not have a direct measure of NFU; thus, we cannot test the mechanism described by using field data. We complement our analysis with a survey on a pool of QB customers and report the evidence in the next section.
We would like to thank an anonymous reviewer for suggesting these points of discussion.
We do not include the network bridging positions (which are usually measured by structural holes, betweenness, etc.) for two reasons. First, the previous literature that also examines the impact of information redundancy uses only network density and degree centrality (e.g., Stephen et al. 2016). Second, the most common metric for network bridging positions (i.e., structural holes) is a global rather than a local network metric.
We would like to thank an anonymous referee for suggesting this point.
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We greatly appreciate the comments and suggestions of seminar participants at 2017 JAMS “Thought Leader Conference on Marketing Strategy in Digital, Data-Rich, and Developing Environments” conference at UIBE, Beijing, China, and thank the editor and three anonymous reviewers for the constructive and developmental review process. We are in debt to the company in China which shares the data and provides extensive discussions. Haiwen Dai and Junwen Huang provided excellent research assistance. Chen and Zhou are grateful for financial support from the National Natural Science Foundation of China (Grant Numbers 71502111, 71872115, 71772126 and 71832015). The authors contribute to the paper equally and are listed alphabetically. The usual disclaimers apply.
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Chen, X., Li, X., Yao, D. et al. Seeking the support of the silent majority: are lurking users valuable to UGC platforms?. J. of the Acad. Mark. Sci. 47, 986–1004 (2019). https://doi.org/10.1007/s11747-018-00624-8
- Information diffusion
- Network density
- Clustering coefficient
- UGC platform