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Seeking the support of the silent majority: are lurking users valuable to UGC platforms?

  • Original Empirical Research
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Abstract

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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Notes

  1. https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/.

  2. https://blog.wechat.com/2017/11/09/the-2017-wechat-data-report/.

  3. https://www.statista.com/statistics/255778/number-of-active-wechat-messenger-accounts/.

  4. 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.

  5. 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).

  6. 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.

  7. Recent studies have developed more sophisticated way to label lurkers using time-series information (Tagarelli and Interdonato 2015), or identify the alternative switch in roles between lurker and poster for the same user in differnet context (Perna et al. 2018).

  8. 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.

  9. 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).

  10. 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.

  11. We would like to thank an anonymous referee for raising this point.

  12. Qiushi-Baike actually means “encyclopedia of funny stories and embarrassing moments in life.”

  13. 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.

  14. We check for selection bias and run the model with all the valid users in the robustness check section.

  15. 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.

  16. We use a “snowball” sampling method to construct the whole dataset; thus, we have all the connections among the users in our dataset. See Figure 6 for a graphical illustration of part of the network for our data, and see Biernacki and Waldorf (1981) for details on the “snowball” sampling method.

  17. 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.

  18. 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.

  19. We would like to thank an anonymous reviewer for suggesting these points of discussion.

  20. 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.

  21. We would like to thank an anonymous referee for suggesting this point.

References

  • Aaltonen, A., & Seiler, S. (2015). Cumulative growth in user-generated content production: evidence from Wikipedia. Management Science, 62(7), 2054–2069.

    Google Scholar 

  • Ahn, D. Y., Duan, J. A., & Mela, C. F. (2016). Managing user-generated content: a dynamic rational expectations equilibrium approach. Marketing Science, 35(2), 284–303.

    Google Scholar 

  • Amaldoss, W., & Jain, S. (2005). Conspicuous consumption and sophisticated thinking. Management Science, 51(10), 1449–1466.

    Google Scholar 

  • Antin, J., & Cheshire, C. (2010). Readers are not free-riders: Reading as a form of participation on Wikipedia. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (pp. 127–130). New York: Academic.

  • Aral, S., & Walker, D. (2014). Tie strength, embeddedness, and social influence: a large-scale networked experiment. Management Science, 60(6), 1352–1370.

    Google Scholar 

  • Arthur, C. (2006). What is the 1% rule? The Guardian.

  • Balasubramanian, S., & Mahajan, V. (2001). The economic leverage of the virtual community. International Journal of Electronic Commerce, 5, 103–138.

    Google Scholar 

  • Barasch, A., & Berger, J. (2014). Broadcasting and narrowcasting: how audience size affects what people share. Journal of Marketing Research, 51(3), 286–299.

    Google Scholar 

  • Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227.

    Google Scholar 

  • Bass, F. M. (2004). Comments on “a new product growth for model consumer durables the bass model”. Management Science, 50(supplement 12), 1833–1840.

    Google Scholar 

  • Berger, J. (2014). Word-of-mouth and interpersonal communication: a review and directions for future research. Journal of Consumer Psychology, 24(4), 586–607.

    Google Scholar 

  • Berger, J., & Heath, C. (2007). Where consumers diverge from others: identity signaling and product domains. Journal of Consumer Research, 34(2), 121–134.

    Google Scholar 

  • Berger, J., & Iyengar, R. (2013). Communication channels and word of mouth: how the medium shapes the message. Journal of Consumer Research, 40(3), 567–579.

    Google Scholar 

  • Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.

    Google Scholar 

  • Berger, J., & Schwartz, E. M. (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48(5), 869–880.

    Google Scholar 

  • Biernacki, P., & Waldorf, D. (1981). Snowball sampling: problems and techniques of chain referral sampling. Sociological Methods & Research, 10(2), 141–163.

    Google Scholar 

  • Brocato, E. D., Baker, J., & Voorhees, C. M. (2015). Creating consumer attachment to retail service firms through sense of place. Journal of the Academy of Marketing Science, 43(2), 200–220.

    Google Scholar 

  • Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring user influence in twitter: the million follower fallacy. Icwsm, 10(10–17), 30.

    Google Scholar 

  • Chae, I., Stephen, A. T., Bart, Y., & Yao, D. (2017). Spillover effects in seeded word-of-mouth marketing campaigns. Marketing Science, 36(1), 89–104.

    Google Scholar 

  • Cheema, A., & Kaikati, A. M. (2010). The effect of need for uniqueness on word of mouth. Journal of Marketing Research, 47(3), 553–563.

    Google Scholar 

  • Chen, Z., & Berger, J. (2016). How content acquisition method affects word of mouth. Journal of Consumer Research, 43(1), 86–102.

    Google Scholar 

  • Chen, Y. J., & Kirmani, A. (2015). Posting strategically: the consumer as an online media planner. Journal of Consumer Psychology, 25(4), 609–621.

    Google Scholar 

  • Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(3), 345–354.

    Google Scholar 

  • Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.

    Google Scholar 

  • Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982–1003.

    Google Scholar 

  • Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences and individual judgment. Journal of Abnormal and Social Psychology, 51, 629–636.

    Google Scholar 

  • Dollinger, S. J. (2003). Need for uniqueness, need for cognition, and creativity. The Journal of Creative Behavior, 37(2), 99–116.

    Google Scholar 

  • Dubois, D., Bonezzi, A., & Angelis, M. D. (2016). Sharing with friends versus strangers: how interpersonal closeness influences word-of-mouth valence. Journal of Marketing Research, 53(5), 712–727.

    Google Scholar 

  • Edelmann, N. (2013). Reviewing the definitions of “lurkers” and some implications for online research. Cyberpsychology, Behavior, and Social Networking, 16(9), 645–649.

    Google Scholar 

  • Fardouly, J., & Vartanian, L. R. (2016). Social media and body image concerns: current research and future directions. Current Opinion in Psychology, 9, 1–5.

    Google Scholar 

  • Fossen, B. L., & Schweidel, D. A. (2016). Television advertising and online word-of-mouth: an empirical investigation of social TV activity. Marketing Science, 36(1), 105–123.

    Google Scholar 

  • Frenzen, J., & Nakamoto, K. (1993). Structure, cooperation, and the flow of market information. Journal of Consumer Research, 20(3), 360–375.

    Google Scholar 

  • Gao, G., Greenwood, B. N., Agarwal, R., & McCullough, J. S. (2015). Vocal minority and silent majority: how do online ratings reflect population perceptions of quality. MIS Quarterly, 39(3), 565–590.

    Google Scholar 

  • Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.

    Google Scholar 

  • Godes, D., & Mayzlin, D. (2009). Firm-created word-of-mouth communication: evidence from a field test. Marketing Science, 28(4), 721–739.

    Google Scholar 

  • Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73(2), 1–13.

    Google Scholar 

  • Goldenberg, J., Libai, B., Muller, E., & Stremersch, S. (2010). Data base submission–the evolving social network of marketing scholars. Marketing Science, 29(3), 561–567.

    Google Scholar 

  • Gourieroux, C., Monfort, A., & Trognon, A. (1984). Pseudo maximum likelihood methods: applications to poisson models. Econometrica, 52(3), 701–720.

    Google Scholar 

  • Hausman, J., Hall, B. H., & Griliches, Z. (1984). Econometric models for count data with an application to the patents R&D relationship. Econometrica, 52(4), 909–938.

    Google Scholar 

  • Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.

    Google Scholar 

  • Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies. Journal of the Academy of Marketing Science, 43(3), 375–394.

    Google Scholar 

  • Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences., 33(2–3), 61–83.

    Google Scholar 

  • Hinz, O., Skiera, B., Barrot, C., & Becker, J. U. (2011). Seeding strategies for viral marketing: an empirical comparison. Journal of Marketing, 75(6), 55–71.

    Google Scholar 

  • Hu, Y., & Li, X. (2011). Context-dependent product evaluations: an empirical analysis of internet book reviews. Journal of Interactive Marketing, 25(3), 123–133.

    Google Scholar 

  • Hung, S. Y., Lai, H. M., & Chou, Y. C. (2015). Knowledge-sharing intention in professional virtual communities: a comparison between posters and lurkers. Journal of the Association for Information Science and Technology, 66(12), 2494–2510.

    Google Scholar 

  • John, L. K., Emrich, O., Gupta, S., & Norton, M. I. (2017). Does “liking” lead to loving? The impact of joining a brand’s social network on marketing outcomes. Journal of Marketing Research, 54(1), 144–155.

    Google Scholar 

  • Katona, Z., Zubcsek, P. P., & Sarvary, M. (2011). Network effects and personal influences: the diffusion of an online social network. Journal of Marketing Research, 48(3), 425–443.

    Google Scholar 

  • Kozinets, R. V. (2002). The field behind the screen: using netnography for marketing research in online communities. Journal of marketing research, 39(1), 61–72.

    Google Scholar 

  • Kozinets, R. V., Valck, K., Wojnicki, A. C., & Wilner, S. J. (2010). Networked narratives: understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(1), 71–89.

    Google Scholar 

  • Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is twitter, a social network or a news media? In Proceedings of the 19th international conference on world wide web (pp. 591–600). New York: Academic.

    Google Scholar 

  • Lai, H. M., & Chen, T. T. (2014). Knowledge sharing in interest online communities: A comparison of posters and lurkers. Computers in Human Behavior, 35, 295–306.

    Google Scholar 

  • Li, X., Bresnahan, T., & Yin, P. L. (2016). Paying incumbents and customers to enter an industry: buying downloads. Available at SSRN: https://ssrn.com/abstract=2834564.

  • Liao, S., & Chou, E. Y. (2012). Intention to adopt knowledge through virtual communities: posters vs lurkers. Online Information Review, 36(3), 442–461.

    Google Scholar 

  • Lin, H. C., & Kalwani, M. U. (2018). Culturally contingent electronic word-of-mouth signaling and screening: a comparative study of product reviews in the United States and Japan. Journal of International Marketing, 26(2), 80–102.

    Google Scholar 

  • Liu, Y. (2006). Word-of-mouth for movies: its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.

    Google Scholar 

  • Liu-Thompkins, Y., & Rogerson, M. (2012). Rising to stardom: an empirical investigation of the diffusion of user-generated content. Journal of Interactive Marketing, 26(2), 71–82.

    Google Scholar 

  • Lovett, M. J., Peres, R., & Shachar, R. (2013). On brands and word of mouth. Journal of Marketing Research, 50(4), 427–444.

    Google Scholar 

  • Lynn, M., & Harris, J. (1997). The desire for unique consumer products: a new individual differences scale. Psychology and Marketing, 14(6), 601–616.

    Google Scholar 

  • Manchanda, P., Packard, G., & Pattabhiramaiah, A. (2015). Social dollars: the economic impact of customer participation in a firm-sponsored online customer community. Marketing Science, 34(3), 367–387.

    Google Scholar 

  • Marett, K., & Joshi, K. D. (2009). The decision to share information and rumors: examining the role of motivation in an online discussion forum. Communications of the Association for Information Systems, 24(1), 47–68.

    Google Scholar 

  • McWilliam, G. (2000). Building stronger brands through online communities. Sloan Management Review., 41(Spring), 43–54.

    Google Scholar 

  • Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.

    Google Scholar 

  • Milkman, K. L., & Berger, J. (2014). The science of sharing and the sharing of science. Proceedings of the National Academy of Sciences, 111(supplement 4), 13642–13649.

    Google Scholar 

  • Mishra, S., & Slotegraaf, R. J. (2013). Building an innovation base: exploring the role of acquisition behavior. Journal of the Academy of Marketing Science, 41(6), 705–721.

    Google Scholar 

  • Moe, W. W., & Schweidel, D. A. (2012). Online product opinions: incidence, evaluation, and evolution. Marketing Science, 31(3), 372–386.

    Google Scholar 

  • Moldovan, S., Steinhart, Y., & Ofen, S. (2015). “Share and scare”: solving the communication dilemma of early adopters with a high need for uniqueness. Journal of Consumer Psychology, 25(1), 1–14.

    Google Scholar 

  • Mousavi, S., Roper, S., & Keeling, K. A. (2017). Interpreting social identity in online brand communities: Considering posters and lurkers. Psychology & Marketing, 34(4), 376–393.

    Google Scholar 

  • Mu, J., Thomas, E., Qi, J., & Tan, Y. (2018). Online group influence and digital product consumption. Journal of the Academy of Marketing Science, 1–27.

  • Nair, H. S., Manchanda, P., & Bhatia, T. (2010). Asymmetric social interactions in physician prescription behavior: the role of opinion leaders. Journal of Marketing Research, 47(5), 883–895.

    Google Scholar 

  • Nonnecke, B., & Preece, J. (1999). Shedding light on lurkers in online communities. Ethnographic Studies in Real and Virtual Environments: Inhabited Information Spaces and Connected Communities.

  • Nonnecke, B., Andrews, D., & Preece, J. (2006). Non-public and public online community participation: needs, attitudes and behavior. Electronic Commerce Research, 6(1), 7–20.

    Google Scholar 

  • Oberholzer-Gee, F. (2014). Buzzfeed - the promise of native advertising. Harvard Business School Case, 714–512.

  • Packard, G., & Berger, J. (2016). How language shapes word-of-mouth’s impact. Journal of Marketing Research.

  • Park, E., Rishika, R., Janakiraman, R., Houston, M. B., & Yoo, B. (2018). Social dollars in online communities: the effect of product, user, and network characteristics. Journal of Marketing, 82(1), 93–114.

    Google Scholar 

  • Perna, D., Interdonato, R., & Tagarelli, A. (2018). Identifying users with alternate behaviors of lurking and active participation in multilayer social networks. IEEE Transactions on Computational Social Systems, 5(1), 46–63.

    Google Scholar 

  • Ransbotham, S., Kane, G. C., & Lurie, N. H. (2012). Network characteristics and the value of collaborative user-generated content. Marketing Science, 31(3), 387–405.

    Google Scholar 

  • Rego, L. L., Billett, M. T., & Morgan, N. A. (2009). Consumer-based brand equity and firm risk. Journal of Marketing, 73(6), 47–60.

    Google Scholar 

  • Ryu, G., & Han, J. K. (2009). Word-of-mouth transmission in settings with multiple opinions: the impact of other opinions on WOM likelihood and valence. Journal of Consumer Psychology, 19(3), 403–415.

    Google Scholar 

  • Schau, H. J., & Gilly, M. C. (2003). We are what we post? Self-presentation in personal web space. Journal of Consumer Research, 30(3), 385–404.

    Google Scholar 

  • Schlosser, A. E. (2005). Posting versus lurking: communicating in a multiple audience context. Journal of Consumer Research, 32(2), 260–265.

    Google Scholar 

  • Seiler, S., Yao, S., & Wang, W. (2017). Does online word-of-mouth increase demand? (and how?) evidence from a natural experiment. Marketing Science, 36(6), 838–861.

    Google Scholar 

  • Shen, Q., & Miguel Villas-Boas, J. (2017). Behavior-based advertising. Management Science, 64(5), 2047–2064.

    Google Scholar 

  • Shriver, S. K., Nair, H. S., & Hofstetter, R. (2013). Social ties and user-generated content: evidence from an online social network. Management Science, 59(6), 1425–1443.

    Google Scholar 

  • Simonson, I., & Nowlis, S. M. (2000). The role of explanations and need for uniqueness in consumer decision making: unconventional choices based on reasons. Journal of Consumer Research, 27(1), 49–68.

    Google Scholar 

  • Snyder, C. R., & Fromkin, H. L. (1977). Abnormality as a positive characteristic: the development and validation of a scale measuring need for uniqueness. Journal of Abnormal Psychology, 86(5), 518–527.

    Google Scholar 

  • Sohn, D. (2009). Disentangling the effects of social network density on electronic word-of-mouth (eWOM) intention. Journal of Computer-Mediated Communication, 14(2), 352–367.

    Google Scholar 

  • Steinhoff, L., & Palmatier, R. W. (2016). Understanding loyalty program effectiveness: managing target and bystander effects. Journal of the Academy of Marketing Science, 44(1), 88–107.

    Google Scholar 

  • Stephen, A. T., Zubcsek, P. P., & Goldenberg, J. (2016). Lower connectivity is better: the effects of network structure on redundancy of ideas and customer innovativeness in interdependent ideation tasks. Journal of Marketing Research, 53(2), 263–279.

    Google Scholar 

  • Stremersch, S., Verniers, I., & Verhoef, P. C. (2007). The quest for citations: drivers of article impact. Journal of Marketing, 71(3), 171–193.

    Google Scholar 

  • Stremersch, S., Camacho, N., Vanneste, S., & Verniers, I. (2015). Unraveling scientific impact: citation types in marketing journals. International Journal of Research in Marketing, 32(1), 64–77.

    Google Scholar 

  • Sun, N., Rau, P. P. L., & Ma, L. (2014). Understanding lurkers in online communities: a literature review. Computers in Human Behavior, 38(2), 110–117.

    Google Scholar 

  • Sun, M., Zhang, X., & Zhu, F. (2016). Nonconformity in online social networks. NET Institute working paper no. 12-15. Available at SSRN: https://ssrn.com/abstract=2164387.

  • Tagarelli, A., & Interdonato, R. (2015). Time-aware analysis and ranking of lurkers in social networks. Social Network Analysis and Mining, 5(1), 46.

    Google Scholar 

  • Takahashi, M., Fujimoto, M., & Yamasaki, N. (2003). The active lurker: Influence of an in-house online community on its outside environment. In Proceedings of the 2003 International ACM SIGGROUP Conference on Supporting Group Work (pp. 1–10). New York: Academic.

  • Thompson, S. A., & Sinha, R. K. (2008). Brand communities and new product adoption: the influence and limits of oppositional loyalty. Journal of Marketing, 72(6), 65–80.

    Google Scholar 

  • Tian, K. T., Bearden, W. O., & Hunter, G. (2001). Consumers’ need for uniqueness: scale development and validation. Journal of Consumer Research, 28(1), 50–66.

    Google Scholar 

  • Toubia, O., & Stephen, A. T. (2013). Intrinsic VS. image-related utility in social media: Why do people contribute content to Twitter? Marketing Science, 32(3), 368–392.

    Google Scholar 

  • Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: findings from an internet social networking site. Journal of Marketing, 73(5), 90–102.

    Google Scholar 

  • Trusov, M., Bodapati, A. V., & Bucklin, R. E. (2010). Determining influential users in internet social networks. Journal of Marketing Research, 47(4), 643–658.

    Google Scholar 

  • Uslay, C., Morgan, R. E., & Sheth, J. N. (2008). Peter drucker on marketing: an exploration of five tenets. Journal of the Academy of Marketing Science, 37(1), 47.

    Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.

    Google Scholar 

  • Villanueva, J., Yoo, S., & Hanssens, D. M. (2008). The impact of marketing-induced versus word-of-mouth customer acquisition on customer equity growth. Journal of Marketing Research, 45(1), 48–59.

    Google Scholar 

  • Wan, E. W., Xu, J., & Ding, Y. (2013). To be or not to be unique? The effect of social exclusion on consumer choice. Journal of Consumer Research, 40(6), 1109–1122.

    Google Scholar 

  • Wang, K., & Goldfarb, A. (2016). Can offline stores drive online sales? Journal of Marketing Research, 54(5), 706–719.

    Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications (Vol. 8). Cambridge: Cambridge University press.

    Google Scholar 

  • Willett, J. (1998). VECO-an online community making a difference, an evaluation of the project. Australian student traineeship foundation and dusseldorp skills forum, Sydney.

  • Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT press.

    Google Scholar 

  • Workman, M. (2014). New media and the changing face of information technology use: The importance of task pursuit, social influence, and experience. Computers in Human Behavior, 31(Supplement C), 111–117.

    Google Scholar 

  • Wu, F., & Huberman, B. A. (2007). Novelty and collective attention. Proceedings of the National Academy of Sciences, 104(45), 17599–17601.

    Google Scholar 

  • Zamudio, C., Wang, Y., & Haruvy, E. (2013). Human brands and mutual choices: an investigation of the marketing assistant professor job market. Journal of the Academy of Marketing Science, 41(6), 722–736.

    Google Scholar 

  • Zeng, X., & Wei, L. (2013). Social ties and user content generation: evidence from Flickr. Information Systems Research, 24(1), 71–87.

    Google Scholar 

  • Zhang, X., & Zhu, F. (2011). Group size and incentives to contribute: a natural experiment at Chinese Wikipedia. The American Economic Review, 101(4), 1601–1615.

    Google Scholar 

  • Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–114.

    Google Scholar 

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Acknowledgements

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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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, China

    Xingyu Chen & Zhimin Zhou

  2. Guanghua School of Management, Peking University, Beijing, China

    Xing Li

  3. NUS Business School, National University of Singapore, Singapore, Singapore

    Dai Yao

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  1. Xingyu Chen
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  2. Xing Li
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  3. Dai Yao
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  4. Zhimin Zhou
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Correspondence to Zhimin Zhou.

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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

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