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Exploring embeddedness, centrality, and social influence on backer behavior: the role of backer networks in crowdfunding

Abstract

This research examines the influence of backers’ social networks on their backing behavior using data from a large social networking site and a reward-based crowdfunding platform. We distinguish the roles of nodes and ties in a backer’s social network and assess the combined and differential effects of these two types of social relationship on the backer’s pledge decisions. As backers have different motives for engaging in different crowdfunding campaigns, which range from commercially oriented technological innovation to community-based social development, we further examine how these effects differ between technology-oriented campaigns and social-oriented campaigns. We find that node-level factors (e.g., centrality) have a greater influence on technology-oriented campaigns than on social-oriented campaigns, while tie-level factors (e.g., embeddedness) have a stronger impact on social-oriented campaigns. Considering the two forms of embeddedness in tandem, we find that the effects of relational embeddedness on backers’ pledge decisions are not only moderated by structural embeddedness but also contingent on campaign type. These results offer important theoretical insights into the drivers of contribution, which should be considered by crowdfunding operators and campaign proponents seeking to stimulate contribution.

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Notes

  1. In Web Appendix A, we have conducted a thorough review on the related literature in Table A1.

  2. Table A2 summarizes the relevant research problems, theoretical issues, and findings.

  3. The notion of structural embeddedness fundamentally differs from that of network density. The former measures dyadic-tie relationships, which are defined as the number of share common peers between two members (Aral & Walker, 2014; Peng et al., 2018). The latter is a global metric of backers’ aggregate network. The concept of structural embeddedness (i.e., the number of friends shared by a potential backer and her connected influencers) instead of network density is used in this study because the theories underlying our hypotheses are more closely connected to egocentric networks than sociometric networks. Theoretically, we assume that the pledging decision of a given potential backer will be influenced more by her connected influencers than by network density, whose calculations involve links between influencers themselves.

  4. An advantage of using this time interval is that website layout and content remains relatively constant throughout this period. In August 2014, rumors spread that Demohour was abandoning the crowdfunding business to become an e-commerce platform for pre-orders and deals on e-products. As this rumor may have influenced backer behavior, we limit our analysis to campaigns posted within the abovementioned time frame.

  5. We extract the technical terms from the Techopedia website (http://techopedia.com/dictionary). As shown in Web Appendix B, the descriptions of technology-oriented campaigns contain more technical terms than those of social-oriented campaigns.

  6. Web Appendix C provides the details of measures. Factor analysis suggested a two-factor solution. The five items of informational influence formed a single factor. Four items of utilitarian influence and five items of value-expressive influence were loaded together. The results suggest that value-expressive and utilitarian influences are not distinct, and should be merged to one normative factor (Bearden et al., 1989).

  7. http://ir.weibo.com/phoenix.zhtml?c=253076&p=irol-homeProfile.

  8. We discuss this potential self-selection bias in Web Appendix D.

  9. We create a sample that comprised backer–campaign pairs on a daily basis. A backer–campaign pair is included if the backer is active during the duration of the focal campaign, with active defined as having pledged at least once (to any campaign) on that day. A campaign is active if it accepts pledges on day t (Kuppuswamy & Bayus, 2017). This setting reduces the risk of including backers who no longer visit the crowdfunding site. However, it may also have omitted backers who visit the site but do not pledge to any campaign on a particular day. We address this potential selection bias in a robustness test (see Web Appendix F).

  10. We consider peers to be socially connected if they are mutual followers on Weibo. In the robustness test, we also use the ratio of connected ties in the focal campaign, defined as the number of connected ties supporting focal campaign k, to the total number of ties of a potential backer (i.e., 3/5 in Figure 3) as an alternative measure of the social influence arising from the number of influencers.

  11. We counted the number of interactions a potential backer interacted with her influencers prior to the potential backer’ decisions on pledging the campaign.

  12. The distribution of the interaction level for all connected dyads is shown in Web Appendix H. In a robustness test, we operationalize relational embeddedness as the combined measure of several types of social ties between dyads: whether the dyad is made up of neighbors (living in the same place), classmates (attending the same college), or coworkers (working in the same firm; Aral & Walker, 2014) in Web Appendix F.

  13. The distribution of the number of common contacts for all connected dyads is shown in Appendix H. We also demonstrate robustness to an alternative measure, the normalized measure of the number of connections shared between the dyads (Bapna et al., 2017).

  14. We provide the correlations of all key variables in Web Appendix E.

  15. To test the hypotheses H2a–H2d, we introduce two dichotomous variables TC and SC to indicate the campaign type. The reference group is other campaigns that are neither technology-oriented nor social-oriented.

  16. To test H2a, we conduct statistical test for the equality of coefficients between TC × Degree(potential backer) and SC × Degree(potential backer). In other words, we compare the coefficients of these two interaction terms (βTC, Degree(potential backer) – βSC, Degree(potential backer) = .219, p < .01) and check the equality of coefficients. We conduct the same statistical tests for hypotheses H2b, H2c, and H2d.

  17. We exclude campaigns that are neither social-oriented nor technology-oriented in the subset analysis.

  18. https://www.kickstarter.com/help/handbook.

  19. https://www.kickstarter.com/help/stats.

  20. In Web Appendix G, we examine how the social ties of backers affect the amount of their contributions.

  21. Please see Web Appendix F for a detailed discussion of reverse causality.

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Acknowledgments

This research is supported by HKIBS Research Seed Fund, Lingnan University (RSF-201-005), Lam Woo Research Fund, Lingnan University, and the National Natural Science Foundation of China (71802166).

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Chung, Y., Li, Y. & Jia, J. Exploring embeddedness, centrality, and social influence on backer behavior: the role of backer networks in crowdfunding. J. of the Acad. Mark. Sci. 49, 925–946 (2021). https://doi.org/10.1007/s11747-021-00779-x

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Keywords

  • Backer behavior
  • Centrality
  • Crowdfunding
  • Embeddedness
  • Social influence
  • Social network
  • Tie strength