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

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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.

References

  • Agrawal, A., Catalini, C., & Goldfarb, A. (2015). Crowdfunding: Geography, social networks, and the timing of investment decisions. Journal of Economics & Management Strategy, 24(2), 253–274.

    Article  Google Scholar 

  • Aitamurto, T. (2011). The impact of Crowdfunding on journalism: Case study of spot. Us, a platform for community-funded reporting. Journalism Practice, 5(4), 429–445.

    Article  Google Scholar 

  • Allcott, H., Gentzkow, M., & Yu, C. (2019). Trends in the diffusion of misinformation on social media. Research & Politics, 6(2), 1–8.

    Article  Google Scholar 

  • Aral, S. (2016). The future of weak ties. American Journal of Sociology, 121(6), 1931–1939.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Asch, S. E. (1956). Studies of Independence and conformity: I. a minority of one against a unanimous majority. Psychological Monographs: General and Applied, 70(9), 1–70.

    Article  Google Scholar 

  • Bapna, R., Qiu, L., & Rice, S. (2017). Repeated interactions versus social ties: Quantifying the economic value of trust, forgiveness, and reputation using a field experiment. MIS Quarterly, 41(1), 115–130.

    Article  Google Scholar 

  • Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497–529.

    Article  Google Scholar 

  • Bearden, W. O., Netemeyer, R. G., & Teel, J. E. (1989). Measurement of consumer susceptibility to interpersonal influence. Journal of Consumer Research, 15(4), 473–481.

    Article  Google Scholar 

  • Belk, R. W. (1988). Possessions and the extended self. Journal of Consumer Research, 15(2), 139–168.

    Article  Google Scholar 

  • Bergstrom, T., Blume, L., & Varian, H. (1986). On the private provision of public goods. Journal of Public Economics, 29(1), 25–49.

    Article  Google Scholar 

  • Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026.

    Article  Google Scholar 

  • Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298.

    Article  Google Scholar 

  • Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895.

    Article  Google Scholar 

  • Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350–362.

    Article  Google Scholar 

  • Burt, R. S. (1987). Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology, 92(6), 1287–1335.

    Article  Google Scholar 

  • Burt, R. S. (1992). Structural holes: The social structure of competition: Harvard university press.

  • Burtch, G., Ghose, A., & Wattal, S. (2013). An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets. Information Systems Research, 24(3), 499–519.

    Article  Google Scholar 

  • Burtch, G., Ghose, A., & Wattal, S. (2016). Secret admirers: An empirical examination of information hiding and contribution dynamics in online Crowdfunding. Information Systems Research, 27(3), 478–496.

    Article  Google Scholar 

  • Chamberlain, G. (1980). Analysis of covariance with qualitative data. The Review of Economic Studies, 47(1), 225–238.

    Article  Google Scholar 

  • Cialdini, R. B. (2009). Influence: Science and practice: Pearson education Boston, MA.

  • Coleman, J. S. (1988). Social Capital in the Creation of human capital. American Journal of Sociology, 94, S95–S120.

    Article  Google Scholar 

  • Colombo, M. G., Franzoni, C., & Rossi-Lamastra, C. (2015). Internal social capital and the attraction of early contributions in Crowdfunding. Entrepreneurship: Theory & Practice, 39(1), 75–100.

    Google Scholar 

  • Dai, H., & Zhang, D. J. (2019). Prosocial goal pursuit in Crowdfunding: Evidence from Kickstarter. Journal of Marketing Research, 56(3), 498–517.

    Article  Google Scholar 

  • Davis, D. L., & Rubin, R. S. (1983). Identifying the energy conscious consumer: The case of the opinion leader. Journal of the Academy of Marketing Science, 11(2), 169–190.

    Article  Google Scholar 

  • DellaVigna, S., List, J. A., & Malmendier, U. (2012). Testing for altruism and social pressure in charitable giving. The Quarterly Journal of Economics, 127(1), 1–56.

    Article  Google Scholar 

  • Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629–636.

    Article  Google Scholar 

  • Feld, S. L. (1997). Structural Embeddedness and stability of interpersonal relations. Social Networks, 19(1), 91–95.

    Article  Google Scholar 

  • Festinger, L. (1957). A theory of cognitive dissonance: Stanford university press.

  • Gerber, L., & Hui, J. (2016). Crowdfunding: How and why people participate. In J. Méric, I. Maque, & J. Brabet (Eds.), International perspectives on crowdfunding. Emerald Group Publishing Limited.

  • Gerber, A. S., Green, D. P., & Larimer, C. W. (2010). An experiment testing the relative effectiveness of encouraging voter participation by inducing feelings of pride or shame. Political Behavior, 32(3), 409–422.

    Article  Google Scholar 

  • Goldenberg, J., Lehmann, D. R., Shidlovski, D., & Barak, M. M. (2006). The role of expert versus social opinion leaders in new product adoption. Marketing Science Institute Report(06-124).

  • Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Granovetter, M. (2005). The impact of social structure on economic outcomes. The Journal of Economic Perspectives, 19(1), 33–50.

    Article  Google Scholar 

  • Harkins, S. G., & Petty, R. E. (1987). Information utility and the multiple source effect. Journal of Personality and Social Psychology, 52(2), 260–268.

    Article  Google Scholar 

  • Heider, F. (1958). The psychology of interpersonal relations: Psychology Press.

  • Herzenstein, M., Dholakia, U. M., & Andrews, R. L. (2011). Strategic herding behavior in peer-to-peer loan auctions. Journal of Interactive Marketing, 25(1), 27–36.

    Article  Google Scholar 

  • Hong, Y., Hu, Y., & Burtch, G. (2018). Embeddedness, Prosociality, and social influence: Evidence from online Crowdfunding. MIS Quarterly, 42(4), 1211–1224.

    Google Scholar 

  • Hu, Y., & Van den Bulte, C. (2014). Nonmonotonic status effects in new product adoption. Marketing Science, 33(4), 509–533.

    Article  Google Scholar 

  • Iyengar, R., Van den Bulte, C., & Valente, T. W. (2011). Opinion leadership and social contagion in new product diffusion. Marketing Science, 30(2), 195–212.

    Article  Google Scholar 

  • Ke, Z., Liu, D., & Brass, D. J. (2020). Do online friends bring out the best in us? The effect of friend contributions on online review provision. Information Systems Research, 31(4), 1322–1336.

    Article  Google Scholar 

  • Kelman, H. C. (1961). Processes of opinion change. Public Opinion Quarterly, 25(1), 57–78.

    Article  Google Scholar 

  • Kim, C., Kannan, P. K., Trusov, M., & Ordanini, A. (2020). Modeling dynamics in Crowdfunding. Marketing Science, 39(2), 339–365.

    Article  Google Scholar 

  • Knoke, D., & Yang, S. (2008). Social network analysis (2nd ed.): SAGE Publications.

  • Kuksov, D., & Liao, C. (2019). Opinion leaders and product variety. Marketing Science, 38(5), 812–834.

    Article  Google Scholar 

  • Kuppuswamy, V., & Bayus, B. L. (2017). Does my contribution to your Crowdfunding project matter? Journal of Business Venturing, 32(1), 72–89.

    Article  Google Scholar 

  • Kuppuswamy, V., & Bayus, B. L. (2018), Crowdfunding creative ideas: The dynamics of project backers, in The economics of crowdfunding, Douglas Cumming and Lars Hornuf, eds.: Palgrave Macmillan.

  • Latané, B. (1981). The psychology of social impact. American Psychologist, 36(4), 343–356.

    Article  Google Scholar 

  • Lawyer, G. (2015). Understanding the influence of all nodes in a network. Scientific Reports, 5(1), 1–9.

    Article  Google Scholar 

  • Lee, J. K., & Kronrod, A. (2020). The strength of weak-tie consensus language. Journal of Marketing Research, 57(2), 353–374.

    Article  Google Scholar 

  • Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of Trust in Effective Knowledge Transfer. Management Science, 50(11), 1477–1490.

    Article  Google Scholar 

  • Lin, M., & Viswanathan, S. (2016). Home Bias in online investments: An empirical study of an online Crowdfunding market. Management Science, 62(5), 1393–1414.

    Article  Google Scholar 

  • Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), 17–35.

    Article  Google Scholar 

  • Liu, D., Brass, D., Lu, Y., & Chen, D. (2015). Friendships in online peer-to-peer lending: Pipes, prisms, and relational herding. MIS Quarterly, 39(3), 729–742.

    Article  Google Scholar 

  • Luo, X., Gu, B., Zhang, J., & Phang, C. W. (2017). Expert blogs and consumer perceptions of competing brands. MIS Quarterly, 41(2), 371–395.

    Article  Google Scholar 

  • Mannes, A. E. (2009). Are we wise about the wisdom of crowds? The use of group judgments in belief revision. Management Science, 55(8), 1267–1279.

    Article  Google Scholar 

  • Mollick, E. (2014). The dynamics of Crowdfunding: An exploratory study. Journal of Business Venturing, 29(1), 1–16.

    Article  Google Scholar 

  • Montgomery, J. D. (1992). Job search and network composition: Implications of the strength-of-weak-ties hypothesis. American Sociological Review, 57, 586–596.

    Article  Google Scholar 

  • Moran, P. (2005). Structural Vs. relational Embeddedness: Social capital and managerial performance. Strategic Management Journal, 26(12), 1129–1151.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Neyman, J., & Scott, E. L. (1948). Consistent estimates based on partially consistent observations. Econometrica, 16(1), 1–32.

    Article  Google Scholar 

  • Ordanini, A., Miceli, L., Pizzetti, M., & Parasuraman, A. (2011). Crowd-funding: Transforming customers into investors through innovative service platforms. Journal of Service Management, 22(4), 443–470.

    Article  Google Scholar 

  • Ostrom, E. (2009). Understanding institutional diversity: Princeton university press.

  • Park, C. W., & Lessig, V. P. (1977). Students and housewives: Differences in susceptibility to reference group influence. Journal of Consumer Research, 4(2), 102–110.

    Article  Google Scholar 

  • Peng, J., Agarwal, A., Hosanagar, K., & Iyengar, R. (2018). Network overlap and content sharing on social media platforms. Journal of Marketing Research, 55(4), 571–585.

    Article  Google Scholar 

  • Peng, L., Cui, G., Chung, Y., & Li, C. (2019). A multi-facet item response theory approach to improve customer satisfaction using online product ratings. Journal of the Academy of Marketing Science, 47(5), 960–976.

    Article  Google Scholar 

  • Peng, L., Cui, G., Chung, Y., & Zheng, W. (2020). The faces of success: Beauty and ugliness premiums in E-commerce platforms. Journal of Marketing, 84(4), 67–85.

    Article  Google Scholar 

  • PewResearch (2016). Collaborative: Crowdfunding platforms, [available at https://www.pewresearch.org/internet/2016/05/19/collaborative-crowdfunding-platforms/].

  • Rindfleisch, A., & Moorman, C. (2003). Interfirm cooperation and customer orientation. Journal of Marketing Research, 40(4), 421–436.

    Article  Google Scholar 

  • Rishika, R., & Ramaprasad, J. (2019). The effects of asymmetric social ties, structural Embeddedness, and tie strength on online content contribution behavior. Management Science, 65(7), 3398–3422.

    Article  Google Scholar 

  • Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: An analysis of structural and relational Embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369–386.

    Article  Google Scholar 

  • Sorenson, O., & Stuart, T. E. (2001). Syndication networks and the spatial distribution of venture capital investments. American Journal of Sociology, 106(6), 1546–1588.

    Article  Google Scholar 

  • Sunder, S., Kumar, V., Goreczny, A., & Maurer, T. (2017). Why do salespeople quit? An empirical examination of own and peer effects on salesperson turnover behavior. Journal of Marketing Research, 54(3), 381–397.

    Article  Google Scholar 

  • Thies, F., Wessel, M., & Benlian, A. (2016). Effects of social interaction dynamics on platforms. Journal of Management Information Systems, 33(3), 843–873.

    Article  Google Scholar 

  • Uzzi, B. (1996). The sources and consequences of Embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61(4), 674–698.

    Article  Google Scholar 

  • Wang, J., Aribarg, A., & Atchadé, Y. F. (2013). Modeling choice interdependence in a social network. Marketing Science, 32(6), 977–997.

    Article  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications: Cambridge university press.

  • Xiang, D., Zhang, L., Tao, Q., Wang, Y., & Ma, S. (2019). Informational or emotional appeals in Crowdfunding message strategy: An empirical investigation of backers’ support decisions. Journal of the Academy of Marketing Science, 47(6), 1046–1063.

    Article  Google Scholar 

  • Zhang, Y., & Godes, D. (2018). Learning from online social ties. Marketing Science, 37(3), 425–444.

    Article  Google Scholar 

  • Zhang, J., & Liu, P. (2012). Rational herding in microloan markets. Management Science, 58(5), 892–912.

    Article  Google Scholar 

  • Zhang, X., Li, S., Burke, R. R., & Leykin, A. (2014). An examination of social influence on shopper behavior using video tracking data. Journal of Marketing, 78(5), 24–41.

    Article  Google Scholar 

  • Zhang, B., Pavlou, P. A., & Krishnan, R. (2018). On direct Vs. indirect peer influence in large social networks. Information Systems Research, 29(2), 292–314.

    Article  Google Scholar 

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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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