Of supranodes and socialwashing: network theory and the responsible innovation of social media platforms

References

• Anderson, L. R., & Holt, C. A. (1997). The American Economic Review, 87(5), 847–18.
   Web of Science ®Google Scholar
• Ansari, S., & Phillips, N. (2011). Text me! New consumer practices and change in organizational fields. Organization Science, 22(6), 1579–1599. https://doi.org/10.1287/orsc.1100.0595
   Web of Science ®Google Scholar
• Aral, S., & Walker, D. (2011). Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Science, 57(9), 1623–1639. https://doi.org/10.1287/mnsc.1110.1421
   Web of Science ®Google Scholar
• Balzarova, M. A., & Castka, P. (2012). Stakeholders’ influence and contribution to social standards development: The case of multiple stakeholder approach to ISO 26000 development. Journal of Business Ethics, 111(2), 265–279. https://doi.org/10.1007/s10551-012-1206-9
   Web of Science ®Google Scholar
• Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The effects of the social structure of digital networks on viral marketing performance. Information Systems Research, 19(3), 273–290. https://doi.org/10.1287/isre.1070.0152
   Web of Science ®Google Scholar
• Bandura, A. (1977). Social learning theory. Prentice Hall.
   Google Scholar
• Barton, A. M. (2009). Application of cascade theory to online systems: A study of email and Google cascades. Minnesota Journal of Law, Science & Technology, 10(2), 473–502. scholarship.law.umn.edu/cgi/viewcontent.cgi?article=1199&context=mjlst
   Google Scholar
• Bastos, M., & Farkas, J. Donald Trump is my President!: The internet research agency propaganda machine. Social Media + Society: 1–13 https://doi.org/10.1177/2056305119865466
   Google Scholar
• Besiou, M., Hunter, M. L., & Van Wassenhove, L. N. (2013). A web of watchdogs: Stakeholder media networks and agenda-setting in response to corporate initiatives. Journal of Business Ethics, 118(4), 709–729. https://doi.org/10.1007/s10551-013-1956-z
   Web of Science ®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. https://doi.org/10.1086/261849
   Web of Science ®Google Scholar
• Bikhchandani, S., Hirshleifer, D., & Welch, I. (1998). Learning from the behavior of others: Conformity, fads and informational cascades. Journal of Economic Perspectives, 12(3), 151–170. https://doi.org/10.1257/jep.12.3.151
   Web of Science ®Google Scholar
• Borgatti, S. P., & Halgin, D. S. (2011). On network theory. Organization Science, 22(5), 1168–1181. https://doi.org/10.1287/orsc.1100.0641
   Web of Science ®Google Scholar
• Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network Analysis in the Social Sciences. Science, 323(5916), 892–895. https://doi.org/10.1126/science.1165821
   PubMed Web of Science ®Google Scholar
• Boudreau, M. C., & Robey, D. (2005). Organization Science, 16(1), 3–18.
   Web of Science ®Google Scholar
• Bovet, A., & Makse, H. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature Communications, 10(1), 7. https://doi.org/10.1038/s41467-018-07761-2
   PubMed Web of Science ®Google Scholar
• boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x
   Web of Science ®Google Scholar
• Breuninger, K. 2019. Robert Mueller’s Russia probe cost nearly $32 million in total, Justice Department says. CNBC, https://www.cnbc.com/2019/08/02/robert-muellers-russia-probe-cost-nearly-32-million-in-total-doj.html
   Google Scholar
• Burt, R. (1992). Structural holes: The social structure of competition. Harvard University Press.
   Google Scholar
• Butler, B. (2001). Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information Systems Research, 12(4), 346–362. https://doi.org/10.1287/isre.12.4.346.9703
   Web of Science ®Google Scholar
• Carroll, C. E., & McCombs, M. E. (2003). Agenda-setting effects of business news on the public’s images and opinions about major corporations. Corporate Reputation Review, 6(1), 36–46. https://doi.org/10.1057/palgrave.crr.1540188
   Google Scholar
• Celen, B., & Kariv, S. (2004). Distinguishing informational cascades from herd behavior in the laboratory. American Economic Review, 94(3), 484–498. https://doi.org/10.1257/0002828041464461
   Web of Science ®Google Scholar
• Cha, M., Haddadi, H., Benevenuto, F., & Gummad, K. P. 2010. “Measuring user influence on Twitter: The million-follower fallacy.” AAAI Conference on Weblogs And Social Media, May 16 https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1538/1826
   Google Scholar
• Chesney, T. (2016). The cascade capacity predicts individuals to seed for diffusion through social networks. Systems Research and Behavioral Science, 34(1), 51–61. https://doi.org/10.1002/sres.2398
   Web of Science ®Google Scholar
• de Bakker, F. G. A., & Hellsten, I. (2013). Capturing online presence: Hyperlinks and semantic networks in activist group websites on corporate social responsibility. Journal of Business Ethics, 118(4), 807–823. https://doi.org/10.1007/s10551-013-1962-1
   Web of Science ®Google Scholar
• Delmas, M., & Burbano, V. (2011). The drivers of greenwashing. California Management Review, 54(1), 64. https://doi.org/10.1525/cmr.2011.54.1.64
   Web of Science ®Google Scholar
• Devaraj, S., & Kohli, R. (2003). Performance impacts of information technology: Is actual usage the missing link? Management Science, 49(3), 273–289. https://doi.org/10.1287/mnsc.49.3.273.12736
   Web of Science ®Google Scholar
• Diani, M. (2000). Social movement networks virtual and real. Information, Communication & Society, 93(3), 386–401. https://doi.org/10.1080/13691180051033333
   Google Scholar
• Dictionary.com. 2019. https://www.dictionary.com/browse/supra-
   Google Scholar
• Donath, J., & Boyd, D. (2004). Public displays of connection. BT Technology Journal, 22(4), 71–82. https://doi.org/10.1023/B:BTTJ.0000047585.06264.cc
   Web of Science ®Google Scholar
• Dunbar, R., & Hill, R. (2002). Social network size in humans. Human Nature, 14(1), 53–72. researchgate.net/publication/281203308_Social_Network_Size_in_Humans
   Web of Science ®Google Scholar
• Ellison, N. B., & boyd, D. M. (2013). Sociality through Social Network Sites. In W. H. Dutton (Ed.), The Oxford Handbook of Internet Studies (pp. 151–172). Oxford University Press.
   Google Scholar
• Facebook. 2019. https://www.facebook.com/help/community/question/?id=765037383609149
   Google Scholar
• Federal Election Commission. 2016. Federal elections 2016 election results for the U.S. President, the U.S. Senate and the U.S. house of representatives. https://www.fec.gov/resources/cms-content/documents/federalelections2016.pdf
   Google Scholar
• Fieseler, C., & Fleck, M. (2013). The pursuit of empowerment through social media: Structural social capital dynamics in CSR-blogging. Journal of Business Ethics, 118(4), 759–775. https://doi.org/10.1007/s10551-013-1959-9
   Web of Science ®Google Scholar
• Freeman, L. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
   Web of Science ®Google Scholar
• George, A., & Bennett, A. (2005). Case studies and theory development in the social sciences. MIT Press.
   Google Scholar
• Gilbert, E., & Karahalios, K. 2009. Predicting tie strength with social media. Proceedings of the 27th International Conference on Human Factors in Computing Systems 211–220.
   Google Scholar
• Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469
   Web of Science ®Google Scholar
• Granovetter, M. (1978). Threshold models of collective behavior. The American Journal of Sociology, 83(6), 1420–1443. https://doi.org/10.1086/226707
   Web of Science ®Google Scholar
• Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425), 374–378. https://doi.org/10.1126/science.aau2706
   PubMed Web of Science ®Google Scholar
• Guo, L., & Vargo, C. (2018). Fake news and emerging online media ecosystem: An integrated intermedia agenda-setting analysis of the 2016 U.S. Presidential election. Communications Research, 47(2), 178–200. https://doi.org/10.1177/0093650218777177
   Web of Science ®Google Scholar
• Hansen, M. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82–111. https://doi.org/10.2307/2667032
   Web of Science ®Google Scholar
• Hinz, O., Skiera, B., Barrot, C., & Becker, J. (2011). Seeding strategies for viral marketing: An empirical comparison. Journal of Marketing, 75(6), 55–71. https://doi.org/10.1509/jm.10.0088
   Web of Science ®Google Scholar
• Jackson, K., & Bazeley, P. (2019). Qualitative data analysis with NVivo. SAGE Publications.
   Google Scholar
• Kahn, R., & Kellner, D. (2004). New media and internet activism: From the ‘Battle of Seattle’ to blogging. New Media & Society, 6(1), 87–95. https://doi.org/10.1177/1461444804039908
   Web of Science ®Google Scholar
• Kane, G. C., Alavi, M., LaBlanca, G., & Borgatti, S. P. (2014). What’s different about social media networks? A framework and research agenda. MIS Quarterly, 38(1), 275–304. https://doi.org/10.25300/MISQ/2014/38.1.13
   Web of Science ®Google Scholar
• Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35(2), 342–369. https://doi.org/10.2307/2393394
   Web of Science ®Google Scholar
• Krackhardt, D., & Kilduff, M. (1999). Whether close or far: social distance effects on perceived balance in friendship networks. Journal of Personality & Social Psychology, 76(5), 770–782. https://doi.org/10.1037/0022-3514.76.5.770
   Web of Science ®Google Scholar
• Lua, A. 2019. 21 Top social media sites to consider for your brand. Buffer Marketing Library. https://buffer.com/library/social-media-sites
   Google Scholar
• Maiz, A., Arranz, N., & de Arroyabe, J. (2016). Factors affecting social interaction on social network sites: The Facebook case. Journal of Enterprise Information Systems, 29(5), 630–649. researchgate.net/publication/307981423_Factors_affecting_social_interaction_on_social_network_sites_the_Facebook_case
   Google Scholar
• Major, A. M. (2000). Norm origin and development in cyberspace: Models of cybernorm evolution. Washington University Law Quarterly, 78(1), 59–111. https://openscholarship.wustl.edu/law_lawreview/vol78/iss1/2/
   Google Scholar
• Malone, T., Laubacher, R., & Dellarocas, C. (2010). The collective intelligence genome. MIT Sloan Management Review, 51(3), 21–31. sloanreview.mit.edu/article/the-collective-intelligence-genome/
   Web of Science ®Google Scholar
• Marineau, J. E., Labianca, G., Borgatti, S. P., & Brass, D. J. (2018). Individuals’ formal power and their social network accuracy. Social Networks, 54, 145–161. https://doi.org/10.1016/j.socnet.2018.01.006
   Web of Science ®Google Scholar
• Mayhew, B., & Levinger, R. (1976). Size and the Density of Interaction in Human Aggregates. American Journal of Sociology, 82(1), 86–110. https://doi.org/10.1086/226271
   Web of Science ®Google Scholar
• McCombie, S., Uhlmann, A., & Morrison, S. (2020). The US 2016 presidential election and Russia’s troll farms. Intelligence and National Security, 35(1), 95–114. https://doi.org/10.1080/02684527.2019.1673940
   Web of Science ®Google Scholar
• McCombs, M. E. (2004). Setting the agenda: The mass media and public opinion. Polity Press.
   Google Scholar
• McPherson, J. M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415
   Web of Science ®Google Scholar
• Mueller, R. 2019. Report on The Investigation into Russian Interference in the 2016 Presidential Election. U.S. Department of Justice. https://www.justice.gov/storage/report.pdf
   Google Scholar
• Naroditskiy, V., Jennings, N., Van Hentenryck, P., & Cebrian, M. (2014). Crowdsourcing contest dilemma. Journal of the Royal Society Interface, 11. http://dx.doi.org/10.1098/rsif.2014.0532
   Web of Science ®Google Scholar
• Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153
   Web of Science ®Google Scholar
• Nass, C., Moon, Y., & Carney, P. (1999). Are people polite to computers? Responses to computer-based interviewing systems. Journal of Applied Social Psychology, 29(5), 1093–1110. https://doi.org/10.1111/j.1559-1816.1999.tb00142.x
   Web of Science ®Google Scholar
• Nass, C., Moon, Y., & Fogg, B. (1995). Can computer personalities be human personalities? International Journal of Human-Computer Studies, 43(2), 223–239. https://doi.org/10.1006/ijhc.1995.1042
   Web of Science ®Google Scholar
• National Archives. 2022. Electoral College. https://www.archives.gov/electoral-college/faq
   Google Scholar
• Netto, S., Sobral, M., & Soares, G. (2020). Concepts and forms of greenwashing: A systemic review. Environmental Science Europe, 32, 1. enveurope.springeropen.com/articles/10.1186/s12302-020-0300-3
   Web of Science ®Google Scholar
• Noyes, D. 2019. Top 10 Twitter Statistics. Zephoria Digital Marketing. https://zephoria.com/twitter-statistics-top-ten/
   Google Scholar
• Oh, W., & Jeon, S. (2007). Membership herding and network stability in the open source community: The ising perspective. Management Science, 53(7), 1086–1101. https://doi.org/10.1287/mnsc.1060.0623
   Web of Science ®Google Scholar
• Oliver, C. (1991). Strategic responses to institutional processes. Academy of Management Review, 16(1), 145–179. https://doi.org/10.2307/258610
   Web of Science ®Google Scholar
• Pollet, T. V., Robert, S. G., & Dunbar, R. I. (2011). Use of social network sites and instant messaging does not lead to increased offline social network size, or to emotionally closer relationships with offline network members. Cyberpsychology, Behavior, and Social Networking, 14(4), 253–258. https://doi.org/10.1089/cyber.2010.0161
   PubMed Web of Science ®Google Scholar
• Polyakova, A. 2019. Want to know what’s next in Russian election interference? Pay attention to Ukraine’s elections. BROOKINGS. https://www.brookings.edu/blog/order-from-chaos/2019/03/28/want-to-know-whats-next-in-russian-election-interference-pay-attention-to-ukraines-elections/
   Google Scholar
• Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.
   Google Scholar
• Ren, Y., Kraut, R., & Kiseler, S. (2007). Applying common identity and bond theory to design of online communities. Organization Studies, 28(3), 377–408. https://doi.org/10.1177/0170840607076007
   Web of Science ®Google Scholar
• Rogers, E. (2003). Diffusion of innovations. Free Press.
   Google Scholar
• Rowley, T. J. (1997). Moving beyond dyadic ties: A network theory of stakeholder influences. Academy of Management Review, 22(4), 887–910. https://doi.org/10.5465/amr.1997.9711022107
   Web of Science ®Google Scholar
• Salge, C., & Karahanna, E. (2018). Protesting corruption on Twitter: Is it a bot or is it a person. Academy of Management Discoveries, 4(1), 32–49. https://doi.org/10.5465/amd.2015.0121
   Web of Science ®Google Scholar
• Shirky, C. (2011). The political power of social media. Foreign Affairs, 90(1), 28. https://www.jstor.org/stable/25800379
   Web of Science ®Google Scholar
• Smith, K. 2019. 53 Incredible Facebook statistics and facts. Brandwatch https://www.brandwatch.com/blog/facebook-statistics/
   Google Scholar
• Smith, L., & Sorensen, P. (2000). Pathological outcomes of observational learning. Econometrica, 68(2), 371–398. https://doi.org/10.1111/1468-0262.00113
   Web of Science ®Google Scholar
• Suchman, M. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571–610. https://doi.org/10.2307/258788
   Web of Science ®Google Scholar
• Sunstein, C. (2001). Republic.com. Princeton University Press.
   Google Scholar
• Wasserman, S., & Faust, K. (1994). Social network analysis: Methods & application. Cambridge University Press.
   Google Scholar
• Watts, D. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences of the United States of America, 99(9), 5766–5771. https://doi.org/10.1073/pnas.082090499
   PubMed Web of Science ®Google Scholar
• Watts, D. J. (2004). Six degrees: The science of a connected age. WW Norton & Company.
   Google Scholar
• Watts, D., Duncan, J., & Dodds, P. (2007). Influentials, networks, and public opinion information. Journal of Consumer Research, 34(4), 441–458. https://doi.org/10.1086/518527
   Web of Science ®Google Scholar
• Welch, I. (1992). Sequential sales, learning and cascades. Journal of Finance, 47(3), 695–732. https://doi.org/10.1111/j.1540-6261.1992.tb04406.x
   Google Scholar
• Wu, Y., Zhang, K., & Xie, J. (2020). Bad greenwashing, good greenwashing: Corporate social responsibility and information transparency. Management Science, 66(7), 3095–3112. https://doi.org/10.1287/mnsc.2019.3340
   Web of Science ®Google Scholar
• Yin, R.K. (2009). Case Study Research Design and Methods. Sage.
   Google Scholar