Research on the wisdom of crowds (WOC) identifies two paradoxical effects of communication. The social influence effect hampers the WOC, whereas the collective learning effect improves crowd wisdom. Yet it remains unclear under what conditions such communication impedes or enhances collective wisdom. The current study examined two features characterizing communication in online communities, communication network centralization and shared task experience, and their effect on the WOC. Both these features can serve as indicators of the likelihood that underlying communication may facilitate either social influence or collective learning. With an 8-year longitudinal behavioral-trace data set of 269,871 participants and 1,971 crowds, we showed that communication network centralization negatively affected the WOC. By contrast, shared task experience positively predicted the WOC. Shared task experience also moderated the effect of communication network centralization such that centralized communication networks became more beneficial for crowd performance as shared task experience increased.
ArgoteL.AvenB. L.KushJ. (2018). The effects of communication networks and turnover on transactive memory and group performance. Organization Science, 29, 191–206. https://doi.org/10.1287/orsc.2017.1176
2.
ArgoteL.Miron-SpektorE. (2011). Organizational learning: From experience to knowledge. Organization Science, 22, 1123–1137. https://doi.org/10.1287/orsc.1100.0621
3.
ArrowK. J. (1962). The economic learning implications of by doing. The Review of Economic Studies, 29, 155–173.
4.
BanduraA. (1977). Social learning theory. Prentice Hall.
BeckerJ.BrackbillD.CentolaD. (2017). Network dynamics of social influence in the wisdom of crowds. Proceedings of the National Academy of Sciences of the United States of America, 114, E5070–E5076. https://doi.org/10.1073/pnas.1615978114
BoudreauK. J.LakhaniK. R. (2015). “Open” disclosure of innovations, incentives and follow-on reuse: Theory on processes of cumulative innovation and a field experiment in computational biology. Research Policy, 44, 4–19. https://doi.org/10.1016/j.respol.2014.08.001
9.
BrabhamD. C. (2012). Motivations for participation in a crowdsourcing application to improve public engagement in transit planning. Journal of Applied Communication Research, 40, 307–328. https://doi.org/10.1080/00909882.2012.693940
10.
BudescuD. V.ChenE. (2015). Identifying expertise to extract the wisdom of crowds. Management Science, 61, 267–280. https://doi.org/10.1287/mnsc.2014.1909
11.
BurghardtK.AlsinaE. F.GirvanM.RandW.LermanK. (2017). The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange. PLOS ONE, 12, Article e0173610. https://doi.org/10.1371/journal.pone.0173610
12.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
13.
DeMarzoP. M.VayanosD.ZwiebelJ. (2003). Persuasion bias, social influence, and unidimensional opinions. The Quarterly Journal of Economics, 118, 909–968.
FestingerL.SchachterS.BackK. (1950). Social pressures in informal groups: A study of human factors in housing (Vol. x). Harper.
16.
GibbsJ. L.KimH.KiS. (2016). Investigating the role of control and support mechanisms in members’ sense of virtual community. Communication Research, 46, 117–145. https://doi.org/10.1177/0093650216644023
17.
GoelS.HofmanJ. M.LahaieS.PennockD. M.WattsD. J. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, 107, 17486–17490. https://doi.org/10.1073/pnas.1005962107
18.
GolubB.JacksonM. O. (2010). Naïve learning in social networks and the wisdom of crowds. American Economic Journal: Microeconomics, 2, 112–149. https://doi.org/10.1257/mic.2.1.112
19.
GuthK. L.BrabhamD. C. (2017). Finding the diamond in the rough: Exploring communication and platform in crowdsourcing performance. Communication Monographs, 84, 510–533. https://doi.org/10.1080/03637751.2017.1359748
20.
HandcockM. S.HunterD. R.ButtsC. T.GoodreauS. M.MorrisM. (2003). statnet: Software tools for the statistical modeling of network data. http://statnetproject.org
HongH.YeQ.DuQ.WangG. A.FanW. (2020). Crowd characteristics and crowd wisdom: Evidence from an online investment community. Journal of the Association for Information Science and Technology, 71, 423–435. https://doi.org/10.1002/asi.24255
HongL.PageS. E. (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences of the United States of America, 101(46), 16385–16389. https://doi.org/10.1073/pnas.0403723101
25.
HoweJ. (2008). Crowdsourcing: Why the power of the crowd is driving the future of business. Crown Business.
26.
IbarraH. (1993). Network centrality, power, and innovation involvement: Determinants of technical and administrative roles. Academy of Management Journal, 36, 471–501.
27.
JanisI. L. (1982). Groupthink: Psychological studies of policy decisions and fiascoes (2nd ed.). Cengage.
28.
JebbA. T.TayL.WangW.HuangQ. (2015). Time series analysis for psychological research: Examining and forecasting change. Frontiers in Psychology, 6, 2–24. https://doi.org/10.3389/fpsyg.2015.00727
29.
KeuschniggM.GanserC. (2016). Crowd wisdom relies on agents’ ability in small groups with a voting aggregation rule. Management Science, 63, 818–828. https://doi.org/10.1287/mnsc.2015.2364
30.
KingA. J.ChengL.StarkeS. D.MyattJ. P. (2012). Is the true “wisdom of the crowd” to copy successful individuals?Biology Letters, 8, 197–200. https://doi.org/10.1098/rsbl.2011.0795
31.
KoehlerD. J.BeauregardT. A. (2006). Illusion of confirmation from exposure to another’s hypothesis. Journal of Behavioral Decision Making, 19, 61–78. https://doi.org/10.3389/fpsyg.2015.00888
LeavittH. J. (1951). Some effects of certain communication patterns on group performance. The Journal of Abnormal and Social Psychology, 46, 38–50.
34.
LorenzJ.RauhutH.SchweitzerF.HelbingD. (2011). How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences of the United States of America, 108, 9020–9025. https://doi.org/0.1073/pnas.1008636108
35.
LuoY.IyengarG.VenkatasubramanianV. (2018). Social influence makes self-interested crowds smarter: An optimal control perspective. IEEE Transactions on Computational Social Systems, 5, 200–209. https://doi.org/10.1109/TCSS.2017.2780270
36.
MajchrzakA.MalhotraA. (2016). Effect of knowledge-sharing trajectories on innovative outcomes in temporary online crowds. Information Systems Research, 27, 685–703. https://doi.org/10.1287/isre.2016.0669
37.
MarwellG.OliverP.PrahlR. (1988). Social networks and collective action: A theory of the critical mass. III. American Journal of Sociology, 94, 502–534.
38.
MasonW.WattsD. J. (2012). Collaborative learning in networks. Proceedings of the National Academy of Sciences of the United States of America, 109, 764–769. https://doi.org/10.1073/pnas.1110069108
39.
McClearyR.HayR. A.MeidingerE. E.McDowallD. (1980). Applied time series analysis for the social sciences. SAGE.
40.
MertonR. K. (1968). The Matthew effect in science: The reward and communication systems of science are considered. Science, 159, 56–63.
41.
MosselE.SlyA.TamuzO. (2015). Strategic learning and the topology of social networks. Econometrica, 83, 1755–1794. https://doi.org/10.3982/ECTA12058
NavajasJ.NiellaT.GarbulskyG.BahramiB.SigmanM. (2018). Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds. Nature Human Behaviour, 2, 126–132. https://doi.org/10.1038/s41562-017-0273-4
44.
Noriega-CamperoA.AlmaatouqA.KrafftP.AlotaibiA.MoussaidM.PentlandA. (2018). The wisdom of the network: How adaptive networks promote collective intelligence. arXiv:1805.04766. http://arxiv.org/abs/1805.04766
45.
PageS. E. (2007). The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton University Press.
46.
RansbothamS.KaneG. C. (2011). Membership turnover and collaboration success in online communities: Explaining rises and falls from grace in Wikipedia. MIS Quarterly, 35, 613–627. https://doi.org/10.1080/01972243.2017.1391909
47.
ReidlC.SeidelV. (2018). Learning from mixed signals in online innovation communities. Organization Science, 29, 1010–1032. https://doi.org/10.1287/orsc.2018.1219
48.
RenR.YanB. (2017, May). Crowd diversity and performance in Wikipedia: The mediating effects of task conflict and communication. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 6342–6351). ACM.
49.
RobertL. P.RomeroD. M. (2016). The influence of diversity and experience on the effects of crowd size. Journal of the Association for Information Science and Technology, 68, 321–332. https://doi.org/10.1002/asi.23653
50.
SalganikM. J.DoddsP. S.WattsD. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854–856. https://doi.org/10.1126/science.1121066
51.
SalganikM. J.WattsD. J. (2009). Web-based experiments for the study of collective social dynamics in cultural markets. Topics in Cognitive Science, 1, 439–468. https://doi.org/10.1111/j.1756-8765.2009.01030.x
SurowieckiJ. (2005). The wisdom of crowds. New York, NY: Anchor Books.
54.
ToyokawaW.WhalenA.LalandK. N. (2018). Social learning strategies regulate the wisdom and madness of interactive online crowds. BioRxiv, Article 326637. https://doi.org/10.1101/326637
55.
WaltherJ. B. (1996). Computer-mediated communication impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43.
56.
WisdomT. N.SongX.GoldstoneR. L. (2013). Social learning strategies in networked groups. Cognitive Science, 37, 1383–1425. https://doi.org/10.1111/cogs.12052
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