On Direct vs. Indirect Peer Influence in Large Social Networks

Published Online:https://doi.org/10.1287/isre.2017.0753

References

  • Agarwal R, Gupta AK, Kraut R (2008) Editorial overview—The interplay between digital and social networks. Inform. Systems Res. 19(3):243–252.LinkGoogle Scholar
  • Ahuja G (2000) Collaboration networks, structural holes, and innovation: A longitudinal study. Admin. Sci. Quart. 45(3):425–455.CrossrefGoogle Scholar
  • Allenby GM, Rossi PE (1998) Marketing models of consumer heterogeneity. J. Econometrics 89(1–2):57–78.CrossrefGoogle Scholar
  • Aral S, Walker D (2011) Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Sci. 57(9):1623–1639.LinkGoogle Scholar
  • Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. 106(51):21544–21549.CrossrefGoogle Scholar
  • Bass F (1969) A new product growth for model consumer durables. Management Sci. 15(5):215–227.LinkGoogle Scholar
  • Bernheim BD (1994) A theory of conformity. J. Political Econom. 102(5):841–877.CrossrefGoogle Scholar
  • Bonacich P (1987) Power and centrality: A family of measures. Amer. J. Sociol. 92(5):1170–1182.CrossrefGoogle Scholar
  • Bott H (1928) Observation of play activities in a nursery school. Genetic Psych. Monographs 4(1):44–88.Google Scholar
  • Bramoullé Y, Djebbari H, Fortin B (2009) Identification of peer effects through social networks. J. Econometrics 150(1):41–55.CrossrefGoogle Scholar
  • Brancheau CJ, Wetherbe CJ (1990) The adoption of spreadsheet software: Testing innovation diffusion theory in the context of end-user computing. Inform. Systems Res. 1(2):115–143.LinkGoogle Scholar
  • Broadcast Music Inc. (2009) BMI announces 2009 mobile music market projections. https://www.bmi.com/press/entry/538235/.Google Scholar
  • Burkhardt ME (1994) Social interaction effects following a technological change: A longitudinal investigation. Acad. Management J. 37(4):869–898.CrossrefGoogle Scholar
  • Burt RS (1982) Toward a Structural Theory of Action: Network Models of Social Structure, Perception, and Action, Quant. Stud. Soc. Relations (Academic Press, New York).CrossrefGoogle Scholar
  • Burt RS (1987) Social contagion and innovation: Cohesion versus structural equivalence. Amer. J. Sociol. 92(6):1287–1335.CrossrefGoogle Scholar
  • Chatterjee R, Eliashberg J (1990) The innovation diffusion process in a heterogeneous population: A micromodeling approach. Management Sci. 36(9):1057–1079.LinkGoogle Scholar
  • Coleman JS (1958) Relational analysis: The study of social organization with survey methods. Human Organ. 17(4):28–36.CrossrefGoogle Scholar
  • Coleman JS, Katz E, Menzel H (1966) Medical Innovation: A Diffusion Study (Bobbs-Merrill, Indianapolis).Google Scholar
  • Doreian P (1982) Maximum likelihood methods for linear models: Spatial effects and spatial disturbance terms. Sociol. Methods Res. 10(3):243–269.CrossrefGoogle Scholar
  • Doreian P (1989) Two regimes of network effects autocorrelation. Kochen M, ed. The Small World (Ablex Publishing, Norwood, NJ).Google Scholar
  • Dunbar R (1992) Neocortex size as a constraint on group size in primates. J. Human Evolution 20:469–493.CrossrefGoogle Scholar
  • Duncan OD, Haller AO, Portes A (1968) Peer influences on aspirations: A reinterpretation. Amer. J. Sociol. 74(2):119–137.CrossrefGoogle Scholar
  • Fang X, Hu P, Li Z, Tsai W (2013) Predicting adoption probabilities in social networks. Inform. Systems Res. 24(1):128–145.LinkGoogle Scholar
  • Fujimoto K, Valente TW (2012) Social network influences on adolescent substance use: Disentangling structural equivalence from cohesion. Soc. Sci. Medicine 74(12):1952–1960.CrossrefGoogle Scholar
  • Goolsbee A, Klenow PJ (2002) Evidence on learning and network externalities in the diffusion of home computers. J. Law Econom. 45:317–343.CrossrefGoogle Scholar
  • Hanneke S, Fu W, Xing EP (2010) Discrete temporal models of social networks. Electronic J. Statist. 4:585–605.CrossrefGoogle Scholar
  • Hanneman R, Riddle M (2005) Introduction to social network methods. University of California Riverside, http://www.faculty.ucr.edu/˜ hanneman/nettext/.Google Scholar
  • Harkola J, Greve A (1995) Diffusion of technology: Cohesion or structural equivalence? Acad. Management Proc., 422–426.Google Scholar
  • Kamakura WA, Russell GJ (1989) A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. 26(4):379–390.CrossrefGoogle Scholar
  • Katona Z, Zubcsek PP, Sarvary M (2011) Network effects and personal influences: The diffusion of an online social network. J. Marketing Res. 48(3):425–443.CrossrefGoogle Scholar
  • Krackhardt D, Stern RN (1988) Informal networks and organizational crises: An experimental simulation. Soc. Psych. Quart. 51(2):123–140.CrossrefGoogle Scholar
  • Lazarsfeld PF, Merton RK (1954) Friendship as a social process: A substantive and methodological analysis. Abel T, Page CH, eds. Freedom and Control in Modern Society (Van Nostrand, New York), 18–66.Google Scholar
  • Leenders RT (1997) Longitudinal behavior of network structure and actor attributes: Modeling interdependence of contagion and selection. Doreian P, Stokman F, eds. Evolution of Social Networks (Routledge, Amsterdam), 165–184.Google Scholar
  • Leenders RT (2002) Modeling social influence through network autocorrelation: Constructing the weight matrix. Soc. Networks 24:21–47.CrossrefGoogle Scholar
  • Leuven E, Sianesi B (2003) PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical Software Components S432001, Department of Economics, Boston College, Chestnut Hill, MA.Google Scholar
  • Ma L, Krishnan R, Montgomery AL (2015) Latent homophily or social influence? An empirical analysis of purchase within a social network. Management Sci. 61(2):454–473.LinkGoogle Scholar
  • Manski CF (1993) Identification of endogenous social effects: The reflection problem. Rev. Econom. Stud. 60(3):531–542.CrossrefGoogle Scholar
  • Manski CF (2000) Economic analysis of social interactions. J. Econom. Perspect. 14(3):115–136.CrossrefGoogle Scholar
  • McPherson JM, Smith-Lovin L (1987) Homophily in voluntary organizations: Status distance and the composition of face-to-face groups. Amer. Sociol. Rev. 52(3):370–379.CrossrefGoogle Scholar
  • McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annual Rev. Sociol. 27:415–444.CrossrefGoogle Scholar
  • Nair HS, Manchanda P, Bhatia T (2010) Asymmetric social interactions in physician prescription behavior: The role of opinion leaders. J. Marketing Res. 47(5):883–895.CrossrefGoogle Scholar
  • Oinas-Kukkonen H, Lyytinen K, Yoo Y (2010) Social networks and information systems: Ongoing and future research streams. J. Assoc. Inform. Systems 11(2):62–67.Google Scholar
  • Ord K (1975) Estimation methods for models of spatial interaction. J. Amer. Statist. Assoc. 70(349):120–126.CrossrefGoogle Scholar
  • Premkumar GRK, Nilakanta S (1994) Implementation of electronic data interchange: An innovation diffusion perspective. J. Management Inform. Systems 11(2):157–186.CrossrefGoogle Scholar
  • Robins G, Pattison P, Kalish Y, Lusher D (2007) An introduction to exponential random graph models for social networks. Soc. Networks 29(2):173–191.CrossrefGoogle Scholar
  • Rogers EM (1962) Diffusion of Innovations (Free Press, New York).Google Scholar
  • Rogers EM, Kincaid LD (1981) Communication Networks: Toward a New Paradigm for Research (Free Press, New York).Google Scholar
  • Salton G (1989) Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer (Addison-Wesley, Reading, MA).Google Scholar
  • Shalizi CR, Thomas AC (2011) Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40(2):211–239.CrossrefGoogle Scholar
  • Shriver SK, Nair HS, Hofstetter R (2013) Social ties and user-generated content: Evidence from an online social network. Management Sci. 59(6):1425–1443.LinkGoogle Scholar
  • Smith TE, LeSage JP (2004) A Bayesian probit model with spatial dependencies. Pace KR, LeSage JP, eds. Advances in Econometrics, Spatial Spatiotemporal Econometrics, Vol. 18 (Elsevier, Oxford, UK), 127–160.CrossrefGoogle Scholar
  • Snijders TAB (2001) The statistical evaluation of social network dynamics. Sobel ME, Becker MP, eds. Sociological Methodology—2001, Vol. 31 (Basil Blackwell, Boston), 361–395.CrossrefGoogle Scholar
  • Snijders TAB, van Duijn MAJ (1997) Simulation for statistical inference in dynamic network models. Conte R, Hegselmann R, Terna P, eds. Simulating Social Phenomena (Springer, Berlin),493–512.CrossrefGoogle Scholar
  • Snijders TAB, Koskinen JH, Schweinberger M (2010) Maximum likelihood estimation for social network dynamics. Ann. Appl. Statist. 4:567–588.CrossrefGoogle Scholar
  • Steglich C, Snijders TAB, West P (2006) Applying SIENA: An illustrative analysis of the coevolution of adolescents’ friendship networks, taste in music, and alcohol consumption. Methodology 2(1):48–56.CrossrefGoogle Scholar
  • Strang D, Tuma NB (1993) Spatial and temporal heterogeneity in diffusion. Amer. J. Sociol. 99(3):614–639.CrossrefGoogle Scholar
  • Susarla A, Oh JH, Tan Y (2012) Social networks and the diffusion of user-generated content: Evidence from YouTube. Inform. Systems Res. 23(1):23–41.LinkGoogle Scholar
  • Trusov M, Bodapati AV, Bucklin RE (2010) Determining influential users in Internet social networks. J. Marketing Res. 47(4):643–658.CrossrefGoogle Scholar
  • Valente TW (2005) Models and methods for innovation diffusion. Carrington PJ, Scott J, Wasserman S, eds. Models and Methods in Social Network Analysis (Cambridge University Press, Cambridge, UK), 98–116.CrossrefGoogle Scholar
  • Van den Bulte C, Lilien GL (2001) Medical innovation revisited: Social contagion versus marketing effort. Amer. J. Sociol. 106(5):1409–1435.CrossrefGoogle Scholar
  • Yang S, Allenby GM (2003) Modeling interdependent consumer preferences. J. Marketing Res. 40(3):282–294.CrossrefGoogle Scholar
  • Zhang B, Krackhardt D, Krishnan R, Doreian P (2011) An effective and efficient subpopulation extraction method in large social networks. ICIS 2011 Proc. 7. https://aisel.aisnet.org/icis2011/proceedings/breakthroughideas/7.Google Scholar
  • Zhang B, Thomas AC, Doreian P, Krackhardt D, Krishnan R (2013) Contrasting multiple social network autocorrelations for binary outcomes: With applications to technology adoption. ACM Trans. Management Inform. Systems 3(4):1–21.Google Scholar
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