Although the role of social networks and consumer interactions in new product diffusion is widely acknowledged, such networks and interactions are often unobservable to researchers. What may be observable, instead, are aggregate diffusion patterns for past products adopted within a particular social network. The authors propose an approach for identifying systematic conditions that are stable across diffusions and thus are “transferrable” to new product introductions within a given network. Using Facebook applications data, the authors show that incorporation of such systematic conditions improves prelaunch forecasts. This research bridges the gap between the disciplines of Bayesian statistics and agent-based modeling by demonstrating how researchers can use stochastic relationships simulated within complex systems as meaningful inputs for Bayesian inference models.
AndersonRoy M., and RobertM. May (1992), Infectious Diseases of Humans: Dynamics and Control. New York: Oxford University Press.
2.
AralSinan, and WalkerDylan (2012), “Identifying Influential and Susceptible Members of Social Networks,”Science, 337(6092), 337–41.
3.
BarabásiAlbert-László, and AlbertRéka (1999), “Emergence of Scaling in Random Networks,”Science, 286(5439), 509–512.
4.
BassFrank M. (1969), “A New Product Growth Model for Consumer Durables,”Management Science, 361057–79.
5.
BemmaorAlbert C. (1994), “Modeling the Diffusion of New Durable Goods: Word-of-Mouth Effect Versus Consumer Heterogeneity,” in Research Traditions in Marketing, LaurentGillesLilienGary L., and PrasBernard, eds. Boston: Kluwer Academic Publishers, 201–223.
6.
BemmaorAlbert C., and LeeJanghyuk (2002), “The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates,”Marketing Science, 21(2), 209–220.
7.
BergerJonah, and MilkmanKatherine L. (2012), “What Makes Online Content Viral,”Journal of Marketing Research, 49(April), 192–205.
8.
BradlowEric T. (2002), “Exploring Repeated Measures Data Sets for Key Features Using Principal Components Analysis,”International Journal of Research in Marketing, 19(2), 167–79.
9.
BraunMichael, and BonfrerAndré (2011), “Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes,”Marketing Science, 30(3), 513–31.
10.
ChandrasekaranDeepa, and TellisGerard J. (2007), “A Critical Review of Marketing Research on Diffusion of New Products,” in Review of Marketing Research, Vol. 3, MalhotraNaresh K., ed. Armonk, NY: Emerald Group Publishing, 39–80.
11.
ChipmanHughGeorgeEdward I., and McCullochRobert E. (2001), “The Practical Implementation of Bayesian Model Selection,” in IMS Lecture Notes—Monograph Series: Model Selection, Vol. 38, LahiriPartha, ed. Beachwood, OH: Institute of Mathematical Statistics, 65–116.
12.
DasguptaKoustuvSinghRahulViswanathanBalajiChakrabortyDipanjanMukherjeaSougataNanavatiAmit A. (2008), “Social Ties and Their Relevance to Churn in Mobile Telecom Networks,” in Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology. New York: Association for Computing Machinery, 668–77.
13.
DoddsPeter S., and WattsDuncan J. (2005), “A Generalized Model of Social and Biological Contagion,”Journal of Theoretical Biology, 232(4), 587–604.
14.
DoverYanivGoldenbergJacob, and ShapiraDaniel (2012), “Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data,”Marketing Science, 31(4), 689–712.
FienbergStephen E., and HollandPaul W. (1973), “Simultaneous Estimation of Multinomial Cell Probabilities,”Journal of the American Statistical Association, 68(343), 683–91.
17.
GoelSharadWattsDuncan J., and GoldsteinDaniel G. (2012), “The Structure of Online Diffusion Networks,” inProceedings of the 13th ACM Conference on Electronic Commerce.New York: Association for Computing Machinery, 623–38.
18.
GoldenbergJacobHanSangmanLehmannDonald R., and HongJae (2009), “The Role of Hubs in the Adoption Process,”Journal of Marketing, 73(March), 1–13.
19.
GoldenbergJacobLibaiBarak, and MullerEitan (2001), “Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth,”Marketing Letters, 12(3), 211–23.
20.
GoldenbergJacobLibaiBarak, and MullerEitan (2002), “Riding the Saddle: How Cross-Market Communications Can Create a Major Slump in Sales,”Journal of Marketing, 66(April), 1–16.
21.
GoldenbergJacobLibaiBarak, and MullerEitan (2010), “The Chilling Effects of Network Externalities,”International Journal of Research in Marketing, 27(1), 4–15.
HaenleinMichael (2011), “A Social Network Analysis of Customer-Level Revenue Distribution,”Marketing Letters, 22(1), 15–29.
25.
HaenleinMichael, and LibaiBarak (2013), “Targeting Revenue Leaders for a New Product,”Journal of Marketing, 77(May), 65–80.
26.
HahnMinhiParkSehoonKrishnamurthiLakshman, and ZoltnersAndris A. (1994), “Analysis of New Product Diffusion Using a Four-Segment Trial-Repeat Model,”Marketing Science, 13(3), 224–47.
27.
HillShawndraProvostFoster, and VolinskyChris (2006), “Network-Based Marketing: Identifying Likely Adopters via Consumer Networks,”Statistical Science, 22(2), 256–75.
28.
IacobucciDawn, and HopkinsNigel (1992), “Modeling Dyadic Interactions and Networks in Marketing,”Journal of Marketing Research, 29(February), 5–17.
29.
IyengarRaghuramVan den BulteChristophe, and ValenteThomas W. (2011), “Opinion Leadership and Social Contagion in New Product Diffusion,”Marketing Science, 30(2), 195–212.
30.
KeelingMatthew J. (1999), “The Effects of Local Spatial Structure on Epidemiological Invasions,”Proceedings of the Royal Society of London—Series B: Biological Sciences, 266(1421), 859–67.
31.
KempeDavidKleinbergJon, and TardosEva (2003), “Maximizing the Spread of Influence Through a Social Network,” inProceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York: Association for Computing Machinery, 137–46.
32.
LemmensAurelieCrouxChristophe, and StremerschStefan (2012), “Dynamics in International Market Segmentation of New Product Growth,”International Journal of Research in Marketing, 29(1), 81–92.
33.
LenkPeter J., and RaoAmbar G. (1990), “New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures,”Marketing Science, 9(1), 42–53.
34.
LewisTed G. (2011), Network Science: Theory and Applications.Hoboken, NJ: John Wiley & Sons.
35.
LibaiBarakMullerEitan, and PeresRenana (2005), “The Role of Seeding in Multi-Market Entry,”International Journal of Research in Marketing, 22(4), 375–93.
36.
LibaiBarakMullerEitan, and PeresRenana (2009), “The Role of Within-Brand and Cross-Brand Communications in Competitive Growth,”Journal of Marketing, 73(May), 19–34.
37.
LilienGary L., and RangaswamyArvind (2004), Marketing Engineering: Computer-Assisted Marketing Analysis and Planning.Victoria, BC: Trafford Publishing.
38.
LilienGary L.RangaswamyArvind, and Van den BulteChristophe (2000), “Diffusion Models: Managerial Applications and Software,” inNew Product Diffusion Models, MahajanVijayMullerEitan, and WindYoram, eds. Norwell, MA: Kluwer Academic Publishers, 295–311.
39.
MahajanVijayMullerEitan, and BassFrank M. (1993), “New Product Diffusion Models,” inHandbooks in OR & MS, Vol. 5, EliashbergJehoshua, and LilienGary L., eds. Amsterdam: Elsevier Science Publishers, 349–408.
40.
ManchandaPuneetPackardGrant, and PattabhiramaiahAdithya (2012), “Social Dollars: The Economic Impact of Customer Participation in a Firm-Sponsored Online Community,” working paper, Stephen M. Ross School of Business, University of Michigan.
41.
MarsdenPeter V. (1987), “Core Discussion Networks of Americans,”American Sociological Review, 52(1), 122–31.
42.
MoeWendy W., and FaderPeter S. (2002), “Using Advance Purchase Orders to Forecast New Product Sales,”Marketing Science, 21(3), 347–64.
43.
MoldovanSarit, and GoldenbergJacob (2003), “Cellular Automata Modeling of Resistance to Innovations: Effects and Solutions,”Technological Forecasting and Social Change, 71(5), 425–42.
44.
NazirAtifRazaSaqib, and ChuahChen-Nee (2008), “Unveiling Facebook: A Measurement Study of Social Network Based Applications,” in Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement.New York: Association for Computing Machinery, 43–56.
45.
NewmanMark E.J., and WattsDuncan J. (1999), “Scaling and Percolation in the Small-World Network Model,”Physical Review E, 60(6) 7332–42.
46.
NitzanIrit, and LibaiBarak (2011), “Social Effects on Customer Retention,”Journal of Marketing, 75(November), 24–38.
RahmandadHazhir, and StermanJohn (2008), “Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models,”Management Science, 54(5), 998–1014.
49.
RamsayJim O., and SilvermanBernard W. (2005), Functional Data Analysis, 2d ed. New York: Springer.
50.
RandWilliam M., and RustRoland T. (2011), “Agent-Based Modeling in Marketing: Guidelines for Rigor,”International Journal of Research in Marketing, 28(3), 181–93.
51.
RogersEverett M. (1962), Diffusion of Innovations.New York: The Free Press.
52.
RyanBryce, and GrossNeal C. (1943), “The Diffusion of Hybrid Seed Corn in Two Iowa Communities,”Rural Sociology, 8(1), 15–24.
53.
SalganikMatthew J.DoddsPeter S., and WattsDuncan J. (2006), “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market,”Science, 311(5762), 854–56.
54.
ShaikhNazrul I.RangaswamyArvind, and BalakrishnanAnant (2005), “Modeling the Diffusion of Innovations Using Small-World Networks,” working paper, Pennsylvania State University.
55.
SoodAshishJamesGareth M., and TellisGerard J. (2009), “Functional Regression: A New Model for Predicting Market Penetration of New Products,”Marketing Science, 28(1), 36–51.
56.
SrinivasanV., and MasonCharlotte H. (1986), “Nonlinear Least Squares Estimation of New Product Diffusion Models,”Marketing Science, 5(2), 169–78.
57.
StephenAndrew T., and ToubiaOlivier (2009), “Explaining the Power-Law Degree Distribution in a Social Commerce Network,”Social Networks, 31(4), 262–70.
58.
StremerschStefan, and LemmensAurelie (2009), “Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes,”Marketing Science, 28(4), 690–708.
59.
ToubiaOlivierGoldenbergJacob, and GarciaRosanna (2008), “A New Approach to Modeling the Adoption of New Products: Aggregated Diffusion Models,” Marketing Science Institute Working Paper Series No. 08-001.
60.
ToubiaOlivierGoldenbergJacob, and GarciaRosanna (2009), “Diffusion Forecast Using Social Interactions Data,” Marketing Science Institute Working Paper Series No. 09-210.
61.
TrajtenbergManuel, and YitzhakiShlomo (1989), “The Diffusion of Innovations: A Methodological Reappraisal,”Journal of Business & Economic Statistics, 7(1), 35–47.
62.
TrusovMichaelBodapatiAnand, and BucklinRandolph E. (2010), “Determining Influential Users in Internet Social Networks,”Journal of Marketing Research, 47(August), 643–58.
63.
ValenteThomas W. (1995), Network Models of the Diffusion of Innovations.Creskill, NJ: Hampton Press.
64.
Van den BulteChristophe (2010), “Opportunities and Challenges in Studying Customer Networks,” inThe Connected Customer: The Changing Nature of Consumer and Business Markets, WuytsStefan H.K.DekimpeMarnik G.GijsbrechtsEls, and Rik PietersF.G.M., eds. London: Routledge, 7–35.
65.
Van den BulteChristophe, and IyengarRaghuram (2011), “Tricked by Truncation: Spurious Duration Dependence and Social Contagion in Hazard Models,”Marketing Science, 30(2), 233–48.
66.
Van denBulte, and JoshiYogesh V. (2007), “New Product Diffusion with Influentials and Imitators,”Marketing Science, 26(3), 400–421.
67.
Van denBulte, and LilienGary L. (1997), “Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models,”Marketing Science, 16(4), 338–53.
68.
Van denBulte, and LilienGary L. (2001), “Medical Innovation Revisited: Social Contagion Versus Marketing Effort,”American Journal of Sociology, 106(5), 1409–1435.
69.
Van denBulte, and StremerschStefan (2004), “Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test,”Marketing Science, 23(4), 530–44.
70.
Van denBulte, and WuytsStefan H.K. (2007), Social Networks and Marketing.Cambridge, MA: Marketing Science Institute.
71.
WassermanStanley, and FaustKatherine (2009), Social Network Analysis: Methods and Applications.New York: Cambridge University Press.
72.
WattsDuncan J. (2002), “A Simple Model of Global Cascades on Random Networks,”Proceedings of the National Academy of Science, 99(9), 5766–71.
73.
WattsDuncan J., and DoddsPeter S. (2007), “Influentials, Networks and Public Opinion Formation,”Journal of Consumer Research, 34(4), 441–58.
74.
WattsDuncan J., and StrogatzSteven H. (1998), “Collective Dynamics of ‘Small-World’ Networks,”Nature, 393(6684), 440–42.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
