Research Limitations and Suggestions for Further Research

Our study has several limitations; some are more general, whereas others specifically apply to the social influence focus of this study. In turn, these limitations could provide

ence from cumulative adoptions decreases from the product introduction onward, whereas the effect of recent adoptions is constant. We are not able to examine what causes these dynamics, because the required data are not available. We provided several theoretical explanations for this effect, but it is likely that many factors influence these dynamics (Eas- ingwood, Mahajan, and Muller 1983). An important next step in this area is to decompose the social influence effect into a contagiousness part (the sender) and a susceptibility part (the receiver) (for recent examples, see, e.g., Aral and Walker 2012; Hu and Van den Bulte 2012, and Iyengar, Van den Bulte, and Valente 2011).

Second, we included only direct marketing and ignored mass-marketing data (e.g., at the brand level) because we did not have access to those data. Although we partially cor- rect for this limitation by including monthly dummies in our hazard model, which capture all monthly fluctuations, future researchers could address it further.

Third, we did not have access to the content of the communication on which the networks are based. We assume that connected people influence one another similarly, but we could not investigate whether they actually discuss mobile phones with one another. These data are probably not perfect, but they still enable us to construct networks in

a way that is well-established in the marketing literature (Haenlein 2013; Haenlein and Libai 2013; Nitzan and Libai 2011). Further research might use social media data (e.g., Twitter, Facebook) to include a substantive analysis of the conversations between consumers in a network.

Fourth, to keep our model parsimonious, we used one homophily measure in which the four variables are equally weighted. However, our results show that homophily is an important dimension in social influence, and thus more research should be done to investigate the underlying dimensions of homophily and their respective contributions to social influence among individuals.

Fifth, it would be worthwhile to use our empirical results for an agent-based model simulation (Delre et al. 2010). This would allow for analyzing the marketing strategy we suggest in the “Discussion” section. Moreover, it would be useful to estimate the indirect effect of direct marketing by performing a moderated mediation analysis in which social influence mediates the effect of direct marketing on adoption and time since product introduction acts as

a moderator. In the context of our study, doing so would require more data on the customers in the ego networks. Sixth, we infer influence from correlations among the behavior of customers who interact. Although this is in line with most research on social contagion, it gives a simplistic view on the influence process. Moreover, it does not allow us to infer either what drives social influence or in which stages of the adoption process social influence and direct marketing are most effective.

Seventh, we assume that findings on organic word of mouth can be used to stimulate firm-initiated word of mouth. There are valid arguments in favor of this assumption because firms may use our findings to target specific consumers hoping that they initiate a cascade of events (e.g., adoptions of a new product by means of organic word of mouth). How-

66 / Journal of Marketing, March 2014 66 / Journal of Marketing, March 2014

iment would be suitable methods to investigate these differ- mation to different contacts) when stimulated to do so by a

ences (Chen, Wang, and Xie 2011; Van den Bulte 2010).

REFERENCES

Allison, Paul D. (1982), “Discrete-Time Methods for the Analysis Feld, Sebastian, Heiko Frenzen, Manfred Krafft, Kay Peters, and of Event Histories,” Sociological Methodology, 13, 61–98.

Peter C. Verhoef (2013), “The Effect of Mailing Design Char- Aral, Sinan, Lev Muchnik, and Arun Sundararajan (2009), “Distin-

acteristics on Direct Mail Campaign Performance,” Interna- guishing Influence-Based Contagion from Homophily-Driven

tional Journal of Research in Marketing , 30 (2), 143–59. Diffusion in Dynamic Networks,” Proceedings of the National

Fruchter, Gila E. and Christophe Van den Bulte (2011), “Why the Academy of Sciences , 106 (51), 21544–49.

Generalized Bass Model Leads to Odd Optimal Advertising ——— and Dylan Walker (2012), “Identifying Influential and Sus-

Policies,” International Journal of Research in Marketing, 28 ceptible Members of Social Networks,” Science, 337 (6092),

337–41. Godes, David (2011), “Commentary—Invited Comment on ‘Opin- Arts, Joep, Ruud T. Frambach, and Tammo H.A. Bijmolt (2011),

ion Leadership and Social Contagion in New Product Diffu- “Generalizations on Consumer Innovation Adoption: A Meta-

sion,’” Marketing Science, 30 (2), 224–29. Analysis on Drivers of Intention and Behavior,” International

——— and Dina Mayzlin (2004), “Using Online Conversations to Journal of Research in Marketing , 28 (2), 134–44.

Study Word-of-Mouth Communication,” Marketing Science, Bass, Frank M. (1969), “New Product Growth for Model Con-

sumer Durables,” Management Science, 15 (5), 215–27. Godfrey, Andrea, Kathleen Seiders, and Glenn B. Voss (2011), Bell, David R. and Sangyoung Song (2007), “Neighborhood

“Enough Is Enough! The Fine Line in Executing Multichannel Effects and Trial on the Internet: Evidence from Online Gro-

Relational Communication,” Journal of Marketing, 75 (July), cery Retailing,” Quantitative Marketing and Economics, 5 (4),

361–400. Greene, William (2012), Econometric Analysis. Boston: Pearson. Berger, Ursula, Juliane Schäfer, and Kurt Ulm (2003), “Dynamic

Haenlein, Michael (2013), “Social Interactions in Customer Churn Cox Modelling Based on Fractional Polynomials: Time-

Decisions: The Impact of Directionality,” International Jour- Variations in Gastric Cancer Prognosis,” Statistics in Medicine,

nal of Research in Marketing , 30 (3), 236–48. 22 (7), 1163–80.

——— and Barak Libai (2013), “Targeting Revenue Leaders for a Bolton, Ruth N., Katherine N. Lemon, and Peter C. Verhoef

New Product,” Journal of Marketing, 77 (May), 65–80. (2008), “Expanding Business-to-Business Customer Relation-

Hann, Il-Horn, Kai-Lung Hui, Sang-Yong T. Lee, and Ivan P.L. ships: Modeling the Customer’s Upgrade Decision,” Journal of

Png (2008), “Consumer Privacy and Marketing Avoidance: A Marketing , 72 (January), 46–64.

Static Model,” Management Science, 54 (6), 1094–1103. Broström, Göran and Henrik Holmberg (2011), “glmmML: Gener-

Hanssens, Dominique M., Leonard J. Parsons, and Randall L. alized Linear Models with Clustering,” R package version

Schultz (2001), Market Response Models Econometric and 0.82-1, (accessed November 15, 2013), [available at http://

Time Series Analysis . Boston: Kluwer Academic Publishers. CRAN.R-project.org/package=glmmML].

Hartmann, Wesley R., Puneet Manchanda, Harikesh Nair, Matthew Brown, Jacqueline J. and Peter H. Reingen (1987), “Social Ties

Bothner, Peter Dodds, David Godes, et al. (2008), “Modeling and Word-of-Mouth Referral Behavior,” Journal of Consumer

Social Interactions: Identification, Empirical Methods and Pol- Research , 14 (3), 350–62.

icy Implications,” Marketing Letters, 19 (3/4), 287–304. Burnham, Kenneth P. and David R. Anderson (2004), “Multi-

Haythornthwaite, Caroline (2005), “Social Networks and Internet model Inference: Understanding AIC and BIC in Model Selec-

Connectivity Effects,” Information, Communication & Society, tion,” Sociological Methods & Research, 33 (2), 261–304.

Chen, Yubo, Qi Wang, and Jinhong Xie (2011), “Online Social Hill, Shawndra, Foster Provost, and Chris Volinsky (2006), Interactions: A Natural Experiment on Word of Mouth Versus

“Network-Based Marketing: Identifying Likely Adopters via Observational Learning,” Journal of Marketing Research, 48

Consumer Networks,” Statistical Science, 21 (2), 256–76. (April), 238–54.

Hinz, Oliver, Bernd Skiera, Christian Barrot, and Jan U. Becker Choi, Jeonghye, Sam K. Hui, and David R. Bell (2010), “Spatio -

(2011), “Seeding Strategies for Viral Marketing: An Empirical temporal Analysis of Imitation Behavior Across New Buyers at

Comparison,” Journal of Marketing, 75 (November), 55–71. an Online Grocery Retailer,” Journal of Marketing Research,

Horsky, Dan and Leonard S. Simon (1983), “Advertising and the 47 (February), 75–89.

Diffusion of New Products,” Marketing Science, 2 (1), 1–17. Cialdini, Robert B. (2007), Influence: The Psychology of Persua-

Hu, Yansong and Christophe Van den Bulte (2012), “The Social sion . New York: HarperCollins.

Status of Innovators, Imitators, and Influentials in New Prod- De Bruyn, Arnaud and Gary L. Lilien (2008), “A Multi-Stage

uct Adoption: It’s Not Just About High Versus Low,” Report Model of Word-of-Mouth Influence Through Viral Marketing,”

No. 12-106, Marketing Science Institute. International Journal of Research in Marketing , 25 (3), 151–63.

Iyengar, Raghuram, Christophe Van den Bulte, and Jeonghye Choi Delre, Sebastiano A., Wander Jager, Tammo H.A. Bijmolt, and

(2011), “Distinguishing Among Multiple Mechanisms of Marco A. Janssen (2010), “Will It Spread or Not? The Effects

Social Contagion: Social Learning Versus Normative Legiti- of Social Influences and Network Topology on Innovation Dif-

mation in New Product Adoption,” working paper, The Whar- fusion,” Journal of Product Innovation Management, 27 (2),

ton School, University of Pennsylvania. 267–82.

———, ———, and Thomas W. Valente (2011), “Opinion Leader- Du, Rex Y. and Wagner A. Kamakura (2011), “Measuring Conta-

ship and Social Contagion in New Product Diffusion,” Market- gion in the Diffusion of Consumer Packaged Goods,” Journal

ing Science , 30 (2), 195–212.

of Marketing Research , 48 (February), 28–47. Kalish, Shlomo and Gary L. Lilien (1986), “Applications of Inno- Easingwood, Christopher J., Vijay Mahajan, and Eitan Muller

vation Diffusion Models in Marketing,” in Innovation Diffu- (1983), “A Nonuniform Influence Innovation Diffusion Model of

sion Models of New Product Acceptance , Vijay Mahajan and New Product Acceptance,” Marketing Science, 2 (3), 273–95.

Yoram Wind, eds. Cambridge, MA: Ballinger, 235–79.

Dynamic Effects of Social Influence and Direct Marketing / 67

Online Social Network,” Journal of Marketing Research, 48 Rust, Roland T. and Peter C. Verhoef (2005), “Optimizing the (June), 425–43.

Marketing Interventions Mix in Intermediate-Term CRM,” Kelman, Herbert C. (1958), “Compliance, Identification, and

Marketing Science , 24 (3), 477–89.

Internalization: Three Processes of Attitude Change,” Journal Ryu, Gangseog and Lawrence Feick (2007), “A Penny for Your of Conflict Resolution , 2 (1), 51–60.

Thoughts: Referral Reward Programs and Referral Likeli- Kumar, V., J.A. Petersen, and Robert P. Leone (2007), “How Valuable

hood,” Journal of Marketing, 71 (January), 84–94. Is Word of Mouth?” Harvard Business Review, 85 (10), 139–46.

Sauerbrei, Willi, Patrick Royston, and Maxime Look (2007), “A Landsman, Vardit and Moshe Givon (2010), “The Diffusion of a

New Proposal for Multivariable Modelling of Time-Varying New Service: Combining Service Consideration and Brand

Effects in Survival Data Based on Fractional Polynomial Time- Choice,” Quantitative Marketing and Economics, 8 (1), 91–121.

Transformation,” Biometrical Journal, 49 (3), 453–73. Leeflang, Peter S.H., Tammo H.A. Bijmolt, Jenny Van Doorn,

Schmitt, Philipp, Bernd Skiera, and Christophe Van den Bulte Dominique M. Hanssens, Harald J. Van Heerde, Peter C. Ver-

(2011), “Why Customer Referrals Can Drive Stunning Prof- hoef, et al. (2009), “Creating Lift Versus Building the Base:

its,” Harvard Business Review, 89 (6), 30–31. Current Trends in Marketing Dynamics,” International Journal

Sethuraman, Raj, Gerard J. Tellis, and Richard A. Briesch (2011), of Research in Marketing , 26 (1), 13–20.

“How Well Does Advertising Work? Generalizations from Lehr, Stephan and Michael Schemper (2007), “Parsimonious

Meta-Analysis of Brand Advertising Elasticities,” Journal of Analysis of Time-Dependent Effects in the Cox Model,” Statis-

Marketing Research , 48 (June), 457–71. tics in Medicine , 26 (13), 2686–98.

Stephen, Andrew T. and Jeff Galak (2012), “The Effects of Tradi- Leone, Robert P. (1995), “Generalizing What Is Known About

tional and Social Earned Media on Sales: A Study of a Temporal Aggregation and Advertising Carryover,” Marketing

Microlending Marketplace,” Journal of Marketing Research, Science , 14 (3), 141–50.

49 (October), 624–39.

Libai, Barak, Ruth N. Bolton, Marnix S. Bügel, Ko De Ruyter, Strang, David and Nancy B. Tuma (1993), “Spatial and Temporal Oliver Götz, Hans Risselada, et al. (2010), “Customer to Cus-

Heterogeneity in Diffusion,” American Journal of Sociology, tomer Interactions: Broadening the Scope of Word of Mouth

99 (3), 614–39.

Research,” Journal of Service Research, 13 (3), 267–82. Terza, Joseph V., Anirban Basu, and Paul J. Rathouz (2008), Manchanda, Puneet, Ying Xie, and Nara Youn (2008), “The Role

“Two-Stage Residual Inclusion Estimation: Addressing Endo- of Targeted Communication and Contagion in Product Adop-

geneity in Health Econometric Modeling,” Journal of Health tion,” Marketing Science, 27 (6), 961–76.

Economics , 27 (3), 531–43.

Manes, Stephen (2004), “Phones with Keyboards: How Smart?” Therneau, Terry M. and Patricia M. Grambsch (2000), Modeling Forbes , 174 (6), 113–14.

Survival Data: Extending the Cox Model . New York: Springer- Manski, Charles F. (2000), “Economic Analysis of Social Interac-

Verlag.

tions,” Journal of Economic Perspectives, 14 (3), 115–36. Van den Bulte, Christophe (2000), “New Product Diffusion Accel- McPherson, Miller, Lynn Smith-Lovin, and James M. Cook

eration: Measurement and Analysis,” Marketing Science, 19 (2001), “Birds of a Feather: Homophily in Social Networks,”

(4), 366–80.

Annual Review of Sociology , 27 (1), 415–44. ——— (2010), “Opportunities and Challenges in Studying Cus- Mundlak, Yair (1978), “On the Pooling of Time Series and Cross

tomer Networks,” in The Connected Customer: The Changing Section Data,” Econometrica, 46 (1), 69–85.

Nature of Consumer and Business Markets , Stefan Wuyts, Nam, Sungjoon, Puneet Manchanda, and Pradeep K. Chintagunta

Marnik G. Dekimpe, Els Gijsbrechts, and Rik Pieters, eds. (2010), “The Effect of Signal Quality and Contiguous Word of

London: Routledge, 7–35.

Mouth on Customer Acquisition for a Video-on-Demand Ser- ——— and Raghuram Iyengar (2011), “Tricked by Truncation: vice,” Marketing Science, 29 (4), 690–700.

Spurious Duration Dependence and Social Contagion in Haz- Narayanan, Sridhar, Puneet Manchanda, and Pradeep K. Chinta-

ard Models,” Marketing Science, 30 (2), 233–48. gunta (2005), “Temporal Differences in the Role of Marketing

——— and Yogesh V. Joshi (2007), “New Product Diffusion with Communication in New Product Categories,” Journal of Mar-

Influentials and Imitators,” Marketing Science, 26 (3), 400–421. keting Research , 42 (August), 278–90.

——— and Gary L. Lilien (1997), “Bias and Systematic Change in Nitzan, Irit and Barak Libai (2011), “Social Effects on Customer

the Parameter Estimates of Macro-Level Diffusion Models,” Retention,” Journal of Marketing, 75 (November), 24–38.

Marketing Science , 16 (4), 338–53.

Onnela, Jukka-Pekka, Jari Saramäki, Jörkki Hyvönen, Gábor ——— and ——— (2001), “Medical Innovation Revisited: Social Szabó, David Lazer, Kimmo Kaski, et al. (2007), “Structure and

Contagion Versus Marketing Effort,” American Journal of Tie Strengths in Mobile Communication Networks,” Proceed-

Sociology , 106 (5), 1409–1435.

ings of the National Academy of Sciences , 104 (18), 7332–36. ——— and Stefan Stremersch (2004), “Social Contagion and Income Osinga, Ernst C., Peter S.H. Leeflang, and Jaap E. Wieringa

Heterogeneity in New Product Diffusion: A Meta-Analytic (2010), “Early Marketing Matters: A Time-Varying Parameter

Test,” Marketing Science, 23 (4), 530–44. Approach to Persistence Modeling,” Journal of Marketing

——— and Stefan Wuyts (2007), Social Networks and Marketing. Research , 47 (February), 173–85.

Cambridge, MA: Marketing Science Institute. Prins, Remco and Peter C. Verhoef (2007), “Marketing Communi-

Van Eck, Peter S., Wander Jager, and Peter S.H. Leeflang (2011), cation Drivers of Adoption Timing of a New E-Service Among

“Opinion Leaders’ Role in Innovation Diffusion: A Simulation Existing Customers,” Journal of Marketing, 71 (April), 169–83.

Study,” Journal of Product Innovation Management, 28 (2), Reingen, Peter H. and Jerome B. Kernan (1986), “Analysis of

187–203.

Referral Networks in Marketing: Methods and Illustration,” Venkatesan, Rajkumar and V. Kumar (2004), “A Customer Lifetime Journal of Marketing Research , 23 (November), 370–78.

Value Framework for Customer Selection and Resource Allo- Roberts, John H. and Glen L. Urban (1988), “Modeling Multiattri-

cation Strategy,” Journal of Marketing, 68 (October), 106–125. bute Utility, Risk, and Belief Dynamics for New Consumer

Verbeek, Marno (2008), A Guide to Modern Econometrics. Chich- Durable Brand Choice,” Management Science, 34 (2), 167–85.

ester, UK: John Wiley & Sons.

Rogers, Everett M. (2003), Diffusion of Innovations. New York: Verhoef, Peter C. (2003), “Understanding the Effect of Customer The Free Press.

Relationship Management Efforts on Customer Retention and Royston, Patrick and Douglas G. Altman (1994), “Regression

Customer Share Development,” Journal of Marketing, 67 Using Fractional Polynomials of Continuous Covariates: Parsi-

(October), 30–45.

68 / Journal of Marketing, March 2014

Research Limitations and Suggestions for Further Research