Integrating the Social Network to Diffusion Model and Evaluation of the Value of Hubs in the Adoption Process

In this paper we analytically study the effect of social hubs on the penetration of new products. Aggregating individual-level social network considerations to the market level, we identify boundary conditions for hubs' effect on diffusion. Our results demonstrate that seeding hubs has a differential accelerating effect on diffusion measured by the additional net present value (NPV) of potential future sales. On the basis of closed-form solutions, we find that where consumers’ decisions to purchase a new product are almost entirely induced by word-of-mouth communications, seeding a small number of hubs whose social-connectedness is about 10 times greater than that of ordinary individuals, may help initiate a valuable diffusion process in which the NPV is increased by several tens of percentage points. On the other hand, seeding such highly connected hubs adds less than 1% to the NPV. Tapping into a category of social influence that is characterized by the number and intensity of social ties, we find that a hub’s “area of influence” has greater impact on NPV than its tie intensity. Focusing on the evolution of adoption in a segment of hubs, we show that the product life cycle in this segment is about two to three times shorter than the life cycle in the entire market. We find that the ratio of hub-to-non-hub degree has the most significant impact on reducing life cycle length, and its effect exceeds other effects (i.e., the average proportion of hubs among individuals' neighbors, the intensity of external influence, or word-of-mouth communications). We examine the proposed analytical framework using empirical data from an online social network.

[1]  L. Shampine,et al.  A 3(2) pair of Runge - Kutta formulas , 1989 .

[2]  C. Moorman,et al.  The Acquisition and Utilization of Information in New Product Alliances: A Strength-of-Ties Perspective , 2001 .

[3]  Michael Trusov,et al.  Determining Influential Users in Internet Social Networks , 2010 .

[4]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[5]  J. Goldenberg,et al.  The NPV of bad news , 2007 .

[6]  Jacob Goldenberg,et al.  Zooming In: Self-Emergence of Movements in New Product Growth , 2009, Mark. Sci..

[7]  A. Polyanin,et al.  Handbook of Exact Solutions for Ordinary Differential Equations , 1995 .

[8]  P. Kotler,et al.  Marketing in the Network Economy , 1999 .

[9]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .

[10]  Jacob Goldenberg,et al.  Uncovering Social Network Structures through Penetration Data , 2009 .

[11]  Jacob Goldenberg,et al.  Distributive immunization of networks against viruses using the ‘honey-pot’ architecture , 2005 .

[12]  G. Weimann The Influentials: People Who Influence People , 1994 .

[13]  Yogesh V. Joshi,et al.  New Product Diffusion with Influentials and Imitators , 2007 .

[14]  Thomas W. Valente Network models of the diffusion of innovations , 1996, Comput. Math. Organ. Theory.

[15]  T. Hotta Mean-Field Approximation , 2003 .

[16]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[17]  Donald R. Lehmann,et al.  A Meta-Analysis of Applications of Diffusion Models , 1990 .

[18]  Olivier Toubia,et al.  Deriving Value from Social Commerce Networks , 2009 .

[19]  D. Iacobucci Networks in Marketing , 1996 .

[20]  Eitan Muller,et al.  When does the majority become a majority? Empirical analysis of the time at which main market adopters purchase the bulk of our sales , 2006 .

[21]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Emanuel Rosen,et al.  The Anatomy of Buzz: How to Create Word of Mouth Marketing , 2000 .

[23]  D. Watts,et al.  Influentials, Networks, and Public Opinion Formation , 2007 .

[24]  Thomas W. Valente,et al.  Opinion Leadership and Social Contagion in New Product Diffusion , 2011, Mark. Sci..

[25]  Donald R. Lehmann,et al.  When giving some away makes sense to jump-start the diffusion process , 2006 .

[26]  John R. Ronchetto,et al.  Embedded Influence Patterns in Organizational Buying Systems , 1989 .

[27]  Mengze Shi,et al.  Social Network-Based Discriminatory Pricing Strategy , 2003 .

[28]  N. I. Shaikh,et al.  Modeling the Diffusion of Innovations Through Small-World Networks , 2010 .

[29]  J. Goldenberg,et al.  The Role of Hubs in the Adoption Process , 2009 .

[30]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[31]  P. Lazarsfeld,et al.  6. Katz, E. Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[32]  R. Burt The contingent value of social capital. , 1997 .

[33]  D. N. Prabhakar Murthy,et al.  A Mathematical Model for New Product Diffusion: The Influence of Innovators and Imitators , 1992 .

[34]  Olivier Toubia,et al.  Explaining the Power-Law Degree Distribution in a Social Commerce Network , 2009 .

[35]  Katherine N. Lemon,et al.  Quantifying the Ripple: Word-of-Mouth and Advertising Effectiveness , 2004, Journal of Advertising Research.

[36]  Philip Rabinowitz,et al.  Methods of Numerical Integration , 1985 .