Seeding strategies for new product launch: The role of negative word-of-mouth

When launching a new product, firms often give away free samples to seed the market. This paper aims to identify the optimal seeding targets, such as early adopters, social hubs, or randomly chosen consumers while considering the presence of negative word-of-mouth (WOM). Using agent-based modeling, it was found that seeding early adopters can generate the highest profit and the largest market penetration, followed by the social hubs and random consumers. Moreover, the results show that seeding early adopters can be more beneficial for a low-quality product, wherein adopters are more likely to spread negative WOM. These findings challenge a widely accepted notion in the related research that social hubs are often the most promising targets for seeding programs.

[1]  Damon Centola.  Homophily, networks, and critical mass: Solving the start-up problem in large group collective action , 2013 .

[2]  E. Muller,et al.  Decomposing the Value of Word-of-Mouth Seeding Programs: Acceleration vs. Expansion , 2012 .

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

[4]  E. Babakus,et al.  Success Factors in Product Seeding: The Role of Homophily , 2014 .

[5]  J. Goldenberg,et al.  The chilling effects of network externalities , 2010 .

[6]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[7]  Amy L. Ostrom,et al.  Complaining to the Masses: The Role of Protest Framing in Customer-Created Complaint Web Sites , 2006 .

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

[9]  H. Watkins,et al.  Collectivism, Individualism and In-Group Membership: , 1996 .

[10]  Yanjun Qian,et al.  Strategies for new product diffusion: Whom and how to target? , 2018 .

[11]  William O. Bearden,et al.  The role of public and private complaining in satisfaction with problem resolution. , 1985 .

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

[13]  V. Mahajan,et al.  Innovation diffusion and new product growth models: A critical review and research directions , 2010 .

[14]  Haihua Hu,et al.  Local Opinion Heterogeneity and Individual Participation in Collective Behavior: A Reconsideration , 2015, J. Artif. Soc. Soc. Simul..

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

[16]  G. Lilien,et al.  Medical Innovation Revisited: Social Contagion versus Marketing Effort1 , 2001, American Journal of Sociology.

[17]  N. Marr,et al.  How Damaging is Negative Word of Mouth , 1995 .

[18]  Eitan Muller,et al.  The role of seeding in multi-market entry , 2005 .

[19]  Jacob Goldenberg,et al.  From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success , 2004 .

[20]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[21]  Markus Strohmaier,et al.  Homophily explains perception biases in social networks , 2017, ArXiv.

[22]  Barak Libai,et al.  Targeting Revenue Leaders for a New Product , 2013 .

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

[24]  Jacob Goldenberg,et al.  Cellular automata modeling of resistance to innovations: Effects and solutions , 2004 .

[25]  Jan U. Becker,et al.  Seeding Strategies for Viral Marketing: An Empirical Comparison , 2011 .

[26]  John Urry,et al.  Small Worlds and the New ‘Social Physics’ , 2004 .

[27]  Ramayya Krishnan,et al.  Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network , 2015, Manag. Sci..

[28]  T. Valente,et al.  Accelerating the Diffusion of Innovations Using Opinion Leaders , 1999 .

[29]  G. Tellis,et al.  Research on Innovation: A Review and Agenda for Marketing Science , 2006 .

[30]  P. Herr,et al.  Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective , 1991 .

[31]  Arun Sundararajan,et al.  Engineering social contagions: Optimal network seeding in the presence of homophily , 2013, Network Science.

[32]  D. Watts.  Networks, Dynamics, and the Small‐World Phenomenon1 , 1999, American Journal of Sociology.

[33]  J. Bartels,et al.  Consumer innovativeness and its correlates: A propositional inventory for future research , 2011 .

[34]  Floriana Gargiulo,et al.  The Role of Homophily in the Emergence of Opinion Controversies , 2016, J. Artif. Soc. Soc. Simul..

[35]  Daniel L. Sherrell,et al.  Influentials and Influence Mechanisms in New Product Diffusion: An Integrative Review , 2014 .

[36]  Sinan Aral,et al.  Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion , 2010, Mark. Sci..

[37]  Marsha L. Richins.  A Multivariate Analysis of Responses to Dissatisfaction , 1987 .

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

[39]  Marsha L. Richins.  Negative Word-of-Mouth by Dissatisfied Consumers: A Pilot Study , 1983 .

[40]  G. Tellis,et al.  Beyond Diffusion: An Affordability Model of the Growth of New Consumer Durables , 1998 .

[41]  Jacob Goldenberg,et al.  Riding the Saddle: How Cross-Market Communications Can Create a Major Slump in Sales , 2002 .

[42]  Lincoln Quillian,et al.  Migration Patterns and the Growth of High‐Poverty Neighborhoods, 1970‐‐19901 , 1999, American Journal of Sociology.

[43]  W. Sasser,et al.  The profitable art of service recovery. , 1990, Harvard business review.

[44]  Zhengrui Jiang,et al.  Virtual Bass Model and the Left-Hand Data-Truncation Bias in Diffusion of Innovation Studies , 2006 .

[45]  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 .

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

[47]  S. Ramaswami,et al.  Consumers’ Responses to Negative Word-of-Mouth Communication: An Attribution Theory Perspective , 2001 .

[48]  Gerard J. Tellis,et al.  A Critical Review of Marketing Research on Diffusion of New Products , 2007 .

[49]  J. Arndt.  Role of Product-Related Conversations in the Diffusion of a New Product , 1967 .

[50]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[51]  Damon Centola,et al.  The Social Origins of Networks and Diffusion1 , 2015, American Journal of Sociology.

[52]  Tammo H. A. Bijmolt,et al.  Dynamic Effects of Social Influence and Direct Marketing on the Adoption of High-Technology Products , 2014 .

[53]  E. Anderson.  Customer Satisfaction and Word of Mouth , 1998 .

[54]  Monic Sun,et al.  How Does the Variance of Product Ratings Matter? , 2010, Manag. Sci..

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

[56]  M. Macy,et al.  Complex Contagions and the Weakness of Long Ties1 , 2007, American Journal of Sociology.

[57]  Vijay Mahajan,et al.  An Approach for Determining Optimal Product Sampling for the Diffusion of a New Product , 1995 .

[58]  Albert C. BemmaorJanghyuk Lee,et al.  Research Note The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates , 2002 .

[59]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[60]  Jagdip Singh.  Voice, exit, and negative word-of-mouth behaviors: An investigation across three service categories , 1990 .

[61]  R. Mizerski.  An Attribution Explanation of the Disproportionate Influence of Unfavorable Information , 1982 .

[62]  Stefan Stremersch,et al.  Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test , 2004 .