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New Product Adoption in Social Networks: Why Direction Matters

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  • Hinz, Oliver
  • Schulze, Christian
  • Takac, Carsten

Abstract

Marketing managers and researchers generally agree that analyzing data from social networks and using them to influence consumers' purchase decisions are useful strategies. However, not all social network data may identify the most influential customers. This empirical study of more than 300 students reveals the low explanatory power of friendship networks (e.g., Facebook) and undirected-advice networks (e.g., LinkedIn). Only directed-advice networks (e.g., Google+) clearly identify influential consumers. In addition, the results challenge conventional wisdom that firms should target advisers assuming that they have the strongest influence on new product adoption. This study contradicts this common assumption and reveals that structural equivalence drives product adoption more than cohesion because advisees' adoption pressures advisers to purchase the product as well. Finally, the study shows the value of social network data beyond the traditional ego-centric psychographic metrics, such as innovativeness or opinion leadership.
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  • Hinz, Oliver & Schulze, Christian & Takac, Carsten, 2014. "New Product Adoption in Social Networks: Why Direction Matters," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69937, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:69937
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/69937/
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    3. Plé, Loïc & Demangeot, Catherine, 2020. "Social contagion of online and offline deviant behaviors and its value outcomes: The case of tourism ecosystems," Journal of Business Research, Elsevier, vol. 117(C), pages 886-896.
    4. Jansen, Nora & Hinz, Oliver, 2022. "Inferring opinion leadership from digital footprints," Journal of Business Research, Elsevier, vol. 139(C), pages 1123-1137.
    5. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.
    6. Miriam Däs & Julia Klier & Mathias Klier & Georg Lindner & Lea Thiel, 2017. "Customer lifetime network value: customer valuation in the context of network effects," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 307-328, November.
    7. Jahanmir, Sara F. & Lages, Luis Filipe, 2016. "The late-adopter scale: A measure of late adopters of technological innovations," Journal of Business Research, Elsevier, vol. 69(5), pages 1701-1706.
    8. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    9. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 179-193, June.
    10. Loïc Plé & Catherine Demangeot, 2019. "Social contagion of online and offline deviant behaviors and its value outcomes: The case of tourism ecosystems," Post-Print hal-02509372, HAL.
    11. Wang, Feng & Zhang, Xueting & Chen, Man & Zeng, Wei & Cao, Rong, 2022. "The influential paradox: Brand and deal content sharing by influencers in friendship networks," Journal of Business Research, Elsevier, vol. 150(C), pages 503-514.
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    13. Zhang, Honghong & Fam, Kim-Shyan & Goh, Tiong-Thye & Dai, Xin, 2018. "When are influentials equally influenceable? The strength of strong ties in new product adoption," Journal of Business Research, Elsevier, vol. 82(C), pages 160-170.

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