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Essays on the Influence of Social Networks on the Marketing Distribution Channel and New Product Diffusion

  • Author / Creator
    Li, Shenyu
  • The first essay studies the channel relationship between the reseller and the manufacturer based on a social network theory framework. We propose a conceptual model that approaches this topic from a relational embeddedness perspective. Our analysis shows how the reseller can strategically develop relational ties with a manufacturer that transform the latter’s common marketing mix into unique resources that enhance the reseller’s own profit. Results from a large scale survey of beer resellers in a local Chinese market suggest that in a channel setting, social norms (e.g. communication effectiveness and conflict resolution) and social relations influence the reseller’s access to the manufacturer’s valuable resources. Furthermore, we find that over embeddedness affects the reseller’s profit in a non-linear manner. That is, a reseller’s effort to develop a relationship with a particular manufacturer may generate information that lacks freshness, objectivity or usefulness, thereby diminishing the reseller’s profitability. Theory of social contagion states that individual’s adoption of new product depends on the adoption of his immediate neighbors in a social network in addition to the influence from other sources. This research models the dynamic diffusion process of new drug in a social network of physicians. We simulated the information transmission process in a social network, where each network entity repetitively influences the probability of connected entity’s new product adoption. The simulation approach integrates two seemingly contradictive concepts of cohesion and structural equivalence into a single modeling framework. Besides, it incorporates a coefficient that describes an individual entity’s efficiency of information transmission. On the one extreme it assumes that information transmits to only one of the network neighbors and on the other extreme it assumes that information transmits to all of the network neighbors. We revisited Medical Innovation data and empirically find an optimum point for each of the four cities in this data set, using a discrete time hazard model. The four cities demonstrate different patterns of information transmission. Managerially, we suggest different ways of pinpointing initial adopters in different types of social networks.

  • Subjects / Keywords
  • Graduation date
    2010-06
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3KF0M
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • School of Business
  • Supervisor / co-supervisor and their department(s)
    • Siva K. Balasubramanian (Stuart School of Business, Illinois Institute of Technology)
    • Peter T.L. Popkowski Leszczyc (Department of Marketing, Business Economics and Law)
  • Examining committee members and their departments
    • Haifang Huang (Department of Economics)
    • Paul R. Messinger (Department of Marketing, Business Economics and Law)
    • Christophe Van Den Bulte (The Wharton School of Business, University of Pennsylvania)
    • Adam Finn (Department of Marketing, Business Economics and Law)