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Choices in networks: a research framework

Author

Listed:
  • Fred Feinberg

    (University of Michigan)

  • Elizabeth Bruch

    (University of Michigan)

  • Michael Braun

    (Marketing, Southern Methodist University)

  • Brett Hemenway Falk

    (Univ. of Pennsylvania)

  • Nina Fefferman

    (University of Tennessee)

  • Elea McDonnell Feit

    (Drexel University)

  • John Helveston

    (George Washington U)

  • Daniel Larremore

    (University of Colorado Boulder)

  • Blakeley B. McShane

    (Northwestern University)

  • Alice Patania

    (Indiana University)

  • Mario L. Small

    (Harvard University)

Abstract

Networks are ubiquitous in life, structuring options available for choice and influencing their relative attractiveness. In this article, we propose an integration of network science and choice theory beyond merely incorporating metrics from one area into models of the other. We posit a typology and framework for “network-choice models” that highlight the distinct ways choices occur in and influence networked environments, as well as two specific feedback processes that guide their mutual interaction, emergent valuation and contingent options. In so doing, we discuss examples, data sources, methodological challenges, anticipated benefits, and research pathways to fully interweave network and choice models.

Suggested Citation

  • Fred Feinberg & Elizabeth Bruch & Michael Braun & Brett Hemenway Falk & Nina Fefferman & Elea McDonnell Feit & John Helveston & Daniel Larremore & Blakeley B. McShane & Alice Patania & Mario L. Small, 2020. "Choices in networks: a research framework," Marketing Letters, Springer, vol. 31(4), pages 349-359, December.
    • Handle: RePEc:kap:mktlet:v:31:y:2020:i:4:d:10.1007_s11002-020-09541-9
    DOI: 10.1007/s11002-020-09541-9
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    References listed on IDEAS

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    Cited by:

    1. Kiran Tomlinson & Austin R. Benson, 2022. "Graph-Based Methods for Discrete Choice," Papers 2205.11365, arXiv.org, revised Nov 2023.

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