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Social Network Influence on New Drug Diffusion: Can the Data-driven Approach Provide Practical Benefits?

Author

Listed:
  • Ágnes Lublóy

    (Stockholm School of Economics in Riga, Latvia
    Institute of Finance and Accounting, Department of Finance, Corvinus University of Budapest, Hungary)

  • Judit Lilla Keresztúri

    (Institute of Finance and Accounting, Department of Finance, Corvinus University of Budapest, Hungary)

  • Gábor Benedek

    (Thesys SEA Pte Ltd., Singapore
    Department of Mathematical Economics and Economic Analyses, Corvinus University of Budapest, Hungary)

Abstract

This article studies the determinants of pharmaceutical innovation diffusion among specialists. To this end, it investigates the influences of six categories of factors—social embeddedness, socio-demography, scientific orientation, prescribing patterns, practice characteristics, and patient panel composition—on the use of 11 new drugs for the treatment of type 2 diabetes mellitus in Hungary. The Cox proportional hazards model identifies three determinants—social contagion (in the social embeddedness category) and prescribing portfolio and insulin prescribing ratio (in the prescribing pattern category). First, social contagion has a positive effect among geographically close colleagues—the higher the adoption ratio, the higher the likelihood of early adoption—but no influence among former classmates and scientific collaborators. Second, the wider the prescribing portfolio, the earlier the new drug uptake. Third, the lower the insulin prescribing ratio, the earlier the new drug uptake—physicians’ therapeutic convictions and patients’ socioeconomic statuses act as underlying influencers. However, this finding does not extend to opinion-leading physicians such as scientific leaders and hospital department and outpatient center managers. This article concludes by arguing that healthcare policy strategists and pharmaceutical companies may rely exclusively on practice location and prescription data to perfect interventions and optimize budgets.

Suggested Citation

  • Ágnes Lublóy & Judit Lilla Keresztúri & Gábor Benedek, 2018. "Social Network Influence on New Drug Diffusion: Can the Data-driven Approach Provide Practical Benefits?," Society and Economy, Akadémiai Kiadó, Hungary, vol. 40(2), pages 227-243, June.
  • Handle: RePEc:aka:soceco:v:40:y:2018:i:2:p:227-243
    Note: This research was supported by the Higher Education Institutional Excellence Program of the Ministry of Human Capacities in the framework of the ‘Financial and Public Services’ research project (1783-3/2018/FEKUTSTRAT) at Corvinus University of Budapest. Ágnes Lublóy’s work on the paper was also supported by the post-doctoral research grant from AXA Research Fund (grant number 2011-Post Doc - Corvinus University of Budapest - Lublóy Á.). The authors are grateful to DoktorInfo Ltd, for waiving the subscription charge in the interest of scientific research; Petra Baji, Edina Berlinger, and László Gulácsi, as well as two anonymous referees for valuable comments and suggestions on earlier drafts; participants at the Production and Operations Management Society’s 27th Annual Conference and at the 23rd Congress of the Hungarian Diabetes Association for helpful discussions; and Anamaria M. Cristescu-Martin, for editorial assistance.
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    Citations

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    Cited by:

    1. Anikó Bíró, 2019. "Reduced user fees for antibiotics under age 5 in Hungary: Effect on antibiotic use and imbalances in the implementation," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-13, June.

    More about this item

    Keywords

    pharmaceutical innovations; Cox proportional hazards model; diffusion; social contagion; pharmaceutical innovations;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • I19 - Health, Education, and Welfare - - Health - - - Other

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