The influence of marketing factors and substance characteristics on pharmaceutical sales in a state-controlled prescriptions pharmaceuticals market

  • Michael Stros

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

The present dissertation investigates the influence of brand as well as substance-related marketing attributes on prescription pharmaceutical sales within a state-controlled market. For this purpose, a systematic literature review was conducted in the first instance, during which
knowledge about the most relevant research within this field was gathered. Consequently, over 538 publications were reviewed and indicated as being potentially relevant, leading to an eventual count of 98 core publications. However, most of these studies had been conducted in
the mainly unrestricted US market. These findings were then summarised and statistically evaluated. In a second step, based on the literature review, a qualitative study, containing focus and Delphi groups, was then performed. The participants in these studies were involved in pharmaceutical marketing within a state-controlled prescriptions pharmaceuticals market. Consequently, the findings were slightly different to those derived by the systematic literature review. Based on this second step, seven hypotheses were proposed. In the third step, these
hypotheses were tested, using collected data and a secondary market dataset provided by a market research institute. A statistical analysis was then performed, applying descriptive as well as multiple regression analytical methods. The evaluation of the results resulted in a
conceptual model of physician targeting, leading to several theoretical, methodological and managerial implications.
Date of AwardDec 2012
Original languageEnglish
SupervisorJohn F Marriott (Supervisor) & Nicholas J Lee (Supervisor)

Keywords

  • pharmaceutical prescriptions marketing
  • state-regulated market
  • marketing mix
  • order-of-entry
  • systemic literature review
  • focus group technique
  • delphi group technique
  • secondary data
  • multiple regression

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