Impact of e-detailing on the number of new prescriptions
Tóm tắt
The recent trend of e-detailing in the pharmaceutical industry aims to increase the effectiveness of promotion of prescription products to physicians at a less expensive way than traditional detailing. In the proposed promotion response model, the effect of e-detailing on new prescriptions is accounted for in the presence of traditional face-to-face detailing and a host of product-specific factors. The model is calibrated on 21 ethical pharmaceutical products in six diverse therapeutic categories over a period of two years using datasets from two industrial sources. We estimate our model once at the aggregate level and once using a fixed-effects methodology to account for unobserved heterogeneity across products. We find that prescription product (Rx) manufacturers appear to benefit from increasing both e-detailing and traditional detailing. Our findings also lead us to conclude that there is room for improving the synergy between the two types of detailing.
Tài liệu tham khảo
Bates A, Bailey E, Rajyaguru I (2003) Navigating the e-Detailing Maze. Int J Med Mark 2(3):255–262. doi:10.1057/palgrave.jmm.5040083
Davidson T, Sivadas E (2004) Details: Physicians are Responding to Electronic Sales Calls. Mark Health Serv (Spring):20–25.
www.lathian.com/pdf/articles/Verispan_June08_YearReview.pdf (2008)
Gönül FF, Carter F, Petrova E, Srinivasan K (2001) Promotion of Prescription Drugs and Its Impact on Physicians’ Choice Behavior. J Mark 65:79–90. doi:10.1509/jmkg.65.3.79.18329
Mizik N, Jacobson R (2004) Are Physicians “Easy Marks”? Quantifying the Effects of Detailing and Sampling on New Prescriptions. Manage Sci 50:1704–1715. doi:10.1287/mnsc.1040.0281
S. Narayanan, P. Manchanda, and P. K. Chintagunta, Temporal Differences in the Role of Marketing Communication in New Product Categories, Journal of Marketing Research, August (2005) 278-290
Moon S, Kamakura WA, Ledolter J (2007) Estimating Promotion Response when Competitive Promotions are Unobservable. J Mark Res:503–515. doi:10.1509/jmkr.44.3.503
Lim CW, Kirikoshi T Understanding the Effects of Pharmaceutical Promotion: A Neural Network Approach Guided by Genetic Algorithm – Partial Least Squares, Health Care Management Science, online, Feb 5, www.springerlink.com/content/0u266h8267683827/ (2008)
News, Medical Marketing & Media, July (2005) 8
www.virsci.com (2007)
Mackintosh A (2004) Innovation in Pharmaceutical Marketing Strategy: How to Overcome the 30-second Detailing Dilemma. Int J Med Mark 4:15–17. doi:10.1057/palgrave.jmm.5040138
www.mmm-online.com/Force-in-the-Field/print/58328 (2007)
(2005) e-Marketing. Med Mark Media (June):26
Chamberlain G (1985) Heterogeneity, Omitted Variable Bias, and Duration Dependence. In: Heckman JJ, Singer B (eds) Longitudinal Analysis of Labor Market Data. Cambridge University, New York, pp 3–38
www.drugs.com/news/verispan-reports-physicians-prefer-merck-s-epromotion-activities-8006.html (2008)
www.imshealth.com/imshealth/Global/Content/StaticFile/New_Product_Spectra.pdf (2008)
HealthScout www.healthscout.com/ency/1/ImagePages/18126.html (2004)