Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes

Published Online:https://doi.org/10.1287/mksc.1080.0440

References

  • Atun R., Gurol-Urganci I. Impact of regulation on the uptake and diffusion of pharmaceutical innovations: Systematic review. (2006) . Monograph, Tanaka Business School, Imperial College, LondonGoogle Scholar
  • Bass F. M., Krishnan T. V., Jain D. C. Why the Bass model fits without decision variables. Marketing Sci. (1994) 13(3):203–223LinkGoogle Scholar
  • Bell R. A., Kravitz R. L., Wilkes M. S. Direct-to-consumer prescription drug advertising and the public. J. General Internal Medicine (1999) 14(11):651–657CrossrefGoogle Scholar
  • Berndt E. R., Bhattacharjya A., Mishol D. N., Arcelus A., Lasky T. An analysis of the diffusion of new antidepressants: Variety, quality and marketing efforts. J. Mental Health Policy Econom. (2002) 5:3–19Google Scholar
  • Berndt E. R., Danzon P. M., Kruse G. B. Dynamic competition in pharmaceuticals: Cross-national evidence from new drug diffusion. Managerial Decision Econom. (2007) 28(4–5):231–250CrossrefGoogle Scholar
  • Bowman D., Heilman C. M., Seetharaman P. B. Determinants of product-use compliance behavior. J. Marketing Res. (2004) 41:324–338CrossrefGoogle Scholar
  • Bronnenberg B. J., Rossi P. E., Vilcassim N. J. Structural modeling and policy simulation. J. Marketing Res. (2005) 42(February):22–26CrossrefGoogle Scholar
  • Brumback B. A., Ruppert D., Wand M. P. Comment to “Variable selection and function estimation in additive nonparametric regression using a data-based prior”. J. Amer. Statist. Assoc. (1999) 94(447):794–797Google Scholar
  • Chandy R. K., Tellis G. J., Macinnis D. J., Thaivanich P. What to say when: Advertising appeals in evolving markets. J. Marketing Res. (2001) 38(November):399–414CrossrefGoogle Scholar
  • Chatterjee R., Eliashberg J., Rao V. R., Mahajan V., Muller E., Wind Y. Dynamic models incorporating competition. New Product Diffusion Models (2000) (Kluwer Academic Publishers, Boston) 49–73Google Scholar
  • Chiang C.-T., Rice J. A., Wu C. O. Smoothing spline estimation for varying coefficient models with repeatedly measured dependent variables. J. Amer. Statist. Assoc. (2001) 96(454):605–619CrossrefGoogle Scholar
  • Chintagunta P., Desiraju R. Strategic pricing and detailing behavior in international markets. Marketing Sci. (2005) 24(1):67–80LinkGoogle Scholar
  • Cleveland W. S., Grosse E., Shyu W. M., Chambers J. M., Hastie T. Local regression models. Statistical Models in S (1992) (Chapman and Hall/CRC, New York) 309–376Google Scholar
  • Cohen E. P. Direct-to-the-public advertisement of prescription drugs. New England J. Medicine (1988) 318:373–376CrossrefGoogle Scholar
  • Coull B. A., Ruppert D., Wand M. P. Simple incorporation of interactions into additive models. Biometrics (2001) 57(2):539–545CrossrefGoogle Scholar
  • Crainiceanu C., Ruppert D., Claeskens G., Wand M. P. Exact likelihood ratio tests for penalized splines. Biometrika (2005) 92(1):91–103CrossrefGoogle Scholar
  • Danzon P. M., Chao L. Cross-national price differences for pharmaceuticals: How large, and why? J. Health Econom. (2000) 19:159–195CrossrefGoogle Scholar
  • Danzon P. M., Ketcham J. D., Cutler D. M., Garber A. M. Reference pricing of pharmaceuticals for medicare: Evidence from Germany, The Netherlands and New Zealand. Frontiers in Health Policy Research (2004) (National Bureau of Economic Research, Cambridge, MA) 1–54Google Scholar
  • Danzon P. M., Wang Y. R., Wang L. The impact of price regulation on the launch delay of new drugs. Health Econom. (2005) 14(3):269–292CrossrefGoogle Scholar
  • Dawar N., Parker P. Marketing universals: Consumers' use of brand name, price, physical appearance, and retailer reputation as signals of product quality. J. Marketing (1994) 58(April):81–95CrossrefGoogle Scholar
  • Dekimpe M. G., Hanssens D. M. Sustained spending and persistent response: A new look at long-term marketing profitability. J. Marketing Res. (1999) 36:397–412CrossrefGoogle Scholar
  • Dekimpe M. G., Parker P. M., Sarvary M. Staged estimation of international diffusion models: An application to global cellular telephone adoption. Tech. Forecasting Soc. Change (1998) 57:105–132CrossrefGoogle Scholar
  • Dekimpe M. G., Parker P. M., Sarvary M., Mahajan V., Muller E., Wind Y. Multimarket and global diffusion. New Product Diffusion Models (2000) (Kluwer Academic Publishers, Boston) 49–73Google Scholar
  • Deleersnyder B., Dekimpe M. G., Steenkamp J.-B. E. M., Leeflang P. S. H. The role of national culture in advertising's sensitivity to business cycles: An investigation across continents. J. Marketing Res. (2009) . ForthcomingCrossrefGoogle Scholar
  • Desiraju R., Nair H., Chintagunta P. Diffusion of new pharmaceutical drugs in developing and developed nations. Internat. J. Res. Marketing (2004) 21:341–357CrossrefGoogle Scholar
  • Ding M., Eliashberg J. A dynamic competitive forecasting model incorporating dyadic decision-making. Management Sci. (2008) 54(4):820–834LinkGoogle Scholar
  • Economist, The From bench to bedside—An unhealthy burden. (2007) June 30):14–16Google Scholar
  • Eilers P. H. C., Marx B. D. Flexible smoothing with B-splines and penalties. Statist. Sci. (1996) 11(2):89–121CrossrefGoogle Scholar
  • Ekelund M., Persson B. Pharmaceutical pricing in a regulated market. Rev. Econom. Statist. (2003) 85(2):298–306CrossrefGoogle Scholar
  • European Commission New products and services: Analysis of regulations shaping new markets. (2004) . http://www.cordis.lu/innovation-policy/studiesGoogle Scholar
  • Gatignon H., Eliashberg J., Robertson T. S. Modeling multinational diffusion patterns: An efficient methodology. Marketing Sci. (1989) 8(3):231–247LinkGoogle Scholar
  • Gönül F. F., Carter F., Petrova E., Srinivasan K. Promotion of prescription drugs and its impact on physicians' choice behavior. J. Marketing (2001) 65(July):79–90CrossrefGoogle Scholar
  • Grunert K. G. Automatic and strategic processes in advertising effects. J. Marketing (1996) 60(October):88–101CrossrefGoogle Scholar
  • Hahn M., Park S., Krishnamurthi L., Zoltners A. A. Analysis of new product diffusion using a four-segment trial-repeat. Marketing Sci. (1994) 13(3):224–247LinkGoogle Scholar
  • Hart J., Salman H., Bergman M., Neuman V., Rudniki C., Gilenberg D., Matalon A., Djaldetti M. Do drug costs affect physicians' prescription decisions? J. Internal Medicine (1997) 241:415–420CrossrefGoogle Scholar
  • Hassett K. A. Price controls and the evolution of pharmaceutical markets. (2004) . Mimeo, American Enterprise Institute, Washington, DCGoogle Scholar
  • Hastie T., Tibshirani R. Varying-coefficient models. J. Roy. Statist. Soc. Ser. B (1993) 55(4):757–796Google Scholar
  • Heeler R. M., Hustad T. P. Problems in predicting new product growth for consumer durables. Management Sci. (1980) 26(10):1007–1020LinkGoogle Scholar
  • Helsen K., Jedidi K., DeSarbo W. S. A new approach to country segmentation utilizing multinational diffusion patterns. J. Marketing (1993) 57(October):60–71CrossrefGoogle Scholar
  • Hofstede G.Culture's Consequences: International Differences in Work-Related Values (1980) (Sage, Beverly Hills, CA) Google Scholar
  • Hofstede G.Culture's Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations (2001) 2nd ed.(Sage Publications, London) Google Scholar
  • Horsky D., Simon L. S. Advertising and the diffusion of new products. Marketing Sci. (1983) 2(1):1–17LinkGoogle Scholar
  • Huang J. Z., Wu C. O., Zhou L. Varying-coefficient models and basis function approximations for the analysis of repeated measurements. Biometrika (2002) 89(1):111–128CrossrefGoogle Scholar
  • Jacobzone S. Pharmaceutical policies in OECD countries: Reconciling social and industrial goals. OECD Labour Market Soc. Policy—Occasional Papers (2000) 40:1–100Google Scholar
  • Janakiraman R., Dutta S., Sismeiro C., Stern P. Physicians' persistence and its implications for their response to promotion of prescription drugs. Management Sci. (2008) 54(6):1080–1093LinkGoogle Scholar
  • Kauermann G. Penalized spline smoothing in multivariate survival models with varying coefficients. Computational Statist. Data Anal. (2005) 49(1):169–186CrossrefGoogle Scholar
  • Kauermann G., Komski P. Additive two-way hazards model with varying coefficients. Computational Statist. Data Anal. (2006) 51(3):1944–1956CrossrefGoogle Scholar
  • Kolsarici C., Vakratsas D. Dynamic market-level effects of highly regulated advertising messages. (2008) . Working paper, McGill University, MontrealGoogle Scholar
  • Kravitz R. L., Bell R. A., Azari R., Kelly-Reif S., Krupat E., Thom D. H. Direct observation of requests for clinical services in office practice: What do patients want and do they get it? Archives Internal Med. (2003) 163:1673–1681CrossrefGoogle Scholar
  • Krishnan T. V., Bass F. M., Kumar V. Impact of a late entrant on the diffusion of a new product/service. J. Marketing Res. (2000) 37(2):269–278CrossrefGoogle Scholar
  • Krivobokova T., Kauermann G., Archontakis T. Estimating the term structure of interest rates using penalized splines. Statist. Papers (2006) 47(3):443–459CrossrefGoogle Scholar
  • Kumar V., Krishnan T. V. Multinational diffusion models: An alternative framework. Marketing Sci. (2002) 21(3):318–330LinkGoogle Scholar
  • Kyle M. K. Pharmaceutical price controls and entry strategies. Rev. Econom. Statist. (2007) 89(1):88–99CrossrefGoogle Scholar
  • Lanjouw J. O. Patents, price controls and access to new drugs: How policy affects global market entry. (2005) . Working Paper 61, Center for Global Development, Brookings Institution, Washington, DCCrossrefGoogle Scholar
  • Leeflang P., Wieringa J. E., Wittink D. R. Modeling the effects of promotion expenditures on the sales of pharmaceuticals. (2005) . Working paper, University of Groningen, Groningen, The NetherlandsGoogle Scholar
  • Lilien G. L., Little J. D. C. The ADVISOR project: A study of industrial marketing budgets. Sloan Management Rev. (1976) 17(Spring):17–31Google Scholar
  • Lynn M., Zinkhan G. M., Harris J. Consumer tipping: A cross-country study. J. Consumer Res. (1993) 20(December):478–488CrossrefGoogle Scholar
  • Mahajan V., Muller E. Innovation diffusion in a borderless global market: Will the 1992 unification of the European community accelerate diffusion of new ideas, products and technologies? Tech. Forecasting Soc. Change (1994) 45:221–235CrossrefGoogle Scholar
  • Mahajan V., Sharma S., Buzzell R. D. Assessing the impact of competitive entry on market expansion and incumbent sales. J. Marketing (1993) 57(3):39–52CrossrefGoogle Scholar
  • Manchanda P., Chintagunta P. K. Responsiveness of physician prescription behavior to salesforce effort: An individual level analysis. Marketing Lett. (2004) 15(2–3):129–145CrossrefGoogle Scholar
  • Manchanda P., Honka E. The effects and role of direct-to-physician marketing in the pharmaceutical industry: An integrative review. Yale J. Health Policy, Law Econom. (2005) 5:785–822Google Scholar
  • Manchanda P., Rossi P. E., Chintagunta P. K. Response modeling with non-random marketing mix variables. J. Marketing Res. (2004) 41(November):467–478CrossrefGoogle Scholar
  • Mantrala M. K., Sinha P., Zoltners A. A. Structuring a multiproduct sales quota bonus plan for a heterogeneous sales force. Marketing Sci. (1994) 13(2):121–144LinkGoogle Scholar
  • Mason C. H. New product entries and product class demand. Marketing Sci. (1990) 9(1):58–73LinkGoogle Scholar
  • Mehta A., Purvis S. C. Consumer response to print prescription drug advertising. J. Advertising Res. (2003) 43(2):194–206CrossrefGoogle Scholar
  • Michaut A. Consumer response to innovative products: With application to foods. (2004) . Unpublished doctoral dissertation, Wageningen University, Wageningen, The NetherlandsGoogle Scholar
  • Mintzes B., Barer M. L., Kravitz R. L., Bassett B., Lexchin J., Kazanjian A., Evans R. G., Pan R., Marion S. A. How does direct-to-consumer advertising (DTCA) affect prescribing? A survey in primary care environments with and without legal DTCA. Canadian Medical Assoc. J. (2003) 169(5):405–412Google Scholar
  • Mizik N., Jacobson R. Are physicians “easy marks”? Quantifying the effects of detailing and sampling on new prescriptions. Management Sci. (2004) 50(12):1704–1715LinkGoogle Scholar
  • Murphy K. M., Topel R. H. Estimation and inference in two-step econometric models. J. Bus. Econom. Statist. (1985) 3(4):370–379CrossrefGoogle Scholar
  • Naik P. A., Mantrala M. K., Sawyer A. G. Planning media schedules in the presence of dynamic advertising quality. Marketing Sci. (1998) 17(3):214–35LinkGoogle Scholar
  • Narayanan S., Manchanda P. The role of free samples in the pharmaceutical industry: An empirical analysis. 2006 INFORMS Marketing Sci. Conf. (2006) (Institute for Operations Research and the Management Sciences, Hanover, MD) Google Scholar
  • Narayanan S., Manchanda P. Heterogeneous learning and the targeting of marketing communication for new products. (2008) . Working paper, http://ssrn.com/abstract=935339Google Scholar
  • Narayanan S., Desiraju R., Chintagunta P. K. Return on investment implications for pharmaceutical promotional expenditures: The role of marketing-mix interactions. J. Marketing (2004) 68(October):90–105CrossrefGoogle Scholar
  • Narayanan S., Manchanda P., Chintagunta P. K. Temporal differences in the role of marketing communication in new product categories. J. Marketing Res. (2005) 42:278–290CrossrefGoogle Scholar
  • Neelamegham R., Chintagunta P. K. Modeling and forecasting the sales of technology products. Quant. Marketing Econom. (2004) 2:195–232CrossrefGoogle Scholar
  • Neslin S. ROI analysis of pharmaceutical promotion. (2001) . Unpublished study conducted for the Association of Medical Publications, http://www.rxpromoroi.orgGoogle Scholar
  • Ngo L., Wand M. P. Smoothing with mixed model software. J. Statist. Software (2004) 9(1):1–54CrossrefGoogle Scholar
  • Opsomer J., Wang Y., Yang Y. Nonparametric regression with correlated errors. Statist. Sci. (2001) 16:134–153CrossrefGoogle Scholar
  • Parsons L. J., Vanden Abeele P. Analysis of sales call effectiveness. J. Marketing Res. (1981) 18(February):107–113CrossrefGoogle Scholar
  • Prosser H., Walley T. New drug uptake: Qualitative comparison of high and low prescribing GP's attitudes and approach. Family Practice (2003) 20(5):583–591CrossrefGoogle Scholar
  • Prosser H., Almond S., Walley T. Influences on GP's decision to prescribe new drugs—The importance of who says what. Family Practice (2003) 20(1):61–68CrossrefGoogle Scholar
  • Reuveni H., Sheizaf B., Elhayany A., Sherf M., Limoni Y., Scharff S., Peled R. The effect of drug co-payment policy on the purchase of prescription drugs for children with infections in the community. Health Policy (2002) 62(1):1–13CrossrefGoogle Scholar
  • Rosenthal M. B., Berndt E. R., Donohue J. M., Frank R. G., Epstein A. M. Promotion of prescription drugs to consumers. New England J. Medicine (2002) 346(7):498–505CrossrefGoogle Scholar
  • Rosenthal M. B., Berndt E. R., Donohue J. M., Epstein A. M., Frank R. G., Cutler D. M., Garber A. M. Demand effects of recent changes in prescription drug information. Frontiers in Health Policy Research (2003) 6(MIT Press, Cambridge, MA) 1–26Google Scholar
  • Roth M. The effects of culture and socioeconomics on the performance of global brand image strategies. J. Marketing Res. (1995) 32(May):163–175CrossrefGoogle Scholar
  • Ruppert D., Wand M. P., Carroll R. J.Semiparametric Regression (2003) (Cambridge University Press, Cambridge, UK) Cambridge Series in Statistical and Probabilistic MathematicsCrossrefGoogle Scholar
  • Schafer J. L.Analysis of Incomplete Multivariate Data (1997) (Chapman and Hall, CRC Press, London) . Chapter 5CrossrefGoogle Scholar
  • Schafer J. L., Graham J. W. Missing data: Our view of the state of the art. Psych. Methods (2002) 7(2):147–177CrossrefGoogle Scholar
  • Schwartz S. H., Zanna M. Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. Advances in Experimental Social Psychology (1992) 25(Academic Press, Orlando, FL) 1–65Google Scholar
  • Schwartz R. K., Soumerai S. B., Avorn J. Physician motivations for nonscientific drug prescribing. Soc. Sci. Medicine (1989) 28(6):577–582CrossrefGoogle Scholar
  • Shugan S. M. Editorial: Defining interesting research problems. Marketing Sci. (2003) 22(1):1–15LinkGoogle Scholar
  • Sloot L. M., Fok D., Verhoef P. C. The short- and long-term impact of an assortment reduction on category sales. J. Marketing Res. (2006) 43:536–548CrossrefGoogle Scholar
  • Steenkamp J.-B. E. M., ter Hofstede F., Wedel M. A cross-national investigation into the individual and national cultural antecedents of consumer innovativeness. J. Marketing (1999) 63(April):55–69CrossrefGoogle Scholar
  • Stremersch S., Tellis G. J. Understanding and managing international growth of new products. Internat. J. Res. Marketing (2004) 21:421–438CrossrefGoogle Scholar
  • Takada H., Jain D. Cross-national analysis of diffusion of consumer durable goods in Pacific Rim countries. J. Marketing (1991) 55(April):48–54CrossrefGoogle Scholar
  • Talukdar D., Sudhir K., Ainslie A. Investigating new product diffusion across products and countries. Marketing Sci. (2002) 21(1):97–114LinkGoogle Scholar
  • Tellis G. J., Fornell C. The relationship between advertising and product quality over the product life cycle: A contingency theory. J. Marketing Res. (1988) 25(February):64–71CrossrefGoogle Scholar
  • Tellis G. J., Stremersch S., Yin E. The international takeoff of new products: The role of economics, culture, and country innovativeness. Marketing Sci. (2003) 22(2):188–208LinkGoogle Scholar
  • Triandis H. C. The self and social behavior in differing cultural contexts. Psych. Rev. (1989) 96(July):506–520CrossrefGoogle Scholar
  • URCHThe Guide to Pharmaceutical Pricing and Reimbursement Systems: Western Europe (2005) (URCH Publishing Ltd., London) Google Scholar
  • Van den Bulte C., Stremersch S. Social contagion and income heterogeneity in new product diffusion: A meta-analytic test. Marketing Sci. (2004) 23(4):530–544LinkGoogle Scholar
  • Van Heerde H. J., Leeflang P. S. H., Wittink D. R., Wedel M., Naert P. A. Non- and semiparametric regression models. Building Models for Marketing Decisions (2000) (Kluwer Academic Publishers, Boston) 396–408Google Scholar
  • Van Heerde H. J., Mela C. F., Manchanda P. The dynamic effect of innovation on market structure. J. Marketing Res. (2004) 41(2):166–183CrossrefGoogle Scholar
  • Venkataraman S., Stremersch S. The debate on influencing doctors' decisions: Are drug characteristics the missing link? Management Sci. (2007) 53(11):1688–1701LinkGoogle Scholar
  • Verbeke G., Molenberghs G.Linear Mixed Models for Longitudinal Data (2000) (Springer-Verlag, New York) Google Scholar
  • Wand M. P. Smoothing and mixed models. Computational Statist. (2003) 18:223–249CrossrefGoogle Scholar
  • Weber B. A., Roberts B. L., McDougall G. J. Exploring the efficacy of support groups for men with prostate cancer. Geriatric Nursing (2000) 21(5):250–253CrossrefGoogle Scholar
  • Wedel M., Leeflang P. S. H. A model for the effects of psychological pricing in Gabor-Granger price studies. J. Econom. Psych. (1998) 19(2):237–260CrossrefGoogle Scholar
  • Weissman J. S., Blumenthal D., Silk A. J., Zapert K., Newman M., Leitman R. Consumers' reports on the health effects of direct-to-consumer drug advertising. Health Affairs—Web Exclusive (2004) W3:82–95Google Scholar
  • West D. Mixing it up in the media. Pharmaceutical Executive DTC Times Supplement (1999) 2(9):6Google Scholar
  • West M., Harrison J. P.Bayesian Forecasting and Dynamic Models (1997) (Springer-Verlag, New York) Google Scholar
  • West M., Harrison P. J., Migon H. S. Dynamic generalized linear models and Bayesian forecasting. J. Amer. Statist. Assoc. (1985) 80:73–97CrossrefGoogle Scholar
  • Wosinska M. Just what the patient ordered? Direct-to-consumer advertising and the demand for pharmaceutical products. (2002) (Harvard Business School, Boston) HBS Marketing Research Paper Series 02–04Google Scholar
  • Wright D. J. The drug bargaining game: Pharmaceutical regulation in Australia. J. Health Econom. (2004) 23:785–813CrossrefGoogle Scholar
  • Wu H., Zhang J.-T.Nonparametric Regression Methods for Longitudinal Data: Mixed-Effects Modeling Approaches (2006) (John Wiley & Sons, New York) Google Scholar
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