Investigating international new product diffusion speed: A semiparametric approach
KAUST Grant NumberKUS-CI-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/598663
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AbstractGlobal marketing managers are interested in understanding the speed of the new product diffusion process and how the speed has changed in our ever more technologically advanced and global marketplace. Understanding the process allows firms to forecast the expected rate of return on their new products and develop effective marketing strategies. The most recent major study on this topic [Marketing Science 21 (2002) 97-114] investigated new product diffusions in the United States.We expand upon that study in three important ways. (1) Van den Bulte notes that a similar study is needed in the international context, especially in developing countries. Our study covers four new product diffusions across 31 developed and developing nations from 1980-2004. Our sample accounts for about 80% of the global economic output and 60% of the global population, allowing us to examine more general phenomena. (2) His model contains the implicit assumption that the diffusion speed parameter is constant throughout the diffusion life cycle of a product. Recognizing the likely effects on the speed parameter of recent changes in the marketplace, we model the parameter as a semiparametric function, allowing it the flexibility to change over time. (3) We perform a variable selection to determine that the number of internet users and the consumer price index are strongly associated with the speed of diffusion. © Institute of Mathematical Statistics, 2012.
CitationHartman BM, Mallick BK, Talukdar D (2012) Investigating international new product diffusion speed: A semiparametric approach. The Annals of Applied Statistics 6: 625–651. Available: http://dx.doi.org/10.1214/11-AOAS519.
SponsorsSupported in part by National Science foundation CMG research Grants DMS-07-24704, DMS-09-14951 and by Award Number KUS-CI-016-04 made by King Abdullah University of Science and Technology (KAUST).Supported in part through the Dean's Faculty Research Fellowship award from the School of Management, State University of New York at Buffalo.
PublisherInstitute of Mathematical Statistics
JournalThe Annals of Applied Statistics