Over a large number of new products and technological innovations, the Bass diffusion model (Bass 1969) describes the empirical adoption curve quite well. In this study, we generalize the Bass model to include decision variables such as price and advertising. The generalized model reduces to the Bass model as a special case and explains why the Bass model works so well without including decision variables. We compare our generalized Bass model to other approaches from the literature for including decision variables into diffusion models, and our results provide both theoretical and empirical support for the generalized Bass model. We also show how our generalized Bass model can be used for product planning purposes.diffusion, marketing mix, new product research, pricing research

We investigate consumer preference for online versus offline purchasing of a complex service (home mortgage), across the three stages of purchasing, namely, pre-purchase, purchase, and post-purchase. Our analysis of data from 300 consumers shows that (1) the offline channel is generally preferred over the online channel across all the stages, and (2) the channel usage intention in a particular stage is moderated by the consumer's Internet experience. Specifically, in both the pre- and post-purchase stages, the usage intention for the online channel is higher when consumers have more favorable Internet experience. In the purchase stage, consumers prefer the offline channel over the online channel, regardless of their Internet experience. Furthermore, we find that the drivers of channel preference are substantially different across the three buying stages due to (in)congruities between channel benefits desired and channel capabilities offered.

The literature on cross-national diffusion models is gaining increased importance today due to the needs of present day managers. New product sales growth in a given nation or society is affected by many factors (Rogers 1995), and of these, sociocontagion (or word of mouth) has been found to be the most important factor that characterizes the diffusion process (Bass 1969, Moore 1995). Hence, it is interesting and perhaps challenging to analyze what would happen if a new product diffuses in parallel in two neighboring but culturally different countries. Not only will we expect the diffusion process in the two countries to be different, but we will also expect some interaction among them, especially if the two societies mingle with each other. There are two streams of research in cross-national diffusion. The first type focuses on exploring the differences between diffusion processes in two countries and finding out whether those differences can be attributed to social and cultural differences between the countries involved. Examples of this type of research are found in Takada and Jain (1991), Gatignon et al. (1989), Helsen et al. (1993), and Kumar et al. (1998). These studies did find some relationship between the cultural differences of the countries studied and the differences in the diffusion process. The second stream of research focuses on modeling explicitly the interaction between the diffusion processes in two countries. The interaction is typically captured through lead-lag effect (Eliashberg and Helsen 1996, Kalish et al. 1995), where the sales process in the lead country (i.e., the country where the product was first introduced) is modeled to affect the sales process in the lag country (i.e., the country where the product was introduced a few years later). Another method to study the interaction among the diffusion processes in two countries was suggested by Putsis et al. (1997), who used a “mixing model” to empirically explore the existence of such interactions. These studies basically observed that, when a new product is introduced early in one country and with a time lag in subsequent countries, the consumers in the lag countries learn about the product from the lead country adopters, resulting in a faster diffusion rate in the lag countries. Ganesh and Kumar (1996) formulized this effect as the learning effect and, subsequently, Ganesh et al. (1997) found this learning effect to be influenced by country-specific factors (cultural similarity, economic similarity, and time lag elapsed between the lead and the lag countries) and product-specific factors (continuous vs. discontinuous innovation and the presence or absence of a standardized technology). A careful analysis of the extant literature on the second stream of research would reveal that neither the learning effect model nor the mixing model can be modified to accommodate the other model. Our contribution to the literature exactly addresses this point. In this paper, an alternative framework is proposed that has two unique features. First, the ...

Starting with Bass's (1969) article, diffusion researchers have predominantly focused on modeling category-level sales growth and issues surrounding it. In this article, the authors propose a brand-level diffusion model and demonstrate its managerial use by applying it to the following issue: If a new brand enters a category that has not attained its peak sales, how can a practicing manager evaluate its impact on the category and on the incumbent brands? The proposed model helps the manager diagnose whether the late entrant affects the market potential and/or the diffusion speed of the category and of the incumbent brands. The authors test the model using brand-level sales data from the cellular telephone industry in multiple markets.