A Generalized Norton–Bass Model for Multigeneration Diffusion

The Norton–Bass (NB) model is often credited as the pioneering multigeneration diffusion model in marketing. However, as acknowledged by the authors, when counting the number of adopters who substitute an old product generation with a new generation, the NB model does not differentiate those who have already adopted the old generation from those who have not. In this study, we develop a generalized Norton–Bass (GNB) model that separates the two different types of substitutions. The GNB model provides closed-form expressions for both the number of units in use and the adoption rate, and offers greater flexibility in parameter estimation, forecasting, and revenue projection. An appealing aspect of the GNB model is that it uses exactly the same set of parameters as the NB model and is mathematically consistent with the later. Empirical results show that the GNB model delivers better overall performance than previous models both in terms of model fit and forecasting performance. The analyses also show that differentiating leapfrogging and switching adoptions based on the GNB model can help gain additional insights into the process of multigeneration diffusion. Furthermore, we demonstrate that the GNB model can incorporate the effect of marketing mix variables on the speed of diffusion for all product generations

Now or Never Revisited: An Analysis of Market Entry Timing for Successive Product Generation

Determining the optimal market entry timing for successive technological innovations is a critical decision for firms. Pioneering studies dealing with this issue have focused one-time sale (e.g., HDTV), and concluded that a new product should be introduced to the market either now or never, or now or at maturity. However, these prior studies do not examine another commonly seen business practice —revenue is generated from continuous services (e.g., Office 365). In this research, we derive the optimal market entry timing under both one-time sale and continuous service, and check whether the prior findings remain valid under today’s diverse market landscape. We find that under one-time sale, the optimal entry timing is not limited to now, maturity, or never; but it can also lie between now and maturity. More interestingly, our results show that the now or never rule holds only under a scenario not considered in the prior studies

Understanding new products’ market performance using Google Trends

This paper seeks to empirically examine diffusion models and Google Trends’ ability to explain and nowcast the new product growth phenomenon. In addition to the selected diffusion models and Google Trends, this study proposes a new model that incorporates the two. The empirical analysis is based on the cases of the iPhone and the iPad. The results show that the new model exhibits a better curve fit among all the studied ones. In terms of nowcasting, although the performance of the new model differs from that of Google Trends in the two cases, they both produce more accurate results than the selected diffusion models

Predicting the Potential Market for Electric Vehicles

Forecasting the potential demand for electric vehicles is a challenging task. Because most studies for new technologies rely on stated preference (SP) data, market share predictions will reflect shares in the SP data and not in the real market. Moreover, typical disaggregate demand models are suitable to forecast demand in relatively stable markets, but show limitations in the case of innovations. When predicting the market for new products it is crucial to account for the role played by innovation and how it penetrates the new market over time through a diffusion process. However, typical diffusion models in marketing research use fairly simple demand models. In this paper we discuss the problem of predicting market shares for new products and suggest a method that combines advanced choice models with a diffusion model to take into account that new products often need time to gain a significant market share. We have the advantage of a relatively unique databank where respondents were submitted to the same stated choice experiment before and after experiencing an electric vehicle. Results show that typical choice models forecast a demand that is too restrictive in the long period. Accounting for the diffusion effect, instead allows predicting the usual slow penetration of a new product in the initial years after product launch and a faster market share increase after diffusion takes place. </jats:p

The changing rationale for governance choices: early vs. late adopters of global services sourcing

This article studies how the logic of firm governance choices varies as a function of the time of adoption of particular sourcing practices. Using data on the diffusion of global business services sourcing as a management practice from early experiments in the 1980s through 2011, we show that the extent to which governance choices are affected by process commoditization, availability of external service capabilities, and past governance choices depends on whether firms are early or late adopters. Findings inform research on governance choice dynamics specifically in highly diverse and evolving firm populations

MEASURING THE EFFECT OF THE ENTRY INTO THE NEW GENERATION UNDER THE COMPETITIVE MARKET

Master'sMASTER OF SCIENCE (BUSINESS