Information processing and innovative choice

In this paper we examine how the information processing of subjects who make an innovative choice (innovators) differs from that of subjects who make a noninnovative choice (noninnovators). The task involves selection of an alternative within a range of prerated product category innovativeness. We propose that subjects who seek 1) impersonal/uncontrollable sources, 2) higher levels of information, 3) more detailed (versus summary) data, and 4) noncomparative (versus comparative) data are more likely to make innovative choices. The research method is a computerized process tracing experiment utilizing Search Monitor (Brucks 1988).The authors wish to express their gratitude to Merrie Brucks for the use of and help with Search Monitor and to Eric Johnson, Dave Schmittlein, and Mita Sujan for helpful comments on an earlier version of this paper.     

Forecasting the diffusion of photovoltaic systems in southern Europe : A learning curve approach

Most analysts disagree upon whether photovoltaic systems (PV) will be able to play an important role in the energy scenarios of the future. A few scholars also question the appropriateness of policies that envisage the use of public subsidies to stimulate the growth of this industry and to accelerate market penetration. This paper contributes to this debate by examining whether carefully designed policies may initiate a process of large-scale diffusion of grid-connected PV, even without the deployment of external subsidies. Building upon a disaggregated characterization of the electricity market, it takes endogenously into account the learning curve phenomenon and simulates the diffusion of PV building-integrated systems in five European countries. The analysis is restricted to crystalline silicon systems and is repeated under four different macroeconomic scenarios corresponding to four different energy policies. The results suggest that already today there are opportunities for PV diffusion in many islands of the Mediterranean region, which may trigger sufficient scale economies to render the technology competitive in larger markets. They also show that the diffusion process could be accelerated through the implementation of carbon-tax policies that support initial penetration. The environmental benefits (net avoided CO₂ emissions over the system life cycle) associated with the forecasted penetration are also evaluated.     

A substitution orbit model of competitive innovations

Successful innovation and diffusion of technology can be attributed to the identification of the orbit of emerging new technologies that complement or substitute for existing technologies. This dynamism resembles the co-evolution process in an ecosystem. In an ecosystem, in order to maintain sustainable development, the complex interplay between competition and cooperation, typically observed in predator-prey systems, create a sophisticated balance. Given that an ecosystem can be used as a masterpiece system, this sophisticated balance can provide suggestive ideas for identifying an optimal orbit of competitive innovations with complement or substitution dynamism.Prompted by such a sophisticated balance in an ecosystem, this paper analyzes the optimal orbit of competitive innovations and, on the basis of an application of Lotka-Volterra equations, it reviews substitution orbits of Japan's monochrome to color TV system, fixed telephones to cellular telephones, cellular telephones to mobile Internet access service, and analog to digital TV broadcasting. On the basis of substitution orbits analyses, it attempts to extract suggestions supportive to identifying an optimal policy option in a complex orbit leading to expected orbit.Key findings include policy options that are effective in controlling parameters for Lotka-Volterra equations leading to expected orbit.     

World Oil Depletion Models: Price effects compared with strategic or technological interventions

World oil depletion, including natural gas liquids, was modelled in the past by many authors. Recently, Guseo and Dalla Valle have introduced and Guidolin has applied a new approach following perturbed life-cycle diffusion models. Here we examine joint effects of economic and strategic or technological interventions using a Generalized Bass Model (GBM). Statistical analysis takes into account three different hierarchical levels: natural diffusion, long memory interventions and stochastic components. The main results confirm the statistical significance of historical 1970s shocks and highlight a strong long memory effect due to an increase in oil production after World War II. The estimated peak-date, 2007, and the 90% depletion time, 2019, are determined under an equilibrium intervention hypothesis.     

Estimating exchange rate dynamics with diffusion processes: an application to Greek EMU data

Recently various exchange rate models capturing the dynamics during the transition from an exchange rate arrangement of floating rates into a currency union have been derived. Technically, these stochastic equilibrium models are diffusion processes which have to be estimated by discretely sampled observations. Using daily exchange rate data prior to the Greek EMU-entrance on 1 January 2001, we develop a rigorous estimation procedure. Our estimates point to an increasing interventionist economic policy in the run-up to the Greek EMU entrance. A comparison of this econometric indication with policy information provided (ex-post) by the Bank of Greece (BoG) in its Annual Report 2000 reveals that the BoG indeed pursued such an active policy stance (so-called institutional frontloading strategies).      

A model of multiple-unit ownership as a diffusion process

This paper develops and tests a new model for multiple-unit adoptions of durable goods based on the diffusion modeling tradition. Multiple-unit adoptions are a major component of sales for many consumer durable product categories. For instance, sales of multiple-unit adoptions for televisions have been higher than both first adoptions and replacement purchases since 1977, while for automobiles, they have represented more than 20% of sales since 1966 in Australia. The structural drivers of multiple-unit adoptions are quite different from either first purchase or replacement purchase. Hence, identifying and modeling the multiple-unit component of sales is important for aggregate sales forecasts. Moreover, consumer requirements for additional units of a product are likely to be considerably different than for the other components of sales (first purchases and replacement purchases). As such, the ratio of the first, multiple, and replacement sales components will strongly influence the product mix requirements of the market.

While forecasting and influencing multiple-unit sales are an important managerial issue, very little attention has been given to multiple-unit ownership in the diffusion modeling literature. The only model available was developed for the purpose of modeling relatively short-term behavior of multiple-unit adoptions, rather than the longer-term pattern of sales. We propose a model of multiple-unit adoptions as a diffusion process.

We apply the model to both color television and automobiles. Analysis of the model's long-term fit and forecasts in these applications provide support for the structure of the new model.     

A Welcome Revolution in Innovation

This review considers a major new book on von Hippel’s concept of user innovation. Von Hippel’s work has led to a welcome revolution in the economics of innovation. When producers are the innovators, there is an important difference in the interests of innovators and users. This can lead to dysfunctional innovation, especially in mature markets that are monopolised. In contrast, when users innovate, there are no such problems, as the innovator and the user are the same. User innovation can help enhance the power of innovation in wealth creation, and this edited collection helps to establish user innovation as a fully fledged field of the economics of innovation.     

Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

This paper contributes to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension-reduction, machine learning and shrinkage methods, including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension-reduction method called the Bloomberg Relevance Index (BRI), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable’s usage by market participants. Our empirical analysis shows that, when specified using dimension-reduction methods (particularly BRI and SPCA), DFMs yield superior predictions relative to both benchmark linear econometric models and simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies that we study (Brazil, Mexico, South Africa, and Turkey). These findings point to the importance of spillover effects across emerging market economies, and underscore the significance of characterizing such linkages parsimoniously when utilizing high-dimensional global datasets.