Deepa Chandrasekaran

A Critical Review of Marketing Research on Diffusion of New Products

We critically examine alternate models of the diffusion of new products and the turning points of the diffusion curve. On each of these topics, we focus on the drivers, specifications, and estimation methods researched in the literature. We discover important generalizations about the shape, parameters, and turning points of the diffusion curve and the characteristics of diffusion across early stages of the product life cycle. We point out directions for future research.

Global Takeoff of New Products: Culture, Wealth, or Vanishing Differences?

The authors study the takeoff of 16 new products across 31 countries 430 categories to analyze how and why takeoff varies across products and countries. They test the effect of 12 hypothesized drivers of takeoff using a parametric hazard model. The authors find that the average time to takeoff varies substantially between developed and developing countries, between work and fun products, across cultural clusters, and over calendar time. Products take off fastest in Japan and Norway, followed by other Nordic countries, the United States, and some countries of Midwestern Europe. Takeoff is driven by culture and wealth plus product class, product vintage, and prior takeoff. Most importantly, time to takeoff is shortening over time and takeoff is converging across countries. The authors discuss the implications of these findings.

Getting a Grip on the Saddle: Chasms or Cycles

The “saddle” is a sudden, sustained, and deep drop in sales of a new product, after a period of rapid growth following takeoff, followed by a gradual recovery to the former peak. The authors test for the generalizability of the saddle across products and countries and for three rival explanations: chasms in adopter segments, business cycles, and technological cycles. They model both boundary points of the saddle—start of the sales drop and recovery to the initial peak—using split-population models. Empirical analysis of historical sales data from ten products across 19 countries shows that the saddle is fairly pervasive. The onset of the saddle occurs in 148 product–country combinations. On average, the saddle occurs nine years after takeoff, at a mean penetration of 30%, and it lasts for eight years with a 29% drop in sales at its depth. The results support explanations of chasms and technological cycles for information/entertainment products and business cycles and technological cycles for kitchen/laundry products. The authors conclude with a discussion of the findings, contributions, and implications.

Design Crowdsourcing: The Impact on New Product Performance of Sourcing Design Solutions from the “Crowd”

The authors examine an increasingly popular open innovation practice, “design crowdsourcing,” wherein firms seek external inputs in the form of functional design solutions for new product development from the “crowd.” They investigate conditions under which managers crowdsource design and determine whether such decisions subsequently boost product sales. The empirical analysis is guided by qualitative insights gathered from executive interviews. The authors use a novel data set from a pioneering crowdsourcing firm and find that three concept design characteristics—perceived usability, reliability, and technical complexity—are associated with the decision to crowdsource design. They use an instrumental variable method accounting for the endogenous nature of crowdsourcing decisions to understand when such a decision affects downstream sales. The authors find that design crowdsourcing is positively related to unit sales and that this effect is moderated by the idea quality of the initial product concept. Using a change-score analysis of consumer ratings, they find that design crowdsourcing enhances perceived reliability and usability. They discuss the strategic implications of involving the crowd, beyond ideation, in helping transform ideas into effective products.

Evolution of an Open Source Community Network

The study attempts to better understand the evolution of the structure of a network using two snapshots of the developer-project affiliations in an Open Source Software (OSS) community. We use complex networks and social network theory to guide our analysis. We proceed by first extracting separate bipartite networks of projects in each of the five development stages – planning, pre-alpha, alpha, beta and production/stables stages. Then, by analyzing changes in the network using degree distributions, assortativity, component sizes, visualizations and p-star models, we try to infer the project-joining behavior of the OSS developers. Simulations are used to establish the significance of some findings. Highlights of our results are the higher levels of assortativity and networking in the Beta and Stable subnetworks, and a surprisingly higher level of connectivity of the Planning subnetwork. Significant clustering of projects is observed based on the programming language but not on other project attributes, including even licenses.