Section 7: Methods

Research and also practical work in distributed innovation can require new methods for dividing up tasks, for experimenting in parallel, and for identifying rare individuals who might have something important to contribute. Here are a few papers offering this type of contribution. With respect to modularity – subject of my first-listed paper in this section – Carliss Baldwin and her coauthor Kim Clark are of course the major architects of that field. (See their book Baldwin, Carliss Y. and Kim B. Clark (2000) Design Rules, Vol. 1: The Power of Modularity, MIT Press, Cambridge, MA.)

von Hippel, Eric. “Task Partitioning: An Innovation Process Variable.” Research Policy 19, no. 5 (October 1990): 407–418. doi:10.1016/0048-7333(90)90049-C. (PDF)

Abstract: Innovation projects are “partitioned” into smaller tasks. Precisely where the boundaries between such tasks are placed can affect project outcome and the efficiency of task performance due to associated changes in the problem-solving inter-dependence among tasks. I propose that problem-solving inter-dependence among tasks can be predicted in many projects and can then be managed by strategies involving (1) adjustment of the task specifications and/or (2) reduction of the barriers to problem-solving interaction across selected or all task boundaries. The potential value of studying and managing task partitioning is illustrated by exploring how problematic areas of the innovation process, such as the design-build and marketing-R&D interfaces, can be better understood through that lens.

Thomke, Stefan, Eric von Hippel, and Roland Franke. “Modes of Experimentation: An Innovation Process–and Competitive–variable.” Research Policy 27, no. 3 (July 1998): 315–332. doi:10.1016/S0048-7333(98)00041-9. (PDF)

Abstract: The outputs of R&D, such as new research findings and new products and services, are generated with the aid of specialized problem-solving processes. These processes are somewhat arcane and have been largely ignored in studies of technical change. However, their improvement can significantly affect the kinds of research problems that can be addressed, the efficiency and speed with which R&D can be performed, and the competitive positions of firms employing them. In this paper, we first describe the general nature of the trial-and-error problem-solving processes and strategies for experimentation used in the development of new products and services. We next discuss the rapid advances being made in problem-solving methods, and the impact such advances can have on the competitive position of adopting firms. Finally, we offer a detailed case study of the impact one novel experimental method, combinatorial chemistry, is having on the economics of the drug discovery process.

von Hippel, Eric, Nikolaus Franke, and Reinhard Prügl. “Pyramiding”: Efficient Identification of Rare Subjects Sloan Working Paper. 4720-08. Sloan School of Management, Massachusetts Institute of Technology, October 2008. (PDF)

Abstract: The need to economically identify rare subjects within large, poorly-mapped search spaces is a frequently-encountered problem for social scientists and managers. It is notoriously difficult, for example, to identify the best new CEO for our company, or the best three lead users to participate in our product development project. Mass screening of entire populations or samples becomes steadily more expensive as the number of acceptable solutions within the search space becomes rarer. Pyramiding can be significantly more efficient under many conditions.

Pyramiding is a search process based upon the view that people with a strong interest in a topic or field tend to know people more expert than themselves. In this paper we empirically explore the efficiency of pyramiding searches relative to mass screening in search spaces where there is only one best solution. In four experiments, we find that pyramiding in each case identifies the best solution within the search space using an average of only 30% of the effort required by mass screening. Based on our findings, we propose conditions under which pyramiding will be a more efficient approach to exploring a search space than screening.