Knowledge that Transforms

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. The search strategy of "pyramiding" is a potential solution to this problem under many conditions. Pyramiding is a search process based upon the idea that people with a strong interest in a topic or field tend to know people more expert than themselves. In this paper we report upon four experiments empirically exploring the efficiency of pyramiding searches relative to mass screening. We find that pyramiding on average identified the most expert individual in a group on a specific topic with only 28.4% of the group interviewed - a great efficiency gain relative to mass screening. Further, pyramiding identified one of the top 3 experts in a population after interviewing only 15.9% of the group on average. We discuss conditions under which the pyramiding search method is likely to be efficient relative to screening.

This paper addresses the emergence and development of firm-centred knowledge networks within learning and innovation systems in late-industrialising countries. A key contribution of the paper is conceptual and methodological: the development of an original typology of knowledge network properties to trace out changes in the form of networks as they evolve over time. A second contribution consists in providing an example of the application of the typology by examining the emergence and development of a firm-centred knowledge network in the case of Petrobras, the Brazilian oil company over more than 30 years between the late 1960s and the early 2000s. This demonstrates that the properties of Petrobras' knowledge networks continuously evolved through a succession of stages towards (i) increasing intentionality in the management decision-making underlying network development, (ii) growing complexity and diversity in selected cognitive characteristics, and (iii) greater complementarity in the division of innovative labour between Petrobras and its network partners. These original results from applying the typology, in conjunction with retrospective historical methods, illustrate only one aspect of its potential value in the analysis of knowledge networks in late-industrialising economies: tracking out organisational evolution over long periods of time. Others include the comparative examination of network differences across different circumstances and the analysis of relationships between changes/differences in network properties and other characteristics of learning/innovation systems and their contexts.