We argue in this paper that when the knowledge base of an industry is both complex and expanding and the sources of expertise are widely dispersed, the locus of innovation will be found in networks of learning, rather than in individual firms. The large-scale reliance on interorganizational collaborations in the biotechnology industry reflects a fundamental and pervasive concern with access to knowledge. We develop a network approach to organizational learning and derive firm-level, longitudinal hypotheses that link research and development alliances, experience with managing interfirm relationships, network position, rates of growth, and portfolios of collaborative activities. We test these hypotheses on a sample of dedicated biotechnology firms in the years 1990-1994. Results from pooled, within-firm, time series analyses support a learning view and have broad implications for future theoretical and empirical research on organizational networks and strategic alliances.*

A recursive analysis of network and institutional evolution is offered to account for the decentralized structure of the commercial field of the life sciences. Four alternative logics of attachment-accumulative advantage, homophily, follow-the-trend, and multiconnectivity-are tested to explain the structure and dynamics of interorganizational collaboration in biotechnology. Using multiple novel methods, the authors demonstrate how different rules for affiliation shape network evolution. Commercialization strategies pursued by early corporate entrants are supplanted by universities, research institutes, venture capital, and small firms. As organizations increase their collaborative activities and diversify their ties to others, cohesive subnetworks form, characterized by multiple, independent pathways. These structural components, in turn, condition the choices and opportunities available to members of a field, thereby reinforcing an attachment logic based on differential connections to diverse partners.

This paper focuses on the spatial concentration of two essential factors of production in the commercial field of biotechnology: ideas and money. The location of both research-intensive biotech firms and the venture capital firms that fund biotech is highly clustered in a handful of key U.S. regions. The commercialization of a new medicine and the financing of a high-risk startup firm are both activities that have an identifiable timeline, and often involve collaboration with multiple participants. The importance of tacit knowledge, face-to-face contact, and the ability to learn and manage across multiple projects are critical reasons for the continuing importance of geographic propinquity in biotech. Over the period 1988-99, more than half of the U.S. biotech firms received locally-based venture funding. Those firms receiving non-local support were older, larger, and had moved research projects further along the commercialization process. Similarly, as VC firms grow older and bigger, they invest in more non-local firms. But these patterns have a strong regional basis, with notable differences between Boston, New York, and West Coast money. Biotechnology is unusual in its dual dependence on basic science and venture financing; other fields in which product development is not as dependent on the underlying science may have different spatial patterns.

The dynamics of innovative search are investigated. A formal learning model is explicated to understand (1) how attention is allocated and (2) how attention and ideas are related during an active search. A particular chaotic regime of the model is shown to have dynamics similar to an actual search process. This motivates an agenda for research along three primary directions: refinements to the model, extensions that link the model to actions and decisions, and surroundings required to embed such a model in a larger context. The findings here suggest that chaos can occur in specific organizational processes over particular periods of time. Whether such chaos is beneficial to organizations is unclear.