Firm Resources and Sustained Competitive Advantage

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

https://arxiv.org/pdf/cond-mat/9910332.pdf

Emergence of Scaling in Random Networks

This article synthesizes the large but diverse literature on organizational legitimacy, highlighting similarities and disparities among the leading strategic and institutional approaches. The analysis identifies three primary forms of legitimacy: pragmatic, based on audience self-interest; moral, based on normative approval: and cognitive, based on comprehensibility and taken-for-grantedness. The article then examines strategies for gaining, maintaining, and repairing legitimacy of each type, suggesting both the promises and the pitfalls of such instrumental manipulations.

https://www.jstor.org/stable/pdf/258788.pdf

Managing Legitimacy: Strategic and Institutional Approaches

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

This research was supported by grants provided to the first author by the Social and Behavioral Sciences Research Institute, University of Arizona, and the Aspen Institute Nonprofit Sector Research Fund and by grants to the second author by the College of Business and Public Administration, University of Arizona. We have benefited from productive exchanges with numerous audiences to whom portions of this paper have been presented: a session at the 1994 Academy of Management meetings, the Social Organization workshop at the University of Arizona, the Work, Organizations, and Markets workshop at the Harvard Sociology Department, the 1994 SCOR Winter Conference at Stanford University, and colloquia at the business schools at the University of Alberta, UC-Berkeley, Duke, and Emory, and the JFK School at Harvard. For detailed comments on an earlier draft, we are extremely grateful to Victoria Alexander, Ashish Arora, Maryellen Kelley, Peter Marsden, Charles Kadushin, Dick Nelson, Christine Oliver, Lori Rosenkopf, Michael Sobel, Bill Starbuck, Art Stinchcombe, and anonymous reviewers at ASQ. We thank Dina Okamoto for research assistance and Linda Pike for editorial guidance. Address correspondence to Walter W. Powell, Department of Sociology, University of Arizona, Tucson, AZ 85721. 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.*

https://www.jstor.org/stable/pdfplus/2393988.pdf

Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology.

http://www2.econ.iastate.edu/tesfatsi/sfiblake.pdf

Time series properties of an artificial stock market

/pdf/artificial-economic-life-a-simple-model-of-a-stockmarket-kndim3r2ef.pdf

Artificial economic life: a simple model of a stockmarket

‘Silicon Valley’ locational clusters: when do increasing returns imply monopoly?

Path-dependent processes and the emergence of macro-structure

http://eclectic.ss.uci.edu/~drwhite/center/ppt_pdf/arthur0703.pdf

W. Brian Arthur

Papers

Time series properties of an artificial stock market

Artificial economic life: a simple model of a stockmarket

‘Silicon Valley’ locational clusters: when do increasing returns imply monopoly?

Path-dependent processes and the emergence of macro-structure

The structure of invention