Ashish Arora

Bio: Ashish Arora is an academic researcher from Duke University. The author has contributed to research in topics: Human capital & Competition (economics). The author has an hindex of 57, co-authored 197 publications receiving 14395 citations. Previous affiliations of Ashish Arora include Northwestern University & Carnegie Mellon University.

Complementarity and external linkages: the strategies of the large firms in biotechnology*

Abstract: In biotechnology, large firms enter into different kinds of linkages with universities and small/medium sized research-intensive firms. We test the hypothesis that the strategies of external linkage of the large firms with other parties are complementary to one another. We show that if any two strategies are complementary (i.e. undertaking more of one strategy raises the marginal value of the other) then they are positively correlated. Using data for a sample large US, European, and Japanese chemical and pharmaceutical producers we find that the strategies above are positively correlated even after controlling for firm-specific characteristics.

Markets for Technology: The Economics of Innovation and Corporate Strategy

Abstract: The past two decades have seen a gradual but noticeable change in the economic organization of innovative activity Most firms used to integrate research and development with activities such as production, marketing, and distribution Today firms are forming joint ventures, research and development alliances, licensing deals, and a variety of other outsourcing arrangements with universities, technology-based start-ups, and other established firms In many industries, a division of innovative labor is emerging, with a substantial increase in the licensing of existing and prospective technologies In short, technology and knowledge are becoming definable and tradable commoditiesAlthough researchers have made significant advances in understanding the determinants and consequences of innovation, until recently they have paid little attention to how innovation functions as an economic process This book examines the nature and workings of markets for intermediate technological inputs It looks first at how industry structure, the nature of knowledge, and intellectual property rights facilitate the development of technology markets It then examines the impacts of these markets on firm boundaries, the division of labor within the economy, industry structure, and economic growth Finally, it examines the implications of this framework for public policy and corporate strategy Combining theoretical perspectives from economics and management with empirical analysis, the book also draws on historical evidence and case studies to flesh out its research results

Markets for Technology: The Economics of Innovation and Corporate Strategy

Abstract: The article reviews the book “Markets for Technology: The Economics of Innovation and Corporate Strategy,” by Ashish Arora, Andrea Fosfuri and Alfonso Gambardella.

The changing technology of technological change: general and abstract knowledge and the division of

Abstract: Abstract In the past, most innovations have resulted from empiricist procedures; the outcome of each trial yielding knowledge that could not be readily extended to other contexts. While trial and-error may remain the primary engine of innovation, developments in many scientific disciplines, along with progress in computational capabilities and instrumentation, are encouraging a new approach to industrial research. Instead of relying purely on trial-and-error, the attempt is also to understand the principles governing the behaviour of objects and structures. The result is that relevant information, whatever its source, can now be cast in frameworks and categories that are more universal. The greater universality makes it possible for the innovation process to be organised in new ways: firms can specialise and focus upon producing new knowledge, and the locus of innovation may be spread across both users and producers. More generally the use of general and abstract knowledge in innovation opens up the possibility for a division of labour in inventive activity -the division of innovative labour. The implications for public policy, especially that on intellectual property rights, are discussed.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

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

Abstract: 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.*

Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study:

Abstract: To assess the effects of a firm's network of relations on innovation, this paper elaborates a theoretical framework that relates three aspects of a firm's ego network—direct ties, indirect ties, an...

Relative absorptive capacity and interorganizational learning

Abstract: Much of the prior research on interorganizational learning has focused on the role of absorptive capacity, a firm's ability to value, assimilate, and utilize new external knowledge. However, this definition of the construct suggests that a firm has an equal capacity to learn from all other organizations. We reconceptualize the firm-level construct absorptive capacity as a learning dyad-level construct, relative absorptive capacity. One firm's ability to learn from another firm is argued to depend on the similarity of both firms' (1) knowledge bases, (2) organizational structures and compensation policies, and (3) dominant logics. We then test the model using a sample of pharmaceutical–biotechnology R&D alliances. As predicted, the similarity of the partners' basic knowledge, lower management formalization, research centralization, compensation practices, and research communities were positively related to interorganizational learning. The relative absorptive capacity measures are also shown to have greater explanatory power than the established measure of absorptive capacity, R&D spending. © 1998 John Wiley & Sons, Ltd.