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KNOWLEDGE DYNAMICS:

Reconciling Competing Hypotheses from Economics And Sociology*

Anne Marie Knott*

The Wharton School

of the University of Pennsylvania

2023 Steinberg Hall-Dietrich Hall

Philadelphia, PA 19104-6370

Phone: (215) 573-9628

Fax: (215) 898-0401

Bill McKelvey

Anderson School at UCLA

110 Westwood Plaza

Box 951481

Los Angeles, CA 90095-1418

Phone: (310) 825-7796

Fax: (310) 206-3337

June 22, 1999

* Please address all correspondence to the senior author, Anne Marie Knott.

The authors would like to thank Dan Levinthal and participants at the Reginald Jones Center seminar series for helpful comments during development of this research. We would also like to acknowledge financial support from the Huntsman Center for Emerging Technologies.


KNOWLEDGE DYNAMICS:

Reconciling Competing Hypotheses from Economics And Sociology

Abstract

Our goal is to reconcile competing null hypotheses from sociology and economics with respect to knowledge flow—sociology assumes that knowledge flow is viscous, whereas economics assumes knowledge flows fluidly thereby discouraging investment in its creation. The concern is that the two fields’ attendant prescriptions might cancel one another. Our vehicle for reconciliation is a simulation of knowledge flow that embodies empirical regularities emerging from studies in both fields. Through simulation we find that both fields are partially correct. More importantly we find that the fields’ prescriptions for knowledge growth, rather than canceling one another, actually complement one another.

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1. INTRODUCTION

Both economics and sociology agree that innovation and human capital formation normally improves social welfare, but they differ in starting assumptions. In general, economics studies knowledge creation—How can public policy create incentives for firms to create new knowledge? Sociology, on the other hand, looks at knowledge diffusion—How can institutions get individuals to share their own knowledge and adopt that of others? Corresponding to these goals, the two fields have opposing null hypotheses regarding the flow of knowledge. In general, economists hold that knowledge flows freely—the challenge is to impede knowledge flow. In contrast, sociologists generally hold that knowledge is inert—the challenge is to facilitate knowledge flow.

The null hypothesis that each field has adopted makes sense given its respective approach to innovation. Economists worry that firms will not invest in knowledge creation if they can not appropriate the returns from those investments. The greatest threat to appropriation is the relative ease with which knowledge can be transferred. Sociologists suspect that organizations will not share knowledge if they derive benefit from controlling it. They also see firms as comprised of boundaries around individuals and subunits that act as barriers to information flow. The problem with the opposing null hypotheses is that they may lead to contrary prescriptions that may potentially offset one another, such that neither innovation nor growth occurs. Thus, it is important to understand the relative correctness of each field’s null hypothesis. Are economists correct that knowledge flow is fluid, or are sociologists correct that knowledge is inert?

We take a step toward resolving the debate by studying the impact of the driving assumptions, given various firm and industry conditions. Our approach begins with harvesting a set of stylized facts or known empirical regularities of knowledge flow from both literatures so as to develop a simple model of knowledge dynamics. We focus on empirical regularities spreading across several fields of research and introduce a computational modeling approach so as to study the dynamics in between the extremes of the economists’ and sociologists’ assumptions—the range of dynamics more likely to be of use by managers interested in speeding up human capital appreciation within their firm, but worried about possible increased flow out to competitors. Thus, we try to answer questions regarding the inherent state of knowledge in a firm or an industry and the effects of various stimuli on knowledge growth in firms or in an industry. The method we use is an interacting agent model derived from the “spin glass” family of models frequently used in physics (Fischer and Hertz, 1993).

In this article we: (1) highlight the debate; (2) resolve the debate such that research findings from the two literatures may be constructively integrated toward a theory of knowledge dynamics between the two extremes; and (3) provide some new insights that might guide public as well as managerial policy. We begin by reviewing the sociology of science, diffusion, technology economics, and management of technology literatures, setting out the empirical regularities as we go. Then we propose a formal model of knowledge dynamics that we evaluate through computer simulation. We validate the simulation relative to the empirical regularities and then utilize it to explore the dynamics affecting knowledge growth. Results and Discussion follow.

2. COMPARING THE PERSPECTIVES

2.1 Sociology of Science and Organization

Sociological perspectives on knowledge flow are probably best exemplified by the sociology of science and the study of bureaucratic structure. Sociology of science is concerned with the interrelationship of science and society. How has science influenced values, education, class structure, ways of life, political decisions, and ways of looking at the world? How has society, in turn, influenced the development of science itself (Kaplan, 1964)? Organizational sociology has uncovered significant deleterious effects of bureaucratic structure that inhibit the flow of information across organizational boundaries.

Sociology of science emerged in the early twentieth century as an outgrowth of studies of the history of invention and technology (Ogburn, 1922; Usher, 1929; Gilfillan, 1933; Merton 1938). Sociology was ripe for a theory of science around the turn of the century, as technology formed a major force that threatened, or at least affected, society. Since its inception the sociology of science has come to be characterized as the “old” and “new” schools. Old sociology of science is best represented by the 240 or so published papers and classic book (1963) by Price and a second edition that includes nine later papers (1986; reissued by Columbia University Press with a foreword by Robert K. Merton and Eugene Garfield). Price’s papers document the quantitative study of science, what he called “scientometrics,” and particularly the study of citation indices. New sociology of science studies science as a social phenomenon, paying particular attention to the social construction aspects of scientific truth claims as characterized by the postmodernist literature (Mirskaya, 1990; Lynch,1993; Hilgartner and Brandt-Rauf, 1994, Pels, 1994; Murphy, 1994; Fuller, 1995a, b; Barnes, Bloor, and Henry, 1996). The impetus for new sociology of science was undoubtedly Kuhn’s 1962 book, The Structure of Scientific Revolutions, with the works of Hanson (1958) and Feyerabend (1970) also instrumental. Mulkay’s 1969 paper, “Some aspects of cultural growth in the natural sciences” sits as a dividing point between old and new—between the quantitative and functionalist views of Merton (1942) and the relativist/postmodernist views of post Kuhnian and postmodern sociology of science. Since we are interested in the causes of knowledge flow dynamics and whatever empirical regularities exist, needless to say, new sociology of science has little to offer once it moved into studying the social aspects of truth claims. This explains why most of the research we cite dates before new sociology of science.

Up until about the 20th century most people, while knowing that change occurs, tended to think of stability as more normal and preferable than change (Dubin, 1958: 117). Partly this was because U. S. society was rural dominated and more homogeneous and change occurs least readily in less heterogeneous and rural societies (Berelson and Steiner, (1964: 615–616). However the combined effects of technological development coupled with consequent social change stemming from the industrial revolution and the growth of factory employment in urban centers transformed all aspects of human life at ever more rapid speed and magnitude (1964: 615–617). The general public’s conception shifted to one in which change was more normal than stability, as well as more desirable. Further impetus came with World War II, as science attained political importance, both because it formed a major element in the national budget, and because it produced technology with substantial societal implications, e.g., the atomic bomb(Hiskes and Hiskes, 1986).

Given technology as the major source of change and science at the heart of technology, a sociology of science emerged to deal with the patterns of change likely to affect society (Barber, 1952; Barber and Hirsch, 1962; Hiskes and Hiskes, 1986; Fuller 1993). Thus, understanding the rate of technological change became a significant goal of sociology of science. Another objective was to identify and promote factors that lead to change.

2.1.1 Knowledge Creation Process

Sociology of science holds that knowledge accumulates. Exponential growth in several measures of scientific activity comprises the primary evidence supporting this view. Sociologists view invention as the cumulative synthesis of many individual items, though the magnitude of each item is small. Thus, scientific growth is largely a diffusion process: accretion of many small innovations each building upon prior innovations (Crane, 1972). Ogburn (1922) goes so far as to contend that when the necessary cultural base is in place, invention is inevitable—if one inventor does not create the new device another will. He offers as supporting evidence the frequency of independent simultaneous invention. This is confirmed more recently by Kuhn (1959), and is evident in the account of the biologists’ search for the DNA “double helix” molecule (Watson, 1968).

If growth in social welfare is the end, and invention the means, one merely needs to facilitate diffusion of the existing knowledge base to ensure efficient discovery of the inevitable invention. Diffusion mechanisms, thus, become the main focus of empirical studies in sociology of science. The science citation index has become a major data source for the investigation of the growth and the diffusion of knowledge since it provides a clear listing of prior innovations.

A number of empirical regularities emerge from the examinations of scientific citations:

  1. The growth of science as a whole follows a logistic curve, and while the total cost of research has increased by a factor of 4.5 since World War II, output has merely doubled. Further the growth of important contributions (heavily cited papers) has remained constant over the same period. “Thus we are multiplying lesser talents faster than the highest ones with half the scientific advance” (Price, 1963: 91). (Diminishing returns)
  2. Output (complete papers per person) is highest for large groups and for solos (Price, 1963: 132). (U-shape productivity)
  3. Per capita science activity in a country correlates with per capita wealth and level of economic development (Price, 1963: 43). (Wealth effect)
  4. Competition (number of specialists attacking problem) increases when agreement about the importance of a field increases (Hagstrom, 1965). (Density dependence).

2.1.2 Stimulus to Knowledge Creation

A parallel theme hinting that progress may not be inevitable, is the stimulus to invention—the extent to which invention is socially determined versus the inherent development of science. Ogburn (1922) acknowledges that necessity (social pull) plays a role in invention, but argues necessity is insufficient without a cultural base, and not necessary, as evidenced by frivolous invention. This view is not universally held. Price for example, holds that the greatest and most useful advances in our technologies have come not from applied research, but from “basic research aimed at furthering understanding and curiosity” (1963: 155).

Stein (1962) presents research findings indicating that personality factors also bear on knowledge creation, independent of culture or the basic/applied continuum. Why individual scientists do what they do is “little science” (Price, 1986). As the century ends, the concerns about the practices and social impact of “big science” have become more pronounced as people worry that big science has been stimulated beyond reason and to the exclusion of other factors affecting modern society—particularly the impact of the military/industrial symbiosis, hi-tech weapons and weapons of mass destruction, and chemical pollutants (Steneck, 1975; Reingold, 1979; Hiskes and Hiskes, 1986; Bell 1992).

2.1.3 Diffusion of Innovation

A related literature dealing with the diffusion of innovation outside science draws conclusions similar to those of the sociology of science. Diffusion research grows out of the rural sociology studies of the 1940s that examined the diffusion of agricultural innovation—the most influential study being Ryan and Gross's (1943) investigation of the diffusion of hybrid corn seed. The research spans a number of disciplines including education (adoption of learning innovations), public health (adoption of health practices), communication (awareness of media events), marketing (the adoption of new products), and geography (role of spatial distance in technology adoption) (Rogers, 1995). The principal theme of this literature is that widespread adoption, even of an idea with obvious advantages, is often difficult. The research examines the factors affecting the rate of adoption, with a goal of devising policies that speed the adoption process.

There are two important differences between the sociology of science and diffusion literatures. Sociology of science is fundamentally interested in the creation of new knowledge, whereas the diffusion literature is concerned with the exploitation of existing knowledge. Therefore, because the sociology of science view is that creation is inevitable, given sufficient prior accumulation and diffusion, it follows that creation of new knowledge directly is isomorphic with the adoption of existing prior knowledge. Consequently, this distinction disappears—both see diffusion as critical. The second distinction is that while both literatures examine knowledge flow between individuals, sociology of science restricts attention to scientists embedded in the scientific institutional context, whereas the diffusion literature, perhaps because of its breadth, examines the implications of diffusion among individuals independent of any institutional context.

Rogers (1995) develops a set of empirical regularities from review of approximately 4000 diffusion publications:

  1. Diffusion follows a logistic curve, where the rapid growth phase is prompted by adoption of the innovation by opinion leaders (Tarde, 1903) (Logistic growth)
  2. Imitation (adoption by one individual prompted by the adoption of another individual) is most frequent between individuals who share similar attributes (Technical proximity/homophily).
  3. The very nature of diffusion requires that some heterophily exists, else there is nothing to diffuse (Heterophily)
  4. Innovators and early adopters differ from later adopters in that they have higher education, social status, upward mobility, wealth, IQ, ability for abstract thought, ability for rational thought, and empathy. In addition, their communication patterns differ from later adopters. In particular, they have more exposure to mass media, engage in more information seeking, have more social ties, and are more cosmopolitan (Wealth effect)
  5. Diffusion is a function of geographic distance (Hagerstrand, 1952) (Geographic proximity)

2.1.4 Boundary Effects