Why information should influence productivity

This article offers a broad set of hypotheses for how information influences productivity. 1 There are three contributions from this work. First, it distills observations from a diverse literature as prelude to testing these theories empirically. Second, it applies two concrete models of information value, relating them to the economic definition of productivity, while considering how network structure influences information flow. Third, examples from an ongoing empirical study illustrate each observation to give it practical significance. Interested readers may also test precise interpretations of these theories in an online simulation environment of networked societies. 1 This work has been generously supported by NSF Career Award #9876233 and by Intel Corporation. 2 The Information Diffusion & Growth Simulator is available at . Studies since the mid-1990s have argued that investments in information technology positively influence productivity (Brynjolfsson and Hitt 1996; Lehr and Lichtenberg 1999; Brynjolfsson and Hitt 2000; Oliner and Sichel 2000; Jorgenson 2001). These firm level studies, however, also show that productivity per information technology dollar varies widely, and may differ with clusters of technology, strategy and organizational practice. Findings in the literature correspond with mangers' conventional wisdom -it is not the presence of the technology itself that influences productivity, but how it is used. Our central question follows from this insight: specifically, how should information management practices influence white-collar productivity and what theories would explain these effects? This analysis differs from earlier work on the relationship between computerization and productivity by focusing on how information and connectivity influence productivity as distinct from computer technology per se. The central question is approached from two distinct theoretical perspectives: the economics of uncertainty and computational complexity theory. Both theories are highly abstracted from social context and emphasize the thorough and rigorous development of results related to information and efficiency. But they ask different questions, use different tools and, most importantly, conceptualize information in different ways. We apply them here intending to inform organizational theory and with the hope of moving theory into practice. In adopting a Bayesian view, Neoclassical economists consider only how information addresses the probabilistic "truth" of a proposition Roughly stated, the Neoclassical view of information and productivity is that if you could reduce uncertainty about the state of the world to zero then solutions to productivity puzzles would be obvious. In contrast, computational complexity theory asks first whether a problem can be solved, given an algorithm, encoding or heuristic. If so, theoretical interest then centers on determining an upper bound for the time it will take specific procedures to locate a satisfactory solution. Intuitive notions that process knowledge contributes to productivity are widely recognized. Hayek (1945) notes that "civilization advances by extending the number of important operations which we can perform without thinking about them"; evolutionary economics and organizational theory emphasize the contribution of "routines" (March and Simon 1958; Cyert and March 1963; Nelson and Winter 1982); corporate strategy speaks of "capabilities" or "competencies" (Wernerfelt 1984; Prahalad and Hamel 1990; Barney 1991; Kogut and Zander 1992). When procedural information is recognized within economics broadly defined, it is most frequently modeled as accumulating stocks of knowledge capital. Examples include the endogenous growth theory literature of macroeconomics (Romer 1986; Adams 1990; Romer 1990; Rivera-Batiz and Romer 1991; Aghion and Howitt 1998) and the industrial organization literature (Griliches 1986; Pakes 1986). Our contribution is to adapt a modeling approach from computational complexity theory as a strategy for developing hypotheses that relate information management techniques to productivity.