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Why information should influence productivity 167influence productivity has renewed long-standing debate among economistsover the sources of productivity growth. We argue that the complexity of therelationship between information and productivity necessitates approachesthat transcend traditional disciplinary boundaries and acknowledge contributionsfrom economics, complexity, and network theories.Our argument begins by linking theoretical notions for valuing informationas data and process to the economic definition of total factor productivity.Formally recognizing the economic value of information as process opens thedoor for integrating theory from multiple traditions.A major contribution of this work is to codify predictions of various theoriesand connect them to white-collar productivity. One set of theories considersquestions of value and information as facts. The economic traditionconnects information to output via risk, precision, push, search, standards, andincentives. Another set of theories helps understand efficiency and informationas instructions. Computational and network models connect information tooutput via topological efficiency, modular design, standards, centrality, modeling,and search.While relationships between information and productivity are clearlycomplex, they should be amenable to testing and validation. Along this line,the second contribution of this work is to provide a glimpse of how eachhypothesis might be interpreted and applied. In the specific context of executivesearch, absolute incentive systems track information sharing, larger socialnetworks are observed with more revenues and higher completion rates,routines correlate with revenue, decentralized data entry parallels perceptionsof information control, and centrality seems connected to revenue. Althoughanecdotal in nature, these illustrations from a continuing multi-year studypoint to the means of probing these predictions further.Empirical verification of hypotheses will undoubtedly involve considerableingenuity in generating suitable controls and translating predictions of theoryinto precise measures of information use and human interaction. This processis ongoing. The greater promise, however, lies in the potential not only toreflect on patterns of organization as they exist, but to generate new lines ofresearch that actively informs business practice in light of the opportunitiesoffered by continuing advances in information, network, and communicationtechnologies.ACKNOWLEDGMENTSWe gratefully acknowledge valuable suggestions from Manuel Castells,Michael Cohen, David Croson, Misha Lipatov, Brian Subirana, and JunZhang. Charles King III provided useful resources and helpful conversations.
168 Marshall Van Alstyne and Nathaniel BulkleyThis work has been generously supported by NSF Career Award #9876233and by Intel Corporation. Interested readers may test precise interpretations ofour theories in an online simulation environment of networked societies (theInformation Diffusion and Growth Simulator available at www.IndigoSim.org).NOTES1. Strategic uses of information are covered in the literature on game theory and industrial organization.On political uses of information within organizations, there is a separate literature(cf. Markus, 1983; Davenport et al., 1992).2. We emphasize that illustrations here are either bivariate correlations or anecdotes based oninterviews. Subsequent research will seek to introduce appropriate statistical controls.3. Participants were paid $25 and $100 for surveys and for permission to capture e-mail respectively.4. Blackwell demonstrates a formal equivalence between increased precision and reduced noise.5. Interestingly, precision also has a computational interpretation as the number of bits necessaryto distinguish different cases (Cover and Thomas, 1991).6. In-bound e-mail contacts are more important, which might indicate a stronger correlation withsocial networks than marketing per se (cf. hypotheses 11 and 12).REFERENCESAdams, J. D. (1990) “Fundamental Stocks of Knowledge and Productivity Growth,”Journal of Political Economy 98 (4): 673–702.Aghion, P. and Howitt, P. (1998) Endogeneous Growth Theory. Cambridge, MA: MITPress.Akerlof, G. A. (1970) “The Market for ‘Lemons’: Quality Uncertainty and the MarketMechanism,” Quarterly Journal of Economics 84 (3): 488–500.Arrow, K. J. (1962a) “The Economic Implications of Learning by Doing,” The Reviewof Economic Studies 29 (3): 155–73.—— (1962b) “Economic Welfare and the Allocation of Resources for Invention,” inRichard R. Nelson (ed.), The Rate and Direction of Inventive Activity: Economicand Social Factors, pp. 609–25. National Bureau of Economic Research,Conference Series. Princeton, NJ: Princeton University Press.—— (1974) The Limits of Organization. New York, W. W. Norton.Arthur, B. W. (1989) “Competing Technologies, Increasing Returns and Lock-in byHistorical Events,” The Economic Journal 99 (394): 116–31.Bain, J. (1956) Barriers to New Competition. Cambridge, MA: Harvard UniversityPress.Baker, W. E. (1984) “The Social Structure of a National Securities Market,” AmericanJournal of Sociology 89 (4): 775–811.Balakrishnan, A., Kalakota, R. et al. (1995) “Document-centered Information Systemsto Support Reactive Problem-solving in Manufacturing,” International Journal ofProduction Economics 38: 31–58.Baldwin, C. Y. and Clark, K. B. (2000) Design Rules: The Power of Modularity.Cambridge, MA: MIT Press.
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- Page 197 and 198: 176 Chris BennerLABOR AND FLEXIBILI
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168 Marshall Van Alstyne and Nathaniel BulkleyThis work has been <strong>ge</strong>nerously supported by NSF Career Award #9876233and by Intel Corporation. Interested readers may test precise interpretations ofour theories in an online simulation environment of networked societies (theInformation Diffusion and Growth Simulator available at www.IndigoSim.org).NOTES1. Strategic uses of information are covered in the literature on game theory and industrial organization.On political uses of information within organizations, there is a separate literature(cf. Markus, 1983; Davenport et al., 1992).2. We emphasize that illustrations here are either bivariate correlations or anecdotes based oninterviews. Subsequent research will seek to introduce appropriate statistical controls.3. Participants were paid $25 and $100 for surveys and for permission to capture e-mail respectively.4. Blackwell demonstrates a formal equivalence between increased precision and reduced noise.5. Interestingly, precision also has a computational interpretation as the number of bits necessaryto distinguish different cases (Cover and Thomas, 1991).6. In-bound e-mail contacts are more important, which might indicate a stron<strong>ge</strong>r correlation withsocial networks than marketing per se (cf. hypotheses 11 and 12).REFERENCESAdams, J. D. (1990) “Fundamental Stocks of Knowled<strong>ge</strong> and Productivity Growth,”Journal of Political Economy 98 (4): 673–702.Aghion, P. and Howitt, P. (1998) Endo<strong>ge</strong>neous Growth Theory. Cambrid<strong>ge</strong>, MA: MITPress.Akerlof, G. A. (1970) “The Market for ‘Lemons’: Quality Uncertainty and the MarketMechanism,” Quarterly Journal of Economics 84 (3): 488–500.Arrow, K. J. (1962a) “The Economic Implications of Learning by Doing,” The Reviewof Economic Studies 29 (3): 155–73.—— (1962b) “Economic Welfare and the Allocation of Resources for Invention,” inRichard R. Nelson (ed.), The Rate and Direction of Inventive Activity: Economicand Social Factors, pp. 609–25. National Bureau of Economic Research,Conference Series. Princeton, NJ: Princeton University Press.—— (1974) The Limits of Organization. New York, W. W. Norton.Arthur, B. W. (1989) “Competing Technologies, Increasing Returns and Lock-in byHistorical Events,” The Economic Journal 99 (394): 116–31.Bain, J. (1956) Barriers to New Competition. Cambrid<strong>ge</strong>, MA: Harvard UniversityPress.Baker, W. E. (1984) “The Social Structure of a National Securities Market,” AmericanJournal of Sociology 89 (4): 775–811.Balakrishnan, A., Kalakota, R. et al. (1995) “Document-centered Information Systemsto Support Reactive Problem-solving in Manufacturing,” International Journal ofProduction Economics 38: 31–58.Baldwin, C. Y. and Clark, K. B. (2000) Design Rules: The Power of Modularity.Cambrid<strong>ge</strong>, MA: MIT Press.