Management Automation
by
Manav Raj, Robert C. Seamans
  • LAST MODIFIED: 31 July 2019
  • DOI: 10.1093/obo/9780199846740-0172

Introduction

Since the first decades of the 20th century, there has been concern that automation, including mechanization, computing, and more recently robotics and artificial intelligence (AI), will take away jobs and damage the labor market. There has also been concern that large, dominant firms will capture whatever value is created by automating technologies. In an effort to understand these issues, a wide variety of scholars have studied automation. Automation has been studied at a number of levels, including country, industry, firm, occupation, and even the occupational-task level, and by a range of disciplines, including economics, innovation, management, organizational theory, sociology, and strategy. This annotated bibliography attempts to include a range of literature that speaks to these different levels and different disciplines. It includes articles that are older, foundational pieces so readers can familiarize themselves with the major work in the area, as well as more recent articles so readers can get a sense of current research interests and opportunities. Notably, much of the recent research is focused on the effects of AI and robotics on workers, firms, and the economy. It is likely that there will be a large increase in research in this space in the coming years, especially as more data on the adoption of these technologies becomes available, and that this research will tell us much more about how these technologies are affecting our economy in the 21st century as well as inform our understanding of automation more generally.

General Overviews

Automation is not a new concept, as innovations described by Mokyr 1992, such as the steam engine or the cotton gin, can be viewed as automating tasks that were previously done in a manual fashion. This section highlights work that takes a historical view of automation, and describes the effects of automation on firms, workers, and society more generally. Keynes 1930 is an early perspective considering the consequences of automation, while Leontief 1983 presents potential opportunities and consequences posed by automation. Current technologies may have the potential to automate certain non-routine tasks, whereas in the past more rote tasks have been subjected to automation. Brynjolfsson and McAfee 2011 thus explores the extent to which the current wave of automation, including digitization, AI and robotics, might be different.

  • Brynjolfsson, E., and A. McAfee. Race against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Lexington, MA: Digital Frontier Press, 2011.

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    In this book, Brynjolfsson and McAfee specifically examine the effects of the digital revolution on productivity and the labor force. They note that digital technologies have increased productivity and collective wealth. However, because these innovations are largely general purpose technologies, parallel innovation in complementary assets and resources is needed to fully harness productivity gains.

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  • Keynes, J. M. “Economic Possibilities for Our Grandchildren.” In Essays in Persuasion. By J. M. Keynes, 321–332. London: Macmillan, 1930.

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    Keynes famously worried that automation would mean his grandchildren would inherit a world in which people work fewer hours and earn lower wages, thanks to technological unemployment. Keynes defined technological unemployment as “unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.”

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  • Leontief, W. “National Perspective: The Definition of Problems and Opportunities.” In The Long-Term Impact of Technology on Employment and Unemployment. Edited by National Research Council, 3–56. Washington, DC: National Academies Press, 1983.

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    Leontief, observing the dramatic improvements in the processing power of computer chips, worried that machines would replace people, just like internal combustion engines replaced horses.

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  • Mokyr, J. The Lever of Riches: Technological Creativity and Economic Progress. Oxford: Oxford University Press, 1992.

    DOI: 10.1093/acprof:oso/9780195074772.001.0001Save Citation »Export Citation »E-mail Citation »

    Mokyr argues that technological creativity is one of the main determinants of economic progress. Mokyr presents a historical account of technological creativity to argue that such creativity leads to economic progress, even if it disrupts labor and existing organizations. For example, the Second Industrial Revolution led to the replacement of sailboats by steamboats; however, it produced new jobs for humans even as it diminished existing jobs such as sailor.

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Innovation, Automation, and Productivity

In the economics and management literature, a large body of work has drawn attention to the importance of innovation in driving growth and increasing productivity, as described in Schumpeter 1934; Solow 1957; Romer 1990; and Aghion, et al. 2014. This section highlights work that has examined the effect of innovation and productivity growth, and evaluates how automation may affect future growth trajectories. In addition, this section includes empirical work that attempts to measure how the adoption of industrial robots, often linked to automation, has affected productivity growth, as described in Graetz and Michaels 2018, and also examines firm-level consequences of innovation, as in Syverson 2011.

  • Aghion, P., U. Akcigit, and P. Howitt. “What Do We Learn from Schumpeterian Growth Theory?” Handbook of Economic Growth 2B (2014): 515–563.

    DOI: 10.1016/B978-0-444-53540-5.00001-XSave Citation »Export Citation »E-mail Citation »

    This paper derives and considers predictions based off of Schumpeterian growth theory. The authors note that faster innovation-driven growth is associated with higher turnover rates of firms and jobs. Additionally, they note that innovation and productivity growth are stimulated by competition and entry by firms near the technology frontier.

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  • Graetz, G., and G. Michaels. “Robots at Work.” Review of Economics and Statistics 100.5 (2018): 753–768.

    DOI: 10.1162/rest_a_00754Save Citation »Export Citation »E-mail Citation »

    Graetz and Michaels use a panel data set on robot adoption across seventeen different countries from 1993 to 2007 to examine the effect of robot usage on productivity. They suggest that increased use of robots increased annual growth of labor productivity by 0.36 percentage points—a contribution similar to the effect of the introduction of steam engines in the 19th century.

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  • Romer, P. M. “Endogenous Technological Change.” Journal of Political Economy 98.5 (1990): S71–S102.

    DOI: 10.1086/261725Save Citation »Export Citation »E-mail Citation »

    In this foundational work, Romer evaluates how growth is driven by technological change and notes the importance of human capital in determining growth rates. This work highlights that general purpose technologies generally lead to economic growth.

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  • Schumpeter, J. A. The Theory of Economic Development: An Inquiry into Profits, Capital, Credits, Interest, and the Business Cycle. Piscataway, NJ: Transaction, 1934.

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    In this book, Schumpeter makes the case that innovation is the critical driver of economic change and business cycles. He suggests that innovation is at the heart of the emergence, growth, decline, and eventual death of industries. Schumpeter viewed innovation by entrepreneurs as the force that sustains economic growth even if it undermines established firms and industries.

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  • Solow, R. M. “Technical Change and the Aggregate Production Function.” The Review of Economics and Statistics 39.3 (1957): 312–320.

    DOI: 10.2307/1926047Save Citation »Export Citation »E-mail Citation »

    Solow proposes a variation to the aggregate production function that incorporates technical change (i.e., innovation). Applying his model to American data from 1909 to 1949, he suggests that greater than 85 percent of the increase in productivity was attributable to technical change. The results suggest that technological progress is the source of long-term economic growth.

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  • Syverson, C. “What Determines Productivity?” Journal of Economic Literature 49.2 (2011): 326–365.

    DOI: 10.1257/jel.49.2.326Save Citation »Export Citation »E-mail Citation »

    While early work on innovation and productivity largely took a macroeconomic perspective, this work examines heterogeneity in performance at the firm level. In decomposing the various factors that lead to variation in productivity, Syverson notes that within-firm productivity growth is in many cases the result of active innovation efforts rather than passive gains in efficiency.

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Automation and Complementary Investment

While innovation has historically been linked to productivity growth, its effect is not uniform. At the individual level, firm level, and macro level, access to and investment in complementary assets, such as experience with specific computing technologies as in Choudhury, et al. 2018, have been linked to the ability to extract value from machine learning technologies. This section presents a number of studies that examine this phenomenon at various levels, including at the firm level, as in Teece 1986, Tripsas 1997, and Brynjolfsson and Hitt 2003, and the occupation level, as in Autor 2015 and Felten, et al. 2019. This body of work shows that the effect of innovation and automation is often dependent on capabilities, skills, and complementary investments.

  • Autor, D. H. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation.” Journal of Economic Perspectives 29.3 (2015): 3–30.

    DOI: 10.1257/jep.29.3.3Save Citation »Export Citation »E-mail Citation »

    Autor focuses on automation and notes that, while automation can substitute for labor at times, it also can serve as a complement. In particular, when work processes rely on a variety of inputs, the automation of one input increases the value of the remaining tasks. While some routine tasks may be automatable, tasks requiring interaction, flexibility, adaptability, and problem solving largely are not, and automation is likely to increase the value of those skills.

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  • Brynjolfsson, E., and L. M. Hitt. “Computing Productivity: Firm-Level Evidence.” Review of Economics and Statistics 85.4 (2003): 793–808.

    DOI: 10.1162/003465303772815736Save Citation »Export Citation »E-mail Citation »

    This paper makes the case that complementary inputs are often needed to achieve potential productivity gains from innovation. Studying the effects of computerization on productivity from 1987 to 1994, the authors find that productivity growth associated with computerization is far greater over long periods. The authors suggest that this is due to investments in complementary assets that are often large and time-consuming.

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  • Choudhury, P., E. Starr, and R. Agarwal. “Machine Learning and Human Capital: Experimental Evidence on Productivity Complementarities.” SSRN Working Paper (2018).

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    Studying machine learning, the authors find that productivity differentials for individuals using machine learning technologies rely on human capital attributes. In particular, both domain-specific expertise and vintage-specific capital help individuals maximize the productivity gains available from new technologies.

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  • Felten, E., M. Raj, and R. C. Seamans. “The Effect of Artificial Intelligence on Human Labor: An Ability-Based Approach.” SSRN Working paper (2019).

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    In this work, the authors utilize an ability-based approach to evaluate the effect of AI on labor and wage growth to investigate whether and under what circumstances AI serves as a complement to or substitute for labor. They find that occupations that feature technology use and automation experience labor growth when affected by AI, suggesting that access to complementary skills and technologies shapes the impact of AI. Available online

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  • Teece, D. J. “Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing, and Public Policy.” Research Policy 15.6 (1986): 285–305.

    DOI: 10.1016/0048-7333(86)90027-2Save Citation »Export Citation »E-mail Citation »

    Teece lays out an argument why innovating firms are often unable to obtain significant returns from innovation. He suggests that innovators often are unable to accrue benefits from new products or processes without access to certain complementary assets.

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  • Tripsas, M. “Unraveling the Process of Creative Destruction: Complementary Assets and Incumbent Survival in the Typesetter.” Strategic Management Journal 18.S1 (1997): 119–142.

    DOI: 10.1002/(SICI)1097-0266(199707)18:1+<119::AID-SMJ921>3.3.CO;2-SSave Citation »Export Citation »E-mail Citation »

    Tripsas builds upon the theoretical argument in Teece 1986 with a multi-method study in the typesetter industry. While radical technological change often challenges established firms, Tripsas finds that incumbent firms with specialized complementary assets are better able to survive and take advantage of the changes in technology.

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Automation and Labor

While automation holds great promise in its ability to spur on productivity growth, it also sparks fears that new technologies may substitute for human labor. Research has attempted to identify the effects of automation on employment in a variety of industries and settings. Acemoglu and Restrepo 2017 studies the effect of industrial robot usage on labor markets, suggesting that it may substitute for labor employment, and Mann and Püttmann 2017 uses patent data to study the effect of automation on employment. While the findings thus far are mixed, there is generally a belief that automation may substitute for routine occupations and may contribute to increased labor polarization and inequality, as discussed in Miller 1964; Autor, et al. 1998; Autor and Dorn 2013; and Katz and Mungo 2014. However, there may be situations where automation leads to employment growth, as is shown in the case of ATMs and bank tellers reviewed by Bessen 2015, and more generally described in Autor and Salomons 2018.

  • Acemoglu, D., and P. Restrepo. “Robots and Jobs: Evidence from US Labor Markets.” NBER Working Paper 23285 (2017).

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    Acemoglu and Restrepo investigate the impact of industrial robot usage in particular on labor markets in the United States between 1990 and 2008, and find that robots may reduce employment and wages. Further, the most pronounced effects are experienced in manufacturing industries, especially among manual and blue collar occupations, and for workers without a college degree.

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  • Autor, D. H., and D. Dorn. “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market.” American Economic Review 103.5 (2013): 1553–1597.

    DOI: 10.1257/aer.103.5.1553Save Citation »Export Citation »E-mail Citation »

    Autor and Dorn note the role of automation in contributing to labor polarization. In particular, they note that automation may substitute for routine tasks, but cannot yet substitute for “manual,” low-education occupations that rely on physical dexterity and interpersonal communication.

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  • Autor, D. H., L. F. Katz, and A. B. Krueger. “Computing Inequality: Have Computers Changed the Labor Market?” The Quarterly Journal of Economics 113.4 (1998): 1169–1213.

    DOI: 10.1162/003355398555874Save Citation »Export Citation »E-mail Citation »

    In this study, the authors study how technological change, and in particular increased computerization, have changed the demand for labor. They note that the technological change is skill-biased in that it has increased the rate of demand for more-skilled and more-highly-educated workers, and through this mechanism, may be increasing income inequality.

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  • Autor, D. H., and A. Salomons. “Is Automation Labor Share-Displacing? Productivity Growth, Employment, and the Labor Share.” Brookings Papers on Economic Activity 49.1 (2018): 1–87.

    DOI: 10.1353/eca.2018.0000Save Citation »Export Citation »E-mail Citation »

    Autor and Salomons show that while automation has not been employment-displacing overall, due to the stimulation of increased growth and demand from cross-industry effects, it has reduced the share of value added by labor. These effects have become more pronounced over time.

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  • Bessen, J. Learning by Doing: The Real Connection between Innovation, Wages, and Wealth. New Haven, CT: Yale University Press, 2015.

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    In this book, Bessen studies the relationship between new technologies, the labor force, and income inequality. He suggests that despite the threat of substitution from automation, it need not worsen labor market conditions. As one example, he notes how the adoption of ATM machines by banks was actually associated with an increase in bank employment to address increased demand for relationship bankers.

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  • Katz, L. F., and R. A. Mungo. “Technical Change and the Relative Demand for Skilled Labor: The United States in Historical Perspective.” In Human Capital in History: The American Record. Edited by L. P. Boustan, C. Frydman, and R. A. Margo, 15–57. Chicago: University of Chicago Press, 2014.

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    Katz and Margo discuss how automation and technical change have “hollowed out” the labor force. The share of “middle-skill” jobs has declined, while those of “high-skill” and “low-skill” workers have increased. These trends have the potential to exacerbate the labor polarization that Autor and Dorn discuss.

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  • Mann, K., and L. Püttmann. “Benign Effects of Automation: New Evidence from Patent Texts.” SSRN Working Paper (2017).

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    In this paper, the authors take an alternative approach to measuring automation. By analyzing patent texts, they identify automation patents and link them to industries and local labor markets. They find that automation is associated with decreased manufacturing employment but the losses are compensated by service sector job growth.

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  • Miller, J. J. “Automation, Job Creation, and Unemployment.” Academy of Management Journal 7.4 (1964): 300–307.

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    This article argues against the notion that automation causes general unemployment, but rather makes the case that it creates occupations of “a higher order—more intellectual, creative, and idealistic.” Miller suggests the largest issue to address is the problem of distribution of the wealth that automation creates.

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Automation and Organization

As automation affects organizations, it often requires reconfiguration of the production process—either at the individual work level, the team level, or the organizational level. Early works, such as Lipstreu 1960 and Lipstreu and Reed 1965, examine how early cases of automation influenced firm organization, and Adler 1988 suggests that innovations have led to increased use of flexible automated systems. Other work, such as Zammuto, et al. 2007 and Bloom, et al. 2012, examines how information technologies affect organization, and Kirkman and Mathieu 2005 suggests that the Internet has led firms to increasingly embrace remote work and virtual teams. Barley 1988 merges theory from sociology, organization theory, and management to study the effect of technical change on organizations, and Barley 1990 conducts an ethnography to study how worker roles and networks change following the introduction of new technologies. Simon 1990 presents an initial perspective on the role of AI on firm organization. Raj and Seamans 2019 describes several important questions for organizational scholars to research the effect of AI, robotics, and automation on organizations.

  • Adler, P. S. “Managing Flexible Automation.” California Management Review 30.3 (1988): 34–56.

    DOI: 10.2307/41166513Save Citation »Export Citation »E-mail Citation »

    Adler discusses how innovations in manufacturing technology have led toward the increased use of automated systems. He suggests that these systems allow for greater flexibility in the manufacturing process but that these changes also pose challenges for management.

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  • Barley, S. R. “Technology, Power, and the Social Organization of Work: Towards a Pragmatic Theory of Skilling and Deskilling.” In Research in the Sociology of Organizations. Edited by N. DiTomaso and S. Bacharach, 33–80. Greenwich, CT: JAI Press, 1988.

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    Barley suggests that current theories in sociology and organizational theory are “too brutish or too brittle” to truly capture the ramifications of technical change on the organization and employees. He suggests that the future lies somewhere in between the pessimistic predictions of Deskilling Theory and the more optimistic ones stemming from the Sociology of Automation.

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  • Barley, S. R. “The Alignment of Technology and Structure through Roles and Networks.” Administrative Science Quarterly 35.1 (1990): 61–103.

    DOI: 10.2307/2393551Save Citation »Export Citation »E-mail Citation »

    The author argues that focusing on worker roles and networks is a useful way to understand how organizations change in response to automation and other technological changes. The author provides support for his theory using ethnographic and other data from radiology departments.

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  • Bloom, N., R. Sadun, and J. V. Reenen. “Americans Do I.T. Better: US Multinationals and the Productivity Miracle.” American Economic Review 102.1 (2012): 167–201.

    DOI: 10.1257/aer.102.1.167Save Citation »Export Citation »E-mail Citation »

    This paper shows that between 1995 and 2006, US multinationals obtained higher productivity from information technology than non-US multinationals did. The authors use a management practices survey to show that the US IT-related productivity advantage is primarily due to better management practices.

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  • Brynjolfsson, E., and L. M. Hitt. “Beyond Computation: Information Technology, Organizational Transformation and Business Performance.” Journal of Economic Perspectives 14.4 (2000): 23–48.

    DOI: 10.1257/jep.14.4.23Save Citation »Export Citation »E-mail Citation »

    This paper argues that adoption of radical new technologies requires changes to organizational structure. It explains how advances in computer integrated manufacturing technology by a large medical products manufacturer led the firm to adopt a more decentralized organizational structure, eliminate piece rates, give more authority to workers, and increase lateral communication and teamwork.

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  • Kirkman, B. L., and J. E. Mathieu. “The Dimensions and Antecedents of Team Virtuality.” Journal of Management 31.5 (2005): 700–718.

    DOI: 10.1177/0149206305279113Save Citation »Export Citation »E-mail Citation »

    This paper defines the construct of virtual teams, reviews the virtual teams’ literature, and describes several antecedents of virtual teams. An important antecedent is team member knowledge, skill, and ability, including skilled use of “virtual tools.”

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  • Lipstreu, O. “Organizational Implications of Automation.” Academy of Management Journal 3.2 (1960): 119–124.

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    Using survey data from large industrial businesses in the United States, this article derives hypotheses regarding the effect of automation on a number of dimensions of organization, such as supervisory responsibility, interdependence, organization structure, and cross-hierarchy communication.

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  • Lipstreu, O., and K. A. Reed. “A New Look at the Organizational Implications of Automation.” Academy of Management Journal 8.1 (1965): 24–31.

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    A follow-up to Lipstreu’s earlier work, this article attempts to test the hypotheses of Lipstreu 1960 using a field study at a baking plant going through the adoption of new, highly automated technology.

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  • Raj, M., and R. Seamans. “Primer on Artificial Intelligence and Robotics.” Journal of Organization Design (2019).

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    Raj and Seamans provide a primer on AI and robotics for organizational scholars. They define relevant terms, review recent literature, and outline a series of outstanding research questions on the ways in which AI, robotics, and other types of automation affect organizations.

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  • Simon, H. A. “Information Technologies and Organizations.” The Accounting Review 65.3 (1990): 658–667.

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    Simon answers a number of questions on the effect of new information technologies, such as AI, on organization structure, control, and hierarchy.

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  • Zammuto, R. F., T. L. Griffith, A. Majchrzak, D. J. Dougherty, and S. Faraj. “Information Technology and the Changing Fabric of the Organization.” Organization Science 18.5 (2007): 749–762.

    DOI: 10.1287/orsc.1070.0307Save Citation »Export Citation »E-mail Citation »

    The authors of this article examine the intersection between information technology and organization, and make the argument that information technology replaces the role of hierarchy in the coordination of firm activities. In this regard, they suggest that information technology has become “one of the threads from which the fabric of organization is continually created” (p. 750).

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Automation and the Nature of Work

While automation may create or eliminate some occupations, it will also affect the nature of work. This section addresses the literature that examines how automation may direct the future of work. Some of these articles, such as Hoos 1960; Matteis 1979; Argote, et al. 1983; and Hall and Krueger 2018, examine how the adoption of automation affects employees at an individual level, while other studies, such as Zuboff 1988, Adler 1992, Susskind and Susskind 2015, and Hall and Krueger 2018, examine how automation may cause a reorganization of how businesses and workforces structure themselves. Hirschhorn 1984 specifically examines how the roles of managers change with increased innovation and automation.

  • Adler, P. S., ed. Technology and the Future of Work. New York: Oxford University Press, 1992.

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    This volume is a compilation of ten essays on the effect of automation and new technologies on organizations. Many of the essays study these effects in manufacturing settings, and find that when new technologies are adopted, there is often a need for more highly skilled workers.

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  • Argote, L., P. Goodman, and D. Schkade. “The Human Side of Robotics.” Sloan Management Review 24.3 (1983): 31–40.

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    Argote, Goodman, and Schkade also address how employees are affected by increased automation and examine how employees react to the introduction of robots in a factory. They suggest that there are both positive and negative psychological reactions to the new technology.

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  • Hall, J. V., and A. B. Krueger. “An Analysis of the Labor Market for Uber’s Driver-Partners in the United States.” ILR Review 71.3 (2018): 705–732.

    DOI: 10.1177/0019793917717222Save Citation »Export Citation »E-mail Citation »

    The authors describe reasons that individuals choose to work for a firm like Uber that automates the process of matching drivers to riders. They compare the demographics of Uber drivers to other workers in the economy, including taxi drivers. They report that Uber drivers often cite the desire to smooth fluctuations in their income as one of their reasons for working for Uber.

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  • Hirschhorn, L. Beyond Mechanization: Work and Technology in a Postindustrial Age. Cambridge, MA: MIT Press, 1984.

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    The book considers how increasing levels and sophistication of automation are increasing the demands on human managers to understand the machines around them and to best use and adjust to them. In short, managers need to be engaged in continuous learning.

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  • Hoos, I. R. “The Sociological Impact of Automation at the Office.” Management Technology 1.2 (1960): 10–19.

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    Hoos studies the impact of the introduction of computer technology across organizations in the San Francisco area, and focuses on the sociological impacts on employees affected. In particular, she addresses the implications in regard to changes in occupation content, location, and trajectory and changes in organizational structure.

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  • Katz, L. F., and A. B. Krueger. “The Rise and Nature of Alternative Work Arrangements in the United States, 1995–2015.” NBER Working Paper 22667 (2016).

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    The authors describe how technological changes have led to an increase in a variety of alternative work arrangements, including use of temporary help agency workers, on-call workers, contract workers, and independent contractors and freelancers. They argue that alternative work arrangements have been responsible for a significant portion of the total growth in jobs in the US economy since 2005.

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  • Matteis, R. J. “The New Back Office Focuses on Customer Service.” Harvard Business Review 57 (1979).

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    In this article, Matteis discusses how Citibank increased automation in the 1970s, and how the company managed the “human” aspect of the transition. Specifically, Matteis addresses how Citibank managed individuals whose jobs and roles changed as a function of the new technology.

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  • Susskind, R. E., and D. Susskind. The Future of the Professions: How Technology Will Transform the Work of Human Experts. Oxford: Oxford University Press, 2015.

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    The authors argue that a variety of technologies are converging to automate the work that is currently being done by various professions, including law and education. The authors argue that this automation will ultimately lead to a radical reorganization of how these professions conduct their business.

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  • Zuboff, S. In the Age of the Smart Machine: The Future of Work and Power. New York: Basic Books, 1988.

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    In this book, Zuboff discusses how information technologies not only automate tasks, but simultaneously “informate” and create information based on the activities, actions, and objects around them. Zuboff lays out the distinct consequences of the automating processes compared to the informating processes.

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Automation and Strategy

Management and strategy scholars have identified a number of important cognitive, managerial, and strategic antecedents that may affect which firms adopt automating technologies, and the effects that these automating technologies may have on the firm. Some work, such as Cowgill 2019 and Simon 1987, examines how AI technologies can be a resource in decision-making, while Powell and Dent-Micallef 1997 more broadly examines how information technology can allow firms to gain a competitive advantage. Other work, such as Katila and Ahuja 2002 and Roy and Sarkar 2016, uses the robotics industry as a setting to show how prior firm experience affects future performance and strategy.

  • Cowgill, B. “Bias and Productivity in Humans and Algorithms: Theory and Evidence from Résumé Screening.” Columbia University Working Paper (2019).

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    Cowgill describes how a number of firms are now relying on machine learning techniques to assist in a variety of organizational tasks, including employee recruitment. Cowgill compares the results of human discretion and machine learning techniques for hiring outcomes, and finds that machine techniques outperform human discretion.

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  • Katila, R., and G. Ahuja. “Something Old, Something New: A Longitudinal Study of Search Behavior and New Product Introduction.” Academy of Management Journal 45.6 (2002): 1183–1194.

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    The authors’ study of the global robotics industry reveals that the nature of firms’ search efforts, including search depth and search scope, affects the number of new products introduced by the firm.

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  • Powell, T. C., and S. Dent-Micallef. “Information Technology as Competitive Advantage: The Role of Human, Business, and Technology Resources.” Strategic Management Journal 19.5 (1997): 375–405.

    DOI: 10.1002/(SICI)1097-0266(199705)18:5<375::AID-SMJ876>3.0.CO;2-7Save Citation »Export Citation »E-mail Citation »

    The authors use data from a retail store setting to argue that firms use information technology to gain competitive advantage by leveraging intangible, complementary human and business resources such as flexible culture, strategic planning–IT integration, and supplier relationships.

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  • Roy, R., and M. B. Sarkar. “Knowledge, Firm Boundaries, and Innovation: Mitigating the Incumbent’s Curse during Radical Technological Change: Mitigating Incumbent’s Curse during Radical Discontinuity.” Strategic Management Journal 37.5 (2016): 835–854.

    DOI: 10.1002/smj.2357Save Citation »Export Citation »E-mail Citation »

    Using the industrial robot industry as a setting, these authors show that prior technological experience and technological knowledge are associated with greater innovative behavior following the introduction of a disruptive technology.

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  • Simon, H. A. “Making Management Decisions: The Role of Intuition and Emotion.” The Academy of Management Executive 1.1 (1987): 57–64.

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    In this work, Simon discusses managerial decision-making and notes how AI holds the potential to be a tool to improve decision-making processes within organizations and can be used to help understand managerial intuition.

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AI, Robotics, and Other New Technologies

As dramatic advances are made in AI and robotics technologies, scholars have begun to study whether these new technologies will have a different impact than prior episodes of automation. Differences from prior episodes of automation include the following: (1) the nature of business activity has shifted dramatically since the 1990s such that many businesses now rely on platform (i.e., two-sided market) business models; (2) AI is likely to affect white collar workers more than blue collar workers (while perhaps robotics may affect blue collar workers more than white collar workers); and (3) AI may affect the links between establishments and firms. A number of these works focus specifically on the effect of AI on firms and the economy, such as Agrawal, et al. 2018; Brynjolfsson and McAfee 2017; Brynjolfsson, et al. 2018; Brynjolfsson, et al. 2019; and Korinek and Stiglitz 2019. A number of other papers, such as Arntz, et al. 2016 and Frey and Osborne 2017, look more broadly at automation and computerization on the whole, while Brynjolfsson and McElheran 2016 studies data-driven decision-making and Pratt 2015 discusses the rapid diversification and expansion of robotics technology.

  • Agrawal, A. K., J. Gans, and A. Goldfarb. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston: Harvard Business Review Press, 2018.

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    The authors present AI as a technology that lowers the costs of prediction and accordingly discuss its potential impact on the economy. With this framing, AI is seen as a tool to improve decision-making under uncertainty.

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  • Arntz, M., T. Gregory, and U. Zierahn. “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis.” OECD Social Employment and Migration Working Paper 189 (2016).

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    Building off of Frey and Osborne’s earlier work, this article presents another attempt at measuring the automatability of jobs. The authors suggest that other studies overestimate the automatability of occupations by not considering the heterogeneity of workers’ tasks within occupations and estimate that 9 percent of occupations in OECD countries are automatable. In addition, they argue that adjustment processes will help to blunt technological unemployment.

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  • Brynjolfsson, E., and A. McAfee. “The Business of Artificial Intelligence: What It Can—and Cannot—Do for Your Organization.” Harvard Business Review (2017).

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    In this article, the authors claim that AI has the potential to be “the most important general-purpose technology of our era.” They discuss the potential of AI to transform the economy and its practical implications for businesses.

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  • Brynjolfsson, E., and K. McElheran. “The Rapid Adoption of Data-Driven Decision-Making.” American Economic Review 106.5 (2016): 133–139.

    DOI: 10.1257/aer.p20161016Save Citation »Export Citation »E-mail Citation »

    This paper studies the diffusion and adoption patterns of data-driven decision-making, a field similar to and with important implications for AI. The authors find that diffusion has been rapid and that data-driven decision-making has largely been productivity-enhancing.

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  • Brynjolfsson, E., T. Mitchell, and D. Rock. “What Can Machines Learn, and What Does It Mean for Occupations and the Economy?” American Economic Association Papers and Proceedings 108 (2018): 43–47.

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    The authors take an abilities and skills-based approach to construct a measure of “Suitability for Machine Learning” at the occupation level. They suggest that most occupations include at least some tasks that are suitable for machine learning; however, few occupations are fully automatable using machine learning.

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  • Brynjolfsson, E., D. Rock, and C. Syverson. “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics.” In The Economics of Artificial Intelligence: An Agenda. Edited by A. K. Agrawal, J. Gans, and A. Goldfarb, 23–57. Chicago: University of Chicago Press, 2019.

    DOI: 10.7208/chicago/9780226613475.003.0001Save Citation »Export Citation »E-mail Citation »

    Despite rapid advances in AI technology, measured productivity growth has not experienced corresponding increases. This article presents a potential explanation for this “modern productivity paradox.” They suggest that lags are likely the biggest contributor and commonly used statistics may fail to measure the benefits of these new technologies.

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  • Frey, C. B., and M. A. Osborne. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change 114 (2017): 254–280.

    DOI: 10.1016/j.techfore.2016.08.019Save Citation »Export Citation »E-mail Citation »

    Frey and Osborne estimate the probability of computerization for a set of 702 occupations using a machine learning algorithm. They suggest that 47 percent of total US employment is at high risk for automation within the next few decades.

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  • Korinek, A., and J. E. Stiglitz. “Artificial Intelligence and Its Implications for Income Distribution and Unemployment.” In The Economics of Artificial Intelligence: An Agenda. Edited by A. K. Agrawal, J. Gans, and A. Goldfarb, 349–390. Chicago: University of Chicago Press, 2019.

    DOI: 10.7208/chicago/9780226613475.003.0014Save Citation »Export Citation »E-mail Citation »

    There are fears that increased automation will exacerbate income inequality. In this chapter, Korinek and Stiglitz discuss how AI may affect inequality and how it can be adopted in a welfare-maximizing manner.

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  • Pratt, G. A. “Is a Cambrian Explosion Coming for Robotics?” Journal of Economic Perspectives 29.3 (2015): 51–60.

    DOI: 10.1257/jep.29.3.51Save Citation »Export Citation »E-mail Citation »

    Pratt compares the rapid diversification and expansion of human life during the “Cambrian Explosion” to the increased use and diversification of robotics technology. He suggests that, with the advancements in “deep learning” and machine learning technologies, society may soon reach a tipping point that greatly accelerates the rate of development of robotics and AI technologies.

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Call to Action

While this bibliography lays out extant literature on automation, there is still clearly much work to be done in this area, as described by Loebbecke and Picot 2015, Dafoe 2018, McElheran 2019, and Raj and Seamans 2019. Especially as technologies such as AI and robotics continue to advance in their complexity and their uses, automation in the economy is likely to continue to evolve in its impact and have significant implications for productivity growth and labor. This final section presents work that highlights areas that need more attention by scholars in the future, including to public policy decisions as described by Furman and Seamans 2019. In addition, this section includes work, such as Simon 1987 and Agarwal and Dhar 2014, that highlights the role of new technologies and data in future information systems and management research.

  • Agarwal, R., and V. Dhar. “Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research.” Information Systems Research 25.3 (2014): 443–448.

    DOI: 10.1287/isre.2014.0546Save Citation »Export Citation »E-mail Citation »

    Agarwal and Dhar discuss how big data, analytics, and new methods such as machine learning can be incorporated into information systems research—in terms of methods as well as research topics.

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  • Dafoe, A. “AI Governance: A Research Agenda.” Report by the Future of Humanity Institute (2018).

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    The research agenda outlined in this report divides the field of AI into three clusters. The first focuses on the technical landscape; the second examines the political dynamics between firms, governments, the public, and scholars; and the third cluster focuses on AI governance structures.

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  • Furman, J., and R. Seamans. “AI and the Economy.” NBER Innovation Policy and the Economy 19 (2019): 161–191.

    DOI: 10.1086/699936Save Citation »Export Citation »E-mail Citation »

    The authors argue that policymakers and practitioners need to understand the economic and policy backdrop against which AI and other types of automating technologies are being adopted. They also review current and potential policies around AI that may help to boost productivity growth while also mitigating any labor market downsides.

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  • Loebbecke, C., and A. Picot. “Reflections on Societal and Business Model Transformation Arising from Digitization and Big Data Analytics: A Research Agenda.” Journal of Strategic Information Systems 24.3 (2015): 149–157.

    DOI: 10.1016/j.jsis.2015.08.002Save Citation »Export Citation »E-mail Citation »

    This article examines how digitization and big data analytics affect business models and labor. In addition, the authors present a research agenda arising from the disruption and automation caused by these technologies.

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  • McElheran, K. “Economic Measurement of AI.” NBER Working Paper (2019).

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    In this paper, McElheran reviews research on how firms utilize new information technologies and argues for better measurement of advances in machine learning and AI.

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  • Raj, M., and R. C. Seamans. “AI, Labor, Productivity and the Need for Firm-Level Data.” In The Economics of Artificial Intelligence: An Agenda. Edited by A. K. Agrawal, J. Gans, and A. Goldfarb, 553–565. Chicago: University of Chicago Press, 2019.

    DOI: 10.7208/chicago/9780226613475.003.0022Save Citation »Export Citation »E-mail Citation »

    One of the difficulties in studying automation and new technologies is the lack of available data. In this chapter, the authors highlight the currently available data on AI and robotics before calling specifically for firm-level data to examine the effect of AI on productivity.

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  • Simon, H. A. “Two Heads Are Better Than One: The Collaboration between AI and OR.” Interfaces 17.4 (1987): 8–15.

    DOI: 10.1287/inte.17.4.8Save Citation »Export Citation »E-mail Citation »

    In this prescient work written in 1987, Nobel laureate Herbert Simon encourages research on operations and management science to incorporate AI as a tool for analysis. He argues that, instead of drawing distinctions between operations research and AI, scholars need to “confuse, blend, and synthesize them as much as possible” (p. 11).

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