Prior work on information technology (IT) adoption and economic impacts typically employs an instrumental logic in which firms lead with innovation when they possess characteristics that make it economically beneficial to do so and lag when they do not. However, firms may deviate from this idealized picture when they possess characteristics of an innovation laggard but exhibit the behavior of an innovation leader (or vice versa), with implications for the returns to IT investment. This study develops a conceptual framework and hypotheses regarding the implications of such deviations, which we call innovation misfits. Using a data set comprising measures of the adoption of electronic networking technologies (ENT) in over 25,000 U.S. manufacturing plants, productivity regression estimation reveals a consistent pattern that the association between IT and productivity is diminished in the presence of innovation misfit. We discuss the implications of innovation misfit for scholarship and management practice, which are numerous.
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Data in Action: Data-Driven Decision Making in U.S. Manufacturing
January 2016
Working Paper Number:
CES-16-06
Manufacturing in America has become significantly more data-intensive. We investigate the adoption, performance effects and organizational complementarities of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for 2005 and 2010, we observe the extent to which manufacturing firms track and use data to guide decision making, as well as their investments in information technology (IT) and the use of other structured management practices. Examining a representative sample of over 18,000 plans, we find that adoption of DDD is earlier and more prevalent among larger, older plants belonging to multi-unit firms. Smaller single-establishment firms adopt later but have a higher correlation with performance than similar non-adopters. Using a fixed-effects estimator, we find the average value-added for later DDD adopters to be 3% greater than non-adopters, controlling for other inputs to production. This effect is distinct from that associated with IT and other structured management practices and is concentrated among single-unit firms. Performance improves after plants adopt DDD, but not before ' consistent with a causal relationship. However, DDD-related performance differentials decrease over time for early and late adopters, consistent with firm learning and development of organizational complementarities. Formal complementarity tests suggest that DDD and high levels of IT capital reinforce each other, as do DDD and skilled workers. For some industries, the benefits of DDD adoption appear to be greater for plants that delegate some decision making to frontline workers.
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Do Market Leaders Lead in Business Process Innovation? The Case(s) of E-Business Adoption
April 2011
Working Paper Number:
CES-11-10
This paper investigates the relationship between market position and the adoption of IT-enabled process innovations. Prior research has focused overwhelmingly on product innovation and garnered mixed empirical support. I extend the literature into the understudied area of business process innovation, developing a framework for classifying innovations based on the complexity, interdependence, and customer impact of the underlying business process. I test the framework's predictions in the context of ebuying and e-selling adoption. Leveraging detailed U.S. Census data, I find robust evidence that market leaders were significantly more likely to adopt the incremental innovation of e-buying but commensurately less likely to adopt the more radical practice of e-selling. The findings highlight the strategic significance of adjustment costs and co-invention capabilities in technology adoption, particularly as businesses grow more dependent on new technologies for their operational and competitive performance.
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The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)
April 2025
Working Paper Number:
CES-25-27
We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications. We find causal evidence of J-curve-shaped returns, where short-term performance losses precede longer-term gains. Consistent with costly adjustment taking place within core production processes, industrial AI use increases work-in-progress inventory, investment in industrial robots, and labor shedding, while harming productivity and profitability in the short run. These losses are unevenly distributed, concentrating among older businesses while being mitigated by growth-oriented business strategies and within-firm spillovers. Dynamics, however, matter: earlier (pre-2017) adopters exhibit stronger growth over time, conditional on survival. Notably, among older establishments, abandonment of structured production-management practices accounts for roughly one-third of these losses, revealing a specific channel through which intangible factors shape AI's impact. Taken together, these results provide novel evidence on the microfoundations of technology J-curves, identifying mechanisms and illuminating how and why they differ across firm types. These findings extend our understanding of modern General Purpose Technologies, explaining why their economic impact'exemplified here by AI'may initially disappoint, particularly in contexts dominated by older, established firms.
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Computer Network Use and Firms' Productivity Performance: The United States vs. Japan
September 2008
Working Paper Number:
CES-08-30
This paper examines the relationship between computer network use and firms' productivity performance, using micro-data of the United States and Japan. To our knowledge, this is the first comparative analysis using firm-level data for the manufacturing sector of both countries. We find that the links between IT and productivity differ between U.S. and Japanese manufacturing. Computer networks have positive and significant links with labor productivity in both countries. However, that link is roughly twice as large in the U.S. as in Japan. Differences in how businesses use computers have clear links with productivity for U.S. manufacturing, but not in Japan. For the United States, the coefficients of the intensity of network use are positive and increase with the number of processes. Coefficients of specific uses of those networks are positive and significant. None of these coefficients are significant for Japan. Our findings are robust to alternative econometric specifications. They also are robust to expanding our sample from single-unit manufacturing firms, which are comparable in the two data sets, to the entire manufacturing sector in each country, as well as to the wholesale and retail sector of Japan.
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Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
December 2020
Working Paper Number:
CES-20-40
We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES), provides comprehensive and timely information on the diffusion among U.S. firms of advanced technologies including artificial intelligence (AI), cloud computing, robotics, and the digitization of business information. The 2018 ABS is a large, nationally representative sample of over 850,000 firms covering all private, nonfarm sectors of the economy. We describe the motivation for and development of the technology module in the ABS, as well as provide a first look at technology adoption and use patterns across firms and sectors. We find that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher. This new data will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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Computer Networks and U.S. Manufacturing Plant Productivity: New Evidence from the CNUS Data
January 2002
Working Paper Number:
CES-02-01
How do computers affect productivity? Many recent studies argue that using information technology, particularly computers, is a significant source of U.S. productivity growth. The specific mechanism remains elusive. Detailed data on the use of computers and computer networks have been scarce. Plant-level data on the use of computer networks and electronic business processes in the manufacturing sector of the United States were collected for the first time in 1999. Using these data, we find strong links between labor productivity and the presence of computer networks. We find that average labor productivity is higher in plants with networks. Computer networks have a positive and significant effect on plant labor productivity after controlling for multiple factors of production and plant characteristics. Networks increase estimated labor productivity by roughly 5 percent, depending on model specification. Model specifications that account for endogenous computer networks also show a positive and significant relationship. Our work differs from others in several important aspects. First, ours is the first study that directly links the use of computer networks to labor productivity using plant-level data for the entire U.S. manufacturing sector. Second, we extend the existing model relating computers to productivity by including materials as an explicit factor input. Third, we test for possible endogeneity problems associated with the computer network variable.
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Predictive Analytics and Organizational Architecture:
Plant-Level Evidence from Census Data
January 2019
Working Paper Number:
CES-19-02
We examine trends in the use of predictive analytics for a sample of more than 25,000 manufacturing plants using proprietary data from the US Census Bureau. Comparing 2010 and 2015, we find that use of predictive analytics has increased markedly, with the greatest use in younger plants, professionally-managed firms, more educated workforces, and stable industries. Decisions on data to be gathered originate from headquarters and are associated with less delegation of decision-making and more widespread awareness of quantitative targets among plant employees. Performance targets become more accurate, long-term oriented, and linked to company-wide performance, and management incentives strengthen, both in terms of monetary bonuses and career outcomes. Plants increasing predictive analytics become more efficient, with lower inventory, increased volume of shipments, narrower product mix, reduced management payroll and increased use of flexible and temporary employees. Results are robust to a specification based on increased government demand for data.
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Delegation in Multi-Establishment Firms: The Organizational Structure of I.T. Purchasing Authority
October 2010
Working Paper Number:
CES-10-35
A rare large-scale empirical study of delegation within firms, this paper investigates how decision rights over information technology investments are allocated within multi-establishment firms. The core results indicate that a relatively high contribution to firm sales is highly correlated with authority being delegated to the local establishment. Firm-wide operational complexity and local information advantages are also associated with local discretion for IT purchases. Certain IT investments are also positively correlated with delegation. On the other hand, significant operational interdependencies evince a positive correlation with centralization, as do productive similarities among establishments. Surprisingly, absolute size of the firm and having a large IT budget are also correlated with centralized IT decision-making. With the exception of these latter effects, the results are consistent with models of organizational design that predict delegation where there is great demand for locally adapted choices and centralization where firm-wide coordination is most important. The findings document and make sense of widespread heterogeneity in decision rights across a range of firm and industry settings ' even among establishments belonging to the same parent firm. Finally, they suggest important considerations for future empirical and theoretical research into the determinants of delegation.
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Nature Versus Nurture in the Origins of Highly Productive Businesses: An Exploratory Analysis of U.S. Manufacturing Establishments
September 2011
Working Paper Number:
CES-11-26
This paper investigates the origins of productivity leaders, those that operate close to and help push out the production frontier. Do such businesses emerge as top performers from the very beginning of their lives, for example as the consequence of an outstanding founding idea, technology, or location? Or, at the other extreme, do they appear initially as completely average (or even underperformers) that exhibit gradual improvement as they learn and develop with age? To answer this question we draw upon five decades of U.S. Census of Manufacturing (CM) establishment-level data, tracing the productivity leaders of the most recent CM (2007) back over their observed life spans. We also examine possible industry-level correlates of variation in the extent of nature versus nurture that are suggested by theories of industry dynamics and economic growth.
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Computer Networks and Productivity Revisited: Does Plant Size Matter? Evidence and Implications
September 2010
Working Paper Number:
CES-10-25
Numerous studies have documented a positive association between information technology (IT) investments and business- and establishment-level productivity, but these studies usually pay sole or disporportionate attention to small- or medium-sized entities. In this paper, we revisit the evidence for manufacturing plants presented in Atrostic and Nguyen (2005) and show that the positive relationship between computer networks and labor productivity is only found among small- and medium-sized plants. Indeed, for larger plants the relationship is negative, and employment-weighted estimates indicate computer networks have a negative relationship with the productivity of employees, on average. These findings indicate that computer network investments may have an ambiguous relationship with aggregate labor productivity growth.
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