No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Technical Progress and Co-Invention in Computing and in the Uses of Computers
... Larger incumbents tend to benefit from economies of scale, as well as from more-developed organizational complements (Bresnahan et al., 2002;Brynjolfsson et al., 2021a;Jin and McElheran, 2024). Yet they also face higher adjustment costs compared to smaller entrants (Henderson, 1993;Bresnahan and Greenstein, 1996;McElheran, 2015;Gans, 2016). This tension is evident in the early AI diffusion data, where larger firms have emerged as leading adopters, but high-growth startups also show a marked propensity to rely on AI in production . ...
... This tension is evident in the early AI diffusion data, where larger firms have emerged as leading adopters, but high-growth startups also show a marked propensity to rely on AI in production . Our approach addresses this puzzle by disentangling size from correlated orga-The theoretical motivation for our study derives from the rich literature on general purpose technologies, or GPTs (Bresnahan and Trajtenberg, 1995) and related work focused on both the challenges and gains associated with technological and organizational "co-invention" (Bresnahan and Greenstein, 1996). Recent macroeconomic studies have highlighted the lag between investment in AI-related technologies and observed economic gains (Furman and Seamans, 2019), highlighting the need for significant investments in intangibles such as process redesign, worker retraining, and complementary software development (Brynjolfsson et al., 2021b). ...
... According to work in this vein, technologies with such broad potential, ongoing improvement, and often-uncertain trajectories typically require significant investments in co-invention (Bresnahan and Greenstein, 1996) or co-specialization (Teece, 1986) to align technological capabilities with core business activities, processes, products, and resources-and vice versa. ...
... В случае технологий общего назначения создание таких прикладных технологий требует тесного взаимодействия поставщиков технологии и ее потребителей. Этот процесс получил в экономической литературе название «со-изобретения» (co-invention) и детально исследован применительно к компьютерным технологиям в [12], [13]. Ключевые выводы из этого анализа следующие. ...
... Во-вторых, именно со-изобретение оказалось узким местом, определяющим реальные темпы прогресса в использовании компьютеров. В [12] прямо подчеркивается: «со-изобретение -это не просто установка компьютера, это изобретение цели, которая будет достигнута при помощи системы». Именно в процессе со-изобретения создается новый технико-экономический режим, адекватный новой технологии. ...
... Наконец, еще позже в немецкой компании AEG появилась система трехфазного переменного тока, обеспечивающая работу электрических двигателей.11 Под прототипом понимается полностью работоспособный образец, который отличается от коммерческого продукта или услуги отсутствием технической поддержки и регулярного обновления12 Знание здесь и далее в настоящем параграфе понимается в самом широком смысле, включая научные знания, патенты, ноу-хау, коммерческие секреты и др. ...
В российской экономической науке самым популярным инструментом анализа технологических революций стала модель технологического уклада. Между тем, хотя эта модель имеет определенные возможности на макроуровне, на микроуровне она не отвечает на вопросы, стоящие перед российскими предприятиями и государственными регуляторами. Речь, прежде всего, идет о таких вопросах, как приоритеты технической политики, построение адекватных цепочек развития новой технологии и, что самое важное, выявление организационного и кадрового обеспечения инноваций, позволяющего трансформировать инженерную разработку в бизнес-модель, способную принести деньги своим создателям. Для этих целей предлагается использовать альтернативные модели: модель S-образной кривой, технологии общего назначения и, наконец, т.н. Саарбрюккенскую модель передачи технологий.
In Russian economics, the model of techno-economic paradigm became the most popular instrument to analyse technological revolutions. Though this model has some analytical power on the macro level, on microlevel it cannot answer many questions facing Russians firms and regulators. One should first mention such question as technical policy priorities, building adequate chains of technology development and, what is most important, identifying organizational and staff complementarities, which allow to transform engineering development into a business model that can bring money to its creators. For this purpose, we propose alternative models: S-shaped curve, general purpose technology and, finally so-called Saarbrucken technology transfer model.
... Consequently, markets should place a higher valuation on nontech firms that engage in digital activities, due to potential future gains in performance. On the other hand, prior work has also suggested that there are frictions associated with new technologies that may delay or limit their benefits (Bresnahan and Greenstein 1996;Brynjolfsson et al. 2019). Consistent with digital technologies providing net benefits to firms, we find that the market-to-book ratio of nontech firms that engage in digital activities is higher than their industry peers in an economically significant way. ...
... There are several reasons that explain why the benefits of IT adoption manifest slowly. First, adopting technologies requires developing complementary organizational capabilities (Bresnahan and Greenstein 1996), which may be difficult without sufficient managerial expertise. Bloom et al. (2012) illustrate this point, as they show that managerial capabilities explain the US-Europe productivity gap in IT adoption. ...
... Second, in contrast to studies in the innovation literature, we study innovation through the deployment of existing digital technologies in nontechnology firms. Thus the principal bottleneck in the digital innovation process is arguably the organizational co-invention costs (Bresnahan and Greenstein 1996), rather than the technology itself (Lakhani and Iansiti 2020). In particular, the uncertainty arising from these costs is primarily strategic and thus quite different from the technological uncertainty observed in typical R&D and patenting. ...
We examine firm value and performance implications of the growing trend of nontechnology companies engaging in activities relating to digital technologies. We measure digital activities in firms based on the disclosure of digital words in the business description section of 10-Ks. Digital activities are associated with a market-to-book ratio 8%–26% higher than industry peers, and only 25% of the differences in market-to-book is explained by accounting capitalization restrictions. To control for selection bias, we implement lagged dependent variable and IV regressions, and our market-to-book findings are robust to these specifications. Portfolios formed on digital activity disclosure earn a Daniel et al. The Journal of Finance 52 (3): 1035–1058 (1997)-adjusted return of 30% over a three-year horizon and a monthly alpha of 44-basis-points. On the other hand, we find weak evidence of near-term, positive improvements in fundamental performance, as we find some evidence of interim productivity increases but declines in sales growth conditional on digital activities.
... While information technology (IT) systems have been shown to create significant value for the firms that adopt them, the returns often appear with a delay (e.g., Brynjolfsson & Hitt, 2003) and may vary greatly across firms (Aral & Weill, 2007;Bloom et al., 2012;Bresnahan et al., 2002). Firms investing in new IT systems must often undertake complementary innovation, sometimes termed co-invention, to adapt general-purpose IT systems to the idiosyncratic needs of organizations (Bresnahan & Greenstein, 1996). While sometimes these innovations are related to technical adaptations to IT hardware and software systems, they also frequently involve changes to organizational elements such as business processes (Bartel et al., 2007;Bresnahan et al., 2002;Dranove et al., 2014). ...
... The effective implementation and use of IT within organizations has emphasized the view of IT as an enabler of business process innovation. Business process innovation requires a range of investments in computing hardware and software as well as changes to process flows, human capital, and other organizational practices (Bresnahan & Greenstein, 1996). In the context of enterprise software, for example, adopters of Enterprise Resource Planning (ERP) systems must incorporate local business rules into ERP software through a process of configuration and customization. ...
... All of these factors make new products difficult to learn. 9 This is consistent with studies showing that for IT knowledge related to new applications, the extent of required co-invention may be greater and more context-dependent because standardized solutions have yet to be deployed and refined (Bresnahan & Greenstein, 1996). Evidence of these differences have been found in other settings as well; for example, von Hippel and Tyre (1995) show that avoidance of problems when using a new process machine may require a great deal of information about the setting where it is to be applied. ...
We examine the productivity implications of external knowledge flows obtained through an internet-mediated discussion forum in which IT professionals help one another solve problems related to the implementation and use of enterprise software. We extend elements of the absorptive capacity (ACAP) framework that have not previously been studied in the information systems (IS) literature to a new context. Consistent with prior results from the IS literature, we first show that IT spillovers—acquired through employees’ participation in this forum—only accrue to firms with prior related investments in enterprise software. We then demonstrate boundary conditions for ACAP based on characteristics of external knowledge affecting the ease of learning. Our results show that IT spillovers are not “free”; the ability to derive the value of IT spillovers through informal channels—such as online communities—critically depends on both prior related IT investments by the recipient firm and the novelty of external knowledge. Less intuitively, when knowledge originates from relatively novel or emergent domains, the role of prior related knowledge in absorbing spillovers becomes more important.
... Organizational complementarity theory suggests that firms that invest in mutually-reinforcing assets (both tangible and intangible) will perform better, though appropriate complements may take time to develop, and a mismatch may be temporarily very costly (Kandel and Lazear 1992;Roberts 1990, 1995;Holmstrom and Milgrom 1994;Brynjolfsson and Milgrom 2013;Brynjolfsson et al. 2018). Empirical studies have validated the importance of complementary investments and organizational alignment for realizing the value of IT (Bresnahan and Greenstein 1996;Black and Lynch 2001;Caroli and Van Reenen 2001;Bresnahan et al. 2002;Melvill et al. 2004;Aral and Weill 2007;Bloom et al. 2012;Bapna et al. 2013), as well as data-centered practices (Aral et al. 2012;Tambe et al. 2012;Brynjolfsson and McElheran 2019). ...
... First, focusing on changes in the smaller subpopulation of late adopters would distort our inference. It is widely believed that later adopters of new technologies tend to be those with low anticipated returns, disproportionately high costs of adoption, and/or lagging awareness of the technology (Griliches 1957;David 1969;Bresnahan and Greenstein 1996). But we are interested in adoption and performance benefits-or the barriers thereto-for firms throughout the diffusion curve. ...
... Thus, firms with existing IT capital investments that are more prepared for the industrial Internet of Things (IoT) and related "big data" innovations at the time of our study may possess fully-depreciated investments in infrastructure to collect and analyze data, as well as richer data inputs, giving them an advantage in analytics. Building and adapting such infrastructure to a particular firm setting is known to be risky and time-consuming (Bresnahan and Greenstein 1996), particularly in our manufacturing context (McElheran 2015). 13 High cross-sectional heterogeneity in firm performance has long been established (e.g., Syverson 2004Syverson & 2011Hopenhayn 2014); however, a large number of recent studies point to increasing firm heterogeneity along a number of economically important dimensions (Andrews et al. 2015;Van Reenen 2018;Song et al. 2019;Decker et al. 2020;Autor et al. 2020;Bennett 2020a). ...
Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics—up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and the timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flow-efficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.
... Organizational complementarity theory suggests that firms that invest in mutually-reinforcing assets (both tangible and intangible) will perform better, though appropriate complements may take time to develop, and a mismatch may be temporarily very costly (Kandel and Lazear 1992;Roberts 1990, 1995;Holmstrom and Milgrom 1994;Brynjolfsson and Milgrom 2013;Brynjolfsson et al. 2018). Empirical studies have validated the importance of complementary investments and organizational alignment for realizing the value of IT (Bresnahan and Greenstein 1996;Black and Lynch 2001;Caroli and Van Reenen 2001;Bresnahan et al. 2002;Melvill et al. 2004;Aral and Weill 2007;Bloom et al. 2012;Bapna et al. 2013), as well as data-centered practices (Aral et al. 2012;Tambe et al. 2012;Brynjolfsson and McElheran 2019). ...
... First, focusing on changes in the smaller subpopulation of late adopters would distort our inference. It is widely believed that later adopters of new technologies tend to be those with low anticipated returns, disproportionately high costs of adoption, and/or lagging awareness of the technology (Griliches 1957;David 1969;Bresnahan and Greenstein 1996). But we are interested in adoption and performance benefits-or the barriers thereto-for firms throughout the diffusion curve. ...
... Thus, firms with existing IT capital investments that are more prepared for the industrial Internet of Things (IoT) and related "big data" innovations at the time of our study may possess fully-depreciated investments in infrastructure to collect and analyze data, as well as richer data inputs, giving them an advantage in analytics. Building and adapting such infrastructure to a particular firm setting is known to be risky and time-consuming (Bresnahan and Greenstein 1996), particularly in our manufacturing context (McElheran 2015). 13 High cross-sectional heterogeneity in firm performance has long been established (e.g., Syverson 2004Syverson & 2011Hopenhayn 2014); however, a large number of recent studies point to increasing firm heterogeneity along a number of economically important dimensions (Andrews et al. 2015;Van Reenen 2018;Song et al. 2019;Decker et al. 2020;Autor et al. 2020;Bennett 2020a). ...
... Investment in new routines, new policies, and enabling technologies are often an essential component of rapid diffusion (Fichman, 1992). But these investments too can also be self-limiting (Bresnahan, et al., 1996). On an individual level, we experience this when we buy something new -from smartphone to air-fryer -and need to acquire and/or understand a bewildering array of supporting and complementary accessories and new routines. ...
... We must acknowledge prediction is very hard. However, a forward-looking tool, however imprecise, can guide R&D decisions and assist in forming innovation alliances (Cassiman & Veugelers, 2006& Cohen 2010 and the purchase and/or outsourcing of third-party services (Attewell 1992& Bresnahan, et al., 1996. Further, such a tool can allow the innovators to better capture the profits from developing complementary technologies (Gambardella, et al., 2021). ...
A general purpose technology (GPT) is an exceptional link in a complex chain of innovations connected through space and time. Common examples include the steam engine, electrification, and the automobile (Bresnahan & Trajtenberg, 1995; Bresnahan & Trajtenberg, 1995; & Bekar, et. al., 2018). A GPT is set apart from other innovations because of the massive breadth, depth, and duration of their impact on our world and, therefore, worthy of special consideration. 'pervasiveness dimensions' of a candidate GPT before its ultimate economic and societal impact is observed. Our candidate GPT is Artificial Intelligence (AI) and a key enabling technology, Deep Learning (DL). We trace the evolution of AI and DL from their birth in the latter half of the 20 th century to the present, with a focus on the diffusion of large-scale applications of AI/DL from 2000 to 2020 (Goldfarb, et. al, 2021). We are aided by a rich base of prior academic research across various disciplines as well as industry analyses and reports. We hope to add a forward-looking tool to the decision-makers toolkit, while also increasing our understanding of AI not only as an innovation but as an extraordinary technological, economic, social, and even political phenomenon. iii To my family and friends who pushed, guided, and encouraged me to pursue my curiosity, wherever it takes me. iv ACKNOWLEDGMENTS
... The main commonality has been the role of complementary innovation, or co-invention. For example, Bresnahan and Greenstein (1996) examined the diffusion of client/server computing systems in large companies. They emphasize the role of co-invention, the invention of new technologies and processes that enable the technology to generate growth. ...
... Bresnahan and Trajtenberg attribute the increase in downstream R&D productivity to "innovational complementarities." These complementarities generate what Bresnahan and Greenstein (1996) label "co-invention". Gans (1995) and Aral, Brynjolfsson, and Wu (2012) note that these innovations can also occur in management and organization, and so they label them "organizational complementarities." ...
... Bresnahan (2020) argues that AI is an information technology and traditionally such technologies have required organisational redesign to be fully adopted. This is readily apparent in patterns of adoption of earlier generations of IT (Bresnahan and Greenstein (1996); Bresnahan et al. (2002); Aral et al. (2012); Dranove et al. (2014)). Bresnahan (2020) challenges the idea that AI adoption can be analysed at the task level independent of the organisational context the task lies in. ...
... The model here is inspired by Van den Steen (2017), although it addresses a distinct research question. 2 Suppose that an organisation's return, R, depends on the outcomes of two decisions, {D 1 , D 2 }, indexed by k. 3 Each decision results in the choice of an action, a k ∈ A k , 1 One interpretation of investment in communication within an organisation is that this is a co-invention that enables AI; see Bresnahan and Greenstein (1996); Bresnahan et al. (2002); Aral et al. (2012); and Dranove et al. (2014). ...
... 2 GPT analytics emphasize the innovational complementarities between the GPT itself and inventions of applications (Bresnahan and Trajtenberg, 1995;Rosenberg and Trajtenberg, 2004;Helpman and Trajtenberg, 1998). Bresnahan and Greenstein (1996) emphasize the role of difficult-to-invent applications in slowing the diffusion of, and easy-to-invent applications accelerating the diffusion of, ICT GPTs. Rosenberg (1997) writes about the role of post-invention uncertainty (often about the most important applications). ...
... A UI is an interfaceit works between two things. In this case, it works between the user and a system or service, as indicated by Al Lindsay, the manager of Amazon's 28 See, e.g., Zuboff (1988) and Bresnahan and Greenstein (1996). 29 Many people recall the Turing test as "Could you tell?"are you conversing with a human or a machine? ...
Although economic growth has historically been an engine of prosperity in the United States, recent trends have generated uncertainty regarding the prospects for sustaining such growth. Economists disagree about the relative importance of many factors affecting future growth, including rapid technological advances, immigration, the growth of the financial sector, problems with the educational system, increasing income inequality, an aging population, and large fiscal imbalances that have not been addressed by the political system. This collection of chapters, authored by many of today's leading economists, addresses the prospects for economic growth in the United States over the next few decades. During a time of great economic uncertainty, this book engages with both sides in the debate over economic growth, focusing on policy options that increase the prospects for vigorous economic growth in the future.
... As leaders plan and perform computerized activities, they should decide how they could use the best of technology to supply the production and distribution needs of the organization (Dlabay et al., 2006;Moussa, 2015). As Bresnahan and Greenstein (1996) noted, the invention of any technology enables but does not direct its use. Different people may apply different criteria regarding what is advantageous or detrimental. ...
... Computers in organizations are usually linked in a computer network to enable users to share hardware, software and data. Remarkable changes have arisen since the late 1980s and early 1990s, when mainframe hardware and software faced genuine competition from networked smaller computers (Bresnahan & Greenstein, 1996). Gitman and McDaniel (2006) stated that a computer network is a group of two or more computer systems connected, often linking thousands of users by communications tools to share data and information and transmit audio and video as well. ...
The main purpose of this study is to ascertain the relationship between business course curriculum and leadership skills development among a sample of business graduates. The required data were collected through a questionnaire administered to MBA and final semester BBA students of different public and private universities in Bangladesh. The total number of respondents in the study was 226. The students were selected using a convenience sampling method. To examine the hypotheses, multivariate correlation and ANOVA tests were used. The results showed that a business course curriculum has a positive impact on leadership skills development among the business graduates. The study suggests guidelines for updating the course curriculum that will provide opportunities to students for developing their leadership skills, from which society will also benefit. The study was unable to include all universities in Bangladesh. Findings would be more significant if the respondents had been selected randomly. A cross-sectional study might provide more insight about the leadership skills development through course curriculum. The study has both theoretical and practical implications. Some future research directions have also been provided in this study.
... On the other hand, the advance in manufacturing technology expands the demand for AI technology applications, motivating developers to further promote AI technology. These complementarities can be termed as technological resonance, or "co-invention" (Bresnahan et al., 1996). ...
Purpose
This research aims to investigate the impact of artificial intelligence (AI) adoption on the innovation dynamics of Chinese manufacturing enterprises, with a specific focus on the intricate interplay with the labor structure.
Design/methodology/approach
Leveraging panel data of listed companies from 2010 to 2022, this study employs the two-way fixed effects (TWFE) model to examine the influence of AI adoption on Chinese manufacturing companies' innovativeness. Firm-level AI adoption is measured by constructing a three-dimensional attention, application and absorption index.
Findings
The results indicate that (1) AI adoption has a positive impact on both internal innovation capability and external innovation interaction, (2) AI adoption has dual effects on the education and skill structure of labor in manufacturing enterprises and (3) enterprises with a highly educated and skilled workforce exhibit a stronger influence of AI adoption on innovativeness.
Originality/value
This research contributes to the academic and practical discourse by unveiling the underlying mechanisms of AI affecting innovation and introducing a new measurement of the AI adoption index. The findings emphasize the need for a highly educated and skilled workforce to navigate the complexities of AI-driven innovation, offering valuable theoretical and practical implications for policymakers and enterprises.
... AI capabilities are a type of information technology investment, and IT investments generally necessitate coinvention that leads to an accumulation of intangible assets [7]. These intangibles include knowhow, business processes, corporate culture, and organizational designs that allow the new technology to increase corporate productivity Generated from the assumption that AI may potentially see people as a threat, comes the fear of AI devices enabling largescale cyber-attacks or causing mass disinformation by generating substantial amounts of unverified data [8]. ...
Abstract. IT based-learning activities are rapidly developing in
world countries. There are a few approaches available for interacting and measuring technological talents. But, in modern days
AI rapidly expands almost all sectors of the national economy.
From this point of view some challenges in human capital development from high IQ level IT personnel. This study denotes the
development challenges of human resources in technology-based
economies. In total, 65 countries from 193 populations were sampled. Such human-AI partnerships are a new form of socio-technical
system in which the potential synergies between humans and machines are much more fully utilized. To achieve this, AI systems
will need to leave their currently solipsistic nature behind and be
able to cooperate, coordinate, and compete with one another and
their human interlocutors. Such partnerships will combine their
complementary skills and capabilities to make the best use of the
distinctive strengths of humans and machines. We used OLS and
robust regression analysis, while the logarithmic transformation linear regression model was found significant as well. Governmental
AI Readiness Index and IQ level (2021) as an independent variable
estimated in various tests in p<0.05 statistically significant level.
Mainly, the generalized hypothesis was found to fulfilled H0 but as
for the data normality, it has been found 3 values are not significant.
The results of the three models can be applied in the public and
business administration of governments. Increasing technological
talents in major economies can take one more advantage by implementing AI with a high level of IQ for further well-being in world countries.
... These three problems stand out in the empirical literature on enterprise systems development (Bresnahan et al., 2002;Bresnahan & Greenstein, 1996). This makes the job-automation path, even with advances in AITs, seem unpromising. ...
Early commercial applications of artificial intelligence technologies (AITs) were narrow but extremely profitable. Comparable uses of those technologies throughout the economy would lead to a growth boom. Firms which emulated the early applications successfully would make tremendous strategic gains. This is a situation familiar from earlier rounds of information and communication technology. However, for AITs to become a general‐purpose technology across many commercial applications sectors will require some new innovations. This paper examines the innovation paths that could lead to that desirable outcome, the ones that have stalled, the ones in the process now, and the ones that might occur in the future. Strikingly, early AIT use, both commercial and with technical customers, occurred where Digital Transformation was not required for it to succeed. The innovation paths all require Digital Transformation as key steps.
... Their far-reaching consequences, which unfold over decades, are difficult to anticipate, particularly in relation to labor demand (Bessen, 2018;Korinek and Stiglitz, 2018;Acemoglu et al., 2020;Benzell et al., 2021). The realization of general purpose technologies' full potential requires extensive co-invention (Bresnahan and Trajtenberg, 1995;Bresnahan et al., 1996Bresnahan et al., , 2002Lipsey et al., 2005;Dixon et al., 2021), a costly and time-consuming process involving the discovery of new business procedures (David, 1990;Bresnahan, 1999;Frey, 2019;Brynjolfsson et al., 2021;Feigenbaum and Gross, 2021). Consequently, many studies of machine learning technologies focus on systems-level adoption, arguing that organizational systems may require redesign to effectively take advantage of novel machine learning advancements (Bresnahan, 2019;Agrawal et al., 2021;Goldfarb et al., 2023). ...
We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT-4. Our findings indicate that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater exposure. Notably, the impact is not limited to industries with higher recent productivity growth. We conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies (GPTs), suggesting that as these models could have notable economic, social, and policy implications.
... Further, cost Efficiency of the banks, measured by the cost-income ratio, had no effect on market share across all banks. There are various frictions associated with new technologies that may delay or limit the benefits of digital transformation (Bresnahan et al., 1996). ...
ABSTRACT Purpose: The aim of this study is to examine the effect of adopting neobanking on the market share of traditional banks in the UAE and test the influence of financial performance indicators on the banks' market share after the digital transformation. Theoretical framework: The financial service sector has been undergoing major transformation due to technological developments and innovations in terms of operating efficiency, client acquisition and organizational structure. Banks are accelerating digital transformation in an attempt to enhance digital presence, lower costs and gain market share. Neobanking is a recent innovation in the Fintech space that has disrupted the financial services sector. Design/methodology/approach: This study employs published data of quarterly financial statements from 2012-2021. Chow Test was applied, with known structural breaks in the data, based on the implementation of neobanking and our results are based on pooled regression. Findings: The results reveal that neobanking has influenced the bank specific factors and those factors have affected the market share. NPL, ROE and NIM are critical for the market share with each variable affecting all banks contrarily. This paper further identifies that NPL and NIM has a favourable impact on the market share of only one bank. Cost efficiency has no effect on the market share of the banks in the period after launching neobanking. Research, Practical & Social implications: The study has important implications for the management of banks as the results affirm that structural changes made to adopt digital transformation by firms is the key to derive the favorable effects in terms of increased revenue, profitability and lower credit risk. Originality/value: Neobanking is the most recent disruptor in the financial services sector and effect of digitalization in banking sector is becoming the focus of literature of commercial banks. This paper provides insights into bank specific variables that impact financial performance after its digital transformation.
... Further, cost Efficiency of the banks, measured by the cost-income ratio, had no effect on market share across all banks. There are various frictions associated with new technologies that may delay or limit the benefits of digital transformation (Bresnahan et al., 1996). ...
Purpose: The aim of this study is to examine the effect of adopting neobanking on the market share of traditional banks in the UAE and test the influence of financial performance indicators on the banks’ market share after the digital transformation. Theoretical framework: The financial service sector has been undergoing major transformation due to technological developments and innovations in terms of operating efficiency, client acquisition and organizational structure. Banks are accelerating digital transformation in an attempt to enhance digital presence, lower costs and gain market share. Neobanking is a recent innovation in the Fintech space that has disrupted the financial services sector. Design/methodology/approach: This study employs published data of quarterly financial statements from 2012- 2021. Chow Test was applied, with known structural breaks in the data, based on the implementation of neobanking and our results are based on pooled regression. Findings: The results reveal that neobanking has influenced the bank specific factors and those factors have affected the market share. NPL, ROE and NIM are critical for the market share with each variable affecting all banks contrarily. This paper further identifies that NPL and NIM has a favourable impact on the market share of only one bank. Cost efficiency has no effect on the market share of the banks in the period after launching neobanking. Research, Practical & Social implications: The study has important implications for the management of banks as the results affirm that structural changes made to adopt digital transformation by firms is the key to derive the favorable effects in terms of increased revenue, profitability and lower credit risk. Originality/value: Neobanking is the most recent disruptor in the financial services sector and effect of digitalization in banking sector is becoming the focus of literature of commercial banks. This paper provides insights into bank specific variables that impact financial performance after its digital transformation.
... Our dating of the actual change in market demand is in keeping with our prior empirical studies of the competition between legacy large-system users and the emerging client-server technologies. See Bresnahan and Greenstein (1996). 51. ...
... There is general agreement that it takes significant efforts to adopt technologies. Entrepreneurs and innovators take time to adopt new technologies, reconfigure existing work, discover new business processes, and co-invent complementary technologies (Bresnahan et al., 1996). Brynjolfsson et al. (2018) argued that unleashing the full potential of AI will require unbundling of tasks in jobs and a significant redesign of the task content of jobs. ...
The availability of parallel and distributed processing at a reasonable cost and the diversity of data sources have contributed to advanced developments in artificial intelligence (AI). These developments in the AI computing environment are not concomitant with changes in the social, legal, and political environment. While considering deploying AI, the deployment context and the end goal of human intelligence augmentation for that specific context have surfaced as significant factors for professionals, organizations, and society. In this research commentary, we highlight some important socio-technical aspects associated with recent growth in AI systems. We elaborate on the intricacies of human-machine interaction that form the foundation of augmented intelligence. We also highlight the ethical considerations that relate to these interactions and explain how augmented intelligence can play a key role in shaping the future of human work.
... The concept has been used in analysis of a wide variety of settings. For example, it contributes to analyzing the speed of the transition between usage of mainframe computers and client-server systems (Bresnahan and Greenstein, 1996), the transition to usage of internet-enabled administrative . ...
This study analyzes the role of co-invention in the creation of a platform for print-on-demand-clothing, or PODC. Co-invention is the invention of a new business process to complement new technology, and turn it into a valuable commercial service. PODC copies a design onto clothing with immaterial effect on the cost, and irrespective of the scale of the batch. In its modern form, PODC extends to more than two dozen different pieces of clothing and other items, enabling buyers to personalize clothing with any art. The digital printing machines used in PODC contain numerous technical inventions, while the electronic commerce platform contains the important business processes. The study examines a pioneering PODC platform from Threadless, and analyzes how this new platform emerged from a sequence of co-inventions. The study highlights the level of discretion given to graphic artists to foster trust with the platform, and it shows how a hierarchy of business process co-inventions overcame the coordination issues inherent in building a large scale and new multi-sided platform.
... Much of the explanation has centered around complementarities. Bresnahan and Trajtenberg (1995), for example, argued that general purpose technologies (GPTs) create value via complementarities, and as a result may be slow to diffuse until complementary technologies develop-although contemporary and subsequent work has debated whether the obstacles to diffusion are organizational or technological (David 1990, Bresnahan and Greenstein 1996, Goldfarb 2005. 4 Through the AT&T example, we contend they are part and parcel of the same problem: making organizations and technology congruent with each other. ...
... After the trial period, people were expected to buy a license, though there was no enforcement mechanism. In the end, many users did pay, especially 142 Bresnahan and Greenstein (1996). 143 Gates (1995, p. 95). ...
This paper is an excerpt from a larger book project called The Corporation and the Twentieth Century, which chronicles and interprets the institutional and economic history-the life and times, if you will-of American business in the twentieth century. This excerpt details the history of the personal computer industry and the Internet. It highlights the process of entrepreneurship and decentralized learning in these industries, and it considers the role of industrial and trade polices (in both the U. S. and Japan) in semiconductors and the development of the Internet. The excerpt ends with a consideration of U. S. v. Microsoft at the close of the century.
... However, although a necessary condition, technical feasibility is not sufficient to generate adoption of a new technology. Firms, institutions, and society at general, take time to adopt technology (Brynjolfsson, Rock, & Syverson, 2017), in some places more than others (Bresnahan & Greenstein, 1996). That is why, for instance, cashiers in the busy city of New York can be expected to get automated sooner than cashiers in the winery region of Napa Valley. ...
Jobs disappear, jobs are created, work changes, in a geographically uneven way. Some places cope better with adverse events that threaten its labour force into unemployment. Some places end up having better jobs. Why? Labour dynamics are particular of each place, influenced by a myriad of local factors, such as the type of jobs in each city. But jobs do not stand alone. Workers interact with and influence each other within and across firms. This ends up reflecting in the structure of labour of each city, region, or country. At each point in time, the structure sets the opportunities and boundaries for labour dynamics to unravel.
This doctoral thesis investigates the way jobs relate to each other – relatedness – and how it shapes the evolving geography of jobs. Several bodies of literature – Evolutionary Economic Geography (EEG), Labour Economics, Urban Scaling, Innovation Studies, Regional Policy – come together to better understand how relatedness shapes labour dynamics in different spatial contexts.
First, using employment data of six industrialized countries, we find that bigger cities have more than proportionally higher levels of relatedness between jobs, than smaller cities – relatedness self-reinforces as cities grow. Second, relatedness promotes job diversification and prevents exit of job specializations in USA cities – “magnet effects” – in three distinct ways: agglomeration of jobs that are complementary, and/or similar, and/or synergic. Third, the impacts of automation spread through the structure of relatedness – “diffusion effects”. More concretely, being complementary, but not similar, to local high-risk-jobs increases employment grow of a job in a USA city. Finally, policy can explore the potential of relatedness effects to reach desired outcomes. For instance, by stimulating the structure of relatedness around highly specialized jobs, which tend to be denser in innovative sectors. Accordingly, we found EU policy business incentives to innovation to have increased job quality in Portuguese firms.
These findings may help design policy instruments that neutralize the negative effects of automation, while promoting the positive impacts. For instance, identifying which jobs in which cities might be at higher risk, given jobs’ technical feasibility of automation, but also how high-risk-jobs spread automation impacts to other jobs in each city. Workers will be in greater need of social support and training programs especially where their similarities to high-risk-jobs in the city out rule the complementarities. Moreover, place-based policy instruments can target the above-mentioned relatedness effects to foster employment with job quality in lagging regions. I hope this thesis inspires future policy-science collaboration to nurture a future with good jobs for all.
Artificial intelligence (AI) may be the next general purpose technology. General purpose technologies, such as the steam engine and computing, can have an outsized impact on productivity through a positive feedback loop between producing and application industries. Along with the discussion of AI's potential to improve productivity come a number of policy concerns related to AI's potential to automate jobs and to create existential risk for humanity. Because of these worries, in March 2023, a widely circulated petition called for a pause in AI research. That letter asked several questions about AI's potential impact on society. This paper examines those questions through an economic lens. It highlights reasons to be optimistic about the long‐run impact of AI, while underscoring short‐run risks. Economic models provide an understanding of where the ambiguity lies and where it does not. Our models suggest no ambiguity on whether there will be jobs and little ambiguity on long‐term productivity growth if AI diffuses widely. In contrast, there is substantial ambiguity on the implications of AI's diffusion for inequality.
We investigate firm decisions to adopt artificial intelligence (AI) technology and how adoption is sourced: by purchasing commercial readymade software, by developing or customizing solutions in‐house, or both. Using a cross‐sectional data set of 3143 firms from across Europe, we examine the extent to which sourcing strategies exhibit complementarity or substitution. We find that adoption of AI using readymade software as a sourcing strategy is now increasingly commonplace, but differs across industrial sectors. Further, complementarities between sourcing strategies are common across sectors, though with some differences in strength and some exceptions. Our results show that sourcing strategies play an important role in shaping AI adoption decisions among firms.
Research Summary
We investigate how worker mobility influences the adoption of a new technology using state‐level changes to the enforceability of noncompete agreements as an exogenous shock to worker mobility. Using data on over 153,000 establishments from 2010 and 2018, we find that changes that facilitate worker movements are associated with a significant decline in the likelihood of adoption of machine learning. Moreover, we find that the magnitude of decline depends upon the size of the establishment, the extent of predictive analytics adoption in its industry, and the number of large establishments in the same industry‐location. These results are consistent with the view that increases in outward worker mobility increase costs for adoption of a new technology that involves significant downstream investments in the early years of its diffusion.
Managerial Summary
Successful business adoption of new technologies such as machine learning requires skilled workers with experience in implementing those technologies. In the early years of technology diffusion workers in early adopting businesses typically acquire these skills through on‐the‐job learning that is paid for by the adopter. So, if such early adopters face an increased risk of those skilled workers quitting, then their incentives to adopt the technology decrease. We examine this possibility using changes in noncompete enforceability as a proxy for changes in worker mobility and find that the likelihood of adopting machine learning decreases as the risk of worker mobility increases, particularly for larger establishments, establishments in industries where adoption may be more beneficial and in locations with many large competing establishments.
We study the early adoption and diffusion of five artificial intelligence (AI)‐related technologies (automated‐guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI‐related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, was found in every sector of the economy and clustered with emerging technologies, such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more‐educated, more‐experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common in startups displaying indicators of high‐growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth‐oriented business strategies. Early AI adoption was far from evenly distributed: a handful of “superstar” cities and emerging hubs led startups' adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing “AI divide” if early patterns persist.
Analyses of artificial intelligence (AI) adoption focus on its adoption at the individual task level. What has received significantly less attention is how AI adoption is shaped by the fact that organizations are composed of many interacting tasks. AI adoption may, therefore, require system‐wide change, which is both a constraint and an opportunity. We provide the first formal analysis where multiple tasks may be part of an interdependent system. We find that reliance on AI, a prediction tool, increases decision variation, which, in turn, raises challenges if decisions across the organization interact. Reducing inter‐dependencies between decisions softens that impact and can facilitate AI adoption. However, it does this at the expense of synergies. By contrast, when there are mechanisms for inter‐decision coordination, AI adoption is enhanced when there are more inter‐dependencies. Consequently, we show that there are important cases where AI adoption will be enhanced when it can be adopted beyond tasks but as part of a designed organizational system.
Using examples from information technology adoption, we emphasize the role of costs, benefits, communications channels and dynamic considerations in the decision to adopt new technology. We discuss differences between adoption by consumers and adoption by firms. We emphasize the adoption of business process innovations, which alter organizational practices and often involve the post-adoption invention of complementary business processes and adaptations. Within the context of business adoption, we discuss the inherent challenges in identifying the decision maker and the role of competition in influencing the benefits to adoption.
The benefits to data analytics and machine learning have been distributed unevenly across firms around the world. Research on IT productivity points to intangible capital as a key driver of value creation from innovation in computing. We argue that a crucial component of intangible capital is organization-wide technological architecture, which is idiosyncratic and difficult to measure. We use a novel survey instrument to quantify large corporations’ data architecture capabilities by their closeness to “best practices” of frontier digital companies. Using the prevalence of third-party maintenance as a proxy for legacy servers before 2016 and an instrument for data architecture coherence, we find that improving data architecture coherence increases machine learning capabilities. Legacy servers reduce data architecture coherence particularly at corporations with complex software systems, consistent with the hypothesis that costs of digital transformation are greater when workers need to develop more complicated co-invention processes to interact with technical systems.
Software represents a major and fast-growing share of firms’ capital investment, impacting demand for labor and what workers do on their jobs. Using U.S. Census Bureau panel data that link firms and workers, this paper estimates the effect of firm software capital on the earnings of workers by age group. We extend the AKM framework to include job-spell fixed effects that account for potential correlation between the worker–firm match and employee age, as well as including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, capitalized software investment raises worker earnings. However, it does so at a rate that declines after the age of 50, to about zero beyond 65. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and earnings in high-wage firms relative to low-wage firms, thereby widening earnings inequality within and across firms.
This paper aims to examine the pervasive role of coaching in enhancing the income
generation capacity of women. It describes how carefully contextualized training programs are critical in that respect. It ensures that intangible investments and the use of knowledge are becoming essential for women's financial wellbeing and economic prosperity. It investigates in which areas more effective training might be necessary. Best practice experience and lessonsthat work in the local context should be learned
Industrial policy was an object of intense discussions for the three recent decades. Now the information revolution raises new challenges. Spread of Artificial Intelligence, which is a general purpose technology (GPT), increases dramatically the uncertainty of technology development. Ubiquitous global IT platforms like Amazon or Google require achieving balance between their interests, on the one hand, and the interests of local consumers and producers, on the other. New class of peer-to-peer networks arising in the last decade challenge both platform markets and modern corporations. This situation leads to a number of tradeoffs to be solved in modern industrial policy. The paper discusses these tradeoffs by example of the Information Society Strategy and Digitalization Program in Russia and draws consequences influencing industrial policy in general.
Long-term public support may encourage the diffusion of emerging technologies by coordinating the generation of knowledge and providing patient funding, but unexpected policy changes may hinder private investment and even lead to situations of technology lockout. Leveraging archival data; insights from 45 interviews across academia, industry, and government; and 75 hours of participant observations, we develop insights about why institutional instability in Portugal affected the adoption of Polymer Additive Manufacturing (PAM) and Metal Additive Manufacturing (MAM) differently. In both cases, Portugal invested in the technology relatively early. While PAM has been widely adopted, including increasingly in high-tech applications, MAM adoption has been modest despite MAM's potential to greatly improve the performance and competitiveness of metal molds. From the comparison between PAM and MAM, we generate theory about technological and contextual factors that affect ‘technological forgiveness’, defined as the resilience of a new technology's adoption to institutional instability.
This study investigated the mediating role of owner/manager in the adoption of ICTs among Pakistani SMEs whiling using the organizational and technological context of TO -E framework. Data were collected from owners/ managers of registered SMEs in Lahore chamber of commerce and trade (N= 223). The empirical results revealed the owner/manager role mediates the link between firm resources, firms size, complexity, compatibility, and SMEs decision to adopt ICTs. Findings of this study indicate that owner/managers can play a significant role in shaping the SMEs decision towards using ICTs. Furthermore, by taking such a decision, SMEs can survive and compete in a complex and ever-changing business environment.
The digital economy researching scientific seminar was created by the decision of the Academic Council of the faculty of Economics of Moscow State University on December 29, 2017, adopted at the suggestion of Professor V. N. Cherkovets. Professor M. I. Lugachev, head of the Department of Economic Informatics, was appointed scientific Director of the seminar. The moderator of the seminar was the Dean of the Faculty of Economics of MSU, Professor A. A. Auzan. The main objective of the seminar was to form the tasks of the faculty based on the analysis of the content and main trends in the development of the digital economy. 13 meetings of the seminar were held, 107 speakers made speeches, 61 were from the Faculty of Economics and 46 were invited.
No careful empirical research has tested the widely held belief that the cost of switching computer vendors tends to produce technological “lock-in”, meaning that the cost of switching between incompatible vendors is prohibitively expensive. Using several studies by federal agencies into the costs of switching mainframe computer vendors, this article concludes that mainframe computers of the late 1970s possessed many of the features typically associated with lock-in. However, many other factors also attenuated tendencies to lock-in. While lock-in was important for the outcomes of several well-documented instances, it is not clear whether lock-in was important for the outcomes of a wide set of cases
This paper provides new empirical insights into the source of investment adjustment costs. In contrast to prior models of investment that gave only ad hoc treatment to the concept of adjustment costs, my analysis pays serious micro-level attention to investment adjustment processes and their dynamics. I construct a structural dynamic model that predicts the lumpy and episodic patterns of mainframe computer investment in the micro-level data and estimate it using a dynamic programming technique. The model identifies the size of implicit lump-sum adjustment costs from the investment observations. Furthermore, the paper investigates how lump-sum adjustment costs differ across organizations. The empirical results show that investment adjustment costs arise due to the specificity of the complementary assets. Adjustment costs are higher when computer applications are more firm-specific. The estimates show, however, the main costs of this firm-specificity are not the engineering costs of producing in-house application software. Instead, the results point to the difficulty of changing users' work routines and coordinating tasks as the source of lump-sum adjustment costs. The idiosyncrasies in firms' internal transactions and tacit organizational knowledge constrain the ability of engineers to promote common, standardized applications. Hence, they also limit firms' ability to economize the computer development efforts by delegating tasks to external markets. The findings of this paper substantiate the importance of internal organization in understanding the source of investment adjustment costs.
This report compares the performance of different computer systems in solving dense systems of linear equations. The comparison involves approximately one hundred computers, ranging from a CRAY Y-MP to scientific workstations such as the Apollo and Sun to IBM PCs.
How should firms decide whether and when to invest in new capital equipment, additions to their workforce, or the development of new products? Why have traditional economic models of investment failed to explain the behavior of investment spending in the United States and other countries? In this book, Avinash Dixit and Robert Pindyck provide the first detailed exposition of a new theoretical approach to the capital investment decisions of firms, stressing the irreversibility of most investment decisions, and the ongoing uncertainty of the economic environment in which these decisions are made. In so doing, they answer important questions about investment decisions and the behavior of investment spending. This new approach to investment recognizes the option value of waiting for better (but never complete) information. It exploits an analogy with the theory of options in financial markets, which permits a much richer dynamic framework than was possible with the traditional theory of investment. The authors present the new theory in a clear and systematic way, and consolidate, synthesize, and extend the various strands of research that have come out of the theory. Their book shows the importance of the theory for understanding investment behavior of firms; develops the implications of this theory for industry dynamics and for government policy concerning investment; and shows how the theory can be applied to specific industries and to a wide variety of business problems.
A theory of intraorganizational power is discussed and applied to theinformation services department. The paper presents the results of a study ofthe power of five departments in 40 manufacturing plants. Hypotheses about thelevels of power of information processing are not supported by the findings,however the power theory in general does receive support. Reasons for theunexpected results that the information services department is perceived ashaving low levels of power and influence in the organization are discussed. Thepaper suggests several explanations for the findings and possible problems inthe organization. Recommendations to senior management and to the informationservices department are offered.
Despite evidence that information technology (IT) has recently become a productive investment for a large cross-section of firms, a number of questions remain. Some of these issues can be addressed by extending the basic production function approach that was applied in earlier work. Specifically, in this short paper we 1) control for individual firm differences in productivity by employing a "firm effects" specification, 2) consider the more flexible translog specification instead of only the Cobb-Douglas specification, and 3) allow all parameters to vary between various subsectors of the economy.
We find that while "firm effects" may account for as much as half of the productivity benefits imputed to IT in earlier studies, the elasticity of IT remains positive and statistically significant. We also find that the estimates of IT elasticity and marginal product are little-changed when the less restrictive translog production function is employed. Finally, we find only limited evidence of differences in IT's marginal product between manufacturing and services and between the "measurable" and "unmeasurable" sectors of the economy. Surprisingly, we find that the marginal product of IT is at least as high in firms that did not grow during 1988-1992 sample period as it is in firms that grew.
This paper investigates empirically the importance of technological catch-up in explaining productivity growth in a sample of countries since the 1960s. New proxies for a country's absorptive capability--based on data for students studying abroad, telecommunications and publications--are tested in regression models. The results indicate that absorptive capability is a factor in explaining growth, with the most robust finding that countries with relatively high numbers of students studying science or engineering abroad experience faster subsequent growth. However, the paper also indicates that the significance of coefficients varies across specifications and samples, suggesting caution in focusing on individual results. Copyright 2004, Oxford University Press.
According to the advocates of a "Generalized Darwinism" (GD), the three core Darwinian principles of variation, selection and retention (or inheritance) can be used as a general framework for the development of theories explaining evolutionary processes in the socioeconomic domain. Even though these are originally biological terms, GD argues that they can be re-defined in such a way as to abstract from biological particulars. We argue that this approach does not only risk to misguide positive theory development, but that it may also impede the construction of a coherent evolutionary approach to "policy implications". This is shown with respect to the positive, instrumental and normative theories such an approach is supposed to be based upon.
Technological Com-petition and the Structure of the Computer Industry Center for Economic Policy Research. Working PaperThe Competitive Crash in Large-Scale Commercial Comput-ing In The Mosaic of Economic Growth
- Timothy F Bresnahan
- Shane Greenstein
Bresnahan, Timothy F., and Shane Greenstein. 1992. "Technological Com-petition and the Structure of the Computer Industry." Stanford University, Center for Economic Policy Research. Working Paper.. 1996. "The Competitive Crash in Large-Scale Commercial Comput-ing." In The Mosaic of Economic Growth, edited by Ralph Landau, Timothy Taylor, and Gavin Wright, pp. 357-97. Stanford University Press. Bresnahan, Timothy F., Shane Greenstein, and Harumi Ito. 1996. "The Sources and Effects of Investment Irreversibility: Large-Scale Computing." Mimeo. Stanford University.
The First Fifty Years of the Computer Industry
- Alfred P Chandler
Chandler, Alfred P. 1997. "The First Fifty Years of the Computer Industry." In Competing in the Age of Digital Convergence, edited by David B. Yoffie. Harvard Business School Press.
Computer Systems Development: History, Organization, and Implementation. Wiley. This content downloaded from 152.3.102.242 on Sun, 14 Jul 2013 15:23:41 PM All use subject to JSTOR Terms and Conditions Gordon The Measurement of Durable Goods Prices
- Andrew L Friedman
- Dominic S Cornford
Friedman, Andrew L., with Dominic S. Cornford. 1989. Computer Systems Development: History, Organization, and Implementation. Wiley. This content downloaded from 152.3.102.242 on Sun, 14 Jul 2013 15:23:41 PM All use subject to JSTOR Terms and Conditions Gordon, Robert J. 1990. The Measurement of Durable Goods Prices. Univer-sity of Chicago Press.
Downsizing Is Ready for Prime Time Midrange Sys-temsThe Aroma Is Appetizing . . . But the Client/Server Main Course Is Still Simmering
- Kador
- John
Kador, John. 1992. "Downsizing Is Ready for Prime Time Midrange Sys-tems." Computerworld, May 12, 1992. Keefe, Patricia. 1990. "The Aroma Is Appetizing... But the Client/Server Main Course Is Still Simmering." Computerworld, January 1, 1990, pp. 35-37.
Size Is Beside the Point: All that Really Matters Is Fit
- Radding
- Alan
Radding, Alan. 1989. "Size Is Beside the Point: All that Really Matters Is Fit." Computerworld, June 12, 1989, pp. 69-71.