-Reviewing the overall development needs and investment priorities of the region -Preparation of statutory Regional Planning Guidelines (RPGS) and reviewing
opportunities identified in the Regional Competitiveness Agenda reports developed by Forfã¡s for each NUTS III region
multiethnic society and a knowledge-based economy -Develop programmes of ongoing and long-term support for activities that are
respect to certain services and that these services cannot be provided in a way that does not require indefinite, ongoing support
merely focus on specific investment programmes alone 4. Implementation/Administrative Requirements Most responsibility for successful implementation of the RDP 2014-2020 will rest
and duplication (even interagency competition) of measure implementation. There should be proper liasion/consultation with local bodies, such as Local authorities
County Enterprise Boards (to be known in future as Local Enterprise Offices) and others In terms of the regional role in this implementation process, the following points are
The Regional Authority, would welcome the opportunity, and is available, to meet with you to further discuss its submission and, in particular, the role that the regional
Economist editor: Big data is a goldmine for companies...p. 6 Boosting e-skills in European higher education
opportunities. The project, which will deliver short †hands on†courses in core computer science for pupils aged 14-15
on fostering entrepreneurship and coding skills among European youth by setting up a coding cup across eight countries in
a six-month-long competition, rewarding national finalists with a camp to enthuse teachers and students to engage with
opportunities for youth, while helping Europe to reap the benefits of the booming digital economy, †Microsoft†s senior director
competition between actors involved on pledges â€oeit†s good that a space has been created that allows people to highlight and showcase
for a better match of demand and supply in EU countries. A cap of a European
explaining that the opportunities in ICT are attractive, go beyond one sector and include jobs within for example the music
the economic downturn, as ICT is widely adopted in all corners of society. Experts believe a new wave of big data and
ever-changing business environment. Last year, the European commission said the EU would lack 700,000 workers in the ICT
â 63 billion to the economy, with a positive impact on youth unemployment â€oethe real issue is that there are going
economy as a whole â€oewe need more highly specialised computer engineers. The ICT sector currently lacks people with the right skills
-end support services such as systems and network administration and user support, †Schaart told Euractiv
exciting job opportunities the industry is providing, especially since fewer people study computer science While the industry still has an
loss for the European economy and for millions of customers who could benefit from ideas contributed by talented women
Continued from Page 2 Photo: Andresr/Shutterstock 4 5-9 may 2014 SPECIAL REPORT ESKILLS FOR GROWTH Euractiv
entrepreneurship is an important part of this effort The letter stated that tech entrepreneurship will power the economic
recovery. The app economy workforce is predicted to triple its revenues from â 17.5 billion to â 63 billion from 2013 to 2018
That translates to 4. 8 million jobs by 2018, according to the EU executive The strategy also aims to â€oedigitiseâ€
entrepreneurship â€oewe support entrepreneurship but we also have to create conditions of the wealth of all of society in general.
We can†t boil the whole ocean, †he said Finance and training Start-up companies can†t begin
without investment. Access to finance for business is another policy area that requires serious attention.
be employed by digital ventures and those who have the skills to actually do those jobs
European entrepreneurship Startup Europe introduced a commission one-stop website for entrepreneurs. Other forums include the
Web Investors Forum, a crowdfunding network and Tech Allstars group run by DG Connect The conference was chosen to
Technology has provided entrepreneurship training for more than 1, 000 students and contributed to the creation of more than
enhancing digital entrepreneurship in Europe, and picked John Higgins, director general of trade association Digitaleurope
Digital entrepreneurship cuts across a huge amount of policy areas and national and European responsibilities.
entrepreneurship. It†s a driving force that will be needed to overcome the obstacles that exist at national and European level
Economist editor: Big data is a goldmine for companies Computer algorithms are better at diagnosing severe cancer than
Economist and co-author with Viktor Mayer -Schã nberger of Big data: A Revolution That Will Transform How We Live Work and
information and communication technologies, according to a report by the Organisation for Economic Cooperation and Development
of open learning environments, open education theories, new business models open education computational tools, and new and emerging technologies in the
educational technologies marketplace, †the Commission said at the launch of the event The platform involves all Slovenian
of Hellenic ICT Enterprises (SEPE) told Euractiv Greece in an interview that Europe will need 900,000 skilled ICT workers
outsourcing services and service-level agreement (SLA), create collaboration with Southern Europe, in order to ensure a
competitive Europe with innovative services and products, †the SEPE head said, adding that the e-skills conference tries to address
Economy is our commitment. We mean it when we say that the digital literacy is on the
economy, despite the fact that in the future â€oe90%of jobs will require at least some basic digital skills. †â€oein Europe, 25%of adults do not
â 800 billion in the European economy. The data mentioned is catalytic and shows us
Digital Business models: Review and Synthesis 2. 1 Origins of Business models While technological disruptions are changing the competitive landscape, their full
impact on business structures, processes, and innovativeness are understood less and vary significantly across companies in the same industry, and may ironically
multiple definitions of business models exist, which pose significant challenges for understanding essential components In general, there is no accepted definition of the term ††business model††(Shafer
expression business model can be traced back to the writings of Peter Drucker Ramon et al. 2009), the concept had gained prominence only in the last decade or
While business model has been part of the business jargon for a long time, it has been argued that the focus initially involved a scientific analysis of firms has been on industry, and
) Others, in fact, some have argued that the concept of a business model, is relatively new, dating back to only the early 1980s.
Kalantari 2010) particularly in economic theory (Teese 2010 The plethora of definitions poses significant challenges for understanding the
essential components of a business model. They also lead to confusion in termi -nology as ††business model, strategy, business concept, revenue model and
economic model are used often interchangeably†(and moreover) the business model has been referred to as architecture, design, pattern, plan, method
For example some definitions of business models a. Baden-Fuller et al. when they define business models ††the logic of the firm, the
way it operates and how it creates value for its stakeholders (2000 b. Timmers defines the business model as architecture for product, service and
information flows, including a description of the various business actors and their roles; and a description of the potential benefits for various business
actors; and a description of the sources of revenue (Timmers 2000 c. Mahadevan defines a business as is a unique blend of three streams that are
d. Johnson et al. define ††Business model consists of four interlocking elements that, taken together create
profit formula†key resources†key processes††e. Ostenwalder et al. define ††A business model is a conceptual tool containing a
set of objects, concepts and their relationships with the objective to express the business logic of a specific firm.
value is provided to customers, how this is done and with which financial consequences (Ostenwalder et al. 2010
It also outlines the architecture of revenues, costs, profits associated with the business enterprise delivering value††(Teese 2010
g. Demil and Lecocq, define ††business model as, the description of the articula -tion between different business model components or building blocks to pro
-duce a proposition that can generate value for consumers and thus for the organization††(Demil and Lecocq 2010
h. Sorescu et al. define ††a business model is specified a well system of interde -pendent structures, activities,
logic for value creation (for its customers) and value appropriation (for itself and its partners) ††(Sorescu et al. 2011
In addition, the concept of business models can be seen as having progressed in 5 stages as shown in Fig. 2. 1 (Gordijn et al. 2005.
term business model started to become prominent, a number of authors suggested business model definitions and classifications.
Then, during the second phase authors started to complete the definitions by proposing what elements belong into
a business models. Initially, these propositions were simple shopping lists, just mentioning the components of a business model.
Only in a third phase followed detailed descriptions of these components (Hamel 2000; Weill and Vitale 2001
-ponents conceptually culminating in business model ontologies. In this phase models also started to be evaluated more rigorously or tested.
14 2 Digital Business models: Review and Synthesis We assert that in the sixth phase, the focus is now on theory building and dynamic
A business model is a representation of the strategic choices that characterize a business venture. These choices are made either intentionally or by default, so the
contribution of a business model is to make them explicit (Morris et al. 2005 Thus, the business model can be seen as a communication or a planning tool.
It allows entrepreneurs, investors, and partners to examine strategic choices for internal consistency, to surface the assumptions of the business plan, and to
understand the vision toward which the business is being built. Business model development may be part of new venture planning,
but is often just as useful in sense making around a going concern, or when new opportunities and threats
indicate a need for reinvention (Johnson et al. HBR 2008 Furthermore, although properly formed business models are very useful and can
be a strategic tool for a firm, many business models however suffer from 4 common problems (Shafer et al. 2005), namely
•Flawed or untested assumptions underlying the key premises of a firm†s busi -ness plan;
these resolve around untested assumptions about future conditions, or implicit or explicit cause-and effect-relationships that are founded not well or
logical •Limitations in the strategic choices considered; addressing and developing the business logic in only one ††component††of the business model, and making
untested assumptions about the others •Misunderstanding about value creation and value capture; the inability of
thus negatively affect the ††revenue generation††aspects of business models •Flawed assumptions about the value network;
Fig. 2. 1 Progression of business model studies 2. 1 Origins of Business models 15 Table 2. 1 summarizes some of the attempts to capture the concept of business
models over the last two decades or so. The number of components proposed in each model ranges from 3 to 9. In general, three general categories of definitions
economic approach focuses on how a firm can make a profit and key variables from this approach include revenue sources, pricing methodologies, cost struc
boundaries, and growth opportunities. This approach espouses the totality of how a firm selects its customers,
defines and differentiates its offerings, creates utility for its customers, define the tasks it will perform
or outsource, configures its resources and ultimately captures profits (Slywotzky 1996. Decision variables focus on stakeholder identification, value creation, visions, values and networks and
alliances 2. 2 Why Digital Business models The role of information technology and its relationship to the business has shifted
over the last 20 years. We have progressively transitioned from a focus on the design of information systems, to the design of IT-enabled business processes, and
more recently to the design of business models for services provided through digital platforms (Fig. 2. 2). While this attention to business models for digital
platforms initially started in the networked digital industry (telecom, media entertainment, gaming. software, etc. it is increasingly being propagated to all
industries whether healthcare, energy, retail, or financial services. As more cus -tomers consume products and services offered through digital platforms, the
managerial stakes in understanding those models is becoming much higher especially when these products and services have to be offered to
and priced for consumers. A review of Table 2. 1 also illustrates that most of the espoused busi
technology-enabled and digitally interconnected environment characterized by new affordances, structures, and rules (El Sawy et al. 1999).
16 2 Digital Business models: Review and Synthesis Table 2. 1 Comparison of business model approaches Source Components Number of
components Eco -system Digital platform Horowitz (1996) Price, product, distribution organizational characteristics and technology 5 No Some
units, services and linkages 5 No No Timmers (1998) Product/service/information flow architecture, business actors
stakeholder network, value interfaces, value ports and value exchanges 8 No No Linder and Cantrell
core technology investments and bottom line 4 No Some Hamel (2001) Core strategy, strategic resources value network and customer
2. 2 Why Digital Business models 17 Table 2. 1 (continued Source Components Number of components
Bertz (2002) Resources, sales, profits and capital 4 No No Hedman and Kalling 2003 Value network, resources
customers, value chain financial flow, goods and services, societal environment 7 No No Osterwalder and
Pignuer (2009 Customer segments, value propositions, channels customer relationships, revenue streams, key resources, key activities, key partnerships
18 2 Digital Business models: Review and Synthesis into one fabric†it no longer makes sense to talk about information technology as
a tool or environment that is kept at arm†s length from business activities (El Sawy 2003).
) To theorize about new business models and by adding a few ††digital††features to the theory would lead to
We realize that a theory of digital business models and digital service must integrate the distinct attributes of digital business ecosystems from the get-go
Responding to the velocity and turbulence of the environment, and taking advantages of the affordances of digital technology, firms and groups of
technologies and the delivery of services (Gawer and Cusumano 2008. Platforms Table 2. 1 (continued
Environment IT as Fabric Fig. 2. 2 Changing role of technology in business 2. 2 Why Digital Business models 19
are standards or architectures that allow modular substitution of complementary assets (West 2003. Taking advantage of the digital affordance of modularity
As business models have become more digital, firm capabilities themselves have become more modular, more easily
interfaces (APIS) and broadband fiber optics, we can ††mash up††digital services like Google†s maps and Facebook†s social newsfeed in no time and on a shoestring
20 2 Digital Business models: Review and Synthesis http://www. springer. com/978-3-642-31764-4
2 Digital Business models: Review and Synthesis 2. 1†Origins of Business models 2. 2†Why Digital Business models
2. 3†New Architectures
Understanding Digital Technology†s Evolution and the Path of Measured Productivity Growth Present and Future in the Mirror of the Past
by Paul A. David Stanford university & All Souls College, Oxford First draft: 20,may 1999 Second draft:
<paul. david@economics. ox. ac. uk >Understanding the Digital economy's Evolution and the Path of
1. The Computer Revolution, the"Productivity Paradox"and the Economists Over the past forty years, computers have evolved from a specialized and limited role in the information
processing and communication processes of modern organizations to become a general purpose tool that can be
telecommunication equipment linking organizations to their suppliers and customers. In the process, computers and networks of computers have become an integral part of the research and design operations of most
enterprises and, increasingly, an essential tool supporting control and decision-making at both middle and top
and, ultimately, prospects for economic growth national security and the quality of life. Not since the opening of â€oethe atomic age, †with its promises of power
performance of the economy at large crystallized around the perception that the U s.,along with other advanced
industrial economies, was confronted with a disturbing"productivity paradox.""The precipitating event in the formation of this"problematic"view of the digital information technology was an offhand (yet nonetheless
pithy) remark made in the summer of 1987 by Robert Solow, Institute Professor at MIT and Economics Nobel
and the divergence of opinion that emerged within the economics profession on this question has persisted,
new economy"or"new paradigm"of macroeconomic behavior It should not be surprising, therefore, that shifting understandings about the nature of the information
subscribed publicly to a strongly optimistic reading of the American economy's prospects for sustaining rapid
economic transformation in which the U s. economy is taking the lead: 2 â€oewe are living through one of those rare, perhaps once-in-a-century events...
and satellite technologies have changed fundamentally the structure of the American economy. †Yet, many economists continue to demur from this view,
and there has been no lack of skepticism regarding the potential of the new information and communications technologies to deliver a sustained surge of
Among academic economists the consensus of optimistic opinion now holds a wait -and-see attitude,
the U s. economy well into the 1990's. The figures relating to the private non-farm business economy are
because the deflation of the current value of output has been carried out by using price indexes that re-weight the component goods and
services'prices in accord with the changing composition of the aggregate. 4 These â€oechain-weighted†output
4 Moving from the private domestic economy to the private business economy concept also eliminates the distortions in the
series, and in the estimated flow of capital input services 3 The first point is that the productivity growth rate†s deviation below the trend that had prevailed during
economic growth. Indeed, most of the labor productivity growth during the interval extending from the 1830's
through the 1880's was accounted for by the increasing capital-labor input ratio, leaving residual rates of total
were biased strongly in the direction of tangible-capital using, involving the substitution of new forms of
and the substitution of increasing volumes of the services of reproducible tangible capital for those of other inputs, dispensing increasingly with the sweat and the old craft skills of workers in the fields and
the rise of computer capital, and OCAM (office, computing and accounting machinery) capital more generally
has been contributing to economic growth in the closing quarter of the twentieth century, except for the fact that
this particular category of capital equipment only recently has begun to bulk large in the economy†s total stock
of reproducible capital. Indeed, Daniel Sichel (1997) recently proposed a â€oeresolution†of the productivity paradox is just such terms,
accounted for by the increasing capital-labor input ratio, leaving residual rates of total factor productivity growth that were
moreover, assume that investments embodying information technology earn only a normal private rate of return and do not yield significantly higher â€oesocial rates of return†due to externalities and other spill
perplexing conjuncture with the wave of ICT-embodying investments in the U s. economy thus have continued
2) there has been a vast overselling of the productivity-enhancing potential of investments in computers and related information equipment and software--due in part to misplaced technological enthusiasm
assets, and much new investment being allocated to ventures that remain experimental, and adaptive in nature
persistence of the slow trend rates of TFP growth experienced in the U s. economy during the past two decades
and business models; typically they are marked also by the accelerated rate of appearance of new goods and
and measure the performance of the economy which is undergoing significant and unprecedented structural changes.
are identified above, it seems to have been rather misleading for economists to have approached these as though
which a national income accounting system devised to deal with ordinary goods and services is able to cope with
the shift towards integrating such commodities with the services of information. These considerations suggest the possibility that the past two decades have been marked by a more pronounced bias towards the
underestimation of the growth of aggregate real output and, consequently, of measured productivity In section 4 the discussion takes up some of the technological realities that justly can be said to underlie
measuring the heterogeneous bundles of labor and capital services, but attention is being directed mainly to the
There are some industries, especially services, for which the concept of a unit of output itself is defined not well,
The introduction of new commodities again raises the problem of comparability in forming the price deflators for an industry
2. 1 Over-deflation of output: will this account for the productivity slowdown That there is a tendency for official price indexes to overstate the true rate of inflation (understate the
which there seems to be broad agreement among economists. The magnitude of the bias, however, is another question.
the private domestic economy over the period 1966-89.10 Were we to allow for this by making an upward
procedural sources of overstatement--the methods of"splicing in"price series for new goods and services.
whether structural changes in the U s. economy have exacerbated the problem of output underestimation, and thereby contributed to the appearance of a productivity
and employment in the"hard-to-measure"sectors of the economy is immediately pertinent. The bloc of the U s
private domestic economy comprising Construction, Trade, Finance, Insurance, and Real estate (FIRE), and miscellaneous other services has indeed been growing in relative importance,
and this trend in recent decades has been pronounced especially. 13 There is certainly a gap in the manhours productivity growth rates favoring
the better-measured, commodity-producing sections, but the impact of the economy†s structural drift towards
growth were measured properly for the rest of the economy. Taking account of the increasing relative weight of
the hard-to-measure sector in the value of current gross product for the private domestic economy, the implied
measurement bias for the whole economy--under the conditions assumed--must have become more 13 Gross product originating in Griliches†â€oehard-to-measure†bloc average 49.6 percent of the total over the years 1947
due to underestimation of output quality improvements associated with new goods and services. The problem arises from the practice of waiting the â€oechain in†new products until they have acquired a substantial share of the
market for the class of commodities for which they can be regarded as substitutes. During that period, however
The difficulties created for price index statisticians by the turnover of the commodity basket due to the
--namely, the higher rate of appearance of new goods in the basket of commodities available to consumers--is
central theme in the business and economics literature on â€oemodern manufacturing†at least since the 1980's. 15
been lowered by application of information and communication technologies, and by the adoption of marketing 18 Jack Triplett (1999), p. 14, correctly points out that â€oea greater number of new things is not necessarily a greater rate of
the result of marketing innovation and the use of information and communication technologies Of course, some progress has been made in resolving the computer productivity paradox by virtue of the
token, the hedonic deflation of investment expenditures on computer equipment contributes to raising the measured growth of the computer capital services,
which are used intensively as inputs in a number of sectors including banking, financial services and wholesale trade within the service sector.
Beyond the technical problems of the way that the national income accountants are coping with accelerating product innovation and quality change lie several deeper conceptual issues.
3. 1 Micro-level evidence on payoffs from IT investment--the â€oeexcess†returns puzzle The first involves the surprising appearance of â€oeexcess rates of return on computer capital. †These
appeared when economists sought to illuminate the macro-level puzzle through statistic studies of the impact of
the economy, which emerges starkly from the growth accounting studies by Stiroh (1998), may be in some part an artifact
IT at the microeconomic level, using observations on individual enterprise performance. 21 This phenomenon points to the conceptual gap between task productivity measures, on the one hand,
efficiency of the economy by calculating TFP as the ratio of aggregate real output to the aggregate inputs of
labor and capital services; whereas, in undertaking comparisons among organizational departments and firms engaged in quite different production activities,
computer investments, and the weaker (time series) effects gauged in terms of task productivity, might indicate simply that very high gross private rates of return are associated with such capital expenditures.
additional intangible investments that were correlatives of high information technology-intensity. Much of the evidence for this is reasonably direct,
capital of other kinds. 22 But there also is some indirect support from the relationship between the reproduction
increasingly widespread as digital information technologies diffuse throughout the economy, deserves further consideration 3. 2 Leaving out investments in organizational change:
the narrow scope of the NIPA How should the investments made by organizations and individuals in learning to utilize a new
technology be treated for national income accounting purposes? The factor payment side of the official National Income and Product Accounts (NIPA) include the expenditures that this may entail--for labor time and the use
of facilities, but the intangible assets formed in the process do not appear on the output side, among the final
goods and services produced. This definition of the scope of GNP and GDP is not problematic so long as the
and non-market investments in learning remains more or less unchanged But that has not been the case
in the national income and product accounts. This carries a potential for the conventional statistical indicators to
intangible investments in the retraining of workers and the reorganization of business operations that, as as been
and communication technology industries. For software designers, Moore's law promises that new computational resources will continue to grow
while others are part of the learning investments being made by firms in formal and informal â€oeon the job†knowledge acquisition about information technology
and communication technologies has complicated enormously the task of maintaining coherence and functionality within the organization.
the new business processes arising from the use of information and communication technologies. Without attention to these issues, it is not surprising that they may often follow a version of Parkinson's law (â€oework
communication resources if their costs were recognized fully. 25 Was this state of affairs a necessary, inescapable burden imposed by the very nature of the new
and communication technologies are likely to be worthy of the same amount of analysis that is devoted to approval paths, logistics,
ever-growing array of application demands. During the same period, efforts to down-scale mainframe computers to allow their use for specialized control
enterprises, and service companies; it specialized instead in hardware production, leaving the software market to
to meet the rapidly rising demands for new, specialized applications software. Moreover, personal computers could use
because the widespread adoption of the new technology raised the demand for compatible printers, the dedicated word processors found themselves unprotected by any persisting special advantages in
"look and feel"of information communication, its quality and style, the capability for an individual to express
ideas, and the quantity of such communications. But singly and severally they made very little progress in
and new demands for"user support"to make the general purpose technology deliver its potential
and variety of services offered internally within the company; and externally to customers who would, through the intermediation of personnel with appropriate
information system access, receive an array of service quality improvements. Arguably, many of these improvements are part of the productivity measurement problem,
29in the medium and large enterprises of 1990, what remained was a deep chasm between the"mission critical"application
application environments was the widespread use of the IBM 3270, the DEC VT-100 and other standards for"intelligent
the real value expenditures on final goods and services There is a more evident"down-side"of the process by
Worse still, by the mid-1990's, the competition among packaged software vendors for extending the
Bresnahan and Trajtenberg (1995), of â€oegeneral purpose technologies†that transform an economy by finding many new lines of application,
the economy. While the positive, long-run growth igniting ramifications of a fundamental technological breakthrough of that kind are stressed in the formalization of this vision by the new growth theory literature, the
can result in long delays in the acceleration of productivity growth in the economy at large.
superior goods and services, and so yield welfare gains that escape being reflected properly in the measured
output, and productivity indexes of the economy. As this theme already has been aired well (in sections 2 and 3
Recent estimates of the growth of computer stocks and the flow of services therefrom are consistent with
the U s. economy could be said 33 See e g.,, chapters by Helpman and Trajtenberg (1998), Aghion and Howitt (1998),
OCAM) were providing only 0. 56 percent and 1. 5 percent, respectively, of the total flow of real services from
â€oecomputerization†that had been achieved in the whole economy by the late 1980's was roughly comparable with
ratio of computer equipment services to all producers†durable equipment services in the U s Does the parallel carry over also,
The index of the computerization of capital services that has been derived here from the work of
growth of the ratio of computer equipment services to all producers'durable equipment services in the U s. turns
Some economists who have voiced skepticism about the ability of computer capital formation to make a substantial contribution to raising output growth in the economy point to the rapid
technological obsolescence in this kind of producer durables equipment, and argue that the consequent high depreciation rate prevents the stock from growing rapidly in relationship to the overall stock of producer capital in the economy.
The latter argument would be relevant were one focussing on the impact on real net output,
computer services for 1987-1993 to have been-7. 9 percent per annum, and compares that to-7. 0 percent per
electricity and quality adjusted computer services hardly warrants dismissing the relevance of seeking some insights into the dynamics of the transition to new general purpose technology by looking back at the dynamo
computer services--and, by implication, the comparison just reported--may be misleading. He contends that the hedonic price indexes for computers that come bundled with software actually would have fallen faster than
the (unbundled) price-performance ratios that have been used as deflators for investment in computer hardware If so, Sichel†s (1997) price indexes of quality adjusted â€oecomputer services†(from hardware and software) would
Furthermore, in the same vein it may be noticed that the slower rate of fall in computer services prices as
putative) discrepancy between the rate at which prices associated with electricity and computer services. Such
surge of readily discernable productivity growth throughout the economy. To say that, however, is not at all the
and transforms the organization of production in many branches of an economy. One cannot simply infer the
opportunities, enterprises and other institutions are forced to reexamine workflow and develop new methods for information system design
computer and mainframe environment by developing the intermediate solution of client-server data processing systems. This development is still very much in progress
networked environment, the re-configuration of work organization becomes a central issue; strategic and practical issues surrounding the ownership
this added capital to an already highly-capital-using industrial power technology, without instigating any reorganization of factory layout and routines for materials handling.
that the major capital-saving contributions to multi-factor productivity growth from thoroughgoing factory redesign could be realized
economy to enjoy a pervasive quickening in the pace of conventionally measured multifactor productivity improvements, alongside the continuing proliferation of new branches of industry offering novel and
qualitatively enhanced goods and services 40 See Gibbs (1997), and especially Norman (1998), Ch. 11
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