Chapter 2 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,
A primary reason for such a seemingly‘‘random process''is the lack of a generally accepted definition of the term‘‘business model''within
In fact, 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 et al. 2005;
Ho et al. 2010; Muller et al. 2011. Although, the origins of the 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 two. Many have observed that the term‘‘business model''became widely adopted by practitioners during the dotcom revolution of the 1990s.
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 resources, as shown by the works of Porter (1980) and Wernerfeld (Hoyer et al. 2009).
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 terminology 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, assumption and statement''(Morris et al. 2005).
O. A. El Sawy and F. Pereira, Business Modelling in the Dynamic Digital Space, Springerbriefs in Digital Spaces, DOI:
10.1007/978-3-642-31765-1 2, The Author (s) 2013 13 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;
d. Johnson et al. define‘‘Business model consists of four interlocking elements that, taken together create and deliver value customer value proposition 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.
and representation of what value is provided to customers, how this is done and with which financial consequences (Ostenwalder et al. 2010).
and delivers value to customers. 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 articulation between different business model components or building blocks to produce 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 interdependent structures, activities, and processes that serves as a firm's organizing 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.
In the initial phase, when the 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; Afuah and Tucci 2003. In a fourth phase researchers started to model the components conceptually culminating in business model ontologies.
In this phase models also started to be evaluated more rigorously or tested. Finally, in the fifth phase, the reference models are being applied in management
and IS applications. 14 2 Digital Business models: Review and Synthesis We assert that in the sixth phase,
the focus is now on theory building and dynamic modeling. 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 business 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 organizations to financially capitalize on the‘‘value''they create,
which may thus negatively affect the‘‘revenue generation''aspects of business models. Flawed assumptions about the value network;
assumptions that the current value created through the network would continue unchanged into the future
Define and Classify Business models List Business model components Describe Business model Elements Business model Ontology Apply Business model concept Dynamic Business Modeling for the Future Definitions & taxonomies Shopping List of components Components
Cigref 2011 AUTHORS OUTCOME ACTIVITY 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 based on their emphasis, namely economic, operational and strategic,
The economic approach focuses on how a firm can make a profit and key variables from this approach include revenue sources, pricing methodologies, cost structures, margins and expected volumes.
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 customers 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 business models do not consider explicitly the effects of digital platforms specifically.
Thus, digital business ecosystems are new and different. Companies operate in a technology-enabled and digitally interconnected environment characterized by new affordances,
structures, and rules (El Sawy et al. 1999. The information systems discipline has explored and explicated many of these differences.
One of its most important conclusions is that technology and business are fused effectively 16 2 Digital Business models:
Review and Synthesis Table 2. 1 Comparison of business model approaches Source Components Number of components Ecosystem Digital platform Horowitz (1996) Price, product, distribution
, organizational characteristics and technology 5 No Some Viscio and Pasternak (1996) Global core, governance, business units, services and linkages 5 No No Timmers (1998) Product/service/information
flow architecture, business actors and roles, actor benefits, revenue sources, and marketing strategy 5 No Some Markides (1999) Product innovation, customer relationship, infrastructure management,
2001) Actors, market segments, value offering, value activity, stakeholder network, value interfaces, value ports and value exchanges 8 No No Linder and Cantrell (2001) Pricing model,
value network and competitive strategy 6 No No Gartner (2003) Market offerings, competencies, core technology investments,
and market model 7 No No (continued) 2. 2 Why Digital Business models 17 Table 2. 1 (continued) Source Components Number of components Ecosystem Digital platform Dubosson
and financial model 4 No No Bertz (2002) Resources, sales, profits and capital 4 No No Hedman and Kalling (2003) Value network, resources, capabilities, revenue and pricing, competitors
, customers, value chain, financial flow, goods and services, societal environment 7 No No Osterwalder and Pignuer (2009) Customer segments, value propositions, channels, customer relationships
cost structures 9 Some No (continued) 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 what we call the‘‘horseless carriage''fallacy.
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 (Yoo et al. 2010).
Responding to the velocity and turbulence of the environment, and taking advantages of the affordances of digital technology, firms and groups of firms have been prolific in establishing digital platforms for the combination of technologies and the delivery of services (Gawer and Cusumano 2008).
Platforms Table 2. 1 (continued) Source Components Number of components Ecosystem Digital platform Al-Debei and Avison (2010) Value proposition, value architecture
-IT IT IT as Tool IT as Environment IT as Fabric Fig. 2. 2 Changing role of technology in business 2. 2 Why Digital Business models 19 are standards
As business models have become more digital, firm capabilities themselves have become more modular, more easily connectable,
we can‘‘mash up''digital services like Google's maps and Facebook's social newsfeed in no time and on a shoestring budget.
and capture value (Schlagwein and Schoder 2011). 20 2 Digital Business models: Review and Synthesis http://www. springer. com/978-3-642-31764-4
<paul. david@economics. ox. ac. uk>Understanding the Digital economy's Evolution and the Path of Measured Productivity Growth:
Present and Future in the Mirror of the Past 1 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 found in use virtually everywhere,
factory workers and shipping clerks, often side by side with the 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 management levels.
and, ultimately, prospects for economic growth, national security and the quality of life. Not since the opening of the atomic age, with its promises of power too cheap to meter and threats of nuclear incineration, has a technology so deeply captured the imagination of the public.
and the productivity 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 laureate:"
"You can see the computer age everywhere but in the productivity statistics.""1 Almost overnight this contrasting juxtaposition achieved the status of being treated as the leading economic puzzle of the late twentieth century,
and communications technologies have given rise during the latter 1990's to"a new economy"or"new paradigm"of macroeconomic behavior.
Alan Greenspan, subscribed publicly to a strongly optimistic reading of the American economy's prospects for sustaining rapid expansion
which the U s. economy is taking the lead: 2 We are living through one of those rare, perhaps once-in-a-century events...
as a consequence, the emergence of modern computer, telecommunication 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
Among academic economists the consensus of optimistic opinion now holds a wait -and-see attitude,
or cyclical movements that, in any case, have yet to materially reverse the profound"slowdown"in the economy's productivity growth trend
so that it brought the TFP growth rate all the way back down to the very low historical levels indicated by Abramovitz and David's (1999) statistical reconstructions of the mid-nineteenth century American economy's performance.
May 6, 1998) that enable us to follow the path of measured productivity gains in the U s. economy well into the 1990's. The figures relating to the private non-farm business economy are regraded generally as providing a more accurate picture of recent movements,
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 chain-weighted output measures lead to productivity growth estimates that reveal two notable points about the slowdown. 2 Testimony of Alan Greenspan
only slightly above the 1890-1929 rate. 4 Moving from the private domestic economy to the private business economy concept also eliminates the distortions in the picture of productivity that arise from the inclusion of the imputed gross rents on the stock of dwellings
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 the 1950-1972 golden age of post-WORLD WAR II growth became even more pronounced during the late 1980's and early 1990's,
and slow measured growth of total factor productivity is not a wholly new, anomalous phenomenon in the history of U s. 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
During the nineteenth century the emergence of technological changes that were biased strongly in the direction of tangible-capital using,
and the substitution of increasing volumes of the services of reproducible tangible capital for those of other inputs,
It could be maintained that there is little that is really novel or surprising in the way in which the rise of computer capital,
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.
Thus, most of the labor productivity growth rate was accounted for by the increasing capital-labor input ratio,
moreover, assume that investments embodying information technology earn only a normal private rate of return and do not yield significantly higher social rates of return due to externalities and other spillover effects.
and the private rates of return on this the new information technology and all of its networked applications. 4 Economists'reactions to questions concerning the anomalous slowdown of TFP growth
and its perplexing conjuncture with the wave of ICT-embodying investments in the U s. economy
1) the productivity slowdown is an artifact of inadequate statistical measurement of the economy's true performance,
or (2) there has been a vast overselling of the productivity-enhancing potential of investments in computers
and much new investment being allocated to ventures that remain experimental, and adaptive in nature--more akin to learning than the implementation of chosen routines.
along with me in the cautious optimist camp share the view that the persistence of the slow trend rates of TFP growth experienced in the U s. economy during the past two decades is unlikely.
however, involve not only the obsolescence of skills, capital assets and business models; typically they are marked also by the accelerated rate of appearance of new goods and products.
and measure the performance of the economy which is undergoing significant and unprecedented structural changes.
it seems to have been rather misleading for economists to have approached these as though they were necessarily independent, mutually incompatible and hence competing explanatory hypotheses.
by briefly considering the implications of the limited way in 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.
in measuring the heterogeneous bundles of labor and capital services, but attention is being directed mainly to the problems that are suspected to persist in the measures of real product growth.
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
but, before tackling less tractable conceptual questions we should briefly review their bearing on the puzzle of the slowdown and the computer productivity paradox. 2. 1 Over-deflation of output:
which there seems to be broad agreement among economists. The magnitude of the bias, however, is another question.
This might well be twice the magnitude of the error introduced by mismeasurement of the price deflators applied in estimating the real gross output of the private domestic economy over the period 1966-89.10 Were we to allow for this by making an upward correction of the real output growth rate by as much as 0. 6
who have pointed out that the published CPI reflects corrections that already are made regularly to counteract some of the most prominent among the suspected 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 slowdown.
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 unmeasurability is not big enough to account for the appearance of a productivity slowdown between the preand post-1969 periods.
The simple re-weighting of the trend growth rates lowers the aggregate labor productivity growth rate by 0. 13 percentage points between 1947-1969 and 1969-1990,
but that represents less than 12 percent of the actual slowdown that Griliches was seeking to explain. 14 A somewhat different illustrative calculation supporting the same conclusion has been carried out by Abramovitz and David (1999).
whereas prices and real output 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'hard-to-measure bloc average 49.6 percent of the total over the years 1947-1969,
but its average share was 59.7 percent in the years 1969-1990. See Griliches (1995:
and productivity growth are being systematically mismeasured has directed hitherto not sufficient attention to the possibility that there has been a growing bias due to underestimation of output quality improvements associated with new goods and services.
The problem arises from the practice of waiting the chain in new products until they have acquired a substantial share of the market for the class of commodities for
The difficulties created for price index statisticians by the turnover of the commodity basket due to the introduction of new goods (before the old staples disappear) are quite ubiquitous across industries
the higher rate of appearance of new goods in the basket of commodities available to consumers--is one that also can be linked to the effects of the emerging information revolution.
This has been a central theme in the business and economics literature on modern manufacturing at least since the 1980's. 15 The increasing proliferation of new goods and its connection with the application of computers,
in other words, whether the rate of turnover of the economy's output mix has increased. 18 Diewert
But, more recently, these costs have 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 a greater number of new things is not necessarily a greater rate of new things,
and distribution should be challenged when the business models of the system change as the result of marketing innovation and the use of information and communication technologies.
the hedonic deflation of investment expenditures on computer equipment contributes to raising the measured growth of the computer capital services,
But, in itself, the substitution of this rapidly rising input for others does nothing to lift the economy's measured growth rate of TFP. 3. Conceptual Challenges:
Beyond the technical problems of the way that the national income accountants are coping with accelerating product innovation
Here space limitations allow only brief notice of two related issues of this kind. 3. 1 Micro-level evidence on payoffs from IT investment--the excess returns puzzle.
These appeared when economists sought to illuminate the macro-level puzzle through statistic studies of the impact of 20 The difference between the measured TFP performance of the computer-producing
and the computer-using sectors of the economy, which emerges starkly from the growth accounting studies by Stiroh (1998),
using observations on individual enterprise performance. 21 This phenomenon points to the conceptual gap between task productivity measures, on the one hand,
The former are closer in spirit to the attempt to measure the productive efficiency of the economy by calculating TFP as the ratio of aggregate real output to the aggregate inputs of labor and capital services;
The contrast between the strong (cross-section) revenue productivity impacts of observed computer investments and the weaker (time series) effects gauged in terms of task productivity,
It also is the case that subsequent investigations along the same lines have found that there were additional intangible investments that were correlatives of high information technology-intensity.
Taking those into account statistically leads to substantial elimination of the apparent excess of the estimated returns ON IT capital vis-à-vis the returns on capital of other kinds. 22
which is becoming 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 21 See Brynolfsson and Hitt (1995,1996), Lichtenberg (1995), Lehr and Lichtenberg (1998).
The early studies used crosssection observations from samples of large corporations. 22 See, e g.,, Brynolfsson and Hitt (1997,1998), Bresnahan, Brynolfsson and Hitt (1999a, 1999b.
and non-market investments in learning remains more or less unchanged. But that has not been the case.
has been marked by a relative rise in the production of intangible assets that have gone unrecorded in the national income and product accounts.
but the same would not appear to be true of intangible investments in the retraining of workers and the reorganization of business operations that,
but in all of the information 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 on the job knowledge acquisition about information technology. 14 performance of microprocessor components and for many applications,
however, the spread of personal information and communication technologies has complicated enormously the task of maintaining coherence and functionality within the organization.
instead, is that most organizations have neither the capability nor interest in performing detailed activity accounting with regard to the new business processes arising from the use of information and communication technologies.
and 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 information technology,
and communication technologies are likely to be worthy of the same amount of analysis that is devoted to approval paths, logistics,
During the 1970's it was recognized that the microprocessor provided a general solution to the problem of the electronic system designer confronted by an ever-growing array of application demands.
Digital Equipment Corporation, the leading minicomputer manufacturer retreated from its vertical marketing strategy of offering computer systems specifically designed for newspapers, manufacturing enterprises, and service companies;
and which left them unable to meet the rapidly rising demands for new, specialized applications software.
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 printing technology. 28similar decisions were made by all of the U s. computer manufacturers.
All of these changes improved the"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 changing the structure of work organization
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.
or entertainment reservations, represent welfare 29in the medium and large enterprises of 1990, what remained was a deep chasm between the"mission critical"application embedded in mainframe computers and the growing proliferation of personal computers.
The primary bridge between these application environments was the widespread use of the IBM 3270, the DEC VT-100 and other standards for"intelligent"data display terminals, the basis for interactive data
Hermon and Land (1996). 17 improvements for the customer that do not appear in the measured real GDP originating in those sectors, nor in the real value expenditures on final goods and services.
by the mid-1990's, the competition among packaged software vendors for extending the generality of their offerings became a syndrome with its own name:"
of general purpose technologies that transform an economy by finding many new lines of application, and fusing with existing technologies to rejuvenate other,
preexisting sectors of 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 first is concerned to show that lags in the diffusion process involving a general purpose technology can result in long delays in the acceleration of productivity growth in the economy at large.
The second facet of the argument is that in the earlier phases of the transition process resources tend to be directed to applying the innovation to provide new, qualitatively superior goods and services,
and productivity indexes of the economy. As this theme already has been aired well (in sections 2 and 3, above),
Recent estimates of the growth of computer stocks and the flow of services therefrom are consistent with the view that
the U s. economy could be said 33 See e g.,, chapters by Helpman and Trajtenberg (1998), Aghion and Howitt (1998),
and computing machinery (OCAM) were providing only 0. 56 percent and 1. 5 percent, respectively, of the total flow of real services from the (nonresidential) stock of producers'durable equipment. 34 But,
the extent of computerization that had been achieved in the whole economy by the late 1980's was roughly comparable with the degree to
the growth rate for 1899-1914 is almost precisely the same as that for the 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 Jorgenson
the estimated average rate of growth of the ratio of computer equipment services to all producers'durable equipment services in the U s. turns out to be precisely the same,
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.
Table 5-2) estimates the rate of change in real prices of computer services for 1987-1993 to have been-7. 9 percent per annum,
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 revolution.
In arguing for the opposite view Triplett (1998) suggests that Sichel's (1997) estimates of the price of computer services--and, by implication,
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.
Sichel's (1997) price indexes of quality adjusted computer services (from hardware and software) would seriously underestimate the relevant rate of decline.
Furthermore, in the same vein it may be noticed that the slower rate of fall in computer services prices as estimated by Sichel (1997) are more in accord with the observation that applications software packages also have ballooned in size,
which prices associated with electricity and computer services. Such attempts are themselves instances of the misuse of historical analogies.
when suggesting (in 1989-1990) that it perhaps was still too soon to be disappointed that the computer revolution had failed to unleash a sustained surge of readily discernable productivity growth throughout the economy.
and transforms the organization of production in many branches of an economy. One cannot simply infer the detailed future shape of the diffusion path in the case of the digital information revolution from the experience of previous analogous episodes;
In attempting to take advantage of these opportunities, enterprises and other institutions are forced to reexamine workflow
and develop new methods for information system design. Firstly, a growing range of information technologies has become available that are purpose-built
and mainframe environment by developing the intermediate solution of client-server data processing systems. This development is still very much in progress
In this new 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.
and the formulation in David and Wright (October 1999), upon which the remainder of this section draws. 23 that the major capital-saving contributions to multi-factor productivity growth from thoroughgoing factory redesign could be realized.
So, significant capital-savings through reductions of required commercial office space and transport infrastructures, are likely to result for the whole service sector as teleworking becomes much more widely
Coupled with the widespread diffusion of information appliances, they appear to hold out a realistic prospect for the older branches of an increasingly digitalized economy to enjoy a pervasive quickening in the pace of conventionally measured multifactor productivity improvements,
and qualitatively enhanced goods and services. 40 See Gibbs (1997), and especially Norman (1998), Ch. 11.41 See David and Wright (April, 1999), for fuller discussion of the interrelatedness of mechanization of materials handling and factory electrification in the U s. during the 1920's and 1930's. 24 References
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