On productivity

1. Introduction

Last year, the World Bank released its Information and Communications for Development Report. The study made clear that access to high-speed internet and mobile phone services is paramount for generating economic growth and job creation, in particular in developing countries.

Interestingly, the report highlights the role of e-government by suggesting that it is an ideal instrument to increase ICT penetration in any country – “…e-government could be a key driver for the penetration of ICT in almost any country regardless of its ICT infrastructure and ICT penetration" R. Sudan, Lead ICT Policy Specialist. The report also adds that the world is moving fast towards knowledge-based activities.

Provided that ICT are the foundation of knowledge-based economies the World Bank suggests that countries must improve and increase its use and application.

It is generally accepted that ICT improve standards of living and incentive economic growth. However, when it comes to measure the impact of such technologies and their contribution to productivity levels in the economy, figures tend to reveal otherwise.

This article tries to identify difficulties and challenges faced by governments in measuring the performance of public services and their contribution to the economy.

  

2. Productivity – Impact in the Economy and Government Policy

Productivity is a fundamental measure for business leaders, investors and policy-makers. In its most basic concept it can be defined as the relationship between output and one or more inputs used in the production process of a good or service (National Academy of Sciences, Washington D.C., 1979).

Its uses include analysing sources of economic growth and measuring and forecasting such economic magnitudes as potential GDP and labour requirements.

A slowdown in productivity invariably means a slower rate of growth in income per capita and consequently, in the standard of living. 

Alan Greenspan, addressing the productivity slowdown of the decade of 1990 in the US, mentioned that it was virtually unimaginable half a century ago “the extent to which concepts and ideas would substitute for physical resources and human brawn in the production of goods and services.” He also remarked that while the economic output is only modestly higher than 50 years before, value added has risen well over threefold.

Greenspan’s comments surged in a period when productivity rates were lower than expected and ICT was a key factor to attract investment. Moreover, statistics were showing the services sector causing the slowdown, establishing grounds for the views put forward by Solow (1987) and Baumol (1967).

Greenspan also touched a topic under debate for a long time – the “productivity paradox” as defined by Solow (1987). However, recent research such as the work carried out by Brynjolfsson (1994) have challenged both Solow and Baumol’s theories - “it appears that the shortfall of IT productivity is as much due to deficiencies in our measurement and methodological tool kit as to mismanagement by developers and users of IT”

In fact, Triplett and Bosworth (2002) analysed productivity growth in the US over the 1995-2000 period, and found that ICT equipment contributed to between 30% and 37% of growth in labour productivity of business services, wholesale trade and transportation.

 3. Measuring the Performance of Service Industries

There are at least two reasons why we need to get a clearer picture of the performance of service industries.

Firstly, services contribute towards an increasing share of economic activity - in 2005 the world average output share of services was almost 70% (Image 1). Secondly, services have become so integrated in manufacturing and industrial activity that even when a service is not traded, potential higher productivity of service providers can play a fundamental role in increasing a country’s market share in industrial products (Van Ark, Monnikhof and Mulder, 1999). It shall be noted that I refer to potential in the same way that David (1990) refers to the introduction of skylights in factories, turning them into lighter and cleaner workshops. In other words, there are a number of qualitative indirect benefits neglected by the current methods of measuring productivity that “come part as a package containing other gains” that “take the form of more readily quantifiable resource savings” (David, 1990).

Image 1 – Manufacturing vs Services. Source: World Resources Institute

 

Measuring productivity of services is neither an accurate process nor even an established one. Commonly, economists apply the same formulae used for measuring productivity in manufacturing industries.

However, there are substantial differences between these two dimensions of productivity. Services, as opposed to goods, are intangible and difficult to quantify. Consequently, it is often virtually impossible to identify what the output of a service is (Andersen and Corley, 2006).

Measuring productivity of producing goods disregards their actual consumption. Take as an example a car manufacturer that produces more vehicles than the rest of the automotive industry with the same input. As per current methods, this firm will be highly productive in spite of the fact that its vehicles might have no buoyant market.

On the other hand, the measurement of productivity of services introduces a new dimension – the consumer. Thus, outputs of service industries are analysed on a ‘transaction perspective’, i.e. services are only considered produced if they are also consumed (Andersen and Corley, 2006).

This raises a number of issues since a service typically involves a mixture of object entities, types of transformations and transformations spheres. Hence, services are generally heterogeneous (Andersen and Corley, 2006).

In all sciences, and indeed all aspects of life, categories consist of homogeneous individual components. Current measures of productivity recognise only services that can be quantifiable (Andersen and Corley, 2006) and grouped into categories. However, services are highly heterogenic making actual productivity measures inadequate and inaccurate, in particular when we consider bundled services. 

4. Productivity in the Public Sector

Similarly to other sectors, there is little evidence about how the widespread adoption of ICT in government bodies contribute towards an increase of productivity. Given the nature of their task, public services are bound to be more problematic than most businesses.

Garicano and Heaton (2007) examined how ICT contribute to organisational change and improved productivity in police organisations using a data set of police departments covering 1987-2003, in the US.

One of their major conclusions was that the adoption of ICT has no net relationship with an increase in productivity - measured in ‘clearance rates’, i.e. the number of crimes ‘cleared’ divided by the total number of crimes recorded - and is actually associated with an increase in offence rates.

Shocking as it may seem, one possible explanation is that offences might be higher in places with higher ICT adoption purely because officers may be more willing to file incident reports on a computer rather than by hand. Another explanation could be that it is now easier to report crimes than ever before.

Other topic that stood out is the assumption that greater use of ICT leads to a larger share of non-production personnel with little impact on productivity.

These findings reveal the static nature of the current methods for measuring productivity missing out on a variable that has been subject to increasing discussion – quality.

4.1  Quality

In the services sector quality is intrinsically associated to price. However, quality change is not reflected in price indexes, hence the adoption of hedonic pricing in some countries.

In the case of public services, the output is generally a non-market one - it is free or at prices not economically significant – thus, adding complexity to identifying the nature of outputs and measuring ‘transactions’.

There is an increasing emphasis on delivering quality services both in the private and in the public sector. “As a result there are greater demands on, and expectations of, measures of government output” (National Statistician, 2005)

In the UK, the Atkison Review (2005) which measures government output and productivity for the national accounts has corroborated previous research by arguing that “the impact of technological change may appear with a delay” and that “the benefits of ICT may appear in quality change that is not recorded.”

Analysing the impact of quality in the provision of a service is a difficult task and the outcome will always be subject to relativity, regardless of the method.

The Eurostat Handbook suggests that “part of the quality change can be captured by differentiating as many qualities of a product as possible. These different qualities are then in fact treated as different products” – an approximate concept to the one of hedonic pricing. However, it is also recognised that the quality adjustment cannot be exclusively limited to differentiation.

In the case of public transportation, Hill (1977) suggests that “the amount of services produced must be based primarily on the number of passengers transported and the distance over which they are transported” but that the quality of the service can be considered to account for comfort, speed, punctuality and safety.

If we make use of the Eurostat Handbook view, and calculate a quality adjustment factor for the example above, the result would be an aggregated indicator of quality which would be a combination of the different dimensions of quality, being comfort, speed, punctuality and safety - perhaps a way forward.

4.2  The Transaction Multiplier Effect

Another factor to account for is the difficulty in separating the different functions that contribute to an output. Going back to the police case, governments are faced with the challenge of quantifying the contribution of police, courts and prisons to the final output. These functions are in most cases interdependent and the effectiveness of each depends on the effectiveness of the others.

This is a very similar situation to the role of different departments within a private firm or even to the transaction multiplier effect in e-commerce. (Image 2)

Image 2

The Transaction Multiplier Effect of a Crime

Simple Illustration of the Transaction Multiplier Effect of a Purchase on Amazon

  

5. Conclusions

Statistics show that in spite of evidence of increased utilization of ICT in services sector, productivity growth has not shown signs of acceleration.

Nonetheless, there are several service industries revealing high productivity growth rates. One possible reason is the presence of returns to scale which might result from network effects in the production and use of ICT (Wölfl, A., 2003). This is particularly visible in “social productivity” where the benefits of using a certain technology increase more than proportionately to the number of users.

Firm share of value added in the service sector within the OECD have increased for market-related services such as finance, insurance and business services (Wölfl, A., 2003). At the same time, OECD studies have shown that small firm size is often found in service industries where productivity growth is negative or low, which is the case of the financial services.

Whatever the reason for low productivity growth of services, there is significant evidence that the problem lies to a great extent in the way it is measured. Research also suggests that there is a potential under-estimation of the contribution of services to the production of intermediate goods - as opposed to services provided to the final consumer.

David (1990) and Wolf (2003) also suggest that we did not allow enough time yet to see technological advancements having a real impact on productivity.

Garicano and Heaton (2007) on their research on police organisation, also suggest that there is a learning process associated with ICT. Hence the adoption of ICT can only become synonymous with productivity growth if police officers have the necessary skills.

Another problem that subsists in the knowledge intensive services is that paper-based procedures are retained alongside the new ones. The so-called Big 4 firms (KPMG, PwC, Ernst & Young and Deloitte) illustrate this by having an established procedure of keeping paper records alongside electronic ones.

There are also problems in categorisation and accounting. Accounting does not record novel technologies and therefore quality changes associated to the introduction of such technologies are not possibly to measure (David 1990).

David (1990) made a parallel between the dynamo and ICT and suggested that the widespread adoption of ICT is a key complementary element in foregoing innovation.

This reveals an urgent need for reviewing the way we measure productivity in the service industries in which quality is a central concern and should be accounted for. There are many dimensions of quality but there is way forward. If quality adjustments cannot be comprehensive, they should be representative of a relevant range of dimensions. That could not make more sense in a measure that assumes involvement of the consumer. 

Essay 1: On 'New Economy'

Introduction

We are faced with great new technological opportunities that are continuing to emerge in ‘the new economy’ and as we get through a period of ‘structural crisis of adjustment’ we must regain our investment confidence in order to achieve a new ideal type of productive organization (OECD, 2000). However, we must continue to emphasize on innovation in order to drive business cycles and create business opportunities.

First, the goal of this essay is discuss the concepts that lead to the emergence of the so-called new economy from the business cycle perspective based on literary work taking roots in Joseph Schumpeter’s business cycles and his notion of ‘creative destruction’ and ‘techno-economic paradigms’. And to contrast it with particular mainstream views in explaining economic cycles and business performance. Secondly, this essay aims to outline the role and nature of various types of innovation that help shape a path for a new business environment. And last, it seeks to highlight the ideal type of productive organization by comparing between our previous and approaching technological paradigm.

The business cycle perspective of the ‘New Economy’

It is evident that we are experiencing a shift in the way business is done in our everyday lives due to the advent of technological progress. Brian Arthur best describes the recent development in his article ‘Myths and Realities of the High Tech Economy’: that “the last few years have seen the emergence of a powerful web economy that operates by different rules from the manufacturing economy” (Arthur 2000: 3). And though Arthur does not believe there is a new economy since nothing structurally has happened; there are various ways to argue that we are experiencing a new economy.

One of the ways to analyze the emergence of a ‘new economy’ is to look into the context of the business cycle behaviour and understand the with the neo classical and Keynesian economic view of internal swings in capital investment that produces booms and crises. Conversely, Freeman and Perez explain Keynes views fall short in coming to terms with the influence of technical change (Freeman, Perez, 1988: 38).

Moreover, Freeman and Perez’s “central theme is that certain types of technical change- defined as changes in ‘techno-economic paradigm’ have such widespread consequences for all sectors of the economy that their diffusion is accompanied by a major structural crisis of adjustment” (Freeman, Perez, 1988: 39); and further explains that social and institutional changes are necessary to bring together a new technology to market (Andersen 2010). It is then pointed out by Freeman, that once a new regime of regulation is in place, a new stable form of long-term investment behaviour can rise (Freeman 2001: 118).

Correspondingly, looking beyond the aggregate view of mainstream economists, Chris Freeman argues that there is a lot of investment in the knowledge based businesses as policy has moved from viewing data at macro level to more micro technology specific ways. Likewise, he explains that in a micro view we will need the labour economy to adjust to new types of technologies (Freeman 2001: 119). This brings us closer to Schumpeter’s micro-oriented evolutionary theories, which focus on the qualitative aspects of innovation and new business opportunities. And thus the crucial importance of technical change for investment behaviour is seen by the confidence that is found on the empirical studies of investment confidence and evaluation in R&D as pointed out by figures in the table 1 (Freeman 1995: 5).

The investment by country show a pattern on gross expenditure on R&D and support the view of Schackle and Schumpeter that investment in new products and processes has elements of uncertainty but yet under favourable conditions business confidence improves leading to an atmosphere of boom (Freeman 1995: 10). And especially, under these favourable circumstances the growth of new markets and the profitability of new investments provide for a fairly stable view of growth, despite the uncertainties (Freeman, Perez, 1988: 43).

Another aspect of this argument is to look at Schumpeter’s theory that booms and crises occur due to the historical specifities in changing ‘technological paradigms’ which include structural crises of adjustment throughout the economic, technological, industrial and institutional system (Andersen 2010). Schumpeter is quick to explain why lagging institutional changes to unleash an upswing in the economy are major factors in respect to structural crises of adjustment. He proposes that business cycle theory must require historical analysis that involves identifying the rise of types of technologies and the rise and demise of entire industries (Freeman 1995: 12). As well as further infra-structural investments with perhaps changes in industry location in order to unleash the paradigm. Finally, Schumpeter proposes that other structural changes must take place in education, composition of workforce and management structures of enterprises (Freeman, Perez, 1988: 49).

A taxonomy of innovation

As previously discussed, the weakness of most neo-classical and Keynesian theories of technical change is that they fail to take into account the specifics of changing technologies through time. One reason for failing in addressing the specifics of technical change is the complexity of changing technologies. Thousands of inventions and innovations are introduced every month and it is tough to bring them to a generalization in order to have analysis. So therefore Freeman and Perez suggest a taxonomy of innovation based on empirical work. And thus they are able to distinguish the various types of innovation as ‘Incremental innovation’, ‘Radical innovation’, ‘new technology systems’ and ‘Changes of techno-economic paradigms.’(Freeman, Perez 1988: 40).

Incremental innovations occur continuously in any industry but at varying rates and they may not occur as a result of research in the private or public sector but may be brought upon by discoveries at the hands of engineers (Freeman, Perez, 2000: 46). It is justifiable then to explain that this method of innovation brings about the constant improving of technology such as the use of factors of production in Hollander’s (1965) study of productivity gains in Du Pont rayon plants. It is clear to see the scaling up of plant and equipment have a direct effect in quality improvement of products and services. Though the combined effect of incremental innovations has great importance, no single incremental innovation has a major impact as is in the case of radical innovations (Andersen 2010).

As for radical innovations, they are revolutionary changes in technology and show clear departures from existing practice as is described by Dewar and Dutton in their study “The adoption of radical and incremental innovations: An empirical analysis” (Dewar, Dutton 1986: 1423). In particular radical innovations are do to discontinued events and as a result of deliberate R&D within enterprises and/or university and government laboratories. Some examples of such are the radical inventions of the mobile telephone, digital television, the new textile technologies, and semi-conductors. Their single effect has stimulated productivity growth and has partly given ground for the expansion of the ‘technology system’ (or technological trajectories, as explained by Dosi). Dosi defines it as a combination of incremental and radical innovations affecting more than one firm (Andersen 2010). An example is nuclear energy that has changed certain sectors such as the way electricity is delivered. As such, technology systems have far reaching grasps in technology affecting various parts of the economy as well as giving rise to entirely new sectors (Freeman Perez, 2000: 46).

Turning to the last of the four, ‘techno-economic paradigms’ are at the heart of Schumpeter's long wave theory for one and on the other called ‘technological revolutions by Freeman, Perez and Louca. Techno-economic paradigms are changes in technology systems that go beyond and permeate variables in the entire economy (Freeman, Perez 2000: 41). Freeman mentions a main characteristic of this fourth type of technical change in that it has an overall effect throughout the economy as it leads to the emergence of a new range of products, services, systems and industries. Freeman further distinguishes “The expression " techno-economic " rather than " technological paradigm " emphasising that the changes are interactive, involving organisational as well as technical changes which go beyond specific product or process technologies” (Freeman 1991: 223). The introduction of electric power or steam power are examples as well as new ways of production and consumption patterns. What is more, changes in techno-economic paradigms involve the combination of product innovations, process innovations, organizational innovations, managerial innovations and formal institutional innovations such as IPR systems (Andersen 2010). In addition techno-economic paradigms affect the input cost structure and conditions of production and distribution throughout the system (Freeman 1991: 223).

It appears as the organizing principle of each successive paradigm that the new technology becomes a dominant technology only after a long period of competition, and yet its full success occurs only after a crisis of structural adjustment with the help of social and institutional changes. Some of the key factors for the paradigm to rise beyond this period are falling costs, the rapidly increasing supply of technology and pervasive applications or (the use of new set of inputs throughout the economy) (Freeman 1991: 229). For example microelectronics and telecommunications have set new standard of applicability. Oil was clearly held up until recently due to the post-war boom as described in the" fourth Kondratieff" upswing (Andersen 2010). Furthermore, one can argue that the burst of the dotcom bubble was an indication of structure adjustment in the sectors of on-line technologies. In addition, a new set of socio-economic rules or inputs are being formulated with the emergence of social networks on the web that are brought upon us via the diffusion of a new techno-economic paradigm.

The ideal type of productive organization

In order to identify the ideal type of productive organization in the new economy, it is essential to distinguish between our previous and our forthcoming paradigm. Our previous paradigm being the Fordist mass production categorized by Kondratieff in comparison to the information age paradigm that we are experiencing. The Fordist mass production growth was led by the automobile industry along with the synthetic and chemical industries where as the information and communication technology (ICT) is led by knowledge based systems, telecommunications, software and more recently with media and entertainment services (Andersen 2010).

Beyond the ICT industry itself new technologies have a profound impact on ‘user industries’ and in “the E-economy most companies do not push technology frontiers themselves. Rather they use technology to innovate and deliver improved or novel goods and services” (Vittet-Philippe, 2002: 25). Such statement depicts not just an economic benefit of the new economy but a further transcending impact on other parts of the socio-economic landscape. Another example of the changing effects in the new economy that takes place in public services is the digitization of medical records within the NHS where 75% of value added jobs go towards the GDP (Andersen 2010).

As far as the organization scheme between the Fordist mass production and ICT, the prior had a separate and complex hierarchical managerial and administrative structure. Where in ICT there is more autocracy in the organization and structures are formed around the innovation. As such there is more flexibility at all levels. There are also benefits in transaction costs with coordinated systems in the supply chain (Andersen 2010).

Concerning investment, there is a new pattern in the location of investment and technological leaders. In the prior economy, the United States, Germany and Japan led the way where nowadays you have the BRIC countries paving a path to growing economies due to technological advance and demands in financial services and an entertainment craze such as in computer gaming (Louca 2003: 779). Below is a chart that compares the growth in GDP over a 40-year period, and it is interesting to notice the upswing in GDP for countries during strong periods of innovation. (Louca 2003: 788).

With regards to the social and institutional adjustments that need to be designed to provide appropriate externalities, the Fordist mass production era ran on supporting the consumer demand via trade unions and a minimum wage; where the ICT design has an independent framework as is in the case of consumer watchdogs online or a forum for sellers and buyers to interact via online rating reviews. And last, the ICT economy has created rapidly expanding branches in the economy via its innovator entrepreneur firms which contribute to new sectors of production (Andersen 2010).

Conclusion

Despite these various distinctive but closely related literary views, there are clear empirical examples of the shaping of a ‘New Economy.’ And thus, the shift into a new ‘technological’ paradigm in relation to previous set of standards shows that the defining characteristic of the new economy is not technology but innovation (Miller, Wilsdon 2001: 276). Whether the theoretical views of technology diffusion point out to a major structural crisis of adjustment through business cycles, or the stance on the behaviour of innovation, investment or labour force; the conclusion is that the new economy is dynamic as we look through history. And yet we are faced again in the new economy with the need of regaining confidence for the next ideal type of productive organization.

my lock in essay

Discuss theoretical analytical contributions as well as empirical evidence regarding how “increasing returns” create lock-in to technological solutions in the new economy”.

“Under certain conditions a single technology may achieve decisive advantage over competing technologies, even though it is not necessarily the most efficient one in the long run”

-Brian Arthur and Paul David

Lock-in is a process where consumers get attached to a specific technological solution in the new economy, and thus find it difficult to change to another technological solution during the same technological evolution. A new economy is created during a technological evolution where the old technological solution gets eradicated and new solutions emerge. When two technological solutions compete in the new economy, one solution will always get further ahead by creating increasing returns. When discussing lock-ins, the term ‘path dependence’ arises and tends to create a lot of confusion. There are two main definitions that are mainly used today. The broad definition simply refers to the concept of “history matters”, and the narrow definition refers to the “manner in which preceding stages may radically narrow the range of possible outcomes”(Andersen 2010). Brian Arthur and Paul David are the two leading figures when discussing the concept of lock-in and path dependence. They believe that path dependence is mainly luck and corporate manoeuvring will allow the chosen technology to gain a larger market share over its rival, allowing it to dominate the market and to lock-in the public. The following essay will be analysing their contribution to understanding how increasing returns creates lock in to technological solutions in the new economy, as well as discussing how monopoly power can also create a lock-in.

Paul A David

Paul David is famous for his example of the ‘QWERTY’ keyboard. He believes “lock-in” to be a random process. The QWERTY keyboard was developed six years after Sholes typewriter was patented in 1867(David 1985). Sholes typewriter had many faults, one of which was that the type bars used to clash. This prevented the commercialisation of the keyboard. In 1873 the production rights were sold to Remington where they marketed the QWERTY keyboard to show that you can spell typewriter on one line. This still didn’t boost any sales as in the early 1880’s only 5000 keyboards were sold (David 1985). The DSK keyboard (Dvorak Simplified Keyboard) Qwerty’s rival technological solution, was believed to type much faster than the QWERTY layout. Apple converted from their QWERTY layout to the DSK layout and marketed it as, “lets you type 20-40% faster”(David 1985). This looked to be the end for the QWERTY keyboard until the innovation of touch-typing in the late 1880’s was developed, and was practised using the QWERTY layout. Touch-typing gave rise to three features of the evolving production system, which was crucially important in causing QWERTY to become locked in as the dominant keyboard (David 1985). Technical interrelatedness is the hardware and software represented by the touch typist’s memory of particular arrangements of keys. The software is the typist and the hardware is the typewriter. It is not easy to change the software as training people can be expensive and time consuming, but it is easy to change the hardware. Economies of scale meant that costs of using a particular good, particular to the system as a whole were decreasing. It is hard for new systems to come in as being small scale as it’s going to be more expensive (David 1985). Quasi-irreversibility of investment meant that the investment was irreversible as it is costly to change from one technology to another (David 1985). These features allowed the QWERTY keyboard to lock in consumers as it made it difficult for users to switch to another layout, and thus QWERTY became the dominant layout as it gained a larger market share over its rival keyboard layout DSK. This meant that all Keyboards would now have to use QWERTY layout as touch-typing became essential for typist. This shows how lock in is a random process and if it wasn’t for the innovation of touch-typing, the QWERTY layout wouldn’t be the modern day standard, we probably would have been using DSK layout (David 1985).

Brian Arthur

Brian Arthur’s main contribution has been to research competing technologies and why some often sub-optimal solutions survive. His main argument is based upon the notion of increasing returns where “Increasing returns cause products that are ahead to go further ahead” (Arthur 1996). Brian Arthur identified six circumstances that make technology prone to increasing returns (Andersen 2010). Scale economies where the cost of the product falls as increased units are produced. Almost all technological products are example of scale economies, for example when the playstation3 was first released it charged customers £425 (boxer 2007) and now customers can pick one up for £279(www.game.co.uk). Learning effects creates innovation and produces better technology, as the more a technology is adopted, the more it is used and learned about, therefore the more it is developed and improved. As specialised skills and knowledge accumulate through production and market experience, they can also reduce the manufacturing cost which will reduce the cost of the technological solution (Foxon 2006). The IPod is an example of this. Since its first release iPod has released several generations of iPods, and has extended its product line from the shuffle costing £46, to touch costing £146, to the iphone costing £449 (www.apple.com/uk). Network externalities can create lock-in, as the more users there are the more it is to ones advantage to adopt the technology (Foxon 2006). Facebook is an example of this. The more people who join the network, the more connected they become to one another. It then becomes more to ones advantage to join the network; this also leads to greater cost of leaving the network. Coordination effects help in creating lock in, as once there is a standard it is hard to change into something else. Credit cards and other type of electronic cards is an example of coordination effect. Almost all electronic cards are the same size in order to fit the standard measurement. The cards can easily be made much smaller but they are produced to fit the standard. Technological interrelatedness also aids in creating lock in, as often when a technology becomes more adopted, a number of sub technologies and products become part of its infrastructure (Foxon 2006). An example of this is the IPod and how most portable sound devices are designed specifically with an IPod dock. Adaptive expectations which is also referred to as informational increasing, aids in creating lock in, as increasing adoption reduces uncertainty for both users and producers, and this makes them both confident in the quality, performance and durability of the technology (Foxon 2006). Bose sound systems is an example of this, consumers automatically assume quality and performance.

Brian Arthurs ball game illustration

Brian Arthur uses the example of two coloured balls on a snooker table to show how path dependence does play a role in creating a lock in, but is purely a random process. The number of balls illustrates the number of adopters, and the colours reflect the two technological systems. The rules are simple every time a ball (technological solution) is touched another one of the same colour is added to the table. The whole point of this game is to show that if one person has chosen a certain technological solution, another person who hasn’t yet chosen will more likely also adopt the same technological solution. The six circumstances explained above will create the lock-in once the solution has been adopted. The game starts with 4 balls in total, 2 blue and 2 red. We start with a 50% chance of choosing a blue and a 50% chance of choosing a red ball. The game starts and a blue ball is chosen at random, therefore another blue ball is added. The total number of balls is now 5, 3 blue and 2 red. This now gives the next person a 60% chance of touching a blue ball and a 40% chance of choosing a red ball. The game continues and in this case the blue ball will dominate the number of red balls as the percentage of touching a blue ball keeps increasing. It is impossible to have a 100% chance of choosing a blue ball but in reality the red ball (technological solution 2) will be forced out. The intend of this game is to show that the first ball chosen is a random process and is more likely the one to be the dominant technological solution. This emphasises his point that path dependence does play a role in creating a lock in because if that first blue ball wasn’t chosen, and the red one was chosen, the other technological solution would have been the dominant one. It is a random process, no one knows which technological solution will be the dominant one, and it doesn’t mean that the better solution will win (Andersen 2010).

VHS vs Beta

The VHS vs Betamax is a real life example of how path dependence and lock in is a random process, and how increasing returns creates lock-in to technological solutions in the new economy. The VCR market had two competing formats, VHS and Betamax. Betamax was Sony’s product and VHS was JVC product. Each format could realise increasing returns as its markets share increased over time. Rental stores decided to stock more VHS format pre-recorded tapes, as they assumed for no apparent reason that VHS would win the battle. This lead to more people buying VHS players as the VHS format was more widely available and more beneficial to have. This thus enhanced the value of owning a VHS player and created a positive network externality for owning a VHS player. Brian Arthur uses this example to show how the event is random process, and is unpredictable. Both formats were introduced at roughly the same time with roughly the same market share. The market shares fluctuated in the beginning until luck and corporate manoeuvring sent VHS further ahead. Once VHS took the early lead they were able to keep increasing their market share until Betamax was abolished. Consumers got locked into VHS and Sony released its own VHS player in the late 1980’s giving up the battle (Arthur 1994).

Monopoly power Lock-in

Lock-in doesn’t always have to be a random process. It can also be forced upon consumers by monopoly power. Apple can be used as an example to show how monopoly power can lock consumers into a product. Apple has been accused of locking in consumers to their music player (iTunes) forcefully, and not giving customers the option to decide whether or not they would like to adopt iTunes. When an iPod is purchased the default audio player provided is iTunes. At first glance consumers don’t realise that they are about to get locked in to iTunes by Apples monopolistic strategy. The iPod is programmed to function with iTunes, which seems harmless at first glance. Once iTunes is installed it then takes control of all you’re existing music and uploads it onto iTunes making it the default audio player on your computer. Overtime consumers get locked in to iTunes as they got used to it and all their music is stored on iTunes. They do not realise that they had no other option on which audio player they would like to make the default audio player. Once locked in, it is difficult to change to another audio player as previously discussed above with Brian Arthurs six circumstances. At first sight it seems harmless as it is the default program for an iPod, and it makes uploading music onto an iPod easy and fast. In reality Apple are forcing consumers to use their audio player by the use of their monopoly power, as they do not provide any other alternative audio player. Once installed it takes control of all the music from your computer. Not many people realise that they have been deliberately forced in, many consumers such as myself believe the reason they use iTunes is because it is the most efficient and easiest audio player. The simple fact is Apple used a monopolistic strategy to lock consumers in (Fisher 2005).

Conclusion

Both Brian Arthur and Paul David argue that path dependence is a random process, and there is no way to figure out which technological solution will end up being the dominant one. Increasing returns creates a lock-in to technological solutions in the new economy as Brian Arthur identified six circumstances, which keep consumers locked in. Lock-in isn’t always a random process, monopoly power can also be used to lock consumers, as Apple has demonstrated. Increasing returns creates a lock in to technological solutions as consumers don’t like change, once they adopt one solution they will not adopt another, until a new technological evolution arises. The battle of technological solutions will also occur during new technological periods and once a solution is adopted by chance, corporate manoeuvring and the six circumstances discussed by Brian Arthur will keep consumers locked in to that particular solution.

Exam Paper - 2009

Question 1
Outline the role of innovation and structural crises of adjustment in explaining business cycles. Use this literature to identify the effect of the micro electronic revolution.



Question 2
Discuss the problems surrounding measuring the true productivity and output performance of knowledge-intensive business services, such as the financial sector, the education sector and the health sector.



Question 3
Discuss if rational profit-maximising behaviour of agents underpins decision-making situations in the information society.



Question 4
How do entire societies of firms and individuals become locked into certain product or technological standards?


Question 5
Outline the theoretical literature of why we have patents, and discuss if you believe that the patent system performs in accordance with its objectives.



Question 6
Apply the theories of the firm to explain the changing boundaries of firms, moving towards value networks and dynamic market configurations, as a result of innovation in information and communication technology.