Embed Size (px)
Citation preview
Five Myths of Technology Change
by
Jeffrey L. Funk
Associate Professor
National University of Singapore
Division of Engineering and Technology Management
Christopher L. Magee
Professor of the Practice
Massachusetts Institute of Technology
Engineering Systems Division
Jianxi Luo
Assistant Professor
Singapore University of Technology and Design
Five Myths of Technology Change
Abstract
This paper summarizes five myths of technology change and it replaces them with more
accurate descriptions of reality. These myths are largely based on metaphors and anecdotal
evidence that were presented decades ago and that have not been systematically re-examined.
This paper summarizes the empirical research that proves these myths wrong and it describes
how the more accurate descriptions of reality suggest more appropriate policies and strategies
that are very different from the ones suggested by the myths.
1. Introduction
It is widely recognized that technology is the driving force behind improvements in economic
productivity and standard of living as well as incumbent failure and new firm formation.
Schumpeter focused on “creative destruction” and the ability of new technology-based industries
to destroy existing ones and replace them with more productive ones. Robert Solow’s Nobel
Prize winning research found that most of productivity growth comes from technical innovation.
However, understanding technology change to the extent necessary to reap the full benefits
from new technologies is still a major challenge. A major problem is that the most widely used
models of technology change are largely based on myths, myths that distort the reality of
technology change and mislead the choices available for government policies, and firm
strategies. Our empirical research has identified five myths that impact strongly on how
managers, policy makers, and university professors think about technology change:
#1: Performance vs. time curves resemble an S-curve
#2: Slowing rates of improvement in old technologies drive the development of new
technologies
#3: Product design changes drives performance increases and process design changes
drives cost reductions, with product preceding process design changes in a technology’s
life cycle.
#4: Costs fall as cumulative production rises in a learning curve
#5: All technologies have the potential for rapid rates of improvements
These myths are largely based on metaphors and anecdotal evidence that were presented
decades ago and that have not been systematically re-examined. The one exception is the
learning curve, but even here the empirical evidence has been selective, ignoring the
improvements that occur for most technologies before commercial production begins. This paper
summarizes the empirical research that proves these myths wrong and it replaces these myths
with much more accurate descriptions of reality. These more accurate descriptions of reality
suggest more appropriate policies and strategies that are very different from the ones based on
the myths.
2. Myth #1: Performance vs. time curves resemble an S-curvei
The S-curve (see left side of Figure 1) forms the basis for most models of technology change
and it is taught in almost every business school and every technology management program in
the world. In such an S-curve, following an initial low rate of improvement, the rate of
improvement accelerates to a higher rate of improvement; later it slows. For the early part of the
purported S-curve, the explanation is that improvements accelerate as firms and government
agencies move research funds from an old to a new technology in response to increases in
demand for the new technology or to a slowdown in the rate of improvement in the old
technology. The acceleration may also occur as the technology is better understood by scientists
and firms, constraints are overcome, and complementary technologies developed and
implemented. The later part of the S-curve is explained by the rates of improvement slowing as
the cost of marginal improvements increases, the number of useful inventions decreases, and
natural limits emerge. Hypothetically, research funds then move to a newer technology and thus
the newer technology’s rate of improvement begins to accelerateii.
This myth encourages firms and governments to believe that rates of improvement will
accelerate through small increases in demand or R&D funding. When one believes in this myth
of accelerations, it is easy to be overly optimistic about breakthroughs and believe that new
technologies are almost ready for the market because the rates of improvement will certainly
accelerate as R&D projects are implemented and as demand increases. Proponents of new
technologies often use this myth to promote their technologies and even social scientists have
used this myth to promote specific technologiesiii.
The reality of performance vs. time curves is very different from the myth of S-curves. Our
empirical analysis of 25 different technologies, 32 unique measures of performance, and about
600 individual data points show that the performance vs. time curves do not match the
predominant viewpoint of an S-curve. On a logarithmic plot, none of the 32 time-series curves
display the classical S-curve. The second half of an S-curve, i.e., limits, is only evident in one
technology, the best laboratory efficiency of amorphous silicon solar cells and the first half of an
S-curve, i.e., acceleration, is also only evident in one technology, cellular telecommunications.
Statistical analysis also suggests that rates of improvements are fairly constant over many
years on a logarithmic plot and thus, performance vs. time curves more closely resemble a
straight line on a logarithmic plot than an S-curve (See right side of Figure 1). A first-order
logarithmic model has high R-squared values (> 0.9) and low p values (<0.001) for 26 of the 32
performance vs. time curves. There are four R-squared values that are between 0.7~0.9 and one
R-squared value at 0.54 and there is one p value at 0.014. Therefore, the first-order logarithmic
model fits with the performance vs. time data with good statistical significance. That suggests
exponentially growing performance of technologies, and a constant percentage rate of
improvement each year.
A major reason for the large difference between the myth and reality of performance vs. time
curves is that there is a large gap between the myth and reality of technology change. The reality
of technology change is incremental and cumulative R&D in which improvements build from
past ones and the extensions to the knowledge base that these improvements bring. So-called
breakthroughs in performance or cost do not really exist. Second, R&D is highly decentralized
and it continues to become even more decentralized in the current world of open innovation iv.
Thus, a few firms or funding agencies moving research funds from old to new technologies will
not lead to accelerations.
For the later period of new technologies, we do not deny that physical limits exist. However,
our analysis suggests that these limits are much further in the future than is ordinarily thought,
particularly if one chooses the right metrics. While efficiencies and other metrics with upper
bounds impose natural limits on the technology, there are fewer limits when the metric is a
performance per time, area, volume, or weight. For these types of metrics and the technologies
we have studied, scientists and engineers often continue to find new ways to improve a
technology for many decades, if not longer.
3. Myth #2: Slowing rate of improvement in old technology drives development of new
technologyv
A second myth is that slowdowns in the rates of improvement for old technologies lead to the
development of new technologies and accelerations in the rates of improvements for these new
technologies (See left side of Figure 2). This myth is derived from the myth of S-curves in which
a slowdown in the S-curve for an old technology coincides with an acceleration in in the S-curve
for a new technology thus suggesting that the slowdown is driving the acceleration. As noted
previously, the purported reason is that slowdowns in the rates of improvement for old
technologies cause R&D resources to move to new technologies thus enabling accelerations in
the rates of improvement for the new technologiesvi.
This myth encourages decision makers to believe in a very centralized world in which
changes in policy or strategy by a few firms or governments cause dramatic shifts from old to
new technologies, and sometimes at the blink of an eye. According to this myth, changes in
R&D funding and the resulting accelerations in the rates of improvement may cause the new
winning technology to become unbeatable by other technologies. Furthermore, this myth
encourages decision makers to focus on the rates of improvement for the old technologies and
perhaps ignore new technologies until it is too late for them to succeed in the new technologies.
Our empirical analysis of seven technology domains shows that slowdowns for old
technologies don’t usually coincide with improvement in new technologies and when they do
coincide, there are better explanations. We find that large numbers of new technologies are being
simultaneously developed even as many of the old technologies are still experiencing relatively
constant rates of improvement. For example, many new forms of lighting, displays, electricity
generation, non-volatile memory, electronic devices, computing, and telecommunications were
and still are being simultaneously improved even as many of the old ones were and are still
experiencing improvements.
Looking more closely at old technologies in these seven domains, we find only a few cases in
which slowdowns occurred in the old technology as the newer ones were being improved. We
found that 7 of the 15 old technologies had slower rates of improvement after performance data
were recorded for a new technology but none of these changes in rates of improvement (i.e.,
slopes) were large and only two were statistically significant. Electricity from fossil fuels
experienced a slowdown as nuclear power and solar cells were being improved.
Even for these technologies, however, looking more closely at them, one can easily explain
the timing of the new technologies with reasons (e.g., supply-side ones) other than slowdowns in
the old technologies. The improvements in nuclear power followed closely the development of
the atomic bomb, improvements in solar cells followed the development of the first silicon solar
cell and the need for lightweight power source for satellites in the 1950s. In other words,
slowdowns may have just happened to occur as the new technologies were being developed and
improved.
The reality is that a large variety of technologies are being developed even as an old
technology experiences rapid rates of improvement (see right side of Figure 2). This is because
in the decentralized world of R&D that was mentioned in the criticism of S-curves, independent
researchers are always looking for something new. They are looking for something new because
they must find something new in order to publish papers and thus get promoted. Similarly, firms
are looking for new technologies in order to differentiate their products and processes from
existing ones.
4. Myth #3: Product design changes drives performance increases and process design changes
drives cost reductions, with product preceding process design changes in a technology’s life
cyclevii
A third myth is that improvements in product performance and cost are driven by different
mechanisms. Increases in functional performance are driven by changes in product design and
cost reductions are driven by changes in process design. Product design changes and thus
performance increases occur early in the technology’s lifecycle and process design changes and
thus cost reduction occur later in the lifecycle (See the left side of Figure 3) viii. The product
design changes include novel combinations of components, scaling, and combinatorial learning.
The process design changes include learning by doing by workers on the factory floor, better
process design and control, better automated manufacturing equipment, and organizational
learningix.
This myth makes it difficult for firm strategies and government policies to emphasize the
proper mix and synthesis of product and process projects. It encourages decision makers to
emphasize process improvements if they want lower costs or to emphasize product
improvements if they want higher performance. This is particularly a problem with government
and corporate R&D since these policy makers and managers must deal with many different
technologies that cross multiple domains. For example, this myth has encouraged public
organizations to focus on process improvements in their attempts to reduce the cost of electricity
from solar cells or the cost of energy storage with batteries, whereas the major cost-related
improvements have mostly come from intrinsic design changes of photovoltaic and battery
technologies.
Our empirical research finds that improvements in cost and performance simultaneously occur
throughout most of the life cycle and they are driven by inter-related product and process
innovations. Our statistical analysis of 23 different paired time series for cost and performance
in different technologies shows that these improvements in cost and performance are highly
correlated and thus being simultaneously implemented. The correlation coefficients exceed 0.9
for 18 of the 23 unique measures and only two of them fall below 0.5; the other three measures
of performance fall between 0.5 and 0.9 with two of them at 0.895. Overall, the results show that
improvement in performance and improvements in cost/price are highly correlated and thus
being simultaneously implemented.
Our interpretation of these results is that firms and research organizations are implementing
product and process innovations at the same time because the innovations are inter-related and
because these inter-related innovations enable improvements in both performance and cost For
example, consider integrated circuits (ICs); they have experienced rapid, yet constant
improvements over the last 50 years through reductions in the size of transistors and memory
cells and these reductions in size enable increases in the numbers of transistors and memory cells
that can be placed on a single IC chip (i.e., Moore’s Law). Simply put, firms are reducing the
feature sizes (i.e., product design) on an IC while at the same time making changes to the process
in order to achieve these smaller feature sizes. Furthermore, the smaller feature sizes enable
improvements in both performance (faster speeds, lower power consumption, higher
functionality through more transistors) and lower costs (fewer materials and less equipment area
per transistor).
A second type of inter-related product and process design change can be found with new
materials (i.e., product design changes) where the use of new materials requires new processes
for their fabrication. For example, scientists and engineers create new forms of semiconductor
materials that better exploit the phenomenon of electro-luminescence in an LED and that results
in higher luminosity per Watt. The higher luminosity per Watt from the new material enables
both higher performance (brighter light, lower power consumption, smaller size, faster
switching) and lower cost (fewer materials and equipment per LED from smaller sizes).
5. Myth #4: Costs fall as cumulative production rises in a learning curvex
A fourth myth is that costs fall as cumulative production increases in a so-called learning or
experience curve (see left side of Figure 4). Although there is some evidence that costs fall as a
single design is manufactured in a single factory, this observation has caused many to assume
that factory level activities are driving the cost improvements even when new product designs
are being introduced. According to this myth, costs fall as workers become better at tasks and
firms introduce better work flows, process control, and automated manufacturing equipment, and
promote organizational learningxi .
This myth focuses firms and governments on commercial production as a means of achieving
reductions in cost. It suggests that commercial production is essential for cost reductions (and
many argue for performance improvements). Even when the technology clearly involves new
product designs, the link between increases in cumulative production and reductions in cost
creates an illusion that most of the improvements are achieved on a factory floor and that these
improvements can be hidden from other firm). This illusion, which was popularized by many
consulting firms, bankrupted many companies that placed too much emphasis on achieving first
mover advantages through early production. More recently this illusion has caused Western
governments to spend hundreds of billions of dollars on subsidizing the installation of solar cells,
wind turbines, and electric vehicles. Proponents of new technologies not surprisingly say that
their technology will get cheaper once manufacturing begins, even though other factors have a
larger impact on costs.
In addition to the evidence presented in disproving the reliance of cost reductions on process
improvements in Myth #3, we also analyzed 14 technologies that experienced rapid
improvements of greater than 10% per year with little or no commercial production. These 14
are: organic LEDs, solar cells, and transistors; quantum dot displays and solar cells: Perovskite
solar cells; superconducting cables and Josephson junctions; quantum computers, carbon
nanotubes for transistors; ferroelectric, phase change, magnetic, resistive RAM. Commercial
production was started for all 14 of the technologies long after improvements in cost and
performance were recorded in engineering and science journals. Furthermore, all of the
technologies have recent sales figures smaller than $1Billion and only printed electronics, which
includes organic transistors, had a market size larger than $300 million. These results clearly
demonstrate that learning in a factory is not a relevant mechanism for the rapid improvements in
cost and performance
These technologies experienced rapid reductions in cost through the types of inter-related
product and process design changes that were discussed in the last section and most of the
improvements were made at universities and to a lesser extent at startups or corporate
laboratories in a system of open innovation. For example, reducing the scale of Josephson
Junctions and new forms of memory cells enable higher densities of Josephson junctions and
memory cells and this enables lower costs per area and often higher speeds since there is less
distance for electrons to travel. Second, creating new materials that better exploit physical
phenomena enables higher performance and lower cost. Scientists and engineers created organic
materials that better exploited the phenomenon of electroluminescence for OLEDs, the
photovoltaic phenomenon for solar cells, and the semiconducting phenomenon for transistors.
They created semiconductor and other materials that better exploited the phenomenon of
quantum dots and other materials that better exploited superconductivity.
We also note that many new systems only become economically feasible as their components
reach specific levels of performance and cost and thus these systems are experiencing
improvements before commercial production begins. This is particularly true of new electronic
products in which more than 95% of the system costs involve the cost of components such as ICs
and the performance of these ICs determine the performance of the system. Thus, improvements
in the cost and performance of new systems are driven mostly by improvements in components.
As the components experience improvements over time, new systems gradually becomes
economically feasible for a first application and subsequently for a growing number of
applications. This has been and continues to be true with many types of computers, mobile
phones, eBook readers, and more complex systems such as wireline and wireless
telecommunication systems.
Disproving Myth #3 has important implications for firms and governments. First, things don’t
need to be manufactured in large amounts in order to reduce costs. New materials can be created
and reductions in the scale of devices can be achieved in research laboratories and both of these
research activities are much cheaper than constructing large scale manufacturing facilities.
Second, since we observe no great changes (either accelerations or decelerations) in
improvement rates after commercial production begins, it is likely that the two mechanisms
mentioned above – materials creation and reductions in scale – continue to drive improvements
in cost and performance thus suggesting that factory level improvements play a much smaller
role than is ordinarily thought.
Third, the constant rates of improvement that we see for these technologies and for the larger
sample that is referenced in the discussion of the first myth suggest that early rates of
improvement provide a signal for the potential for a new technology. Rather than believe in the
usual song and dance that a new technology will get cheaper once commercial production for the
technology increases, managers and policy makers should expect technologies to experience
improvements before commercial production begins. If improvements aren’t emerging, decision
makers should question the long-term viability of the technology.
6. Myth #5: All Technologies have the Potential for Rapid Rates of Improvementsxii
A fifth myth is that all technologies have the same potential for rapid rates of improvements
and thus strategies and policies determine the actual rates of improvements and the winning
technologies. According to this myth, the extent to which firms and governments promote their
technologies through early production, moving down the learning curve, and experiencing the
acceleration in the rates of improvements that occur as the effects from demand and early
production kick in, the faster the rates of improvement. In some sense, this myth builds from the
other four myths. The myths of S-curves, slowdowns, cost reductions from process
improvements, and learning in factories support the notion that all technologies have the
potential for rapid rates of improvements. An acceleration in the rate of improvement occurs
(Myth #1, S-curves) as demand and commercial production begin (Myth #3, cost reductions
from process improvements and Myth #4, learning in factories) or as a slowdown in the rate of
improvement in the old technology occurs (Myth #2, slowdowns). This acceleration causes the
new technology to have a rapid rate of improvement and be commercially successful.
The most damaging part of this fifth myth is that by removing the most important objective
signal for a technology’s potential, its rate of improvement, it becomes difficult for managers and
policy makers to make rational decisions about new technologies. This causes public and private
debates about new technologies to revolve around what Nobel Laureate Daniel Kahnemanxiii calls
“instinctive and emotional” thought. People tend to assess the relative importance of issues,
including new technologies, by the ease with which they are retrieved from memory and this is
largely determined by the extent of coverage in the media. For example, the media talks about
solar, wind, battery-powered vehicles, and bio-fuels and thus many people think these
technologies are experiencing rapid rates of improvement when many are not (e.g., wind, 2% a
year; Li-ion batteries, 5%xiv) in spite of the large improvements that are needed before they will
become economically feasible.
Our empirical analysis (along with the discussion of the other myths) suggests that rates of
improvement vary dramatically across different technologies. Most technologies experience very
slow rates of improvement, less than 5% per year. For example, one study of rates of
improvement found that most materials, beverages, electrical appliances, and foods experienced
improvements of much less than 5% per year over decades while some chemicals experienced
slightly faster rates of improvement, but rarely reaching 10% a yearxv. One reason for the slow
rates of improvement in electrical appliances is because they didn’t contain rapidly improving
components. A reason for the slow rates of improvement in the other technologies is that the
chemical compositions for them are fixed and thus there are few opportunities for improvements
through changes in product design.
Table 1 summarizes technologies that are experiencing rapid rates of improvement (>10%).
Most of the technologies listed in Table 1 are information related ones such as integrated circuits,
MEMS, superconducting Josephson junctions, computers (both digital and quantum), magnetic
storage, and wireline and wireless transmission that benefit from reductions in the scale of
feature sizes or from improvements in ICs. Furthermore, many of the ones classified under
energy transformation can also be considered information related technologies since it is data
that is transformed from one type of energy to another (e.g., electrical to light or vice versa)
rather than work being done. This includes various types of lighting, displays, and lasers (e.g.,
LEDs, OLEDs, GaAs lasers, LCDs, and quantum dot displays). Similarly DNA sequencers and
synthesizers can be considered information-related technologies. Energy-related technologies
that have rapid rates of improvement are limited to solar cells, superconducting cables, and
cellulosic ethanol. Cellulosic ethanol is the only technology in Table 1 whose reductions in cost
are primarily driven by production.
The technologies listed in Table 1 will probably have a greater impact on our world than will
ones with slower rates of improvement. They are more likely to become economically feasible
for a growing number of applications and/or to impact on the design of higher level systems than
are technologies that experience slower rates of improvement. They will probably change the
way we design our computer, telecommunication, transportation, environmental, energy, health
care, and other systems. Understanding these technologies and their potential impact on new
systems is important for firms looking for new opportunities.
One goal for R&D managers is to look for technologies that are or will likely experience rapid
rates of improvement. We have already noted that early rates of improvement are important
signals since rates of improvement are fairly constant over time, both before and after
commercial production begins. Second, we have already noted that certain types of
improvements lead to rapid rates of improvements. Technologies such as ICs that benefit from
reductions in feature size have experienced rapid rates of improvement as have technologies that
benefit from the creation of new materials. Thus, one way to find technologies that have the
potential for rapid rates of improvement is to look for technologies that benefit from reductions
in scale or the creation of new materials and determine whether the degree to which these types
of improvement are possible.
7. Conclusions
The five myths described in this paper have an enormous impact on how decision makers
view technology change and how R&D is done and R&D decisions are made. This is particularly
true for government or corporate R&D where decision makers must deal with many types of
technologies that cross many technology domains, and they must make R&D investment
decisions whose outcomes depend on rates of improvement. Few decision makers have the
breadth and depth of knowledge to deal with multiple domains and this can cause the best of
managers or policy makers to depend on the myths of technology change that are discussed in
this paper. The worst case scenario is public debates about new technologies such as clean
energy ones. Public debates on clean energy completely ignore rates of improvement and
decision makers have easily fallen for the myths of technology change summarized in this paper
and thus implemented policies that are often expensive and ineffective.
The realities that are described in this paper suggest a world of technology change that
requires decision makers to consider and evaluate many different technologies and rates of
improvement, rather than make their decisions on the metaphor-based myths. Luckily, dispelling
the myths greatly simplifies the world of decision makers. Without the myth of S-curves,
decision makers can look for the constant rates of improvement that are to some extent
predictable. Without the myth of slowdowns, we can focus on new technologies and their rates
of improvement. Without the myth of commercial production driving cost reductions, early rates
of improvement provide important signals for new technologies. Without the myth of product
and process design changes, we can focus on specific types of inter-related design changes that
enable rapid rates of improvement. In the end, effective R&D, particularly public R&D, is
largely about finding those technologies that have the potential for rapid improvements.
Table 1 Technologies with Recent Rapid Rates of Improvement
Technology
Domain
Sub-Technology Dimensions of measure Time
Period
Improvement
Rate Per
Year
Energy
Trans-
formation
Light Emitting
Diodes (LEDs)
Luminosity per Watt, red 1965-2005 16.8%
Lumens per Dollar, white 2000-2010 40.5%
Organic LEDs Luminosity/Watt, green 1987-2005 29%
GaAs Lasers Power density 1987-2007 30%
Cost/Watt 1987-2007 31%
Liquid Crystal
Displays (LCDs)
Square meters per dollar 2001-2011 11.0%
Quantum Dot
Displays
External Efficiency, red 1998-2009 36.0%
Solar Cells Peak Watt Per Dollar 1977-2013 13.7%
Efficiency, Organic 2001-2012 11.4%
Efficiency, Quantum Dot 2010-2013 42.1%
Efficiency, Perovskite 2009-2013 46.5%
Energy
Transmission
Superconducting
cables
Current-length per dollar 2004-2010 115%
Current x length – BSSCO 1987-2008 32.5%
Current x length - YBCO 2002-2011 53.3%
Information
Trans-
formation
Microprocessor
Integrated Circuits
Number of transistors per
chip
1971-2011 38%
Power ICs Current Density 1993-2012 16.1%
Camera chips Pixels per dollar 1983-2013 48.7%
Light sensitivity 1986-2008 18%
MEMS for Artificial
Eye
Number of Electrodes 2002-2013 45.6%
MEMS Printing Drops per second 1985-2009 61%
Organic Transistors Mobility 1984-2007 94%
Single Walled
Carbon Nano-tube
1/Purity 1999-2011 32.1%
Density 2006-2011 357%
Transistors
Super-
conducting
Josephson Junctions
1/Clock period 1990-2010 20.3%
1/Bit energy 1990-2010 19.8%
Qubit Lifetimes 1999-2012 142%
Number of bits/Qubit
lifetime
2005-2013 137%
Photonics Data Capacity per Chip 1983-2011 39.0%
Computers Instructions per unit time 1947-2009 36%
Instructions per kw-hour 1947-2009 52%
Quantum Computers Number of Qubits 2002-2012 107%
Sources: Funk J and Magee C. Exponential Change: What drives it? What does it tell us about
the future?
i This section is largely based on a paper entitled, “on the shape of the performance vs. time curve.”ii Foster, R. 1986. The Attacker’s Advantage, NY: Basic Books. Butler J 1988. Theories of
Technological Innovation as Useful Tools for Corporate Strategy, Strategic Management Journal 9: 15-29. Utterback, J., 1994, Mastering the dynamics of innovation, Harvard Business School Press. Loch C and Huberman B 1999. A Punctuated-Equilibrium Model of Technology Diffusion, Management Science 45(2): 160-177. Adner R and D Levinthal, 2001. Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation, Management Science 47(5): 611-628. Garcia R and Calantone R 2002. A critical look at technological innovation typology and innovativeness terminology: a literature review, Journal of Product Innovation Management 19(2): 107-182. Adner R 2002. When are Technologies Disruptive? A Demand-Based View of the Emergence of Competition, Strategic Management Journal 23: 667-688. Adner R 2004. A Demand-Based Perspective on Technology Life Cycles, Advances in Strategic Management 21:25-43. Gaimon C, 2008. The Management of Technology: A Production and Operations Management Perspective, Productions and Operations Management 17(1): 1-11. Mudambi R and Swift T 2013. Knowing when to Leap: Transitioning between Exploitative and Explorative R&D, Strategic Management Journal 35: 126-145.
iii Schilling M and Esmundo M 2009. Technology S-curves in renewable energy alternatives: Analysis and implications for industry and government, Energy Policy 37(5): 1767-178iv Chesbrough H 2003. Open Innovation: The new imperative for creating and profiting from technology. Boston: Harvard Business School Press.v This section is largely based on a paper entitled, Do Slowdowns in Old technologies lead to Improvements in New Technologies?vi (Foster, 1986; Utterback, 1994; Garcia and Calantone, 2002; Sood and Tellis, 2005)vii This section is largely based on a paper entitled, “Improvements in Performance and Cost:Are they driven by independent or related activities?”viii (Abernathy and Utterback, 1978; Utterback, 1994; Klepper, S. 1996. Entry, exit, growth and
innovation over the product life cycle, American Economic Review 86(3) 562–583; Adner and Levinthal 2001; Adner, 2002, 2004; Adner R and Zemsky P 2006. A Demand-Based Perspective on Sustainable Competitive Advantage, Strategic Management Journal 27:215-239.
ix Wright T P, 1936. Factors Affecting the Cost of Airplane, Journal of Aeronautical Sciences, 3(4): 122 – 128; Arrow K 1962. The economic implications of learning by doing, The review of economic Studies 29(3): 155-173. Basalla G 1988. The Evolution of Technology, Cambridge University Press. Argote L and Epple D 1990. Learning Curves in Manufacturing, Science 247(4945): 920- 924. Adler P and Clark K 1991. Behind the Learning Curve: A Sketch of the Learning Process, Management Science 37(3): 267-281. Utterback, 1994; Lapre M, Mukherjee A, Wassenhove L 2000. Behind the Learning Curve: Linking Learning Activities to Waste Reduction, Management Science 46(5):597-611. Lipsey, R. Carlaw, K. and Bekar, C. 2005. Economic Transformations, NY: Oxford Univ Press. Winter S 2008. Scaling heuristics shape technology! Should economic theory take notice? Industrial and Corporate Change 17(3): 513–531.x This section is based on a paper entitled, “Rapid Improvements with Little or No Commercial Production: What Drives the Improvements?”xi (Wright, 1936, Arrow, 1962; Argote and Epple, 1990; Adler and Clark, 1991; Utterback, 1994; Lapre et al, 2000)xii This section is based on a paper entitled, “Thinking about the Future: Rapid Rates of Improvement and Economic Feasibility”xiii Daniel Kahneman, Thinking Fast and Slow, 2011xiv Renewable Energy Sources and Climate Change Mitigation: Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. 2013. Tarascon, J. 2009. Batteries for Transportation Now and In the Future, presented at Energy 2050, Stockholm, Sweden, October 19-20.
xv Nagy B, Farmer D, Bui Q, Trancik J 2013. Statistical Basis for Predicting Technological Progress. PLoS ONE 8(2): e52669. doi:10.1371/journal.pone.0052669NREL, 2013.
