Examining the dynamics between aligning a
company’s internal processes to the external
environment and the company’s performance with
a temporal dimension in the aircraft and
semiconductor industry
By
Hannes S. Dietz
in partial fulfilment of the requirements for the degree of
Master of Science in Management of Technology
at the Delft University of Technology,
to be defended publicly on Wednesday May 4, 2016 at 15:00 PM.
Graduation committee
Chairman:
Dr. Robert M. Verburg
Associate Professor, Faculty of Technology Policy & Management, TU Delft
First Supervisor
Dr. Zenlin Roosenboom-Kwee
Assistant Professor, Faculty of Technology Policy & Management, TU Delft
Second Supervisor:
Dr. Haiko van der Voort
Assistant Professor, Faculty of Technology Policy & Management, TU Delft
An electronic version of this thesis is available at http://repository.tudelft.nl/.
This page is intentionally left blank
Executive Summary
Since Bourgeois III and Eisenhardt (1988) have introduced their ground breaking study discussing the
concept of environmental velocity, which describes how fast and continuous/discontinuous the
organizational environment of a company changes in all the relevant dimensions which affect the
company i.e. the dimensions of technology, demand, regulation and competition, much research has
followed on this topic. However the research has come up short in several ways. First of it has assumed
the industry to have the same type of speed for every dimension and thus termed industries as high
or low velocity industries without taking into account that dimensions can differ in terms of their
speed which makes it unjustified to term an industry merely as a high or low velocity industry. An
example for this is the reference of the biotechnology industry as a high velocity industry even though
product development times are around 10-20 years in this industry. Secondly the research only takes
into account the speed of change, and mostly neglected the continuity of change measured though
the concept of direction of change. However the direction of change is an important concept which
can help characterize the environment and in turn enable researchers and managers alike to
understand the industry in which a company is operating in better. Furthermore most of the research
has been done on a conceptual level and neglected actual operationalisations and measurements of
the velocities of the industries. There are only few studies that have operationalized and measured
the environmental velocity, and those that have done so have neglected the direction of change.
Alignment literature has found that matching internal processes and capabilities to the external
environment has positive performance implications for the firm. Regarding a temporal dimension
previous studies have found that matching the internal rates of change to the external rates of change
is beneficial for the company and should be strived for (Kwee 2009, Ben-Menahem, Kwee et al. 2013).
We aim to bring together these two research streams and build upon the theory of environmental
velocity as well as the alignment literature.
One research objective is to advance research about environmental velocity by taking into account
the discontinuity through the concept of direction of change and operationalizing and measuring it in
a comprehensive way. Another one is to challenge the predominant view in existing literature that an
environment can be described with one single velocity which sums up all dimensions. This is done with
the help of velocity homology, a concept which assesses how dis(similar) the different dimensions of
the industry are to each other. Thus the fact that dimensions have different speeds and continuities
is taken into account which results in a multidimensional conceptualization of the environmental
velocity concept. In order to get a better understanding of the performance of companies in different
velocity conditions this concept will then be used to see how companies have managed to align their
internal actions to the environment. Furthermore an objective is to test the interrelationship of the
alignment of internal and external rates and directions of change and the performance of the
companies in the aircraft and the semiconductor industries, two industries which are both high
technology industries and have been previously described as low and high velocity industries
respectively.
In order fulfil the research objective several different steps were taken. First a thorough literature
review was conducted on the topic of environmental velocity with the aim of finding all possible
relevant dimensions which were deemed to be product, technology, demand, regulation and
competition. Subsequently the possible operationalisations and measurements for the speed and
continuity of the five dimensions was assessed and the difficulties that have limited previous research
highlighted. One of the main findings is that the continuity of an industry (direction of change) must
be assessed through qualitative analysis which limits the possibility of researching this concept due to
i
the required time of assessing it. Furthermore literature review on alignment theory with a temporal
context revealed that alignment of internal processes and capabilities to the external environment
was found to be positively related to performance and that positive misalignment is superior to
negative misalignment which built the propositions for our analysis.
Followingly a short introduction and informative background about the two industries and the focal
companies, namely Intel and Boeing were given. Then each measure for the rate of change (speed
characteristic) and direction of change (continuity characteristic) for the three chosen dimensions,
namely product, technology and demand was discussed and analysed in detail. Whereas for both the
aircraft and semiconductor industry the rate of changes were measured through equal indicators,
namely change in number of new product generations (product), change in number of new patents
(technology), change in sales (demand), the direction of change was different and customized for each
industry except for the demand dimension (change in trend in sales). For the semiconductor industry
this was the minimum feature size (technology) the ratio of clock speed to price (product), whereas
for the aircraft industry it was the range, capacity and fuel efficiency per seat (product). For the
technology dimension a purely qualitative study was undertaken which indicated that no
discontinuous change had taken place over the last 25 years. As the operationalization of the direction
of change requires an in depth case study of the industries it becomes clear why there has almost
been no study measuring the concept of direction of change despite its relevance when analyzing an
industry in terms of its velocity.
On the basis of this analysis the velocity homologies of the industries were assessed. It was found that
there were considerable heterogeneity in between the dimensions for both industries. Nonetheless
we find that the semiconductor industry has rather high rates and directions of change in comparison
with the aircraft industry.
Finally the interrelation between aligning the internal rates and directions of change and the
performance of the firm are assessed. For the rate of change closer alignment is connected to higher
performance in the semiconductor industry. Furthermore positive misalignment is associated with
better performance than negative misalignment which is in line with our expectations. For the aircraft
industry at first no effect of alignment on performance could be detected. However after controlling
for the extreme high fluctuations in the product dimension results are in line with the propositions.
Even though further research is needed to confirm our findings we can say that in general our results
show that alignment is beneficial for the company.
We thus find that it is crucial for a manager to understand the environment the company is operating
in taking into account all the different dimensions and then try to align the company to the external
conditions. This however is connected to some challenges. If the velocities associated with the
different environmental dimensions are similar (high homology environment) all organizational
activities should be aligned to this uniform environmental velocity. This is rather straightforward and
simpler to manage. If, on the other hand, the velocity dimensions differ significantly (low-homology
environment), the firm will have to align its internal activities to these dissimilar rates and directions
of change, which will lead to heterogeneous sets of paces and directions of activities within the firm.
This situation can pose a real challenge since it will bring about potential incoherence among subunits.
A possible solution to this are modular and flexible structures which allow room for experimentation.
This can possibly help the company to be more open and flexible to change and operate at the
necessary speed at all levels.
ii
Further studies should take into account the other dimensions of the environmental velocity concept.
Furthermore more industries or the same industries with more data points should be studied and
other factors influencing the performance should be controlled for.
iii
Acknowledgements
I would like to thank all the members of my graduation committee for guiding me to through the
graduation process. Foremost, I would like to express my sincere gratitude to my first supervisor Dr.
Zenlin Roosenboom-Kwee for her continuous support, guidance and motivation. I really appreciate
her time and flexibility whenever I was stuck and appreciate the feedback and insightful comments
during our meetings which helped me regain my focus and overcome the challenges during this
research. I would also like to thank Dr. Haiko van der Voort and Dr. Robert Verburg for their
constructive and valuable feedback during our meetings. Your challenging remarks helped me
understand the shortcomings of my research and enabled me to improve my document. I also really
appreciate the freedom I was given in the research project by all of you.
I would like to thank all my friends for the support and encouragement during this period. Last but not
least I would like to thank my parents for everything they have done for me. You have always
supported and encouraged as long as I can remember and I am extremely grateful for that!
Hannes Dietz
Delft, April 2016
iv
This page is intentionally left blank
v
Contents
Executive Summary .................................................................................................................................. i
Acknowledgements................................................................................................................................ iv
Table of Figures ...................................................................................................................................... ix
Table of Tables ....................................................................................................................................... ix
1 Introduction ......................................................................................................................................... 1
1.1 Research Background .................................................................................................................... 1
1.2 Shortcomings of current research ................................................................................................ 2
1.3 Research Objective ....................................................................................................................... 3
1.4 Research Questions....................................................................................................................... 4
1.5 Research Approach ....................................................................................................................... 4
1.6 Relevance ...................................................................................................................................... 5
1.6.1 Scientific Relevance ............................................................................................................... 5
1.6.2 Managerial Relevance ............................................................................................................ 5
1.7 Outline of thesis ............................................................................................................................ 6
2 Literature Review ................................................................................................................................. 7
2.1 Environmental velocity ................................................................................................................. 7
2.2. Industry clockspeed ................................................................................................................... 12
2.3 Hypercompetition ....................................................................................................................... 13
2.4 Key summary of overlooked aspects of environmental velocity ................................................ 14
2.5 Velocity homology: Incorporating rates and directions of change ............................................. 15
2.6 Effect of environmental velocity on organization....................................................................... 16
2.7 Alignment .................................................................................................................................... 20
2.7.1 Effects of alignment on performance with a temporal dimension...................................... 21
2.7.2 Effect of alignment using the concept of rate of change..................................................... 22
2.8 Summary ..................................................................................................................................... 23
3 Methodology ...................................................................................................................................... 25
3.1 Operationalisation of Five Dimensions of Environmental Velocity ............................................ 25
3.1.1 Technology ........................................................................................................................... 25
3.1.2 Product ................................................................................................................................. 28
3.1.3 Demand ................................................................................................................................ 29
3.1.4 Regulation ............................................................................................................................ 30
3.1.5 Competition ......................................................................................................................... 30
3.1.6 Summary .............................................................................................................................. 31
3.2 Measurement.............................................................................................................................. 32
3.2.1 Measuring rate of change .................................................................................................... 32
vi
3.2.2 Measuring direction of change ............................................................................................ 32
3.2.3 Measuring alignment for rate and direction of change ....................................................... 34
3.2.4 Performance Measurement ................................................................................................. 35
3.3 Short introduction of study sample ............................................................................................ 35
3.3.1 Industries ............................................................................................................................. 35
3.3.2 Companies............................................................................................................................ 35
4 Empirical Settings ............................................................................................................................... 36
4.1 Semiconductor ............................................................................................................................ 36
4.1.1 Semiconductor Industry ....................................................................................................... 36
4.1.2 Intel and competitors........................................................................................................... 37
4.1.3 Rates and directions of change ............................................................................................ 39
4.2 Aircraft ........................................................................................................................................ 42
4.2.1 Aircraft Industry ................................................................................................................... 42
4.2.2 Boeing .................................................................................................................................. 44
4.2.3 Rates and directions of change ............................................................................................ 45
4.3 Summary ..................................................................................................................................... 50
5 Analysis and Discussion ...................................................................................................................... 53
5.1 Descriptive Statistics ................................................................................................................... 53
5.2 Homology comparisons of industries ......................................................................................... 54
5.3 Homology alignment ................................................................................................................... 55
5.4 Alignment of rates of change ...................................................................................................... 57
5.4.1 Semiconductor ..................................................................................................................... 58
5.4.2 Aircraft industry ................................................................................................................... 59
5.5 Direction of change ..................................................................................................................... 62
5.5.1 Semiconductor industry ....................................................................................................... 62
5.5.2 Aircraft industry ................................................................................................................... 64
5.6 Conclusion ................................................................................................................................... 64
6 Conclusion and recommendations .................................................................................................... 66
6.1 Conclusion ................................................................................................................................... 66
6.2 Contribution to Literature ........................................................................................................... 67
6.2.1 Theoretical contribution ...................................................................................................... 67
6.2.2 Managerial contribution and implication ............................................................................ 68
6.3 Reflection .................................................................................................................................... 69
6.3.1 Reflection about choice of companies ................................................................................. 69
6.3.2 Reflection about managerial view ....................................................................................... 70
6.4 Relation to Management of Technology Curriculum.................................................................. 71
vii
6.5 Limitations and future research .................................................................................................. 72
7 Bibliography ....................................................................................................................................... 73
8 Appendix ............................................................................................................................................ 80
8.1 Rate of change ............................................................................................................................ 80
8.1.1 Semiconductor ..................................................................................................................... 80
8.1.2 Aircraft ................................................................................................................................. 86
8.2 Direction of change ..................................................................................................................... 87
8.2.1 Technology Semiconductor ................................................................................................. 87
8.2.2 Product Semiconductor ....................................................................................................... 91
8.2.3 Demand Semiconductor ...................................................................................................... 92
8.2.4 Product Aircraft .................................................................................................................... 93
8.2.5 Demand Aircraft ................................................................................................................... 95
8.3 Tobin’s Q ..................................................................................................................................... 96
8.3.1 Intel ...................................................................................................................................... 96
8.3.2 The Boeing Company ........................................................................................................... 97
8.4 Aggregated alignment rate and directions of change with Tobin’s Q ........................................ 98
8.4.1 Semiconductor ..................................................................................................................... 98
8.4.2 Aircraft ................................................................................................................................. 99
viii
Table of Figures
Figure 1: Development of GDP and innovation-driven growth in between 1960 and 2007, Source:
Jorgensen, Ho et al. 2011 ...................................................................................................................... 37
Figure 2: Absolute airplane speed records, Source: McMaster and Cummings, 2002 ......................... 49
Figure 3: Homology comparison for aircraft and semiconductor industry for entire period of study . 55
Figure 4: Comparison of homologies in semiconductor industries during entire period of study ....... 56
Figure 5: Comparison of homologies in Aircraft Industry during entire period of study ..................... 57
Figure 6: Effect of alignment of rates of change on performance of Intel ........................................... 58
Figure 7: Effect of alignment of rates of change on performance for Intel .......................................... 59
Figure 8: Effect of alignment of absolute rates of change on performance for Boeing ....................... 59
Figure 9: Effect of alignment of rates of change on performance for Boeing ...................................... 60
Figure 10: Effect of alignment with weighted absolute rates of change for Boeing ............................ 61
Figure 11: Effect of alignment with weighted rates of change for Boeing ........................................... 61
Figure 12: Effect of alignment of absolute direction of change on performance for Intel .................. 62
Figure 13: Effect of alignment of direction of change on performance for Intel ................................. 62
Figure 14: Effect of alignment of absolute weighted direction of change on performance for Intel .. 63
Figure 15: Effect of alignment of weighted direction of change on performance for Intel ................. 63
Figure 16: Effect of alignment of absolute direction of change on performance for Boeing ............... 64
Figure 17: Effect of alignment of direction of change on performance for Boeing .............................. 64
Table of Tables
Table 1: Studies on environmental Velocity; Source: Adapted from McCarthy et al. 2010. .................. 8
Table 2: Differences in concepts related to environmental velocity .................................................... 13
Table 3: Set of dimensions used to define environmental velocity ...................................................... 14
Table 4: Examples of high- and low-velocity environments ................................................................. 15
Table 5: Organizational enablers of success in different velocity-environments ................................. 17
Table 6: Suggested and selected operationalizations of rate and direction of change ........................ 26
Table 7: Calculation of measurements ................................................................................................. 32
Table 8: Approach to operationalizing direction of change ................................................................. 33
Table 9: Coding example for alignment of direction of change ............................................................ 34
Table 10: Development of Intel's market share in DRAM, Source: (Burgelman, 1991) ....................... 38
Table 11: Measurements and sources for semiconductor industry ..................................................... 52
Table 12: Measurements and sources for aircraft industry.................................................................. 52
Table 13: Descriptive statistics for rate and direction of change ......................................................... 53
Table 14: Comparison of speed in the industries ................................................................................. 54
ix
1 Introduction
1.1 Research Background
The organizational environment of a company has received significant attention from relevant
literature. This is due to the fact that organizations are heavily influenced and affected by their
external environment in which they are operating. How well they cope with the special conditions and
changes of the environment can decide about survival and failure of the organization (Drazin and Van
de Ven 1985, Venkatraman and Prescott 1990, Zajac, Kraatz et al. 2000, Miles and Snow 2001). Existing
literature argues that alignment of the organization and its internal capabilities to the environment
and to the changes in the environment increases chances of survival of the firm (Drazin and Van de
Ven 1985, Venkatraman and Prescott 1990, Zajac, Kraatz et al. 2000, Miles and Snow 2001).
The organizational environment is comprehensively defined by a set of interrelated key concepts,
namely munificence, complexity and dynamism (Child 1972, Dess and Beard 1984, McCarthy,
Lawrence et al. 2010). Munificence refers to the extent to which the environment is able to support
continuous growth, in other words the degree to which resources are available and accessible to firms
(Dess and Beard 1984, Ketchen, Thomas et al. 1993). Complexity on the other hand, can be defined as
“the heterogeneity and range of an organization’s activities” (Child 1972, p. 3), which includes the
nature of buyers, suppliers and competitors (Ketchen, Thomas et al. 1993). Finally, environmental
dynamism is described by the level and predictability of change in an environment (Dess and Beard
1984). This is determined by the amount of turbulence and instability in an environment, the
frequency as well as direction of changes, which determines the amount of uncertainty for
organizations (Ketchen, Thomas et al. 1993). Each of these concepts is composed of several aspects,
which have in turn different subdimensions. There are in general two approaches to study the
organizational environment. One is to analyse the organizational environment as a whole whereas
another one is to focus on single aspects of it.
The studies which take into account all three major concepts of environmental velocity aim for
conceptualizing and measuring the general environment with the set of core concepts on the highest
level and are characterized by generalizability and simplicity. They give a broad overview, however
they do not achieve to describe the core concepts, their aspects and subdimensions in more detail,
sacrificing accuracy and depth (Dess and Rasheed 1991). This broad approach to analysing the
environment as a whole without looking in more detail at aspects and sub dimension of each construct
is not sufficient for achieving a deeper understanding and insight of the dynamics and interrelatedness
between the single aspects of the environment and the organizational factors.
To counteract this, other scholars have focused on single aspects and subdimensions of the concept
of organizational environment. This allows for deeper examination of specific phenomena (McCarthy,
Lawrence et al. 2010). Hence the researcher can draw detailed conclusions about the interaction of
the subdimension of a specific aspect of organizational environment and the organization itself, which
also facilitates more specific recommendations for practical application. Singular aspects which have
been analysed to this regard, include, inter alia, uncertainty (Milliken 1987) and munificence
(Castrogiovanni 1991).
Another construct which has been singularly analysed and which is a specific aspect of dynamism, is
environmental velocity (McCarthy, Lawrence et al. 2010). Environmental velocity was first introduced
in the management literature by Bourgeois III and Eisenhardt (1988), who described high-velocity
environments as having “rapid and discontinuous change in the dimensions of demand, competitors,
technology and regulation so that information is often inaccurate, unavailable or obsolete” (Bourgeois
1
III and Eisenhardt 1988, p. 816). Thus they defined environmental velocity along two ways, namely
along the multiple subdimensions (demand, competitors, technology, regulation), and for each
subdimensions the rate and direction of change. Rate of change describes the speed or pace of change,
while the term direction of change is used to describe the (dis)continuity of the change.
Even though a decent amount of literature has followed the study of Bourgeois III and Eisenhardt
(1988) there remain some shortcomings in existing research.
1.2 Shortcomings of current research
Some of the research carried out on the topic of environmental velocity following the study of
Bourgeois III and Eisenhardt (1988), has overlooked the fact that the different dimensions like
technology, regulation demand and competitors of the environmental velocity concept differ in their
speed and continuity and cannot always be aggregated and summed up to have a single velocity. Thus
instead of defining the environment to have distinct velocities for each of the different dimension,
authors define the environment to have one single velocity across all the dimensions, like a high,
medium or low velocity. The simple aggregation might be true for some industries, however in other
industries there are different velocities across the different dimensions that cannot be aggregated to
one common level. An example for this is studies which have classified the biotechnology industry as
a high-velocity environment even though for example the product development lead times are around
10-20 years (Judge and Miller 1991, McCarthy, Lawrence et al. 2010). In this case the most prominent
dimension, namely technology, is used to classify the velocity of the industry while other relevant
dimensions besides technology, like product dimension, are not considered. Thus the usage of the
term high-velocity environment in this context is misleading.
To this end and in order to further facilitate understanding the interrelation of the velocities of the
different dimensions, McCarthy, Lawrence et al. (2010) have introduced the concept of velocity
homology. The term homology has been used in the management literature to describe the extent to
how similar two constructs are (Glick 1985, Hanlon 2004, Chen, Bliese et al. 2005). In the case of
environmental velocity it describes the condition as to how similar the different dimensions like e.g.
competitors or technology of environmental velocity are to each other in terms of rate and direction
of change over a specific period of time. An environment is then called to have a high homology if the
rates and directions of change of the different dimensions are relatively similar. Low homology on the
other hand describes the condition of the different dimensions showing dissimilar rates and directions
of change. This in turn has implications on the activities of the firm. If the velocities associated with
the different environmental dimensions are similar (high homology environment) all organizational
activities should be aligned to this uniform environmental velocity. This is rather straightforward and
simpler to manage. If, on the other hand, the velocity dimensions differ significantly (low-homology
environment), the firm will have to align its internal activities to these dissimilar rates and directions
of change, which will lead to heterogeneous sets of paces and directions of activities within the firm.
This situation can pose a real challenge since it will bring about potential incoherence among subunits.
Another caveat of existing studies is that most of them only take into account the rate of change
(meaning the amount or magnitude of change), while neglecting the direction of change (Eisenhardt
1989, Judge and Miller 1991, Eisenhardt and Tabrizi 1995, Nadkarni and Narayanan 2007, Nadkarni
and Narayanan 2007). However this is a very important aspect which should be considered, since it
shows whether change is continuous or discontinuous.
An additional problem of research on environmental velocity has been the associated
operationalisations. Either no direct operationalization or measurement of environmental velocity has
been undertaken, as is the case when illustrative statistics and examples are being used. This leads,
2
inter alia, to environments merely being classified as having a certain velocity without actual
justification for this classification. Or studies that actually operationalize and measure velocity use
quite unidimensional measurements which are not able to capture environmental velocity
comprehensively, by neglecting that environmental velocity is composed of several dimensions. This
is arguably the case due to the difficulties in operationalizing environmental velocity, especially
regarding the direction of change. This study aims to contribute to closing this gap and operationalize
the concept of environmental velocity in a multidimensional way using rates and directions of change
for the different dimensions.
As mentioned it has been proposed that the alignment of internal resources to the external
environment, especially regarding the rates of change is beneficial for the performance of the
company. Similar propositions and insights are missing for the concept of direction of change since it
has been less researched and only been discussed on a conceptual level.
1.3 Research Objective
As seen in the discussion in the previous section, there are still several issues surrounding the concept
of environmental velocity, regarding especially its multidimensionality and the associated
operationalization and measurement of it. The research objective then is to build on the
environmental velocity research and alignment theory and connect them by looking at two
organizations in two different environments with different velocity conditions. The two different
environments chosen are the aircraft and the semiconductor industry. The choice for these industries
was based on several arguments. First of whereas the semiconductor industry has been classified in
previous research as a high-velocity environment, the aircraft industry has been characterized as
having a low velocity (Nadkarni and Narayanan 2007, Nadkarni and Barr 2008, Nadkarni, Chen et al.
2015). Even though one aim of the thesis is to challenge the applicability and truth of giving an industry
one single velocity it will be nonetheless interesting to see how the analysed phenomena namely
homology and alignment differ for different velocity conditions. Secondly, besides having different
velocity conditions the industries have been shown to have similar industry conditions and can thus
minimize the confounding effects of the differences between the industries (Nadkarni and Narayanan
2007, Nadkarni and Barr 2008). Last but not least both of these industries are high technology
industries which make them specifically interesting with regard to the master programme in which
this thesis is embedded. The companies chosen are Intel and Boeing. Both of them are generating
more than 70% of their revenue from the industries under study, which makes them ideal for the
research as the interrelation between the company and the industry can be assumed to be strong.
First objective is the operationalization of the directions of change for different industries. This will
help understand the environment better from a standpoint of continuity/discontinuity. Even though
there is knowledge about the continuity/discontinuity of change available it is rather vague and
implicit and not explicit and measurable. This thesis aims to change that for the industries under study.
This enables researches and managers to better analyse the environment in terms of its
(un)predictability. Furthermore research on this specific concept will enable us to understand the
difficulties which are associated with the measurement of the direction of change which is another
objective.
A furthe objective is to apply and visualize the concept of velocity homology. This concept which has
only been discussed on a conceptual level can help to understand industries better in terms of their
homogeneity or heterogeneity of rate and direction of change of the different dimensions. Thus the
notion that an environment has only one single velocity across all dimensions, which is predominant
in current literature, will be challenged.
3
Lastly another objective is to test the concept of alignment to see whether there are positive
performance implications of aligning internal and external rates and directions of change. This will be
done by looking at how the two organizations have managed to align their rates and directions of
changes and how this is related to their performance.
1.4 Research Questions
The following section will detail the research questions. Firstly, an overview of the research questions
will be presented, followed by a short discussion about the connection between the research question
and the research objective.
RQ1: What are possible dimensions of environmental velocity?
RQ2: What are possible operationalisations of rates and directions of change of the different
dimensions in generic terms and what are the associated difficulties?
RQ3: What have previous studies found regarding the effect of alignment of internal activities to the
external environment regarding the temporal dimension?
RQ4: What are the operationalisations of rate and directions of change of the different dimensions of
for the chosen industries?
RQ5: How can the homologies of the two industries be characterized?
RQ6: What is the relationship between the alignment of the rates and directions of change of a
company and the industry?
Since it is of extreme importance to have reliable and valid measurement the definitions of the
directions and rates of changes of the different dimensions must be done very carefully and
thoroughly. Furthermore it is interesting to see why there has been difficulties associated with
operationalisations of rate and direction of change. RQ1 and RQ2 aim to lay the groundwork to proper
operationalization and answer the aforementioned questions. RQ3 assesses the findings of existent
literature on the performance implication of aligning internal and external rates of change in order to
derive some general hypotheses that can then be tested later on. In order to do so we need a specific
analysis of the different dimensions with valid measurements for the two industries (i.e.
semiconductor and aircraft), which is sought to achieve by answering RQ4. RQ5 and RQ6 then build
upon the previously gained knowledge and analyse the homologies and the connection of alignment
to performance.
1.5 Research Approach
The research will be conducted in three phases. Firstly a literature review will be conducted, followed
by an in depth study of the industries and a qualitative analysis of the industries and their rates and
directions of change. Finally the analysis will be carried out.
In the first phase a literature review will be conducted which will help to identify a list of relevant
dimensions that describe the environmental velocity concept comprehensively. Furthermore the
literature review seeks to identify possible operationalisations and measurements for the rates and
directions of change for the different dimensions. This will also expose the difficulties associated with
the operationalisations which can give us insight into why only little research has been done on specific
concepts. Furthermore literature review on the alignment theory combined with a temporal
dimension will give an overview of what type of effect can be expected from aligning internal and
external rates and directions of change. Hence the literature review provides answers to research
questions 1, 2 and 3.
4
Following the literature review will be a qualitative analysis of the two industries under study. This will
help understand the industries better and enable us to derive meaningful rates and directions of
change for each industry. This is particularly important for the direction of change which, due to the
lack of studies, can be expected to be difficult to operationalize.
Once the rate and direction of change of the two industries have been defined and operationalized it
is possible to analyse the homologies of the industries. This can be done by aggregating the rates and
directions of changes over the period of study and deriving an overall rate and direction of change for
every dimension. This can then be used to characterize the overall environmental velocity of the
industries. Lastly the effect of alignment on the performance of the company can be assessed through
comparing the performance of the company to the aggregated alignment in directions and rates of
change. This will be done by clustering periods with high alignment and low alignment and comparing
the performance of the companies in these heterogeneous clusters via Tobin’s Q.
1.6 Relevance
Since the environment is an important factor influencing how firms fare is need for both managers
and academics to understand the underlying concepts of it. This is especially the case in hightechnology industries where which are relatively dynamic with high rates and directions of change.
The relevance in each aspect is outlined in the following paragraphs.
1.6.1 Scientific Relevance
There are several gaps in the literature regarding the environmental velocity.
1. Most studies on environmental velocity have merely used the definition of Bourgeois III and
Eisenhardt (1988) without providing empirical evidence. They have merely adopted the
definition and discussed it on conceptual level only without providing empirical evidence. This
issue will be tackled in the study by discussing the difficulties and problems of the
operationalisations and measurements, and by building on the theory of McCarthy, Lawrence
et al. (2010) to create a holistic theoretical framework of multidimensional environmental
velocity.
2. Secondly even studies that have empirically tested the environmental velocity through the
rate of change of an industry have neglected the direction of change of the industry. In this
study the direction of change will be empirically tested and the difficulties in operationalizing
it will be highlighted. This can help open up further research on these topics.
3. Furthermore the concept of velocity homology, which has been introduced by McCarthy,
Lawrence et al. (2010) will be empirically tested.
4. Lastly the concept of alignment will be tested with the help of velocity homology and direction
of change in order to see whether there are positive performance implications of aligning
internal and external rates and directions of change. Even though it has been tested how
alignment is interrelated with performance, it has not been done with the help of velocity
homology and direction of change.
1.6.2 Managerial Relevance
From the standpoint of an innovation manager or strategic planner in high technology industries it is
crucial to understand the industry its company is competing in. A part of this is how fast and
continuous the industry is moving. The research will help better understand the two industries under
study in regard to rate and direction of change. Additionally a better understanding of the different
velocity homologies will enable managers to understand the industries better in terms of the different
rates and directions of change for the different dimensions. Measuring and testing the alignment will
help gain an understanding how internal activities should be managed in order to achieve increased
5
performance. Thus both managers of innovation as well as planners in high technology industries can
benefit from the study.
1.7 Outline of thesis
The first section served as an introduction to the topic and provides background information as well
as motivation for the research. Furthermore it introduced the research question and discussed the
scientific and managerial relevance.
In chapter 2 a through literature review will be conducted on the concepts of environmental velocity
in general and specifically with regard to the dimensions of environmental velocity as well as the
interrelation of aligning the internal rate of change to the external rate of change and performance of
the company. This will result in a coherent set of dimensions used to describe the environmental
velocity as well as hypotheses regarding the relationship between alignment and performance.
Chapter 3 follows up on that by discussing the measures and operationalisations for the five
dimensions of environmental velocity through a further literature review. Additionally it introduces
the methodology used in the study. The chapter is closed by a short summary and a discussion of the
associated difficulties in operationalizing and measuring the direction of change which is arguably a
reason for the lack of use in literature of these concepts.
In Chapter 4 the industries as well as companies under study are looked at more closely. First of an
overview of the respective industries is given followed by an introduction of the companies. Then the
measures of rate and direction of change are discussed in more detail, giving special attention to the
direction of change of technology and product through qualitative assessment. The chapters are
closed out by short summaries.
Chapter 5 assesses the velocity homologies of the 2 industries under study analysing both the industry
itself as well as elaborating on the key differences between the two industries. It further analyses the
relationship between the rates and directions of changes of the company and the industry and the
performance of the company. The findings are evaluated and implications are discussed.
The thesis is concluded by Chapter 6 which provides an overview and conclusion of the research and
discusses its implications and limitations as well as avenues for future research.
6
2 Literature Review
In this chapter the main concepts and theories of the thesis will be discussed. First an overview of the
environmental velocity literature and related concepts will be given. Thus we will gain insight into the
most relevant concepts and their definitions in the context of environmental velocity. This will also
help answer research question 1. Following will be a discussion of what studies have found out about
the alignment of organizational factors to the external environment regarding performance which in
turn helps answer research question 2. The chapter is concluded by a short summary and discussion
of the results.
In order to find relevant articles for the field of both environmental velocity and alignment articles
were searched through Google Scholar. Since the field of literature of both environmental velocity and
alignment are not extremely large there was no need to predetermine a certain amount of articles at
which to cut off the search. Furthermore no latest publishing year was determined in order to prevent
excluding relevant but old literature. The used approach were queries on Google Scholar using
relevant search terms such as environmental velocity, industry velocity, industry clockspeed. For the
alignment theory terms that were used were alignment, entrainment, rates of change both on their
own as well as in combination with the above mentioned terms such as environmental velocity. The
queries were sorted for relevance and the abstract of the articles was scanned, and then taken into
account if found appropriate. Furthermore articles which were referenced multiple times by the
selected sources were also scanned and taken into account when deemed to be helpful.
2.1 Environmental velocity
The concept of environmental velocity was first introduced by Bourgeois III and Eisenhardt (1988), in
the context of strategic decision making in the microcomputer industry. They termed the
microcomputer industry as a “high-velocity environment”. They defined this as having high rates of
“rapid and discontinuous change in the dimensions of demand, competitors, technology and
regulation so that information is often inaccurate, unavailable or obsolete” (Bourgeois III and
Eisenhardt 1988, p. 816). However they do not further elaborate on the exact definitions of the
different dimensions (demand, competitors, technology and regulation). Additionally this study does
not actually measure the industry in terms of its velocity. It merely explains the concept of
environmental velocity shortly through the classification of the exemplary industries. Besides having
continuous dynamism or volatility, which describes unpredictability or variation of the industry under
study, a high velocity environment is, according to them, also characterized by high rates of change.
The difference between an environment that has a high velocity and one that is merely volatile lies in
the fact that a volatile environment is described by constant change in the environment, however this
change is not large in nature or rather the rates of change of the dimensions are not high. For example
the forest products and machine tools industries score high on volatility indices due to the fact that
they are very cyclical. However they are not classified to be high-velocity environments by Bourgeois
III and Eisenhardt (1988) because there are no high rates of change in these industries. The
microcomputer, banking and airlines industry on the other hand are classified as high velocity
industries since in these industries the change has large variation and the rates of change are very
high.
Thus Bourgeois III and Eisenhardt (1988) created a construct of environmental velocity with multiple
dimensions, each of which is defined by rate and direction of change. However, even though this is a
path breaking study on which most studies on environmental velocity are based, they discuss the
concept of environmental velocity on a conceptual level only.
7
Table 1: Studies on environmental Velocity; Source: Adapted from McCarthy et al. 2010.
Study
Discussed Phenomenon
Definition of velocity
Measurement of environmental velocity
Bourgeois &
Eisenhardt (1988)
Pace and style of strategic decision making in high velocity
industry
Rapid and discontinuous change in demand, competitors,
technology and/or regulation, such that information is often
inaccurate, unavailable or obsolete
Illustrative statistics and example
Eisenhardt &
Bourgeois (1988)
Effect of politics on strategic decision making in high velocity
environment
As per Bourgeois and Eisenhardt
Illustrative statistics and example
Eisenhardt (1989)
Antecedents of rapid decision making in high velocity
environments
As per Bourgeois and Eisenhardt
Illustrative statistics and example
Judge & Miller
(1991)
Antecedents and outcomes of rapid decision-making in
industries with different velocities
Industry growth coupled with changes in technology and
such other disruptive forces as governmental regulations
- Growth: Change in industry (1) employment and (2) sales
- Technological change assessed through ratings of CEOs and
high-level executives
- Archival data to qualitatively describe additional
competitive, technological and governmental discontinuities
Smith et al (1994)
Effect of top management teams demography and process
on performance in high-velocity environments
Rate of change in technology, demand and competition
Illustrative statistics
Brown &
Eisenhardt (1997)
Effect of continuous change through product innovations on
performance in computer in high velocity environment
Short product cycles and rapidly shifting competitive
landscapes
Illustrative statistics and example
Stephanovisch &
Uhrig (1999)
Strategic decision making practices in a high velocity
environment
Rate of change in demand, competition, technology and
regulation
An illustrative example
Bogner & Barr
2000
Cognitive and sense making abilities in hypercompetitive
environments
Hypercompetition
None
Eisenhardt &
Martin (2000)
Nature of dynamic capabilities in different velocity
conditions
Ambiguous industry structure, blurred boundaries, fluid
business models, ambiguous and shifting market players,
nonlinear and unpredictable change
Illustrative example
Baum & Wally
(2003)
Effect of strategic decision speed on firm performance
Unpredictability and rapid growth
None
8
Oliver & Roos
(2005)
Team-based decision making in high velocity environments
As Bourgeois and Eisenhardt
None
Brauer & Schmidt
(2006)
Temporal development of a firm's strategy implementation
consistency in industries with different velocities
A form of dynamism and volatility
Annual capital market raw beta-value of the industries'
market returns compared to general market returns.
Nadkarni &
Naryanan (2007b)
Relationship between strategic schemas, strategic flexibility
and firm performance in different velocity conditions
Rate of change for product and process technologies and in
competitors’ strategic actions (Industry clockspeed)
- Product dimension: No of new products introduced
- Process clockspeed: Average number of years over which
firms depreciated capital equipment
- Organizational dimension: Average time span between new
corporate strategic actions introduce by all firms in industry
Nadkarni &
Narayanan
(2007a)
How cognitive construction by firms drives industry velocity
The rate of new products, processes and competitive
changes
- Product dimension: No of new products introduced
- Process clockspeed: Average number of years over which
firms depreciated capital equipment
- Organizational dimension: Average time span between new
corporate strategic actions introduce by all firms in industry
Davis & Shirato
(2007)
Selection of WTO disputes in different velocity conditions
Number of product lines and speed of product turnover
- Ratio of R&D expenditure to total revenue
- New product ratio
- patent registrations
Wirtz, Mathieu,
Schilke (2007)
Effect of Strategy on business performance in high velocity
environments
As per Bourgeois and Eisenhardt
Illustrative statistics and example
Nadkarni & Barr
(2008)
Relationship between industry velocity, the structure of top
management’s cognitive representation of the environment,
and the speed of response to environmental events
As Bourgeois and Eisenhardt
Rate:
- Number of new products introduced
- Time span between new products introduced
- depreciation rate of capital equipment
Volatility:
- In accordance with Dess & Beard (1984), regressing a
variable each year on a variable for net industry sales
Davis, Eisenhardt
& Bingham (2009)
The implications of velocity on structure and performance
Speed of rate at which new opportunities emerge
Rate that new opportunities flow into the environment using
a Poisson distribution model
9
McCarthy,
Lawrence, Wixted
& Gordon (2010)
Multidimensional conceptualization of environmental
velocity
Rate and direction of change in product, technology,
demand, competitor and regulatory dimension
Illustrative example and statistics
Jones & Mahon
(2012)
Relationship between explicit and tacit knowledge in high
velocity environments
Environments where change is large, rapid and
discontinuous
Illustrative example
Nadkarni, Chen &
Chen (2015)
Effect of interplay between executive temporal depth and
industry velocity on competitive aggressiveness and firm
performance.
Rate at which new opportunities emerge and disappear in an
industry
Competitive actions (total competitive actions/number of
firms) of these dominant firms in a given year.
10
Table 1 lists major studies which since have used environmental/industry velocity or very similar
concepts as a core of their research. Since some studies use the term industry velocity instead of
environmental velocity and refer to the same concept, the two terms will be used interchangeably in
the following.
As can be seen in Table 1, many studies built on the definition of Bourgeois III and Eisenhardt (1988).
However, these studies which use the definition of Bourgeois III and Eisenhardt (1988) usually just
provide illustrative statistics and examples and do not explicitly measure the environmental velocity.
This means that, in many cases industries or environments are postulated to have a certain velocity
without further justification. An example is the study by Wirtz, Mathieu et al. (2007), in which the
effect of strategy in high-velocity environments is analysed. Their study focuses on the ICT-industry
which they describe as a high velocity environment. To prove their claim, they provide qualitative
examples of discontinuous change in the different dimensions. They do not discuss rate or direction
of change in more detail or in a more comprehensive way.
A notable exception, which actually measures environmental velocity is the study by Nadkarni and
Barr (2008). In this study three indicators are used to measure industry change, namely number of
new products introduced, time span between new products introduced and depreciation rate of
capital equipment. Additionally they use the concept of volatility, calculating it by regressing a variable
for each year on a variable for net industry sales. Thus they assess four different industries regarding
their velocity. With the concept of volatility the authors take into account how unpredictable an
environment is, however they disregard whether changes are continuous or discontinuous (direction
of change).
There are also studies that have provided their own definition of environmental velocity, even though
they are usually at least loosely based upon the definition of Bourgeois III and Eisenhardt (1988).
Definitions of environmental velocity, which have been used without actually measuring it: short
product cycles and rapidly shifting competitive landscapes (Brown and Eisenhardt 1997);
unpredictability and rapid growth (Robert Baum and Wally 2003); ambiguous industry structure,
blurred boundaries, fluid business models, ambiguous and shifting market players, nonlinear and
unpredictable change (Eisenhardt and Martin 2000); and environments where change is large, rapid
and discontinuous (Jones and Mahon 2012). In all of these studies illustrative statistics and examples
are being used to assess the velocity of the discussed industry. Nonetheless the overview shows that
even the authors that have come up with their own definitions of environmental velocity or rather
definition of a high velocity environment characterize it through some type of rapid and
unpredictable/discontinuous change in some specific dimensions of the environment.
The issue of measurement remains problematic however. Stepanovich and Uhrig (1999), which define
environmental velocity as rate of change in demand, competition, technology and regulation, e.g.
state that “it should be apparent that health-care is a high velocity environment” (p.198) to then
provide a short qualitative descriptions of why this assessment is justified. No further justification for
this claim is provided. This shows the issue of rather weak measurement, which also leads to
inconsistencies, as Judge and Miller (1991) e.g. have defined health care environments to have a
medium velocity.
There are also studies providing their own definition of environmental velocity which provide more
than merely illustrative examples and statistics. Davis and Shirato (2007) define velocity as the number
of new product lines and the rate of product turnover and operationalize it with the ratio of R&D
expenditure to total revenue, the new product ratio as well as patent registrations. Davis, Eisenhardt
et al. (2009) and Nadkarni, Chen et al. (2015) which define environmental velocity as rate or speed at
11
which new opportunities emerge (and disappear) in an environment operationalize it with the rate
that new opportunities flow into the environment using a Poisson distribution model, and competitive
actions (total competitive actions/number of firms) of the dominant firms in a given year, respectively.
Even though these studies provide proper operationalisations and measurements of their definitions
of environmental velocity they do disregard the direction of change and thus fail to comprehensively
describe the environmental velocity.
A study which is an exception and does measure the concept of environmental velocity explicitly and
also takes into account the direction of change, is that of Judge and Miller (1991). They analyse the
rate of change through industry growth with the indicators of employment growth, sales growth and
perceived pace of technological change while they assess the discontinuities in the environment
(direction of change of environment) through changes in competitive actions, new technologies and
government initiatives. Thus they generate an overarching velocity of the industry through assessing
rate and direction of change of its different aspects. However direction of change is not explicitly
measured, an environment is just defined to have a high or low direction of change through some
exemplary changes in the assessed dimensions. Another weakness of their approach, which they have
in common with all other studies listed in Table 1 and which characterizes the current literature on
environmental velocity is that they have neglected the possibility that a firm’s environmental velocity
is composed of multiple, distinct rates and directions of change and instead aggregate all dimensions
describing the environment to have one single velocity. Judge and Miller (1991) e.g. judge the
biotechnology industry to be a high-velocity environment, even though it has long productdevelopment times and product life cycles of around 10-20 years each. By not taking into account the
product dimension they thus arguably miss the fact that the biotechnology industry is not an overall
high velocity environment. This illustrates the difficulty of assigning an environment a single velocity
aggregated over several dimensions.
Even though McCarthy, Lawrence et al. (2010) also merely use illustrative statistics and examples to
discuss the concept of environmental velocity they help clarify the issues of the existing literature.
They propose five dimensions, namely technology, demand, competitor and regulatory, and the
product dimension. Furthermore this study is the first one to actually properly define each dimension
in regards to the rate and direction of change, as opposed to other studies which have mostly used
the concept of Bourgeois III and Eisenhardt (1988) without clear definition.
There have also been other advancements towards the velocity of an industry/environment which are
quite similar but have been termed differently. Two of the most similar constructs are industry
clockspeed and hypercompetition which are respectively explained in the following sections.
2.2. Industry clockspeed
Industry clockspeed is defined by the 3 facets of product, process technology and the organization
(Fine 1998). Taken together these dimensions reflect changes on the industry-level based on the
aggregate actions by all the incumbent firms in the industry. Industry clockspeed is determined by the
collective actions of the incumbent firms and thus is an endogenous concept.
The industry clockspeed concept – like many other studies carried out on environmental velocity only takes into account rate of change, and neglects direction of change. The product dimension
describes how fast new product launches are being introduced into the market. The process
technology dimension describes how fast process technology ages and is renewed over the course of
the years. The organizational dimension takes into account the rate of change of strategic actions and
structures of incumbent firms.
12
The concept of industry clockspeed clearly has commonalities with the previously discussed definition
of environmental velocity and its dimensions. The product and process technology dimensions capture
the same as the product and the technological dimensions respectively. The organizational dimension
on the other hand, takes into account some but not all of the aspects of the competitive dimension.
Environmental velocity furthermore takes into account the demand and regulatory dimensions, which
are not taken into account by the industry clockspeed concept. This is due to the fact that industry
clockspeed only captures changes that are endogenous to an industry (Nadkarni and Narayanan 2007).
Furthermore studies using the industry clockspeed concept also do not differentiate between the
speeds of the different dimension but assign a single speed to the whole industry thus are in line with
other studies on environmental velocity in this regard.
2.3 Hypercompetition
Hypercompetition is another concept which is closely related to an environment with high velocities.
Hypercompetitive environments are defined by rapid changes in environmental factors such as
technology and regulation, low barriers of entry and ambiguous consumer demand. Thus the concept,
equivalently to environmental velocity, also takes into account competitors, technology, regulation
and demand. The core premise is that firms in hypercompetitive environments cannot earn above
average profit for a sustainable period of time based on a single innovation or competitive advantage.
Hypercompetition is triggered by changes that are large in scale and scope in terms of technology,
competition or regulatory changes (Bogner and Barr 2000). Thus the construct of a hypercompetitive
environment is also an aggregated construct similar to environmental velocity. However there are two
differences to the concept of environmental velocity
1. First, the concept of hypercompetition describes a specific (binary) situation of an
environment. There is no differentiations between states of hypercompetition as is the case
for industry velocity. Industries can have different velocities for the different dimensions but
they can only be classified as either being hypercompetitive or not being hypercompetitive.
2. Another difference is that the basic premise of hypercompetitive environments is that in these
type of environments competitive advantage cannot be sustained. While this is the case for
many environments that have high levels of velocity across their dimension it is not a
requirement for them. Thus one can say an environment has high levels of velocity across the
dimensions and can be called a high velocity environment even if advantage is sustainable in
that specific environment.
It can be argued that a hypercompetitive environment is a special form of an industry or environment
with high velocities in all dimensions. As is the case for industry clockspeed, hypercompetition does
not take into account direction of change but only rate of change. Table 2 summarizes the key traits
and differences of the three discussed concepts.
Table 2: Differences in concepts related to environmental velocity
Concept
Perspective
Defining
dimension
Environmental velocity
Industry clockspeed
Exogenous
and Endogenous
endogenous
 Product
 Product
 Technology
 Process
technology
 Demand

Organization
 Competition
 Regulation
Hypercompetition
Exogenous
and
endogenous
 Technology
 Demand
 Competition
 Regulation
13
Key difference
to
environmental
velocity
concept


Only
endogenous
perspective,
which explains
missing
of
demand
and
regulation
dimensions
No direction of
change



Hypercompetition
describes specific
(binary) situation
Only
environments in
which
no
competitive
advantage can be
sustained
are
hypercompetitive
No direction of
change
2.4 Key summary of overlooked aspects of environmental velocity
All in all we can say that even though many different concepts have been used to analyse the
environment we can comprehensively sum these up to five dimensions namely the four original ones
mentioned by Bourgeois III and Eisenhardt (1988) which are technology, competition, regulation and
demand and an additional one namely product dimension. These five dimension cover the
environmental velocity comprehensively. The dimensions and studies that have used them are shown
in Table 3. All of these five dimension have distinct rates and directions of change.
Table 3: Set of dimensions used to define environmental velocity
Dimension
Product
Technology
Demand
Regulation
Competition
Studies
Brown & Eisenhardt (1997), Nadkarni & Naryanan (2007b), Nadkarni
& Narayanan (2007a), McCarthy, Lawrence, Wixted & Gordon (2010)
Bourgeois & Eisenhardt (1988), Judge & Miller (1991), Smith et al
(1994), Stephanovisch & Uhrig (1999), Nadkarni & Naryanan (2007b),
Nadkarni & Narayanan (2007a)
Bourgeois & Eisenhardt (1988), Smith et al (1994), Stephanovisch &
Uhrig (1999), McCarthy, Lawrence, Wixted & Gordon (2010)
Bourgeois & Eisenhardt (1988), Stephanovisch & Uhrig (1999),
Bourgeois & Eisenhardt (1988), Smith et al (1994), Brown &
Eisenhardt (1997), Stephanovisch & Uhrig (1999), Nadkarni &
Naryanan (2007b), Nadkarni & Narayanan (2007a)
The rate of change is a relatively straightforward concept, which is also called pace, frequency or clock
rate. It describes the amount of change in a certain dimension over a specific period of time. This can
be measured by assessing the quantity of changes in a specified amount of time, e.g. the number of
new products introduced in a year compared to the number of products introduced the year before.
The percentage change is then an indicator of the rate of change. This concept is quite easy to
understand and is also relatively straightforward to calculate once adequate indicators have been
found (Bourgeois III and Eisenhardt 1988).
The direction of change on the other hand is a construct which has been disregarded in most of the
existing research. Arguably this is because direction of change is not an intuitive and easily
understandable concept when applied to the change of an environment. Whereas the direction of an
object can be easily described by the cardinal points or by relative directions such as up, down, left,
right, forward and backward, the concept of direction of change of an environment is much harder to
14
grasp. The original definition by Bourgeois III and Eisenhardt (1988) describes the direction of change
by characterizing change in terms of its continuity/discontinuity. Continuous change represents a
change that is an extension of past development. This includes minor seasonal changes that are
relatively regular and predictable. It also includes change which is relatively predictable and follows a
recognizable pattern. Discontinuous change on the other hand is described by more severe changes
that alter the status quo and bring about great changes in the industry. Greater unpredictability and
magnitude are further characterization of discontinuous change (McCarthy, Lawrence et al. 2010).
Thus the direction of change of an environment can be characterized by either continuity or
discontinuity. This gives a good indication how the dimensions are changing while at the same time
ensuring that the concept can be measured consistently across different industries.
2.5 Velocity homology: Incorporating rates and directions of change
In order to be able to better conceptualize the concept of environmental velocity and make use of the
fact that there are several different dimensions with different velocities the concept of velocity
homology is introduced.
Velocity homology describes how similar or dissimilar the rates and direction of change of the different
dimensions are. An environment is defined to have a high velocity homology if the rates and directions
of change of the different dimensions are relatively similar. Low homology on the other hand describes
the condition of the different dimensions showing dissimilar rates and directions of change. Most of
the existing literature on environmental velocity has implicitly postulated the environment to have a
high velocity homology, since environments have been simply termed as having a high or low
environmental velocity, thus aggregating the velocity for an environment over all the dimensions. This
aggregation is justifiable only if the environment indeed has a high velocity homology, because only
then a simple aggregation over the different dimensions would make sense, because the rates and
directions of them are very similar. For example an environment with low levels of rate and direction
of change in each dimension has a high velocity homology and can be called a low velocity
environment. Accordingly an environment with high levels of rate and direction of change across all
dimensions, would also have a high velocity homology and could be called a high velocity
environment. However there are arguably also industries where some of the dimensions have a high
velocity in terms of rate and direction of change, while others have a low or medium velocity. These
environments then in turn have a medium or low velocity homology and cannot simply be called high
or low velocity environments since a single aggregation does not make sense.
Table 4: Examples of high- and low-velocity environments
Examples of low-velocity environment
Examples of high-velocity environment
- Aircraft industry
- Food packaging industry
- Metal and plastic industry
- Office furniture industry
- Paper industry
- Petrochemical industry
- Ship-building industry
- Steel industry
- Textile industry
- Tire and rubber industry
- Tobacco industry
- (Personal) computer industry
- Athletic footwear industry
- Biomedical industry
- Biotechnology industry
- Computer software industry
- Cosmetics industry
- Electrical industry
- Electronics industry
- Healthcare industry
- Informational industry
- IT industry
- Microcomputer industry
- Motion picture and entertainment industry
- Movie industry
- Semiconductor industry
- Semiconductor industry
- Telecommunications industry
- Toys and games industry
15
The fact that these types of environments exist, has been mostly neglected by studies regarding
environmental velocity because existing studies have consistently used a single velocity and defined
it to be high, medium or low. Consequently only little is known about environments with a low or
medium level of velocity homology. Thus we can conclude that the concept of velocity homology can
be very helpful in characterizing the velocity of an environment.
Table 4 gives an overview over which industries have been classified as high or low velocity industries
in previous studies. One should be aware that for some of these industries the pure label of high or
low velocity will not work since they have a low velocity homology. Nonetheless the table provides a
good overview over how different industries have been assessed regarding their environmental
velocity.
2.6 Effect of environmental velocity on organization
Several studies carried out in the environmental velocity literature underline the fact that in order to
be successful firms must adjust their actions according to the velocity of the environment they are
operating in. Table 5 lists relevant studies which incorporate the performance of a firm as a key
concept in different velocity settings.
As can be seen most existing studies which have analysed different phenomena which lead to superior
performance in environments with high and low velocities are related to the timing of internal actions.
Existing studies suggest that that firms in fast- and slow-clockspeed industries need to have different
capabilities, speeds of decision making, strategic responses and organization structures. Furthermore
successful strategizing differs significantly in low and high velocity environments (Brauer and Schmidt
2006, Nadkarni and Narayanan 2007). All in all, the findings in existing literature underline the fact
that, in order to be successful, firms must have very different approaches, that are tailored to and
depending on the velocity of the environment/industry they are operating in
However it can also be seen that existing empirical studies analysing the concepts of environmental
velocity and performance have focused mainly on the speed and frequency of product innovations as
well as decision speed rather than giving fuller consideration to organization-wide factors regarding
the rate and direction of change.
16
Table 5: Organizational enablers of success in different velocity-environments
Study
Methodology
Relevant findings
Independent
Variable
Measurement of dependent
variable
Measurement of
performance
Sample
Design
Analysi
s
Bourgeois &
Eisenhardt
(1988)
4
microcomput
er firms
Multiple case
study;
longitudinal;
multi-method;
actual strategic
decision-making
process
Content
analysis
Strategic decision making
positively affects firm
performance in high velocity
environments
Strategic
decision making
Decision (1) involving strategic
repositioning or redirection of firm, (2)
having high stakes, (3) involve as many
functions as possible, (4) considered
representative of major decisions
(1) Market acceptance
of each company's major
product
(2) CEO's numerical selfreport of effectiveness
(3) sales and profitability
Eisenhardt,
(1989)
8
microcomput
er firms
Multiple case
study;
longitudinal;
multi-method;
actual strategic
decision-making
process
Content
analysis
Decision speed is positively
related to firm performance
in high velocity
environments
Decision speed
Duration, using beginning (first reference
to deliberate action) and end
(commitment to act) of each decision
(1) CEOs' numerical selfreports of company
effectiveness (0 to 10
scale), (2) a comparison
of that rating to ratings
CEOs gave to
competitors, and (3)
sales growth and
profitability figures
before and after the
study
Judge & Miller
(1991)
Executives
from 32
organizations
in the
biotechnolog
y, hospital
and textiles
industries
Field study;
cross-sectional;
semi-structured
interviews;
archival data;
recent SDs
made by the
firms
Correlati
on and
regressio
n analysis
Decision speed is positively
related to firm performance
in high velocity
environments
Decision speed
Duration using beginning (first reference
to deliberate action) and end
(commitment to act) of each decision
- profitability
- sales growth
Mod.
Var.
Med.Var.
Real time
information; multiple
simultaneous
alternatives; Twotier advice process;
- Consensus with
qualification; Decision integration
Environm
ental
velocity
- Number of
alternatives
considered; Board
experience
17
Brown &
Eisenhardt
(1997)
81 interviews
of low- and
high-level
respondents
in 6 firms in
computer
industry,
Interviews;
questionnaires;
observations;
secondary
sources
Case
writing,
cross
case
analysis
Successful product portfolios
have positive affect on firm
performance
Successful
product
portfolio
Presence of positive portfolio
characteristics (i.e., on schedule, on time
to market, on target to market projects)
and the absence of negative ones (e.g.,
make-work, competing, stop-gap,
stripped, endless, stuttering projects).
-Market position
- revenue
- profitability
Nadkarni &
Naryanan
(2007)
225 firms
from 14
industries
COMPUSTAT
database
Causal
mapping;
Structura
l
Equation
Modellin
g
Complexity and focus of
strategic schemas influence
strategic flexibility which has
effect on strategic
performance
- Complexity of
Strategic
Schemas (+)
- Focus of
Strategic
Schemas (-)
Complexity: Comprehensiveness (total
and connectedness (measured through
causal maps)
Focus: Centralization and eigenvector
centrality (measured through causal
maps)
- Sales growth
- ROI
- Net income growth
Wirtz, Mathieu
& Schilke (2007)
754 senior
executives of
companies in
the ICT
industry in
Germany
Survey
questionnaire
Structura
l
Equation
Modellin
g
Positive effect of strategy
construct on business
performance in high velocity
environments
- Strategy
- Product
differentiation
- Image
differentiation
- Focus
- Pro activeness
- Replication
-Re
configuration
- Cooperation
- Differentiation in a market place by
distinguishing products and services from
those of competitors
- Company’s uniqueness caused by
psychological, attitudinal positioning
- Concentration on a narrow market
segment
- Continuous search for opportunities of
improvement and early pursuit of those
opportunities
- Redeployment of knowledge and
competencies from one economic setting
to another
- Creation of new knowledge and
competencies in a company
- Access to external resources through
cooperative arrangements
- Growth
- Profitability
Industry
Clockspe
ed
Strategic Flexibility
(+ for high-velocity
environments, - for
low-velocity
environments)
(measured by
variety in resource
deployment, shifts
in resource
deployment,
competitive
simplicity, and shifts
in competitive
action)
18
Nadkarni et al
2015
258 firms in
23 industries
Triangulation
between Letter
to
shareholders;
management's
discussion and
analysis in the
10-K forms;
executive
conference calls
with analysists
Generaliz
ed least
squares
model
Executive temporal depth
exhibits different patterns of
relationships with
competitive aggressiveness
in low- and high-velocity
industries. Competitive
aggressiveness has a positive
main effect on firm
performance, which is
stronger in high than in low
velocity environments
- Executive
temporal depth
- Competitive
Aggressiveness
3 step content analysis on time horizon;
structured content analysis
Return on sales (ROS)
and return on assets
(ROA)—at the end of the
same year as the
competitive
aggressiveness measure.
Afterwards combination
of the z-scores of the
two measures into a
composite measure of
firm performance
(Brimley and Harris,
2014).
Industry
velocity
(industry
clockspe
ed)
19
2.7 Alignment
The empirical findings that organizations must act differently and accordingly to the environmental
velocity they are operating in, is backed up by alignment theory, which states that in order to survive
over time - and thus accordingly to perform well – firms should achieve fit or alignment with their
environment. Several studies have proposed and examined this principle. The agreement is that if the
organization manages to create alignment/fit between its organizational capabilities, internal
processes resources and the different aspects of the environment this has positive implications on the
survival and performance of the firm (Drazin and Van de Ven 1985, Venkatraman and Prescott 1990,
Zajac, Kraatz et al. 2000, Miles and Snow 2001). Venkatraman and Prescott (1990) for example, analyse
how the coalignment of the strategy of a firm and its environment influences the performance of
firms. They first propose an ideal profile of strategic resource deployment for each environment and
then measure how the deviation from this ideal profile influences the performance of the companies.
They provide empirical evidence that a more fitting coalignment of strategic resource deployment and
environment leads to superior performance.
Whereas the concept of alignment itself seems to imply a static match between an organization and
the environment at given time, it is necessary to also understand it from a dynamic perspective. It is
important to analyse whether an organization can achieve fit with over time changing environmental
conditions, because a static fit between the company and the environment at any given time does not
mean that the fit will remain, once time passes and environmental conditions change. This is why
there are two differing theories which discuss how alignment/fit is achieved over time.
The environmental selection perspective proposes that the environment is the deciding force which
determines which firm characteristics best fit the environment (Hannan and Freeman 1984, Hannan
and Freeman 1993). According to this perspective firms can only improve their existing routines,
however they cannot change them. If firms have capabilities and characteristics that do not fit the
environment they are sorted out. Firms with characteristics and capabilities matching the
environment’s requirements on the other hand will be successful. They then, in turn, will do more of
what has made them successful to further increase their success. This, however will lead them to
become relatively inert after some time. This means that in the selection perspective organizations
are not able to respond to changes in the environment. They are either matching the environment a
priori and thus become successful or they do not match the environment, which leads them to failure.
However there are also other, less deterministic views of that theory which suggest that firms can
actively manage to achieve fit with their environment. But they can only do so in response to external
change. This principle is called responsive fit. It is further assumed that mostly, organisations are not
able to manage to change at the necessary speed to match the changes in the external environment.
This in turn means that even though organizations can achieve fit with their environment, in
environments with high velocities they will not be able to match the rate of change of the
environment.
On the other end of the spectrum is the adaptation perspective which hypothesizes that firms can
actively manage the change in the organization so that it matches the external changes (Child 1972).
This perspective proposes that well managed companies can achieve a firm-environment fit and thus
increase chances of superior performance. Furthermore according to this perspective, these firms can
also actively try to induce changes in the environment which alter and shape the firms environment.
This means that there is interdependence between the firm and the environment and changes can be
endogenously induced. Firms are not always passive recipients of the influence of the environment
but can also actively influence the environment (Child 1972, Miles and Snow 2001). This means that
firms should be aware of the different ways in which they can influence the environment.
20
Organizations do not just react to the environment but actually enact their own environments (Tan
and Tan 2005). If firms achieve fit in this environment it is called proactive fit (Eisenhardt and Martin
2000). This view is in stark contrast to the selection perspective which sees the environment as the
dominant force which cannot be influenced by the firm.
Merging the environmental selection and the adaptation perspectives, a co-evolutionary approach is
taken, which interprets survival of the firm as a joint outcome of selection pressures from the
environment and adaptation of the firm (Volberda and Lewin 2003, Kwee 2009). With this approach
it can be argued that in order to be successful over a sustained period of time, the firm must achieve
to co-align its internal pace of change to the change in the environment. Matching organizational
transformation to environmental shifts is thus crucial in order to achieve organizational survival.
This is supported by Volberda and Lewin (2003) who discuss the interrelation between the pace of the
environment and the organization. They suggest that one of the three prerequisites of a self-renewing
organization, which describes an organization that adapts itself over time according to its
environment, is matching or exceeding the pace of the external rate of change with the rate of change
within the organisation. This means that organizations, which manage to stay on top of the changes
in the external environment are able to perform better in the industry. This proposition has also been
recognized by business leaders. Jack Welch, CEO of General Electric (GE) stated in the 2000 annual
report of GE “when the rate of change inside an institution becomes slower than the rate of change
outside, the end is in sight.” This supports the claim that internal rate of change must not be lower
than the external rate of change. This claim is also supported by the concept of entrainment, which
has been discussed in literature and is closely related to the one of alignment. Entrainment describes
the condition of one system synchronizing its activity cycles to those of another, more dominant
system, which is called the time giver.
In the case of environmental velocity the environment is the time giver and the organization is the
part which should synchronize its activity cycles to the environment (Ancona 1996, Pérez-Nordtvedt,
Payne et al. 2008). Equivalently to the alignment concept, the consensus is that firms that manage to
match the temporal aspects of their competitive actions to the temporal characteristics of the
environment achieve superior performance, whereas firms that fail to do so face major losses
(Nadkarni, Chen et al. 2015). However as can be seen there is a discrepancy in the exact type of
alignment that must be achieved. Whereas some authors (Volberda and Lewin 2003, Kwee 2009)
argue that the internal activities should be not slower, which means as fast or faster than the
environment, others (Ancona 1996, Pérez-Nordtvedt, Payne et al. 2008) argue that it should be
completely matched or aligned, meaning that if internal activities are faster than external activities,
this is also detrimental for the company. The consensus however is that alignment will be beneficial
for the company.
As can be seen there does exist a fair amount of literature which recognizes the importance of
organizations being aligned to the temporal aspect of the environment. However there have not been
many studies that have actually analysed and empirically tested this temporal dimension of strategic
change. Even fewer studies have linked the alignment of internal and external rate of change to
performance. The following section will discuss studies that have done so.
2.7.1 Effects of alignment on performance with a temporal dimension
Tan and Tan (2005) show in their study how organizations have managed to adapt and coevolve their
strategies to the changing conditions in the Chinese business environment. They find that the firms,
which have implemented new strategies fitting to the changed environment have enjoyed superior
performance. Specifically they first assess how the environment has changed regarding environmental
21
dynamism, complexity and hostility. They then hypothesize and also provide empirical evidence that
these changes in the environment require from firms a higher willingness to take risks, which is in turn
positively related to performance of the firms.
Zajac, Kraatz et al. (2000) take a similar approach and analyse how firms adapt their strategies to the
changing environmental conditions and what implications their changing strategies have on
performance. They find that organizations that match their strategies to the changing environmental
conditions outperform those that do not. Furthermore they find that if the environmental conditions
change significantly and the organization manages to match that change, this is even more beneficial
than when there is only little or no change in the environment and firms manage to match the change
in this case. On the other hand they also find that organizations that change less than necessary (show
lower rates of change than the environment), perform worse than organizations that change more
than necessary (higher rates of change than the environment). This leads them to their conclusion
that insufficient change is a greater danger than excessive change.
2.7.2 Effect of alignment using the concept of rate of change
Kwee (2009), tests in her study –inter alia- to what extent 2 long-lived firms in the oil industry, namely
Royal Dutch Shell plc and British Petroleum have managed to align their internal rates of change to
the external rate of change. Thus in this study the earlier mentioned proposition by Volberda and
Lewin (2003) is put to the test, namely that in order to survive over time companies must align their
internal rate of change to the external rate of change. Measures of rates of change are divided into
homogeneous (same measures for industry and firm level) and heterogeneous parts (different
measures for firm and industry level). This is done due to limited data availability. Homogeneous
measures are rate of change of oil production, patents, research and development intensity, and
external venturing (mergers, acquisitions, joint ventures, and interorganizational alliances).
Heterogeneous measures on the other hand are the rates of change of oil prices and competition for
the industry level. For the firm level the heterogeneous measures are rates of change of new product
and services, new process technology, restructuring in the organization and internal expansion.
They find that both firms, which have survived and performed well in the market for a long time have
managed to match or exceed the external rate of change with their internal rates of change. Thus they
confirm the principle that in order to survive over time, organization should manage to align their
internal rates of change to the external rates of change. However there is no direct test of the relation
between alignment and performance. It is merely stated that the companies under study have overall
managed to align their rates of change to that of the environment over the entire period of study.
In a similar vein Ben-Menahem, Kwee et al. (2013) research the effect of alignment on performance.
They take a knowledge based perspective in their study. They analyse how absorptive capacity, which
describes the capability of a firm (Royal Dutch Shell plc) to attain and assimilate externally generated
knowledge, influences the ability of a firm to align its internal rate of change to the external rate of
change. They measure internal rate of change by calculating the yearly percentage change of strategic
renewal actions undertaken by the firm under study. To achieve a comprehensive picture they divide
the strategic renewal actions in 5 different categories namely: 1) new products and services, 2) process
innovations, 3) internal venturing (e.g., business start-up and termination), 4) external venturing (e.g.,
mergers and acquisitions, joint ventures, alliances), and 5) organizational restructuring. The external
rate of change on the other hand is measured by the rate of change in the price of crude oil, which is
justified by the relevance for Shell in its reflection of changes in the environment, and its effects on
the company’s strategic decisions and profitability. They find that there is a positive relationship
between absorptive capacity and the ability to align internal and external rates of change.
22
Furthermore they also assess how the alignment of internal and external rate of change affects the
performance, which is measured in terms of market share and confirmed by gross profit margin. They
find that performance is higher in times when the internal rate of change is close to or exceeds the
external rate of change than performance in times when the internal rate of change lacks behind the
external rate of change. Thus they provide initial evidence for the suggested theory that alignment of
internal rate of change to external rate of change is associated with superior performance.
As can be seen there is initial empirical evidence that it is beneficial for a firm to adapt its internal rate
of change to the external rate of change in order to be successful. However similar insights are missing
for the direction of change. This is the case due to the fact that there are only very few studies that
have taken into account the direction of change. No study up to this date has analysed what
interrelation exists between the direction of change that the company is taking and the direction of
change of the industry as a whole. However, even though these insights are missing for the concept
of direction of change, it can be argued that similarly to the rate of change, organizations should also
manage to align the direction of change of their internal actions to the direction of change of the
environment. This is the case since, as described in the previous paragraphs, it is crucial for a firm to
achieve a fit to the environment. This means that the organization should achieve fit in all important
aspects, direction of change being one of them. Thus it will be interesting to see whether an alignment
of direction of change is indeed connected to superior performance.
2.8 Summary
Now that the variables and their connection has been emphasized, the next section will sum up our
findings of the literature review.
To sum up we can say that studies carried out on the topic of velocity in the environment have used
several different dimensions to define environmental velocity. Most of the extant studies have only
used some and not all dimensions simultaneously. It can be argued that with the dimensions of
technology, demand, competition, regulation and product the environmental velocity can be
described in a collectively exhaustive way. No concepts were found in studies that would not fit under
one of these dimensions. However it must be noted that the separation is not completely mutually
exclusive as there is still some minor overlap in between the dimensions. Nonetheless it is relatively
exclusive and seems to be the best possible way to clearly and collectively describe the environmental
velocity. This provides an answer to the first research question.
Furthermore each of these dimension has a distinct rate of change and direction of change. The rate
of change describes the amount of change over a period of time in the specific dimension, is easily
understandable and has been used in most existing research. The direction of change on the other
hand describes how continuous or discontinuous the changes are. Most existing literature does not
take into account direction of change, most likely due to its difficulty in operationalisation and
measurement. Existing studies also neglect the fact that industries have different velocities across the
different dimensions. A useful concept to counteract against this trend is the one of velocity homology
which describes how similar/dissimilar the different dimensions of the industry are in respect to each
other.
There is evidence that co-alignment of internal organizational activities to the environment positively
affects the performance of a company. Internal activities must be different for different velocity
environments and firms should try to achieve a fit with their environment. Specifically with regard to
the temporal dimension studies have found that alignment of the internal change to the external
change is beneficial for a company. Furthermore it has been found that whereas alignment of the
organization and the environment is always associated with superior performance, it is even more
23
beneficial in environments that exhibit stronger changes. However there are also some discrepancies.
Whereas some studies have found that it is only necessary to not have a lower rate of change and that
a similar or higher rate of change internally than externally is positive for performance, others have
found that it is necessary to be completely aligned and to not surpass the rate of change of the
environment. Thus there is no consensus among existing literature as to which effect a higher internal
rate of change compared to the external rate of change has on the performance. With regard to the
direction of change no studies were found that tested performance implications. This is very likely
because the direction of change is a very little used concept. However it can be argued that also the
internal direction of change should be aligned to the external direction of change since alignment of
relevant internal processes to the external environment is necessary and beneficial in every regard.
Thus we can sum up how previous studies have found alignment to be interrelated with performance.
Closer alignment of rates and directions of change is associated with better performance than
misalignment. Firms outrunning their environment in terms of rate and direction of change are
connected to higher performance than firms being outpaced by their environment.
24
3 Methodology
This chapter will discuss each of the five previously named dimensions and their rates and directions
of change in more detail, including the methodology used in analysing the concepts. Furthermore the
concepts of measuring rates and directions of change will be discussed.
However first, since a lot of terms and variables have been introduced in the previous chapter, a short
review of them and their interrelation will be presented. This will help understand the following
sections explaining the measurements and operationalisations. The velocity of the environment
describes how fast (rate of change) and discontinuous (direction of change) an industry changes in the
relevant dimensions of technology, product, demand, regulation and competition. Since for each of
the dimension the speed and continuity can be different the concept of velocity homology is
introduced which explains how similar or dissimilar the dimensions are to each other in terms of their
speed and continuity. Literature has found a positive interrelation between aligning the organization
to the environment. This is why we argue that the same can be expected for aligning the organization
to the environment in terms of its velocity. Thus we argue that the applicable internal processes or
capabilities need to be aligned to the external environment (e.g. rate of new product introductions or
improvements by competitors, changes in customer expectations, changes in technology) in terms of
the velocity. For both the company and the industry the velocity homology will then be measured to
then analyze the alignment of these two. If there is stronger match they are then stronger aligned and
positive performance implications are expected.
3.1 Operationalisation of Five Dimensions of Environmental Velocity
In order to evaluate the velocity homologies of the firm, the industry and the effect of alignment on
firm performance, the key concepts must be clearly defined in a way that enables operationalization
and measurement. As previously discussed in the literature review, especially for the direction of
change this can be a challenge. In order to gain insight into how a proper operationalization can be
achieved, each dimension will be first discussed on generic terms to then analyse it for the 2 specific
industries in detail in the following chapter. The specific analysis will be necessary for some
dimensions, for which a generic operationalization and measurement is very difficult. Whereas this is
necessary for the later operationalization and measurement in this study it is will also help shed light
on the difficulties of operationalization which is arguably a cause for the lack of studies in this field.
Table 6 provides an overview of the studies that have operationalized and used different measures
for direction of change. Bold means that these are the measures which are selected while italics signify
that this measure has only been suggested on a conceptual level but not been empirically tested or
used.
3.1.1 Technology
The technology dimension describes the change in production processes and component technologies
of a specific industry. The rate of change captures the amount of change of the technologies of an
industry over a specific period of time, which includes the creation of new technologies the
improvement of current technologies and the combination of technologies (Bourgeois III and
Eisenhardt 1988, McCarthy, Lawrence et al. 2010). This can vary significantly for different industries.
In terms of direction of change of technologies one can distinguish between continuous and
discontinuous change. Continuous change is the improvement/refinement of existing technologies,
whereas discontinuous change is a more radical change which will improve the technology by orders
of magnitude. It can be argued that “major technological innovations represent technical advance so
significant that no increase in scale, efficiency, or design can make older technologies competitive with
the new technology“ (Tushman and Anderson 1986, p. 441). .
25
Table 6: Suggested and selected operationalizations of rate and direction of change
Dimension
Product
Operationalization of rate of change

Studies
Nadkarni & Naryanan
(2007b), Nadkarni &
Narayanan (2007a), Davis
& Shirato (2007), Nadkarni
& Barr (2008)
Judge & Miller (1991),
Nadkarni & Naryanan
(2007b), Nadkarni &
Narayanan (2007a), Davis
& Shirato (2007)


No of new products introduced
measured through total product
introductions or product generations
Time span between new products
introduced
high-level executives' perceptions of
the pace of technological change in
their industries (Judge & Miller)
Average number of years over which
firms depreciated capital equipment
Number of patents
Ratio of R&D to total sales
Demand

Change in sales
Judge & Miller (1991)
Regulation


Technology


McCarthy, Lawrence,
Number of laws and regulations
Wixted & Gordon (2010)
introduced
Competition
(1999), Nadkarni &
 Average time span between new
Naryanan (2007b),
corporate strategic actions introduce
Nadkarni & Narayanan
by all firms in industry
 Competitive actions (total competitive (2007a), Nadkarni, Chen &
Chen (2015)
actions/number of firms) of these
dominant firms in a given year
Italics: Operationalization only suggested in literature but not applied in study
Bold: Operationalization, which will be used for this study
Operationalization of direction of
change
The change in the nature of product
features as perceived by the market
in a given period
Studies
Archival data to qualitatively describe
technological discontinuities
Judge & Miller
(1991)
The change in the trend (e.g., growth
versus decline) and nature (e.g.,
personal versus impersonal) of
demand in a given period
Archival data to qualitatively describe
governmental discontinuities
Archival data to qualitatively describe
competitive discontinuities
McCarthy,
Lawrence,
Wixted &
Gordon (2010)
Judge & Miller
(1991)
Judge & Miller
(1991)
McCarthy,
Lawrence,
Wixted &
Gordon (2010)
26
Literature suggests that one can then further separate between competence enhancing and
competence destroying discontinuities (Tushman and Anderson 1986). This classification is done on
the basis of whether the discontinuity destroys or enhances the capabilities knowledge and skills that
have accumulated over the years in the industry. Competence enhancing discontinuities are order-of
magnitude improvements in performance or price performance of the process technology. Where
they do bring about enormous improvement in the production of the product they rely on the same
skills and knowledge of the previous mode of producing and thus benefit the incumbents in the
industry. Competence destroying discontinuities represent a new way of making a given product, as
is the case when new processes or technologies are used. This can be achieved by combining steps
that were previously discrete into a more continuous flow or it may involve a completely different
process (Tushman and Anderson 1986). Competence destroying discontinuities are so different that
previously required knowledge and skills are not helpful anymore and such portray a shift in core
technology. These type of discontinuities are beneficial for newcomers and problematic for
incumbents as new skills and knowledge are required in order to successfully adapt.
Judge and Miller (1991) measure the rate of change of technology through high-level executives'
perceptions of the pace of technological change in their industries. Whereas this will certainly permit
a good understanding of the rate of change of an industry it is too time consuming to be used in this
study. Other authors using the clockspeed concept (Nadkarni and Narayanan 2007, Nadkarni and Barr
2008) have used the average number of years over which firms depreciated capital equipment as an
indicator for the speed of the process technology. Whereas this is also a good measurement no yearly
indicator can be created with this approach. Again others have used the ratio of R&D to total sales
(Davis and Shirato 2007). Also this is a valid indicator however it is more focused on the input rather
than the technological output. This is why we decided to measure the rate of change of the technology
dimension through the number of patents of an industry/firm is granting per year/the change in that
number over the years. Even though it is clear that not all changes in the technologies are patented
because either they are not patentable or simply because they are not patented due to strategic
reasons, the number of patents over a specific period of time is nonetheless a good indicator of the
technological output of an industry (McCarthy, Lawrence et al. 2010). Furthermore it is relatively easily
available and accessible. This measurement will be stable across all industries that are analysed since
it is does not include any factors that need to be specifically taken into account for differing industries.
The direction of change on the other hand, is as described before, characterized by either continuous
or discontinuous change. In order to operationalize this dimension it is helpful to think about the
trajectory of the technology. The direction of change is continuous if the performance steadily
improves along a continuous trajectory. If on the other hand there is a change in the technology,
triggered for example by a radical innovation, the performance curve will have an injection point. As
can be seen in Table 6 only one study has actually operationalized this measure and done so through
qualitative assessment of the industry.
One suggestion has been to show this trajectory along a performance/price curve of a technology
(McCarthy, Lawrence et al. 2010). Continuous change means that the performance/price curve moves
steadily downward. If there is a radical innovation, this will alter the shape of the performance/price
curve so that it becomes concave upward until the benefits are reaped and the curve becomes
concave downward again. This signifies a discontinuous direction of change. However there is only
little data available which concerns the price and performance relationship of specific technologies.
Thus one possible reason for the lack of use of the concept of direction of change, besides the difficulty
in interpreting it, could be the limited data availability especially regarding performance price curves
27
of technologies. This is why it will be helpful to have other possible operationalisations and
measurements at hand in case price data is not available.
One alternative possibility is to omit the price and merely look at the development of the performance
of the technology and assess it regarding its continuity (Abernathy and Clark 1985, Tushman and
Anderson 1986). Steady development means continuous direction of change while inflection points
hint to discontinuities in direction of change. A drastic change in the performance of the process
technologies used to fabricate the product or a change in the technology that alters the competitive
situation in the industry and forces firms to readjust their technological processes are discontinuities.
Furthermore it can then be evaluated if it is a competence-enhancing or competence-destroying
discontinuity by assessing how it affected the previously required knowledge and skills of the
incumbents in the industry.
In order to properly operationalize and measure the direction of change, binary coding can be applied.
Continuous changes can be coded with a 0 whereas discontinuous changes, which radically alter the
performance of technologies, can be coded as 1. Thus direction of change of the technology dimension
is split into continuous and discontinuous through binary coding.
As can be seen the direction of change for the technology dimension is not as straightforward as the
rate of change. Whereas for the rate of change, one simply needs to measure the patents, for the
direction of change the technology and its trajectory needs to be assessed. This brings about several
difficulties.
There are many technologies which are used and combined in order to deliver a final product. In order
to measure direction of change one needs to find out what the crucial technologies are for the specific
industry under study, which in turn means that each industry has specific indicators for the direction
of change of technology that depend on its specific conditions. Furthermore one then needs to find
out how to assess the technology regarding the continuity/discontinuity. A third difficulty is the
availability of required data. This is why for each industry the direction of change of the technology
will be discussed in more detail.
3.1.2 Product
The rate of change of products is the amount of change in new product introductions and
enhancements. In the case of direction of change, continuous change is at hand when the changes are
built on existing products or are improvements of existing products. Discontinuous changes are
represented by products that offer something new for the consumer.
The rate of change of the product dimension can be defined by how many new products or product
enhancements are entering the market or being introduced into the market over a certain period of
time. This concept has also been termed product clockspeed by Fine (1999) and since been used
frequently in studies related to environmental velocity (Mendelson and Pillai 1999, Nadkarni and
Narayanan 2007, Nadkarni and Narayanan 2007). It can be measured in 2 distinct ways. The first way
to measure it is by analysing the “intervals between new product generations” (Fine 1999, p.2). An
alternative approach is to measure the rates of new product introductions on the individual product
level. Basically both the measures give an indication how many new products are entering the market
over a specific period of time. Again this measurement is straightforward and is comparable across
different industries.
The direction of change of the product dimension on the other hand is described by how customers
and consumers perceive the changes in products over time. If the changes in products are merely
enhancements or small improvements of previous existing attributes this is continuous. If the changes
28
however, introduce fundamentally new attributes and characteristics or radical change in the most
important characteristics this is discontinuous change. As seen in Table 6 no study was found that
measured this concept in the environmental velocity research. It has merely been suggested on a
conceptual level.
In order to measure this specific construct, one needs to familiarize oneself with the specific industry
under study. The question at hand is what exactly the most important attributes of a certain product
are in the eyes of the customer/consumer. This can vary greatly depending on the industry and
product, differing products for which price will be the main factor to products for which performance
metrics are the main factor affecting purchasing decisions. The measurement will be done
equivalently to the direction of change for the technology dimension as that it will be binary coded
along continuity/discontinuity, depending on the changes in the product. The direction of change of
the product will also be discussed in more detail for each industry as it is an indicator which needs to
be developed individually for each industry.
The product dimension was not included in the original definition by Bourgeois III and Eisenhardt
(1988), and subsequently research which has followed their definition has ignored the product
dimension and has implicitly included it in the technological dimension by lumping these two
dimensions together. This is problematic however since, even though while they might seem similar
on the first look, they are distinct dimensions with different meanings, and thus there is an important
differentiation between the product and the technology dimension. There can be stark differences in
the rate and direction of change in technology and products in an industry. In some cases the products
will change fast while the underlying technologies remain relatively stable, whereas in another
industry the exact opposite will be the case. For example in some industries the technologies and
processes used to fabricate the products have changed severely while the end products themselves
have changed only little in comparison. An example is the car industry (McCarthy, Lawrence et al.
2010). The opposite might be the case as well, of which the fashion industry is an example. Of course
there can be overlapping as is the case when a breakthrough in technology is at the same time a
breakthrough for the product itself, however this is not always the case. This notion is captured by
Abernathy and Clark (1985,p.4) who argue that ”technological innovation may influence a variety of
economic actors in a variety of ways, and it is this variety that gives rise to differing views of the
significance of changes in technology. What may be a startling breakthrough to the engineer, may be
completely unremarkable as far as the user of the product is concerned.” This is why the differentiation
between the product and the technology dimension is quite important. A separation between the two
dimensions helps get a clearer insight into the industry.
3.1.3 Demand
Demand is defined by the change in willingness of customers and consumers to pay for certain goods
or products. This includes changes in the number and types of transactions. This dimension is
influenced by a variety of factors, like changes in consumer preferences, competitors, substitute
products as well as switching costs. The rate of change is how the overall demand changes over a
specific period. The direction of change on the other hand is continuous when the development in
demand is steady along a certain trend and discontinuous when there are significant and
unpredictable shifts in the demand.
The demand dimension displays the rate and direction of change in the “willingness and ability of the
market to pay for goods and services” (McCarthy, Lawrence et al. 2010, p. 609).
The rate of change of demand, which is influenced by many different factors like changes in taste, new
competitors or changes in relative prices, can be measured by sales figures. The amount of sales per
29
year for a certain company/industry gives an easy to track and reliable measure of how the demand
changes over time (Judge and Miller 1991, McCarthy, Lawrence et al. 2010).
The direction of change in the demand dimension can be assessed along 2 criteria. The first indicator
is the trend in the sales over the years. If the sales are steadily increasing or decreasing over a period
of time it can be said to be continuous. Thus if sales are increasing at a similar rate this will be coded
as continuous direction of change. If, on the other hand, the sales are suddenly increasing or
decreasing at a very different pace, or if the trend is reversed, it will be coded as discontinuous change.
Additionally the direction of change can be conceptualized by the change in the nature of the buyers.
If the customer group stays the same it is continuous, if there are major shifts in customer groups the
change is discontinuous.
3.1.4 Regulation
The regulatory dimension captures the change in laws and regulations which affect the firms in an
industry. It encompasses both governmental action like changes in laws, as well as industry selfregulation like voluntary standards or codes (Bourgeois III and Eisenhardt 1988). Changes in this
dimension can open or close whole markets or require large strategic shifts. This dimensions is
dependent on several other factors like technology or business developments, demographic
developments or health and safety issues. The rate of change is merely the amount of changes to this
regard in an industry in a certain period of time. The direction of change on the other hand is
continuous when new regulations are similar to existing ones in scope and form and discontinuous
when new regulations are introduced dealing with distinct issues not dealt with previously.
Again the rate of change is can be measured in a straightforward approach, namely by the number of
laws and regulations that are passed in a certain amount of time in or regarding an industry (McCarthy,
Lawrence et al. 2010).
The direction of change on the other hand describes how these regulations equal or differ the existing
and previously passed ones. In order to analyse that, a qualitative analysis of the laws their purpose
and their implications must be carried out (Judge and Miller 1991).
3.1.5 Competition
Finally the competitive dimension is about the change of structure of competition within an industry
regarding its profitability. It is influenced by entry and exit of firms as well as speed and scale of
competitive responses to strategic action. The rate of change is the amount of change in industry
population size and density. The direction of change can be mapped across the continuitydiscontinuity continuum with regard to the supply-chain or nature of rivals. Continuity is characterized
by stable changes while discontinuity is at hand if there are major shifts (McCarthy, Lawrence et al.
2010).
The rate of change can be assessed by the number of firm entries and exits in the industry and the size
of the competing firms as well as the speed of competitive responses in the industry. Another
measurement would be the average time span between new corporate strategic actions introduce by
all firms in industry (Fine 1999, Nadkarni and Narayanan 2007).
The direction of change of the competitive dimension can be measured by assessing changes in the
competitive environment of the company regarding the nature of rivals, the change in the
contestability meaning changing barriers to entry and exit or the value chain of the industry (Porter
2008).
30
3.1.6 Summary
As discussed each dimension has a distinct rate and direction of change. Having described and
analysed each dimension and its rate and direction of change in more detail as well as possible
measures of rate and direction of change several things can be highlighted.
The lack of literature on the concept of direction of change despite its importance in environmental
velocity was an indication of the difficulty in operationalization and measurement of this very concept.
Whereas the rate of change is rather straightforward and can be measured by calculating the changes
in the number of indicators which are more or less publicly and easily available the measurement of
the direction of change requires a far more tedious and complicated approach. In order to properly
operationalize and measure the direction of change, specifically for the technology and product
dimension one needs to have a good understanding of the industry and its underlying dynamics. The
continuity or discontinuity of change can only be assessed qualitatively and not merely by change of
some indicators. Thus a qualitative in depth analysis of the industry under study is necessary in order
to assess the direction of change of an industry.
For the direction of change of the product dimension one needs to be able to pin down the most
important aspects of the product(s) which the company is producing to then measure the change and
development in these aspects. There are two distinct difficulties associated with this approach. First
of all the identification of the main important aspects of the product. This can be challenging because
a product has many different attributes and several of them are important in the eye of the customer.
Furthermore a lot of times there is not only one typical customer but several ones with different
requirements, which means that attributes have different importance depending on the customer.
The second challenge is to find measurements for these attributes. While this can be less of a challenge
for technical products in which the attributes are mainly performance based, it will be a very
challenging in industries in which the products are not rated based on their performance but on less
easy to measure attributes like in the fashion industry. Whereas one can argue that it is possible to
discuss the changes in such an industry it will be very difficult to measure them.
For the direction of change of technology similar problems are at hand. There are many different
technologies and production techniques used in the fabrication or production of products. Of course
this is again heavily dependent on the industry and varies significantly between them. The problem in
this case is that it is almost impossible to find data on the ratio between performance and price as
suggested by authors as McCarthy, Lawrence et al. (2010). This is why indicators need to be looked
for that can depict the change in the underlying production technologies of the industry that can
indicate whether changes were continuous or discontinuous. This requires an in-depth study of the
industry under investigation and a thorough search process for the right indicators. This will have to
be done through qualitative research and many times coding, otherwise no reliable and valid results
can be achieved. This means that an in-depth study of the industry must be undertaken, while yet the
danger of oversimplification of the variable remains.
These two dimensions will be the most difficult to analyse in terms of direction of change due to the
limited data availability and difficulty in comprehensively capturing the key characteristics. In order to
measure the direction of change of the other three dimensions qualitative analysis will have to be
done. Even though this is also a challenging task it will be easier due to less complex attributes of these
dimensions.
This subchapter provided an answer to research question 3 by discussing operationalisations of the
rates and directions of change for the different industries and highlighting the associated difficulties.
31
3.2 Measurement
In this section the approach of measuring rate and direction of change as well as the alignment will be
discussed. As this study is limited due to time and resource constraints, it will focus only on the 3
dimensions of product, technology and demand for both the firm and the industry level. Furthermore
these three dimensions are the ones that a company needs to seek alignment with the most. One can
argue that the parts of the competitive dimension are indirectly included in the product and
technology dimension, since on the industry level it will be measured how fast and discontinuous new
products are technologies are introduced by competitors. Furthermore whereas the regulatory
dimension is also an important one for the company it will be hard to actually be aligned to it, besides
by being able to be flexible and ready to change in case of laws that alter the competitive situation in
the market. Hence we believe that the three dimensions used in the research are the most relevant
ones. Thus even though the competitive dimension and the regulatory dimension, are omitted, the
three dimensions used, already provide a detailed analysis of environmental velocity.
3.2.1 Measuring rate of change
As mentioned before the rate of change indicates how fast the magnitude increases or decreases per
unit of time. Thus in order to calculate the rate of change, the formula shown in Table 7 will be used.
The rates of changes can have positive and negative values as well as a value of zero. A negative value
means that there is a decrease of rate of change over time while a positive value signifies an increase
in rate of change over time. A value of zero on the other hand means that there is no change at all.
This formula was used for the industry level as well as for the firm level.
For the rate of change of the products a 3 year moving average was used. This is done due to the fact
that new product introductions did not happen every year. This can result in high fluctuations in the
computational results of the rates of change. To counter this problem, the three-year moving average
for smoothing out the fluctuations was used.
Table 7: Calculation of measurements
Measure
Rate of change
Formula
𝑅𝐶 (%) = (
𝑋𝑡
− 1) ∗ 100
𝑋𝑡−1
Symbol
 RC: rate of change (in
percentage)
 Xt: value from the later
point in time
 Xt-1: value from the
earlier point in time
3.2.2 Measuring direction of change
For the direction of change on the other hand there is need for a qualitative analysis of the industry
and the underlying key factors. Table 8 shows what key questions must be answered in order to
achieve relevant characterizations of the direction of change for the industry. These questions are
derived from the previous discussion about the measurement and operationalization of rate and
direction of change.
For the technology dimension the most important questions are what type of technologies are used,
how the progress of these technologies can be measured and how the change over the years can be
characterized in terms of its continuity/discontinuity. For the product dimension the most relevant
questions to ask are what the most important product attributes are from the point of the customer,
how these can be measured and how they have changed over time. The demand dimension analyses
32
how the trend has changed. While one part is numerical and will analyse the discontinuity in sales, it
will also be analysed whether there have been significant shifts in the customer demographics.
By answering these key questions useful indicators will be achieved which can then be used to analyse
the industries regarding its direction of change.
Table 8: Approach to operationalizing direction of change
Dimension
Technology
Product
Customers
Key questions
 What are the most important technologies used in the production
process?
 What indicators are there for these technologies?
 What are the most important characteristics of the product from the
view of the customer?
 What indicators are there for these characteristics/how can these
characteristics be measured?
 How has the demand changed in terms of its nature?
Once the relevant indicators have been found out the measurement is straightforward. The specific
indicators for each industry will be discussed in the following chapter. Every time a discontinuous
change is taking place this is coded as a 1, if there is no change or only continuous change this is coded
as a 0.
The direction of change for the product dimension is also assessed through binary coding. The
indicators and ratios that are used will be discussed in the next chapter. Specific ratios will be derived
for each industry. If there is a change which improves the status quo by a significant amount this can
be seen as a product discontinuity, since it is an order of magnitude improvement in product
characteristics. Previous studies on discontinuities have used a similar approach (Tushman and
Anderson 1986, Anderson and Tushman 1990). If there is a discontinuous change this will be coded
with a 1 all other years in which no discontinuous change is taking place this will be coded with a 0.
The exact coding for each industry can be found in the appendix.
The direction of change of demand is determined through several steps. In the first step the
percentage change in sales for each year was calculated. This shows how much the sales in- or
decreased percentage wise and is already an indicator for the trend in sales development. However
there is still some refinement needed in order to get a clearer picture of when the shifts in sales are
significant or unpredictable which signifies discontinuous change. This is due to the fact that even if
the change was extremely high this would not mean it is discontinuous if it was continuously (meaning
each subsequent period) that high. Only if there is a change in the trend the change can said to be
discontinuous. This is why the difference in change of each year was calculated. The average of the
absolute change values was then calculated. If the change was more than double of the average of all
absolute values for the entire period change this was coded as discontinuous change. This is the
threshold because while variation can be expected in sales an in- or decrease of more than double the
average of absolute values is a significant change and is thus coded as discontinuous. The exact coding
is shown in the appendix.
Thus for all the dimensions of the direction of change it is assessed through binary coding. Each time
a discontinuous change takes place this is coded as a 1, if on the other hand there is no change or only
small continuous change this is coded as 0. This coding scheme neglects the possibility that some
discontinuous change is stronger as others. However it would be very difficult to numerically assess
33
the actual difference in strength and magnitude in discontinuous change and would arguably lead to
some flawed and biased results. Furthermore this would further complicate the comparability
between different industries.
3.2.3 Measuring alignment for rate and direction of change
Alignment for the rate of change will be measured by the deviation of the internal measure from the
external measure with the formula:
∆𝑅𝐶 = 𝐼𝑅𝐶 − 𝐸𝑅𝐶.
IRC: Internal rate of change
ERC: External rate of change
ΔRC: difference between IRC and ERC
This is a valid approach since both the internal and external indicators are based on the same measures
and thus are additive. This deviation score approach has been used in several other studies and is a
well-accepted way of measuring alignment or co-alignment (Drazin and Van de Ven 1985,
Venkatraman and Prescott 1990, Zajac, Kraatz et al. 2000, Kwee 2009). Thus a negative value means
that the rate of change of the environment exceeds the rate of change of the company while a positive
value signifies the opposite.
In order to measure whether alignment is at hand for the direction of change a different approach is
taken. If the same approach was taken and the difference would be calculated this would lead to
results which have not much meaning. In order to show the used approach and explain the motivation
for the used approach an example is given which is depicted in Table 9. If for example a discontinuous
change in the technology dimension was introduced by the company in period 1 and the industry
would introduce a similar discontinuity in period 3 and the approach of measuring alignment of rate
of change would be used this would result in a coding of 1, 0, and -1, as shown in the Table. This
however would not make sense since e.g. in period 2 there is still no alignment, alignment however is
indicated by a 0 which would be the result of coding with the same approach of coding the rate of
change. Since the company still is operating with the discontinuous approach which the industry did
not pick up on yet it should still be coded as a +1. Furthermore in period 3 a coding of -1 would mean
that the industry had a discontinuous change which the company did not have yet and there was
misalignment. However in reality the discontinuous change in the industry similar to the one the
company introduced 2 periods earlier actually signifies alignment. This explains why the coding would
be 1, 1, and 0, meaning that misalignment is coded as long as there is misalignment. This approach
was used for the direction of change of the product and technology dimension.
Table 9: Coding example for alignment of direction of change
Period
1
2
3
Industry
0
0
1
Company
1
0
0
Approach for measuring rate of change
1
0
-1
Used approach
1
1
0
In case of the alignment of the direction of change of the demand, the difference between the
percentage changes of industry and company was assessed and if it superseded a threshold it was
coded as 1 otherwise as 0. The exact coding is found in the appendix.
34
3.2.4 Performance Measurement
In order to measure the performance of the companies, Tobin’s Q was used. This is a common
measure in order to capture the performance of a firm and has the advantage of capturing short term
performance and long-term prospects (Uotila, Maula et al. 2009). Tobin’s Q is defined as the ratio
between a physical assets market value and its replacement value. In general it is very laborious and
difficult to calculate Tobin’s Q due to the difficulties in estimating the replacement value. This is why
the replacement value will be approximated by the book value. There are many different formulas for
Tobin’s Q, we will use the approximation as book assets minus book equity plus market value of equity
all divided by book assets. This calculation is consistent with much of the literature (Gompers, Ishii et
al. 2001, Brown and Caylor 2006, Coles, Daniel et al. 2008). Because no data on replacement cost of
assets or market value of debt is available, it is only an approximation. However this measure avoids
the ad hoc assumptions about depreciation and inflation rates that some other measures of Q require.
Furthermore the approximation is likely to be highly correlated with actual Q. Studies have shown that
this proxy explains at least 96% of the variability of Tobin’s Q of Lindenberg and Ross (1981) (Chung
and Pruitt 1994).
3.3 Short introduction of study sample
3.3.1 Industries
The study will focus on the semiconductor industry (which has previously been termed a high-velocity
industry) and the aircraft industry (which has been previously classified as a low-velocity industry) in
the USA and one company in each of the industries. The USA is chosen as the market to analyse, since
data is more easily and readily accessible for this region. The choice for these industries is deliberate
and was based on several arguments. First of whereas the semiconductor industry has been classified
in previous research as a high-velocity environment, the aircraft industry has been characterized as
having a low velocity (Nadkarni and Narayanan 2007, Nadkarni and Barr 2008, Nadkarni, Chen et al.
2015). Even though one aim of the thesis is to challenge the applicability and truth of giving an industry
one single velocity it will be nonetheless interesting to see how the analysed phenomena namely
homology and alignment differ for different velocity conditions. So even though the term high and
low velocity industry may not be true we believe that there are certain general differences in the
speed of the industries which make it interesting enough to contrast them. Secondly, besides having
different velocity conditions the industries have been shown to have similar industry conditions and
can thus minimize the confounding effects of the differences between the industries (Nadkarni and
Narayanan 2007, Nadkarni and Barr 2008). Both the semiconductor and aircraft industry are hightechnology industries.
3.3.2 Companies
The companies chosen are Intel and Boeing. The choice is based on the fact that both these companies
are prominent firms in the industries they are operating in and are single-industry firms, meaning that
they draw more than 70 percent of their revenue from the core business (Rumelt 1974).This is
important so that realistic conclusions can be drawn from the interrelation of the firm and the industry
they are operating, since this specific industry is the main focus of the companies.
A further and very pragmatic reason for the choice of companies was the amount of data that was
available for these companies. Especially in the period dating back 20 or more years, only limited
amount of data can be expected to be found for smaller companies, especially regarding also the
direction of change. Many scientific articles and books have been published about the companies in
the study which increased the likeliness of gathering much relevant information.
35
4 Empirical Settings
First of a general overview of the respective industries and companies will be given. This is done
through qualitative research of relevant articles, industry accounts and trade journals. This will help
gain have a better understanding of the industry structure, competitive environment and future
direction which is necessary for the next part. Subsequently the history of and most important
characteristics of the company under study will be reviewed shortly. Then the rates and directions of
changes for the industries will be discussed. Since the measures for rate of change are rather selfexplanatory and will remain the same for any industry these will be only shortly discussed
4.1 Semiconductor
4.1.1 Semiconductor Industry
The semiconductor industry is a very prominent industry that produces devices, which are ubiquitous
in many modern day products and has shaped the world we live in today. The origins of the
semiconductor industry can be traced back to the late 1930s when first works on semiconductors
started. The first applications of semiconductors were in radios. A major step in the development of
the industry was the invention of the transistor in 1948 by Bell Laboratories. The transistor, a solidstate switching device, enabled a large decrease of size in electric devices and was thus very popular.
Around a decade later, in the late 1950s the integrated circuit (IC) was invented. The IC is also simply
called microchip, computer chip or chip. Whereas the first integrated circuits had around 12
transistors, todays have more than 20,000,000 (Lojek 2007). This was a breakthrough which enabled
the birth of the semiconductor industry as we know it today. In 1964 the still very young industry
surpassed 1$ billion sales for the first time. Since then continuous progress in process technology and
performance of semiconductors have helped the industry grow continuously.
Today the number of semiconductor components which is used in products and our daily lives is
constantly expanding. Semiconductor chips form the core of many devices that are cutting edge in
terms of technology across all types of industries and products. Examples include smartphones and
tablets, flat-screen monitors and televisions but also modern cars, new aircraft or also medical
devices. This ubiquitous usage is also one of the reasons for the constant growth of the industry.
Besides the rapid growth one of the main characteristics of the industry are the fast innovation cycles
which are taking place in the industry. The famous Moore’s Law states that the number of transistors
on a single computer chip double every 24 months. Even though it has been argued many times that
Moore’s Law would at some point face insurmountable physical limitations the trend has continued
until today. The rapid technological process and the growth in overall demand in turn brings pressure
on the firms and explains another key characteristic of the semiconductor industry, namely large
amounts of capital which is needed to support those two trends (Lojek 2007).
The semiconductor industry is a crucial enabler of innovation also in other industries and thus also a
driver for economic growth. As figure 1 shows from 1960-2007 the U.S. semiconductor industry
accounted for 30 % of all economic growth due to innovation in the United States. Because of its rapid
innovation the impact of the semiconductor industry outsizes its U.S. Gross Domestic Product (GPD)
share. The contribution of the semiconductor industry to real economic growth was more than seven
times its share of U.S. nominal GDP (Jorgenson, Ho et al. 2011). This large effect of the semiconductor
industry can be illustrated by dividing U.S. industries into IT-using, IT-producing and non IT-using
industry groups. IT-producing or IT-using industries – which are both heavily reliant on semiconductor
technology – had a 52.7 percent share of nominal GDP and accounted for a 59.7 percent share of real
GDP growth Most importantly however, all innovation occurred due to IT-using and IT-producing
industries. Even though the semiconductor industry does not have a very long history it is an important
36
industry for the growth and development of the overall economy. It has now grown to be a $336
billion industry.
Figure 1: Development of GDP and innovation-driven growth in between 1960 and 2007, Source: Jorgensen, Ho et al. 2011
Originally the companies in the semiconductor industry were vertically integrated, meaning that the
whole supply chain was owned by the companies. Companies owned and operated their own siliconwafer fabrication facilities and developed their own process technology for manufacturing the chips.
However this trend discontinued when small innovative start-ups began introducing innovative IC
designs and innovative chip solutions. As the manufacturing process is extremely capital intensive and
thus has very high barriers to entry these small companies started to outsource the fabrication to
producers who had excess capacity. This development was the start of the so called fabless business
model. Companies were then able to manufacture ICs without owning a fabrication plant. The
advantage of this business model is that the companies are not burdened by the high overhead costs
and thus have less risk (Lojek 2007).
Now that the impact of the semiconductor industry and its overall structure are clear the development
of competition of Intel and other prominent companies will be analysed.
4.1.2 Intel and competitors
Intel was founded in 1968 by leading semiconductor engineers Robert Noyce and Gordon Moore. It
entered the semiconductor business with the goal of revolutionizing the way data could be stored in
the active memory systems of mainframe computers.
Intel’s first product was a bipolar static random access memory (SRAM) chip, which was introduced in
1969 (Burgelman 1991). This was a breakthrough since it replaced the magnetic core memory which
is a magnet that stores information, and was introduced in the 1940s. This product had become so
refined and entrenched in existing systems that in order to be superseded a huge reduction in cost
per bit was required. Since Intel managed to offer such an advantage with its product the SRAM was
successfully adapted by the market.
However, shortly after, in 1970 Intel produced the world’s first dynamic random access memory
(DRAM) chip, propelled by its process technology breakthrough. At this point in time Intel was well
ahead of its competitors in the DRAM market, since many companies had tried to produce a DRAM
but only managed to design it and not been able to develop the process technology for successful
37
manufacturing. Intel was able to break the DRAM production barrier because of its new MOS process
technology.
Even though DRAMs were a similar to SRAMs in that they were both high density, random access
memory silicon chips, the DRAM offered the advantage of being more cost-effective to manufacture.
This is why DRAMS replaced the magnetic cores, subsequently becoming the standard technology
used by computed to store and process information (Burgelman 1991).
The DRAM, SRAM and also Read-only memory (ROM) market, which can also be described as the
memory business, was the core business of Intel from its beginnings until the late 1970s early 1980s.
In the early years of Intel it accounted for more than 90% of its revenue (Burgelman 1991, Burgelman
1994, Burgelman and Andrew 2001).
However around the mid-1970s the success and mass marketability of DRAM attracted larger
competitors into the market. At that point in time, Intel was still a very young company which then
was competing against large foreign multibillion conglomerates such as Mitsubishi or Hitachi. These
Japanese semiconductor companies were integrated into computers telecommunications and other
similar devices and heavily used their own products. Furthermore they had access to cheap capital
and were much further in manufacturing capabilities, which gave them significant cost advantages.
Whereas production yields for DRAM were as high as 70%-80% for Japanese companies Intel and other
U.S. firms only achieved around 50%-60%. They also possessed better supplier relationships and
superior production facilities (Casadesus-Masanell, Yoffie et al. 2002).
Other external developments were that the nature of the DRAM industry changed dramatically,
namely customers demanding high quality DRAMS with guaranteed performance, reliability and price.
These developments shifted the competitive advantage towards the Japanese manufacturers, which
were superior in production efficiency and high volume production. The strenght of Intel which had
been innovative design were not decisive anymore, since it lacked the necessary manufacturing
capabilities to satisfy the customers. Thus market share started to shift to the more manufacturingoriented firms, such as Texas Instruments and the larger Japanese firms. BY 1984 DRAM accounted
only for 3 % Intel’s sales revenue and production was restricted to one Fab in Intel’s network of eight
plants. Already in October of 1985 Intel produced its last DRAM chip. Table 10 shows the development
of Intel’s market share in the DRAM business over the different generations of the product. It can be
clearly seen that there was a fast and drastic decline in the success in the DRAM business measured
in market share and already towards the end of the 1970s Intel had a rather small share in the DRAM
market.
Table 10: Development of Intel's market share in DRAM, Source: (Burgelman, 1991)
Product
4K
16K 3PS
16K 5V
64K
256K
Total Share
1974
82.9
82.9
1975
45.6
45.6
1976
18.7
37
19
Intel DRAM market share (%)
1977
1978
1979
1980
18.1
14.3
8.7
3.2
27.9
11.5
4.4
2.1
100
94
0.7
20
12.7
5.8
2.9
1981
1982
1983
1984
2.4
66.5
0.2
2.3
33.1
1.5
1.9
11.7
3.5
4.1
3.5
3.6
1.4
12.3
1.7
0.1
1.3
The microprocessor on the other hand, which performs the computing tasks in electric devices, was
also invented in the beginning of the 1970s. However it is unclear which company actually brought
the processor first to the market with different sources citing different companies/people as inventors
(Lojek 2007). While the microprocessor would end up becoming the main business for Intel, in the
38
beginning Intel did not see it as a breakthrough innovation or product. This early attitude towards the
microprocessor is seen in the quote by Gordon Moore, former CEO of Intel: “We never considered the
microprocessor as an invention. We just felt it was integrating more stuff onto one chip. Initially we
didn’t even try to patent the basic microprocessor.” (Casadesus-Masanell, Yoffie et al. 2002). Initially
the microprocessor was mainly used in calculators and as component in industrial controls. Neither
the producing companies nor their customers had much knowledge about microprocessor and its
applicability and huge potential in the early stages. This can be seen by the fact that personal
computers were not included among the top fifty potential applications for the 286 Intel
microprocessor which was introduced in 1982. This is very curious since the IBM PC which entered the
market just shortly after, was the main reason for the huge success of the microprocessor (CasadesusMasanell, Yoffie et al. 2002).
From the mid-1970s Intel had competitors in the microprocessor market by companies such as
Motorola or Texas Instruments.
After the downfall of the DRAM business for Intel and its exit of the same the microprocessor business
became the core for Intel. Microprocessors became a great success and started to generate a large
part of Intel’s revenue. By the beginning of the 1980s microprocessors had become the largest
component of Intel’s revenue. Microprocessors remained the core business of Intel for a rather long
period. By 1993 industry analysts estimated that Intel’s 486 microprocessor accounted for 75 percent
of the company’s revenues and 85 percent of its profits. In 1997, sales of Pentium microprocessors
and related board-level products comprised around 80 percent of the company’s revenues and a large
part of its profits (Burgelman and Andrew 2001). This trend continued until the early 2000s, were it
was estimated that in 2002 microprocessors and related products were generating approximately 75%
of Intel’s revenue and most of its profits. Until 2004 the microprocessors remained Intel’s core
business.
The study will be carried out over a time span from 1979 to 2004. Since the microprocessor was the
dominant selling product for Intel during this period in time it will be used as an indicator on the
product level. The 25 year period chosen is believed to be sufficient as previous studies have shown
that such a period of time is sufficient to capture the upturns and the downturns in various important
factors such as performance, growth, technology and competition (Nadkarni and Barr 2008).
4.1.3 Rates and directions of change
4.1.3.1 Technology
The rate of change of technology is measured, as explained above, by the number of patents that are
granted. The office of interest is the United States Patent and Trademark office (USPTO). There are
several different patent classification systems, which are used to assess the category/industry to
which a patent belongs.
One way patents are classified is by the World Intellectual Property Organization (WIPO) that classifies
patents according to its technology and has semiconductors as one classification. Even though this
would be a good source, the problem is that the data available starts only in 1980. Another possible
source is the database of the organization for economic co-operation and development (OECD). In
order to classify patents the International Patent Classification (IPC) is used. The relevant subclass is
H01L which covers “semiconductor devices; electric solid state devices not otherwise provided for”.
However the chosen source is the database is the one of the USPTO itself which classifies patents
through the North American Classification System (NAICS), which is the standard used by Federal
statistical agencies for classifying business establishments. The applied classification is 3344 which is
for “Semiconductor and other electronic component manufacturing”.
39
There are many technologies that play a role in developing microprocessors. The manufacturing of a
single chip involves a combination of chemical, mechanical, thermal, and optical processes, including
lithography, deposition, clean, and etch (Turley 2003, Pillai 2011). The development of a chip takes
around 6-8 weeks. It is a highly technological process and is performed in specialized facilities which
are called fabs. Thus it would be very difficult if not impossible and time consuming to analyse each
technological factor that is relevant during the production singularly and assess its development in
terms of continuity discontinuity.
This is why a different indicator, which is able to show the development of process technology over
time in the semiconductor industry is needed. A good measurement for that is the so called
manufacturing technology or process technology, which measures how small the smallest feature on
a chip is. This is expressed in micrometres and more recently in nanometres. The reason why this is a
good indication of the process technology is because the ability to produce smaller transistors requires
progress and innovation in all of the afore mentioned technologies and processes, which are needed
to manufacture a chip (Grimm 1998, Pillai 2011). This simple concept thus summarizes a large amount
of technical material and progress in the semiconductor industry and is at the same time easy to
comprehend. This concept has been used as the key measurement of pace of technological innovation
in the semiconductor industry by the International Technology Roadmap for Semiconductors, and has
also been used in several other studies to assess the technological progress in the semiconductor
industry (Aizcorbe, Oliner et al. 2008, Pillai 2011).
Furthermore, in order to achieve the new and smaller level of feature size, a new process technology
along with new equipment is needed. This means that for each new process technology new
equipment and accordingly a new fab/retooling of the fab is needed, because the new feature size
cannot be produced on the old equipment. This is why every time a new feature size is introduced, it
will be modelled as discontinuous change. If companies do not invest in new fabs they do not have
the technological resources to build chips with state of the art microprocessors. And since the process
technology is crucial in terms of performance this is a discontinuity. This is the case, since it classifies
as an improvement that brings about a change which alters the competitive dynamics of the industry.
The company which will not manage to go to the next level on feature size will not be successful for a
long time. Thus it is a discontinuity, however it is a competence enhancing and not a competence
destroying discontinuity since it builds on the skills and knowledges already available and acquired by
the incumbents in the industry. This means it favours the incumbent companies over new entrants.
Once new equipment and tools to produce a smaller feature size are introduced continuous
improvement is strived for to achieve better quality and reliability of the chips.
The data on feature sizes was retrieved from several different sources including the Stanford CPU
Database and manufacturer’s websites.
4.1.3.2 Products
The rate of change of products is calculated by the introduction of new product generations. This
approach is favoured over the rate of change on the single product level because there is only little
and inconsistent data about single product introductions in the early years. This would confound the
results and is the reason why the approach of analysing the time between new product introductions
is favoured. The exact way of measuring can be found in the appendix.
The direction of change for the product assesses how the attributes of the product from the
perspective of the customer change over time. As semiconductors in general, and microprocessors in
specific, are very technical products that do not have many features which go above the mere purpose
40
of fulfilling their computing tasks the performance and the price are the key attributes for the
customers. Other factors are secondary compared to price and performance. Thus it will be analysed
how these 2 indicators changed over time. Specifically it will be analysed whether they developed
smoothly or they had inflection points. Smooth development is equal to continuous change whereas
inflection points are a sign for discontinuous change.
Traditionally performance of microprocessors is compared through benchmarks. There are many
different benchmarks when it comes to comparing microprocessors. However there are some issues
which make them inappropriate for this study. First many benchmarks only have results for current
chip generations that were recently introduced into the market. This makes comparison to older chips
introduced in the beginning of the microprocessor era impossible (Benchmark 2016). The earliest
benchmarks found were in 1995 which leaves the period from 1971 to 1995, which are 24 years of
microprocessor development, uncovered. In some instances there are benchmarks that provide
information for microprocessors of earlier dates. However the benchmarks measures for these chips
are outdated and not used anymore. Since there is no reliable way of converting these outdated
performance benchmarks to currently used benchmarks, comparisons between old and current chips
are not possible. SPEC, the leading benchmark provider for microprocessors states about convertibility
of its own benchmarks of different periods: “There is no formula for converting CPU2000 results to
CPU2006 results and vice versa; they are different products. There probably will be some correlation
between CPU2000 and CPU2006 results (i.e., machines with higher CPU2000 results often will have
higher CPU2006 results), but there is no universal formula for all systems.”(Spec.org 2011). This means
that it does not make sense to convert them, since an indicator is needed which is consistent over
time and is actually able to directly compare the performance. This is why classic performance
benchmarks are not used in this study.
There are many different technical specifications of microprocessors, which influence its performance.
At this point in time the performance of a microprocessor is dependent on many different factors.
However for the period under study a good approximation of overall microprocessor is the clock rate
or clock speed (Grade 2015). The clock speed is the speed at which a microprocessor executes
instructions. It is measured in Hertz and shows how many clock cycles a CPU can perform per second.
Several other factors have come to the forefront in the last 12 years, which also play a major role in
determining the performance of a microprocessor. These include the number of cores of a
microprocessor (multi-core technology was made widely commercially available around 2005) or the
power consumption and heat generation of microprocessors. However until the early 2000s it was
possible to improve performance by increasing the clock rates consistently, and power consumption
as well as heat generation were not important yet. After that point in time, performance increase
could not simply be achieved anymore by increasing frequency without excessive power consumption
and heat generation. When this became a problem solutions were sought which included the
development of multi-core architecture and the slowing down of the trend to increase frequency.
However in the period under study the clock speed is a good approximation of performance. Byrne,
Oliner et al. (2015p. 3) e.g. state that until 2004: „clock speed [...] had been highly correlated with user
performance...“. The statement by Grade (2015) that “either directly or indirectly, processor clock
speed, expressed in Megahertz (MHz) or Gigahertz (GHz), was once the common reference point used
to predict how a given processor would perform for a given application and where a processor ranked
on performance comparisons“ further underlines the fact that clock rate is a good proxy for
performance.
This has also been empirically validated. In their study Byrne, Oliner et al. (2015) find that the
coefficients on clock speed are positive and significant correlated with the performance as measured
41
by SPEC. Other coefficients that are also positive and significant are number of cores and number of
threads. However, since these have only started to play a role after 2004 those are not of relevance
for our study. There have also been studies which have used percentage improvement in CPU speed
as indicator for progress in the semiconductor industry (Tushman and Anderson 1986, Anderson and
Tushman 1990).
Since data availability was one of the main problems, many different sources had to be used.
Producer’s websites, the CPU Database as well as other reports were used to gather data on the prices
and clock rates of each chip. While clock rate were easily and widely available the prices were much
less available.
4.1.3.3 Demand
The rate of change is calculated by the change in sales. Data sources were annual reports of Intel and
for firm level data and the database of the Semiconductor Industry Association (SIA) for the Industry
level.
The buyers of microprocessors are for the large part large corporations, mostly computer and mobile
phones manufacturing companies such as HP, Dell, Samsung, Nokia, Alcatel and others. This will likely
remain the same and there will be no discontinuous change as the microprocessor will only be bought
on a large scale by large manufacturing companies. This is why in order to assess the discontinuity we
will turn to analysing the sales figures. This will be measured through the same sources as mentioned
above. The process of how to determine if it is continuous or discontinuous is described in the next
chapter.
4.2 Aircraft
4.2.1 Aircraft Industry
Today the aerospace industry is one of the largest manufacturing industries in the world in terms of
employed people and in terms of value of output. It has shaped the 20thcentury in a decisive way by
pushing the boundaries of existing technologies. It has been driven by large R&D funding that were
invested inter alia due to the prestige and power connected to a nation being at the forefront of this
very industry.
The beginning of the aircraft industry were in the early 20th century when the Wright brothers secured
a contract to make an aircraft for the U.S. Army in 1908, 5 years after their famous first flight in 1903.
This marked the start to aircraft manufacturing as also other companies started to manufacture
Aircraft, like the Astra Company in France. The first scheduled flight occurred in 1914, by the between
St. Petersburg and Tampa, Florida, offshore of Tampa Bay (Bugos 2001).
Whereas the beginnings of the industry are located in the United States the Europeans soon took a
lead in the industry. By the outbreak of World War I in 1914 French, Germany and Britain had built
almost 4000 aircraft together while American firms had managed to build only less than a hundred.
However war created demand for more aircraft, which then suddenly fell away after the end of the
war leading to the restructuring of the aircraft companies. Nonetheless the war had driven the aircraft
industry to become more organized and less fragmented. In the years between 1920 and 1930 much
change happened, especially in terms of design and used materials. The previously predominant used
manufacturing material of wood was replaced by metal and the biplane (two main wings stacked
above each other) was replaced by the monoplane (single pair of wing) design. Thus basically by the
mid-1930s the design used today was roughly similar (Rae 1968).
42
Meanwhile the customers also developed. In the United States the main use was airmail systems,
however air transport companies also started to fly passengers in the 1920s. However this remained
a side business until advances in the 1930s addressed critical comfort and safety related issues such
as cabin pressurization, improved instrumentation or retractable landing gear. The Douglas DC-3 and
Boeing 247 airliner market de start of true transport aircraft mainly carrying passengers.
In the year previous to the wars and during the war the development and production of aircraft hit
new heights. Both technological improvements and innovation spurred the performances of aircraft.
Planes reached speeds of over 640 km/h at the end of the second war, 200km/h more than at its
beginning. Furthermore during the war large amounts of aircraft were built, 300,718 military aircraft
were built by American firms. Compared to the previous six year period where only 19,857 aircraft
were built this is a 15 fold increase in production. This push in demand made the aviation industry the
biggest in the U.S with 1,345,600 employees in 1943. The end of the war brought a stop to the huge
demand. Whereas the total sales were $16 billion in 1944, they were only $1.2 billion in 1947 (Pattillo
2001).
However the next major political event namely the cold war which started in 1947 also had a big
influence on the development in the aircraft industry. The cold war started a development race which
aimed for records in speed and altitude. In December 1947 the rocket-powered Bell X-1 became the
first aircraft to break the sound barrier. Other advances during this period of time include the
invention of the helicopter as well as the introduction of the jet engine, a disruptive technology. After
Sputnik 1 was the first to travel in space the cold war entered a new phase and the NASA was given
the mission in 1961 to send a an American moon to the earth and return him safely before the end of
the decade, which they famously achieved in 1969. However in other areas the Soviet Unions
outpaced the Americans like in space medicine or heavy lifting rockets. Whereas the Soviets also sold
civil aircraft the purchasing decisions of customers in the early years were based upon politics rather
than performance or other relevant criteria of aircraft. When the Soviet Union dissolved in 1991 the
once dominant Soviet firms were not competitive, anymore. Instead European firms managed to
increase their competitiveness and challenge the U.S. dominance in aircraft (Pattillo 2001).
Following Second World War the European aircraft industry was very weak. Whereas the German and
Italian industries were basically prohibited to do anything of importance, the British and French firms
did keep producing aircraft. However due to the fact that they mostly sold to their nations militaries
and national airlines there was only limited demand. Amortization of engineering cost was very hard
to achieve which limited the progress in the industry. In the 1960 however the countries started to
collaborate to achieve a state in which different firms produced aircraft together and sold to the
different markets. The fact that many national firms participated in various transnational projects
meant that the European industry operated neither as monopoly nor monopsony (Bugos 2001).
This was also the period in which the commercial flights started to boom. Between 1960 and 1974
passenger volume on international flights grew six fold. In 1970 the Boeing 747, a jumbo jet with 360
seats was introduced. This was a ground-breaking airplane which increased the level of comfort and
safety in international air travel. Soon after the Airbus A300 followed, flying for the first time in 1972.
Whereas in the beginning the American companies Boeing and Douglas were the clear leaders, a surge
in the demand in the 1980s which could not be sufficiently supplied by them gave Airbus a rise in
orders and consequently market position. By the 1990s Airbus had built a contractor network and had
successfully established its position with several airliners serving different markets.
43
Whereas also other nations besides the American and European countries are operating in aircraft,
especially in the military and defence market the commercial aircraft industry is dominated by
European and American firms. In the time after the cold war, once again there was less demand
especially in the military and defence sector. In the 1990s large consolidations took place with several
companies overtaking others. Whereas during the cold war the aerospace firms had a fairly even sales
split between military and civil aircraft and one quarter space vehicles the rest being missiles and
ground support equipment, in the 1990s a large shift towards civil aircraft was in place (Pattillo 2001).
As of today there are several major players in the aircraft market. Whereas Boeing and Airbus
dominate the market there are a few other notable players. Bombardier is a Canadian aircraft
manufacturer which originally started out as a snowmobile-manufacturer in 1942. It is now a major
player in the aircraft market especially in business jets and regional airliners. Another company
challenging the European and American dominance in the aircraft market is Embraer, a Brazilian
commercial aircraft manufacturer that was founded in 1969 however started to flourish only in 1994
when the company was privatized. As has been discussed before there was much consolidation in the
years leading up to 2000 with aircraft companies being overtaken by bigger aircraft companies.
Recently established companies are the Russian JSC United Aircraft Corporation, which was founded
in 2006 and the Chinese Commercial Aircraft Corporation of China, Ltd. (Comac) which was founded
in 2008 and delivered its first jet in 2015.
Due to the high entry barriers because of large amount of needed capital as well as long learning curve
due to its complex assembly and its high content of labor performing complicated tasks it is very
difficult for these companies to establish themselves in the aircraft industry. Nonetheless since they
are backed up by government, there is a chance to capture share in the market.
4.2.2 Boeing
Boeing was founded in 1916 by Yale engineering college graduate William Boeing who incorporated
his airplane manufacturing business as Pacific Aero Products Company. A year later, the name was
changed to Boeing Airplane Company. In the same year, 1917 Boeing produced its first production
airplane: the Model C seaplane. Fifty of these planes were ordered by the United States Navy for the
use in World War I. During the 1920s, Boeing produced several different models of fighter, mail, and
passenger planes, with its largest customers being governmental institutions such as the United States
Navy and the Post Office. Also in World War II Boeing as well as its competitors were called on again
and produced several fighters and bombers in collaboration. Other aircraft companies at that time
were Douglas Aircraft Company, Lockheed Aircraft Corporation, Bell Aircraft Company, and Glenn L.
Martin Company. However the demand that the World War had generated led to large excess capacity
once it had finally ended. The absence of orders from the military forced Boeing to close factories and
lay off 70,000 employees. However the missing orders from the military created urgency to
successfully develop commercial airplanes. After several unsuccessful attempts, the company finally
produced the world’s first commercial trans- Atlantic jetliner, the Boeing 707. This was a major step
which gave Boeing the leading position in the commercial aircraft market. During the 1960s Boeing
heavily benefitted from the space race and saw a booming aerospace business which came through
contracts with NASA and the U.S. military. However like before with the end of World War II, this time
after the moon landing in 1969 and the relaxation of the relation between the UDSSR and the USA the
boom stopped and Boeing had to cut over 40,000 jobs between 1970 and 1971. Again Boeing entered
other markets trying to find new sources of revenue. However this time the ventures were unrelated
in nature and only short-lived. Examples include computer products, housing project management,
water treatment, and light rail vehicles (Yenne 2005).
44
In the late 1990s a couple of mergers further strengthened Boeing’s position. In 1996, Boeing merged
with Rockwell International Corporation’s aerospace and an attempt to defence units an attempt to
improve its defence equipment production abilities. Boeing’s defence business now operates as a
wholly-owned subsidiary, Boeing North American. One year later, The Boeing Company merged with
McDonnell Douglas Corporation, a competing manufacturer of both commercial and defence aircraft.
In 2000, Boeing purchased Hughes Electronics Corporation’s space and electronics business. Jepsen
Sanderson, Inc., provider of aeronautical charts. Presently, Boeing is operating in 70 countries with
22,000 suppliers and 170,000 employees (Yenne 2005).
Boeing commercial airplanes and Boeing defence, space & security are the two systematic business
units of the organization where the products and modified services are based on providing commercial
and military aircrafts, satellites, weapons, electronic and defence systems, launch systems, advanced
information and communication systems, and performance-based logistics and training. Around 6070% of the revenue is generated from the commercial airplanes which is why this will be taken as the
product on the product level (Yenne 2005).
4.2.3 Rates and directions of change
The rate of change of technology is measured by the number of patents that are granted. The office
of interest is the United States Patent and Trademark office (USPTO). For the rate of change of
technology of the aircraft industry, the patents of NAICS class 3364 were used. This was not further
split in between military and civil aircraft and the like. Besides the fact that it is hardly possible it also
does not make sense to split down the categorization any further because companies use patents that
were originally developed for e.g. the military sector in the civil sector as well (Begemann 2008).
4.2.3.1 Technology
Even though the final product of the aircraft industry, namely the airplane is a very complex and high
technology product, the main work of the aircraft industries like Boeing assembly does not rely as
heavily on high technology as one would expect. This characteristic is unavoidable due to the nature
of air aircraft manufacturing which makes labor saving technology very hard to implement. The
production and assembly of aircraft is still very much dependent on individual workers and skill craft.
Aircraft manufacturing can been described as a craft industry organized and managed as a traditional
mass production system. This is due to the fact that the aircraft industry combines the quantitative
needs of a large manufacturing operation, which means a great labor force for production with the
qualitative requirements of small handcraft which relies on skill and experience of the workers. This
can also be seen by the fact that the percent of the industry’s workers involved in craft and technical
jobs is significantly higher than for manufacturing in general (Kronemer and Henneberger 1993,
Murman, Walton et al. 2000, Bozdogan 2010). Now that an overview is gained the different trends
and challenges in the manufacturing of aircraft will be discussed in more detail.
A primary driver for progress in manufacturing and technologies regarding the production of aircraft
was the emergence of the computer, which allowed increase in automation of manufacturing
processes as well as the development of new manufacturing technologies, such as laser cutting or the
design by computers (Andersen 1998). Also the development of the supercomputer and the
associated software tools like computational fluid dynamics (CFD), which illustrates how airflows
impact the aircraft at various angles, and under differing conditions of temperature and air density
and has joined the wind tunnel and flight test as tools to design and test planes. These were major
innovation. CFD for example is a major discontinuous innovation because it has „revolutionized the
process of aerodynamic design“ (Johnson, Tinoco et al. 2005, p.1117). This development helped
aircraft manufacturers reducing development time and required hours of flight testing, thus allowing
45
them to investigate a greater number of design options over a shorter period of time(Andersen 1998).
These developments which started in the late 1960s still have an impact today.
Another key characteristic of the aircraft manufacturing industry is the systems integration approach
of the companies. This approach is described by the fact that key components and sub-assembles are
outsourced to external suppliers, while the company maintains design authority and the task of final
assembly. There are several reasons for this approach. The first one is that in a product as
sophisticated and complex as the airplane there are many different parts (e.g. wing, center wing box,
front fuselage, aft fuselage, empennage and nose) which require specialized manufacturing. Due to
the great variety and very specialized manufacturing process there are simply other companies that
are able to better manufacture the products due to superior production technologies and
manufacturing know-how. Another reason for this approach are the extremely high costs that are
associated with the launch of an aircraft (e.g. research and development, facilities, capital and
equipment). The system integration is used to help minimize the risks and costs (Pritchard and
MacPherson 2004). For example for the Boeing 787 most of the design and construction was
outsourced, along with around 40 percent of the estimated $8 billion on development costs. This
illustrates how system integration enables cost sharing
Problems with automation
Even though there have been advances towards automation in the production of aircraft during the
last decade there is still much less automation than in other industries like e.g. car manufacturing due
to several reasons. The factors which make it difficult and which will be explained in more detail below
are the scale or size of aircraft, the necessary precision and tight tolerances, the low quantity of parts
as well as tight tolerances.
First of the production volume is rather low in aircraft manufacturing. Whereas in automotive
production there are rates of 50 to 60 units per hour, a fast production rate for the aircraft industry is
one aircraft per day. However during that time multiple manual tasks would interrupt the operation
which would leave the robot idle. This means that there are shared workspaces which also need to be
made safe, which takes extra floor space which is already short in supply. All these factors make it less
cost effective to invest in extremely expensive automation manufacturing.
Secondly the product is very complex and very demanding in terms of reliability and tolerance.
Whereas in the other industries manufacturing requirements like tolerance limits or coating
consistency are not as strict, which helps the case for automation, this cannot be said for aircraft
manufacturing. Such demanding tolerances cannot yet be achieved by a machine without a huge
expense, which in most instances is not cost effective (Kronemer and Henneberger 1993).
Additionally the used materials are rather exotic and the shapes are complex. Airlines also request
customized cabin and cockpit configurations and individual paint schemes which make constant
adjustment and retooling on the shop floor necessary, which in turn limits the opportunities for
automation. This also requires human decisions which obviously is not within the scope of automation.
Overall it can be said that “Aircraft assembly is [traditionally] a manual process because the tasks
require a high level of skill and dexterity. People are constantly making decisions during the assembly
process and adapting to the exact situation”. This will not change in the near future as is pointed out
by Curtis Richardson, associate technical fellow for assembly and automation technology “there are
also a tremendous number of complex operations involved in building aircraft that will always need
to be people-based”. This is why up to this date the aeronautical industry is still very labour intensive
with a big workforce (Weber 2009).
46
Nonetheless there has been continuous improvement in the areas of automation and cost cutting
technology improvements driven by technologies such as Computer Aided Manufacturing (CAM) and
Computer Aided Design (CAD) as well as Computer Aided Production Planning (CAPP). Examples of
technologies are automated fiber placement, friction stir welding, sealing and assembly. Most of the
automation has taken place in materials processing rather than in assembly.
Key role of process technologies and composite materials
Process technologies which aim at improving the cost and efficiency of assembly and production of
aircraft and have been introduced since 1990s include laser beam, electron beam and friction stir
welding (Fonta 2010) as well as jigless assembly , gaugeless tooling, inline assembly and automatic
riveting.
There are several other process technologies which have incrementally improved the efficiency and
the performance of the manufacturing and assembly of aircraft. However there have not been any
breakthrough innovations in this field.
Another field in which a lot of progress has been made is the material of aircraft such as advanced
alloys and composite materials. The most accepted definition for a composite material is a material
with two or more distinct phases bonded with each other forming a material with different properties
than the properties of its constitutes (Groover 2007). Usually on these phases different materials are
used, which have different properties and crystalline structure. The new material in turn has some
type of advantage for example presenting low density, good resistance to fatigue, creep and corrosion,
or excellent mechanical behavior. On the downside these types of materials are quite expensive, are
fragile and are more susceptible to humidity and high temperatures. Examples of composites used in
aircraft are aramid, carbon and boron fibers.
Whereas in the 1990s aircraft was based on metallic structures having around 12% of composite or
advanced materials, in 2005 there was already 25 % of advanced lightweight composite material
(leading to 8% weight reduction) and in 2015 around 70% (leading to 15% weight reduction) (Fonta
2010). The push for these technologies and materials was due to requirements of air transport for not
only less costs of production but also of operation. Thus investigation in lightweight materials became
even more relevant.
Lean Approaches
Another important characteristic of the aircraft manufacturing industry was the introduction of lean
principles. Even though it is not a technology per se it is a process improvement in manufacturing
which was crucial for the companies to become more cost efficient and improve the quality of its
products. Major pillars of the lean principles are employee empowerment and commitment of the
employees. The lean approach and its major improvements in manufacturing highlight once more the
crucial importance of people in the airplane manufacturing. One article in the Boeing company
magazine puts it this way (Jenkins 2002): "To make planes is to make and develop people […] "We use
the word 'kaizen' (continuous improvement), but all it's really about is training the people who make
it happen." This again shows how important the human factor still is in aircraft manufacturing still is.
Overview
As can be seen the aircraft manufacturing industry has gone through some change in the last 25 years.
Even though there are several barriers to automation and human labour is still a major factor in the
production and especially assembly of the aircraft some technological progress has been made. Most
of this progress has been made in the material processing and used materials. Furthermore lean
production principles have brought down costs increased production speed and eliminated
47
unnecessary waste in the whole supply chain. It can also be seen that the aircraft manufacturers have
taken the approach to further increase the outsourcing of production taking the system integrator
approach.
To conclude it can be said that while there has been continuous advancements in manufacturing
processes and lean manufacturing, there has not been one single event or innovation which can be
described as a discontinuity which has brought about order of magnitude improvements in production
or which has induced change as strong as to change the competitive dynamics in the industry. Rather
there have been many small improvements and continuous enhancements of the production of
aircraft.
4.2.3.2 Product
The rate of change for products will be measured by the time between new product generations. This
ensures that the measurement displays how fast new products are being introduced into the market
and enables comparison between the company and the industry. Sources used were company
prospects as well as trade journals.
The direction of change for the product assesses how the attributes of the product from the
perspective of the customer change over time. The customers in this case are the airline companies
since they are the ones purchasing the aircraft. In general there are many different factors affecting
the purchasing decision of an airline. While the most important ones are arguably safety and security,
this can be seen as requirements that have to be fulfilled by all aircraft in order to even be considered
by purchasing customers. If not enough safety or security is ensured the aircraft will not be bought.
This is why the safety is not taken into account as it is a minimum requirement for an aircraft to even
be purchased. There are many other tangible and intangible characteristics that are associated with
an aircraft and have influence on its function and appeal from the perspective of the customer.
However only measurable characteristics can be taken into account within the scope of this analysis.
Arguably one of the biggest factors affecting the purchasing decisions of an airline are the costs
associated with the aircraft. Thus optimally the price should be taken into account. However the
problem in the aircraft market is that prices are not available for the public. Even though there are list
prices for some of the aircraft available this price is not what the airlines actually pay. There is a lot of
bargaining involved and the price the customer actually pays in the end is also dependent on many
different factors, like quantity, bargaining power and relations with the aircraft manufacturer.
Discounts vary between 20% and 60%, however the actual prices paid are very closely guarded and
do not reach the public. This is why inclusion of the list price would not make sense in determining the
direction of change of the product (Michaels 2012).
Another major factor influencing the cost model of airlines is the operating cost, which is heavily
influenced by fuel costs. Fuel costs compromise around one fourth of the total airline’s operating cost
(Association 2009). This is why fuel efficiency is a crucial factor for deciding which airplane to choose.
Other than fuel costs, pilot salaries and maintenance are the largest parts of operating costs (Lee,
Lukachko et al. 2001, Association 2009). Pilot salaries are not related to the purchasing decisions of
aircraft and maintenance costs cannot be taken into account by a measure. Especially rising fuel prices
have driven the development of more efficient aircraft. This is why the fuel efficiency of the aircraft
will be taken into account as it is a critical factor to consider for airlines before purchasing new aircraft.
Factors that are also very important and influence the performance of an aircraft in a decisive way
and thus have an effect of the purchasing decision of the airline and which have been used in previous
48
studies (Company 1995, Commission 1998, Frenken and Leydesdorff 2000) to depict the performance
of aircraft are:






Maximum take off weight
Speed
Range
Wing span
Engine power
Fuselage length
Physical measures and characteristics of the aircraft as size, wing span or engine power are very
important but were in previous studies found to be highly correlated with the range and capacity of
the aircraft(Commission 1998). This is the case as length will most likely be correlated with passenger
capacity and engine power inter alia with fuel efficiency and range. This is why these specifications
are not taken into account.
Furthermore the speed was not taken into account due to the fact even though it is an important
performance characteristics, it has not changed for commercial aircraft since the 1970s. As can be
seen in figure 2 the speed has stayed at around the same level since the beginning of the 1960s which
is at around 0.8 mach (around 550 miles per hour or 980 kilometres per hour). Even though there
were attempts at supersonic aircraft (aircraft that is able to fly faster than the speed of sound, which
is 1 mach) well before the 1990s, namely in the 1960s and 1970s these have failed (examples are the
Concorde and the Tuploev TU-144, which both were commercial failures). It must be noted that there
is aircraft which is able to fly considerably faster, however these are not used for commercial
purposes.
Figure 2: Absolute airplane speed records, Source: McMaster and Cummings, 2002
Previously aircraft productivity has been measured by multiplying payload by cruise speed and
dividing that by gross weight. However since the speed has been constant over the last years this is
not very appealing. Furthermore even though it is interesting how much an airplane weighs for the
airline this is mainly due to its influence on other characteristics like fuel efficiency. Other than that
the weight is not all that interesting for the airline. Another reason of why this this indicator would
not be a good measurement is that the supersonic airplanes like the Concorde have a high aircraft
49
productivity while actually it is not a very attractive aircraft for the airlines due to its high initial cost
and also its high fuel cost per seat. This is why a different ratio will be used to indicate direction of
change. In order to measure the changes a ratio will be created which equals
𝑟𝑎𝑛𝑔𝑒∗𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦
𝑓𝑢𝑒𝑙 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑝𝑒𝑟 𝑠𝑒𝑎𝑡
.
Thus the measure that will be taken into account are range, capacity and fuel efficiency This measure
will be the better the higher it is since range and capacity are favourable and fuel efficiency per seat
will be favourable if it is lower. Fuel efficiency per seat is taken into account since this makes the fuel
efficiency comparable across different capacities and does not favour smaller planes with less seats
over bigger ones. While this ratio itself does not have a specific meaning, the relationship in the change
can very well be interpreted as having continuous or discontinuous rates of change.
In the analysis the airplanes will be split into different segments of airlines. There are two distinct
segments, namely narrow-body, and wide-body planes. This differentiation is necessary in order to
compare the decisive characteristics to each other in a meaningful way.
Narrow body airplanes are those that host a single aisle with a maximum of 6-abreast seating. The
diameter of the fuselage is consequently shorter than four meters. This results in capacities of up to
290 passengers. Narrow body airplanes are used for short-range flights. Even though opinions differ
on the exact definition of a short haul flight one can say that it will be shorter than three hours. A
wide-body aircraft is an airliner with a wide enough fuselage to accommodate two passenger aisles
with seven or more seats. This results in diameters of the fuselage of around five to six meters and 7
to 10 passengers sitting abreast. Capacities range around 200 to 850 passengers. Flights range from
above three hours to around 12 hours (Doganis 2002).
These differentiations are necessary in order to be able to make the comparisons between the
different airplanes meaningful.
4.2.3.3 Demand
The rate of change is calculated by the change in sales. Data sources were annual reports of Boeing
for firm level data and the database of Aeroweb.
The nature of the demand has stayed relatively continuous in the period of study. The largest
customers of commercial aircraft are airline companies. Governments, companies, and individuals
also make up a smaller portion of sales. This means there has not been any shift in this regard. This is
also very unlikely to change.
The direction of change in the demand dimension was measured through the same sources as
mentioned above. The process of how to determine if it is continuous or discontinuous is described in
the methodology chapter.
4.3 Summary
Table 11 and 12 provide an overview of the used measurements and the sources.
One problem of data collection is that many times for the semiconductor industry, analyses were
carried out on the basis of Intel specific data which were then generalized for the whole industry. This
was a problem insofar that then it was not possible to separate between Intel and the Industry in our
analysis. This is why many sources which had some relevant information could not be used. For
example for the price data a problem occurring is the limited availability, especially before the 1990s.
For chips produced in the 1990s there are several sources for prices, however the early microprocessor
price data only for very few chips could be found. The problem of availability of prices is also occurring
in other studies where hypothetical prices have been constructed on the base of characteristics. Other
50
studies have used Intel chips as representatives of microprocessors and thus not taken into account
other company’s chips (Aizcorbe 2002, Aizcorbe, Oliner et al. 2008).
51
Table 11: Measurements and sources for semiconductor industry
Technology
Measure
Source
USPTO
Database
(NAICS 3344 )
Product
Measure
Source
- CPU Museum
New product - CPU world
introductions - CPU collection
Rate of change
Patents
Direction of change
- Stanford CPU
Database
- Manufacturer's
- Price
Feature size websites
- Clockspeed
Measure
Demand
Source
Sales
- Annual reports
- Semiconductor
Industry Association
-Product Catalogues
- Websites of companies
- Stanford CPU Database
- Chronology of
Microprocessors
Sales
- Annual reports
- Semicondcutor
Industry Association
Table 12: Measurements and sources for aircraft industry
Technology
Measure
Source
Rate of change
Direction of change
Product
Measure
Patents
USPTO
Database
(NAICS 3364 )
Qualitative
Assessment
- Range
- capacity
- fuel
Trade journals, efficiency
Source
New product
introductions Product Catalogues
-Product Catalogues
-Websites of companies
Measure
Demand
Source
Sales
- Annual reports
- Aerospace Industry
Association
Sales
- Annual reports
- Aerospace Industry
Association
52
5 Analysis and Discussion
This chapter will first give a short overview over the two industries and discuss the descriptive
statistics. Then the homologies of the two different industries will be visualized and analyzed. Finally
the dynamics between aligning internal rates and directions of change to the environment and
performance will be examined.
5.1 Descriptive Statistics
First of the descriptive statistics of the industries will be analyzed to gain an overview of the different
types of environments. These descriptive statistics will then be visualized in homology graphs, both
for the two industries under study and then for the industry and the companies.
Table 13 shows the descriptive statistics for the rate and direction of change for the two industries
under study.
Table 13: Descriptive statistics for rate and direction of change
Rate of change
Industry
Rate of
Mean
change
Standard deviation
Direction of Mean
change
Standard Deviation
Product
Aircraft
-2.5
47.22
0.05
0.224
Semiconductor
28.78
14.44
0.12 0.33 -
Technology
SemiAircraft conductor
4.57
9.75
12.69
10.56
0.32
0.48
Demand
SemiAircraft conductor
3.53
12.17
4.96
24.76
0.1
0.16
0.31
0.37
As can be seen every dimension has higher rates and directions of change for the semiconductor
industry than for the aircraft industry. Especially regarding the rate of change the semiconductor
industry has significantly higher values than the aircraft industry. However it can be noted that the
standard deviation for the technology as well as the product dimension is higher for the aircraft
industry than it is for the semiconductor industry. With regard to the product dimension it can be
explained to the fact that only very few times new product generations are introduced into the
market. Even though the rates of changes for products were smoothed using a 3-year average, every
time a new product was introduced this resulted in high rates of change during and after the
introduction. That is why this high standard deviation does not necessarily mean that the aircraft
industry is more unpredictable than the semiconductor industry. For the technology dimension on the
other hand it is notable that even though the rate of change of the aircraft industry is lower the
standard deviation is higher. This is partially caused by the fact that in the aircraft industry there are
several years in which the rate of change is negative, while this is only 3 times the case in the
semiconductor industry. This reduces the mean for the aircraft industry and on the other hand can
increase the standard deviation.
Table 14 on the other hand shows how much duration is passing between the prominent events
occurring in the industry. Whereas previous studies have found that the product speed of aircraft is
around ten years (Nadkarni and Narayanan 2007, Nadkarni and Barr 2008), this study finds product
speeds of around five to seven years. This means that a new product generation was introduced every
5-7 years in the period of our study. In the semiconductor industry the product speed is much faster,
with new product generations being introduced every one to two years.
53
Table 14: Comparison of speed in the industries
Product
Industry
speed
Aircraft
5-7 years
Semiconductor 1-2 years
Technology
discontinuities
2-4 years
Product
discontinuities
20 years
7-10 years
Demand
discontinuities
10 years
5-7 years
This is in line with previous studies on the product speed in the semiconductor industry (Nadkarni and
Narayanan 2007, Nadkarni and Barr 2008). The timespan between technological discontinuities shows
the most drastic difference between the two industries. Where there were no drastic discontinuities
found in the aircraft industry, a discontinuity is taking place every 2-4 years in the semiconductor
industry. However one must note that these discontinuities are competence-enhancing
discontinuities as opposed to competence-destroying discontinuities. This means that they help and
favor the already established firms and incumbents in the industry. Product discontinuities on the
other hand are taking place every 7-10 years in the semiconductor industry and around every 20 years
in the aircraft industry. Interestingly it seems that for both the aircraft and semiconductor industry
around every 4-5 product generations a product discontinuity is reached. Demand discontinuities take
place every 5-7 years in the semiconductor industry and around every 10 years in the aircraft industry.
However as the time of study was only 20 years for the aircraft industry, statistics about such rare
occurrences as product discontinuities must be taken with caution.
5.2 Homology comparisons of industries
Figure 3 visualizes the homologies for both industries over the total period of study. It shows that
there are large differences in the rate and direction of change within the semiconductor and the
aircraft industry.
In the semiconductor industry for example the technology dimension is much more discontinuous
than the demand and the product dimension. While the technology scores more than double as high
as the demand dimension it is almost three times as high as the product dimension. The product and
demand dimensions are quite similar in terms of direction of change. Regarding the rate of change the
product dimension scores much higher (almost three times as high) than the demand and technology
dimension, which are more homogenous. We can thus conclude that the velocity homology of the
semiconductor industry is not very high. Since there are differences in between the dimensions of two
or three times the velocity homology can be characterized as being medium to low.
The aircraft industry on the other hand is a little bit more homogeneous, thus has a higher homology,
which can be seen by the fact that the points for the different dimensions are closer together
absolutely but also relatively. However there also exist some differences in the dimensions. The
technology dimension is not discontinuous at all where there is small discontinuity in products and a
decent amount of discontinuity in the demand dimension. Regarding the rate of change the
technology dimension is highest with the demand dimension close to it and the product dimension far
off. The rate of change of the product dimension for the entire period of study is negative which means
that more often than not the change in product shows a decreasing trend. Thus also the aircraft
industry does not have a very high velocity homology but rather a medium one.
All in all we can see that both the industries do not show high velocity homologies. Both in the aircraft
as well as semiconductor industry there exist large differences in the rates and directions of change.
Some dimensions score very high on rate of change while they score low on direction of change as
well as the other way round. This provides empirical evidence to the fact that in order to characterize
54
an industry it is important to distinguish between the different dimensions because they can be very
different and aggregation of these dimensions to one single velocity would not make sense. Also
interesting to see is that for both industries the differences for the product and technology dimension
are very large which shows the importance of treating them separately and not lumping them together
as previous research has done. Furthermore the findings emphasize the fact that direction of change
is an important concept that should not be omitted since it adds a second characteristic to the
dimension, which again distinguishes the dimensions from each other.
Figure 3: Homology comparison for aircraft and semiconductor industry for entire period of study
When comparing the two industries with each other, one can see that overall the semiconductor
industry is faster and more discontinuous than the aircraft industry. Very interesting is also that the
technology dimension scores highest on direction of change in the semiconductor industry, whereas
it scores lowest in the aircraft industry. The same applies to the rate of change of the product
dimension, which is the highest for the semiconductor industry and the lowest for the aircraft industry.
Thus while we cannot say, as discussed before, that the overall industry has a certain aggregated
velocity, it can be said that overall the semiconductor industry is more fast-paced and has more
discontinuous directions of change than the aircraft industry.
5.3 Homology alignment
First of the homologies of the industry and the company over the entire period of study will be
compared. This will show whether in general, the companies managed to align their rates and
directions of change to that of the environment.
As can be seen in Figure 4 in which the homologies of both the industry and Intel is plotted, there is
some general alignment achieved by Intel over the course of the entire period of study. Whereas the
product dimension is almost completely identical and the demand dimension is relatively similar,
there seems to be some misalignment in the technology dimension. Even though Intel has managed
to achieve a very similar direction of change, it has a considerably higher rate of change than the
55
industry. In the demand dimension on the other hand Intel has a very similar rate of change and a
slightly lower direction of change.
Figure 4: Comparison of homologies in semiconductor industries during entire period of study
Figure 5 shows the homologies of Boeing and the aircraft industry over the period of study. Boeing
seems to also achieve a fit. The rates of change of Boeing surpass that of the industry in each
dimension. As authors have argued before this could actually be beneficial for the company, meaning
that it is good that a company surpasses the external rate of change with its internal rate of change
Furthermore the demand dimension of the industry is more discontinuous than that of Boeing.
56
Figure 5: Comparison of homologies in Aircraft Industry during entire period of study
Even though it is interesting to see how the homologies compare to each other over the entire period
of study, we can gain even more insight by looking how the yearly alignment is interrelated with
performance. This will be done in the next section.
5.4 Alignment of rates of change
In this section it will be discussed how the alignment of internal to external rates of change is related
to the performance of the company. The limited amount of data does not allow for statistic regression,
however it is possible to cluster the years according to their rates of change. This means that periods
in which the difference between internal and external rate of change are taken together and being
compared to other periods in which the differences in internal and external rates of change were
lower. Clusters were made in such a way that they were homogenous within each other and
heterogeneous with other clusters regarding the alignment. For these homogenous clusters the
average Tobin’s Q will be compared. This then in turn can enable us to derive some initial statements
about what interaction of the alignment of rate and direction of change and the performance of a
company is.
57
5.4.1 Semiconductor
In Figure 6 the performance of Intel is mapped against the alignment of internal and external rates of
change. It is also shown how many data points are in each cluster pointed out by the label count. For
this figure only absolute values were taken into account, meaning that negative values were converted
into positive ones. This approach was taken in order to see how the alignment changes once the rates
of change get larger independent of which type of misalignment was at hand.
As can be seen in periods in which the highest aggregate performances were achieved there also was
the closest alignment between company and industry. Furthermore in periods in which there was
higher misalignment the performance as measured by Tobin’s Q was lower. The trend which can be
seen is that for each cluster of periods lower performance was apparent in periods with higher
misalignment. Only in the third cluster there was slightly higher performance even though the
alignment was less in the second cluster of periods. Whereas this is a result which does not fit in the
hypothesis that higher alignment is connected to better performance it must be noted that in general
the trend of performance for a higher ΔRC is downward and that the highest performance was
Figure 6: Effect of alignment of rates of change on performance of Intel
achieved by Intel in the periods in which the ΔRC was the lowest. Thus we can conclude that in the
periods under study higher performances were achieved by Intel in periods in which their alignment
to the environment was high.
In order to find out more about the alignment of Intel and the industry a different approach is now
taken, in which also negative values are taken into account. This is shown in Figure 7. In this figure it
can be seen that the highest performances were apparent in periods in which there was very close
alignment followed closely by periods in which there was some positive misalignment. Both in periods
in which negative misalignment as well as extreme positive misalignment were taking place there
were relatively bad performance of Intel at hand. In general this is in line with previous research on
alignment that has suggested that closer alignment is associated with relatively higher performance.
58
Figure 7: Effect of alignment of rates of change on performance for Intel
The fact that in the cluster of periods in which there was very high misalignment there was worse
performance than in the cluster of periods in which there was less misalignment is interesting as there
has been some inconsistencies in previous research on how higher internal rates of change are
connected to the performance.
A general observing is that Tobin’s Q for Intel over the period of study was very high.
5.4.2 Aircraft industry
Figure 8 shows the alignment for Boeing and the aircraft industry and its relationship with
performance. As can be seen the performance for Boeing was the highest in periods with very little
alignment.
Figure 8: Effect of alignment of absolute rates of change on performance for Boeing
The second highest performance was achieved in periods with very high alignment. Nonetheless it
must be said that these results do not support our proposition that the higher alignment is associated
with relatively good performance.
59
The next figure differentiates between negative and positive values. In this figure we can see that
again our propositions are not supported. As we can see the highest performance was achieved in
periods with the highest positive misalignment while the second highest performance was achieved
in periods with negative misalignment.
Figure 9: Effect of alignment of rates of change on performance for Boeing
One reason for these results could be the fact that for the alignment of the rates of change, when
aggregating the product dimension is dominating the other dimensions since it has extremely high
rates of change compared to the other dimensions. This is the fact that since there are only so few
product introductions every time a product is introduced the change is immediately 100 and the year
afterwards -100. Even the moving 3-year average does not smooth out the large fluctuations. In order
to see whether this is a reason and to make sure that the product dimension which has much higher
rates of change than another dimension does not dominate the aggregate measure a weighted
average is taken. This was done by first calculating the average of the absolute values of the rates of
change of a singular dimension over the entire period of study. Then the rates of change for this
dimension for each year were divided by the average. This was done for all dimensions which were
then summed up to give the aggregate rate of change over all dimensions. The result is given in figure
10. As can be seen the results here are more in line with our propositions namely that in periods with
close alignment the performance of Boeing was relatively better than in other periods. However there
are no large differences in the clusters regarding the performance. This could hint to the previously
discussed fact that the effect of alignment is less important and has less effect in industries with lower
velocity.
60
Figure 10: Effect of alignment with weighted absolute rates of change for Boeing
Next we take a look at the weighted alignment and rates of change when also negative values are
taken into account. As can be seen the highest performance is at hand in the cluster in which the
alignment is the closest. Furthermore the lowest performance is achieved for the cluster with negative
misalignment. However the 4th cluster in which there is the highest positive misalignment shows to
higher performance than the one with lower positive misalignment which again goes against our
propositions.
Figure 11: Effect of alignment with weighted rates of change for Boeing
All in all aligning the rates of change for the aircraft industry seem ambiguous. Where it seems that
relatively better performance was achieved in periods when high alignment was at hand when
taking the weighted average of the rates of change of all dimensions it still remains rather weak in
comparison with Intel and the semiconductor industry. This could be due to the fact that the
semiconductor industry is a faster and more discontinuous environment and that alignment is more
beneficial in these environments. This has been proposed by previous research (Zajac, Kraatz et al.
2000).
61
5.5 Direction of change
The next section discusses the implications of the alignment of direction of change and its connection
to the performance.
5.5.1 Semiconductor industry
Figure 12 shows only the absolute values. It can be seen that the highest performance was achieved
in periods in which complete alignment was at hand. In periods with lower alignment there was also
lower performance at hand. This in general supports existing theory that a closer alignment is
associated with better performance.
Tobin's Q
Tobin's Q for different Δ DC
3.5
3.3
3.1
2.9
2.7
2.5
2.3
2.1
1.9
1.7
1.5
Tobin's Q
Count
0
1
2
2.866116458
2.722392835
1.770084762
10
12
2
Difference in direction of change
Figure 12: Effect of alignment of absolute direction of change on performance for Intel
The next figure differentiates between positive and negative values and shows how the performance
changes accordingly. It can be seen that the highest performance is achieved in periods in which there
was slight positive misalignment followed by the cluster with complete alignment.
Figure 13: Effect of alignment of direction of change on performance for Intel
62
Whereas periods with slight positive misalignment were associated to superior performance, periods
with negative misalignment were associated with worse performance. The worst aggregate
performance was at hand in periods with stark positive misalignment.
Since there are some dimensions in which there are many different discontinuities or misalignment in
discontinuities and others in which there are only few it can be interesting to see whether it has an
effect if the dimensions are weighted in such a way that the ones occurring more rarely have more
weight. In order to do so the average of the misalignment for each dimension is calculated. Then each
discontinuity is divided by that average to give it a special weight. These weighted dimensions are then
aggregated. Figure 14 and 15 show the result on alignment with this approach.
Figure 14: Effect of alignment of absolute weighted direction of change on performance for Intel
Figure 15: Effect of alignment of weighted direction of change on performance for Intel
In periods with close alignment the best performance was apparent. Furthermore periods with
positive misalignment showed higher performances than periods with negative misalignment. This
could mean that discontinuities that are rarer have a stronger effect. However more research would
be needed to confirm these findings.
63
5.5.2 Aircraft industry
Since for Boeing and the aircraft industry only very few changes took place these findings should be
taken with caution. This is especially the case since one dimension, namely the technology dimension
there were no discontinuous changes at all.
Figure 16 shows the connection between alignment and performance for absolute values. It can be
seen that absolute alignment is connected to better performance than misalignment.
Figure 16: Effect of alignment of absolute direction of change on performance for Boeing
The next figure also takes negative values into account. Again the highest performance was achieved
in periods when there was close alignment. Periods with misalignment was associated with worse
performance, with negative misalignment showing worse performance than positive misalignment.
Figure 17: Effect of alignment of direction of change on performance for Boeing
5.6 Conclusion
All in all our findings support the existing literature in the notion that the semiconductor is a more fast
paced industry and the aircraft is a more slow paced industry. Furthermore the directions of change
64
are rather low for the aircraft and rather high for the semiconductor industry. Nonetheless it must be
said that even though the semiconductor industry has higher rates and directions of change and the
aircraft industry rather lower ones, there do exist substantial differences in between the dimensions
of a singular industry which means that both these industries do not have a high velocity homology
but rather a medium one. The rates and directions of change differ significantly for each industry, one
cannot describe the dimensions to be homogeneous. This finding contradicts previous research which
has postulated industries to possess one singular velocity.
Overall we have found that both Intel and Boeing have managed to align or exceed with internal rates
and directions of change the rates and directions of change of the industry over the whole period of
study. Since both of them have survived and performed well overall over the period of study this is a
first indication that it is beneficial to align or exceed the external rates and directions of change with
internal rates and directions of change.
Going in more detail it was assessed how the alignment of rates and directions of change was related
to the performance measured through Tobin’s Q by aggregating periods with similar alignment rates.
For the rate of change for Intel it was found that in periods with the closest alignment there was also
the highest performance at hand, whereas in periods with positive misalignment there was low
performance. These findings are in consensus with existing literature that close alignment of rates of
change are connected with high performance. For the alignment of the rate of change of the aircraft
industry the initial results were not unambiguous as there was no clear trend seen in clusters of with
less or more alignment. Since the results could have been confounded through the product dimension
with its extremely high rates of change, once a weighted aggregate was taken results were more
favorable and in general supported existing theories even though there were still some outliers and
the difference was rather weak. This could also be due to the fact that the alignment has lower
performance benefits in environments with lower rates of change as has been proposed in previous
studies (Zajac, Kraatz et al. 2000).
The results for the direction of change for the aircraft industry were as expected as in periods with
high alignment there was higher performance than in periods with lower alignment. For the
semiconductor industry our findings were partially in line with expectations. Even though the
performance was high in the cluster with close alignment the cluster with small positive misalignment
had even higher performance. However when a weighted average was taken the periods with close
alignment showed the highest performance, the periods with positive misalignment showed the
second highest performance and periods with negative misalignment showed the worst
performances. Furthermore periods with strong misalignment had worse performances than periods
with little misalignment. These findings might suggest that rarer changes have more significance since
they do not occur that often and are thus potentially more powerful, which is why the weighting was
done. However more research is needed to confirm these suggestions.
All in all we found that in general the trend was that in periods with close alignment performance of
the companies was better than in periods with less alignment both for the rate and direction of
change. However these findings must be taken with caution, as they are based on limited amount of
data.
65
6 Conclusion and recommendations
6.1 Conclusion
Most of previous research has adopted the definition of environmental velocity of Bourgeois III and
Eisenhardt (1988) without actually operationalizing or measuring it, thus only discussing it on a
conceptual level. Empirical evidence is scarce especially for the concept of direction of change.
Therefore we set our research objective to operationalize and measure the environmental velocity in
a multidimensional way with the help of rate and direction of change and additionally to test how the
alignment of the rates and directions of change of the company to the industry’s is interrelated with
performance of the firms.
In order to do so we first conducted a thorough literature review on the environmental velocity and
specifically with regard to the current shortcomings and the different dimensions of environmental
velocity. Five dimensions namely product, technology, demand, competition and regulation which are
deemed to be able to define the concept of environmental velocity in a collectively exhaustive way
were found. The operationalizations of the rates and directions of changes of these dimensions, which
can be seen in Table 6, were discussed along with difficulties in operationalizing them. This was done
with the help of a further literature review. Furthermore the literature review gave indication on the
relationship of aligning internal rates of change to external rates of change and the performance of
companies. There was consensus that close alignment was connected with superior performance
whereas controversies existed whether higher internal than external rates of change are also
connected to higher performance or are detrimental to performance. No literature was found about
the relationship between alignment of direction of change and performance, however the same
relation as for the rates of change were expected.
Subsequently the operationalization of the rates of change were shortly discussed and the directions
of change were operationalized for the industries under study in a qualitative approach (see Table 10
& Table 11). Whereas for both the aircraft and semiconductor industry the rate of changes were
measured through equal indicators, namely change in number of new product generations (product),
change in number of new patents (technology), change in sales (demand), the direction of change was
different and customized for each industry except for the demand dimension (change in trend in
sales). For the semiconductor industry this was the minimum feature size (technology) the ratio of
clock speed to price (product), whereas for the aircraft industry it was the range, capacity and fuel
efficiency per seat (product). For the technology dimension a purely qualitative study was undertaken
which indicated that no discontinuous change had taken place over the last 25 years. As the
operationalization of the direction of change requires an in depth case study of the industries it
becomes clear why there has almost been no study measuring the concept of direction of change
despite its relevance when analyzing an industry in terms of its velocity. However it is very interesting
as it shows how continuous or discontinuous the changes which are occurring in an industry are.
Once all the measures for the direction of change and rate of change had been gathered the velocity
homologies of the two industries were analyzed. It was observed that both the semiconductor and
the aircraft industry were quite heterogeneous in terms of their dimensions, for which there were
large differences in rates and directions of change, meaning they both had rather medium velocity
homologies. This contests the notion in existing research of describing an industry as simply a high or
low velocity industry. Nonetheless the semiconductor industry has in general, for the period of study,
higher rates and directions of change than the aircraft industry which is in line with previous research
on these industries.
66
Subsequently the relation between alignment of rates and directions of change and performance of
the companies was assessed. For the aircraft industry we saw that in periods with close alignment for
the directions of change there was the highest performance at hand, whereas periods with negative
misalignment showed the lowest performance and periods with positive misalignment were in the
middle regarding the performance. For the semiconductor industry on the other hand the findings the
findings were more ambiguous. Even though the cluster with close alignment was associated with high
performance, the cluster of periods with small positive misalignment had even higher performance.
However when a weighted average was taken the expected results were apparent. Periods with close
alignment showed high performance, periods with negative misalignment displayed the lowest
performance and periods with close positive misalignment were in between being better than
negative misalignment but worse than close alignment. Since the weighting of the changes meant that
findings were more in line with previous theory and expectations this could mean that rarer changes
have more significance since they do not occur that often and are thus potentially more powerful.
However more research is needed to confirm this proposition.
For the rate of change for the semiconductor industry we found that periods with close alignment
showed the highest performance while periods with positive misalignment were slightly worse but
still significantly better than periods with negative misalignment. For the aircraft industry on the other
hand there were ambiguous results. While at first the results did not show any trend or pattern, after
an aggregate average of the dimension was taken the results in general were in line with our
expectations. This was done since the product dimension dominated the other dimensions in the
aggregate measure of the rate of change which could confound the results. Even though the cluster
with the closest alignment periods was associated with the highest performance when absolute values
of the differences between internal and external rate of change were taken into account (thus ignoring
positive vs negative misalignment), there was no clear decreasing trend to be seen as periods in which
there was higher misalignment (low alignment) had higher performance than periods with lower
misalignment (more alignment). Nonetheless overall the results showed that in periods in which there
was closer alignment there was also higher performance at hand than in periods with lower alignment.
This effect was much smaller for the aircraft industry than for the semiconductor industry however.
This could mean that the relation between alignment and performance is smaller in industries with
lower rates and directions of change of velocity, which has also been suggested in previous research.
Nonetheless more research is needed to confirm and strengthen our findings as limited amount of
data was used to reach the conclusions.
6.2 Contribution to Literature
6.2.1 Theoretical contribution
We have seen that the velocity of an industry is made up of several different dimensions which have
distinct directions and rates of changes. Previous research has ignored this fact and only termed
industries to have an overarching high or low velocity. While it has been suggested in literature that
this is a wrong assumption (McCarthy, Lawrence et al. 2010), it has not been empirically validated up
to this study. To this end the concept of velocity homology has been used. Furthermore previous
literature has provided only little empirical studies about the environmental velocity. The concept has
mostly been used on a conceptual level.
Most of previous research has neglected the direction of change. Thinking about an industry in terms
of merely high or low velocity in terms of rate of change while neglecting the direction of change
however, does not capture the whole characteristics of the industry. This is because it would leave
out the fact that the directions of change add a very important characteristic, namely how continuous
or discontinuous change the industry is. This study has operationalized and measured the direction of
67
change for the two different industries under study. Besides having classified the two industries under
study in terms of their direction of change the research has thus also provided insight as how to go
about operationalizing the direction of change as the relevant questions for doing so were developed
and shown in Table 8. Thus avenues for further research regarding the direction of change, especially
the measurement have been opened.
Thirdly the multidimensional concept of velocity with also directions of change have not been used in
connection to the alignment theory, which has been done in this study. The findings about the aligning
rates and directions of change to the industry and its relation with performance are a further
contribution.
6.2.2 Managerial contribution and implication
We have seen that industries show different rates and directions of change across their dimensions.
If the velocities differ significantly the industry is in turn called a low homology environment.
Furthermore we have seen that it seems that it is beneficial for a company to align the internal rates
and directions of change of the company to the environment. Whereas there have been inconsistent
findings whether higher rates of change of the company than the industry are better or worse than
lower rates of change we can conclude that overall a close alignment is in general to be strived for.
Also for the direction of change it was seen that close alignment was at hand when on average better
performance was apparent.
This suggests that firms must pay close attention to the environment in order to achieve alignment,
this is especially important in high technology industries which are expected to be especially fast and
volatile. This is relevant for innovation managers and managers of product and technological
processes as well as strategic planners in high technology industries. They should aim for alignment of
the relevant processes but have to pay attention to avoid too little or too high rates and directions of
change in comparison to the industry. This can be explained by the fact that through changing too fast
or too slow they may drift into chaos or suffer from inertia. For example a firm trying to change first
by applying a fast mover approach can give the company benefits by being the first to capture a
market. However it can also be dangerous and risky due to too short reaction times which can lead to
chaos. One of the reasons is that in order to outdo external rates and directions of change companies,
especially incumbent ones, must mobilize resources which have accumulated over the years and
which are connected to long-term partnerships with key customer suppliers or partners. The problem
in this case then is that it is uncertain how the payoffs are, meaning that it is uncertain whether they
are favorable or not. The other extreme is that firms take too long to respond to external changes.
What this means is that they miss the opportunity to act and then are stuck in inertia because they
missed the change to respond to the environment. This is then connected with lower performance
over the long term.
Again this suggests that alignment is the favored solution. Achieving alignment is associated with
several difficulties however. First off, in order to know and understand how fast, and in which direction
the changes go and what the drivers for these changes are an industry must first be understood
comprehensively by the managers. These developments are affected by other developments and
trends such as where the society in general is developing, how the business environment is
developing, and most importantly how technology and science in the relevant fields are developing.
This is especially difficult in environments with low homologies since in these environments the rates
and directions of change are not similar but have several differences. Once this is understood the
manager of the relevant product or technological process must aim to align the internal rates and
68
directions of changes to that of the business environment. Again this will be the most difficult in
environments with a low velocity homology.
In order to align the company with the environment, different rates and directions of change must be
achieved within the company. This can be a challenge since it means that internally subunits and
processes must operate at different speeds which can create tension in between them. To confront
this problem on a very high level possible solutions are modular and flexible structures which allow
room for experimentation. This can possibly help the company to be more open and flexible to change
and operate at the necessary speed at all levels. Furthermore there are differences regarding the
velocity of the environment. In case the environment has rather high rates and directions of change,
they should be more enactive by searching for new ideas and experimenting beyond existing areas. In
environments in which the rates and directions of change are rather low the firms should rather aim
to establish strong ties with constituents of their environment to exchange information and resources.
Whereas this will restricts the inflow of novel and innovative idea it is not a big problem in
environments with lower rates and directions of change since the changes are not frequent the
changes are not discontinuous in nature. This will automatically lead the firm to be more reactive by
changing more slowly and only if their performance declines and the established methods do not work
anymore. Firms in environments with high rates and directions of change should establish weak ties
with constituents both within and outside of their existing domain. Thus they can validate their ideas
based on experimentation rather than on well-developed feedback mechanisms (Nadkarni and
Narayanan 2007). However it must be noted that the environment of a company cannot be purely
understood as exogenous. Firms can change the environment through their assumptions on the
environment and their actions resulting from these assumptions.
6.3 Reflection
Referring to the previous mentioned point that environments are not purely exogenous but
endogenous there are a few things which must be taken into account when looking at the results of
the research. In the case of our research very large and dominant market players have been chosen.
While the choice for these firms has been motivated earlier it does bring about some limitations and
implications which should not be neglected.
6.3.1 Reflection about choice of companies
Since the companies chosen, namely Intel and Boeing are large companies which are dominating
forces in the market it can also be argued that instead of aligning with the environment, they have a
shaping effect on the environment, and also on the environmental velocity. This is because as
mentioned the environment is not purely exogenous but endogenous, meaning that it is determined
by all the firms in the industry. And since the companies analyzed in this study, are dominating players
in the industry they are actually very significant factors in determining the velocity of the industry and
are thus almost automatically aligned whereas other smaller firms have to follow that lead. To sum
up this means that, since the large companies possess a lot of power they determine where the
industry is headed, also in terms of velocity. Then instead of reacting to the changes that are
happening in the environment, they induce changes into the environment which would mean a lack
of need to align to the environment. Due to the power the companies have over the industry including
the suppliers and customers, their actions set the standard for the entire industry, which is then
adopted as the norm. Since they supply a large part of the market and get served by many suppliers
the requirements of the dominant company can become the standard and then others have to follow
that lead or will go out of business soon. Thus in this line of reasoning, for example they set the
standard for product cycles or for the characteristics which are important to customers, which then
becomes the standard which every other player has to follow.
69
An interesting question to ask then would be what we would have observed if the firms were rather
small than large dominant players in the industry. In general two options can be thought of then. The
first one is that the small companies should then as mentioned before follow the lead of the dominant
company in order to be able to also compete in this specific market. This is because all the customers
have adapted to the cycle of change of the dominant player and expect the same from other
companies. Furthermore suppliers have synchronized to the large players and ensured to meet their
requirements, which makes it almost necessary for the smaller companies to follow the lead of the
large dominant companies. By following the dominant player they then in turn align with the
environment, and meet the requirements of the market place.
On the other hand it is possible to think that since they are smaller and not so dominant they will not
be able to compete on the same terms as the large dominant players, they will try to find an alternative
way. Whereas this is completely normal and well understood mechanism in terms of a general
strategy, which can be called a niche strategy it is a bit different for the velocity concept. It basically
can be imagined that they are faster, meaning that they bring products faster to the market or that
they change the characteristics of the products in a different (discontinuous way) than the
competitors. Furthermore it can also be imagined that they bring about a technological discontinuity
which helps them improve their operations in such a way that they become more efficient and thus
more competitive. In this case the company innovates or does something different from the rest of
the industry, which means it does not have alignment with the environment. For example a company
changing faster and thus being not aligned than the environment may benefit exactly because it is not
aligned, because this will give it some type of competitive advantage. However as mentioned before
there are also risks associated with that strategy.
To conclude there is no definite answer to the question what would happen with a smaller company
but we have outlined two different scenarios. We can argue that for small companies alignment can
also be beneficial. However also in some cases the lack of alignment, more specifically the conscious
decision to be faster than the environment could prove beneficial since then the competitors would
be outpaced. The advantage of a small company is that this is easier to achieve since they are more
flexible and quicker in the marketplace. Nonetheless one can say that this is a risky undertaking since
it is possible that the company is trying to outpace the market but the customers are not in favor of
the fast or discontinuous change which then limits the success of this attempt.
6.3.2 Reflection about managerial view
Another point which is interesting to discuss is that in this study a very process oriented and analytical
view is taken for managing internal resources in order to compete successfully in the marketplace.
The premise which was made is that innovation managers or managers of R&D which are responsible
for product development and technological development should seek to understand the velocity of
the industry comprehensively to then try to align their internal processes to the external environment.
Thus a rather mechanical and analytical process namely the analysis of the industry in terms of its
velocity and then the act of aligning the internal ongoings to the external environment is proposed
Even though it can be argued that decision makers will analyze and try to understand the industry
their company is operating in thoroughly they might not merely try to achieve alignment in this
mechanical way but act more on intuition than rely on this purely analytical view. This is due to many
different reasons, but the main one might be that managers trust their own intuition and experience
more than any models or tools for achieving a decision (Pfeffer and Sutton 2006). Thus instead of
trying to merely align they might try to outpace or do something different than the rest of the
environment. In the end it is a human process and not thus this purely analytical and mechanical view
might not mirror it perfectly. Nonetheless it can be said that even if they act more on intuitive bases,
70
if the end result is alignment of the internal processes to the environment the reason for achieving it
will not be a big factor. This means that even if the thought process of the responsible manager of the
innovation process behind achieving the alignment is not purely to achieve alignment with the
external environment, but this is what actually happens in the end the performance implications
remain the same. Of course it is also possible that no alignment is achieved due to an intuitive and
less process oriented approach.
6.4 Relation to Management of Technology Curriculum
From a Management of Technology (MoT) angle, the research is relevant at it is structured around
some core concepts of the curriculum. The MoT programme which educates students as technology
managers, analysts of technological markets (either as scientists or consultants), and entrepreneurs
in high technology-based, internationally-oriented and competitive environments in a variety of
sectors. Managing technology, research methods and the industry lie at the centre of this curricula.
The research fits within this programme as the central actor in the research is the business in high
technology industries. Furthermore as the goal is to see how managing internal resources and most
importantly products and technology can interact with performance the managing of technology is a
crucial part. Lastly several research methods were employed to reach that goal.
Specifically related to the courses I was able to apply several theories and key topics from the MoT
programme and the courses it offered. The courses which were most helpful and useful for the thesis
and their specific contribution are outlined in the following paragraph.
Technology Dynamics [MOT1411] introduced the concepts of competence enhancing and
competence destroying discontinuities. Furthermore it was very helpful in understanding and
analysing the qualitative aspects of the developments of the dimensions of products and technologies
of the different industries throughout the time period under study. In Technology and Strategy
[MOT1433] I learned that a company must understand the industry and environment it is operating in
and formulate its strategies accordingly. Thus it builds the basis on the proposition that a company
needs to build a strategy fitting to the environment. For this to happen it needs to build important
competencies and capabilities. Furthermore the course discussed that the industry is composed of
several different elements which need to be taken into account when formulating a strategy, which is
also the basis for the different dimensions used in the research. The lectures of Innovation
Management [MOT2420] offered insights into innovation mechanisms. In fact the dimensions of
product and technology are driven by innovations and alignment can only be achieved if the
innovations in the field of the product and technology are managed properly. Also some hints how an
organization can achieve alignment in case of a low homology environment which is associated with
difficulties could be derived from innovation management lectures. High tech Marketing [MOT1530]
helped understand the difference between product and process innovations (differentiation between
product and technology dimension in the research), and the importance of differentiating the two as
they are very different and have different implications for the organization and the market. It also
helped understand the difference between incremental vs breakthrough innovations (also called
continuous vs discontinuous changes in this research). Furthermore it was helpful in discussing the
implications of alignment and lack thereof on the companies’ strategies by discussing the different
options namely taking a market leader or a follower approach. Lastly “Preparation for the master
thesis” [MOT2003], was a good introduction on how to prepare design and carry out a research
project. Although, this very research project is not a continuation from the master thesis assignment,
I still gained relevant insight and knowledge and was prepared on tackling the actual Master thesis.
71
6.5 Limitations and future research
One of the obvious limitations is the limited amount of data available meaning the limited amount of
years that were analyzed. While this is due to the fact that more data could not be retrieved it still
limits the reliability of the findings. The more years an industry can be studied the more data points
will be available for analyzing the relationship between the alignment and the performance. This goes
in hand with the issue is that while the results have mostly confirmed our hypothesis, which increases
confidence in the results, they are only based on two industries. Even though these industries are
quite different and still show similar results which raises confidence in the findings it must be said that
two industries are no proof that these findings can be generalized over many other industries. As
suggestion for future research we recommend to repeat this analysis with a longer period of time if
data allows it. Since the indicators for direction of change have already been found it would be doable
with relatively little effort if data is available for the other dimensions. With more data points an actual
regression analysis can also be carried out which will improve the findings and enable to achieve more
insight. Another suggestion would be to repeat the research with different industries.
Another issue, which is related to the measurement is the measurement of the direction of change
especially for the product and technology dimensions. This is the case because there are arguably
many different factors that influence the process technology as well as the product attractiveness as
viewed by the customer. To boil these down to few, yet meaningful indicators which can then be used
to measure the direction of change is very challenging. While we are confident that our indicators are
meaningful as they have been validated through previous studies, it can be argued that there are
several important characteristics which have an influence on these dimensions and are left out.
Furthermore the conversion of these qualitative assessment regarding the direction of change into
binary coding without any weight bears the danger of losing important information on the significance
or importance of the change.
Even though we derived the hypothesis from previous research and are thus confident that the results
are meaningful, it can be argued that there are many different factors which influence the
performance of a firm and which are not taken into account in the analysis. This means that possibly
there are other factors confounding the results which are disregarded in our analysis. Furthermore
the performance is measured for the same company in different time periods as opposed to firms in
the same industry for same periods. This means that the term superior performance must be taken
with caution. Even though we have found relation between alignment and performance as mentioned
before many other factors can influence the performance. By comparing the same company to itself
over time important influencing factors such as the overall economic situation cannot be controlled
for. Future research could include more companies from the same industry and try to control for other
factors influencing the performance.
Another limitation is that while the products chosen do have the highest revenue and profit share in
the company there still exist other products within the company. They may also influence the
performance of the company. While this is not a problem for the technology or demand dimension it
is problematic for the product dimension. Future research could take into account also other products
of the companies and thus achieve a more representative picture of reality.
Furthermore not all dimensions that were found to be relevant to define the environmental velocity
dimension were included in the research. Thus another key point to improve upon the existing
research would be to include the other dimensions which shave been listed as defining the
environmental velocity which however have not been included in the analysis, namely regulation and
competition. The possible operationalisations have been discussed in this report.
72
7 Bibliography
Abernathy, W. J. and K. B. Clark (1985). "Innovation: Mapping the winds of creative destruction."
Research policy 14(1): 3-22.
Aizcorbe, A., et al. (2008). "Shifting trends in semiconductor prices and the pace of technological
progress." Business Economics 43(3): 23-39.
Aizcorbe, A. M. (2002). "Price measures for semiconductor devices." Board of Governors of the
Federal Reserve System FEDS(2002-13).
Ancona, D. (1996). "Entrainment." Wiley Encyclopedia of Management.
Andersen, P. A. (1998). "The Changing Structure of the Global Large Civil Aircraft Industry and
Market: Implications for the Competitiveness of the US Industry." US International Trade
Commission, Investigation: 332-384.
Anderson, P. and M. L. Tushman (1990). "Technological discontinuities and dominant designs: A
cyclical model of technological change." Administrative science quarterly: 604-633.
Association, I. A. T. (2009). The IATA Technology Roadmap Report, International Air Transport
Association (IATA).
Begemann, A. (2008). Die Rolle von Patenten in der zivilen Luftfahrtindustrie aus historischer und
rechtsvergleichender Sicht, Herbert Utz Verlag.
Ben-Menahem, S. M., et al. (2013). "Strategic renewal over time: the enabling role of potential
absorptive capacity in aligning internal and external rates of change." Long Range Planning 46(3):
216-235.
Benchmark, C. (2016). "CPU Benchmarks." Retrieved 11.01.2016, 2016.
Bogner, W. C. and P. S. Barr (2000). "Making sense in hypercompetitive environments: A cognitive
explanation for the persistence of high velocity competition." Organization Science 11(2): 212-226.
Bourgeois III, L. J. and K. M. Eisenhardt (1988). "Strategic decision processes in high velocity
environments: Four cases in the microcomputer industry." Management science 34(7): 816-835.
Bozdogan, K. (2010). "Evolution of the lean enterprise system." Encyclopedia of aerospace
engineering.
Brauer, M. and S. L. Schmidt (2006). "Exploring strategy implementation consistency over time: The
moderating effects of industry velocity and firm performance." Journal of Management &
Governance 10(2): 205-226.
73
Brown, S. L. and K. M. Eisenhardt (1997). "The art of continuous change: Linking complexity theory
and time-paced evolution in relentlessly shifting organizations." Administrative science quarterly: 134.
Bugos, G. (2001). "History of the Aerospace Industry." from http://eh.net/encyclopedia/the-historyof-the-aerospace-industry/.
Burgelman, R. A. (1991). "Intraorganizational ecology of strategy making and organizational
adaptation: Theory and field research." Organization Science 2(3): 239-262.
Burgelman, R. A. and S. Andrew (2001). Strategy is destiny: How strategy-making shapes a
company's future, The Free Press.
Byrne, D. M., et al. (2015). How fast are semiconductor prices falling?, National Bureau of Economic
Research.
Casadesus-Masanell, R., et al. (2002). "Intel Corporation: 1968-2003."
Castrogiovanni, G. J. (1991). "Environmental Munihcence; A Theoretical Assessment." Academy of
Management Review 16(3): 542-565.
Chen, G., et al. (2005). "Conceptual framework and statistical procedures for delineating and testing
multilevel theories of homology." Organizational Research Methods 8(4): 375-409.
Child, J. (1972). "Organizational structure, environment and performance: The role of strategic
choice." Sociology 6(1): 1-22.
Chung, K. H. and S. W. Pruitt (1994). "A simple approximation of Tobin's q." Financial management:
70-74.
Commission, U. I. T. (1998). "The changing structure of the global large civil aircraft industry and
market: implications for the competitiveness of the US industry." Washington DC.
Commission, U. S. I. T. (1998). The Changing Structure of the Global Large Civil Aircraft Industry and
Market: Implications for the Competitiveness of the U.S. Industry, U.S. International Trade
Commission.
Company, D. P. (1995). Global Competitiveness of U. S. Advanced-Technology Manufacturing
Industries: Large Civil Aircraft, DIANE Publishing Company.
Davis, C. L. and Y. Shirato (2007). "Firms, governments, and WTO adjudication: Japan's selection of
WTO disputes." World Politics 59(02): 274-313.
74
Davis, J. P., et al. (2009). "Optimal structure, market dynamism, and the strategy of simple rules."
Administrative science quarterly 54(3): 413-452.
Dess, G. G. and D. W. Beard (1984). "Dimensions of organizational task environments."
Administrative science quarterly: 52-73.
Dess, G. G. and A. M. Rasheed (1991). "Conceptualizing and measuring organizational environments:
A critique and suggestions." Journal of Management 17(4): 701-710.
Doganis, R. (2002). Flying off course: The economics of international airlines, Psychology Press.
Drazin, R. and A. H. Van de Ven (1985). "Alternative forms of fit in contingency theory."
Administrative science quarterly: 514-539.
Eisenhardt, K. M. (1989). "Making fast strategic decisions in high-velocity environments." Academy
of management journal 32(3): 543-576.
Eisenhardt, K. M. and J. A. Martin (2000). "Dynamic capabilities: what are they?" Strategic
Management Journal 21(10-11): 1105-1121.
Eisenhardt, K. M. and B. N. Tabrizi (1995). "Accelerating adaptive processes: Product innovation in
the global computer industry." Administrative science quarterly: 84-110.
Fine, C. H. (1998). Clockspeed: Winning industry control in the age of temporary advantage, Basic
Books.
Fine, C. H. (1999). Industry clockspeed and competency chain design: an introductory essay,
Springer.
Fonta, P. (2010). "Pushing the technology envelope." BAN Ki-moon.
Frenken, K. and L. Leydesdorff (2000). "Scaling trajectories in civil aircraft (1913–1997)." Research
policy 29(3): 331-348.
Glick, W. H. (1985). "Conceptualizing and measuring organizational and psychological climate: Pitfalls
in multilevel research." Academy of Management Review 10(3): 601-616.
Grade, Y. K. (2015). "White Paper. Fifty years of Microprocessor Technology Advancements: 1965 to
2015.".
Grimm, B. T. (1998). "Price indexes for selected semiconductors." Survey of Current Business 78(2):
8-24.
75
Groover, M. P. (2007). Fundamentals of modern manufacturing: materials processes, and systems,
John Wiley & Sons.
Hanlon, G. (2004). "Institutional forms and organizational structures: homology, trust and
reputational capital in professional service firms." Organization 11(2): 186-210.
Hannan, M. T. and J. Freeman (1984). "Structural inertia and organizational change." American
sociological review: 149-164.
Hannan, M. T. and J. Freeman (1993). Organizational ecology, Harvard University Press.
Johnson, F. T., et al. (2005). "Thirty years of development and application of CFD at Boeing
Commercial Airplanes, Seattle." Computers & Fluids 34(10): 1115-1151.
Jones, N. B. and J. F. Mahon (2012). "Nimble knowledge transfer in high velocity/turbulent
environments." Journal of knowledge management 16(5): 774-788.
Jorgenson, D. W., et al. (2011). "Information technology and US productivity growth: evidence from
a prototype industry production account." Journal of Productivity Analysis 36(2): 159-175.
Judge, W. Q. and A. Miller (1991). "Antecedents and outcomes of decision speed in different
environmental context." Academy of management journal 34(2): 449-463.
Ketchen, D. J., et al. (1993). "Organizational configurations and performance: A comparison of
theoretical approaches." Academy of management journal 36(6): 1278-1313.
Kronemer, A. and J. E. Henneberger (1993). "Productivity in aircraft manufacturing." Monthly Labor
Review 116(6): 24-33.
Kwee, Z. (2009). Investigating Three Key Principles of Sustained Strategic Renewal: A Longitudinal
Study of Long-Lived Firms, Erasmus Research Institute of Management (ERIM).
Lee, J. J., et al. (2001). "Historical and future trends in aircraft performance, cost, and emissions."
Annual Review of Energy and the Environment 26(1): 167-200.
Lindenberg, E. B. and S. A. Ross (1981). "Tobin's q ratio and industrial organization." Journal of
business: 1-32.
Lojek, B. (2007). History of semiconductor engineering, Springer.
McCarthy, I. P., et al. (2010). "A multidimensional conceptualization of environmental velocity."
Academy of Management Review 35(4): 604-626.
76
Mendelson, H. and R. R. Pillai (1999). "Industry clockspeed: Measurement and operational
implications." Manufacturing & Service Operations Management 1(1): 1-20.
Michaels, D. (2012). "The Secret Price of a Jet Airliner." The Wall Street Journal.
Miles, R. E. and C. C. Snow (2001). Fit, failure & the hall of fame, Free Press.
Milliken, F. J. (1987). "Three types of perceived uncertainty about the environment: State, effect,
and response uncertainty." Academy of Management Review 12(1): 133-143.
Murman, E. M., et al. (2000). "Challenges in the better, faster, cheaper era of aeronautical design,
engineering, and manufacturing."
Nadkarni, S. and P. S. Barr (2008). "Environmental context, managerial cognition, and strategic
action: an integrated view." Strategic Management Journal 29(13): 1395-1427.
Nadkarni, S., et al. (2015). "The clock is ticking! Executive temporal depth, industry velocity and
competitive aggressiveness." Strategic Management Journal.
Nadkarni, S. and V. Narayanan (2007). "The evolution of collective strategy frames in high-and lowvelocity industries." Organization Science 18(4): 688-710.
Nadkarni, S. and V. K. Narayanan (2007). "Strategic schemas, strategic flexibility, and firm
performance: the moderating role of industry clockspeed." Strategic Management Journal 28(3):
243-270.
Pattillo, D. M. (2001). Pushing the Envelope: The American Aircraft Industry, University of Michigan
Press.
Pérez-Nordtvedt, L., et al. (2008). "An entrainment-based model of temporal organizational fit,
misfit, and performance." Organization Science 19(5): 785-801.
Pfeffer, J. and R. I. Sutton (2006). "Evidence-based management." Harvard business review 84(1): 62.
Pillai, U. (2011). "Technological Progress in the Microprocessor Industry." Survey of Current Business
91(2): 13-16.
Porter, M. E. (2008). Competitive strategy: Techniques for analyzing industries and competitors,
Simon and Schuster.
77
Pritchard, D. and A. MacPherson (2004). "Outsourcing US commercial aircraft technology and
innovation: implications for the industry’s long term design and build capability." United States
Trade Center, Department of Geography, Buffalo, NY.
Rae, J. B. (1968). Climb to Greatness: The American Aircraft Industry, 1920-1960, The MIT Press.
Robert Baum, J. and S. Wally (2003). "Strategic decision speed and firm performance." Strategic
Management Journal 24(11): 1107-1129.
Rumelt, R. P. (1974). "Strategy, structure, and economic performance."
Spec.org (2011). "SPEC CPU2006." from https://www.spec.org/cpu2006/Docs/readme1st.html.
Stepanovich, P. L. and J. D. Uhrig (1999). "Decision making in high-velocity environments:
Implications for healthcare." Journal of Healthcare Management 44(3): 197.
Tan, J. and D. Tan (2005). "Environment-strategy co-evolution and co-alignment: A staged model of
Chinese SOEs under transition." Strategic Management Journal 26(2): 141.
Turley, J. L. (2003). The essential guide to semiconductors, Prentice Hall Professional.
Tushman, M. L. and P. Anderson (1986). "Technological discontinuities and organizational
environments." Administrative science quarterly: 439-465.
Uotila, J., et al. (2009). "Exploration, exploitation, and financial performance: analysis of S&P 500
corporations." Strategic Management Journal 30(2): 221-231.
Venkatraman, N. and J. E. Prescott (1990). "Environment‐strategy coalignment: an empirical test of
its performance implications." Strategic Management Journal 11(1): 1-23.
Volberda, H. W. and A. Y. Lewin (2003). "Co‐evolutionary dynamics within and between firms: From
evolution to co‐evolution." Journal of management studies 40(8): 2111-2136.
Weber, A. (2009). "High-Flying Robots." from http://www.assemblymag.com/articles/86900-highflying-robots.
Wirtz, B. W., et al. (2007). "Strategy in high-velocity environments." Long Range Planning 40(3): 295313.
Yenne, B. (2005). The Story of the Boeing Company, Zenith Imprint.
Zajac, E. J., et al. (2000). "Modeling the dynamics of strategic fit: A normative approach to strategic
change." Strategic Management Journal 21(4): 429-453.
78
79
8 Appendix
8.1 Rate of change
8.1.1 Semiconductor
Products
Year
IRC
ERC_Alig
nment
ΔRC
1978
1979
0
1980
-50
12.5
-62.5
1981
200
37.5
162.5
1982
0
0
0
1983
0
20
-20
1984 -66.6667 -19.4444 -47.2222
1985
0 16.66667 -16.6667
1986
0 14.28571 -14.2857
1987
0 21.42857 -21.4286
1988
400
6.25
393.75
1989
0 -16.6667 16.66667
1990
-20 44.44444 -64.4444
1991
-100 22.22222 -122.222
1992
100
31.25
68.75
1993
0 21.2963 -21.2963
1994
100 18.51852 81.48148
1995
-50 -5.55556 -44.4444
1996
0 5.185185 -5.18519
1997
0 -3.7037 3.703704
1998
100
0
100
1999
50 -21.875
71.875
2000 33.33333 -33.3333 66.66667
2001
0 -16.6667 16.66667
2002
0
20
-20
2003
0
0
0
2004
25 -33.3333 58.33333
Patents: Source: USPTO.
Indication/Classification: NAICS 3344
IRC
ERC
Patents ERC
Sales Sales
Patents IRC
Indicat Patents
Sales Intel
Sales Industry Intel
Industr ΔRC
Intel
Patents or 3
Indicat ΔRC
313788
2594069
17
4163
489615
3567420 56.0337 37.5222 18.5115
10 -41.176
3193 -23.301 -17.876
621540
4615954 26.9446 29.3919 -2.4473
8
-20
3910 22.4554 -42.455
597909
4539554 -3.802 -1.6551 -2.1469
16
100
4211 7.69821 92.3018
651574
4704165 8.97545 3.62615 5.3493
14
-12.5
3999 -5.0344 -7.4656
809035
5614591 24.1662 19.3536 4.81263
18 28.5714
4160 4.02601 24.5454
1159392
9804482 43.3055 74.625 -31.32
24 33.3333
4709 13.1971 20.1362
893410
6718011 -22.942 -31.48 8.5387
16 -33.333
5419 15.0775 -48.411
760895
7186264 -14.832 6.97011 -21.803
15
-6.25
5761 6.31113 -12.561
1166943
8635479 53.3645 20.1665 33.1981
24
60
7358 27.7209 32.2791
1640216
11284360 40.5567 30.6744 9.88226
33
37.5
6965 -5.3411 42.8411
1774585
12466446 8.19215 10.4754 -2.2833
49 48.4848
8489 21.8808 26.604
2115957
12169486 19.2367 -2.3821 21.6188
45 -8.1633
8345 -1.6963 -6.467
2329000
12848431 10.0684 5.57908 4.48932
59 31.1111
9632 15.4224 15.6887
3018000
15309886 29.5835 19.1576 10.4259
75 27.1186
9730 1.01744 26.1012
4416000
20717821 46.3221 35.3232 10.9989
128 70.6667
9698 -0.3289 70.9955
5826000
27987577 31.9293 35.0894
-3.16
207 61.7188 11189 15.3743 46.3444
7922000
39181372 35.9767 39.9956 -4.0189
271 30.9179 11963 6.91751 24.0004
8668000
35196347 9.41681 -10.171 19.5875
423 56.0886 12476 4.28822 51.8003
11053000
38675143 27.515 9.88397 17.631
405 -4.2553 13092 4.93748 -9.1928
11663000
34349517 5.51886 -11.185 16.7034
701 73.0864 18454 40.9563 32.1301
12740000
39294281 9.23433 14.3954 -5.1611
733 4.56491 20045 8.62144 -4.0565
13912000
53803532 9.19937 36.9246 -27.725
795 8.45839 22305 11.2746 -2.8162
8233000
29348483 -40.821 -45.452 4.63162
809 1.76101 25499 14.3197 -12.559
7698000
26206688 -6.4982 -10.705 4.2069
1077 33.1273 27028 5.99631 27.131
7644000
26907659 -0.7015 2.67478 -3.3763
1592 47.818 28491 5.41291 42.4051
6563000
33080183 -14.142 22.9397 -37.081
1601 0.56533 29392 3.1624 -2.5971
Sales
80
8.1.1.1 Basis for rate of change product semiconductor
Family
Processor
Year
AMD
AM9080
1974
AMD
AM8085
1978
AMD
AM8086
1981
AMD
AM8088
1979
AMD
AMD 80186
1982
AMD
AMD 80188
1991
AMD
AMD 80286
1982
AMD
386dX
1991
AMD
386 SX
1991
AMD
5x86
1995
AMD
5k86
1996
AMD
486 SX
1993
AMD
AMD 2900
1975
AMD
AM286
1984
AMD
29000
1988
AMD
29030
1994
AMD
29040
1994
AMD
29050
1990
AMD
K5
1996
AMD
K6
1997
AMD
K6-2
1998
AMD
K6-III
1999
AMD
K7
1999
AMD
K8
2003
AMD
K10
2007
AMD
Bobcat
2011
AMD
Bulldozer
2011
AMD
Jaguar
2013
AMD
Puma
2014
ARM
SA-110
1996
CYRIX
486SLC/DLC
1992
CYRIX
5x86
1995
CYRIX
6x86
1995
CYRIX
Digital Equipment
Corporation
Digital Equipment
Corporation
Digital Equipment
Corporation
Digital Equipment
Corporation
Digital Equipment
Corporation
Digital Equipment
Corporation
GX1
199?
T-11
1981
MicroVAX
1984
CVAX
1987
Rigel
1989
NVAX
1991
21064
1992
81
Digital Equipment
Corporation
Digital Equipment
Corporation
Ferranti
Alpha 21164
1995
Alpha 21264
1998
F100-L
1976
Hitachi
6309
1988
IDT
Winchip C6
1997
IDT
Winchip 2
1999
Intel
4004
1971
Intel
8008
1972
Intel
4040
1974
Intel
8080
1974
Intel
8085
1976
Intel
8086
1978
Intel
8088
1979
Intel
80186
1982
Intel
80188
1982
Intel
80286
1982
Intel
80386
1985
Intel
80960
1988
Intel
80376
1989
Intel
1989
Intel
80486
80486
overdrive
80860
Intel
Pentium
1993
Intel
Pentium II
1995
Intel
Celeron
1998
Intel
Pentium III
1999
Intel
Pentium 4
2000
Intel
Xeon
2001
Intel
Itanium
2001
Intel
Itanium 2
2002
Intel
Pentium M
2003
Intel
Celeron D
2004
Intel
Celeron M
2004
Intel
2005
Intel
Pentium D
Pentium
Extreme
Edition
Core Solo
Intel
Core Duo
2006
Intel
2006
Intel
Core 2
Pentium DualCore
Celeron DualCore
Atom
Intel
Core i7
2008
Intel
Core i5
2009
Intel
Intel
Intel
Intel
1989
1989
2005
2006
2007
2008
2008
82
Intel
Core i3
2010
Intel
Core M
2014
Intersil
6100
1975
MIPS Technologies
R2000
1986
MIPS Technologies
R3000
1988
MIPS Technologies
R4000
1991
MIPS Technologies
R4400
1993
MIPS Technologies
R4600
1994
MIPS Technologies
R5000
1996
MIPS Technologies
R10000
1996
MOS Technology
650x
1975
Motorola
6800
1974
Motorola
6809
1978
Motorola
68000
1979
Motorola
68010
1982
Motorola
68020
1984
Motorola
68030
1987
Motorola
68040
1991
Motorola
68060
1994
Motorola
PowerPC 603
1994
National Semiconductor
PACE
1974
National Semiconductor
SC/MP
1976
National Semiconductor
INS8900
1977
National Semiconductor
SC/MP II
1977
National Semiconductor
32016/32
1982
National Semiconductor
32332
1985
National Semiconductor
32532
1987
NEC
V20
1984
NEC
V30
1984
NEC
V40
198?
NEC
V50
198?
NexGen
Nx586
1994
Philips
68070
1988
RCA
1802
1976
Rise Technology
MP6
1998
Signetics
2650
1975
Signetics
1976
SUN Microsystems
8X300
SPARC
MB86900
SuperSPARC
Sun Microsystems
UltraSparc I
1995
Sun Microsystems
UltraSparc II
1997
Sun Microsystems
UltraSparc IIi
1998
Sun Microsystems
UltraSparc IIe
2001
Sun Microsystems
UltraSparc III
2000
Sun Microsystems
UltraSparc IIIi
2003
Sun Microsystems
UltraSparc IV
2004
SUN Microsystems
1987
1992
83
Sun Microsystems
UltraSparc IV+
2005
Texas Instruments
TMS1000
1974
Texas Instruments
TMS9900
1976
Texas Instruments
TMS99105
1981
Texas Instruments
TMS99110
1981
Transmeta
TM5800
2001
VIA
Cyrix III (C3)
2000
VIA
C7-M
2005
VIA
C7-D
2006
VIA
Nano
2008
VIA
Nano X2
2011
Western Electric
WE 32100
1985
Ziloq
Z80
1976
Ziloq
Z800x
1979
Ziloq
Z180
1986
HP
PA-7000
1991
HP
PA-8000
1996
IBM
Power1
1990
IBM
Power2
1993
IBM
386SLC
1991
IBM
486SLC
1993
IBM
Power 3
1998
IBM
Power PC 970
2002
IBM/Motorola
PowerPC 601
1993
IBM/Motorola
PowerPC 603
1994
IBM/Motorola
PowerPC 604
1994
IBM/Motorola
Power PC 602
1995
IBM/Motorola
Power PC 620
1997
IBM/Motorola
Power PC 740
1997
84
8.1.1.2 Rate of change product
Year
Intel
Movi
Prod ng
ucts avg. RC
AMD
Movi
Prod ng
ucts avg. RC
Digital Equipment
Corporation
IDT
Movi
Movi
Prod ng
Prod ng
ucts avg. RC
ucts avg. RC
Cyrix
Movi
Prod ng
ucts avg. RC
MIPS
Technologies
Movi
Prod ng
ucts avg. RC
National
Semiconductor
Movi
Prod ng
ucts avg. RC
Motorola
Movi
Prod ng
ucts avg. RC
SUN Microsystems
IBM
Movi
Movi
Prod ng
Prod ng
ucts avg. RC
ucts avg. RC
IBM/Motorola
Movi
Prod ng
ucts avg. RC
1971
1
0
0
0
0
0
0
0
0
0
0
1972
1
0
0
0
0
0
0
0
0
0
0
1973
0
0
0
0
0
0
0
0
0
0
0
1974
2
1
0
0
0
0
1
1
0
0
0
1975
0
1
0
0
0
0
0
0
0
0
0
1976
1
0
0
0
0
0
0
1
0
0
0
1977
0
0
0
0
0
0
0
2
0
0
0
1978
1
0.667
1
0
0
0
0
1
0
0
0
0
1979
1
0.667
0
1
0.667
0
0
0
0
0
1
0.667
0
0
0
1980
0
0.333
-50
0
0.667
0
0
0
0.333
100
0
0
0
0
0
0.333
1981
0
1
200
1
1
50
0
1
0.333
0
0
0
0
0
0
1982
3
1
0
2
1
0
0
0
0.333
0
0
0
0
0
1983
0
1
0
0
1
0
0
0
0.333
0
0
0
0
0
1984
0
0.333 -66.67
1
0.333 -66.67
0
1
0.333
0
0
0
0
0
1985
1
0.333
0
0
0.333
0
0
0
0.333
0
0
0
0
0.333
1986
0
0.333
0
0
0
-100
0
0
0.333
0
0
0
1
0.333
1987
0
0.333
0
0
0.333
100
0
1
0.333
0
0
0
0
0.667
1988
1
1.667
400
1
0.333
0
0
0
0
0.667
100
0
0
1
0.333
1989
4
1.667
0
0
0.667
100
0
0
1
0.333
-50
0
0
0
1990
0
1.333
-20
1
1.333
100
0
0
0
0
0.667
100
0
0
1991
0
0
-100
3
1.333
0
0
0.333
100
1
0.667
0
0
0
1992
0
0.333
100
0
1.333
0
1
0.333
0
1
0.667
0
0
0
1993
1
0.333
0
1
1
-25
0
0.333
0
0
0.333
-50
0
0
1994
0
0.667
100
2
1.333
33.33
0
0.667
100
0
0.333
0
0
0
1995
1
0.333
-50
1
1.667
25
2
0.667
0
1
0.333
0
0
1996
0
0.333
0
2
1.333
-20
0
0.667
0
0
0.333
0
0
1997
0
0.333
0
1
1.333
0
0
0
-100
0
0.333
0
1
1998
1
0.667
100
1
1.333
0
0
0
0
1
0.333
0
1999
1
1
50
2
1
-25
0
0
0
0
0.333
2000
1
1.333
33.33
0
0.667 -33.33
0
0
0
2001
2
1.333
0
0
0
-100
0
0
2002
1
1.333
0
0
0.333
100
0
0
2003
1
1.333
0
1
0.333
0
0
2004
2
1
-25
0
0.333
0
0
Industry total
RC total
positve:
RC total
0
0
-50
0
0
0
0
0
12.5
37.5
0.333
0
0
0.333
100
0
0
0
0
0
37.5
37.5
1
0.333
0
1
0.333
0
0
0
0
0
0
0
0
0
0
0.667
100
0
0.333
0
0
0
0
0
0
0
0
20
20
0
1
0.333
-50
0
0.333
0
0
0
0
0
0
0
0
-19.444444
19.445
100
0
0.333
0
1
0.333
0
0
0
0
0
0
0
0
16.666667
16.666667
0
0
0.333
0
0
0.667
100
0
0.333
100
0
0
0
0
0
14.285714
42.857143
100
1
0.333
0
1
0.333
-50
1
0.333
0
0
0
0
0
0
21.428571
35.714286
-50
0
0.333
0
0
0.333
0
0
0.333
0
0
0
0
0
0
6.25
18.75
0.333
0
0
0
-100
0
0
-100
0
0
-100
0
0.333
100
0
0
0 -16.666667
61.111111
0
0.333
0
0
0.333
100
0
0
0
0
0
0
1
0.667
100
0
0
0
44.444444
44.444444
1
0.333
0
1
0.333
0
0
0
0
0
0.333
100
1
0.667
0
0
0
0
22.222222
22.222222
0
0.667
100
0
0.333
0
0
0
1
0.333
0
0
1
50
0
0.333
100
31.25
31.25
0
1
0.667
0
0
0.667
100
0
0
0
0.333
0
2
0.667 -33.33
1
1
200
21.296296
45.37
0
1
0.667
0
2
0.667
0
0
0
0
0.333
0
0
0.667
0
2
1.333
33.33
18.518519
18.518519
0
0
0
1
50
0
0.667
0
0
0
1
0.333
0
0
0
-100
1
1
-25 -5.5555556
22.222222
0.333
100
2
0.667 -33.33
0
0
-100
0
0
0
0.667
100
0
0
0
0
1
0.333
0
0
0.667
0
0
0
0
0
0
1
0.667
0
0
0.333
100
0
0.667
100
0
0
-100
0
0
0
0
0
1
0.667
0
1
0.333
0
1
0.333
-50
0
0
0
0
0
0
0
0
0.667
0
0
0
-100
0
0.333
0
0
0
0
0
0
0
0
1
0.667
0
0
0
0
0
0
-100
0
0
0
0
0
0
1
0.667
0
0
0
0
0
0
0
0
0
0
0
0
0
0.667
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
5.1851852
39.258889
2
0.667 -33.33 -3.7037037
25.925556
0
0
0.667
0
0
22.222222
0.333
0
0
0
-100
-21.875
21.875
0
0
-100
0
0
0 -33.333333
33.332857
0
0
0.333
100
0
0
0 -16.666667
50
0
1
0.333
0
0
0
20
20
0.667
0
0
0.333
0
0
0
0
0
0.667
0
0
0
-100
0
0
-33.333333
33.333333
In order to not confound the results for each company itself the rate of change was calculated which was then aggregated. This was done in order to not
have much lower rates of change for the industry than for Intel. If all the products of all companies would have been combined and then the rate of change
would have been calculated there would have been much lower rates of change due to the fact that we assume that the individual product introductions of
each company would have spread out evenly which would have made the rates of change go down considerably. Since there were a lot of companies
85
entering and exiting the business we did not take all of them into account for the whole period. They were taken into account form the date of their
founding until 2 years after they introduced their last product.
8.1.2 Aircraft
Products
Patents
Sales
Moving Moving
RC
Boeing
RC
Product Products avg.
avg.
RC
Industr
Boeing Industry RC
RC
Sales Industr RC
Industr
Year
Boeing Industry Boeing Industry Boeing y
ΔRC
Patents Patents Boeing Industry ΔRC
Total
y Total Boeing y
ΔRC
1993
0
1
0
1
107
2233
39.711
112
1994
0
1 0.33333
1
100
0
100
82
2065 -23.3645 -7.52351 -15.841 34.969
109 -11.941 -2.6786 -9.2627
1995
1
0 0.33333 0.6666667
0
-50
50
92
2007 12.19512 -2.80872 15.00384 32.96
107 -5.7451 -1.8349 -3.9102
1996
0
0 0.33333
0
0
-100
100
97
2163 5.434783 7.772795 -2.33801 35.453
115 7.56371 7.47664 0.08708
1997
0
0 0.33333
0
0
0
0
114
2048 17.52577 -5.31669 22.84246
45.8
130 29.1851 13.0435 16.1416
1998
1
0 0.33333
0
0
0
0
157
2669 37.7193 30.32227 7.397033 56.154
145 22.607 11.5385 11.0685
1999
0
0 0.33333
0
0
0
0
147
2762 -6.36943 3.484451 -9.85388 57.993
152 3.27492 4.82759 -1.5527
2000
0
0
0
0
-100
0
-100
136
3083 -7.48299 11.62201 -19.105 51.321
147 -11.505 -3.2895 -8.2154
2001
0
0
0
0
0
0
0
163
3746 19.85294 21.50503 -1.65209 58.198 151.63
13.4 3.14966 10.2503
2002
0
0
0
0
0
0
0
199
3826 22.08589 2.135611 19.95028 54.069 154.35 -7.0947 1.79384 -8.8886
2003
0
0
0
0
0
0
0
266
3540 33.66834 -7.47517 41.14351 50.256 152.59 -7.0521 -1.1403 -5.9118
2004
0
0
0
0
0
0
0
420
3240 57.89474 -8.47458 66.36931 52.457 156.66 4.37958 2.66728 1.7123
2005
0
0
0
0
0
0
0
403
3144 -4.04762 -2.96296 -1.08466 54.845 168.59 4.5523 7.61522 -3.0629
2006
0
0
0 0.3333333
0
100
-100
478
3509 18.61042 11.60941 7.001007 61.53 184.68 12.1889 9.54386 2.64503
2007
0
1
0 0.3333333
0
0
0
428
3060 -10.4603 -12.7957 2.335417 66.387 203.87 7.89371 10.3909 -2.4972
2008
0
0
0 0.3333333
0
0
0
421
2872 -1.63551 -6.14379 4.508277 60.909 211.1 -8.2516 3.54638 -11.798
2009
0
0
0
0
0
-100
100
532
2792 26.3658 -2.78552 29.15131 68.281 210.66 12.1033 -0.2084 12.3117
2010
0
0 0.66667
0
100
0
100
658
3379 23.68421 21.02436 2.659855 64.306 209.36 -5.8215 -0.6171 -5.2044
2011
2
0 0.66667
0
0
0
0
695
3690
5.6231 9.203906 -3.58081 68.735 214.9 6.88738 2.64616 4.24122
2012
0
0 0.66667
0
0
0
0
672
4597 -3.30935 24.57995 -27.8893 81.698 222.45 18.8594 3.51326 15.3461
2013
0
0
0 0.3333333
-100
100
-200
788
17.2619
86.623 219.44 6.0283 -1.3531 7.38141
2014
0
1
0 0.3333333
0
0
0
898
13.95939
90.762 228.4 4.77818 4.08312 0.69506
86
8.2 Direction of change
8.2.1 Technology Semiconductor
Database for technology
Manufacturer_id
1
1
1
1
1
1
1
1
1
1
1
2
2
3
3
3
3
3
4
4
4
4
4
4
4
4
4
5
6
6
6
6
6
6
7
7
7
7
7
7
Manufacturer year
AMD
1991
AMD
1993
AMD
1997
AMD
1998
AMD
1999
AMD
1999
AMD
1999
AMD
2003
AMD
2005
AMD
2006
AMD
2007
Cypress
1989
Cypress
1992
DEC
1992
DEC
1993
DEC
1993
DEC
1995
DEC
1996
Fujitsu
1994
Fujitsu
1995
Fujitsu
1996
Fujitsu
1996
Fujitsu
1997
Fujitsu
2000
Fujitsu
2002
Fujitsu
2005
Fujitsu
2008
Hitachi
1994
HP
1991
HP
1992
HP
1994
HP
1995
HP
1996
HP
1996
IBM
1993
IBM
1993
IBM
1994
IBM
1994
IBM
1994
IBM
1996
name
CS44E-mod
CMOS-4
CMOS-4S
CMOS-5
CMOS-6
CMOS-6
CS-55
CS-60
CS-60ALE
CS-70
CS-80
CS-85
CS-100
CS-200
CMOS26B
CMOS26B
CMOS26B
CMOS14A
CMOS14C
CMOS14C
CMOS-4S
CMOS-5X
technology
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
BICMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
Feature_size
0.8
0.5
0.35
0.25
0.18
0.25
0.25
0.13
0.09
0.065
0.045
0.8
0.65
0.75
0.675
0.5
0.35
0.35
0.5
0.4
0.35
0.35
0.24
0.18
0.15
0.09
0.065
0.5
1
0.8
0.75
0.55
0.5
0.5
0.72
0.6
0.35
0.35
0.5
0.65
87
7
7
7
7
7
7
7
7
7
8
8
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
10
10
10
10
10
10
10
11
11
11
IBM
IBM
IBM
IBM
IBM
IBM
IBM
IBM
IBM
IDT
IDT
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
Intel
1996
1997
1997
1998
1999
2001
2004
2006
2010
1993
1996
1971
1974
1976
1982
1987
1992
1992
1992
1993
1994
1994
1994
1994
1995
1997
1997
1999
2002
2003
2005
2007
2010
CMOS-6S
Intel
Motorola
Motorola
Motorola
Motorola
Motorola
Motorola
Motorola
NEC
NEC
NEC
2012
1979
1984
1987
1991
1994
1999 HiPerMOS5
2000 HiPerMOS6
1988
1991
1992
CMOS-6S2
CMOS-8S3
CMOS-7S
CHMOS
CHMOS III
CHMOS IV
CHMOS V
P854
P854.3
P856
P858
Px60
P1262
P1264
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
PMOS
PMOS
NMOS
NMOS
NMOS
BICMOS
NMOS
CMOS
CMOS
BICMOS
BICMOS
BICMOS
CMOS
BICMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS_TRIGATE
NMOS
NMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
CMOS
0.29
0.26
0.25
0.18
0.22
0.13
0.09
0.065
0.045
0.65
0.35
10
6
3.2
1.5
1
0.8
0.8
1.2
0.8
0.6
0.35
0.35
0.6
0.5
0.25
0.28
0.18
0.13
0.09
0.065
0.045
0.032
0.022
3.5
2.25
1.3
0.8
0.6
0.22
0.18
1.2
0.8
0.6
88
11 NEC
1995
CMOS
0.35
11 NEC
2000
CMOS
0.18
11 NEC
2001
CMOS
0.13
11 NEC
2001
CMOS
0.13
12 Samsung
1998
CMOS
0.25
12 Samsung
1998
CMOS
0.28
12 Samsung
2001
CMOS
0.18
13 TI
1991
BICMOS
0.8
13 TI
1991
CMOS
0.8
13 TI
1993
CMOS
0.65
13 TI
1995 EPIC-3
CMOS
0.5
13 TI
1997
CMOS
0.35
13 TI
1999
CMOS
0.25
13 TI
2000 GS30
CMOS
0.18
13 TI
2001
CMOS
0.13
13 TI
2005
CMOS
0.09
13 TI
2007
CMOS
0.065
13 TI
C07a
CMOS
0.18
13 TI
CMOS
0.13
14 Toshiba
1986
CMOS
2
14 Toshiba
1994
CMOS
0.3
14 Toshiba
1994 VHMOSIII
CMOS
0.7
14 Toshiba
1998
CMOS
0.25
16 TSMC
2000
CMOS
0.18
15 unnamed
CMOS
0.09
15 unnamed
CMOS
0.13
Some feature sizes had to be aggregated since in some cases larger feature sizes were introduced
after smaller feature sizes. Furthermore for the industry there were several different feature sizes
being introduced at the same time. This is why aggregation was done for those.
Aggregated technology change for the Industry
Industry
Year
Feature Size Included
Total frequency
1975
5
1
1979
3.5
1
1985
2.125
2.25;2
2
1989
1.16666667
1.3;1.2;1
3
1992
0.77454545
0.8;0.75;0.72;0.7
11
1995
0.48303571
0.675;0.65.0.6;0.55;0.5;0.40;0.35
28
1998
0.25428571
0.3; 0.29; 0.28; 0.26; 0.25; 0.24; 0.22
14
2000
0.18
0.18
8
2002
0.13333333
0.15;0.13
6
2004
0.09
1
Aggregated technology change for Intel
89
Year
Intel
Feature Size Included
Total frequency
1974
6
1
1976
3.2
1
1982
1.5
1
1987
1
1992.25
0.9
1.2;0.8
4
1994.2
0.48
0.6;0.5;0.35
5
1997
0.265
0.28;0.25
2
1999
0.18
1
2002
0.13
1
2003
0.09
1
2006
0.065
1
Coding for technology direction of change
Year
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Feature size
Intel
3.2
3.2
3.2
3.2
3.2
1.5
1.5
1.5
1.5
1.5
1
1
1
1
1
0.9
0.9
0.48
0.48
0.48
0.265
0.265
0.18
0.18
0.18
0.13
0.09
0.09
0.09
0.065
Feature size
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
1
0
1
0
0
1
0
1
0
0
1
1
0
0
1
5
5
3.5
3.5
3.5
3.5
3.5
3.5
2.125
2.125
2.125
2.125
1.16
1.16
1.16
0.77
0.77
0.77
0.48
0.48
0.48
0.25
0.25
0.18
0.18
0.13
0.13
0.09
0.09
0.09
Industry Alignment
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
1
0
0
1
0
0
1
0
1
0
1
0
1
0
0
1
1
0
0
0
1
1
1
1
1
1
1
0
0
0
0
0
1
0
0
1
0
1
0
0
0
1
0
0
1
90
8.2.2 Product Semiconductor
Intel
Year
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Industry
Clocksp
Price eed
Ratio
Change
727.3
2 0.00274989
549.94
171.21
5 0.00909192
8 0.04672536
371.56
6 0.01614793
276.77
722.9
114.31
385.38
176.41
378.14
418.78
668.05
272.71
395.92
185.15
272.94
199.9
181.53
199.11
157.29
304.53
16 0.05780983
25
20
20
29.1429
46.4
67.875
144.2
137.7
195.9
316.5
552.818
751.2
1149.39
1846.25
2496.19
2343.83
0.03458278
0.17496653
0.05189639
0.16519773
0.12270738
0.1620778
0.21585147
0.50493244
0.49479973
1.70946161
2.02541468
3.75782827
6.33173296
9.27260829
15.8702328
7.69663252
231%
414%
24%
203%
23%
134%
239%
18%
86%
68%
46%
71%
Cross improvement
Cross
improvemen Coding
t
Alignment
Clocksp
Price
eed
Ratio
Change
Coding
1070.75
1.5 0.00140089
46.2825
2 0.04321285
2985%
Coding
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
1
0
0
0
0
0
0
185
466.845
15.5 0.08378378
16 0.03427261
620.931
35 0.05636701
416.473
60.202
32.5 0.07803635
14.25 0.23670314
93.817
308.359
391.228
204.777
232.442
200.876
571.554
227.067
226.79
115.884
159.517
201.486
257.304
43.0769
63.5789
106.158
114.333
139.389
261.1
367.375
481.438
733.3
1140.67
1695.36
2090.67
2146.15
0.4591591
0.20618474
0.27134567
0.5583312
0.59967087
1.29980999
0.64276483
2.12024633
3.23339417
9.84313961
10.6281298
10.3762633
8.3409141
94%
183%
94%
22%
7%
117%
63%
53%
204%
8%
375%
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
1
0
0
0
79%
45%
35%
162%
159%
19%
157%
32%
5%
14%
55%
15%
49%
0
0
0
0
0
0
0
0
0
0
0
-1
-1
-1
-1
0
-1
0
0
0
0
0
0
0
91
Each time a change in the ratio of more than 100% was achieved this was coded as a discontinuity. The same goes for alignment.
8.2.3 Demand Semiconductor
Americas/USA
Year
1977
Industry
%Δ
Sales
% change
2137652.667
Abs %Δ
Coding
Sales
Intel
%Δ
% change
Abs %Δ
Coding Diff.
Alignment
Coding -Above 25
234460
1978
2,594,069
21.35
313,788
33.83
1979
3,567,420
37.52
16.17
1980
4,615,954
29.39 -
8.13
16.17 0
489,615
56.03
22.20
22.20 0
8.13 0
621,540
26.94 -
29.09
29.09 0
1981
4,539,554 -
1.66 -
31.05
31.05 0
597,909 -
1982
4,704,165
3.63
5.28
5.28 0
651,574
3.80 -
30.75
30.75 0
-
8.98
12.78
12.78 0
-
1983
5,614,591
19.35
15.73
15.73 0
809,035
24.17
15.19
15.19 0
1984
9,804,482
74.63
1,159,392
1985
6,718,011 -
31.48 -
1986
7,186,264
1987
1988
1989
12,466,446
1990
12,169,486 -
1991
12,848,431
1992
1993
55.27
55.27 1
43.31
19.14
19.14 0
106.11
106.11 1
893,410 -
22.94 -
66.25
66.25 1
6.97
38.45
38.45 0
760,895 -
14.83
8.11
8.11 0
8,635,479
20.17
13.20
13.20 0
1,166,943
53.36
68.20
68.20 1
11,284,360
30.67
10.51
10.51 0
1,640,216
40.56 -
12.81
12.81 0
10.48 -
20.20
20.20 0
1,774,585
2.38 -
12.86
12.86 0
2,115,957
19.24
5.58
7.96
7.96 0
2,329,000
10.07 -
15,309,886
19.16
13.58
13.58 0
3,018,000
20,717,821
35.32
16.17
16.17 0
4,416,000
1994
27,987,577
35.09 -
0.23
0.23 0
1995
39,181,372
40.00
1996
35,196,347 -
10.17 -
1997
38,675,143
1998
34,349,517 -
1999
39,294,281
14.40
2000
53,803,532
2001
29,348,483 -
2002
26,206,688 -
2003
2004
8.19 -
29.58
19.52
19.52 0
-
46.32
16.74
16.74 0
-
5,826,000
31.93 -
14.39
14.39 0
35.98
9.88
20.05
20.05 0
11,053,000
11.18 -
21.07
21.07 0
11,663,000
5.52 -
25.58
25.58 0
12,740,000
9.23
36.92
22.53
22.53 0
13,912,000
45.45 -
82.38
82.38 1
8,233,000 -
10.71
34.75
34.75 0
7,698,000 -
26,907,659
2.67
13.38
13.38 0
7,644,000 -
33,080,183
22.94
20.26
20.26 0
6,563,000 -
9.42 27.51
55.00 -1
23.32 0
9.17 0
8,668,000
25.6137558
-
9.17
7,922,000
39.86 -1
30.34 1
11.04 0
4.91 0
7.50 0
36.13 1
-
32.36 0
50.17 1
0.30 0
0.54 0
32.36
4.91
6.03 0
20.96 0
11.04
50.17
Average
-
12.17 0
-
23.90 0
17.13 0
5.94 0
0.57 0
14.16 0
4.05
4.05 0
26.56
26.56 0
0.86 0
18.10
18.10 0
22.00
22.00 0
0.93 0
3.72
3.72 0
21.86 0
-
23.61 0
1.96 0
9.20 -
0.03
0.03 0
40.82 -
50.02
50.02 1
6.50
34.32
34.32 0
0.70
5.80
5.80 0
7.58 0
13.44
13.44 0
33.71 1
14.14 -
Average
22.56 0
-
32.36 -1
0.42 0
21.75996919
92
8.2.4 Product Aircraft
Manufactur
er
Boeing
Boeing
Boeing
Airbus
Airbus
Boeing
Airbus
Boeing
Type
pax
Boeing 757-200 190
Boeing
190
757-200
Boeing
737-300
Airbus
A330-300
Airbus
A340-300
Boeing
777-200
Airbus
A319
Boeing
777200ER
range
3900 3.02 L/100 km (78 mpg-US)[31]
3.02 L/100 km (78 mpgUS)[31]
3900
126
2300
262
6350
262
305
124
fuel per seat
3.46 L/100 km (68 mpgUS)[28]
2.98 L/100 km (79 mpgUS)[36]
3.25 L/100 km (72 mpg7300
US)[36]
2.73 L/100 km (86 mpg5240
US)[40]
2.95 L/100 km (80 mpg3600
US)[31]
Ratio
Improvem Disconti
ent
nuity
Year
Classification
Type
245364.2384
1982 short-medium narrow body
245364.2384
1982 Short haul
narrow body
83757.22543
1984 Regional
narrow body
558288.5906 -
1992 Medium-haul
Wide body
588492.3077 -
1992 Medium-haul
Wide body
585421.2454
1994 Medium-haul
Wide body
151322.0339 -
1995 Short haul
narrow body
301
7730
2.89 L/100 km (81 mpgUS)[36]
805096.8858 0.3752437 x
1996 Medium-haul
Wide body
Boeing
Boeing
777200ER
301
7730
3.08 L/100 km (76 mpgUS)[36]
755431.8182 -
1996 Long haul
Wide body
Boeing
Boeing
777200ER
301
7730
3.01 L/100 km (78 mpgUS)[40]
773000 -
1996 Long haul
Wide body
180
3000
216000
1996 Short haul
narrow body
527006.0241 -
1997 Long haul
Wide body
Wide body
AIrbus
Airbus
Airbus
A321-200
Airbus
A330-200
241
2.5 L/100 km (94 mpgUS)[31]
3.32 L/100 km (71 mpg7260
US)[36]
Boeing
Boeing
777-300
386
6005
2.61 L/100 km (90 mpgUS)[40]
888095.7854 0.5170201
1997 Medium-haul
Boeing
Boeing
737-600
108
3230
3.5 L/100 km (67 mpgUS)[29]
99668.57143 -
1998
Boeing
Boeing
737-600
110
3050
3.08 L/100 km (76 mpgUS)[29]
108928.5714 -
1998 Short haul
Boeing
Boeing
737-700
128
3200
2.71 L/100 km (87 mpgUS)[31]
151143.9114 -
1998
short to
medium
narrow body
Boeing
Boeing
737-800
162
2930
2.38 L/100 km (99 mpgUS)[29]
199436.9748 -
1998
short to
medium
narrow body
245
5625
2.93 L/100 km (80 mpgUS)[39]
470349.8294 -
1999 Medium-haul
Wide body
386
7370
2.84 L/100 km (83 mpgUS)[40]
1001697.183 5.6196386 x
2004 Long haul
Wide body
525
8200
3.27 L/100 km (72 mpgUS)[43]
1316513.761 0.7427301 x
2005 Long haul
wide body
301
8555
3.25 L/100 km (72 mpgUS)[37]
792324.6154 -
2006 Long haul
Wide body
177
3140
2.59 L/100 km (91 mpgUS)[29]
214586.8726 0.0759633
2007 Medium-haul
narrow body
100
1425
3.33 L/100 km (71 mpgUS) [30]
42792.79279 -
2009 Regional
narrow body
934522.3881 -
2011 Long haul
Wide body
666632.9588 -
2011 Long haul
Wide body
Boeing
Boeing
AIrbus
Boeing
Boeing
Bombardier
Boeing
Boeing
AIrbus
Boeing
Bombardier
Boeing
Boeing
767400ER
Boeing
777300ER
Airbus
A380
Boeing
777200LR
Boeing
737900ER
Bombardi
er
CRJ1000
Boeing
747-8
Boeing
787-8
Airbus
A350-900
Boeing
787-9
Bombardi
er
CSeries 1
00
Boeing
787-9
3.35 L/100 km (70 mpgUS)[43]
2.67 L/100 km (88 mpg242 7355.00
US)[35]
405
7730
short to
medium
narrow body
narrow body
315
7750
2.39 L/100 km (98 mpgUS)[37]
1021443.515 -
2013 Long haul
Wide body
304
7635
2.37 L/100 km (99 mpgUS)[37]
979341.7722 0.1027434
2013 Medium-haul
Wide body
115
3100
2.14 L/100 km (110 mpgUS)[26]
166588.785 -
2013 Regional
narrow body
290
7635
2.37 L/100 km (99 mpgUS)[37]
934240.5063 -
2014 Long haul
Wide body
93
Coding for Wide body segment
Year
Model
Range
Cpacity
Range
Fuel Efficiency
Ratio
Change
Coding
Model
1992
Airbus A330-300
Mediumhaul
1992
Airbus A340-300
Mediumhaul
1994
1996
1996
1996
Boeing 777-200
Boeing 777-200ER
Boeing 777-200ER
Boeing 777-200ER
Medium-haul
Medium-haul
Long haul
Long haul
305
301
301
301
5240
7730
7730
7730
1997 Boeing 777-300
Medium-haul
386
6005 2.61 L/100 km (90 mpg-US)[40]
888095.7854
1999 Boeing 767-400ER
2004 Boeing 777-300ER
Medium-haul
Long haul
245
386
5625 2.93 L/100 km (80 mpg-US)[39]
7370 2.84 L/100 km (83 mpg-US)[40]
470349.8294 1001697.183
2.73 L/100 km (86 mpg-US)[40]
2.89 L/100 km (81 mpg-US)[36]
3.08 L/100 km (76 mpg-US)[36]
3.01 L/100 km (78 mpg-US)[40]
585421.2454
805096.8858
755431.8182 773000 -
38%
301
8555 3.25 L/100 km (72 mpg-US)[37]
405
7730 3.35 L/100 km (70 mpg-US)[43]
242 7355.00 2.67 L/100 km (88 mpg-US)[35]
792324.6154 934522.3881 666632.9588 -
2013 Boeing 787-9
Medium-haul
304
7635 2.37 L/100 km (99 mpg-US)[37]
979341.7722
2014 Boeing 787-9
Long haul
290
7635 2.37 L/100 km (99 mpg-US)[37]
934240.5063 -
262
Coding
558288.5906 -
588492.3077
1.88809E-06
Diff
Alignment
0
0
0
0
0
0
0
0
52%
1 Airbus A330-200
33%
0
0
Long haul
241
3.32 L/100 k
7260 m (71 mpgUS)[36]
527006.0241 -
0
0
90%
Airbus A380
Long haul
Long haul
Long haul
262
Change
0
0
0
0
2005
2006 Boeing 777-200LR
2011 Boeing 747-8
2011 Boeing 787-8
Fuel
Efficiency
Ratio
2.98 L/100 k
6350 m (79 mpgUS)[36]
3.25 L/100 k
7300 m (72 mpgUS)[36]
Capacity Range
Long haul
525
3.27 L/100 k
8200 m (72 mpgUS)[43]
1316513.761
74%
1
0
0
0
0
10%
0
1
0
0
0
0 Airbus A350-900
Long haul
315
2.39 L/100 k
7750 m (98 mpgUS)[37]
1021443.515 -
0
0
0
0
Only the percentage change for those deemed to be discontinuous were written down. Even though previous research has only given discontinuous
directions of change for changes that were extremely large in order of magnitude (<100%) we can argue that such an improvement is not possible for this
specific ratio. Since still major improvements are achieved through higher fuel efficiency (improvement larger than 50%) which alter the status quo, we
coded it as discontinuous if large changes are achieved. Also misalignment is coded if the company achieves more than 50% improvement over the industry
or the other way round.
Coding for narrow body segment
Year
Model
1984 Boeing 737-300
1995
1996
1998 Boeing 737-600
1998 Boeing 737-600
1998 Boeing 737-700
1998 Boeing 737-800
2007 Boeing 737-900ER
2009
2013
Cpacity Range
Fuel Efficiency
Ratio
Change
126
2300 3.46 L/100 km (68 mpg-US)[28]
83757.22543
108
110
128
162
177
3230 3.5 L/100 km (67 mpg-US)[29]
99668.57143 3050 3.08 L/100 km (76 mpg-US)[29]
108928.5714 3200 2.71 L/100 km (87 mpg-US)[31]
151143.9114 2930 2.38 L/100 km (99 mpg-US)[29]
199436.9748 3140 2.59 L/100 km (91 mpg-US)[29]
214586.8726 -
Coding
Range
0 Regional
Model
Capacity Range
Fuel
Efficiency
Ratio
Change
Coding
Alignment
Airbus A319
Airbus A321-200
124
180
3600 2.95 L/100 km (80 mpg-US)[31]151322.0339 3000 2.5 L/100 km (94 mpg-US)[31]
216000
0 Short haul
0 Short haul
narrow body
narrow body
Bombardier CRJ1000
Bombardier CSeries 100
100
115
1425 3.33 L/100 km (71 mpg-US) [30]
42792.79279 3100 2.14 L/100 km (110 mpg-US)[26]166588.785 -
0 Regional
0 Regional
narrow body
narrow body
0 short to medium
0 Short haul
0 short to medium
0 short to medium
0 Medium-haul
0
0
0
0
0
0
0
0
0
0
94
8.2.5 Demand Aircraft
Sales
Sales direction of change
Boeing
RC
Δ%
Δ%
Sales Industr RC
Industr
% change Δ %
Boeing Discont % change Δ %
Industry Discontin Differenc Alignme
Year
Total
y Total Boeing y
ΔRC
Boeing Boeing positive inuity industry Industry positive uity
e
nt
1993 39.711
112
1994 34.969
109 -11.941 -2.6786 -9.2627 -11.9413
0 -2.67857
0
0
0
1995 32.96
107 -5.7451 -1.8349 -3.9102 -5.74509 6.196187 6.196187
0 -1.83486 0.843709 0.843709
0 5.352478
0
1996 35.453
115 7.56371 7.47664 0.08708 7.563714 13.3088 13.3088
0 7.476636 9.311498 9.311498
1 3.997304
0
1997
45.8
130 29.1851 13.0435 16.1416 29.18512 21.6214 21.6214
0 13.04348 5.566843 5.566843
0 16.05456
1
1998 56.154
145 22.607 11.5385 11.0685 22.60699 -6.57813 6.578131
0 11.53846 -1.50502 1.505017
0 -5.07311
0
1999 57.993
152 3.27492 4.82759 -1.5527 3.274923 -19.3321 19.33206
0 4.827586 -6.71088 6.710875
0 -12.6212
0
2000 51.321
147 -11.505 -3.2895 -8.2154 -11.5048 -14.7798 14.77976
0 -3.28947 -8.11706 8.11706
1 -6.6627
0
2001 58.198 151.63
13.4 3.14966 10.2503 13.39997 24.90481 24.90481
1 3.14966 6.439134 6.439134
0 18.46568
1
2002 54.069 154.35 -7.0947 1.79384 -8.8886 -7.09475 -20.4947 20.49472
0 1.79384 -1.35582 1.35582
0 -19.1389
1
2003 50.256 152.59 -7.0521 -1.1403 -5.9118 -7.0521 0.042645 0.042645
0 -1.14027 -2.93411 2.934106
0 2.976751
0
2004 52.457 156.66 4.37958 2.66728 1.7123 4.379577 11.43168 11.43168
0 2.667278 3.807544 3.807544
0 7.624133
0
2005 54.845 168.59 4.5523 7.61522 -3.0629
4.5523 0.172723 0.172723
0 7.615218 4.947939 4.947939
0 -4.77522
0
2006 61.53 184.68 12.1889 9.54386 2.64503 12.1889 7.636596 7.636596
0 9.543864 1.928646 1.928646
0 5.70795
0
2007 66.387 203.87 7.89371 10.3909 -2.4972 7.89371 -4.29519 4.295186
0 10.39095 0.847083 0.847083
0 -5.14227
0
2008 60.909 211.1 -8.2516 3.54638 -11.798 -8.25162 -16.1453 16.14533
0 3.546378 -6.84457 6.844569
0 -9.30076
0
2009 68.281 210.66 12.1033 -0.2084 12.3117 12.1033 20.35492 20.35492
0 -0.20843 -3.75481 3.75481
0 24.10973
1
2010 64.306 209.36 -5.8215 -0.6171 -5.2044 -5.82153 -17.9248 17.92483
0 -0.61711 -0.40868 0.408676
0 -17.5162
0
2011 68.735 214.9 6.88738 2.64616 4.24122 6.887382 12.70891 12.70891
0 2.64616 3.263268 3.263268
0 9.445646
0
2012 81.698 222.45 18.8594 3.51326 15.3461 18.85939 11.97201 11.97201
0 3.513262 0.867102 0.867102
0 11.1049
0
2013 86.623 219.44 6.0283 -1.3531 7.38141 6.028299 -12.8311 12.83109
0 -1.35311 -4.86638 4.866375
0 -7.96471
0
2014 90.762 228.4 4.77818 4.08312 0.69506 4.778177 -1.25012 1.250123
0 4.083121 5.436234 5.436234
0 -6.68636
0
12.1991
3.987815
Since the aircraft industry is much less volatile than the semiconductor industry we can argue that alignment is achieved if the percentage differences are
smaller since it is much easier to achieve in this type of environment. Alignment is then achieved if it the differences are not larger than 15%.
95
8.3 Tobin’s Q
8.3.1 Intel
deferred taxes
Common Shares
Common Stock
Market value Total common
(deferred tax
Stock Price Year outstanding (Bold=
outstanding and
Retained
Year
Total Assets
of equity
equity
liabilities)
End (unadjusted) Stock split)
capital in excess of
earnings Tobin's Q
1980
767
880
432.86
23.266
20.1249605
43.72
127.979 304.881
1.55233960887315
1981
872
1,006
487.817
44.019
22.500481
44.7
155.577
332.24
1.54380522778098
1982
1,056
1,793
551.853
67.744
38.750399
46.271
189.567 362.286
2.11072032816351
1983
1,680
2,321
1121.74
89.318
21
110.544
643.343 478.397
1.66106986574584
1984
2,029
3,269
1360.163
112.69
27.999839
116.765
683.577 676.586
1.88526120335873
1985
2,152
3,447
1421.481
133.956
29.25024
117.85
743.325 678.156
1.87909965727404
1986
2,080
2,458
1275.227
132.441
21
117.025
770.236 504.991
1.50472292706097
1987
2,597
3,186
1306.425
105.395
17.66656
180.358
736.941 569.484
1.68323991543080
1988
3,550
4,285
2079.873
56.461
23.75008
180.437
1087.467 992.406
1.60541380681831
1989
3,994
6,513
2548.618
111.474
34.49984
188.778
1165.191 1383.427
1.96463074467768
1990
5,376
12,961
3591.306
126.446
63.875038
202.911
1572.555 2018.751
2.71924596500387
1991
6,292
8,046
4417.852
143.596
38.49984
208.989
1640.636 2777.216
1.55380442881427
1992
8,089
10,522
5424.634
180.304
49
214.729
1755.536 3669.098
1.60786666350171
1993
11,344
19,184
7500
297
43.5
441
2194
5306
2.00374647390691
1994
13,816
27,094
9267
389
62
437
2306
6961
2.26215981470759
1995
17,504
25,084
12140
620
28.375
884
2583
9557
1.70403907678245
1996
23,735
116,273
16872
997
130.937515
888
2897
13975
5.14592430250685
1997
28,880
63,049
19237
1076
35.125
1795
3311
15926
2.47979137811634
1998
31,471
208,492
22774
1387
59.2812385
3517
4822
17952
6.85717377282260
1999
43,849
285,624
28744
3130
82.3125
3470
7316
21428
6.78691361262515
2000
47,945
105,008
37234
1266
15.03125
6,986
8496
28738
2.38717932005423
2001
44,395
216,345
35983
945
31.45
6,879
8,833
27,150
5.04136839734204
2002
44,224
105,238
35488
1232
15.57
6,759
7,641
27,847
2.54933135853835
2003
47,143
212,203
37770
1482
32.05
6621
6,754
31,016
4.66864751924994
2004
48,143
151,895
38431
855
23.39
6494
6,143
32,288
3.33904534407910
2005
48,314
154,203
36055
703
24.96
6178
6,245
29,810
3.43086641553173
2006
48,368
119,070
36809
265
20.25
5880
7,825
28,984
2.69525305987430
Annual
Source
Annual report (I32*L32)
O32+R32
Annual Report Yahoo Finance
Annual report
Annual Report
Report
(E32+F32-G32-H32)/E32
96
8.3.2 The Boeing Company
Year
deferred taxes
Common Shares
Market value Total common
(deferred tax
Stock Price Year outstanding (in millions) Common Stock
of equity
equity
liabilities)
End (unadjusted) (Bold Stock split)
outstanding
9,684
11,517
1215
28.15082196
344.0
13,289
14,792
1505
38.25279156
347.4
47,641
14,237
1780
48.9375
973.5
30,599
14,912
1906
32.625
937.9
36,084
17,230
1295
41.4375
870.8
55,196
19,842
2197
66
836.3
30,943
21,374
3914
38.779999
797.9
26,382
21,462
4691
32.990002
799.7
33,725
22,346
7110
42.139999
800.3
41,064
24,044
7516
51.77
793.2
53,425
26,708
7646
70.239998
760.6
67,323
28,169
8284
88.839996
757.8
64,432
31,194
8272
87.459999
736.7
29,788
31,192
9492
42.669998
698.1
39,315
31,531
9652
54.130001
726.3
47,986
33,711
10736
65.260002
735.3
54,624
36,618
12939
73.349998
744.7
56,942
39,220
14046
75.360001
755.6
102,013
42,440
15664
136.490005
747.4
91,857
45,866
16028
129.979996
706.7
Total Assets
1995
31,877
1996
37,880
1997
38,293
1998
38,002
1999
36,952
2000
43,504
2001
48,987
2002
54,225
2003
55,171
2004
56,224
2005
59,996
2006
51,794
2007
58,986
2008
53,779
2009
62,053
2010
68,565
2011
79,986
2012
88,896
2013
92,663
2014
99,198
Source/
Formula Annual report (I40*K40)
N40+P40+O40
Annual Report
Yahoo Finance
Annual report
Annual Report
Capital surplus of
common stock
Retained
(APIC)
earnings Tobin's Q
1802
1951
7764
0.90437879205516
4976
920
8896
0.92059186349213
5000
1090
8147
1.82583386650302
5,059
1147
8706
1.36263847955371
5,059
1684
10487
1.47517793353540
5,059
2693
12090
1.76215520411916
5,059
1975
14,340
1.11542983244738
5,059
2141
14,262
1.00422507329461
5,059
2880
14,407
1.07737110437911
5,059
3420
15,565
1.16903749288560
5,061
4371
17,276
1.31786356555104
5,061
4655
18,453
1.59601399715797
5,061
4757
21,376
1.42324926700064
5,061
3456
22,675
0.79739165108686
5,061
3724
22,746
0.96989057299889
5,061
3866
24,784
1.05161058077153
5,061
4033
27,524
1.06334537932388
5,061
4122
30,037
1.04135188035007
5,061
4415
32,964
1.47385288342704
5,061
4625
36,180
1.30205108140487
Annual
Annual Report
Report
(E40+F40-G40-H40)/E40
97
8.4 Aggregated alignment rate and directions of change with Tobin’s Q
8.4.1 Semiconductor
Rate of change
Year
Direction of change
Technolog
Weighted Weighted
Weighted
Product
Technology Demand
Aggregate
Product
y
Demand Product
Technology Demand
Aggregated
1981
162.5 92.30179028 -2.14687877 252.6549115
0
0
0
0
0
0
1982
0 -7.465566374
5.34929692 -2.116269454
0
1
0
0 2.181818182
0
1983
-20 24.54542207 4.812634368 9.358056437
0
1
0
0 2.181818182
0
1984 -47.22222222 20.13621795 -31.3195009 -58.40550518
0
1
1
0 2.181818182
4
1985 -16.66666667 -48.41084448 8.538697393 -56.53881376
0
1
-1
0 2.181818182
-4
1986 -14.28571429 -12.56112751 -21.80260934 -48.64945114
0
1
1
0 2.181818182
4
1987 -21.42857143 32.27911821 33.19806544 44.04861222
0
1
-1
0 2.181818182
-4
1988
393.75 42.84112531 9.882255299 446.4733806
0
1
0
0 2.181818182
0
1989 16.66666667 26.60401575 -2.283284058 40.98739836
0
0
0
0
0
0
1990 -64.44444444 -6.466952431
21.6187972 -49.29259967
0
0
0
0
0
0
1991 -122.2222222 15.68870248 4.489322265 -102.0441975
0
0
0
0
0
0
1992
68.75 26.10120221 10.42588124 105.2770834
-1
0
0 -4.7619048
0
0
1993 -21.2962963 70.99554642 10.99891104 60.69816116
-1
0
0 -4.7619048
0
0
1994 81.48148148 46.34444602 -3.160037298 124.6658902
-1
1
0 -4.7619048 2.181818182
0
1995 -44.44444444 24.00036613 -4.018928101 -24.46300642
-1
0
0 -4.7619048
0
0
1996 -5.185185185 51.80033887
19.5875272 66.20268088
0
0
0
0
0
0
1997 3.703703704 -9.19279911 17.63102877 12.14193336
-1
1
0 -4.7619048 2.181818182
0
1998
100 32.13011056 16.70337563 148.8334862
0
0
0
0
0
0
1999
71.875 -4.056529811 -5.16110889
62.6573613
0
1
0
0 2.181818182
0
2000 66.66666667
-2.8162419 -27.72521501 36.12520976
0
0
0
0
0
0
2001 16.66666667 -12.55865298 4.631623365 8.739637052
0
0
-1
0
0
-4
2002
-20
27.1310041
4.20689714 11.33790124
0
0
0
0
0
0
2003
0 42.40510786 -3.376259953 39.02884791
0
1
0
0 2.181818182
0
2004 58.33333333 -2.597075529 -37.08146504 18.65479277
0
0
1
0
0
4
DC
DC
Abs.
Abs.
Abs.
Product/ Technology/
Average: Average: Average Average
Abs Average DC Demand/Ab
0.21
0.46
: 0.25
Abs DC
DC
Average Demand
Weighted Aggregate
0
1
1
2
0
2
0
1
0
0
0
-1
-1
0
-1
0
0
0
1
0
-1
0
1
1
0
2.181818182
2.181818182
6.181818182
-1.818181818
6.181818182
-1.818181818
2.181818182
0
0
0
-4.761904762
-4.761904762
-2.58008658
-4.761904762
0
-2.58008658
0
2.181818182
0
-4
0
2.181818182
4
Weigted Aggregate
0
0.727272727
0.727272727
2.060606061
-0.606060606
2.060606061
-0.606060606
0.727272727
0
0
0
-1.587301587
-1.587301587
-0.86002886
-1.587301587
0
-0.86002886
0
0.727272727
0
-1.333333333
0
0.727272727
1.333333333
Tobin's Q (t+1)
1.552339609
1.543805228
2.110720328
1.661069866
1.885261203
1.879099657
1.504722927
1.683239915
1.605413807
1.964630745
2.719245965
1.553804429
1.607866664
2.003746474
2.262159815
1.704039077
5.145924303
2.479791378
6.857173773
6.786913613
2.38717932
5.041368397
2.549331359
4.668647519
98
8.4.2 Aircraft
Product
Year
Technolo
gy
Demand
Rate of change
Weighted
Aggregate
Product
Weighted
Tech.
Weighted
Demand
Weighted
aggregate
Direction of change
Aggregat
Demand Product
e
Tobins Q
ΔRC
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
ΔRC
ΔRC
100 -15.841 -9.2627
50 15.00384 -3.91023
100 -2.33801 0.087078
0 22.84246 16.14164
0 7.397033 11.06853
0 -9.85388 -1.55266
-100 -19.105 -8.21536
0 -1.65209 10.25031
0 19.95028 -8.88859
0 41.14351 -5.91183
0 66.36931 1.712298
0 -1.08466 -3.06292
-100 7.001007 2.645032
0 2.335417 -2.49724
0 4.508277 -11.798
100 29.15131 12.31173
100 2.659855 -5.20442
0 -3.58081 4.241222
0 -27.8893 15.34613
Average Average Average
of abs:
of abs:
of abs:
34.21
15.77
7.07
74.8963207
61.0936125
97.7490655
38.984103
18.465558
-11.4065415
-127.320369
8.59822646
11.0616925
35.2316771
68.0816114
-4.14757377
-90.3539608
-0.16181888
-7.28971629
141.463045
97.4554317
0.6604163
-12.5431728
7.6 -2.19664157 -13.0363787
3.8 2.08055727 -5.50327282
7.6 -0.32420831 0.12255415
0 3.16752622 22.7178291
0 1.02573417 15.5779008
0 -1.3664208 -2.18522702
-7.6 -2.64925933 -11.5623453
0 -0.22909206 14.4263442
0 2.76647179 -12.5098424
0 5.7053021 -8.32034693
0 9.2033219 2.40989755
0 -0.15040745 -4.31076647
-7.6 0.97081797 3.72263219
0 0.32384841 -3.51462325
0 0.62515523 -16.6045576
7.6 4.04236366 17.3275987
7.6 0.36883769 -7.32473298
0 -0.49654442 5.96911883
0 -3.86736246 21.5982178
RC Product/ RC Tech/
RC
Average of Average of Demand/
abs Product abs Tech
Average of
abs
Demand
-7.63302032
0.37728445
7.39834584
25.8853554
16.603635
-3.55164782
-21.8116046
14.1972522
-9.74337061
-2.61504483
11.6132194
-4.46117392
-2.90654984
-3.19077484
-15.9794024
28.9699624
0.64410471
5.47257441
17.7308553
0
0
0
1
0
0
0
1
-1
0
0
0
0
0
0
1
-1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
-1
0
1
0
0
0
0
1
-1
0
0
0.904378792
0.920591863
1.825833867
1.36263848
1.475177934
1.762155204
1.115429832
1.004225073
1.077371104
1.169037493
1.317863566
1.596013997
1.423249267
0.797391651
0.969890573
1.051610581
1.063345379
1.04135188
1.473852883
99