Examining the Dynamics Between Aligning a Company's Internal Processes to the External Environment and the Company's Perform

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/.

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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

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.

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.

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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

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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

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

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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

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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

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

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,

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.

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.

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 high- technology 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

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.

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.

Table 1: Studies on environmental Velocity; Source: Adapted from McCarthy et al. 2010.

Study Discussed Phenomenon Definition of velocity Measurement of environmental velocity

Bourgeois & Pace and style of strategic decision making in high velocity Rapid and discontinuous change in demand, competitors, Illustrative statistics and example Eisenhardt (1988) industry technology and/or regulation, such that information is often inaccurate, unavailable or obsolete

Eisenhardt & Effect of politics on strategic decision making in high velocity As per Bourgeois and Eisenhardt Illustrative statistics and example Bourgeois (1988) environment

Eisenhardt (1989) Antecedents of rapid decision making in high velocity As per Bourgeois and Eisenhardt Illustrative statistics and example environments

Judge & Miller Antecedents and outcomes of rapid decision-making in Industry growth coupled with changes in technology and - Growth: Change in industry (1) employment and (2) sales (1991) industries with different velocities such other disruptive forces as governmental regulations - 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 Rate of change in technology, demand and competition Illustrative statistics on performance in high-velocity environments

Brown & Effect of continuous change through product innovations on Short product cycles and rapidly shifting competitive Illustrative statistics and example Eisenhardt (1997) performance in computer in high velocity environment landscapes

Stephanovisch & Strategic decision making practices in a high velocity Rate of change in demand, competition, technology and An illustrative example Uhrig (1999) environment regulation

Bogner & Barr Cognitive and sense making abilities in hypercompetitive Hypercompetition None 2000 environments

Eisenhardt & Nature of dynamic capabilities in different velocity Ambiguous industry structure, blurred boundaries, fluid Illustrative example Martin (2000) conditions business models, ambiguous and shifting market players, nonlinear and unpredictable change

Baum & Wally Effect of strategic decision speed on firm performance Unpredictability and rapid growth None (2003)

Oliver & Roos Team-based decision making in high velocity environments As Bourgeois and Eisenhardt None (2005)

Brauer & Schmidt Temporal development of a firm's strategy implementation A form of dynamism and volatility Annual capital market raw beta-value of the industries' (2006) consistency in industries with different velocities market returns compared to general market returns.

Nadkarni & Relationship between strategic schemas, strategic flexibility Rate of change for product and process technologies and in - Product dimension: No of new products introduced Naryanan (2007b) and firm performance in different velocity conditions competitors’ strategic actions (Industry clockspeed) - 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 & How cognitive construction by firms drives industry velocity The rate of new products, processes and competitive - Product dimension: No of new products introduced Narayanan changes - Process clockspeed: Average number of years over which (2007a) firms depreciated capital equipment - Organizational dimension: Average time span between new corporate strategic actions introduce by all firms in industry

Davis & Shirato 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 (2007) - New product ratio - patent registrations

Wirtz, Mathieu, Effect of Strategy on business performance in high velocity As per Bourgeois and Eisenhardt Illustrative statistics and example Schilke (2007) environments

Nadkarni & Barr Relationship between industry velocity, the structure of top As Bourgeois and Eisenhardt Rate: (2008) management’s cognitive representation of the environment, - Number of new products introduced and the speed of response to environmental events - 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 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 & Bingham (2009) a Poisson distribution model

McCarthy, Multidimensional conceptualization of environmental Rate and direction of change in product, technology, Illustrative example and statistics Lawrence, Wixted velocity demand, competitor and regulatory dimension & Gordon (2010)

Jones & Mahon Relationship between explicit and tacit knowledge in high Environments where change is large, rapid and Illustrative example (2012) velocity environments discontinuous

Nadkarni, Chen & Effect of interplay between executive temporal depth and Rate at which new opportunities emerge and disappear in an Competitive actions (total competitive actions/number of Chen (2015) industry velocity on competitive aggressiveness and firm industry firms) of these dominant firms in a given year. performance.

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

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 product- development 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.

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 Environmental velocity Industry clockspeed Hypercompetition Perspective Exogenous and Endogenous Exogenous and endogenous endogenous Defining  Product  Product  Technology dimension  Technology  Process  Demand  Demand technology  Competition  Competition  Organization  Regulation  Regulation

Key difference  Only  Hypercompetition to endogenous describes specific environmental perspective, (binary) situation velocity which explains  Only concept missing of environments in demand and which no regulation competitive dimensions advantage can be  No direction of sustained are change 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 Studies Product Brown & Eisenhardt (1997), Nadkarni & Naryanan (2007b), Nadkarni & Narayanan (2007a), McCarthy, Lawrence, Wixted & Gordon (2010) Technology Bourgeois & Eisenhardt (1988), Judge & Miller (1991), Smith et al (1994), Stephanovisch & Uhrig (1999), Nadkarni & Naryanan (2007b), Nadkarni & Narayanan (2007a) Demand Bourgeois & Eisenhardt (1988), Smith et al (1994), Stephanovisch & Uhrig (1999), McCarthy, Lawrence, Wixted & Gordon (2010) Regulation Bourgeois & Eisenhardt (1988), Stephanovisch & Uhrig (1999), Competition 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

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 - (Personal) computer industry - Food packaging industry - Athletic footwear industry - Metal and plastic industry - Biomedical industry - Office furniture industry - Biotechnology industry - Paper industry - Computer software industry - Petrochemical industry - Cosmetics industry - Ship-building industry - Electrical industry - Steel industry - Electronics industry - Textile industry - Healthcare industry - Tire and rubber industry - Informational industry - Tobacco industry - IT industry - Microcomputer industry - Motion picture and entertainment industry - Movie industry - Semiconductor industry - Semiconductor industry - Telecommunications industry - Toys and games industry

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.

Table 5: Organizational enablers of success in different velocity-environments

Study Methodology Relevant findings Independent Measurement of dependent Measurement of Mod. Med.Var. Variable variable performance Var. Sample Design Analysi s Bourgeois & 4 Multiple case Content Strategic decision making Strategic Decision (1) involving strategic (1) Market acceptance Eisenhardt microcomput study; analysis positively affects firm decision making repositioning or redirection of firm, (2) of each company's major (1988) er firms longitudinal; performance in high velocity having high stakes, (3) involve as many product multi-method; environments functions as possible, (4) considered (2) CEO's numerical self- actual strategic representative of major decisions report of effectiveness decision-making (3) sales and profitability process

Eisenhardt, 8 Multiple case Content Decision speed is positively Decision speed Duration, using beginning (first reference (1) CEOs' numerical self- Real time (1989) microcomput study; analysis related to firm performance to deliberate action) and end reports of company information; - er firms longitudinal; in high velocity (commitment to act) of each decision effectiveness (0 to 10 multiple multi-method; environments scale), (2) a comparison simultaneous actual strategic of that rating to ratings alternatives; Two- decision-making CEOs gave to tier advice process; process competitors, and (3) - Consensus with sales growth and qualification; - profitability figures Decision integration before and after the study

Judge & Miller Executives Field study; Correlati Decision speed is positively Decision speed Duration using beginning (first reference - profitability Environm - Number of (1991) from 32 cross-sectional; on and related to firm performance to deliberate action) and end - sales growth ental alternatives organizations semi-structured regressio in high velocity (commitment to act) of each decision velocity considered; Board in the interviews; n analysis environments experience biotechnolog archival data; y, hospital recent SDs and textiles made by the industries firms

Brown & 81 interviews Interviews; Case Successful product portfolios Successful Presence of positive portfolio -Market position Eisenhardt of low- and questionnaires; writing, have positive affect on firm product characteristics (i.e., on schedule, on time - revenue (1997) high-level observations; cross performance portfolio to market, on target to market projects) - profitability respondents secondary case and the absence of negative ones (e.g., in 6 firms in sources analysis make-work, competing, stop-gap, computer stripped, endless, stuttering projects). industry,

Nadkarni & 225 firms COMPUSTAT Causal Complexity and focus of - Complexity of Complexity: Comprehensiveness (total - Sales growth Strategic Flexibility Naryanan from 14 database mapping; strategic schemas influence Strategic and connectedness (measured through - ROI Industry (+ for high-velocity (2007) industries Structura strategic flexibility which has Schemas (+) causal maps) - Net income growth Clockspe environments, - for l effect on strategic - Focus of Focus: Centralization and eigenvector ed low-velocity Equation performance Strategic centrality (measured through causal environments) Modellin Schemas (-) maps) (measured by g variety in resource deployment, shifts in resource deployment, competitive simplicity, and shifts in competitive action) Wirtz, Mathieu 754 senior Survey Structura Positive effect of strategy - Strategy - Differentiation in a market place by - Growth & Schilke (2007) executives of questionnaire l construct on business - Product distinguishing products and services from - Profitability companies in Equation performance in high velocity differentiation those of competitors the ICT Modellin environments - Image - Company’s uniqueness caused by industry in g differentiation psychological, attitudinal positioning Germany - Focus - Concentration on a narrow market - Pro activeness segment - Replication - Continuous search for opportunities of -Re improvement and early pursuit of those configuration opportunities - Cooperation - 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

Nadkarni et al 258 firms in Triangulation Generaliz Executive temporal depth - Executive 3 step content analysis on time horizon; Return on sales (ROS) Industry 2015 23 industries between Letter ed least exhibits different patterns of temporal depth structured content analysis and return on assets velocity to squares relationships with - Competitive (ROA)—at the end of the (industry shareholders; model competitive aggressiveness Aggressiveness same year as the clockspe management's in low- and high-velocity competitive ed) discussion and industries. Competitive aggressiveness measure. analysis in the aggressiveness has a positive Afterwards combination 10-K forms; main effect on firm of the z-scores of the executive performance, which is two measures into a conference calls stronger in high than in low composite measure of with analysists velocity environments firm performance (Brimley and Harris, 2014).

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.

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

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.

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

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.

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). .

Table 6: Suggested and selected operationalizations of rate and direction of change

Dimension Operationalization of rate of change Studies Operationalization of direction of Studies change Product  No of new products introduced Nadkarni & Naryanan The change in the nature of product McCarthy, measured through total product (2007b), Nadkarni & features as perceived by the market Lawrence, introductions or product generations Narayanan (2007a), Davis in a given period Wixted &  Time span between new products & Shirato (2007), Nadkarni Gordon (2010) introduced & Barr (2008) Technology  high-level executives' perceptions of Judge & Miller (1991), Archival data to qualitatively describe Judge & Miller the pace of technological change in Nadkarni & Naryanan technological discontinuities (1991) their industries (Judge & Miller) (2007b), Nadkarni &  Average number of years over which Narayanan (2007a), Davis firms depreciated capital equipment & Shirato (2007)  Number of patents  Ratio of R&D to total sales

Demand  Change in sales Judge & Miller (1991) The change in the trend (e.g., growth McCarthy, versus decline) and nature (e.g., Lawrence, personal versus impersonal) of Wixted & demand in a given period Gordon (2010) Regulation  Number of laws and regulations McCarthy, Lawrence, Archival data to qualitatively describe Judge & Miller introduced Wixted & Gordon (2010) governmental discontinuities (1991) Competition  Average time span between new (1999), Nadkarni & Archival data to qualitatively describe Judge & Miller corporate strategic actions introduce Naryanan (2007b), competitive discontinuities (1991) by all firms in industry Nadkarni & Narayanan  Competitive actions (total competitive (2007a), Nadkarni, Chen & actions/number of firms) of these Chen (2015) 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

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

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

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

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 self- regulation 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 continuity- discontinuity 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).

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.

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 Formula Symbol Rate of change 푋푡  RC: rate of change (in 푅퐶 (%) = ( − 1) ∗ 100 푋푡−1 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

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 Key questions Technology  What are the most important technologies used in the production process?  What indicators are there for these technologies? Product  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? Customers  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

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:

∆푅퐶 = 퐼푅퐶 − 퐸푅퐶. 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 Industry Company Approach for measuring rate of change Used approach 1 0 1 1 1 2 0 0 0 1 3 1 0 -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.

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 high- technology 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.

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 self- explanatory 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 solid- state 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

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 silicon- wafer 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

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 manufacturing- oriented 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)

Intel DRAM market share (%) Product 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 4K 82.9 45.6 18.7 18.1 14.3 8.7 3.2 16K 3PS 37 27.9 11.5 4.4 2.1 2.4 2.3 1.9 1.4 16K 5V 100 94 66.5 33.1 11.7 12.3 64K 0.7 0.2 1.5 3.5 1.7 256K 0.1 Total Share 82.9 45.6 19 20 12.7 5.8 2.9 4.1 3.5 3.6 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

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 (Casadesus- Masanell, 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”.

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

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

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).

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.

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).

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 60- 70% 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

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).

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

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

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

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 푟푎푛푔푒∗푐푎푝푎푐푖푡푦 change. In order to measure the changes a ratio will be created which equals . 푓푢푒푙 푒푓푓푖푐푖푒푛푐푦 푝푒푟 푠푒푎푡 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

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).

Table 11: Measurements and sources for semiconductor industry

Technology Product Demand Measure Source Measure Source Measure Source

USPTO - CPU Museum - Annual reports Database New product - CPU world - Semiconductor Rate of change Patents (NAICS 3344 ) introductions - CPU collection Sales Industry Association - Stanford CPU -Product Catalogues Database - Websites of companies - Manu- - Stanford CPU Database - Annual reports facturer's - Price - Chronology of - Semicondcutor Direction of change Feature size websites - Clockspeed Microprocessors Sales Industry Association

Table 12: Measurements and sources for aircraft industry

Technology Product Demand Measure Source Measure Source Measure Source USPTO - Annual reports Database New product - Aerospace Industry Rate of change Patents (NAICS 3364 ) introductions Product Catalogues Sales Association

- Range - capacity - Annual reports Qualitative - fuel -Product Catalogues - Aerospace Industry Direction of change Assessment Trade journals, efficiency -Websites of companies Sales Association

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 Product Technology Demand Semi- Semi- Semi- Industry Aircraft conductor Aircraft conductor Aircraft conductor Rate of Mean -2.5 28.78 4.57 9.75 3.53 12.17 change Standard deviation 47.22 14.44 12.69 10.56 4.96 24.76 Direction of Mean 0.05 0.12 - 0.32 0.1 0.16 change Standard Deviation 0.224 0.33 - 0.48 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.

Table 14: Comparison of speed in the industries

Product Technology Product Demand Industry speed discontinuities discontinuities discontinuities Aircraft 5-7 years - 20 years 10 years Semiconductor 1-2 years 2-4 years 7-10 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

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

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.

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.

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.

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.

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.

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).

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 for different Δ DC 3.5 3.3 3.1 2.9 2.7 2.5 2.3 Tobin's Q Tobin's 2.1 1.9 1.7 1.5 0 1 2 Tobin's Q 2.866116458 2.722392835 1.770084762 Count 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

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.

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

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.

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.

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

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

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.

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,

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.

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.

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8 Appendix 8.1 Rate of change 8.1.1 Semiconductor

Patents: Source: USPTO. Products Sales Indication/Classification: NAICS 3344 IRC ERC Patents ERC ERC_Alig Sales Sales Patents IRC Indicat Patents Year IRC nment ΔRC Sales Intel Sales Industry Intel Industr ΔRC Intel Patents or 3 Indicat ΔRC 1978 313788 2594069 17 4163 1979 0 489615 3567420 56.0337 37.5222 18.5115 10 -41.176 3193 -23.301 -17.876 1980 -50 12.5 -62.5 621540 4615954 26.9446 29.3919 -2.4473 8 -20 3910 22.4554 -42.455 1981 200 37.5 162.5 597909 4539554 -3.802 -1.6551 -2.1469 16 100 4211 7.69821 92.3018 1982 0 0 0 651574 4704165 8.97545 3.62615 5.3493 14 -12.5 3999 -5.0344 -7.4656 1983 0 20 -20 809035 5614591 24.1662 19.3536 4.81263 18 28.5714 4160 4.02601 24.5454 1984 -66.6667 -19.4444 -47.2222 1159392 9804482 43.3055 74.625 -31.32 24 33.3333 4709 13.1971 20.1362 1985 0 16.66667 -16.6667 893410 6718011 -22.942 -31.48 8.5387 16 -33.333 5419 15.0775 -48.411 1986 0 14.28571 -14.2857 760895 7186264 -14.832 6.97011 -21.803 15 -6.25 5761 6.31113 -12.561 1987 0 21.42857 -21.4286 1166943 8635479 53.3645 20.1665 33.1981 24 60 7358 27.7209 32.2791 1988 400 6.25 393.75 1640216 11284360 40.5567 30.6744 9.88226 33 37.5 6965 -5.3411 42.8411 1989 0 -16.6667 16.66667 1774585 12466446 8.19215 10.4754 -2.2833 49 48.4848 8489 21.8808 26.604 1990 -20 44.44444 -64.4444 2115957 12169486 19.2367 -2.3821 21.6188 45 -8.1633 8345 -1.6963 -6.467 1991 -100 22.22222 -122.222 2329000 12848431 10.0684 5.57908 4.48932 59 31.1111 9632 15.4224 15.6887 1992 100 31.25 68.75 3018000 15309886 29.5835 19.1576 10.4259 75 27.1186 9730 1.01744 26.1012 1993 0 21.2963 -21.2963 4416000 20717821 46.3221 35.3232 10.9989 128 70.6667 9698 -0.3289 70.9955 1994 100 18.51852 81.48148 5826000 27987577 31.9293 35.0894 -3.16 207 61.7188 11189 15.3743 46.3444 1995 -50 -5.55556 -44.4444 7922000 39181372 35.9767 39.9956 -4.0189 271 30.9179 11963 6.91751 24.0004 1996 0 5.185185 -5.18519 8668000 35196347 9.41681 -10.171 19.5875 423 56.0886 12476 4.28822 51.8003 1997 0 -3.7037 3.703704 11053000 38675143 27.515 9.88397 17.631 405 -4.2553 13092 4.93748 -9.1928 1998 100 0 100 11663000 34349517 5.51886 -11.185 16.7034 701 73.0864 18454 40.9563 32.1301 1999 50 -21.875 71.875 12740000 39294281 9.23433 14.3954 -5.1611 733 4.56491 20045 8.62144 -4.0565 2000 33.33333 -33.3333 66.66667 13912000 53803532 9.19937 36.9246 -27.725 795 8.45839 22305 11.2746 -2.8162 2001 0 -16.6667 16.66667 8233000 29348483 -40.821 -45.452 4.63162 809 1.76101 25499 14.3197 -12.559 2002 0 20 -20 7698000 26206688 -6.4982 -10.705 4.2069 1077 33.1273 27028 5.99631 27.131 2003 0 0 0 7644000 26907659 -0.7015 2.67478 -3.3763 1592 47.818 28491 5.41291 42.4051 2004 25 -33.3333 58.33333 6563000 33080183 -14.142 22.9397 -37.081 1601 0.56533 29392 3.1624 -2.5971

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 GX1 199? Digital Equipment T-11 1981 Corporation Digital Equipment MicroVAX 1984 Corporation Digital Equipment CVAX 1987 Corporation Digital Equipment Rigel 1989 Corporation Digital Equipment NVAX 1991 Corporation Digital Equipment

21064 1992 Corporation

Digital Equipment Alpha 21164 1995 Corporation Digital Equipment Alpha 21264 1998 Corporation

Ferranti 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 80486 1989 80486 Intel 1989

overdrive

Intel 80860 1989

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 Pentium D 2005 Pentium Intel Extreme 2005

Edition

Intel Core Solo 2006

Intel Core Duo 2006

Intel Core 2 2006 Pentium Dual- Intel 2007

Core Celeron Dual- Intel 2008

Core

Intel Atom 2008

Intel Core i7 2008

Intel Core i5 2009

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 8X300 1976 SPARC SUN Microsystems 1987 MB86900 SUN Microsystems SuperSPARC 1992

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 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

8.1.1.2 Rate of change product Digital Equipment MIPS National Intel AMD Cyrix Corporation IDT Technologies Motorola Semiconductor SUN Microsystems IBM IBM/Motorola Industry total Movi Movi Movi Movi Movi Movi Movi Movi Movi Movi Movi Prod ng Prod ng Prod ng Prod ng Prod ng Prod ng Prod ng Prod ng Prod ng Prod ng Prod ng RC total Year ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC ucts avg. RC RC total positve: 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 0 0 1980 0 0.333 -50 0 0.667 0 0 0 0 0.333 100 0 0 0 0 0 0.333 -50 0 0 0 0 0 0 12.5 37.5 1981 0 1 200 1 1 50 0 1 0.333 0 0 0 0 0 0 0.333 0 0 0.333 100 0 0 0 0 0 37.5 37.5 1982 3 1 0 2 1 0 0 0 0.333 0 0 0 0 0 1 0.333 0 1 0.333 0 0 0 0 0 0 0 0 0 1983 0 1 0 0 1 0 0 0 0.333 0 0 0 0 0 0 0.667 100 0 0.333 0 0 0 0 0 0 0 0 20 20 1984 0 0.333 -66.67 1 0.333 -66.67 0 1 0.333 0 0 0 0 0 0 1 0.333 -50 0 0.333 0 0 0 0 0 0 0 0 -19.444444 19.445 1985 1 0.333 0 0 0.333 0 0 0 0.333 0 0 0 0 0.333 100 0 0.333 0 1 0.333 0 0 0 0 0 0 0 0 16.666667 16.666667 1986 0 0.333 0 0 0 -100 0 0 0.333 0 0 0 1 0.333 0 0 0.333 0 0 0.667 100 0 0.333 100 0 0 0 0 0 14.285714 42.857143 1987 0 0.333 0 0 0.333 100 0 1 0.333 0 0 0 0 0.667 100 1 0.333 0 1 0.333 -50 1 0.333 0 0 0 0 0 0 21.428571 35.714286 1988 1 1.667 400 1 0.333 0 0 0 0 0.667 100 0 0 1 0.333 -50 0 0.333 0 0 0.333 0 0 0.333 0 0 0 0 0 0 6.25 18.75 1989 4 1.667 0 0 0.667 100 0 0 0 1 0.333 -50 0 0 0 0.333 0 0 0 -100 0 0 -100 0 0 -100 0 0.333 100 0 0 0 -16.666667 61.111111 1990 0 1.333 -20 1 1.333 100 0 0 0 0 0.667 100 0 0 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 1991 0 0 -100 3 1.333 0 0 0.333 100 1 0.667 0 0 0 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 1992 0 0.333 100 0 1.333 0 1 0.333 0 1 0.667 0 0 0 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 1993 1 0.333 0 1 1 -25 0 0.333 0 0 0.333 -50 0 0 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 1994 0 0.667 100 2 1.333 33.33 0 0.667 100 0 0.333 0 0 0 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 1995 1 0.333 -50 1 1.667 25 2 0.667 0 1 0.333 0 0 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 1996 0 0.333 0 2 1.333 -20 0 0.667 0 0 0.333 0 0 0.333 100 2 0.667 -33.33 0 0 -100 0 0 0 0.667 100 0 0 0 0 1 0 5.1851852 39.258889 1997 0 0.333 0 1 1.333 0 0 0 -100 0 0.333 0 1 0.333 0 0 0.667 0 0 0 0 0 0 1 0.667 0 0 0.333 100 2 0.667 -33.33 -3.7037037 25.925556 1998 1 0.667 100 1 1.333 0 0 0 0 1 0.333 0 0 0.667 100 0 0 -100 0 0 0 0 0 1 0.667 0 1 0.333 0 0 0.667 0 0 22.222222 1999 1 1 50 2 1 -25 0 0 0 0 0.333 0 1 0.333 -50 0 0 0 0 0 0 0 0 0.667 0 0 0.333 0 0 0 -100 -21.875 21.875 2000 1 1.333 33.33 0 0.667 -33.33 0 0 0 0 -100 0 0.333 0 0 0 0 0 0 0 0 1 0.667 0 0 0 -100 0 0 0 -33.333333 33.332857 2001 2 1.333 0 0 0 -100 0 0 0 0 0 0 0 -100 0 0 0 0 0 0 1 0.667 0 0 0.333 100 0 0 0 -16.666667 50 2002 1 1.333 0 0 0.333 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.667 0 1 0.333 0 0 0 20 20 2003 1 1.333 0 1 0.333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.667 0 0 0.333 0 0 0 0 0 2004 2 1 -25 0 0.333 0 0 0 0 0 0 0 0 0 0 0 0 0 1 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

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

8.2 Direction of change 8.2.1 Technology Semiconductor Database for technology Manufacturer_id Manufacturer year name technology Feature_size 1 AMD 1991 CMOS 0.8 1 AMD 1993 CMOS 0.5 1 AMD 1997 CMOS 0.35 1 AMD 1998 CMOS 0.25 1 AMD 1999 CMOS 0.18 1 AMD 1999 CMOS 0.25 1 AMD 1999 CS44E-mod CMOS 0.25 1 AMD 2003 CMOS 0.13 1 AMD 2005 CMOS 0.09 1 AMD 2006 CMOS 0.065 1 AMD 2007 CMOS 0.045 2 Cypress 1989 CMOS 0.8 2 Cypress 1992 CMOS 0.65 3 DEC 1992 CMOS-4 CMOS 0.75 3 DEC 1993 CMOS-4S CMOS 0.675 3 DEC 1993 CMOS-5 CMOS 0.5 3 DEC 1995 CMOS-6 CMOS 0.35 3 DEC 1996 CMOS-6 CMOS 0.35 4 Fujitsu 1994 CMOS 0.5 4 Fujitsu 1995 CS-55 CMOS 0.4 4 Fujitsu 1996 CS-60 CMOS 0.35 4 Fujitsu 1996 CS-60ALE CMOS 0.35 4 Fujitsu 1997 CS-70 CMOS 0.24 4 Fujitsu 2000 CS-80 CMOS 0.18 4 Fujitsu 2002 CS-85 CMOS 0.15 4 Fujitsu 2005 CS-100 CMOS 0.09 4 Fujitsu 2008 CS-200 CMOS 0.065 5 Hitachi 1994 BICMOS 0.5 6 HP 1991 CMOS26B CMOS 1 6 HP 1992 CMOS26B CMOS 0.8 6 HP 1994 CMOS26B CMOS 0.75 6 HP 1995 CMOS14A CMOS 0.55 6 HP 1996 CMOS14C CMOS 0.5 6 HP 1996 CMOS14C CMOS 0.5 7 IBM 1993 CMOS 0.72 7 IBM 1993 CMOS-4S CMOS 0.6 7 IBM 1994 CMOS 0.35 7 IBM 1994 CMOS 0.35 7 IBM 1994 CMOS-5X CMOS 0.5 7 IBM 1996 CMOS 0.65

7 IBM 1996 CMOS-6S CMOS 0.29 7 IBM 1997 CMOS 0.26 7 IBM 1997 CMOS-6S2 CMOS 0.25 7 IBM 1998 CMOS-8S3 CMOS 0.18 7 IBM 1999 CMOS-7S CMOS 0.22 7 IBM 2001 CMOS 0.13 7 IBM 2004 CMOS 0.09 7 IBM 2006 CMOS 0.065 7 IBM 2010 CMOS 0.045 8 IDT 1993 CMOS 0.65 8 IDT 1996 CMOS 0.35 9 Intel 1971 PMOS 10 9 Intel 1974 PMOS 6 9 Intel 1976 CHMOS NMOS 3.2 9 Intel 1982 CHMOS III NMOS 1.5 9 Intel 1987 CHMOS IV NMOS 1 9 Intel 1992 BICMOS 0.8 9 Intel 1992 CHMOS V NMOS 0.8 9 Intel 1992 CMOS 1.2 9 Intel 1993 CMOS 0.8 9 Intel 1994 BICMOS 0.6 9 Intel 1994 P854 BICMOS 0.35 9 Intel 1994 P854.3 BICMOS 0.35 9 Intel 1994 CMOS 0.6 9 Intel 1995 BICMOS 0.5 9 Intel 1997 P856 CMOS 0.25 9 Intel 1997 CMOS 0.28 9 Intel 1999 P858 CMOS 0.18 9 Intel 2002 Px60 CMOS 0.13 9 Intel 2003 P1262 CMOS 0.09 9 Intel 2005 P1264 CMOS 0.065 9 Intel 2007 CMOS 0.045 9 Intel 2010 CMOS 0.032 CMOS_TRI- 9 Intel 2012 GATE 0.022 10 Motorola 1979 NMOS 3.5 10 Motorola 1984 NMOS 2.25 10 Motorola 1987 CMOS 1.3 10 Motorola 1991 CMOS 0.8 10 Motorola 1994 CMOS 0.6 10 Motorola 1999 HiPerMOS5 CMOS 0.22 10 Motorola 2000 HiPerMOS6 CMOS 0.18 11 NEC 1988 CMOS 1.2 11 NEC 1991 CMOS 0.8 11 NEC 1992 CMOS 0.6

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

Intel Year Feature Size Included Total frequency 1974 6 1 1976 3.2 1 1982 1.5 1 1987 1 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 Coding for technology direction of change Year Feature size Intel Feature size Industry Alignment 1977 3.2 0 5 0 1 1978 3.2 0 5 0 1 1979 3.2 0 3.5 1 0 1980 3.2 0 3.5 0 0 1981 3.2 0 3.5 0 0 1982 1.5 1 3.5 0 1 1983 1.5 0 3.5 0 1 1984 1.5 0 3.5 0 1 1985 1.5 0 2.125 1 1 1986 1.5 0 2.125 0 1 1987 1 1 2.125 0 1 1988 1 0 2.125 0 1 1989 1 0 1.16 1 0 1990 1 0 1.16 0 0 1991 1 0 1.16 0 0 1992 0.9 1 0.77 1 0 1993 0.9 0 0.77 0 0 1994 0.48 1 0.77 0 1 1995 0.48 0 0.48 1 0 1996 0.48 0 0.48 0 0 1997 0.265 1 0.48 0 1 1998 0.265 0 0.25 1 0 1999 0.18 1 0.25 0 1 2000 0.18 0 0.18 1 0 2001 0.18 0 0.18 0 0 2002 0.13 1 0.13 1 0 2003 0.09 1 0.13 0 1 2004 0.09 0 0.09 1 0 2005 0.09 0 0.09 0 0 2006 0.065 1 0.09 0 1

8.2.2 Product Semiconductor Intel Industry Cross improvement Cross Clocksp Clocksp improvemen Coding Year Price eed Ratio Change Coding Price eed Ratio Change Coding t Alignment 1974 727.3 2 0.00274989 1070.75 1.5 0.00140089 1975 46.2825 2 0.04321285 2985% 375% 1976 1977 1978 549.94 5 0.00909192 231% 1 0 1979 171.21 8 0.04672536 414% 1 0 1980 0 0 1981 0 0 0 1982 371.56 6 0.01614793 0 0 0 1983 0 185 15.5 0.08378378 94% 0 79% 0 1984 0 466.845 16 0.03427261 0 0 1985 276.77 16 0.05780983 24% 0 0 45% 0 1986 0 0 0 1987 0 620.931 35 0.05636701 0 0 1988 0 0 0 1989 722.9 25 0.03458278 0 416.473 32.5 0.07803635 0 0 1990 114.31 20 0.17496653 203% 1 60.202 14.25 0.23670314 183% 1 35% 0 1991 385.38 20 0.05189639 0 0 0 1992 176.41 29.1429 0.16519773 0 93.817 43.0769 0.4591591 94% 0 162% -1 1993 378.14 46.4 0.12270738 0 308.359 63.5789 0.20618474 0 -1 1994 418.78 67.875 0.1620778 0 391.228 106.158 0.27134567 0 -1 1995 668.05 144.2 0.21585147 23% 0 204.777 114.333 0.5583312 22% 0 159% -1 1996 272.71 137.7 0.50493244 134% 1 232.442 139.389 0.59967087 7% 0 19% 0 1997 395.92 195.9 0.49479973 0 200.876 261.1 1.29980999 117% 1 157% -1 1998 185.15 316.5 1.70946161 239% 1 571.554 367.375 0.64276483 0 32% 0 1999 272.94 552.818 2.02541468 18% 0 227.067 481.438 2.12024633 63% 0 5% 0 2000 199.9 751.2 3.75782827 86% 0 226.79 733.3 3.23339417 53% 0 14% 0 2001 181.53 1149.39 6.33173296 68% 0 115.884 1140.67 9.84313961 204% 1 55% 0 2002 199.11 1846.25 9.27260829 46% 0 159.517 1695.36 10.6281298 8% 0 15% 0 2003 157.29 2496.19 15.8702328 71% 0 201.486 2090.67 10.3762633 0 49% 0 2004 304.53 2343.83 7.69663252 0 257.304 2146.15 8.3409141 0 0

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 Industry Intel Alignment Year Sales % change % Δ Abs %Δ Coding Sales % change % Δ Abs %Δ Coding Diff. Coding -Above 25 1977 2137652.667 234460 1978 2,594,069 21.35 313,788 33.83 1979 3,567,420 37.52 16.17 16.17 0 489,615 56.03 22.20 22.20 0 - 6.03 0 1980 4,615,954 29.39 - 8.13 8.13 0 621,540 26.94 - 29.09 29.09 0 20.96 0 1981 4,539,554 - 1.66 - 31.05 31.05 0 597,909 - 3.80 - 30.75 30.75 0 - 0.30 0 1982 4,704,165 3.63 5.28 5.28 0 651,574 8.98 12.78 12.78 0 - 7.50 0 1983 5,614,591 19.35 15.73 15.73 0 809,035 24.17 15.19 15.19 0 0.54 0 1984 9,804,482 74.63 55.27 55.27 1 1,159,392 43.31 19.14 19.14 0 36.13 1 1985 6,718,011 - 31.48 - 106.11 106.11 1 893,410 - 22.94 - 66.25 66.25 1 - 39.86 -1 1986 7,186,264 6.97 38.45 38.45 0 760,895 - 14.83 8.11 8.11 0 30.34 1 1987 8,635,479 20.17 13.20 13.20 0 1,166,943 53.36 68.20 68.20 1 - 55.00 -1 1988 11,284,360 30.67 10.51 10.51 0 1,640,216 40.56 - 12.81 12.81 0 23.32 0 1989 12,466,446 10.48 - 20.20 20.20 0 1,774,585 8.19 - 32.36 32.36 0 12.17 0 1990 12,169,486 - 2.38 - 12.86 12.86 0 2,115,957 19.24 11.04 11.04 0 - 23.90 0 1991 12,848,431 5.58 7.96 7.96 0 2,329,000 10.07 - 9.17 9.17 0 17.13 0 1992 15,309,886 19.16 13.58 13.58 0 3,018,000 29.58 19.52 19.52 0 - 5.94 0 1993 20,717,821 35.32 16.17 16.17 0 4,416,000 46.32 16.74 16.74 0 - 0.57 0 1994 27,987,577 35.09 - 0.23 0.23 0 5,826,000 31.93 - 14.39 14.39 0 14.16 0 1995 39,181,372 40.00 4.91 4.91 0 7,922,000 35.98 4.05 4.05 0 0.86 0 1996 35,196,347 - 10.17 - 50.17 50.17 1 8,668,000 9.42 - 26.56 26.56 0 - 23.61 0 1997 38,675,143 9.88 20.05 20.05 0 11,053,000 27.51 18.10 18.10 0 1.96 0 1998 34,349,517 - 11.18 - 21.07 21.07 0 11,663,000 5.52 - 22.00 22.00 0 0.93 0 1999 39,294,281 14.40 25.58 25.58 0 12,740,000 9.23 3.72 3.72 0 21.86 0 2000 53,803,532 36.92 22.53 22.53 0 13,912,000 9.20 - 0.03 0.03 0 22.56 0 2001 29,348,483 - 45.45 - 82.38 82.38 1 8,233,000 - 40.82 - 50.02 50.02 1 - 32.36 -1 2002 26,206,688 - 10.71 34.75 34.75 0 7,698,000 - 6.50 34.32 34.32 0 0.42 0 2003 26,907,659 2.67 13.38 13.38 0 7,644,000 - 0.70 5.80 5.80 0 7.58 0 2004 33,080,183 22.94 20.26 20.26 0 6,563,000 - 14.14 - 13.44 13.44 0 33.71 1 Average 25.6137558 Average 21.75996919

8.2.4 Product Aircraft Manufactur Improvem Disconti Type pax range fuel per seat Ratio Year Classification Type er ent nuity Boeing Boeing 757-200 190 3900 3.02 L/100 km (78 mpg-US)[31] 245364.2384 1982 short-medium narrow body Boeing 3.02 L/100 km (78 mpg- Boeing 190 3900 245364.2384 1982 Short haul narrow body 757-200 US)[31]

Boeing 3.46 L/100 km (68 mpg- Boeing 126 2300 83757.22543 1984 Regional narrow body 737-300 US)[28]

Airbus 2.98 L/100 km (79 mpg- Airbus 262 6350 558288.5906 - 1992 Medium-haul Wide body A330-300 US)[36] Airbus 3.25 L/100 km (72 mpg- Airbus 262 7300 588492.3077 - 1992 Medium-haul Wide body A340-300 US)[36] Boeing 2.73 L/100 km (86 mpg- Boeing 305 5240 585421.2454 1994 Medium-haul Wide body 777-200 US)[40] Airbus 2.95 L/100 km (80 mpg- Airbus 124 3600 151322.0339 - 1995 Short haul narrow body A319 US)[31] Boeing 2.89 L/100 km (81 mpg- Boeing 777- 301 7730 805096.8858 0.3752437 x 1996 Medium-haul Wide body US)[36] 200ER Boeing 3.08 L/100 km (76 mpg- Boeing 777- 301 7730 755431.8182 - 1996 Long haul Wide body US)[36] 200ER Boeing 3.01 L/100 km (78 mpg- Boeing 777- 301 7730 773000 - 1996 Long haul Wide body US)[40] 200ER Airbus 2.5 L/100 km (94 mpg- AIrbus 180 3000 216000 1996 Short haul narrow body A321-200 US)[31] Airbus 3.32 L/100 km (71 mpg- Airbus 241 7260 527006.0241 - 1997 Long haul Wide body A330-200 US)[36]

Boeing 2.61 L/100 km (90 mpg- Boeing 386 6005 888095.7854 0.5170201 1997 Medium-haul Wide body 777-300 US)[40]

Boeing 3.5 L/100 km (67 mpg- short to Boeing 108 3230 99668.57143 - 1998 narrow body 737-600 US)[29] medium

Boeing 3.08 L/100 km (76 mpg- Boeing 110 3050 108928.5714 - 1998 Short haul narrow body 737-600 US)[29]

Boeing 2.71 L/100 km (87 mpg- short to Boeing 128 3200 151143.9114 - 1998 narrow body 737-700 US)[31] medium

Boeing 2.38 L/100 km (99 mpg- short to Boeing 162 2930 199436.9748 - 1998 narrow body 737-800 US)[29] medium

Boeing 2.93 L/100 km (80 mpg- Boeing 767- 245 5625 470349.8294 - 1999 Medium-haul Wide body US)[39] 400ER Boeing 2.84 L/100 km (83 mpg- Boeing 777- 386 7370 1001697.183 5.6196386 x 2004 Long haul Wide body US)[40] 300ER Airbus 3.27 L/100 km (72 mpg- AIrbus 525 8200 1316513.761 0.7427301 x 2005 Long haul wide body A380 US)[43] Boeing 3.25 L/100 km (72 mpg- Boeing 777- 301 8555 792324.6154 - 2006 Long haul Wide body US)[37] 200LR Boeing 2.59 L/100 km (91 mpg- Boeing 737- 177 3140 214586.8726 0.0759633 2007 Medium-haul narrow body US)[29] 900ER Bombardi 3.33 L/100 km (71 mpg- Bombardier er 100 1425 42792.79279 - 2009 Regional narrow body US) [30] CRJ1000 Boeing 3.35 L/100 km (70 mpg- Boeing 405 7730 934522.3881 - 2011 Long haul Wide body 747-8 US)[43] Boeing 2.67 L/100 km (88 mpg- Boeing 242 7355.00 666632.9588 - 2011 Long haul Wide body 787-8 US)[35]

Airbus 2.39 L/100 km (98 mpg- AIrbus 315 7750 1021443.515 - 2013 Long haul Wide body A350-900 US)[37]

Boeing 2.37 L/100 km (99 mpg- Boeing 304 7635 979341.7722 0.1027434 2013 Medium-haul Wide body 787-9 US)[37] Bombardi er 2.14 L/100 km (110 mpg- Bombardier 115 3100 166588.785 - 2013 Regional narrow body CSeries 1 US)[26] 00

Boeing 2.37 L/100 km (99 mpg- Boeing 290 7635 934240.5063 - 2014 Long haul Wide body 787-9 US)[37]

Coding for Wide body segment Fuel Year Model Range Cpacity Range Fuel Efficiency Ratio Change Coding Model Capacity Range Efficiency Ratio Change Coding Diff Alignment 2.98 L/100 k Medium- 1992 Airbus A330-300 262 6350 m (79 mpg- 558288.5906 - 0 0 haul US)[36] 3.25 L/100 k Medium- 1992 Airbus A340-300 262 7300 m (72 mpg- 588492.3077 1.88809E-06 0 0 haul US)[36] 1994 Boeing 777-200 Medium-haul 305 5240 2.73 L/100 km (86 mpg-US)[40] 585421.2454 0 0 1996 Boeing 777-200ER Medium-haul 301 7730 2.89 L/100 km (81 mpg-US)[36] 805096.8858 38% 0 0 1996 Boeing 777-200ER Long haul 301 7730 3.08 L/100 km (76 mpg-US)[36] 755431.8182 - 0 0 1996 Boeing 777-200ER Long haul 301 7730 3.01 L/100 km (78 mpg-US)[40] 773000 - 0 0 3.32 L/100 k 1997 Boeing 777-300 Medium-haul 386 6005 2.61 L/100 km (90 mpg-US)[40] 888095.7854 52% 1 Airbus A330-200 Long haul 241 7260 m (71 mpg- 527006.0241 - 0 0 US)[36] 1999 Boeing 767-400ER Medium-haul 245 5625 2.93 L/100 km (80 mpg-US)[39] 470349.8294 - 0 0 2004 Boeing 777-300ER Long haul 386 7370 2.84 L/100 km (83 mpg-US)[40] 1001697.183 33% 0 90% 1 3.27 L/100 k 2005 Airbus A380 Long haul 525 8200 m (72 mpg- 1316513.761 74% 1 0 US)[43] 2006 Boeing 777-200LR Long haul 301 8555 3.25 L/100 km (72 mpg-US)[37] 792324.6154 - 0 0 2011 Boeing 747-8 Long haul 405 7730 3.35 L/100 km (70 mpg-US)[43] 934522.3881 - 0 0 2011 Boeing 787-8 Long haul 242 7355.00 2.67 L/100 km (88 mpg-US)[35] 666632.9588 - 0 0 2.39 L/100 k 2013 Boeing 787-9 Medium-haul 304 7635 2.37 L/100 km (99 mpg-US)[37] 979341.7722 10% 0 Airbus A350-900 Long haul 315 7750 m (98 mpg- 1021443.515 - 0 0 US)[37] 2014 Boeing 787-9 Long haul 290 7635 2.37 L/100 km (99 mpg-US)[37] 934240.5063 - 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 Fuel Year Model Cpacity Range Fuel Efficiency Ratio Change Coding Range Model Capacity Range Efficiency Ratio Change Coding Alignment 1984 Boeing 737-300 126 2300 3.46 L/100 km (68 mpg-US)[28]83757.22543 0 Regional 0 1995 Airbus A319 124 3600 2.95 L/100 km (80 mpg-US)[31]151322.0339 - 0 Short haul narrow body 0 1996 Airbus A321-200 180 3000 2.5 L/100 km (94 mpg-US)[31] 216000 0 Short haul narrow body 0 1998 Boeing 737-600 108 3230 3.5 L/100 km (67 mpg-US)[29]99668.57143 - 0 short to medium 0 1998 Boeing 737-600 110 3050 3.08 L/100 km (76 mpg-US)[29]108928.5714 - 0 Short haul 0 1998 Boeing 737-700 128 3200 2.71 L/100 km (87 mpg-US)[31]151143.9114 - 0 short to medium 0 1998 Boeing 737-800 162 2930 2.38 L/100 km (99 mpg-US)[29]199436.9748 - 0 short to medium 0 2007 Boeing 737-900ER 177 3140 2.59 L/100 km (91 mpg-US)[29]214586.8726 - 0 Medium-haul 0 2009 Bombardier CRJ1000 100 1425 3.33 L/100 km (71 mpg-US) [30]42792.79279 - 0 Regional narrow body 0 2013 Bombardier CSeries 100 115 3100 2.14 L/100 km (110 mpg-US)[26]166588.785 - 0 Regional narrow body 0

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%.

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

8.3.2 The Boeing Company deferred taxes Common Shares Capital surplus of Market value Total common (deferred tax Stock Price Year outstanding (in millions) Common Stock common stock Retained Year Total Assets of equity equity liabilities) End (unadjusted) (Bold Stock split) outstanding (APIC) earnings Tobin's Q 1995 31,877 9,684 11,517 1215 28.15082196 344.0 1802 1951 7764 0.90437879205516 1996 37,880 13,289 14,792 1505 38.25279156 347.4 4976 920 8896 0.92059186349213 1997 38,293 47,641 14,237 1780 48.9375 973.5 5000 1090 8147 1.82583386650302 1998 38,002 30,599 14,912 1906 32.625 937.9 5,059 1147 8706 1.36263847955371 1999 36,952 36,084 17,230 1295 41.4375 870.8 5,059 1684 10487 1.47517793353540 2000 43,504 55,196 19,842 2197 66 836.3 5,059 2693 12090 1.76215520411916 2001 48,987 30,943 21,374 3914 38.779999 797.9 5,059 1975 14,340 1.11542983244738 2002 54,225 26,382 21,462 4691 32.990002 799.7 5,059 2141 14,262 1.00422507329461 2003 55,171 33,725 22,346 7110 42.139999 800.3 5,059 2880 14,407 1.07737110437911 2004 56,224 41,064 24,044 7516 51.77 793.2 5,059 3420 15,565 1.16903749288560 2005 59,996 53,425 26,708 7646 70.239998 760.6 5,061 4371 17,276 1.31786356555104 2006 51,794 67,323 28,169 8284 88.839996 757.8 5,061 4655 18,453 1.59601399715797 2007 58,986 64,432 31,194 8272 87.459999 736.7 5,061 4757 21,376 1.42324926700064 2008 53,779 29,788 31,192 9492 42.669998 698.1 5,061 3456 22,675 0.79739165108686 2009 62,053 39,315 31,531 9652 54.130001 726.3 5,061 3724 22,746 0.96989057299889 2010 68,565 47,986 33,711 10736 65.260002 735.3 5,061 3866 24,784 1.05161058077153 2011 79,986 54,624 36,618 12939 73.349998 744.7 5,061 4033 27,524 1.06334537932388 2012 88,896 56,942 39,220 14046 75.360001 755.6 5,061 4122 30,037 1.04135188035007 2013 92,663 102,013 42,440 15664 136.490005 747.4 5,061 4415 32,964 1.47385288342704 2014 99,198 91,857 45,866 16028 129.979996 706.7 5,061 4625 36,180 1.30205108140487 Source/ Annual Formula Annual report (I40*K40) N40+P40+O40 Annual Report Yahoo Finance Annual report Annual Report Annual Report Report (E40+F40-G40-H40)/E40

8.4 Aggregated alignment rate and directions of change with Tobin’s Q 8.4.1 Semiconductor Rate of change Direction of change Technolog Weighted Weighted Weighted Year Product Technology Demand Aggregate Product y Demand Product Technology Demand Aggregated Weighted Aggregate Weigted Aggregate Tobin's Q (t+1) 1981 162.5 92.30179028 -2.14687877 252.6549115 0 0 0 0 0 0 0 0 0 1.552339609 1982 0 -7.465566374 5.34929692 -2.116269454 0 1 0 0 2.181818182 0 1 2.181818182 0.727272727 1.543805228 1983 -20 24.54542207 4.812634368 9.358056437 0 1 0 0 2.181818182 0 1 2.181818182 0.727272727 2.110720328 1984 -47.22222222 20.13621795 -31.3195009 -58.40550518 0 1 1 0 2.181818182 4 2 6.181818182 2.060606061 1.661069866 1985 -16.66666667 -48.41084448 8.538697393 -56.53881376 0 1 -1 0 2.181818182 -4 0 -1.818181818 -0.606060606 1.885261203 1986 -14.28571429 -12.56112751 -21.80260934 -48.64945114 0 1 1 0 2.181818182 4 2 6.181818182 2.060606061 1.879099657 1987 -21.42857143 32.27911821 33.19806544 44.04861222 0 1 -1 0 2.181818182 -4 0 -1.818181818 -0.606060606 1.504722927 1988 393.75 42.84112531 9.882255299 446.4733806 0 1 0 0 2.181818182 0 1 2.181818182 0.727272727 1.683239915 1989 16.66666667 26.60401575 -2.283284058 40.98739836 0 0 0 0 0 0 0 0 0 1.605413807 1990 -64.44444444 -6.466952431 21.6187972 -49.29259967 0 0 0 0 0 0 0 0 0 1.964630745 1991 -122.2222222 15.68870248 4.489322265 -102.0441975 0 0 0 0 0 0 0 0 0 2.719245965 1992 68.75 26.10120221 10.42588124 105.2770834 -1 0 0 -4.7619048 0 0 -1 -4.761904762 -1.587301587 1.553804429 1993 -21.2962963 70.99554642 10.99891104 60.69816116 -1 0 0 -4.7619048 0 0 -1 -4.761904762 -1.587301587 1.607866664 1994 81.48148148 46.34444602 -3.160037298 124.6658902 -1 1 0 -4.7619048 2.181818182 0 0 -2.58008658 -0.86002886 2.003746474 1995 -44.44444444 24.00036613 -4.018928101 -24.46300642 -1 0 0 -4.7619048 0 0 -1 -4.761904762 -1.587301587 2.262159815 1996 -5.185185185 51.80033887 19.5875272 66.20268088 0 0 0 0 0 0 0 0 0 1.704039077 1997 3.703703704 -9.19279911 17.63102877 12.14193336 -1 1 0 -4.7619048 2.181818182 0 0 -2.58008658 -0.86002886 5.145924303 1998 100 32.13011056 16.70337563 148.8334862 0 0 0 0 0 0 0 0 0 2.479791378 1999 71.875 -4.056529811 -5.16110889 62.6573613 0 1 0 0 2.181818182 0 1 2.181818182 0.727272727 6.857173773 2000 66.66666667 -2.8162419 -27.72521501 36.12520976 0 0 0 0 0 0 0 0 0 6.786913613 2001 16.66666667 -12.55865298 4.631623365 8.739637052 0 0 -1 0 0 -4 -1 -4 -1.333333333 2.38717932 2002 -20 27.1310041 4.20689714 11.33790124 0 0 0 0 0 0 0 0 0 5.041368397 2003 0 42.40510786 -3.376259953 39.02884791 0 1 0 0 2.181818182 0 1 2.181818182 0.727272727 2.549331359 2004 58.33333333 -2.597075529 -37.08146504 18.65479277 0 0 1 0 0 4 1 4 1.333333333 4.668647519 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

8.4.2 Aircraft Rate of change Direction of change Technolo Weighted Weighted Weighted Weighted Aggregat Product gy Demand Aggregate Product Tech. Demand aggregate Demand Product e Tobins Q

Year ΔRC ΔRC ΔRC 1994 100 -15.841 -9.2627 74.8963207 7.6 -2.19664157 -13.0363787 -7.63302032 0 0 0 0.904378792 1995 50 15.00384 -3.91023 61.0936125 3.8 2.08055727 -5.50327282 0.37728445 0 0 0 0.920591863 1996 100 -2.33801 0.087078 97.7490655 7.6 -0.32420831 0.12255415 7.39834584 0 0 0 1.825833867 1997 0 22.84246 16.14164 38.984103 0 3.16752622 22.7178291 25.8853554 1 0 1 1.36263848 1998 0 7.397033 11.06853 18.465558 0 1.02573417 15.5779008 16.603635 0 0 0 1.475177934 1999 0 -9.85388 -1.55266 -11.4065415 0 -1.3664208 -2.18522702 -3.55164782 0 0 0 1.762155204 2000 -100 -19.105 -8.21536 -127.320369 -7.6 -2.64925933 -11.5623453 -21.8116046 0 0 0 1.115429832 2001 0 -1.65209 10.25031 8.59822646 0 -0.22909206 14.4263442 14.1972522 1 0 1 1.004225073 2002 0 19.95028 -8.88859 11.0616925 0 2.76647179 -12.5098424 -9.74337061 -1 0 -1 1.077371104 2003 0 41.14351 -5.91183 35.2316771 0 5.7053021 -8.32034693 -2.61504483 0 0 0 1.169037493 2004 0 66.36931 1.712298 68.0816114 0 9.2033219 2.40989755 11.6132194 0 1 1 1.317863566 2005 0 -1.08466 -3.06292 -4.14757377 0 -0.15040745 -4.31076647 -4.46117392 0 0 0 1.596013997 2006 -100 7.001007 2.645032 -90.3539608 -7.6 0.97081797 3.72263219 -2.90654984 0 0 0 1.423249267 2007 0 2.335417 -2.49724 -0.16181888 0 0.32384841 -3.51462325 -3.19077484 0 0 0 0.797391651 2008 0 4.508277 -11.798 -7.28971629 0 0.62515523 -16.6045576 -15.9794024 0 0 0 0.969890573 2009 100 29.15131 12.31173 141.463045 7.6 4.04236366 17.3275987 28.9699624 1 0 1 1.051610581 2010 100 2.659855 -5.20442 97.4554317 7.6 0.36883769 -7.32473298 0.64410471 -1 0 -1 1.063345379 2011 0 -3.58081 4.241222 0.6604163 0 -0.49654442 5.96911883 5.47257441 0 0 0 1.04135188 2012 0 -27.8893 15.34613 -12.5431728 0 -3.86736246 21.5982178 17.7308553 0 0 0 1.473852883 Average Average Average RC Product/ RC Tech/ RC of abs: of abs: of abs: Average of Average of Demand/ 34.21 15.77 7.07 abs Product abs Tech Average of abs Demand