Analysis of the Commercial
Launch Industry
BACHELOR THESIS WITHIN: Economics NUMBER OF CREDITS: 15 ECTS
PROGRAMME OF STUDY: International Economics AUTHORS: Patrick Andersson & Alexander Brewer JÖNKÖPING May 2021
Determining Competitiveness using
Porter’s Five Forces Framework
Bachelor Thesis in Economics
Title: Analysis of the Commercial Launch Industry; Determining Competitiveness using Porter’s Five Forces Framework
Authors: Patrick Andersson and Alexander Brewer Tutor: Andrea Schneider
Date: 2021-05-24
Key terms: Porter’s Five Forces, Competitiveness, Industry analysis, Industrial Organization, Strategy
Abstract
This thesis aims to analyze the rules of competition of the commercial launch industry between the years 2010-2020 to better understand the market and gain strategic insights for market share captivation or profitability. The industry is analyzed quantitively within the theoretical framework of Porter's five competitive forces. By the means of a Pooled OLS model, we conduct a regression analysis with five industry proxies that closely relate to Porter’s five forces to explain competition in the industry. The results provided us with enough evidence that quantitively applying Porter’s five forces with industry specific proxies leads us to a better understanding of competition in the commercial launch industry. Furthermore, the analysis reveals that the strongest competitive force is the threat of buyers. While the threat of competitive rivalry, power of suppliers and new entrants are relatively weak but highly significant, the threat of substitutes is of very low significance to affect market competition. The result of the analysis is then used as a tool to provide strategic insights for industry actors for optimal positioning in the market. Finally, we present some suggestions for future research on the subject, as well as other industry analysis using the theoretical framework.
Table of Contents
1. Introduction ... 1
2. Institutional Background ... 3
3. Literature Review ... 6
3.1. New Space Industry ... 6
3.2. Academic application of Porter’s five forces framework ... 7
4. Theoretical Framework ... 10 5. Data ... 17 5.1. Variables ... 17 5.2. Descriptive statistics ... 20 6. Empirical Model ... 23 7. Empirical results ... 25 7.1. Regression results ... 25 7.2. Robustness test ... 25 7.3. Discussion ... 27 8. Conclusion ... 31 References ... 33
Tables and Figures
Table 1 The neoclassical theory of the firm: Typology of market structures ... 4
Table 2 Descriptive statistics ... 21
Table 3 Correlation matrix ... 22
Table 4 Abbreviations, definitions, and expected effect of variables ... 24
Table 5 Regression results ... 27
1. Introduction
The 1960s marked the first space race propelled not just by rocket fuel but heavily by government funding. The 21st century marks the second leg of this race which may come to be
known as the “commercial” or “new” space race funded instead by private firms and investors (George, 2018). In 2016, the overall space economy had $345 billion in both private industry revenues and government budgets. Moreover, nearly $6 billion of this was revenue generated by the global space transportation industry, particularly from space-based services (Federal Aviation Administration, 2021). As this nascent commercial launch industry grows to service the ever-increasing needs of satellite providers, space infrastructure, such as resupplying the International Space Station and providing a means to explore the reaches of space, there comes the question of in the fight for market share, where is competition most highly concentrated. Competition in an industry is rooted in its underlying economics, and competitive forces exist that go well beyond the established combatants in a particular industry (Porter, 1979). Whether weak competitive forces lead to greater opportunity for superior performance or strong competitive forces stunt the possibilities of superior gains, a corporate strategist’s goal is to find a position in the industry where their company can best defend itself against said forces (Porter, 2008). In the commercial launch market, this research is left undiscovered to academia, stunting the abilities of future strategists to position their respective firms to the greatest payoffs.
This thesis aims to analyze the rules of competition of the commercial launch industry by investigating the shaping factors of Porter's five forces (Porter, 1979). We hope to accomplish filling the research gap found in describing competitive behaviour and means of capturing market share found in the current commercial launch market through a deductive approach and with a quantitative research method. We investigate the significance and relative importance of the five forces framework in describing the competitive behaviors found in the newly privatized commercial launch market, and from that, what forces act most strongly in a firm’s captivation of market share.
In Porter's paper "The five competitive forces that shape strategy", published in 2008 through the Harvard Business Review he laments two factors of the application of his framework, which we do our best to correct in this thesis. His first large disappointment is the misapplication of the five forces framework to assess the attractiveness levels of the industry, rather than to gain strategic
insight regarding the competitiveness of the industry. He also laments the lack of quantitative applications in the typical usage of the framework. Other authors who apply the framework typically use expert opinions on the chosen industry to rank the importance of factors defined by the five forces analysis ordinally, and then run a quantitative analysis on the results, which leads to a hybrid blend of a qualitative, turned quantitative, analysis. We attempt to avoid these behaviors by discussing only the competitiveness of the commercial launch industry rather than its effect on attractiveness to investors, and by using industry-specific proxies for the defined five forces to allow for a fully quantitative analysis (Porter, 2008). Hence, this thesis seeks to answer our research question:
Does applying Porter’s five forces analysis provide a better understanding of competitiveness in the commercial launch industry?
This paper is structured as follows. Section 2 provides an institutional background of the space industry, and the differences between government provided and commercial launch vehicle providers through its evolution. Section 3 is a literature review of published articles by industry experts, economic theorists, and academics that have applied the five forces framework to provide us a base on which to begin our analysis. Section 4 introduces our primary framework to analyze industry competitiveness and provides the hypothesis we will be investigating. Section 5 elaborates regarding each variable and shows how they will be modeled, the reasoning behind its choice as a proxy, and its expected effect on competitiveness. In section 6 the variables are combined using the estimation method. The results of our modelling are shown in section 7 and the models' effects and significance are then analyzed and discussed. Finally, the results of our model are concluded in section 8 to analyze strategic positioning within the commercial launch market.
2. Institutional Background
The terms Old Space and New Space are not formally defined; therefore, it is important to provide definitions for the scope of this thesis. Their names imply a chronological difference which is referenced within the industry to be between 2010 and 2011 with the retirement of the space shuttle and the turn towards usage of privately operated launch vehicles and international emphasis on the privatization of spaceflight as a commercial industry. The launch of New Space also came with the supported importance of developing a commercial launch sector through the Obama administration’s National Space policy of 2010, and the 2011 memorandum jointly signed by multiple United States agencies, to promote efficient launch certification processes and stabilization of the launch industry (Obama, 2010).
The factors of Old Space identified after the launch of Sputnik, which gave importance, urgency, and inevitability to entering space are listed as exploration, national defense, prestige, and science (NASA, 2005). The industries servicing these four requirements of Old Space were ruled by public agencies, whose utility was difficult to measure in monetary terms, while the newer private actors of New Space are more typical utility seekers who see their investment in space to pay dividends through profit. (Olofsson, 2018). New Space comes not only with private actors but also with new factors which describe the shape of the industry. These factors are formally listed as satellite launch, internet provision, deep space exploration, lunar landing, Earth observation, asteroid mining, space debris management, space tourism, astronomical research, and manufacturing (Stanley, 2021). The requirements of commercial launch vehicles to fulfill the new demands posed by New Space are summarized as lower costs, less strict quality requirements, and a capacity to meet increased and fluctuating volume (Olofsson, 2018).
Commercial launch vehicles are differentiated from the government-funded projects of Old Space. To be commercial in the full sense means to invest significant amounts of private funds, to put them at risk to develop privately-owned capital assets that form the basis for offering products and/or services into a market in which price is based on an agreed value (Hamill, 2002). Launch vehicles of Old Space fail these classifications as they are not based on capital assets acquired at private risk, their risk is limited to the costs of preparing a proposal, not building significant hard assets, and they are designed in response to specific government requests (Hamill, 2002).
Other proponents describe the clear shift between Old and New Space with market-wide descriptors. Greg Autry, who wrote his Ph.D. on the application of strategic management and
organizational theory to the emerging commercial space community, states that that Old Space and New Space should be considered as two separate markets within the same community. When the players of Old Space and New Space are analyzed, they are shown to not compete, signifying a shift of markets. (Hubbard, 2013).
Market structure is an important factor as it affects the outcome of the market through its influence on strategic behavior or conduct of economic actors involved in the market. There are two main classifications we provide for the commercial launch market in the New Space Industry. First, whether the market is complete or not, through checking whether there are negligible transaction costs and access to perfect information. Secondly, what the structure of the market is, following the classifications provided by neoclassical theories. Perfect competition, monopoly, monopolistic competition, and oligopoly are the four main types of market structure this theory considers in Industrial Organization. These four types of market structures vary from each other mainly based on the number of firms, the extent of barriers to entry, and the degree of product differentiation (Lipczynski et al., 2017). Table 1 shows a standard typology of the market structures.
Table 1 The neoclassical theory of the firm: Typology of market structures
Number of firms Entry Conditions Product
differentiation
Perfect competition Many Free entry Identical products
Imperfect competition
Monopolistic competition
Many Free entry Some differentiation
Oligopoly Few High barriers to
entry
Some differentiation
Monopoly One No entry Complete
differentiation Source: (Lipczynski et al., 2017, p. 68)
One of the many reasons launch services saw commercialization is the shared belief that launch costs can be reduced through market competition. Many still believe in this, and the full competition of the space-launch industry. Such is true if the market is stable, and all the factors of a complete market exist. One problem is that the launch industry, as Moon (2018) argued, is homogeneous with the transportation industry and is therefore highly subsidized by the government. It has the same unique characteristics which are unfavorable for a competitive market such as high fixed cost and high barriers to entry. Furthermore, the commercial launch industry lacks the factors of perfect information and clear supply and demand behaviors. The ambiguous supply and demand seen comes from the production of launch vehicles, which are typically only produced after a contract is signed, therefore equating demand to output. Because they are not ready-made products, and the market size is small, demand and supply cannot freely function. Due to these reasons, the commercial launch market is labeled as an incomplete market.
Therefore, to provide the second classification we are left to analyze whether the commercial launch market most closely resembles oligopoly or monopolistic competition conditions, as they are the two which can exist under imperfect information. In studying the market, we were able to distinguish only five firms (Arian Space, NRIS, Rocket lab, SpaceX, and ULA) which could be considered commercial, and provided services ranging to orbit. We see that not enough product differentiation exists between these firms. However, there are distinctly different characteristics of launch vehicles that create differences between competitors. Furthermore, the market has very high barriers to entry. Because of this, we describe the commercial launch market as following oligopoly conditions. Hertzfeld (2007) also describes the market as an oligopoly that heavily relies on funds from the government for research and development purposes.
3. Literature Review
3.1. New Space Industry
The field of study on the New Space industry is relatively new and small in size, thus making the literature defining the New Space industry inadequate. Nevertheless, the literature we have chosen is validated through being peer-reviewed and provides information for understanding the development of the space industry.
Hertzfeld (2007) states that the government needs to be willing to surrender some of its control on space activities to make it feasible for space assets to be privatized when markets become large enough to be profitable for some space activities such as launch services. However, he argues that “space would never be deregulated, the philosophical shift meant more attention to commercial capabilities and opportunities along with the recognition that the government could be a customer rather than a producer for some space goods and services” (Hertzfeld, 2007, p. 215). As we have mentioned earlier Moon (2018) states that the reason for government intervention in the space industry is that it has similar distinctive characteristics as the transportation industry such as high fixed cost, high barrier to entry, and long payback period to realize the return of investment. Thus, the degree of government intervention is highly influential on space development. The space industry is maturing and after several years of centralized control of space activities which in general defines Old Space, NASA and US politicians have given way for space activities to private companies which in this matter portrays the New Space market. According to a recent study by Weinzierl (2018, p. 176), “The shift from public to private priorities in space is especially significant because a widely shared goal among commercial space’s leaders is the achievement of a large-scale, largely self-sufficient, developed space economy.” He describes the centralized control as creating weak incentives for the efficient allocation of resources, poor aggregation of dispersed information, and resistance to innovation due to lack of competition. This portrayal provided by Weinzierl (2018) is generally not the key to economic growth. The Commercial Space Launch act of 19841 was enacted to move the launch industry away from the governments and
when the shuttle program ended in 2011 bigger roles started to shift towards the private sector in space. This in turn increased the number and quality of actors in the market describing them as “New Space” or the new generation of companies (Weinzierl, 2018). However, Weinzierl (2018)
1 A United States federal law to facilitate commercial space launches and the foundation for providing broader space
argues that the economic aspect of it suggests that the outcome would feature an industry with a high degree of concentration due to the characteristics of the market segment they operate wherein many of the key questions for the economic development is technological.
The characteristics of the Old Space given by Weinzierl (2018) is a form of market failure which could explain the unsuccessful U.S. development of new launch vehicles and several cancelled programs in the Old Space era due to technological problems. Hertzfeld et al. (2007) said that the economic aspects of these space activities are equally as important as the technical aspects. Specifically, the launch costs, overall demand for launch vehicles, the size and type of role that the government and NASA play in the overall launch market, have a significant impact in influencing the industry. They claimed that the failures by public actors in the Old Space were because of excessively optimistic technological evaluations rather than thorough economic analysis which led to misinterpretations of the future. Accordingly, Hertzfeld et al. (2007) contributed with an economic analysis of their own based on the lessons learned from the past on launch vehicle programs. Based on their economic analysis on years 1999-2004 launches, they provided the following inputs: (1) the lower the price of launches the more that was launched into space, (2) lower price equals more opportunity to increase flight reliability which over time helps to capture the market and (3) lower prices are the result of economies of scale and the internal cost for producing a launch vehicle is not publicly available therefore an issue arises in determining the price of the product compared to its true cost.
The radical changes in the space sector have opened new opportunities in space which can be seen by the increased number of new privately funded companies that are involved in space activities. According to Yazici and Darici (2019), the transformation of the space economy will have an inevitable effect on the socio-economic growth targets of the countries involved. For this reason alone, R&D programs must be the priority from policies by public actors to develop a larger powerful economic competition for space products and services. Policies that are aimed to protect and isolate them only result in similar products (Hertzfeld, 2007).
3.2. Academic application of Porter’s five forces framework
Many of the works of literature that applied the five forces framework have contributed to Porter’s lamentation on his theory. Assessing the attractiveness levels of an industry in terms of profitability instead of gaining strategic insights based on the competitiveness of the industry and
the lack of quantitative application are the two main factors that he thought were not the intended purpose of his framework (Porter, 2008). Nevertheless, a lot of researchers have found the framework useful in a variety of ways and have led to a generation of academic research and business practice.
Assessing the profitability level of an industry can be seen in Wellner and Lakotta's (2020) research in which they investigated the profitability potential of the German railway industry. They analyzed the industry using a qualitative research method with interviews to evaluate the strength of each of the five forces. In their research, they had two additional forces, namely, government interventions and complementary goods. With the expanded framework, they revealed that the German railway industry has a low profitability level (Wellner and Lakotta, 2020). A similar industry analysis was done by Bhatia (2016) to assess the profitability potential of the Indian Passenger car industry which is a key industry in India. The research was done through a descriptive analysis with a mix of reports and statistics. They found that the overall attractiveness of the Indian car industry is moderate in the sense that there is an expected growth rate in the industry in the coming years due to favorable government initiatives (Bhatia, 2016). More research with the same purpose can be seen in Muchiri (2008), Azim and Alam (2010) wherein they assess the attractiveness of the Mailing industry in Kenya and Tea industry in Bangladesh, respectively.
Even though there is a lack of quantitative application of the framework, there are many academic studies that analyze the industry competitiveness for strategic and policy purposes. Pringle and Huisman (2011) applied the five forces to analyze the higher education industry competitiveness in Ontario, Canada. Their findings revealed that competition in Ontario’s higher education industry is mixed. Through the analysis, Pringle and Huisman (2011) suggested Ontario policymakers emphasize the effects of technology and the globalisation of higher education due to the disruptions these could have on the shape of the higher education industry in Ontario. Similarly, Siaw and Yu (2004) performed an analysis on the banking industry to investigate the impact of the internet on competition in the market. According to them, taking advantage of the competitive benefits of the internet would provide significant business potential for market actors. With the analysis, they discovered that the internet has affected the competition in the banking industry in various ways. It has changed the industry structure, created new opportunities for banks to gain competitive advantage against its rivals, and initiated the creation of new businesses that are seen as an innovative banking domain. Therefore, propositions to fully understand the impact of the
internet to grasp the evolving industry and develop a strategic plan to help deal with these changes were given (Siaw and Yu, 2004). More academic papers that seek to gain strategic insights from factors that affect competition in an industry can be seen in Eskandari et al. (2015), Hokroh (2014), Rasouli and Malabad (2014) wherein they did an industry analysis on Food, Oil, and Gas, and Air Transport industry, respectively.
4. Theoretical Framework
Assume a perfectly competitive market where all the firms earn only normal profit and if any firm is unable to do the same, will be forced to leave from the market. This nature of discipline forces all actors in the market to produce as efficiently as the current state of technology will allow. According to Lipczynski, et al. (2017), this produces industries with a relatively small number of large firms. All or some firms in such industries can earn abnormal profits in the long run and have enough market power to determine their price. However, this is only achievable if one knows how to respond to the state of competition in strategic terms. The main objective of this section is to provide a theoretical framework to analyze the nature and degree of competition in the Commercial Launch market with the help of Porter’s five competitive forces (Porter, 1979;1980;2008).
Michael E. Porter argues that, “competition in an industry is rooted in underlying industry economics and goes well beyond the established competitors” (Porter, 1980, p. 30). According to this framework, competitors are not the sole foundation of competitiveness in an industry rather there are other underlying factors to it. Porter identified five basic competitive forces which according to him are definite forces that shape the structure of an industry, play a significant part in establishing the rules of competition, and determine the degree of profitability for firms. The five forces identified by Porter are the threats of (1) competitive rivalry, (2) powerful buyers, (3) powerful suppliers, (4) new entrants, and (5) availability of substitute products on competitiveness. In figure 1 below, it can be seen the threat of competitive rivalry is at the center of the model, this is because this force is an internal threat that influences the dynamics of industry competitiveness while the other four forces are what we call external threats. All five forces are used to understand the level of competition as they jointly determine the intensity of industry competitiveness. More importantly, identifying which force is the strongest is crucial as different forces play a part in shaping competition in each industry, and so are of greatest importance in strategy formulation (Porter, 1980). Each industry has distinctive underlying market economics that shape competition and hence the strength of each force differ for each market. For example, the market for commercial aircraft in the US could be seen to have a high threat of the power of buyers due to huge orders by airlines for aircraft (Porter, 2008).
Figure 1 The Five Forces that Shape Industry Competition. Source: (Porter, 2008, p. 4)
In Porter’s (1979;1980;2008) articles, the terms competition and profitability are used interchangeably which can lead to misunderstanding. In Microeconomics, competition is associated with the number of firms in the market and is also measured by the concentration of market power, which follows one of the main characteristics in classifying the four types of the market structure mentioned in section 2. In contrast, Porter used the term profitability as if it has the same definition as the economic term competition which is common from a business point of view. Profitability is typically utilized to determine a firm’s success or failure in an industry. It can be applied as a measurement of financial gain to see if a firm is yielding enough profit to stay in the market and grow. Generally, profitability in an industry is negatively related to the number of firms (Bain, 1951). Market concentration is positively related to leaders’ profits while it is also inversely related to the number of firms (Porter, 1979). Furthermore, Porter (2008, p. 6) states that, “the strength of the competitive forces affects prices, costs, and the investment required to compete; thus, the forces are directly tied to the income statements and balance sheets of industry participants”. Therefore, we argue that Porter uses the term profitability as an indicator of the degree of competition in a market and vice versa. Considering this, it is important to distinguish these two terms (competition and profitability) although they have a recognizable link with each other, they are different things. In this thesis, our dependent variable will be the degree of
competition and can thus be realized as an indicator of profitability as explained above. The degree of competition can be directly measured with the help of a market concentration ratio. We use the Herfindahl-Hirschman Index (HHI) as the measure for our dependent variable, degree of competition. While the five forces identified by Porter (2008) will act as our independent variables. These five underlying factors of competition with their respective proxies which will be provided later will explain the degree of competition in the commercial launch market. We will expand more on this in detail in our empirical model in Section 5. We define an intense competition as follows:
Definition 1 (Intense Competition) Competition is intense if the HHI is low.
Understanding the five forces of competition and their fundamental origins provides a framework for shaping competition. According to Porter (2008), one should look at the industry structure quantitatively as many of the elements of the five forces can be quantified rather than being satisfied with qualitative factors. This is naturally dependent on the accessibility of data of the chosen market. Based on this we produce our main hypothesis and hypothesize that by quantitively applying Porter’s Five Forces framework to the commercial launch market we can deem a better understanding of industry competitiveness. This is realized through the rejection of the F-test null hypothesis which states that the regression coefficients of the selected explanatory variables are equal to zero or that the model has no predictive capability. A main hypothesis is produced:
Main hypothesis
Null: The five proxies chosen for Porter’s five forces to model competitiveness, have no effect on market competitiveness.
Alternative: At least one of the named variables is not equal to zero or that one of them influences the intensity of competition in the Commercial launch market.
After having discussed the degree of competition, which is our dependent variable, we now discuss the five factors that shape it and examine what the relative effects of the five forces are on the industry’s competitiveness.
1.The threat of competitive rivalry on competitiveness depends on the number of established firms and their size distribution. The strength of this competitive force is dependent on the intensity of how they compete and the basis on which they compete. The threat of rivalry is high if competitors are many or are similar in size and power. If industry growth is seen to be relatively slow, the threat is high because this precipitate fights for market captivation. Similarly, when exit barriers are high forcing firms to stay and compete even in losses (Porter, 2008). In contrast, if one or a few firms are dominant competition is less intense. Therefore, the higher the threat there is by rivals the more intense the competition will be. This is typically measured through the number of existing competitors, the growth of the industry, fixed costs, product differentiation, switching costs, strategic stakes, capacity for expansion, and exit barriers (Dobbs, 2014). Competitive rivalry is a measure of the extent of competition among existing firms. The difference between competitive rivalry and degree of competition is that degree of competition is the overall state and measure of competing while rivalry is the relationship between two or more rivals who regularly compete. The number of firms only increases by one through the addition of Rocket lab throughout the scope of this thesis, therefore, we choose the number of active firms in the market as the indicator for competitive rivalry. A higher number of active firms indicates more intensive rivalry between them which results in higher industry competitiveness. This will be explained more in detail later. We expect the number of active firms to have a negative effect on the HHI therefore, hypothesis 1 is produced.
Hypothesis 1 The higher the number of active firms is, the lower the HHI.
2. The threat of the power of buyers acts as a competitive force because buyers can bid down prices, demand more services or higher quality, and force competitors to compete against each other (Porter, 1980). The power of buyers depends on their level of dependence on the firm’s output. More importantly, the number and size distribution as a buyer has an influence on how strong the threat of buyer power is. If close substitutes are available, or only a few buyers are present, the power of buyers is likely to exercise significant market power. It is said that in industries with high fixed costs, large-volume buyers are powerful as they intensify the pressure on competitors to keep volume filled through discounting (Porter, 2008). Thus, we can say that the threat of the power of buyers is high if buyers have negotiating advantage relative to industry
participants. This drives up the intensity of competition. Others typically measure the power of buyers by the number of buyer orders, degree of buyer information, the feasibility of buyer backward integration, differentiation of industry products, buyer switching costs, overall buyer costs, buyer profitability, and impact of buyer product or service (Dobbs, 2014). Our proxy for this competitive force will be the volume of completed orders for a customer. We do recognize the difference between the number of orders completed and the number of orders placed, as the number of orders completed does not directly reflect demand. However, without having detailed information on the communication between launch vehicle operators and payload operators for each launch we cannot truly determine demand. Therefore, we use the more discernable amount of launches completed. From this, hypothesis 2 is produced.
Hypothesis 2 The higher the customer volume is, the lower the HHI.
3. The threat of supplier power can apply a competitive force by raising prices or reducing the quality of the goods they sell to firms in the industry which can shift up input costs. Firms depend on a wide range of different supplier groups for inputs and if they are small in number and large in size, they are likely to exercise significant market power as firms would depend more on the supplier. The stronger the power of suppliers the more intense the competition will be. This is typically measured by the concentration of suppliers, supplier volume/profit, feasibility of supplier forward integration, differentiation of supplier products, industry switching costs, and availability of supplier substitutes (Dobbs, 2014). In the commercial space launch market, private firms do not detail the material cost or sourcing for producing launch vehicles, so calculating a proxy for the power of suppliers in the production of launch vehicles becomes difficult. However, there is an important factor in the launching of said vehicles that is discernable, and without it, the launch vehicles become near useless. This factor is the usage of purpose-built launch sites at which vehicle operators pay rent to use. Launch sites become as important to firms supplying commercial launches as the physical materials used to build the launch vehicles because, without either, the launches could not take place. The rent paid by the vehicle operator is not public therefore we take the number of times a specific launch site is used to model supplier volume. Based on this, our proxy for this competitive force is created, and hypothesis three follows.
Hypothesis 3 The more a specific launch site is being used relative to the other launch sites the lower the HHI will be.
4.The threat of new entrants depends importantly on barriers to entry in the industry and the reaction new competitors expect from the established firms (Porter, 2008). Established firms in an industry behave differently when new competitors wish to enter the market. Earning abnormal profits will likely have a higher threat of entrants as this attracts firms outside. Therefore, they would want to raise entry barriers in some way. If an entrant that wishes to enter expects the established firms to not respond or retaliate in a way that makes them regret entering, it may well decide to enter. If the threat of entry is high, this will result in intense competition in the industry. This is usually measured by the degree of supply-side economies of scale, demand-side benefits of scale, switching costs, capital requirements, whether there are first or late mover benefits, access to distribution channels, levels of government policy, and anticipated incumbent responses (Dobbs, 2014). Levels of government policy can either hinder or make entry easier. It can make it easier through subsidies or acting as a customer to indirectly fund firms (Hertzfeld, 2007). According to Hubbard (2013), the competitiveness of the commercial launch market is directly tied to the proportion of government customers. Long-term reliance on the government by either being a customer or subsidizer will lead to firms that could not exist without the government. Especially when the government becomes more than 50% of the market customer. This will end up with a non-competitive market (Hubbard, 2013). The fact that this industry is highly subsidized due to various reasons, such as high fixed cost which makes the barriers to entry high, it is interesting to see if government activity interacting with commercial activity in space affects the degree of competition in the market in a certain way. Therefore, our proxy for this competitive force will be the proportion of government payload relative to commercial payloads and this will be calculated in detail later. Based on Hubbard’s (2013) statements above, we expect the government payload ratio to have a positive effect on the HHI therefore, hypothesis 4 is produced.
Hypothesis 4 The higher the government payload ratio is the higher the HHI will be.
5.Naturally, when substitutes are available the intensity of competition increases (Lipczynski et al., 2017). In this case, the threat of substitutes is high. This competitive force places a ceiling
on the profit potential of an industry in exchange for intense competition that causes price reduction or performance improvement (Porter, 1980). This is typically measured by price/indirect costs, buyer price sensitivity, performance, buyer switching costs, buyer risk profile, and price/performance trends (Dobbs, 2014). Our proxy for this competitive force is the payload mass advantage which is our defined measure of performance. We supply this as our proxy because payload mass capabilities and launch cost closely measures the price/performance ratio of launch vehicles and due to a behavior seen in the commercial launch market called ridesharing. Ridesharing occurs when multiple customers bundle together their payloads to share the cost of the launch and to reduce the amount of unused payload capabilities. The calculations for having a performance advantage over the industry are shown in section 5.1.5. We expect the payload mass advantage to have a negative effect on the HHI therefore, hypothesis 5 is produced.
5. Data
Our time series data reflects all launches between periods 2010 to 2020 with the exact date of the launch and because of this, our time unit is daily. For days where no launches occur our data set contains zeros. The cross-sectional data gives data on launches performed by five commercial firms that all service international customers, namely, Ariane Space which operates out of Europe, and NRIS, Rocket lab, SpaceX, and ULA which operate out of the US. The data for all the five firms for all the launches gives us an unbalanced pooled data2, with a total of 284 launches. The
data set used in this thesis is structured as panel data but is specifically aimed at pooled data which is the pooling of time series and cross-sectional observations (Gujarati and Porter, 2009). Advantages and challenges with panel data are well described in Hsiao (1985) in which according to him gives us more informative data, greater variability, less collinearity between variables, additional degrees of freedom, and better efficiency. Since we are doing an industry analysis this is required to measure the effects that cannot be observed with only time-series and cross-section data alone.
The data provided from Gunter’s Space Page is on a per launch basis, and details the specific launch vehicle, vehicle operators, payload operators, launch sites, and payload mass (Krebs, 2021). The vehicle cost is retrieved from a U.S. Government Accountability Office report which details the costs of the Antares, Atlas, Delta, Minotaur, Pegasus, and Ariane vehicle families (United States Government Accountability Office, 2017). The data for the Rocket Lab Electron launch system is taken directly from Rocket Lab (Lab, 2017). While the launch costs of SpaceX’s Falcon family rockets are taken directly from SpaceX (SpaceX, 2017). Where payloads have been redacted due to matters of national security, we omit these entities as there is no publicly available payload information. The amount of payload redacted does not exceed 5% of our total observations. Orbital Sciences is included when mentioning the firm Northrup Grumman as during the scope of this thesis it was absorbed.
5.1. Variables
Each of the five forces identified by Porter (2008) is typically made up of many variables which are ranked to describe which behaviors are most important in each factor. However, to
2 An unbalanced panel is when each entity has a different number of observations. While a balanced panel is when
quantitatively model what affects the intensity of competitiveness in the commercial launch market using the five forces framework we define five independent variables which closely relate to the five forces identified by Porter to see which factors are statistically significant and what their effects are.
Degree of competition (COMP)
Our measure of the dependent variable is market concentration, measured through the Herfindahl-Hirschman Index (HHI) by Hirschman (1945) and Herfindahl (1950). The concentration is based on the sum of the squared market shares of all firms in the market (Hirschman, 1945). We believe HHI to be a good measure for the degree of competition since it takes into account all the firms and their performed launches which provide a precise measure. It is also said that “when an industry is highly concentrated or dominated by one or a few firms, relative power will be stable and apparent to everyone” (Porter, 1980, p. 35). We use a count of individual launch entries to calculate HHI by summing the squares of each firm’s market share. Market share is calculated by having a rolling total for the scope of our study of the number of launches each firm has completed, and for the total number of launches. For each launch, the HHI is recalculated using the sum of a firm’s launches over the total number of launches.
To show an example, we will take the 96th through the 99th launches in our data set. The 96th
launch was performed by Ariane Space and was their 27th launch. The 97th launch was performed
by SpaceX and was their 13th launch. The 98th Launch was performed by NRIS and was their 14th
launch. The 99th launch was performed by ULA and was their 44th launch. The firm’s market share
for the 99th launch is then .2727 for Ariane Space, .1313 for SpaceX, .1414 for NRIS, and .4444
for ULA. These market shares are squared, then summed equating to an HHI of .31823.
Threat of Rivalry (RIV)
To model the threat of competitive rivalry on the degree of competition, we use the number of active firms. The number of firms varies between one and five, throughout the scope of our thesis. We define an active firm as one which has successfully launched a payload within 90 days of an individual launch entry, this reflects any competitors launching in the same time frame to reflect deadlines posed by future commercial or government contracts, and any seasonal changes in weather.
The date of each launch allows us to calculate the number of active firms. We specify a range of 90 days of the latest launch, any firms operating in this range take on a value of 1, firms that do not take on a value of zero. The number of unique firms operating in the 90-day range is summed to provide us with a tally of the number of active firms.
Threat of power of buyers (BUY)
The number of orders a customer has placed at the time of a specific launch is our proxy for the power of buyers. This follows the publishing’s of Porter, who states that a buyer group is powerful if it is concentrated or purchases in large volumes. (Porter, 1979). We use a count of unique customer orders to model buyer power, even though there can be differences between demand and consumer power. This allows us to focus on creating a quantitative study of competitiveness using Porter’s Five Forces analysis.
Threat of power of suppliers (SUP)
We measure the degree of supplier power in the market through supply-side economies of scale, supply being the launch site used. This is because powerful suppliers capture more of the value for themselves by charging higher prices (Porter, 2008). There are a total of four different launch sites used by commercial launch firms, the sites being Mahia, New Zealand, French New Guiana, Cape Canaveral, Florida, and Boca Chica, Texas. Vehicle operators pay rent to use these launch sites, however, data is not available regarding the specific amount paid in rent. The number of times vehicle operators use a specific launch site is summed on a per-launch basis which provides a measure of supplier power in the case of the commercial launch market.
Threat of substitutes (SUBS)
The threat of Substitutes is measured through a payload mass advantage variable. The variable measures the launch vehicles’ payload capabilities compared to the industry average, these capabilities being the total mass of the payload. Following Porter’s logic, we use this to measure the threat of substitutes because differences in payload capabilities will directly affect the price-performance trade-off as the price-performance quota customers look at in commercial launch vehicles is the measure of payload capabilities (Porter, 1979). We calculate this variable by taking the average payload mass for the market on a per launch basis and comparing it to the payload mass
of the vehicle being launched, this provides launch-specific data for advantages of payload capabilities. A negative payload mass shows that the specified launch is below the industry average for payload mass, the less capable a rocket is for lifting payloads, the less of a threat it is as a substitute.
Threat of new entrants (ENTR)
To measure the threat of new entrants on market competitiveness we take the proportion of government payloads to commercial payloads. Hertzfeld (2007, p. 213) argues that, “governments may opt to purchase space services directly from domestic commercial private firms…they may be precluded by regulation or contract from offering services to customers in the general marketplace.”. More importantly, this is in link with Hubbard’s (2013) statement that the competitiveness of the commercial launch market is directly tied to the proportion of government customers as an industry in which less than 50% of the market customers are non-government. With our data set, we have labeled each of the launches that contain government payloads or do not. We can calculate the government payload ratio by dividing the number of government payload’s serviced by the commercial launch industry up until the specific launch with the total number of payloads serviced.
5.2. Descriptive statistics
Table 2 presents the descriptive statistics of our data included in our analysis with their mean, standard deviation, minimum and maximum values. For all launches between the years 2010 and 2020, the data shows that the commercial launch industry is highly concentrated with a mean value of 0.32 for the variable COMP. This is expected since this is a relatively new industry in the New Space economy with only five firms. This observation is consistent with what Weinzierl (2018) argued about the outcome of a privatized space sector wherein it would feature an industry with a high degree of concentration due to the characteristics of the market segment they operate in involves technological factors that are key answers to its economic development. The average number of active firms (RIV) is 3.39 which shows that overall, commercial launch providers are relatively active. We also observe that the number of supplier orders (SUP) is relatively high in relation to the number of customer orders (BUY) on average. The values could mean that the commercial launch market is more dependent on suppliers for components that are used in launch
activities than customer’s orders. According to Porter (1980), suppliers have more power than buyers if buyers do not have a negotiating advantage relative to industry participants which could argue the values of their means. When it comes to the current launch capabilities in terms of payload mass in the commercial launch market, it is can be seen that the market has a payload mass advantage on average with a value of 512.42 for the variable SUBS which is higher than the industry average since it has a positive value. The government contains more than 50% of the payload proportion for all the launches taken in this analysis.
Table 2 Descriptive statistics
Statistic Mean Std. Dev. Minimum Maximum
HHI (COMP) 0.32 0.08 0.27 1.00
Number of active firms
(RIV) 3.39 0.80 1.00 5.00 Number of customer orders (BUY) 28.00 33.62 1.00 115.00 Number of supplier orders (SUP) 46.97 30.89 1.00 113.00 Payload mass advantage (SUBS) 512.41 3944.35 -5864.45 15440.90 Government proportion (ENTR) 51.51 8.33 40.49 100.00
Table 3 presents the correlation between the variables included in our analysis. Overall, one can distinctly see a negative correlation between our dependent variable, COMP, with the rest of the independent variables except for one, the ENTR variable, which also shows a slightly high correlation with a value of 0.87. Nevertheless, the direction of the relationship is in line with our sub-hypotheses above in terms of the expected effects on the degree of competition.
Table 3 Correlation matrix
Variables COMP RIV BUY SUP SUBS ENTR
COMP 1.00 RIV -0.25 1.00 BUY -0.19 0.05 1.00 SUP -0.41 0.15 0.62 1.00 SUBS -0.04 -0.02 -0.14 0.15 1.00 ENTR 0.87 -0.36 -0.22 -0.55 -0.11 1.00
6. Empirical Model
To the best of our knowledge, no one has quantitively applied Porter’s framework which explains the scarcity of panel data studies providing recommendations for econometric methods and models for this framework.
Based on our data set and objectives on this thesis, we are running a pooled OLS Regression (OLS). With this method, we pool together all 284 observations assuming that the regression coefficients and the intercept to be constant for all which means no distinction between the cross-sectional data or in this case the launches performed by each firm. We are doing this for one critical reason. The time unit of our observations is on a daily basis since it contains the exact date of each launch performed by the firms and for days where no launches occur our data set contains zeros. Consequently, this makes our data unbalanced thus doing a Pooled OLS regression disregards this. We pool all the observations together and estimate a regression that neglects the nature of the panel data and thus the days that do not have a launch. Since we are interested in seeing the statistical significance of the five proxies selected to represent Porter’s five forces, their overall significance, and not necessarily their estimated effects, we believe that this method is suitable. To test our main and sub-hypotheses, the following model is estimated (1).
Where i captures the launches performed by a firm and t captures the exact launch date on a daily basis from the years 2010 to 2020. COMP is the variable for the degree of competition which is our dependent variable measured by the HHI. RIV is the variable for the threat of rivalry and is proxied by the number of active firms in a range of 90-days. BUY is the variable for the threat of the power of buyers and is proxied by the number of performed orders a customer has placed at the time of a specific launch. SUP is the variable for the threat of the power of suppliers and is proxied by the number of times a launch operator has launched at a specific launch site at the time of a specific launch. SUBS is the variable for the threat of substitutes and is proxied by the mass payload advantage that measures the launch vehicle's payload capabilities compared to the industry average.
ENTR is the variable for the threat of new entry and is proxied by the proportion of government
payload relative to the overall payload. The variable u is our error term.
With this model (1), we are going to test the overall significance of quantitatively using Porter’s five forces framework to describe competitiveness in the commercial launch market with the help of the F-test which will tell us whether the including independent variables better describes the intensity of competitiveness. We also test the strength of each of the five forces used in the framework through the sign of the coefficients to describe which forces are high or low in threat when affecting competitiveness within the framework. For example, if the threat of rivalry (RIV) negatively affects our dependent variable HHI which we expect in this thesis, this means that the force, the threat of rivalry presents a high threat since it reduces the HHI and intensifies the degree of competition. The abbreviations, definitions, and expected signs of Eq. (1) are shown in table 4.
Table 4 Abbreviations, definitions, and expected effect of variables
Abbreviations Definitions
Expected signs
COMP Degree of competition measured by HHI
Dependent variable RIV
The threat competitive rivalry proxied by
the number of active firms Negative
BUY
The threat of buyer power proxied by No.
of performed orders by customers Negative
SUP
The threat of supplier power proxied by number of times a specific launch site is
used Negative
ENTR
The threat of new entrants proxied by
government payload ratio Positive
SUBS
The threat of substitutes proxied by
7. Empirical results
The empirical results proceed in three steps. In Subsection 7.1, we present the results of the pooled OLS regression for our chosen proxies for Porter’s five forces. In Subsection 7.2, we do a robustness test by modifying our regression model (1) through estimating a reduced model and a fixed firm-specific model to check if there are large changes in the results when testing our main and sub-hypotheses. In Subsection 7.3, we discuss the results and from that analyze strategic positioning within the commercial launch market.
7.1. Regression results Pooled OLS
Table 5 presents the results of the pooled OLS regression for our chosen proxies for Porter’s five forces. We find that there is a negative relationship between the threat of buyers (BUY) and the HHI (COMP) but is slightly significant. The sign of the coefficient is in line with our expectation and confirms hypothesis 2. Hence, the force of the threat of buyer power presents a high threat since HHI decreases when this variable increase, intensifying the degree of competition. We also find that there is a positive relationship between the HHI (COMP) and the rest of the remaining forces, the threat of rivalry (RIV), the threat of supplier power (SUP), threat of substitutes (SUBS), and the threat of entry (ENTR) wherein all show a high level of significance except for the threat of substitutes (SUBS) which is not significant. These four forces present a low threat since they increase the HHI which diminishes the degree of competition. More importantly, only the threat of entry (ENTR) satisfies our expectations and confirms hypothesis 4.
Furthermore, the regression results show a highly significant F-statistics which provide us with adequate reason to reject the null of our main hypothesis that the five proxies chosen for Porter's five forces to model competitiveness have no effect on the market competitiveness. Hence, our theoretical model (1) is significant enough to explain the degree of competition in the Commercial Launch industry.
7.2. Robustness test
To examine the reliability and validity of our results, it is necessary to conduct robustness checks. One issue with the pooled OLS model performed in Section 7.1 is that it neglects the heterogeneity of the cross-section data included in the analysis which means there is a risk that the regressors might be correlated with the error term since the uniqueness of the cross-section data is subsumed in there. Furthermore, we might have proxies that are not ideal for a specific force simply
due to data availability. Particularly, the proxies for the threat of buyers, threat of suppliers, and threat of entry could be replaced with more accurate proxies if data were obtainable.
Therefore, in this section, we run a reduced model with only the threat of rivalry (RIV) and the threat of substitutes (SUBS) since we strongly believe that these are the only two variables that contain proxies that are ideal for their respective forces. The threat of rivalry is typically measured by the number of firms in a market. When a market only contains five firms over a ten-year period, with long gaps between launches we believe that the number of active firms accurately represents the threat of competitive rivalry. Launch vehicle's primary performance metric is their lifting capabilities therefore having a payload mass advantage directly shows their performance relative to the industry. This leads us to believe that the proxy chosen for the threat of substitutes is accurate. In addition to the reduced model, we will also run a fixed firm effect model with the help of firm dummies to see whether firm effects provide evidence that can only be produced by firm-specific characteristics. Estimating these two modified models and checking for deviations from previous results will help us validate our results.
Pooled OLS reduced model and Fixed effect model (firm effect)
Results presented in Table 5 by running a pooled OLS reduced model and fixed effect model for firm effect give us a slightly different outcome compared to the original pooled OLS model (1). Starting with the reduced model, we find a highly significant negative relationship between our dependent variable HHI (COMP) and the threat of rivalry (RIV) which in this case satisfies our expectation and confirms hypothesis 1. This means that the threat of rivalry presents a high threat as it reduces the HHI and intensifies the degree of competition which contrasts with the previous results for this force. We also find a negative relationship between the HHI (COMP) and the threat of substitutes (SUBS) which satisfies our expectation, but it is still not statistically significant. The results obtained from this restricted model put emphasis on including all the control variables (forces) to capture the overall picture. However, this is difficult since better proxies exist for some of the forces than for others.
Based on the results from the fixed firm effect model, we find that the threat of buyers and threat of new entrants still satisfies our expectations, and the sign of their coefficients does not change. However, we see the increased significance for all the variables, with only one being slightly significant which is the threat of substitutes (SUBS). This shows that there might be effects
that could only be explained by firm-specific characteristics in explaining the competitiveness in the Commercial launch market. Nevertheless, the results seem to be robust. Both the modified models still present a highly significant F-test.
Table 5 Regression results
Dependent variable: COMP
Pooled OLS Pooled OLS Fixed effect model
reduced model firm effects
RIV 0.008248** -0.026402*** 0.006714** (0.003204) (0.005972) (0.002994) BUY -0.000186* -0.000208** (9.72E-05) (9.82E-05) SUP 0.000397*** 0.000968*** (0.000123) (0.000141)
SUBS 7.39E-07 -1.03E-06 1.15E-06*
(6.44E-07) (1.21E-06) (6.93E-07)
ENTR 0.009635*** 0.011800***
(0.000371) (0.000459)
CONSTANT -0.216337*** 0.411765*** -0.367247***
(0.027310) (0.020827) (0.033699)
Firm dummies NO NO YES
Observations 284 284 284
DW-stat 0.215504 0.132763 0.272657
𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅$ 0.766966 0.060161 0.805250
F Statistic 187.2835*** 10.05775*** 131.0160***
note: *** denotes significance at 1%, ** denotes significance at 5% and * denotes significance at 10% Standard errors in parentheses
7.3. Discussion
Across both the pooled OLS and firm fixed effect models the threat of buyers acts the strongest in affecting market competitiveness due to its negative relationship with market concentration. We believe that the threat of powerful buyers acts the strongest because the market size is small, which leads to increased relative importance of each buyer order. This finding is synonymous to Porter’s finding that the power of buyers is the strongest force in the commercial
airline industry due to the large orders by airlines for aircraft (Porter, 2008). Considering that the demand for national security launches will decline and that the global satellite industry growth rate has also been downward trending the global demand for commercial launches will most likely stay between 25 and 30 launches for the foreseeable future (Moon, 2018; Satellite Industry Association, 2020). Without increased demand for commercial launches, we believe that the threat of buyers will continue to act as the strongest force in affecting competitiveness. The commercial launch market is a relatively small part of the overall global space economy which can be the reason why the threat of competitive rivalry is weak due to its positive relationship with the market concentration. Even though the firms are relatively active, Porter (2008) states that the intensity of rivalry is high when competitors are numerous. This is not the case for this market with only five commercial firms. The threat for new entrants is weak in the commercial launch market mainly because of the characteristics of the market structure that this industry presents. As we have described it in Section 2, an oligopoly market has high barriers to entry because of its high fixed costs. In addition, this is in line with Hertzfeld’s (2007) description of the industry which due to the high fixed cost requires funds from the government just to operate. The threat of suppliers is shown as a weak force as the proxy for the threat of suppliers which we reason is a result of commercial launch firms producing many of the components for launch vehicles in-house and that material suppliers are numerous and supply many other industries.
By applying Porter's five forces framework to better understand competitiveness in the commercial launch market we do find limitations in data capture and the correctness of modeling our proxies. While in the pooled OLS and firm fixed effects model the threat of the power of buyers is the strongest acting force, it is not included in the reduced model. This is because our proxy for the power of buyers does not directly model demand, rather it models demand which has been fulfilled. We cannot perfectly model demand because information regarding buyer orders is not available. Our proxy variable measuring the power of buyers is the number of launches completed for a customer because the total number of orders a buyer has placed is unknown. If more information were available regarding the buyers of commercial launch services, we recommend that the proxy for the power of buyers be replaced by the number of orders placed by buyers to better show demand, or by buyer profitability. For the threat of suppliers, we chose supplier volume in the form of launch site usage, however, while this is an important aspect in supplying launches,
it does not describe the supplier power in the production of launch vehicles which is how the threat of powerful suppliers is typically measured. If firms published information regarding material suppliers a better proxy would then be the concentration of suppliers of materials and construction components for launch vehicles. We measure the threat of new entrants as the proportion of government payloads to signify contracts and subsidies received by firms. This measures the government volume which firms service. However, a better proxy would be the aggregate sum of government payments at specific launch date. This is not available, if it were, we would recommend that the proxy for the threat of new entrants be changed to reflect the new information.
We believe that the firm-specific characteristics which affect market competitiveness are the firm's business objectives, and marketing targets. The business objectives of the firms analyzed in this thesis are not uniform, however, some objectives are shared between two firms. Ariane Space and Rocket Lab have similar business objectives which are to open space access and to use space for a better life on Earth (Ariane Space, 2020) (Lab, 2017). NRIS and ULA also have similar business objectives which are to innovate, create new technologies, and deliver maximum capabilities with maximized cost efficiencies (United Launch Alliance, 2021) (Grumman, Northrup, 2021). SpaceX’s business objective stands alone in its goal to goal to “to revolutionize space technology, with the ultimate goal of enabling people to live on other planets” (Pereira, 2020). The firm's conduct is also reflected in its marketing and advertising. All firms except for ULA and NRIS actively advertised and marketed for commercial payloads while ULA and NRIS relied more heavily on the availability of government contracts. The firm's business goals, and consumer group targeting may help in creating a relationship between launch providers and buyers which then leads to buyer hesitancy in switching between firms.
From an economic viewpoint, the degree of market competition in the commercial launch industry is low which does not incentivize the efficient allocation of resources nor does it support innovation within the industry. This leaves the growth of the industry to technological progress which the past has shown to underperform many analyst’s expectation. The lack of support for innovation, and underperformance of technological progress forms a cycle of negative effects for the commercial launch industry. This is economically significant since as Yazici and Darici (2019) argued earlier, the transformation of the space economy will have inevitable effect on the socio-economic growth targets of the countries involved. Thus, policies that are aimed to develop a larger powerful economic competition should be a priority.
While applying Porter’s five forces framework helps us better understand competitiveness in this market, the framework itself comes with some limitations which may limit the application of our findings. The framework assumes a classic perfect market and a relatively static market structure; however, the commercial launch market is far from perfect and has seen radical change in just the last two decades (Essays, 2018).
8. Conclusion
The aim of this thesis was to provide a better understanding of competitiveness in the commercial launch market by applying Porter’s (2008) five forces framework and simultaneously correcting for the lamentations of Porter on the application of his framework. As well as filling the research gap found in describing the competitive behaviors in the commercial launch market, and from that, which forces act positively or negatively to influence industry competitiveness. Our research question of “Does applying Porter’s five forces analysis provide a better understanding
of competitiveness in the commercial launch industry?” was answered through the rejection of the
f-test which allows us to say that by quantitively applying Porter’s five forces framework our model better explains what affects competitiveness in the commercial launch market. We then found that the threat of powerful buyers acts as a strong force in affecting the competitiveness of the market. Porter (1979) states that the strongest forces are of the greatest importance in the formulation of strategy and a that a corporate strategist’s goal is to pose their company in such a way as to defend itself against said forces. The results of our research show that the threat of powerful buyers acts as the strongest force in the commercial launch industry and that without an increase in annual launches this is not subject to change. To defend against this threat, we recommend that firms seek to differentiate their product lines to focus on ride-sharing services instead of providing overly capable rockets, which then launch half empty. Another way to defend against powerful buyers can be to allocate warehouse space for the buyer near the production site of the launch vehicle to decrease payload transport times, which benefits the buyer, and to implement a form of a customer loyalty program to increase switching costs which helps defend commercial launch vehicle suppliers from the threat of powerful buyers. The development of increased ridesharing capabilities requires further research and development or launch vehicle fittings. The nature of having built to order launch vehicles decreases the applicability of renting warehouse space to buyers. The above-mentioned recommendations are only based on information used in this thesis and are not expert based propositions.
Further research should be conducted on this subject with more accurate proxy variables which may only be available to actors within the industry. From what we can tell this thesis is the first to provide a description of competitiveness within the commercial launch industry. With billions of dollars being spent by venture capitalists on the industry, maximizing profitability and creating a self-sustaining free market should be in all stakeholder’s best interest. We also encourage future
researchers to apply Porter’s five framework quantitatively in another industry with relatively easy data accessibility to gain strategic insights for attaining optimal position such as the bank, agriculture and construction industry. This will contribute to increasing the framework’s credibility in terms of quantitative analysis.
