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IN DEGREE PROJECT INDUSTRIAL MANAGEMENT,SECOND CYCLE, 30 CREDITS
, STOCKHOLM SWEDEN 2019
Capitalising on Big Data from SpaceHow Novel Data Utilisation Can Drive Business Model Innovation
MARIA BREMSTRÖM
SUSANNE STIPIC
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT
www.kth.se
Capitalising on Big Data from Space
How Novel Data Utilisation Can Drive Business Model
Innovation
Maria Bremstrom
Susanne Stipic
Master of Science Thesis TRITA-ITM-EX 2019:380
KTH Industrial Engineering and Management
Industrial Management
SE-100 44 STOCKHOLM
Kapitalisera pa stora datamangder
fran rymden
Hur nya satt att utnyttja data leder till innovation av
affarsmodeller
Maria Bremstrom
Susanne Stipic
Examensarbete TRITA-ITM-EX 2019:380
KTH Industriell teknik och management
Industriell ekonomi och organisation
SE-100 44 STOCKHOLM
Master of Science Thesis TRITA-ITM-EX 2019:380
Capitalising on Big Data from Space How Novel Data Utilisation Can Drive Business
Model Innovation
Maria Bremström
Susanne Stipic
Approved
2019-06-03 Examiner
Matti Kaulio Supervisor
Ebba Laurin Commissioner
Swedish Space Corporation Contact person
Tobias Roos
Abstract
Business model innovation has in recent year become more important for firms looking to gain competitive advantage on dynamic markets. Additionally, incorporating data into a firm’s business model has been shown to lead to improved performance. This development has led to interest in the connection between data utilisation and business model innovation.
This thesis provides an in-depth case study of a Swedish space firm active within the satellite industry. The firm operates within an increasingly dynamic market, and ongoing disruptions in the form of new market entrants and rapid technological advancements has led to a search for new business opportunities. As a result, novel ways of utilising the increased amounts of data from space are of significant importance. While the firm is still realising profits utilising their incumbent business model, the firm must simultaneously explore new business opportunities to avoid extinction.
The findings show that novel data utilisation, in the form of data processing, leads to business model innovation. Furthermore, the degree of business model transformation is dependent on how many of the business model's underlying elements are affected by data utilisation. Furthermore, the study concludes that a lack of trial-and-error learning impedes radical innovation efforts and hinders the development of ambidextrous capabilities within the firm. Lastly, the study finds a novel connection between the introduction of large-scale projects and improved ambidextrous capabilities.
Keywords: Business Model Innovation, Data-Driven Business Model Innovation, Organisational Ambidexterity, Satellite Data, Big Data
Examensarbete TRITA-ITM-EX 2019:380
Kapitalisera på stora datamängder från rymden
Hur nya sätt att utnyttja data leder till innovation av affärsmodeller
Maria Bremström
Susanne Stipic
Godkänt
2019-06-03
Examinator
Matti Kaulio
Handledare
Ebba Laurin Uppdragsgivare
Swedish Space Corporation Kontaktperson
Tobias Roos
Sammanfattning
Innovation av affärsmodeller har under senare år blivit alltmer viktigt för företag som vill uppnå ökad konkurrenskraft på dynamiska marknader. Vidare har det visat sig att företag som använder data för att förändra sin affärsmodell når bättre resultat än sina konkurrenter. Detta har lett till ett intresse för kopplingen mellan datautnyttjande och innovation av affärsmodeller.
Detta examensarbete består av en fallstudie av ett svenskt rymdföretag, som har del av sin verksamhet inom satellitbranschen. Företaget verkar på en alltmer dynamisk marknad, och pågående störningar i form av nya marknadsaktörer och tekniska framsteg har lett till att företaget nu måste söka efter nya affärsmöjligheter. Som ett resultat av detta blir nya sätt att använda de ökade mängderna data från rymden av stor betydelse. Fastän företaget fortfarande framgångsrikt nyttjar sin befintliga affärsmodell, måste företaget samtidigt undersöka nya affärsmöjligheter för att undvika att hamna efter marknadsutvecklingen.
Studiens resultat visar att nya sätt att använda data, i form av databehandling, leder till innovation av företagets affärsmodell. Dessutom beror graden av innovation på hur många av affärsmodellens underliggande byggstenar som påverkas av införandet av data. Studien drar vidare slutsatsen att en brist på lärande genom ’trial-and-error’ inom företaget hindrar radikala innovationsinsatser och leder till begränsade förutsättningar för att hantera organisatorisk ambidexteritet. Slutligen finner studien att storskaliga innovationsprojekt kan förbättra förutsättningarna för organisatorisk ambidexteritet.
Nyckelord: Affärsmodellsutveckling, innovation, datadriven affärsutveckling, organisatorisk ambidexteritet, satellitdata, big data
Table of Contents
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problematisation and Scientific Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.5 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Theoretical Framework 4
2.1 Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 Business Model Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Business Model Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Organisational Ambidexterity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Data Driven Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.5 Data from Satellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.5.1 Big Data as a Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5.2 Satellite Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3 Conceptual Framework 15
3.1 Establishing the Business Model Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.1.1 Innovation of a Business Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2 Dynamics of Data Utilisation and Ambidexterity . . . . . . . . . . . . . . . . . . . . . . . 17
4 Method 19
4.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2.1 Interview Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.4 Research Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.5 Research Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5 Case Study 24
5.1 Industry Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.2 Case Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
i
5.2.1 The ’Global Watch Center’ Project . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.2.2 Current Business Model of Satellite Management Services . . . . . . . . . . . . . . 27
5.2.3 Collection and Distribution of Satellite Data . . . . . . . . . . . . . . . . . . . . . 31
6 Findings and Analysis 32
6.1 Data Utilisation as a Driver for Business Model Innovation . . . . . . . . . . . . . . . . . 32
6.1.1 Data Processing as a Form of Utilisation . . . . . . . . . . . . . . . . . . . . . . . . 32
6.1.2 Potential of Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
6.1.3 Partnerships as an Approach to Face Changes . . . . . . . . . . . . . . . . . . . . . 34
6.1.4 Impact on the Current Business Model . . . . . . . . . . . . . . . . . . . . . . . . . 35
6.2 The Need for Organisational Ambidexterity . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.2.1 Innovation Management at SSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6.2.2 Innovation Projects at SaMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
6.2.3 Trial-and-Error Within the Organisation . . . . . . . . . . . . . . . . . . . . . . . . 39
6.2.4 Exploitation at the Expense of Exploration . . . . . . . . . . . . . . . . . . . . . . 40
6.2.5 Promoting a Culture of Innovation Using Large-Scale Projects . . . . . . . . . . . 41
7 Discussion 42
7.1 Data Utilisation as a Means to Reach Innovation . . . . . . . . . . . . . . . . . . . . . . . 42
7.1.1 Data Processing’s Transformational Effect on the Business Model . . . . . . . . . . 43
7.2 Balancing of Exploration and Exploitation . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
7.3 Answering the Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
7.4 Sustainability Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
8 Conclusions 48
8.1 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
References
Appendix A - List of Informants
Appendix B - Interview Protocol
ii
List of Figures
1 The Three Stages of a Business Model’s Journey. Christensen, Bartman and Van Bever
(2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Double Ambidexterity Framework. Adapted from Kaulio, Thoren and Rohrbeck (2017). . 10
3 Summary of the Business Model Elements. . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Framework Illustrating Organisational Response and its Corresponding Business Model. . 18
5 Translation of the Business Model Elements Outlined by Schuritz and Satzger (2016). . . 18
6 Current Business Model of SaMS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
7 Current Business Model of SaMS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
8 Low-Level Processing as an Organisational Response and its Corresponding Business
Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
9 High-Level Processing as an Organisational Response and its Corresponding Business
Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
10 SaMS’ Responses to Disruptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
11 Transformation of SaMS’ Business Model Over Time. . . . . . . . . . . . . . . . . . . . . 44
iii
List of Tables
1 Business Model Elements. Adapted from Hartmann et al. (2016). . . . . . . . . . . . . . . 4
2 Categorisation of Organisational Responses. Adapted from Kaulio, Thoren and Rohrbeck
(2017). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Patterns of Data-Infused Business Models. Adapted from Schuritz and Satzger (2016). . . 12
4 Levels of Data Processing. Adapted from Parkinson, Ward and King (2006). . . . . . . . 14
5 Case Study Tactics for Three Relevant Tests. Adapted from Yin (1994). . . . . . . . . . . 22
iv
Acknowledgements
Conducting this Master’s thesis has indeed been a exceptional journey, filled with intellectual challenges
where we had the opportunity to immerse ourselves in highly interesting areas of research. This research is
the final and concluding step of our Master of Science degree in Industrial Engineering and Management,
and it would not have been possible without the help and support from several people.
First, we would like to sincerely thank our supervisor at the Royal Institute of Technology, Ph.D. Ebba
Laurin. You stood by our side throughout the whole process, highly engaged in everything from thor-
oughly reading our thesis to engaging in long discussions with us. Your guidance truly challenged us in
a positive way, and your profound academic support enabled us to improve our research.
We would also like to express our gratefulness to our supervisors at the investigated company; Tobias
Roos and Stefan Gustafsson. You have given us support, shown immense interest in our work and we
thank you for the opportunity to explore the exciting space industry. Our discussions and your insights
considerably assisted us in arriving at our findings and conclusions. We also thank all the informants who
dedicated their time and efforts, together with other employees who made us feel welcome and guided us
through practical aspects. Without you, this thesis would simply not have been possible.
Finally, we would also like to acknowledge our examiner, Associate Professor Matti Kaulio, together with
our peers in the seminar group. Your participation in discussions and insights contributed to our research
and guided us along the way.
Maria Bremstrom and Susanne Stipic
Stockholm, June 2019
v
1 Introduction
In this section, the foundation of the thesis is presented. A brief background on the subject is given,
followed by a problematisation which leads into the purpose and research questions of thesis. The delimi-
tations of the thesis are also outlined.
1.1 BackgroundIn today’s dynamic and fast changing markets, firms must stay competitive to survive (Wirtz, Gottel and
Daiser 2016). A way of achieving competitiveness is to innovate the firm’s business model, which in recent
years has gained more attention (Wirtz, Gottel and Daiser 2016). Firms engage less and less in process or
product innovation due to their time consuming and resource intensive nature, and are increasingly turn-
ing to business model innovation as an alternative or complement (Amit and Zott 2012). When exploring
business model innovation, organisational ambidexterity comes into play. Meaning, an organisation has to
manage evolutionary and revolutionary change at the same time to ensure long term survival and success
(Kaulio, Thoren and Rohrbeck 2017). A common explanation for why organisations fail to innovate is
the lack of organisational ambidexterity (Tushman and O’Reilly 1996). Furthermore, ambidexterity also
brings to light the interplay between business model innovation, technological innovation, exploration
and exploitation (Kaulio, Thoren and Rohrbeck 2017).
Many recent technological innovations are focused on utilising data. Analysing and utilising data is
becoming increasingly vital for firms to stay competitive and to survive in the long-term (Brownlow et al.
2015; Hartmann et al. 2016; LaValle et al. 2011). Hunke et al. (2017) highlight the opportunity for firms
to make data the central value offering of the firm’s business model. By enriching their business model
with data, firms are more likely to stay competitive within their industry (LaValle et al. 2011). The main
focus is on utilisation of large data sets, commonly referred to as big data (Gandomi and Haider 2015),
with data analysis playing an important role in achieving competitive advantage (Morabito 2015). The
space industry is characterised by advanced technology and is operating in a data-heavy environment,
with increased big data generation. In line with aforementioned, data utilisation is thus an important
part of future business (Hunke et al. 2017). Even industries without data-heavy settings are realising
the potential of data utilisation (Schuritz and Satzger 2016). Moreover, the satellite industry is facing
an increasing amount of big data, contributing to data utilisation being a major market trend (Soille,
Loekken and Albani 2019).
Today the space industry is not only about races to the Moon, pioneering exploration and new discover-
ies; it is also about contributing to technological development and finding solutions to global challenges.
Thanks to emerging technologies, the application areas for space-enabled technology are rapidly increas-
ing and becoming part of people’s every day life. It is a new space era, characterised by high-speed
change which presents new business opportunities. Traditionally, the space industry was characterised
by large government programs, funded by governmental and institutional capital, resulting in a rather
slow moving industry with high entry barriers. Now, the new era brings forth innovative technologies
and new commercial entrants, leading to a market shift towards a commercial and competitive global
market with lower entry barriers. This is driving the market towards lower prices, new applications and
a higher speed of change. In recent years, more and more satellites have been launched into orbit. With
the development of a new generation of small satellites, the cost of building and launching satellites has
dropped, leading to a growing commercial interest and new actors entering the market (Simonis 2019).
The development is having a disruptive impact on the satellite management service sector, resulting in
1
diminishing profit margins on satellite management services. New commercial customers, often called
’new space’, are pressing prices downwards which is affecting pricing within the sector as a whole. To
offer services within the satellite industry is thereby becoming more challenging. The incumbents within
the sector are characterised by their value offering, focused on providing high-quality services. There
is currently little differentiation amongst the incumbents, with the main distinction between incumbent
firms being the added services that can be offered. However, with the industry-wide disruption, the
sector of satellite management services is also facing new entrances and increased competition. In 2018, a
capital-heavy IT firm announced plans to build their own ground station network. In light of this devel-
opment, incumbent firms offering satellite management services need to ensure their competitiveness by
exploring new business opportunities. Emerging technologies for processing satellite data into a variety
of different products and services open up possibilities to reach new markets. Thus, there is an emerging
opportunity within the satellite management sector to move up in the value chain by utilising satellite
data to expand the current value offering.
1.2 Problematisation and Scientific ContributionThe changes in the space industry are affecting even established technology firms with viable business
models, within all areas of the industry. The satellite management sector, which is characterised by
long-time players who operate using established business models, is highly affected by the emerging ’new
space’ customer segment and the prices being pressed downwards. Thus, within the sector, existing
business models need to stay competitive and new business areas must emerge in order to not risk firm
obsoletion in the long term. By engaging in business model innovation, the shifting market can be met and
new business opportunities seized. Additionally, within both the research community and the satellite
industry, there is a novel area of data utilisation receiving increased attention. Innovating a business
model by infusing data, where data is seen as the main resource, is a way for the firm to stay competitive.
A major trend in the satellite industry is data utilisation in the form of satellite data processing, which
makes the area suitable to investigate as a way of innovating an existing business model within the
satellite management industry. To succeed with such an endeavour, the existing business model must
stay operative while the firm simultaneously explores new business model alternatives that incorporate
data utilisation.
Within the literature, there is a noticeable research gap regarding data driven business models, due to
its novelty as a research area. In particular, there is a lack of in-depth case studies regarding data driven
business models and the transition from an existing business model into a data driven one. Moreover, there
is a great deal of research done regarding the space industry and its technological aspects. However, due to
the industry previously being dominated by governmental agencies, there is little research connecting the
technological and business aspects of the industry. For instance, no research connecting business models
with satellite data has yet been conducted within the satellite industry. Due to the increasing importance
of data within the industry, research regarding data driven business models and their adaptation to
satellite data is of high relevance. This area therefore needs to be examined to establish a link between
business models and satellite data, furthering a academic discourse on the subject.
1.3 PurposeThe purpose of this thesis is to explore how novel ways of data utilisation can drive business model inno-
vation, and investigate how a space firm can exploit their incumbent business model while simultaneously
exploring new alternatives.
2
1.4 Research QuestionsTo fulfil the stated purpose, a main research question, as well as two sub-questions, are formulated.
MRQ: How can novel data utilisation drive business model innovation for an established
technology firm still reaping benefits from its incumbent business model?
SQ1: How does novel data utilisation, in the form of data processing, contribute to business
model innovation?
SQ2: How can an incumbent space firm explore new business models while simultaneously
exploiting their existing business model?
1.5 DelimitationsThe thesis will only explore implementation from a business model perspective, and not address the
change management perspective on implementing new business practices. The produced insights will
be applicable on other industries, but only the satellite data industry will be investigated. The thesis is
limited to an embedded study at SSC, with emphasis on the Satellite Management Services business unit,
one out of total three units. However, empirical data has also been obtained from other units, to enable
a deeper understanding. Finally, the thesis will only investigate the firm from an internal perspective,
not taking into account the customer perspective.
3
2 Theoretical Framework
In this section, an in-depth theoretical background of business models, as well as business model innovation
and ambidexterity, is presented. Use of data, in relation to business models, is also outlined.
2.1 Business ModelsWhen exploring the term ’business model’, it becomes noticeable that a commonly accepted definition
does not exist. Originally associated with web-based firms, the increased importance of information
and communication technology (ICT) for other types of firms has lead to the business model concept
becoming more widespread (Morris, Schindehutte and Allen 2005; DaSilva and Trkman 2014). Several
papers have concluded the lack of a commonly accepted definition of what a business model is (Morris,
Schindehutte and Allen 2005; Huarng 2013; DaSilva and Trkman 2014; Zott, Amit and Massa 2011), with
Zott, Amit and Massa (2011) noting that because literature on the subject is produced within separate
fields, it hinders the development of cumulative research on the topic of business models. Out of the
103 publications on business models studied by Zott, Amit and Massa (2011), 37 % did not provide
any definition of the term ’business model’ at all, which suggests that many believe the term to be
self-explanatory. Nevertheless, several researchers have proposed frameworks aimed at identifying and
describing the components of a business model (cf. Wirtz, Gottel and Daiser (2016)).
2.1.1 Business Model FrameworksEven though business model frameworks have different perspectives on the components of a business
model, there are still several common denominators. In Table 1, common elements from eight frameworks
are shown and later described in more detail.
Business Model Element
Value Customer/market Cost Model Revenue Model Activities Resources Others
Chesbrough &
Rosenbloom (2002)D D D D
Value chain,
network,
strategy
Hedman &
Kalling (2003)D D D D Competitors,
supply
Mitchell &
Coles (2003)D D D D Pricing
Morris et al. &
(2005)D D D D D Internal advantage,
ambition
Zott & Amit
(2010)D D Design themes
Teece (2010) D D D D Strategy filter
Osterwalder &
Pigneur (2010)D D D D D D
Relationships,
channels,
partnerships
Mason &
Spring (2011)D D D Technology,
architecture
Table 1: Business Model Elements. Adapted from Hartmann et al. (2016).
One of the early business model frameworks is proposed by Chesbrough and Rosenbloom (2002), who
define a business model as performing six functions; articulating a value proposition, identifying market
4
segments, defining the firm’s value chain, estimating the firm’s cost structure and profit potential, identi-
fying the firm’s place within the value network, and formulating the firm’s strategy. The business model
is developed by mapping out these functions in the described order.
Hedman and Kalling (2003) study previous research on business and strategy and build upon their findings
to develop a framework consisting of six components. These components are customers, competitors,
value offering, firm activities and organisation, resources, and supply of input from the capital and
labour market. A seventh ’longitudinal process’ component that focuses on changes over time is also
added to ensure that the framework is adapted for dynamic nature of a firm.
Mitchell and Coles (2003), when looking into business model innovation, define the business model as
consisting of five distinct elements. The first one is the ’who’, as in who are the customer that the firm
serves. The second is the ’what’, meaning what value proposition the firm is offering. The third is the
’where’, meaning where geographically is the firm operating. The fourth is the ’how’, as in how will the
firm deliver its value (through what activities). The fifth and final is ’how much’, as in how much will it
cost for the customer.
Morris, Schindehutte and Allen (2005) develop a framework that is divided into three levels, starting
from a broad perspective and becoming increasingly firm-specific. At the foundation level, the basic
decisions for the firm are outlined. Morris, Schindehutte and Allen (2005) identify six components that
are needed to produce this set of decisions; value creation, identification of customers, internal sources of
advantages, positioning in the marketplace, economic model, and ambition of the entrepreneur. At the
proprietary level, the firm must focus on finding innovative ways of achieving the basic decisions outlined
in the previous level. It is at this level the firm creates advantages that cannot be replicated, as the
proprietary level is strategy-specific and builds upon creating unique combinations of the variables at the
foundation level. At the rules level, a set of rules or guidelines are defined that will guide the strategic
decisions and actions of the entire firm. These rules will ensure that all strategic decisions are linked to
the foundation and proprietary level.
A concept called Business Model Canvas (BMC) was first introduced by Osterwalder and Pigneur (2010),
and is derived from Osterwalder’s previous research on business models. Here, the business model is de-
veloped further which results in a tool promoting visual thinking when creating or mapping business
models. The tool consists of nine building blocks; value proposition, customer segments, customer rela-
tionships, channels, revenue streams, cost structures, key activities, key resources and key partnerships.
It was created with the intent of providing a standardised approach for designing or mapping business
models and resulted in a comprehensive framework. According to Cosenz (2017), the BMC has been
widely recommended in recent business modelling literature, as well as by academic incubators and ven-
ture capital associations worldwide. The main reason for the BMC framework’s popularity, Osterwalder
and Pigneur (2010) and Sort and Nielsen (2018) argue, is that it provides a clearer understanding of
how a firm creates value. Furthermore, Osterwalder and Pigneur (2010) claim that the concept has been
tested and used around the world, for instance by organisations like IBM, Ericsson and Deloitte. Zott
and Amit (2010) describe business models from an activity system perspective, where the business model
is seen as an activity system, and the main objective to generate value by taking advantage of a business
opportunity. An activity is defined as using resources, such as human or capital resources, to achieve
value creation. The term activity system is defined as a ’system of independent activities that transcends
the focal firm and spans its boundaries’ (Zott and Amit 2010, p. 217). To design an activity system, two
5
sets of parameters need to be analysed. The first set is called design elements, which consists of con-
tent (selection of activities to be performed), structure (how activities are linked and their importance)
and governance (who will perform the activities). The second set of parameters is called design themes,
which are configurations of the design elements used for identifying what drives value creation within the
activity system.
According to Teece (2010, p. 179), a business model ’articulates the logic, the data and other evidence
that support a value proposition for the customer, and a viable structure of revenues and costs for the
enterprise delivering that value’. Five elements of business model design are outlined. When combined,
these elements will lead to value creation for the firm’s customers, which will in turn lead to payments
that are converted into profits. The five elements are: identification of benefit for customers, market
segmentation, confirmation of available revenue streams, design of value-capturing mechanisms, and
selection of technologies and features to be embedded in the product or service. One key aspect of
creating a business model that has a sustainable competitive advantage, according to Teece (2010), is
strategic analysis. A business model on its own will not be enough to create competitive advantage, but
when coupled with firm-specific strategic analysis, the firm’s activities becomes hard to imitate.
Mason and Spring (2011) examine the concept of business models, analysing previous literature on the
subject to reach a conclusion regarding the core elements of a business model. The underlying under-
standing of what a business models is can be said to be a description of the way a particular business
work. Mason and Spring (2011) argue that the value of a business model originates from capturing
actions and the connections between them, which provides a shared understanding of the firm’s actions.
The business model framework that Mason and Spring (2011) arrive at consists of three core elements.
The first element is technology, in the form of a product, process, core or infrastructure, and its delivery
and management. The second element is market offering, i.e., what really is offered to the customers and
how it is offered. The offering can be in the form of artefacts, activities or access. The third element is
network architecture, in the form of markets and standards, transactions, capabilities and relationships.
Hence, the network of suppliers and buyers that make the market offering possible.
2.2 Business Model InnovationIntensified competition on the global market has lead to a growing interest into how firms can stay
competitive in dynamic, fast-changing markets (Wirtz, Gottel and Daiser 2016). The notion of innovating
a firm’s business model to adapt to shifting market conditions has therefore gained prominence in recent
years (Wirtz, Gottel and Daiser 2016). Using examples of technology invented at Xerox that the company
was unable to utilise, but later became successful spin-offs, Chesbrough and Rosenbloom (2002) provide
one of the earliest links between innovation and the business model concept, concluding that a viable
business model is crucial for extracting value from technological innovations. Several authors has since
demonstrated the connection between business model innovation and superior performance, and shown
that successful business model innovation can be linked to sustainable competitive advantages (Amit and
Zott 2012; Casadesus-Masanell and Zhu 2013; Mitchell and Coles 2003).
The novelty of business model innovation means that consensus on the phenomenon is still lacking,
precipitating considerable heterogeneity of the concept’s definition in published literature (Wirtz, Gottel
and Daiser 2016). One description proposed by Amit and Zott (2012) builds upon their activity-based
view of the business model (presented in the previous section), conceptualising business model innovation
as changes made to one or more of the business model’s core elements. Mitchell and Coles (2003) instead
6
define business model innovation as the substitution of a majority of the current business model elements
by completely novel ones, dubbing this ’business model replacement’. Gambardella and McGahan (2010)
choose to conceptualise business model innovation as the adoption of new ways to commercialise the
firm’s fundamental assets, resulting in their continued relevance.
Chesbrough (2010) highlights one aspect of the challenges associated with business model innovation:
many times, managers simply do not know what the right business model for their firm is, and are
unsure of how to find a suitable model. When a firm strives for business model innovation, Chesbrough
(2010) therefore suggests mapping out the business using frameworks such as the business model canvas
proposed by Osterwalder (2004). By mapping out the current business model components, the firm can
more easily construct experiments to test new business models and ideas. These experiments should be
designed to promote cumulative learning within the organisation, a notion reiterated by Kaulio, Thoren
and Rohrbeck (2017) and Sosna, Trevinyo-Rodriguez and Velamuri (2010). In an extensive case study,
Sosna, Trevinyo-Rodriguez and Velamuri (2010) illustrate how a trial-and-error approach to business
model innovation leads to sustainable competitive advantage. Building an organisation that has a positive
view on experimentation and sees failure as an opportunity for learning creates favourable conditions for
successful business model innovation, according to Sosna, Trevinyo-Rodriguez and Velamuri (2010). The
authors also highlight that the firm’s response to early failure is an important factor in how the trial-
and-error process within the firm will subsequently develop.
Teece (2010) instead focus on strategy as a success factor for business model innovation, expressing that
business model innovation must take into account the overall company strategy in order to be successful.
To ensure a proper fit, the business model must pass through a ’strategic filter’. The notion of strategy
as an important differentiator is echoed by DaSilva and Trkman (2014) as well, who argue that a well-
planned corporate strategy enables dynamic capabilities, which in turn facilitates transformation of the
business model.
Christensen, Bartman and Van Bever (2016) investigate the interdependencies between business model
elements, arguing that business models are not designed for change. This view is further supported
by Amit and Zott (2012), who highlight interdependencies between business model elements as well
as interdependencies between the business model itself and the firm’s revenue model. Additionally,
Mitchell and Coles (2003) argue that companies that are efficient in reducing costs and streamlining
their current business model are less likely to achieve continuous business model innovation. Improving
upon the current business model means strengthening the interdependencies, which in turn leads to a
lower degree of flexibility since one element cannot be changed without influencing the entire organisation
(Christensen, Bartman and Van Bever 2016; Mitchell and Coles 2003). Sosna, Trevinyo-Rodriguez and
Velamuri (2010) however, note that business models are frequently revisited and revised by management,
and instead present a dynamic view on the business model as a product of continuous trial-and-error
learning.
According to Christensen, Bartman and Van Bever (2016), the business model of any organisation follows
a three-stage evolution, becoming less flexible for each stage (see figure 1). At the creation stage, the
emphasis is on value creation and the organisation focuses almost exclusively on customer needs. No
routine exists yet and the organisation usually consists of a small team that is in an exploratory mode. If
the team is successful in creating a value proposition, the next stage is sustaining innovation. Processes
are repeated and a routine begins to form, which means that the processes are no longer a flexible element.
7
However, there is still room for flexibility regarding how profits should be made. Finally, the organisation
evolves into the efficiency stage. By standardising processes the organisation can reduce costs and gain
efficiency, but by doing so the interdepencencies are strengthened and flexibility is lost. Since the focus
is on the bottom-line and generating high return on investment with as little risk as possible, managers
are unlikely to favour value creating innovation over cost-reduction and innovation to improve efficiency
(Christensen, Bartman and Van Bever 2016).
Figure 1: The Three Stages of a Business Model’s Journey. Christensen, Bartman and Van Bever (2016)
2.3 Organisational AmbidexterityThe topic of how firms can manage their incumbent business model while simultaneously exploring po-
tential new business models is the central theme of the academic field of organisational ambidexterity.
One early account of the phenomenon is provided by Tushman and O’Reilly (1996), who describe am-
bidexterity as the ability to compete in a mature market while simultaneously developing new products or
services. The former requires a focus on cost efficient and incremental innovation, while the latter instead
demands speed, flexibility and radical innovation. Gibson and Birkinshaw (2004) define organisational
ambidexterity as the ability to achieve alignment with today’s business demands while simultaneously
adapting to the future demands. Kaulio, Thoren and Rohrbeck (2017) describe organisational ambidex-
terity as an organisation managing evolutionary and revolutionary change at the same time to ensure
prolonged survival and success. Several researchers show that an organisation has to be ambidextrous in
order to be successful when existing in a dynamic environment, exploiting the current business oppor-
tunities while still exploring potential future opportunities (Gibson and Birkinshaw 2004; He and Wong
2004; Tushman and O’Reilly 1996).
Markides (2013) proposes that organisational ambidexterity literature can be leveraged to explore business
model innovation. However, there are differing views on how to achieve ambidexterity within a firm
(Markides 2013). Markides (2013) identifies three distinct solutions for the ambidexterity challenge;
spatial separation, temporal separation and contextual ambidexterity. Spatial separation means that the
firm separates innovation efforts that are radically different from the firm’s current operations, usually by
creating separate business units. Tushman and O’Reilly (1996) argue that an organisational architecture
consisting of small, autonomous business units is a vital aspect of achieving ambidexterity. Applying
the same logic on the business model field, Christensen, Bartman and Van Bever (2016) proposes that
new business models that do not align with the firm’s incumbent business model should be managed
separately in a new business unit to avoid conflict and create the conditions necessary to develop the new
business model. Temporal separation utilises a similar logic, but instead of separating conflicting activities
using organisational structure, the activities are performed at different points in time (Markides 2013).
Contextual ambidexterity was first proposed by Gibson and Birkinshaw (2004), describing it as a perceived
8
conflict between alignment and adaptability within an organisation. Opposing the ’trade-off’ view on
alignment and adaptability which leads to spatial or temporal separation, the authors instead argue that
ambidexterity is best achieved by designing a context within the firm that allows for individual judgement
of what activities to perform. Gibson and Birkinshaw (2004) present four dimensions (discipline, stretch,
support, and trust) that are vital in creating said context. Discipline refers to the creation of clear
standards, rapid feedback and consistency in rules. Stretch is the willingness of employees to strive
beyond the ’bare minimum’ and towards more ambitious goals, which is achieved by creating a sense of
individual contribution to a larger goal. Support refers to an environment where employees assist each
other and share resources. Trust is the sense of relying on the commitment of others, and is achieved by
creating a sense of ’fairness’ within the organisation. Too little discipline and stretch leads to a ’country
club’ environment, while a lack of trust and support leads to overworked and disillusioned employees, so
these four dimensions need to be properly balanced in order to create optimal conditions for achieving
ambidexterity (Gibson and Birkinshaw 2004). Gibson and Birkinshaw (2004) further criticises temporal
and spatial separation (referring to them collectively as ’structural separation’), arguing that they lead
to an increase in coordination costs.
Ambidexterity has also been used investigate the balance of exploration and exploration outside a firm-
level perspective. Holmqvist (2004) illustrates how the balance of exploration and exploitation within a
firm is connected to exploration and exploitation between organisations. Kauppila (2010) further shows
how interorganisational partnerships can aid firms in achieving radical innovation. Further advantages
of interorganisational partnerships are reduced risks, access to complimentary skills and knowledge, and
access to novel technologies or new markets (Mohr and Spekman 1994). However, Mohr and Spekman
(1994) also highlight negative consequences in the form of lost autonomy and information asymmetry.
Drawing upon previous research on ambidexterity and business model innovation, recent research has
highlighted the need for double ambidexterity (Kaulio, Thoren and Rohrbeck 2017; Tongur and Engwall
2014). The concept is described by Tongur and Engwall (2014, p. 534) as ’not just the ambidexterity
to simultaneously foster incremental and radical innovation, but also the ambidexterity to simultaneously
advance both technological and business model innovation’. Kaulio, Thoren and Rohrbeck (2017) further
the concept by investigating the interplay between business model innovation, technological innovation,
exploration and exploitation using a framework illustrated in figure 2.
9
Figure 2: Double Ambidexterity Framework. Adapted from Kaulio, Thoren and Rohrbeck (2017).
The framework is used to map organisational responses to disruptions over time, highlighting the vari-
ation in how a firm may react to different types of turbulence within their industry. Responses are
categorised using two principal dimensions, technology and business model innovation, which are divided
into exploitative and exploratory actions, respectively. To further nuance the categorisation, exploratory
responses are further divided into incremental and radical responses. However, it is important to note
that radical innovation is not necessarily preferable over incremental. Sorescu (2017) notes that many
successful business model innovations are not radical and disruptive in nature, and that incremental busi-
ness model innovation also provides potential for competitive advantages. The criteria of categorisation
are detailed in Table 2.
Technology Business Model
Exploitation
Closely related to existing
technology or minor adaption
of current technology
Minor adjustment or fine
tuning of one or several of
the business model’s elements.
Exploration (incremental)
Substantial change in technology,
significant improvement of existing
product, process, or service.
Significant improvement or
upgrade of existing product,
process, or service.
Exploration (radical)
Substantial change in technology,
unprecedented performance features
of product, process, or service, or
drastic changes that enable new
application domains.
Unprecedented performance
features of product, process,
or service, or drastic changes
that enable new application
domains.
Table 2: Categorisation of Organisational Responses. Adapted from Kaulio, Thoren and Rohrbeck (2017).
Kaulio, Thoren and Rohrbeck (2017) highlight that the categorisation should be based on the organ-
isation’s action, and not the outcome of said action. The authors also illustrate the importance of a
10
longitudinal approach when investigating business model innovation, arguing that diffusion of innovation
within the firm needs to be accounted for. The longitudinal approach allows for the identification of
three distinct response patterns that occur either due to a market disruption, a technology disruption
or a combination of the two. Kaulio, Thoren and Rohrbeck (2017) show that market disruptions pro-
voke exploitative responses, while technological disruptions instead induce exploratory responses. When
facing a combination of technological and market disruption, exploratory responses occurs mainly on
the business model axis, while the technological axis consists of mainly exploitative responses. Kaulio,
Thoren and Rohrbeck (2017) note that the focal firm becomes more willing to partner with other firms
and thereby open up their innovation process when facing a combined technological and market disrup-
tion, even if the focal firm previously has had a tradition of in-house value creation. Furthermore, their
work demonstrates the need for further research on the interplay between technological innovation and
business model innovation.
2.4 Data Driven Business ModelsAcquiring, analysing and applying various types of data is seen as increasingly vital for businesses to
not only stay competitive, but to survive in the long-term (Brownlow et al. 2015; Hartmann et al. 2016;
LaValle et al. 2011). This highlights the possibility to enrich existing business models with data utilisation
and moving towards making data the central value offering of the firm (Hunke et al. 2017).
Brynjolfsson, Hitt and Kim (2011) show a positive correlation between data driven practices and firm
performance, indicating that incorporating data utilisation into the firm’s current practices can improve
output and productivity. LaValle et al. (2011) show that firms who identify data utilisation as their main
source of differentiation are twice as likely to be top performers within their industry. Thus, there is
currently a strong focus on how firms can incorporate data into their businesses, with analysis of large
data sets becoming a focal point in this endeavour (Gandomi and Haider 2015). Hunke et al. (2017)
highlight that firms can stay ahead of competitors by using business models exploiting large volumes
of data. Additionally, giving data a more central role within an organisation may improve the value
creation of the firm, with data analytics playing an important role for firms aiming to stay ahead of their
competitors (Morabito 2015).
One of the main obstacles to incorporating data into a firm’s business is that firms are unsure of how to
utilise data for value-adding purposes (Hartmann et al. 2016; LaValle et al. 2011). Therefore, new models
focused on capturing the value of data are becoming increasingly important. These new business models,
frequently called ’data driven business models’, constitute a new field of research which still lacks widely
accepted definitions (Schuritz and Satzger 2016). Hartmann et al. (2016, p. 1385) choose to define a
data driven business model as ’a business model that relies on data as a key resource’. Morabito (2015,
p.65) uses a similar definition, characterising data driven business models as business models that ’rely
on big data to achieve a key value proposition’.
Schuritz and Satzger (2016) take a different stance, arguing that there is no such thing as a data driven
business model. Instead, incorporating data into the firm’s activities leads to a range of alternative
business model transformations, depending on what element of the business model that is affected. The
business model is condensed into three elements; value creation, value proposition and value capturing.
Value creation is described as the arrangement of activities, processes and resources needed to create
and deliver the value proposition to the firm’s customers. By enriching existing products or services,
streamlining current operations to reduce costs, or offering a new product or service, the firm’s value
11
creation is expanded (Schuritz and Satzger 2016). Value proposition is the actual value offered to the
firm’s customers or stakeholders. Value capturing is how the firm turns the value proposition into mon-
etary value for the firm itself. It can be performed by identifying novel revenue streams or reaching new
customer segments. This creates five potential patterns of data infusion, presented in Table 3.
Pattern Value Creation Value Proposition Value Capturing
I: Data-Infused Value
CreationD
II: Data-Infused Value
CapturingD
III: Data-Infused Value
Proposition via Value CreationD D
IV: Data-Infused Value
Proposition via Value CapturingD D
V: New Data-Infused Business
Model (DiBM)D D D
Table 3: Patterns of Data-Infused Business Models. Adapted from Schuritz and Satzger (2016).
Schuritz and Satzger (2016) also claim that new technology, such as technology aimed at novel data
utilisation, can drive business model innovation and help the firm find new sources of value. However,
Schuritz and Satzger (2016) stress that the firm should not focus on how to turn the existing business
model into a data driven one, but instead focus on novel data utilisation and let this process guide the
firm to more innovative practices.
Data utilisation and analytics are now impacting a broad range of industries (Hunke et al. 2017). This
development means that firms across various industries are becoming increasingly aware of the importance
of data. Examples of successful implementation of novel data utilisation can be found in industries such
as traditional manufacturing, raw material processing and retail (Schuritz and Satzger 2016). Within the
satellite sector, the utilisation and analysis of satellite data is one of the focal points of the industry’s
development (Soille, Loekken and Albani 2019).
2.5 Data from SatellitesTraditionally, the satellite industry has had high entry barriers and has therefore been dominated by
large space agencies. However, in recent years the cost of building and launching satellites has dropped
considerably, which has lead to a growing commercial interest within the field (Simonis 2019). One
trend within the satellite industry is the increased deployment of small satellites (Sandau 2010). Using a
constellation of several small satellites instead of a solitary large satellite has several advantages, such as
more frequent mission opportunities and faster adaptation to technological developments (Sandau 2010).
The commercialisation of space has lead to a wide array of new entrants on the market, both in the
form of start-up firms and established, capital-heavy IT firms looking to expand their business into space
(Denis et al. 2017). As costs for building and launching satellites decreases, so do the entry barriers into
the industry. As a result, the satellite industry is currently undergoing a disruptive transformation with
12
an uncertain outcome (Denis et al. 2017).
The rising number of satellites in orbit means a greater amount of data being generated, which poses a
challenge since these data volumes need to be analysed in a quick and efficient manner (Milcinski et al.
2019). Denis et al. (2017) describes how the antenna systems on the ground used to receive satellite
data, the so-called ground station infrastructure, will become of increased importance as the amount of
generated data grows. Such large data sets are often referred to as ’big data’, a concept that has gained
prominence in several industries in recent years (Gandomi and Haider 2015).
2.5.1 Big Data as a ConceptThe term ’big data’ is a relatively new concept that is not well defined by academics (Gandomi and
Haider 2015). In a global survey of IT and business professionals, Schroeck et al. (2012) found that there
was considerable confusion as to what big data actually is. Laney (2001) suggests an approach consisting
of three V’s; Volume, Velocity and Variety. This approach has been widely used and expanded upon to
convey the concept of big data (McAfee et al. 2012; Gandomi and Haider 2015; Hartmann et al. 2016). A
fourth V, veracity, is often added to the three others (Schroeck et al. 2012). According to Sorescu (2017),
the three V’s can be found within business models that are achieving competitive advantages.
The first V, volume, refers to the amount of data transferred and stored (Gandomi and Haider 2015;
Schroeck et al. 2012). Big data, as the name indicates, involves handling large amounts of data. The
amount of generated data is growing at an increased speed and is estimated to reach over 44 trillion
gigabytes in 2020 (Schuritz and Satzger 2016). Velocity refers to the speed of data creation, transfer and
analysis (Gandomi and Haider 2015; Schroeck et al. 2012). The popularity of smartphones and similar
devices has increased the pressure on processing data quickly, since large amounts of the large amount
of data generated by mobile devices presents an opportunity for retailers and marketers (Gandomi and
Haider 2015). Variety refers to the heterogeneity of the data that is generated and analysed (Gandomi
and Haider 2015; Schroeck et al. 2012). Companies must be able to process and analyse both structured
and unstructured data, from a variety of sources and in a wide range of formats, in order to utilise
it. Veracity refers to the varying reliability of data (Gandomi and Haider 2015; Schroeck et al. 2012).
Unreliable data is now commonplace due to the large amounts generated. One dimension of big data is
therefore the ability to handle uncertainty that is built into the data. Data fusion of multiple sources
of unreliable data is one way to reduce uncertainty and facilitate more accurate data analysis (Schroeck
et al. 2012).
The four V’s are highly applicable to satellite data, since it consists of large volumes, is required at a
high speed, with varying reliability (Baumann et al. 2016). Even though all satellite data originates
from satellites, it is still heterogeneous due to a lack of standards within the industry, with custom
solutions for various satellites being commonplace. Furthermore, big data from satellites has the added
difficulty of requiring further conversion into several, distinct layers of information in order to represent
the information to its full extent (Tiede et al. 2019). Many satellite owners and satellite data customers
lack the expertise and resources for processing and analysing large data sets originating from satellites,
which constitutes an obstacle for widespread utilisation of satellite data (Siqueira et al. 2019).
2.5.2 Satellite Data ProcessingAs highlighted by several researchers, artificial intelligence and machine learning applications are starting
to play key roles within the field of Earth observation (Moumtzidou et al. 2019; Sumbul, Demir and Markl
2019; Datcu et al. 2019). Machine learning is becoming a necessity due to the volume, availability and
13
quality of satellite data (Milcinski et al. 2019). While there are many machine learning options available
for general imagery, not many support the complexity of EO data (Milcinski et al. 2019). Additionally,
there is an increased demand for data that has been processed and is ready to use for the end customer
(Siqueira et al. 2019). Since transfer of large amounts of data is resource-consuming, one important
aspect of efficient data handling is to perform the processing close to the source of the data (T. Huang
2019; Neteler et al. 2019).
There are several levels of processing for satellite data. NASA’s Earth Observation System (EOS) has
developed a classification system for processing that has become widely used (Y. Huang et al. 2018;
Piwowar 2001). This classification system is detailed in Table 4.
Level Description
0Reconstructed, unprocessed instrument data. Full resolution, no information lost. All communications
artifacts (synchronisation frames, communications headers, et cetera) are removed.
1A
Reconstructed, unprocessed instrument data at full resolution, time-referenced, and annotated with
ancillary information, including radiometric and geometric calibration coefficients and georeferencing
parameters. Level 0 data are fully recoverable from level 1A data (no loss).
1BLevel 1A data that have been processed to sensor units (radar backscatter cross section, brightness
temperature, optical, et cetera). Level 0 data are not recoverable from Level 1B data.
2Derived geophysical variables, such as sea ice concentration, ocean wave height, et cetera. At the same
resolution and location as Level 1A source data.
3Variables mapped on uniform spatial grid scales, usually with some completeness and consistency
(missing points interpolated, complete regions mosaicked together from multiple orbits, et cetera).
4Model output or results from analyses of lower level data (i.e., variables that were not measured by the
instruments but instead are derived from these measurements).
Table 4: Levels of Data Processing. Adapted from Parkinson, Ward and King (2006).
14
3 Conceptual Framework
In this chapter, a conceptual framework that presents the relevant frameworks for the thesis is outlined.
Arguments for why the presented frameworks are relevant for this thesis are also given.
3.1 Establishing the Business Model ElementsIn order to build the thesis on a generalisable framework with a strong theoretical foundation, the business
model is defined as consisting of the six elements outlined in the previous chapter, found in Table 1. These
six dimensions were chosen due to the lack of a commonly agreed upon definition, thus leading us to choose
the most agreed upon elements in order to attain a reliable definition. Furthermore, a definition of a
framework for a business model was needed to fulfil the purpose and answer the first sub-question. Hence,
this categorisation enabled the mapping of the firm’s current business model using a clearly structured
framework, facilitating the identification of potential new business models, in accordance with Chesbrough
(2010). We chose to define the meaning of our business model elements in line with Osterwalder and
Pigneur’s (2010) elements, since their work is designed primarily for practical applications rather than
academic purposes. It is therefore easy to apply and use their general classifications that are suitable for
a wide range of firms.
Following are the six elements briefly described in accordance with Osterwalder and Pigneur (2010):
1. Value Proposition
The value delivered to a customer, helping to solve a problem or fulfil a need. This is achieved by
products or services. Value can be qualitative or quantitative, meaning it can be created for instance
in the form of customer experience or price. A value proposition could satisfy an entirely new
customer need, which many times is the case regarding technology related products. Accessibility
is one example of a value proposition, meaning that customers who previously lacked access to
a product or service are offered availability. A firm can also create value by ’getting the job
done’, meaning that the firm takes care of the customers needs without hassle, as exemplified by
Rolls-Royce and how they provide manufacturing and full service of jet engines, which allows their
customers to focus on other aspects of their business.
2. Customers
The different groups of people or organisations the firm intends to reach and serve by it’s value
proposition. Customers are grouped into different segments sharing common needs, behaviours,
relationship requirements or willingness to pay for the same aspect of the offer. There could be
one to several customer segments defined within a business model, which need’s are important to
understand to have a successful business model.
3. Revenue Model
The revenue generated from each customer segment, by delivering value. There can be several
revenue streams from the same customer segment, however the revenue streams themselves are
either one time occurring as a result from one-time customer payment, or reoccurring as ongoing
payments for value delivery or post-purchase support.
4. Cost Structure
15
The most important costs incurred to operate a business model and its elements. A business model
can be more or less cost-driven, which intent is to minimise costs wherever possible. In opposite, a
value-driven business model is less concerned with costs and more focused on value creation.
5. Key Activities
The most important activities or actions a firm must take to keep the business model operational
and successful. Thus, the most important activities to be able to create the value proposition,
generate revenue and keep customers.
6. Key Resources
The most important assets required to make a business model work. Key resources can be in the
form financial, human, intellectual or physical, and the resources can be owned, leased or accessed
by partnerships.
It is important to note that this type of business model framework represents a static state, while business
model innovation is a dynamic process (Sosna, Trevinyo-Rodriguez and Velamuri 2010). However, the
framework was in spite of this chosen because it provided a useful way of comparing the business model
in two different points in time. In order to incorporate the dynamic aspects of the business model, a
longitudinal component was added, similar to what is suggested by Hedman and Kalling (2003). This is
further strengthened by Kaulio, Thoren and Rohrbeck (2017), who stress the importance of longitudinal
approaches as well as that of the contextual setting. This made us expand our business model framework.
Further reasoning for this is that by placing the model in a longitudinal setting, the business model trans-
formation can be more accurately described, strengthening the validity of our work. The organisational
ambidexterity theory was leveraged to capture processes occurring during the longitudinal component,
as the firm shifts from its current to its future business model.
In summary, our proposed business model framework is connected to a dynamic aspect by being in-
tertwined with organisational ambidexterity, and the business model elements are summed up in figure
3.
Figure 3: Summary of the Business Model Elements.
16
3.1.1 Innovation of a Business Model
Building on the previously described framework, aspects of innovation also had to be considered due
to the thesis’ purpose of innovating a business model. Hence, the following further defines our business
model framework by adding innovation aspects, once again intertwined with organisational ambidexterity.
The aggregated literature on business model innovation does not agree on a mutual understanding or
definition of what constitutes innovation. To enable a structured analysis, a definition of business model
innovation in accordance with Amit and Zott (2012) was selected, since their definition is widely cited
which provides credibility. Business model innovation was thus conceptualised as changes made to one or
more of the business model’s core elements. Furthermore, the double ambidexterity framework presented
by Kaulio, Thoren and Rohrbeck (2017) was chosen and take into consideration when analysing business
model innovation. This choice was made in order to gain a deeper understanding and identify links
between business model innovation and organisational ambidexterity. By using the framework of Kaulio,
Thoren and Rohrbeck (2017) another dimension was added to the analysis, enabling the evaluation of
innovation as being exploitative or exploratory. Exploratory innovation was further categorised as either
incremental or radical, which provided the analysis with a greater degree of nuance.
3.2 Dynamics of Data Utilisation and AmbidexterityIn the aggregated literature on data driven business models, the implicit understanding is that data driven
or data-infused business models rely on utilisation of data to create value. This distinction has a significant
impact for our thesis, since the investigated firm has a value offering that is centred on delivering data.
However, the firm does not actually utilise data to create value for its customers. Therefore, delivery of
data can be likened to delivery of a physical good or service, in the sense that simply delivering data
does not require a data driven business model. Such a distinction enabled us to postulate that the firm’s
current business model is not data driven, thus enabling the application of a framework for data driven
business model innovation.
Furthermore, in accordance with Schuritz and Satzger (2016), novel data utilisation is equated to techno-
logical innovation in the sense that both act as drivers for business model innovation. Connecting novel
data utilisation with technological innovation made the framework on double ambidexterity presented by
Kaulio, Thoren and Rohrbeck (2017) a suitable selection for this thesis, since it explores the interplay
between technological innovation and business model innovation. The framework developed by Kaulio,
Thoren and Rohrbeck (2017) was also selected since it is used to map organisational responses. When the
focal firm’s earlier responses were mapped, evaluation of how novel data utilisation compares to previous
organisational responses was made possible. The double ambidexterity framework was also used to map
novel data utilisation as an organisational response (see figure 4), further strengthening its suitability.
17
Figure 4: Framework Illustrating Organisational Response and its Corresponding Business Model.
Since the result of novel data utilisation within the business model was investigated, the patterns of data-
infused business model innovation developed by Schuritz and Satzger (2016) were used. These patterns
were identified using a wide range of firms, and was therefore deemed suitable for the investigation of
the focal firm of this study. The elements described by Schuritz and Satzger (2016) were translated into
the six elements of our previously outlined business model framework (see figure 5). This translation was
necessary to assess what degree of business model transformation was achieved by novel data utilisation,
by investigating the change to each of the business model’s underlying elements and categorising the
resulting business model using the patterns described by Schuritz and Satzger (2016). Due to the novelty
of the research field, this framework was the first of its kind and was therefore a natural choice.
Figure 5: Translation of the Business Model Elements Outlined by Schuritz and Satzger (2016).
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4 Method
This chapter presents the scientific method used for the research process conducted in this thesis. The
method guided our research process and was used for gathering necessary empirical data to fulfil the
purpose of the thesis. The choice of method is described in detail and critically argued for.
4.1 Research DesignThe study consists of two parts, the first being an exploratory pre-study which generated empirical
data that help outline the direction of our research (Blomkvist and Hallin 2014). During this phase the
researchers acted as insiders, initiated unstructured conversations with different employees, in particular
our supervisors. Access was also gained to organisation-specific documents, which facilitated a better
understanding of the organisation. This contributed to our understanding of the complexity of the
research area, helping us deciding on the scope of our thesis. Further, problems regarding the creation
of new business models based on market shifts and utilisation of new technology emerged, and especially
technology regarding data processing. In this regard, a perception of a generally negative attitude towards
data utilisation within the firm was noted. This collided with the ambition of finding new business models,
which helped to further decide on the focus of the study. Aforementioned guided us into the second part,
the main study, in which our analysis and conclusions are generated.
The phenomena studied in this thesis are the shifts caused by rapid technological development within
the satellite industry, leading to new opportunities and potential new business models. Due to its re-
cent emergence, this area has been subjected to relatively little research. An exploratory approach was
therefore considered appropriate for this thesis work (Blomkvist and Hallin 2014; Yin 1994). The studied
phenomena occurs in a real-world context, thus the phenomena should not be isolated from it, which
suggests a case study approach that emphasises the context in which the phenomena occurs (Eisenhardt
and Graebner 2007). Yin (1994) argues that case studies are suitable for ’how’ and ’why’ research ques-
tions, also noting that case studies are useful when the focus of the research is on contemporary events
and the researcher does not need to control behavioural events, which holds true for our study. In order
for the research to move in a coherent direction, Yin (1994) highlights the importance of establishing
the propositions of the case study. If the case study is exploratory, Yin (1994) instead suggests that the
researcher should state a clear purpose of the study. Since our work is of an exploratory nature, we have
avoided stating propositions that may bias our result, and instead specified the purpose of the study to
help guide us forward.
The study is decided to be a single case study, focusing only on SSC and investigating the phenomena in
the context of a single firm. Yin (1994) argues that a single case study is suitable when researchers are
investigating an extreme example, gain unusual research access, or have an opportunity to investigate
a particular phenomena under rare circumstances, something which Eisenhardt and Graebner (2007)
acknowledge as well. The studied phenomena occurs within an emerging business area that has been
subject to a limited amount of research. A single case study can therefore make a relevant research
contribution. However, Eisenhardt and Graebner (2007) stress that in comparison to a multiple case
study, a single case study cannot provide an equally strong base for theory building. They also claim that
a multiple case study can generate a more vigorous theory and provide a broader exploration of the posed
research questions. Yin (1994) argues that the same criticism applies to performing a single experiment,
noting that single case studies, like single experiments, are generalisable to theoretical propositions. A
single case study can therefore provide valuable contributions to research by providing the a means of
19
generalising theories.
One important aspect of research design is to establish what the case actually is, and what it is not.
By establishing a unit of analysis, the case study can be more clearly defined. Yin (1994) explains that
the selection of the study’s unit of analysis should be based on the selected research questions, arguing
that well formulated research questions give the researcher a clear indication of the appropriate unit of
analysis. Our research question provided us with the firm as a natural unit of analysis, and in particular
one business unit that is close to the subjects highlighted in the question. Case studies can be of a
holistic nature or have an embedded design, with the latter having multiple levels of analysis within a
single study (Eisenhardt 1989; Yin 1994). This case study is an embedded single-case study, since the
unit of analysis was SSC at a firm-level but the firm’s business units functioned as sub-units of analysis.
In particular, one business unit was selected as the focal unit since it was most closely related to the
investigated phenomena. By using an embedded design, we were able to look further into the investigated
phenomena and help focus the study’s inquiry (Yin 1994).
According to Dubois and Gadde (2002) a linear work flow with planned phases does not bring forth all the
potential and advantages from case research. However, the process of systematic combining does. Dubois
and Gadde (2002) state that systematic combining is the process in case research that consists of going
back and forth between empirical and theoretical activities. Blomkvist and Hallin (2014) note that an
abductive approach that alternates between theory and empirical findings creates a greater responsiveness
to the observed phenomena. In agreement with aforementioned, we let our empirical findings guide the
evolving framework, and vice versa. By alternating back and forth between theory and practice, we were
able to develop deeper insights about the studied subject area.
4.2 Data CollectionCase studies can generate various types of data, including interview data and documents (Eisenhardt
and Graebner 2007; Blomkvist and Hallin 2014). Eisenhardt and Graebner (2007) suggest that when the
phenomena of interest is episodic and infrequent, interview data is an efficient way to gather important
empirical information. This is in line with the studied company and innovation of the incumbent business
model, which is not a frequent undertaking. Hence, the study has mainly focused on data collection
through interviews. However, archival data regarding overall information about the organisation and
ongoing projects have been gathered to deepen the understanding and broaden the empirical material.
An approach used throughout the whole data collection and analysis is in accordance with Eisenhardt
(1989), who explains that it is advantageous to carry out data collection and analysis simultaneously
when performing theory building from a case study, making a key feature of theory building case research
the freedom to make adjustments during the data collection phase. Accordingly, we have analysed data
directly after it was collected, giving us the opportunity to adjust or investigate further based on novel
empirical findings.
4.2.1 Interview Data
For the first interviews, aimed at gaining an understanding about SSC as a company and deciding
on the thesis’ scope, we used the unstructured interview method. The selection of this method was
rooted in Blomkvist and Hallin’s (2014) rationale that unstructured interviews are suitable for exploring
an unknown subject in the early phase of the empirical work. The unstructured method gave us the
flexibility to explore the subject by leaving room for questions and opening up the interview to a free
dialogue (Ritchie et al. 2013). For the interviews aimed at mapping the current business model of the
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business unit in focus, we instead used semi-structured interviews. Because the subject area of business
models has a well developed theoretical foundation, a semi-structured approach allowed us to steer the
interviews to explore certain topics related to scientific theory while still maintaining flexibility (Yin 1994).
Eisenhardt (1989) suggests that additional adjustments can be made to the original interview protocols
to take advantage of a new presented opportunity. Aforementioned helped us steer the interviews even
more, making our research discover new areas of interest that gave us the opportunity to explore a
broader spectrum than intended from the beginning. Thus during interviews, new facts arose and by
analysing these facts directly we could decide on further investigation. We conducted a total of 14
interviews, with people at different positions and levels within the firm. The roles of the informants and
interview protocols can be found in Appendix A and B. The interviews led to additional adjustments of
the original interview protocol, as in line with aforementioned, making us initiate further investigation
by performing a second round of interviews with three interviewees. Furthermore, being able to include
new interview questions gave us a more in-depth empirical finding. Since informants in different roles
had different perspectives, additional questions were sometimes needed to gain a deeper understanding of
the uncovered information. Furthermore, in accordance with Blomkvist and Hallin (2014) all interviews
were audio recorded and notes were taken, to be able to revisit the empirical data collected.
When using interview data, there is a risk of the data being biased, with reinforced impressions and ret-
rospective sense making being the main sins (Eisenhardt and Graebner 2007). To limit biases associated
with interview data collection, Eisenhardt and Graebner (2007) suggest a key approach of using several
informants with different perspectives on the studied area, such as organisational actors from different
hierarchical levels or from different groups. To mitigate biases in this study’s interview data, interviews
with employees at SSC have been carried out within both the Satellite Management Services business
unit and the Science Services business unit, with employees of different roles at different levels of the
units. Three members from the executive committee have also been included.
4.3 Data AnalysisAfter every interview, the audio recording was transcribed in line with what is suggested by Blomkvist and
Hallin (2014), which gave us the chance to ensure all information was included and nothing was forgotten
about. When analysing the collected data, interview notes were paired together with the recorded audio of
an interview. The recording was listened to two times, to make sure nothing was neglected. Immediately
after, company documents were analysed with the aim to find connections between the two data types.
Analysis was carried out parallel with the data collection, and with the use of an abductive approach
connecting the data to the literature, in accordance with Blomkvist and Hallin (2014). This allowed us
to move between the collected data and the literature, enabling us to enrich the analysis which yield
unforeseen findings. To ensure the reliability, each interview was analysed separately and directly after
it was carried out. It was also deemed important with immediate analysis due to the large amount of
interview data that was generated due to the semi-structured method. Additionally, each interview was
also analysed individually by the researchers, to ensure that the respondents were correctly interpreted
and that no data was left out.
4.4 Research QualityTo ensure that the case study is of high quality, several criteria for ensuring the quality of the research
design can be used. Yin (1994) describes four common tests that help researchers judge the quality of their
design. Construct validity (1) ensures that the operational measures for data collection, analysis et cetera
are valid and objective. Internal validity (2) establishes causal relationships between the investigated
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variables, but is only applicable for explanatory or causal studies. Therefore, an internal validity test
will not be performed, since our work is of an exploratory nature. External validity (3) ensures that the
findings of a case study are generalisable, and establishes to what domain the results can be generalised.
Reliability (4) establishes that the operations of the study are replicable and will yield the same results
if performed again. Yin (1994) suggests case study tactics to perform each of these tests, and identifies
at what stages the tactics should be deployed. Table 5 details the case study tactics for the three tests
deemed relevant for our work.
Test Case Study Tactic Research Phase
Construct Validity
Use multiple sources of evidence
Establish chain of evidence
Have key informants review case study draft
Data collection
Data collection
Composition
External Validity Use theory in single-case studies Research design
ReliabilityUse case study protocol
Develop case study data base
Data collection
Data collection
Table 5: Case Study Tactics for Three Relevant Tests. Adapted from Yin (1994).
Construct validity was established by including several informants for interviews and data collection, at
different levels of the firm as well as from different departments. This enabled us to present a nuanced
and balanced view of the investigated phenomena. The diverse selection of informants also aided in
establishing a chain of evidence throughout the research process. By concluding each interview by asking
the informant if they could suggest other potential informants with valuable insights, the original span
of informants was increased and valuable information was accessed. Finally, a draft of the case study
has been reviewed at three different points in time by three different informants, further strengthening
the construct validity of the thesis. Building upon existing theory to develop the case study created
favourable conditions for generalising the findings to a broader set of theories, thereby strengthening the
external validity of the research. Reliability was achieved by continually using a case study protocol, and
including said protocol in the thesis to facilitate potential replication of the study. The protocol, in the
form of the interview questions and list of interviewed informants, is available in appendix A and B. The
reliability was enhanced further by the use of a cloud based service to store and organise information
regarding the case study and literature, together with storage of collected data, acting as the study’s
personalised structured data base.
4.5 Research EthicsEthics and integrity have been taken into substantial consideration during the entire research process.
From the beginning, a dialogue was held with the case company about confidentiality in regard to sensitive
information, and a confidentiality agreement was signed. The most common ethical codes within social
science in Sweden according to Blomkvist and Hallin (2014) are the principles of the Swedish Research
Council. Since the thesis investigates phenomena within the social science sphere, these principles were
therefore followed to assure proper research ethics throughout the duration of the study.
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Hence, the research was conducted in line with the formal guidelines from The Swedish Research Council’s
codex ’Principles of research ethics’, as follows:
1. Information Requirements
The researchers shall inform the interviewees about the purpose of the study.
2. Consent Requirements
Interviewees in the study shall decide on their own participation.
3. Confidentiality Requirements
Information about all interviewees shall bee given the greatest possible confidentiality and personal
data must bee stored in such a way that unauthorised persons cannot access it.
4. Utilisation Requirements
Data collected shall only be used for the research purpose.
The information requirements were met by starting every interview with an introduction of the researchers
as well as the research itself, explaining the study’s background and purpose before initiating the interview.
To fulfil the consent requirement all interviewees was asked to participate in the study, explaining the
objective of the interview, together with asking about consent for conducting an audio recording. The
confidentiality requirements were met by anonymisation of participating interviewees. All informants
are described by their role in the thesis, to give a general view of the informant’s background and
perspective. Since the focal firm is relatively small, the role’s were reworded to avoid identification by
process of elimination. Finally, all collected data was used for the purpose of this thesis only, and no
data was used for any other purposes than those explicitly stated to the interviewed informants.
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5 Case Study
In this section, the industry setting and case company are presented to provide a background to the case
study. The project ’Global Watch Center’, which represents an important potential driver of novel data
utilisation at SSC, is further detailed. The business model of the case study’s focal business unit is also
outlined.
5.1 Industry SettingThe satellite industry is currently undergoing a transformation, with the market shifting towards an in-
creased degree of commercialisation and new entrants changing the status quo. On the Earth observation
market, commercial constellations of small satellites capable of updating their imagery daily now exists
(Business Development Manager, 2019). This development means that increased amounts of data are
generated, leading to new businesses with utilisation of said data as a main value offering. The potential
application domains are steadily increasing due to advancements within machine learning and artificial
intelligence, which in turn enables processing of big data from satellites for analysis and applications.
The aforementioned development has lead a combined market and technological disruption in several
sectors of the space industry. Firms offering satellite management services are affected by the emergence
of a new customer segment which demands low prices, which in turn causes diminishing profit margins
on existing satellite management services. Furthermore, the industry for satellite management services
and ground station networks is characterised by high entry barriers due to the large cost associated with
building the ground stations (i.e., building antenna networks). Also, complicated legislation associated
with building new ground stations, both nationally and internationally, further raise the entry barriers.
However, the industry is now facing an increased degree of commercialisation and with the entrance of
new, capital-heavy firms.
The main market participants in the satellite management service industry are incumbent firms, which
have operated in the industry for a long time and offer more advanced services. The incumbents are
characterised by their value offering, which is focused on providing high-quality services with low latency
and high Service Level Agreement (SLA). Amongst the incumbents, there is generally little differentiation
(Business Development Manager, 2019). The main distinction between incumbent firms are the added
services offered, which range from data analytics to Launch and Early Orbit Phase (LEOP) services.
Presently, an increasing number of new entrants are emerging, who primarily cater to the budget segment
of the market (Executive Committee Member 1, 2019). For instance, recently a capital-heavy IT firm
announced plans for building their own ground station network which will be integrated with data services,
making for a potential competitor. This entry has caused suspense amongst incumbent firms, since it is
unclear how the market will be affected by the entry of a large IT player. Current indications are that
the entrant will offer their customers a lower SLA, meaning that they will primarily compete with the
new entrants of the market (Executive Committee Member 1, 2019).
5.2 Case CompanyThe Swedish Space Corporation (SSC) is a company wholly owned by the Swedish state, operating
in the space industry with almost 50 years of experience. The company’s net sales in 2018 was SEK
945 million, with almost 500 employees (Swedish Space Corporation 2018). Founded in 1972, SSC is
now a global firm offering advanced space services with the mission to help Earth benefit from space.
The firm’s biggest market is Europe, contributing with 63 percent of sales, followed by the US, and
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subsequently Asia (Swedish Space Corporation 2018). SSC is comprised of both a commercial part and
a government-defined social mission. The commercial assignment, with a yield requirement, is to run
commercial operations globally within advanced space services. The commercial part consists of two
business divisions; Satellite Management Services (SaMS) and Engineering Services. The social mission
is conducted within the business division Science Services, which includes ownership, operations and
development of Esrange Space Center in Kiruna. The social mission is evaluated against two distinct
measures, without a requirement on financial returns (Swedish Space Corporation 2018).
Esrange Space Center is a rocket base and research centre, with infrastructure and expertise for conducting
large projects, managing large rocket engines and collaborating internationally on technically complicated
projects. The centre is located in the northernmost part of Sweden, giving access to a vast uninhabited
impact and recovery area, with a low grade of air traffic, in close proximity to settlement and national
communications. SSC owns and operates ground stations around the world, with Esrange acting as a
hub in the global satellite ground station network. The ground stations mainly manage satellite data
acquisition and data transfer, as well as telemetry tracking and command (TT&C). The station at Esrange
has an advantageous location for accessing satellites in a polar orbit, provide the opportunity to collect
data more frequently when compared to ground stations closer to the equator. A new project has been
initiated at Esrange in order to meet the increased demand for launching opportunities aimed at small
satellites (Project Manager 2, 2019). The project is called SmallSat Express and has the ambition to
result in a European launch site for small satellites, located at the Esrange Space Center. The goal is to
perform the first satellite launch in the end of 2021.
The business division Science Services provides design and launch of sounding rocket vehicles and strato-
spheric balloon systems, from the Esrange Space Center. The rockets and balloons are equipped with
instruments or experiments for research and technological development. The launches are often carried
out in cooperation with international customers, and the division also offers development of experiments
and payloads. Science Services is currently working on a test bed project, aimed at test and development
of reusable rockets. The test bed will also have the possibility to demonstrate new components for space
applications (Business Development Director 1, 2019).
The business division Engineering Services offers expertise to organisations within the space industry and
their most advanced space projects. They also have the ability to supply on-site support for customers.
The division provides engineering services to space organisations such as the European Space Agency and
the German Aerospace Center. Engineering services can support projects from specification to launch
and also offer operational service of satellites or spacecrafts in orbit. Engineering Services also develop
software that streamlines satellite related services.
The business division SaMS offers satellite communication and satellite control services via one of the
world’s largest network of ground stations, SSC Ground Network. SSC’s network consists of the firm’s
own stations together with partner stations, creating a wide geographical spread. SSC Ground Network
is monitored around the clock, via control centres. SaMS provide satellite owners and satellite operators
with a wide range of services and offer use of the entire network, part of the network, or individual stations.
Services are ranging from contracts where customers purchase an entire solution, including use of the
infrastructure together with satellite communications and control, to hosting services where customers
own and place their antennas at a ground station (Systems Engineer, 2019). SSC is developing the ground
network and are increasing capacity and automation. To meet the demand for more flexible services for
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smaller satellite constellations, SSC are also developing more cost-effective alternatives with new types of
antennas and higher bandwidths, located at SSC’s existing stations and new locations. The new service,
SSC Infinity, is specifically aimed at operation of constellations of smaller satellites that require more
frequent satellite contact. SSC manage launch support services, such as LEOP services. SSC Ground
Network provides on-orbit services from their multi-mission ground station network (Sales Engineer 2,
2019). There are also teleport and media services offered. A big part of SaMS service offering are satellite
and network operations, offering full mission management services, performed from the mission control
centre at Esrange Space Center. The service addresses geostationary and polar satellites, with both prime
and backup operations. In connection with these services, SSC also offers data handling and processing.
A satellite in orbit collects an extensive amount of data, which needs to be collected and distributed to the
satellite owner. This service is provided by SaMS and there are large data downlink capacities at Esrange
Satellite Station, which also can be expanded to the global ground stations. Hardware and software for
processing can be provided by customers and placed at ground stations, enabling processing directly on
site. The main focus of this thesis will be the SaMS business unit, since they deal with collection and
distribution of satellite data.
SSC is operating in a fast changing environment and thereby aim to keep up with the speed of change,
seizing new market opportunities to generate profitable growth. Operating with the vision ’Leading global
provider of advanced space services’, SSC are looking to expand in the US and Asian market alongside
with efforts towards providing innovative services. One aspect of achieving the vision, as explicitly stated
in the firm’s strategic goals, is to develop a corporate culture that embraces change and drives innovation.
Another is to embrace new emerging commercial customer segments, alongside with nurturing the existing
institutional customers that will continue to be a key segment. With present market disruptions and rapid
change, there could also be a need for developing partnerships going forward.
5.2.1 The ’Global Watch Center’ Project
SSC continuously run innovation projects to find new opportunities and grow their business. The project
Global Watch Center (GWC) is described as a visionary one, having a long term perspective for com-
petitiveness and innovation (Executive Committee Member 2, 2019). Furthermore, it is run outside the
original line organisation, but within the context of the executive team, and with expertise assistance
acquired from a consultant company. The project was initiated in 2018, and with funding secured the
project took off with phase one, consisting of a pre-study. The first phase was completed in April 2019,
yielding results that promoted the project to be prolonged. Now the project moves on to phase two,
securing continued funding and with plans of conducting a use-case to provide proof of concept.
There are two main reasons for the initiation of GWC, with the first being changes in the market for Earth
observation. The market changes are due to miniaturisation of hardware, digitisation, machine learning
and artificial intelligence, which enables new applications and possibilities for handling big data from space
(Project Manager 1, 2019). There are currently a large amount of Earth observation satellites in orbit,
generating vast amounts of Earth observation data. Many of these satellites are owned by agencies, such
as the European Space Agency or NASA, but there is an increasing number of private satellite owners.
Furthermore, with the market shifting towards increased commercialisation, new entrants are emerging
who are making use of new technology aimed at constellations of small satellites. Aforementioned changes
are deemed to bring new possibilities for future business, and promote a Swedish high-tech growth and
global partnerships (Executive Committee Member 2, 2019). The second reason for initiating GWC is
the aspiration of contributing to a more sustainable planet. The project objective is to contribute to
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the UN Global sustainability goals (Agenda 2030), also providing opportunities for refugee aid and crises
management.
The hope is that GWC can provide a ’planetary eye’ which can inform citizens and nations on environ-
mental changes and natural disasters. The need can be transformed into a global platform for integrated
social and environmental observations, by utilising GWC as a global ’event room’. This is planned to
be achieved by using Earth observation data from multiple satellites paired with data generated from
multiple sources on Earth. In order to ensure openness and credibility, it is planned that such a centre
shall be established in a globally coordinated partnership. To this end, the UN is involved in the GWC
project.
With the initiative of SSC, Sweden has an opportunity for the creation of a global watch centre, partic-
ularly due to the country’s neutral political position and esteemed research engagement. The resources
needed for a watch centre are to a high extent already in place, and Earth observation data is continually
generated from multiple satellites in orbit (Executive Committee Member 2, 2019). However, its full
potential is not believed to captured. Hence, there is a gap in the market and a platform combining data
from multiple sources, with a user-friendly interface generating information in real-time, is thought to
be lacking. The EU has created resources for generating data from space via satellites. Within the EU,
the program Copernicus delivers Earth observation data within a very wide spectrum. The US has both
commercial and governmental actors, and there is a rapid growth in the Chinese market. Aforementioned
will increase data generation and thus further increase the opportunities of using satellite data to extract
more value (Project Manager 1, 2019). The world does not yet have large-scale data analysis with high
access, which is recognised by the EU who is engaged in and positive towards the GWC project (Executive
Committee Member 2, 2019).
The platform will be based on the idea of collecting and merging data from different space-based sensors,
making the information more accessible. With modern technology, information from various satellite-
borne sensors together with information from ground-based sources, can be processed and merged to
create a situational picture of our planet. SSC have good conditions for this, with the space base Esrange
particularly in mind (Project Manager 1, 2019). With one of the world’s largest global networks of
antennas and satellite data acquisition in place, data from satellites owned by operators worldwide is
readily available (Executive Committee Member 2, 2019).
5.2.2 Current Business Model of Satellite Management ServicesThe SaMS business model is not clearly outlined by the firm, as there is no documentation that explicitly
details the business model. However, the business model elements as implicitly known by SaMS personnel
are presented below.
Value Proposition
SaMS’ value proposition is to offer access to their ground station network for customers needing satellite
management services. The idea is that it is cheaper and more convenient for customers to buy antenna
capacity from SaMS, since investment in an antenna is costly and the utilisation rate (for a single satellite
with a dedicated antenna) is very low. This setup offers customers access to an asset which they could
not afford to procure in its entirety. As expressed by the Executive Committee Member 1:
’If you want to travel you buy a plane ticket, not the entire air plane. Similarly, customers
buy antenna capacity and not an entire antenna’ (Executive Committee Member 1, 2019)
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The value proposition also has the added advantage of offering lower latency, since a network of antennas
comes into contact with the satellite more frequently than a single antenna. The customer can therefore
receive their data much quicker (Business Development Manager, 2019; Sales Engineer 1, 2019)
Sometimes, however, the customer wants to access the full capacity of an antenna, or do not wish to share
the antenna with other customers. This need is met by the alternative for customers to buy an antenna
and set it up at an SSC location, so called ’antenna hosting’. The market demand for this service varies
across geographical regions, with demand in Asia being especially high (Business Development Director 3,
2019). For SaMS, hosting is not the preferred business to deliver their value proposition, since they would
rather see that the customer buys capacity and associated services, where they will have the possibility
to deliver a broader aspect of their value. The hosting business in some ways cannibalises on the main
business, but so far SaMS have managed to sustain a balance between the two business areas.
Something that increases SaMS’ value proposition even more is their main source of differentiation; their
wide service offering. They can offer satellite management integrated with science services, as well as
provide satellite control and LEOP services to a higher extent than their competitors. They also offer
services related to control of geostationary satellites. These offerings can be seen as ’getting the job
done’, since SaMS can perform all aspects of satellite data management for their customers. Also, SSC
have offices locally in several other countries providing more on-location staff than their competitors,
thus adding to the value offering.
Customers and Market
The satellite industry is a diffuse landscape, but two main customer segments can still be identified;
commercial actors and governmental (or intergovernmental) agencies. The commercial segment consists
of a few, capital-intensive commercial actors. They value price, efficiency and delivery reliability, with
the latter being especially important. Their procedure for choosing a ground station network supplier
usually starts with a thorough price analysis (Sales Engineer 1, 2019; Business Development Director
3, 2019). If the price is deemed acceptable, the technical aspects of the offer are then considered. The
agencies differ in their approach, since they place a higher value on technical expertise. To attract this
customer segment, it is important to exhibit technical expertise when making sales and to demonstrate
’thought leadership’ within the industry. It is therefore important to be visible at industry conferences
and similar events. Agencies’ primary priority when choosing a supplier is that the technical aspects are
satisfactory, with price of the service being less important (Sales Engineer 1, 2019). These two customer
segments are both willing to pay for guaranteed performance and delivery. As one informant puts it:
’Both segments want a higher service level, and be able to schedule their passes with high
precision. They pay more, and require that capacity is available when they need it’
(Executive Committee Member 1, 2019)
However, there is an emerging customer segment that does not share this sentiment. The new space
segment is not clearly defined by SaMS, but it can be described as a sort of budget segment consisting of
commercially oriented firms that are very price-sensitive (Executive Committee Member 1; Sales Engineer
1, 2019). These firms are not willing to pay extra for guaranteed passes at certain times, and instead
accept taking the ’leftover’ capacity after the higher-paying customers have been served, in return for a
lower price (Executive Committee Member 1, 2019). Substantial growth is expected within the new space
segment, but the opinions on its future importance for SaMS vary within the organisation. As stated by
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a sales engineer at SaMS:
’I believe that there is not a lot of revenue in the new space segment today. The firms that
evolve their services will likely drift into the commercial segment over time’ (Sales Engineer
1, 2019)
The business is divided into several market regions, each with differing business approaches. The Asian
market offer new opportunities, since it is a fast-emerging market. The Asian market has been lagging in
comparison to USA and Europe, but is now catching up and expected to grow more than its European
counterpart, since Europe is a more mature market. This growth is mainly due to new, commercial actors
and an increase of private capital being invested in the market (Business Development Director 3, 2019).
Revenue Model
At SaMS, the offering to customers is flexible and designed after the customers’ needs. However, the
most common revenue model is recurring revenue from ongoing payments in the form of a usage fee that
the customers pay in exchange for ’renting’ the antenna network capacity. A common arrangement is
payment based on usage, such as capacity used, amount of data collected, or minutes used. Another
type of arrangement is payment based on a set number of passes each time period, with the option to
add passes at a different price level. A third type of model is insurance, where customers who have their
own ground station network will pay an insurance premium to use SaMS’ antenna network ’in case of
emergency’, should their own network malfunction. Charges are also based on the service provided during
the pass (data collection, satellite control, et cetera). The revenue model is aimed at covering incurred
costs (Business Development Manager, 2019). It is not value based pricing, and one informant describes
the revenue models as simple, noting:
’This is something [SaMS] need to work with, finding new revenue models for their services’
(Business Development Manager, 2019)
One key factor is that the offer is flexible based on customer needs, meaning that the pricing mechanism
is dynamic, rather than fixed. For commercial customers, a negotiation pricing mechanism is employed,
meaning that the price is negotiated based on the circumstances of each deal. Agencies usually use public
procurement and this process therefore involves little dialogue. SaMS need to be compliant with certain
demands, and there is limited room for negotiations (Sales Engineer 1, 2019).
The contract times are usually quite long. However, there is a trend towards shorter contract time frames,
due to an increase in commercial actors, more intense competition and pressure on prices (Business
Development Manager, 2019).
Cost Structure
As mentioned earlier, the revenue model uses a cost-based pricing approach. The most substantial costs
are investments in new antennas, operations at the control centres, and maintenance (Sales Engineer 1,
2019, Business Development Manager, 2019). These are primarily fixed costs, and the business model
overall can be said to be more cost-driven than value-driven. However, SaMS are more value-driven
than many competitors, since they offer extended services and higher delivery security than competitors
catering exclusively to budget segments of the market.
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Key Activities
For the value to be delivered successfully to satisfied customers and in turn generate revenue streams, the
critical part is keeping the satellite management services running. This means that the most important
activities are all connected to operations. One informant states clearly:
’Operational, we work to a very high extent with operations’ (Business Development
Manager, 2019)
Another informant also states that securing ground for future expansion, together with replacing antennas
so they all stay modern, are key activities for the firm (Business Development Director 3, 2019).
Key Resources
The most commonly named key resources of SaMS are the antenna network and the competent person-
nel within the organisation. Many emphasise that while the antennas are the most obvious asset, the
personnel are of equal importance, with one informant remarking:
’The easy answer is to name the global antenna network [as the most important asset], but
that alone is not enough. We have an incredibly strong foundation of competence within the
company’ (Business Development Manager, 2019)
Another informant highlights the fact that SSC is a state owned company which comes with a good
international reputation. The strong brand helps SaMS establish new antennas in various geographical
areas, since Sweden is seen as a neutral country that maintains good relationships with most countries.
Esrange in Kiruna is also emphasised as a key resource (Business Development Director 3, 2019). One
reason for this is the unique combination of being a remote location close to the North pole that still has
a well developed infrastructure.
Summary of the Current Business Model
Based on the previously described elements, the business model of SaMS is summed up by Figure 6.
Figure 6: Current Business Model of SaMS.
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5.2.3 Collection and Distribution of Satellite Data
Management of satellites in a low Earth orbit (LEO) is the main business of SaMS. Satellites in LEO
usually completes one revolution around the Earth in circa 90 minutes. This type of orbit is common
for Earth observation satellites. Earth observation satellites have a polar orbit, meaning that they pass
above or close to the North and South pole each revolution. This means that if the ground station is close
to the poles, the satellite will pass over the station on almost every revolution, facilitating data transfer.
A station situated at the equator would come in contact with the satellite fewer times, due to the Earth’s
rotation. SSC have ground stations in Kiruna, northern Canada, southern Chile and southern Australia,
creating a kind of virtual north and south pole.
When the satellite passes a ground station, data from the satellite can be transferred to Earth. One
’pass’ takes about 15 minutes during which the antenna turns, following the satellite’s orbital path so
that it is constantly pointed towards the satellite (Systems Engineer, 2019). The customer chooses how
many of their satellite’s passes they want monitored and from what stations. Because the amount of data
is very large and customers usually want the data as fast as possible, the data is partitioned into files
that are sent one by one. Programs called accelerators can be used to make the transfer more efficient.
Files are then sent to the customer’s server. SaMS’ first step when accepting new customers is to define
all interfaces, what servers to use and what file formats are necessary, facilitating data transfer directly
into the customer’s system. Some customers want the data to be stored and transferred less frequently,
in which case regulations must be considered since many countries have laws that regulate data storage
(Systems Engineer, 2019). The process of data transfer is detailed in Figure 7.
Figure 7: Current Business Model of SaMS.
The data that is transferred to customers is unprocessed (level 0) data. There are customers who want
some processing done on site, in which case the first part of the processing is located at the ground station
and the processing systems are provided by the customers. This processing is done automatically and
SaMS personnel do not have access to the data. Their task is to intervene if the system malfunctions
and ensure that everything runs smoothly.
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6 Findings and Analysis
In this section, we present our findings and analysis based on the collected empirical material. The
backdrop is new disruptive technology that enables smaller, cheaper satellites, resulting in a larger data
collection and new entrants. The disruption causes concerns over diminishing returns in the traditional
business model, which leads into two distinct findings presented in this chapter.
6.1 Data Utilisation as a Driver for Business Model InnovationSSC has operated within their current market for several years with a strong business model, utilising
a unique value proposition of advanced technological capabilities and human resources. The firm has
cultivated this value proposition by engaging in incremental process innovation to optimise the incumbent
business model, thereby growing stronger on the current market. However, several informants express
concern over a market shift in the satellite sector, threatening profit margins and signalling the need to
encounter new business opportunities as a response to the experienced disruption. A consensus among
the informants about how the business model can be innovated lies within data utilisation, in the form
of satellite data processing. On this basis, the first finding is introduced:
Finding 1: Novel data utilisation, in the form of satellite data processing, acts as a driver
for business model innovation in the face of a market shift.
6.1.1 Data Processing as a Form of Utilisation
At SaMS, a frequently discussed form of data utilisation is the processing of satellite data. SaMS current
role is to function as a ’data pipe’ provider, meaning that they receive and transfer satellite data but
do not access or process it themselves. However, concerns have been raised about emerging technology
causing disruptions on the market. The technology enables a market trend towards smaller satellites and
larger satellite constellations, especially within the field of Earth observation. These constellations provide
imagery that is updated daily or even hourly, resulting in a large amounts of data. An important tool for
utilisation of these large sets of data is machine learning and artificial intelligence (Business Development
Manager, 2019). This development has brought up the discussion of how SaMS could monetise satellite
data processing.
Within the firm, there is a generally positive view on data processing. Increasing the firm’s data services
offering is seen as an important aspect of ensuring future competitiveness (Executive Committee Member
2, 2019; Project Manager 2, 2019). Several informants highlight that SSC has a distinct advantage within
this area; performing data processing is most effective as close to the source as possible. Instead of
transferring large amounts of data on the ground, data processing at the ground station requires less
bandwidth and ensures quicker delivery. Since SSC receives data at their ground stations, the firm is
closer than anyone to the data. Processing data offers a new potential business area, with one informant
noting:
’Some business areas may disappear in the future and therefore it is good to have versatility,
which data processing could provide. I personally believe that it is good to have ”more legs to
stand on”, which processing would offer’ (Project Manager 2, 2019)
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The sentiment is reiterated by another informant, who expresses the need for processing capabilities at
ground stations:
’Data processing at ground stations is preferable, and I would like to have processing on all
ground stations. [...] I believe we should do more processing locally’ (Business Development
Director 3, 2019)
6.1.2 Potential of Data Processing
Data processing could be performed with two main types of objectives; facilitate data management for the
customer or distribute the data further. Either the processing is performed separately on a customer’s
data sets, followed by direct distribution of the processed data to said customer. This enables the
customer to abstain from themselves performing the processing, adding more value for the customer.
The processing also enhances the value of the service provided by SaMS, since it means that transfer and
storage of data will require less resources. The second objective means that the processing service could
be complemented with SaMS taking an active role in further selling and distributing the data to third
parties. These third parties could be initiatives such as GWC.
Within the firm, there are varying opinions on what level of processing that could be suitable for SaMS to
perform, with consideration to the technical aspect. Some informants see no major obstacles and claim
that processing, at least up to level 2, is doable without drastic changes to the firm’s current activities.
Performing data processing up to level 4, where the result is a data analysis product, is however viewed
as more challenging. Several informants note that this move would require a substantial effort, such as
acquisition of an analytics firm or a strategic partnership with another firm that possesses data analytics
expertise. However, this may result in reaching a new type of customer. Overall, reaching a new customer
base by processing is something that is suggested by a number of informants. Furthermore, another
reason to perform processing is expressed by one informant who stresses the fact that one of SaMS’ main
competitors already work with data analysis towards niche markets (Business Development Manager,
2019), pointing out that the firm needs to increase efforts within this area to stay competitive.
One of SSC’s main competitors is helping their customers sell data at an increasing rate. One informant
with insight into the Asian market notes that existing customers want to start selling more data to grow
their business. However, it is uncertain that these customers will want SSC to sell or process their data,
but in light of competitors’ actions and customers’ new intentions it would be wise to at least offer it
(Business Development Director 3, 2019). Highlighting aforementioned, the informant with insight into
the Asian market expresses:
’Most customers are positive to sharing data, it’s all about revenue. SSC is trying to sell
more data for their customers. To maintain competitive advantage, selling data is a good
idea’ (Business Development Director 3, 2019)
There is a lot happening on the Asian market and it is developing faster than other markets at this
time, with many new capital-heavy entrants. Many of the new and smaller commercial customers lack
the technical know-how of the big agencies, and will therefore be in need of data processing from an
external actor. This is especially the case in the Chinese market, a capital-intensive market in growth
with a strong drive that has not yet developed the technical expertise necessary (Business Development
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Director 3, 2019). Furthermore, it is suggested that another way to sell processed data is by distributing
it into large organisations, such as the GWC project performed by SSC (Executive Committee Member
2, 2019). In order to succeed with this type of venture, the ability and know-how for processing will be
the vital part. Within the EU, there is a focus on handling big data from space, where processing is
a vital part of turning the data into accessible, useful products or services. With a Swedish initiative,
Sweden can become a major player in this expansive segment (Executive Committee Member 2, 2019).
Other future possibilities are the contribution to major projects addressing advanced data processing, as
well as research on the subject.
There is a positive tone towards data processing within the firm, as well as an awareness of processing
being a new market trend with considerable potential. This raises the question of why SaMS is not
already participating in this trend. Several informants, when asked about data processing at SSC, bring
up that SSC performed processing and analysis over a decade ago, producing maps based on satellite
imagery. But the subsidiary was shut down due to poor performance and became a major financial
setback for the firm. The firm has subsequently has actively avoided this type of business due to the
previous bad experience. There is also a belief that since data processing is not in the scope, the firm
does not present it to customers as an option and it is therefore difficult to assess potential interest for
the service (Business Development Manager, 2019). Even though this subsidiary bears little resemblance
to the type of processing that SaMS could perform today, SSC employees still make a connection between
the two. One informant explains that the failure was due to SSC being fifteen years too early with the
business idea, and a viable market did not yet exist. The current situation is different since the market is
now ready, and one informant even expresses that SaMS has lost sales because data services could not be
offered (Business Development Director 3, 2019). Several informants point to the fact that SSC still have
employees who worked with the subsidiary and therefore have valuable knowledge of data processing. To
perform processing to a higher level would require new expertise, but SSC still has personnel that has
worked with data processing before (Business Development Director 3, 2019). Nevertheless, there are
employees who worked at SSC at the time of the failed subsidiary who are hesitant towards data processing
and development in that direction (Executive Committee Member 1, 2019). But one informant stresses
that the firm is now facing new times, meaning that it is time to start looking into data processing again.
When considering data processing, some concerns are raised from informants. Since SaMS currently func-
tions as a data pipe provider that transfers data without accessing the content, the question of whether
or not customers are willing to provide access to their data arises. This concern is especially important to
consider regarding the objective to further distribute and sell processed data. One informant is hesitant,
comparing it to an internet provider accessing their customers’ internet data. Others believe that com-
mercial customers are willing to provide access in exchange for payment or lower prices on SaMS services,
since their objective is to earn a profit on their collected data. Furthermore, one potential negative impli-
cation of data processing is that several of SaMS customers perform data processing themselves, meaning
that they may perceive SaMS as a competitor and choose another ground station network provider. There
is also a substantial amount of firms currently focused on satellite data analytics, highlighting that careful
selection of what area to enter is vital (Executive Committee Member 1, 2019).
6.1.3 Partnerships as an Approach to Face Changes
One informant notes that SaMS do not focus on value-adding in regard to their data services, and that
they could take on a bigger role. Large companies with data processing expertise, such as Google, Amazon
and Microsoft, are showing interest in building their own antenna networks to provide data into their own
34
data centres. Several informants express that this development is a potential threat to SSC’s business,
with one employee noting:
’If actors from the ”data world” build their own antenna networks and offer data knowledge
as well, SSC might have the carpet pulled from underneath their feet’ (Business Development
Manager, 2019)
The entrance of these new actors has raised questions about what actions SSC should take in order to
stay competitive. There is no consensus on exactly how SSC should establish data processing, but one
commonly expressed opinion among the informants is to establish processing through a partnership. One
informant notes that there are many competitors within the processing and analytics area, with SSC
being far behind the leaders within the field, so it may be a better idea to form a strategic partnership
with another firm with more processing expertise. Another reason to collaborate is based on the fact
that processing requires processing power, which could be found with other companies having access to
data centres. Thus, it might be hard for SSC to compete and perform processing cheaper and more
efficient than such firms (Sales Engineer 1, 2019). Finding the right partners to collaborate with poses a
major challenge, but finding an appropriate partner can lead to higher level data processing resulting in
applications ready for customer use (Executive Committee Member 1, 2019). One informant highlights
a possible partnership, expressing:
’It would be good if we can find some way to cooperate. However, at the moment it is
unclear what form of cooperation it should be. [...] One thing that is clear though, emerging
entrants will be a threat to some extent’ (Executive Committee Member 1, 2019)
These new, capital-heavy entrants are not interested in LEOP services since this type of service requires
larger and more expensive antennas. However, SaMS’ service of collecting and transferring satellite data
for their customers is potentially threatened and there is a risk that SaMS thereby become redundant in
this business area. One informant expresses that due to the aforementioned development, it is important
for SSC to develop the antenna network in a rapid pace, so that it is hard for competitors to keep up.
6.1.4 Impact on the Current Business Model
A majority of the informants make an implicit connection between data processing and business model
innovation, noting that processing has the potential to reach new customers that lack expertise within
data management. Several informants note that the level of processing will have a substantial impact on
how the business model is changed. It is therefore evident that the change in business model depends on
the level of processing, with informants expressing a ’breaking point’ between level 2 and level 3.
Up to and including level 2 (’low-level processing’), processing will mainly function as a value-added
service that compliments SaMS’ other services. When viewed as a response to a combined market and
technological disruption, low-level processing constitutes a significant improvement of the existing service
and is therefore a technological and business model incremental exploration. The value proposition will
be extended by improvement of an existing service, which will serve as a way of reaching customers within
the ’new space’ segment. The revenue model will likely remain the same, since processing will be viewed
as an added service and charged as such. SaMS’ cost structure will not be dramatically changed, since
processing is primarily associated with fixed costs. Since processing on its own will not be vital to the
35
delivery of SaMS’ value proposition, the key activities and key resources remain unchanged. Low-level
processing thereby transforms the incumbent business model’s value proposition and value capturing.
Low-level processing as an organisational response and its corresponding business model are detailed in
Figure 8.
Figure 8: Low-Level Processing as an Organisational Response and its Corresponding Business Model.
For processing up to level 3 and above (’high-level processing’) the resulting changes on the incumbent
business model are more substantial. Processing at this level results in new products (images, analysed
material et cetera) rather than a continuation on the existing services. Introduction of high-level process-
ing transforms almost all elements of the incumbent business model and amounts to a radical change that
enables new application domains. A partnership is therefore, according to a majority of the interviewed
informants, the most likely way to go if SaMS is to start to offer high-level processing to its customers.
As a result, the partnership becomes a key resource to SaMS. The potential of a partnership acts as a
disruption of the current business model, since SaMS is not engaged in any type of large partnerships.
Enabling value creation and value capturing from high-level data processing requires new key activities,
as expressed by several informants when discussing how the firm would need to adapt to accommodate
processing. The revenue model will be transformed, since high-level processing can result in a variety
of new products and services, as well as new methods of charging for these products and services. A
subscription model for satellite data products is one potential new type of revenue model. Since high-level
data processing becomes a more substantial part of the value proposition, the key activities necessary to
deliver the value proposition also extend to data processing. High-level data processing thereby trans-
forms the incumbent business model’s value proposition, value creation and value capturing. The business
model and its translation into an organisational response is detailed in Figure 9.
36
Figure 9: High-Level Processing as an Organisational Response and its Corresponding Business Model.
Overall, this leads back to our first finding and illustrates that data processing acts as a business model
innovation by transforming the incumbent business model.
6.2 The Need for Organisational AmbidexterityDue to cost-reduction programmes, SSC management adheres to a policy of keeping overhead costs to a
minimum and requiring that new costs to be assigned to the firm’s operational activities. This has incited
to a sense of competition between innovation projects and the day-to-day business, leading to internal
conflicts between innovation efforts and keeping costs down. Questions on how the firm will do both arise;
how can SSC continue exploiting their current business to the fullest, while simultaneously exploring and
adapting a new business model? Several informants note the difficulties in promoting innovation within
the firm, which leads to our second finding:
Finding 2: There is a need for organisational ambidexterity within SaMS to facilitate the
success of business model innovation efforts.
6.2.1 Innovation Management at SSC
Historically, SSC’s management have worked with innovation in a cyclical manner. A special strategy
meeting is held on a five-year basis, where selected innovation ventures deemed to be of strategic impor-
tance are presented to the board of directors, who in turn decide which ventures will be realised. These
innovation ventures are developed beforehand in workshops by SSC personnel with certain expertise.
The ventures are closely tied to SSC’s strategy, and evaluated annually (Executive Committee Member
3, 2019).
The recent turbulence on the market has caused management to reconsider their current work on innova-
tion. To this end, the new role of ’Head of Business and Technology Innovation’ within SSC’s executive
committee has been created, replacing the former role of ’Head of Technology’. This role has overarching
responsibility for business and technological innovation and is tasked with evaluating market trends and
working to promote innovation within the firm. Evaluating the strategic innovation ventures annually
is no longer enough since the market is changing faster than before and a higher degree of alertness
and flexibility is now required (Executive Committee Member 3, 2019). By continually monitoring the
industry’s development, SSC can take on a more proactive stance when facing new disruptions. However,
several informants note that this is just the first step needed to promote innovation within the firm.
37
6.2.2 Innovation Projects at SaMSSeveral informants express that SaMS mainly focus on process innovation. Reducing costs is the primary
objective of the majority of innovation efforts, with automation and streamlining of the current operations
being especially important. As a result, SaMS’ business model innovations usually consist of small
adjustments to the current business model. One such example is that SaMS recently switched from
charging their customer per satellite pass to instead charging per minute used. This enabled a somewhat
higher degree of flexibility towards the customer by adjusting one of the business model elements (the
revenue model) slightly. The switch was a response to demands from customers that want higher flexibility
and a lower cost, made possible by technological advances on the automation front.
One example of a more extensive business model innovation at SaMS is their hosting business. As a
response to a market disruption in the form of an emerging Asian satellite market with a different set of
demands, SaMS in addition to selling capacity from their own antennas also begun hosting entire antennas
at their ground stations. Hosting was not part of SaMS original value offering and is not in line with
the firm’s overarching idea of providing advanced space services (Executive Committee Member 1, 2019).
Nevertheless, the firm has managed to balance the hosting business with their main business, even though
there is a risk of cannibalisation since customers that host their own antenna will not buy capacity from
SaMS’ existing antenna network. To maintain a balance between the two businesses, the firm employs
a strategy of only hosting antennas that do not compete directly with SaMS’ business, i.e., antennas for
dedicated missions that do not sell excess capacity to other users. This strategy has enabled SaMS to
maintain and develop their antenna network while simultaneously utilising their existing resources to reap
the benefits of an additional profit stream. While not constituting a substantial change in technology,
venturing into hosting was a significant expansion of SaMS’ (then) existing service. Hosting was not the
result of a conscious effort to innovate the business model, but rather the result of the firm responding
to market demands (Executive Committee Member 1, 2019).
Another innovation project is SSC Infinity, a service aimed at the emerging constellations of smaller
satellites. The project was initiated due to a shift in the market caused by commercial entrants that
require different and cheaper services than SaMS’ traditional customer segments, as well as a technological
disruption in the form of new technology enabling smaller and cheaper satellites. One informant describes
that the idea of SSC Infinity is to cater to the new segment of price-sensitive customers by offering the
same services in a scaled-down version, noting:
’[SSC Infinity] is sort of a ”light version” of SaMS’ traditional business’ (Executive
Committee Member 3, 2019)
By utilising smaller antennas and relying on standardised mission configurations, as well as web-based
interfaces for scheduling satellite passes, the price point for SSC Infinity can be kept lower than that of
SaMS’ traditional service offering. Developing the service required venturing into software development,
which has previously not been done at a large scale by SSC. The project, however, has taken longer than
expected to launch and has not yet gained the desired traction amongst customers.
One trend within the ground segment is a move towards higher frequencies, since they allow quicker data
transfer from satellite to the ground. With more satellites, lower frequencies risk becoming overcrowded.
This represents a combined technological and market disruption, since new technology may soon be-
come the dominant design as the market demands quicker and more efficient data transfer. Optical
38
communication is a promising alternative to using radio frequencies that is starting to emerge (Systems
Engineer, 2019; Executive Committee Member 1, 2019). SaMS are researching the potential of optical
communications, and SaMS personnel have been looking into possible applications.
These innovation efforts can be seen as organisational responses to disruptions. Figure 10 illustrates the
organisational responses; (1) charging per minute, (2) hosting, (3) SSC Infinity, (4) optical communica-
tion.
Figure 10: SaMS’ Responses to Disruptions.
6.2.3 Trial-and-Error Within the Organisation
Historically, the space industry has had little room for failure. Before the recent market disruptions, the
industry was dominated by large agencies. These agencies would evolve by initiating extensive project
where all aspects had to be evaluated and tested before anything was launched, which meant that a
project could run for over a decade before being ready (Executive Committee Member 2, 2019). One
hallmark of the ’new space’ entrants is that they are willing to accept errors and failures in order to reach
results quicker. One informant highlights the efforts of SpaceX as an example of this development, noting
that they have had several missions that did not go according to plan, but instead of being deterred by
apparent failures they have learned from the errors and tried again.
Within SSC, a self-identified ’legacy’ firm, the mentality of trial-and-error as an instrument of organ-
isational learning has not yet gained foothold. One informant who works with innovation within SSC
believes that extensive efforts will be required to promote a culture of trial-and-error within the firm.
The issue largely concerns corporate culture, since it evolves around new ways of working that the organ-
isation is not accustomed to (Executive Committee Member 2, 2019). Finding new work methods and
promoting a mentality of experimentation, where failure is not always viewed as negative, is therefore
something that several informants believe is vital to ensure that SSC does not fall behind the market
development. At SaMS, however, trial-and-error is not an option for the existing business since SaMS’
traditional customers pay for a high SLA. However, one informant notes that by working in close collab-
oration with new customers that are willing to accept a lower SLA in exchange for lower cost, SaMS can
create an arena for experimentation and exploration of new ideas. The sentiment is echoed by another
informant, who highlights the importance of working close to the ’new space’ customers in order to adapt
39
to their work processes.
At SSC, projects connected directly to the line organisation are normally evaluated using a ’lessons
learned’ approach at the project conclusion. However, for innovation projects there is no established
method for evaluating what has been learned and incorporating these findings into the organisation. One
informant expresses that the firm tries to learn from previous innovation projects, but the follow-up could
be improved greatly. However, the informant also believes that there is a risk that innovation efforts are
stifled if the evaluation becomes too convoluted and standardised into a complicated process. At SaMS,
there is a great deal of processes for all types of tasks, which may cause friction with innovation efforts
that are more exploratory in nature.
6.2.4 Exploitation at the Expense of Exploration
As a result of the recent cost-reduction programme, SaMS is under pressure to continuously deliver
results. The strict demands on cost efficiency means that there is little space left for innovation efforts.
Implementing the cost-reduction programme was a necessity in order to streamline the business and
ensure profitability, but with the changing market conditions there is now a need for increased innovation
efforts and exploration (Executive Committee Member 2, 2019).
Several informants note the need to connect innovation projects to the line organisation in order to utilise
employee ideas and knowledge. Strict economic demands, however, lead to division heads being focused
on their respective division’s bottom line and running their day-to-day business. As a result, conflict
arises between investing in innovation project aimed at developing the division and managing the line or-
ganisation. There is no room to hire new personnel to work primarily with innovation, since management
requires all hires to be connected to operations to keep overhead costs at a minimum. The responsibility
for innovation therefore falls to a few individuals within the organisation. Personnel working with inno-
vation projects are given a certain amount of hours each week to work on the project, and are expected
to spend the remainder of their time on their regular tasks. This temporal separation has resulted in a
feeling that the innovation projects amounts to more work for the individual employee (Executive Com-
mittee Member 3, 2019). One informant expresses the need for more resources allocated to innovation,
enabling innovation projects to have a dedicated team consisting of a specified time frame, budget and
project leader. These employees do not necessarily need to work full-time with these innovation projects,
but it is important to improve the current structure for how innovation projects are performed, with one
informant stating:
’Today, no one feels responsible for the results of innovation efforts’ (Executive Committee
Member 3, 2019)
Another informant highlights the importance of integration between the line organisation and the firm’s
innovation efforts, noting that innovation should not be performed separately from the main organisation
but rather in parallel to it. Several informants also state the need for a more dynamic process for
employees to pursue innovative ideas. Today, ideas get stuck somewhere along the line due to the regular
business operations being prioritised over innovation projects. A large majority of the interviewed
40
informants highlight that SSC staff has a lot of ideas, creativity and expertise, meaning that employee
creativity is likely wasted due to a poor structure for idea realisation.
’Ideally, when an SSC employee has an idea, it should go via their nearest manager to a top
manager in charge of innovation. Today, ideas don’t make it past middle-management’
(Executive Committee Member 2, 2019)
6.2.5 Promoting a Culture of Innovation Using Large-Scale Projects
SSC is described by several informants as a ’line organisation’ that doesn’t work project-based on a larger
scale. However, several large-scale innovation projects have been initiated in the past year within the
Science business unit. For instance, one project at Esrange aimed at establishing a test bed for reusable
rockets and other spacecraft development projects. The idea is to work close to customers and suppliers
to illustrate how new work methods can reduce costs and increase efficiency. The test bed is a unique
project that represents a substantial departure from Science’s regular business. From a business model
viewpoint, it is a radical exploration that has the capability of creating a whole new business segment
for SSC. It also represents a radical technology exploration since establishing the test bed requires a
drastic change in technology that enables a whole new application domain. Furthermore, the test bed is
a stepping stone to establishing satellite launching capabilities at Esrange, which would result in another
novel business area. So far, the project has exceeded expectations and been well-received from both SSC
employees and the market. One informant notes that the transformation has been remarkable, since just
a few years ago the Science division was facing substantial challenges and had difficulties departing from
their regular work methods.
’The old way of working has changed, the Science division has really loosened the reins and
it has worked fantastically’ (Executive Committee Member 2, 2019)
One informant notes that introducing large-scale projects created the necessary circumstances to allow
the Science division to work outside the strict demands of the line organisation, which in turn created
an opportunity utilise employee expertise and creativity. At SaMS, few projects are initiated at all. One
informant states that it would be better to initiate a larger number of projects in order to increase the
chances of success. Another informant shares the sentiment, but instead sees large-scale projects as a
solution, arguing that large projects with a wider target range will have a greater success rate.
The Science division’s quick adaption to more innovative work methods illustrates the presence of am-
bidextrous capabilities within the firm. The Science division is successfully managing their incumbent
business model while at the same time exploring radically different business ideas, illustrating how a
large-scale project provided an opportunity for the organisation to utilise the expertise and commitment
of its employees. The success of the Science division’s innovation efforts connects back to the second
finding that there is a need for organisational ambidexterity within SaMS, which could create better
conditions for innovation efforts.
41
7 Discussion
In this section, the findings outlined in the previous section are revisited and discussed in relation to the
thesis’ purpose and theoretical framework. Using the findings as a basis, the research questions are then
answered.
7.1 Data Utilisation as a Means to Reach InnovationTo address the purpose, the study has aimed its research towards it continuously. In order to fulfil the
purpose, two sub-question were formulated, guiding the research. Thus, answering both sub-questions
will entail us the possibility of answering the main research question. In order to arrive at a purpose
fulfilment, action was first taken towards answering sub-question one, followed by the second sub-question.
SQ1: How does novel data utilisation, in the form of data processing, contribute to business
model innovation?
Looking at the market trends and developments described by Sandau (2010), as well as Simonis (2019),
they align well with the internal perception of future development at SSC. This corroborates the notion
expressed by several researchers that the current disruption have a broad impact on the space industry
as a whole (Soille, Loekken and Albani 2019; Sandau 2010). The argument that satellite data processing
as a novel form of data utilisation would benefit SSC aligns with the fact that utilisation of big data from
space is one of the strongest observed market trends, with considerable research being conducted on the
subject (cf. T. Huang (2019) and Datcu et al. (2019)) which highlight data utilisation and analysis as
one of the focal points of the satellite sector’s development.
A major theme of the academic discourse on challenges of implementing EO solutions is how to process
big data from satellites (cf. Siqueira et al. (2019), Tiede et al. (2019) and Soille, Loekken and Albani
(2019). This illustrates the need for finding new ways to manage satellite data. As noted by T. Huang
(2019) and Neteler et al. (2019), the paradigm of processing as close to the data source as possible is
important in this regard since it enables more efficient processing. This is further supported by our
empirical findings from the case company. SSC are therefore in an advantageous position. Furthermore,
SaMS has promising prerequisites for processing in terms of both infrastructure and human resources,
coupled with preserved know-how on processing from the firm’s previous processing venture. Moving
towards processing would be in line with the market development described by Denis et al. (2017) where
ground station infrastructure becomes of increasing importance for satellite system performance.
However, it is important to note that there is division within the firm regarding the potential customer
demand of processed data. Since value is at the centre of a business model’s purpose, as evidenced by
the aggregate literature outlined in table 1, creating a product or service that customers actually want
is fundamental. Nevertheless, there are presumptions within SSC regarding customers having interest in
processing to some extent. Since data processing will create access to novel customer segments, judgement
on whether there is a demand for processing should not be made based only upon SSC’s current customers.
Within the firm there is also acknowledgement of the need to respond to a fast changing market in order to
stay competitive, aligning with Wirtz, Gottel and Daiser (2016). Furthermore, there is an explicit interest
to engage more in business model innovation since it is viewed as more effective than process innovation,
a view coinciding with the one presented by Amit and Zott (2012). One implication of introducing data
processing at SaMS would be access to new business areas with new customer segments as a result. When
42
juxtaposing the overall strategy of SSC with the question of data utilisation in form of processing, there
is a clear alignment between the two. As expressed by Teece (2010) and DaSilva and Trkman (2014),
taking strategy into account is a vital aspect of successful transformation of a business model. Hence,
data processing is deemed to be ideal for business model innovation in this sense. It would also be in line
with the firm’s strategy to expand on the Asian market since our empirical evidence suggests that this
market would respond positively to an increased service offering. This development is thought to be a
possibility for several markets, which is further supported by Siqueira et al. (2019).
In regard to new entrants on the ground station segment, it is evident that the development described
by Denis et al. (2017) is causing apprehension within SaMS. The capital-heavy and agile new entrants
have the potential to transform the industry, and there is uncertainty on how this advancement should
be handled. This points further to the initiation of data processing, as a solution to not be outrun by the
capital-heavy new entrants. However, new entrants possessing expertise within data storage and handling
may be difficult to compete with. Empirical evidence suggests that partnering with another firm may
provide a way of entering the market of data processing, which is in line with Mohr and Spekman’s
1994 notion that partnerships provide access to novel technology and complimentary skills. Furthermore,
strategic partnerships align with SSC’s strategy goals where partnerships are highlighted as a way of
meeting the fast changing market.
7.1.1 Data Processing’s Transformational Effect on the Business Model
Low-level processing is a way of giving data a more central role within SaMS, which in accordance with
Morabito (2015) can result in an enhanced value creation. Thus, the value proposition may improve with
relatively little risk and effort compared to high-level processing. Furthermore, as highlighted by Sorescu
(2017), incremental innovation can lead to competitive advantage that is as sustainable as that emerging
from radical innovation. This means that venturing into radical innovation just for the sake of it is not
necessarily a good idea, and that innovation efforts should not strive to be radical but rather be evaluated
based on other merits. Thus it is interesting to note that the response of high-level processing does not
necessarily equal a better outcome, even though it is more aggressive. The business model of low-level
processing does show that processing is doable without drastic changes to the firm’s current activities,
which was the presumption expressed by several informants. Hence, the low-level business model is not
deemed to bring major obstacles.
If SaMS venture into high-level processing, the radical transformation of the business model will enable
the reaching of new customer segments that can be served by the firm’s value proposition. A possible
implication of offering data analytics is the attainment of completely new, third-party customers who are
not directly associated to the space industry but interested in analytics products. However, our empirical
evidence suggests that venturing into data analytics may place SSC in a position of direct competition
with their current customers, in which case the firm risks damaging its incumbent business. This potential
trade-off situation must therefore be carefully assessed if high-level processing is to become a successful
venture.
In SaMS’ current business model there are no partnerships established, meaning that there is no experi-
ence on how to form a strategic alliance with another firm. This may prove a challenge for SSC if they are
to enter into a partnership to establish high-level data processing capabilities. Osterwalder and Pigneur
(2010) highlight the importance of partnerships in their framework, which illustrates that a well-planned
partnership can be a considerable asset to the focal firm. Another advantage of a strategic partnership is
43
the risk-reducing abilities (Mohr and Spekman 1994). Since the satellite sector is currently undergoing a
transformation with uncertainties regarding what technology will become dominant and what customers
will demand, it may be preferential to share the risk with another firm. However, since the potential
partnership would likely be with a multinational, capital-heavy type of firm, the risk for information
asymmetry and decreased autonomy expressed by Mohr and Spekman (1994) is likely larger than if SSC
were to partner with a smaller firm. Entering into a partnership as a response to a combined market and
technological disruption is also described by Kaulio, Thoren and Rohrbeck (2017), which highlights that
forming an alliance with another firm may be a natural response to a drastically shifting environment.
It is important to note that the introduction of processing is not necessarily an either-or scenario. Low-
level processing could act as a stepping stone towards higher-level processing. It can also function as a way
to ease into the market, so that when the future market scenario becomes more clear SaMS already has a
foothold on the market. Nevertheless, there still needs to be a decision on what path is most suitable for
SSC, as the emergence of two distinct business models is evident from the empirical evidence. When the
business models are categorised according to the patterns described by Schuritz and Satzger (2016), it is
clear that low-level processing results in transformation of value proposition and value capturing (pattern
IV), while high-level processing results in transformation of all the underlying elements (pattern V). This
indicates that the degree of business model transformation due to novel data utilisation is dependent
on which (and how many) of the business model elements are transformed. Furthermore, this suggests
that the more business elements are transformed, the more radical the transformation is, which is also
indicated by Schuritz and Satzger (2016). Figure 11 illustrates how the incumbent business model is
transformed based on what decision is taken regarding level of data processing.
Figure 11: Transformation of SaMS’ Business Model Over Time.
44
In accordance with Schuritz and Satzger (2016), as well as Hartmann et al. (2016) and Brownlow et al.
(2015), our research shows that novel data utilisation, here in the form of data processing, acts as a driver
for business model innovation. Furthermore, the study shows that the degree of change in the business
model is dependent on how many of the business model elements are affected, which corroborates the
patterns of data infused business models put forth by Schuritz and Satzger (2016).
7.2 Balancing of Exploration and ExploitationHaving answered the first sub-question, the second sub-question is now revisited and answered.
SQ2: How can an incumbent space firm explore new business models while simultaneously
exploiting the existing business model?
Our empirical evidence suggests that SSC does not have the organisational capabilities necessary to
fully support exploratory innovation efforts. The firm’s initial reaction to a failed subsidiary has had
long-lasting effects in the form of aversion to performing data processing, which is in line with Sosna,
Trevinyo-Rodriguez and Velamuri’s (2010) finding that early responses to failure is a determining factor
of future trial-and-error efforts. The lack of experimentation can also be attributed to external factors,
such as a corporate culture of perfection that has historically permeated the space industry. Evidence
suggests that as the firm faces a disruption, the lack of experience with trial-and-error makes the firm
slow to develop and launch their responses. This is demonstrated by the SSC Infinity project, which
despite being a vital effort to stay competitive in the market has taken too long to launch and not yet
gained full traction. The ability to quickly shift activities to meet changing demands is the key aspect
of adaptability (Gibson and Birkinshaw 2004). Hence, adaptability becomes imperative as the previous
culture of perfection within the space industry is disrupted by new entrants who utilise trial-and-error
learning to quickly reach results.
It is interesting to note that in the reviewed literature, the main criticism of temporal separation put forth
by Gibson and Birkinshaw (2004) is that it requires managerial decisions about how to divide time between
tasks aimed at alignment and adaptability, respectively. SSC currently applies temporal separation
to their innovation projects, which has not been functioning satisfactorily according to management.
However, the managerial criticism is that temporal separation leads to a perceived increase in tasks
for the employees, impeding the willingness to participate in firm innovation. Ineffectiveness in the
form of time spent on planning the division between innovation project and regular tasks is not viewed
as the most problematic. The aforementioned suggests that for a firm with a strict line organisation,
temporal separation causes friction with the regular operations by creating a trade-off scenario for the
firm’s employees. Gibson and Birkinshaw (2004) highlight that perceived trade-off scenarios such as the
aforementioned often lead to structural separation, which is illustrated by the fact that several managers
at SSC expressed a preference for separate innovation teams.
By mapping SaMS’ responses to a market disruption (hosting), a technological disruption (charging per
minute) and a combination of the two (SSC Infinity and optical communication), we are not able to
observe the pattern detailed by Kaulio, Thoren and Rohrbeck (2017). However, since only four responses
are mapped there is not have sufficient basis for commenting on the validity of the described pattern. It
is interesting to note that SaMS, a technology-driven business unit, seems to choose exploration primarily
through technology as they face a combined technological and market disruption, which was predicted
by Kaulio, Thoren and Rohrbeck (2017).
45
SSC has moved from a loosely governed ’start-up’ phase towards a less flexible configuration, consistent
with the stages described by Christensen, Bartman and Van Bever (2016). SSC is now firmly planted
in the ’efficiency’ stage where focus lies on incremental improvement of existing processes and reducing
costs by standardising. However, the Science business unit has recently managed to produce radical
responses to market and technology disruptions. We attribute this newly-found ambidextrous capability
to a balancing of the four contextual dimensions put forth by Gibson and Birkinshaw (2004). The Science
business unit is described as having moved from a strict focus on delivering results within their current
business area, causing an emphasis on alignment where discipline and stretch dominate the organisational
context. By introducing large-scale projects with a broad target objective, there was an opportunity for
the trust and support dimensions to develop. Consequently, a balance between the four dimensions arose
which in turn enabled ambidexterity in the form of simultaneous alignment and adaptability within the
business unit. Contrary to the idea of structural separation proposed by Christensen, Bartman and
Van Bever (2016) and Tushman and O’Reilly (1996), the Science division’s successful management of
two highly distinct business models within the same business unit presents a case for the advantages of
contextual ambidexterity.
Following the success of the Science division, one potential way for SaMS to move towards radical innova-
tion from both a technological and business model standpoint is to leverage data processing. Out of the
two business models presented in the previous section, it is the business model for higher-level processing
that offers a radical form of innovation. The project GWC provides an opportunity for SaMS to initiate a
large-scale project similar to those started at the Science business unit, and could be a potential catalyst
for achieving ambidexterity within SaMS. Since GWC has shown promise and been approved for phase
2, it provides a clear frame within which SaMS could develop experiments. A possible first step to design
experimentation is to utilise the mapped business models for data processing presented in 7.1 to deter-
mine what type of experiments are needed, in accordance to Chesbrough (2010). Data processing would
require venturing into a new business area and investing in new forms of technology, so when viewed as
an organisational response it is mapped into the radical quadrant of the double ambidexterity framework
by Kaulio, Thoren and Rohrbeck (2017).
By following the positive example of the Science division and creating an arena for experimentation, in
the form of a large-scale project that has a broad target, while also balancing out the contextual elements
and rethinking the temporal separation design of innovation projects, SaMS can develop their exploration
capabilities while simultaneously maintaining their exploitation of the incumbent business model.
7.3 Answering the Research QuestionHaving answered both sub-questions allows us to meet the purpose of this thesis by answering the main
question:
MRQ: How can novel data utilisation drive business model innovation for an established
technology firm still reaping benefits from its incumbent business model?
The findings that emerged during this research indicates that novel data utilisation contributes to inno-
vation of the incumbent business model by driving it towards development resulting in two new business
models. Thus, data utilisation can result in two new variants of the incumbent business model, with
more or less radical changes made. The two alternative are built upon the incumbent model leaving
it to continue being a vital part of the firm. Meaning that the incumbent business model should be
46
kept viable as the same time as a new one is developed. Furthermore, our main research question is
answered by combining the answers to our previously discussed sub-questions. Novel data utilisation in
the from of satellite data processing can drive business model innovation by transforming the business
model’s underlying elements. The degree of transformation is dependent on which and how many of the
underlying elements are transformed. Furthermore, by balancing the contextual dimensions of discipline,
stretch, trust, and support, while also creating conditions that promote trial-and-error learning, the firm
can exploit their current business model while exploring potential new models.
7.4 Sustainability AspectsGenerally, consumers as well as society at large have become more aware of environmental impacts and
demand companies to conduct sustainable business. Our empirical evidence is not in close relation to
sustainability, however it is worth noting the potential sustainability impacts associated to the subject.
On a firm level, SSC may have a negative environmental impact, especially considering hardware manu-
facturing and handling, as well as rocket launches. However, this is not highlighted in our findings, which
only touch on the firm’s service offerings and how they may become more efficient. Hence, there are
no major sustainability impacts originating from our empirical evidence. However, the findings of this
case study may have contributed to increased sustainability by enabling a less resource-consuming data
transfer. From a long-term perspective, data utilisation in the form of satellite data processing could
enable a higher degree of sustainability by contributing to the global sustainability goals expressed in
the UN’s Agenda 2030. This by SSC’s participation in the project GWC, which is a long-term objec-
tive of transforming the business model. The mission of the GWC project is to work towards fulfilling
the sustainability goals of Agenda 2030, and would constitute a major contribution to promote global
sustainable development.
The business model of low-level processing indicates that the firm will keep ownership of the hardware
and products needed for the service offering. This control can enable a more efficient use of resources as
further hardware development and product designs may adapt an increased sustainability aspect, both
in economic and environmental terms. However, choosing the business model alternative of high-level
processing will result in a partnership, meaning that SSC may lose some control over the sustainability
aspects of their operations. This illustrates the importance of taking sustainability into consideration
when choosing a partner. Furthermore, the concept of being an ambidextrous organisation makes SSC
resilient to change, enabling easier adaptation to their environment and making the organisation more
dynamic and sustainable. Finally, it is worth mentioning that the innovation efforts suggested, to lower the
burden on personnel participating in innovation projects, will have a positive affect on the social dimension
of sustainability. The innovation efforts will also contribute to employees feeling more encouraged and
appreciated for their exploratory initiatives, further contributing to increased social sustainability.
47
8 Conclusions
In this section, the thesis’ conclusions are presented. The limitations of the thesis are also highlighted in
this section, and future research topics are suggested.
This study aimed to investigate how novel data utilisation can drive business model innovation in a
firm that is operating using a still successful business model. The investigation was conducted using a
embedded single case study approach, with the focal firm as the unit of analysis.
We found that data utilisation, in the form of data processing, drives business model innovation by trans-
forming the business model’s underlying elements. The study also found that the degree of transformation
is dependent on how many elements are transformed, discovering two potential business models that result
from data processing. Furthermore, the study indicates that a lack of a culture of trial-and-error learning
inhibits the firm’s innovative capabilities and impedes exploratory innovation efforts. The success of one
business unit in managing organisational ambidexterity provides a potential case for large-scale projects
as a catalyst for organisational ambidexterity.
This study contributed to theory in that it built upon the limited stream of literature on data driven
business models to provide a practical case study. Since the literature on the subject is limited and
few in-depth case studies have been performed within the field, our thesis work has aided in testing the
proposed theories in a real-world setting. Our work has also bridged the subjects of data utilisation and
business model innovation, effectively demonstrating the connection between two distinct research fields.
8.1 Limitations and Future ResearchThis study is not without limitations, one being the choice of conducting a single case study. If the same
research could have been performed by multiple cases, results could have been corroborated using multiple
sources, thereby improving the accuracy of our results. Since the case study was performed in an industry
with few actors, however, it was still deemed to be unique enough to provide valid insights. Furthermore,
the research area is only investigated from an internal perspective and not a customer perspective, due to
difficulties regarding sensitive information. Further research into the subject should therefore investigate
the customer perspective to evaluate the viability of data processing and its resulting business model.
Access to data has to some extent been limited, thus impacting the study since sensitive data may have
been withheld.
Based on our findings, we consider it to be of interest for future research to tackle the interplay between
business model innovation and organisational ambidexterity, and in particular the novel double ambidex-
terity field. Furthermore, our empirical findings provided new insights regarding how big data can change
a business model, but there is still room for further questions and thus further research on how big data
can drive business model innovation is necessary. Furthermore, questions regarding corporate culture
have also been detected. This was however deemed to fall outside of the thesis’ scope. In this sense, our
first finding was initially not evident, seen to the pre-study conducted that indicated a generally negative
attitude towards satellite data utilisation. However, when investigated further this turned out to be
in opposite, and we found that no one knew who the resisting persons really were. This is interesting
and indicates a corporate culture that is partly inhibitory, making us conclude that the opportunities
of satellite data utilisation have not been realised partly due to the corporate culture blocking the way.
This leads us to conclude that the area of corporate culture, in regard to it acting as a potential hinder
for innovation and its effects on a business model, is suitable for future research.
48
The phenomenon of large-scale projects providing an arena for improved ambidexterity capabilities is
interesting from a research perspective, since it is an indication not found in the reviewed literature.
A suggestion for future research is therefore to investigate if a correlation exists between the size of an
innovation project and its resulting degree of exploration.
49
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Appendix A - List of Informants
The list below details the interviewed informants and the corresponding interview date.
Title Interview Date
Project Manager 1 2019-02-11
Project Manager 2 2019-03-14
Business Development Director 1 2019-02-13
Business Development Director 2 2019-02-19
Business Development Director 3 2019-03-14
Systems Engineer 2019-02-12
Sales Engineer 1 2019-03-05
Sales Engineer 2 2019-02-12
Business Development Manager 2019-03-15
Executive Committee Member 1 2019-04-26
Executive Committee Member 2 2019-04-29
Executive Committee Member 3 2019-04-25
Appendix B - Interview Protocol
The following questions were used for the semi-structured interviews carried out to map the current
business model of SaMS. The open-ended questions asked about data processing are also detailed. Lastly,
questions regarding innovation are detailed, which is the additional adjustment to the protocol as in line
with our method.
Interview Questions Regarding the Business Model
• What value does SaMS offer its customers?
• Who are the customers of SaMS?
– Are there distinct customer segments?
• What market does the firm operate on?
• What is revenue model for SaMS?
• What are the most substantial costs incurred for SaMS?
• What are SaMS’ key activities?
• What are SaMS’ key assets?
• Are you part of any partnership with other companies?
– Why?
– Who?
Interview Questions Regarding Data Utilisation
• What are your thoughts on satellite data utilisation in the form of processing?
• At present, SSC only supplies raw data. What do you think about the possibility for SaMS to start
offering its customers more processing?
• What challenges do you believe are associated with satellite data processing, from SaMS’ point of
view?
Interview Questions Regarding Innovation
History of innovation
• What major changes have occurred within SSC due to innovation?
– How has the company managed innovation historically?
Hosting, an example of Business Model Innovation
• How did Hosting occur?
– Does it classifies as radical or incremental innovation?
• Was there any problems or resistance associated with the implementation?
– In that case, how was it handled?
• How was the reasoning for that activity of hosting, considering that it potentially threatens the
established business?
• How did SSC succeed in making the two competing businesses coexist as they do today?
Organisation
• Have there been initiative to promote innovation within the organisation?
• How is ’new vs. old’ handled, when a new idea potentially is threatening the existence and estab-
lished business/operations?
• Is the work with new innovations separate from the rest of the work, for example by ’separate
teams’ or business units?
