To what extent do Strategic
Technology Alliances
influence the competitive
position of service providers
in data analytics?
A two-part explorative qualitative study
Bachelor Thesis
University of Amsterdam Faculty of Economics and Business
By Tsi Kwan Lam Student number: 10327568 1st Supervisor: Erik Dirksen MSc.
Statement of Originality
This document is written by Student Tsi Kwan Lam who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text
and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
This research is a study on the extent that Strategic Technology Alliances (STA) influence the competitive position of service providers in data analytics. The research question of this study is: To what extent do Strategic Technology Alliances influence the competitive position of service providers in data analytics? The study is done through a two-part explorative, qualitative research. The first part, an industry vendor analysis, is done through analyst reports. The second part is a case study with a semi structured interview method. The analyst reports offered insight in four service providers’ competitive position in a course of 5 years and the case study offers an in-depth look into a specific STA and the influence it has on the service provider in data analytics. The case study consists of 6 high level employees interviews, four from Atos and two from Siemens, all working in functions related to the alliance and/or to data analytics. The results of the two-part study revealed that the
competitive position of service providers improved with a new alliance but this depends on the size of the firm and the scope of the alliance. However, when service providers engage in an alliance formation the experienced benefit are increased ease of market entry and shared performance risk. This thesis used literature from the strategic management literature including theories revolving around the motives of STA’s and the current forms of STA’s.
Table of content
Abstract p. 3
Introduction p. 6
Research question p. 7
Literature review p. 8
Strategic Alliances and Strategic Technology Alliance p. 8 Influence of Alliances on the competitive position p. 9
§ Market entry p. 9
§ Performance Risk p. 9
§ Knowledge and expertise p. 10
§ Competitive position p. 10
Big Data & Data Analytics by Service Providers p. 11
Methodology p. 12
Research selection p. 12
Service Providers selection p. 12
§ Accenture p. 13
§ IBM p. 13
§ Capgemini p. 13
§ Atos p. 13
Research method selection p. 14
Industry vendor analysis p. 14
§ Gartner p. 14
§ Nelson Hall p. 16
Case study interview analysis p. 16
§ Case study selection motivation p. 17
§ Atos & Siemens p. 17
§ Semi structured interview method p. 18
§ Propositions p. 19
§ Interview structure p. 20
§ Validity and reliability p. 21
§ Participants p. 21
§ Data analysis method p. 21
Results p. 22
Industry vendor analysis results p. 22
§ Accenture p. 23
§ IBM p. 23
§ Capgemini p. 24
§ Atos p. 24
Case study interview results p. 25
§ The joint approach of the Alliance in data analytics solutions p. 25 § The influence of the Alliance on the capabilities of the firms p. 26
in data analytics solutions
- Market entry p. 26
- Performance risk p. 27
- Additional point p. 29
Discussion p. 31
Industry vendor analysis results discussion p. 31
§ Accenture & IBM p. 32
§ Capgemini p. 32
§ Atos p. 33
Case study interview results discussion p. 33
§ Propositions results p. 34
Conclusion p. 36
Conclusion p. 36
Contribution and limitation p. 36
Managerial implication p. 37
Future research recommendation p. 37
Reference list p. 39 Appendix p. 47 Appendix A p. 47 Appendix B p. 48 Appendix C p. 49 Appendix D p. 50 Appendix E p. 51 Appendix F p. 52 Appendix G p. 53 Appendix H p. 82
Introduction
Strategic alliances refer to an agreement between firms in which each firm collaboratively pursues a common set of objectives (Elmuti & Kathawala, 2001). These alliances can be between firms and their suppliers, competitors or firms within another market. An alliance is called a Strategic Technology Alliance or better known as STA when the collaboration of the parties aims to have a joint innovation effort and/or technology transfer that can have a lasting effect on the product-market positioning of the involved firms (Hagedoorn & Schakenraad, 1994).
Almost every day new alliances are being reported and firms engage in a partnership,
especially on the technology level (Strategy&, 2018). The popularity of alliances is increasing, due to the increased competition arising from the fast pace changing global market (Nielsen, 2007). The increase in competition causes firms to have difficulty answering the pressure for global demand and a global market presence (Isoraite, 2009). The use of alliances can support firms to become market leaders and at the same time seize opportunities in
innovation to strengthen their place in the competitive environment (Thompson et al. 2004). Many researchers have claimed the benefits of alliances on the competitive advantage of corporations (Bowersox, 1990; Khanna et al., 1998; Park et al., 2004) as they offer speed and flexibility in accessing new markets, help achieve economies of scales and further support development of skills and capabilities of the firms involved (Larsson et al., 1998). The attractive benefits explain the flourishing amount of alliances in high technology industries in the last decades (Yasuda, 2005). Alliances are growing in this industry specifically due to its benefits in sharing risk and finances. The advantage of interfirm sharing helps supporting innovation endeavours and seizing opportunities within the climate of globalization where there is a high pace of frontier technology development, rising cost of product development and complexity of products (Teece, 1992).
Today’s businesses are more and more invested in the data they possess and receive (Chen et al., 2012). They are focussed on the increased usage and storage of large amount of data and the applicability of it (Lee et al., 2014). The large data amount, also known as Big data, is large in volume, is generated in high velocity and takes on a variety of forms (Watson, 2014). Big data due to its characteristics requires specialized analytical capabilities of firms to put the information generated into use. The process of these large amount of data and the applicability of it is known as data analytics (Lavalle et al., 2011). Data analytics is about the understanding of data and how this can be put to use in full effect for the exploitation of the owner (Lavalle et al., 2011).
Data analytics is not new as different analysis techniques of data have existed for many years (regression, simulation, statistics etc.) but the analytics of Big data is new in its form due to computer technology (Davenport et al., 2012). The sources of information and applicability have made big data analytics unique in its potential of usage (Watson, 2014). The potential of data analytics is immense as it can help predict behaviour and analyse past behaviour in reference to efficiency for future decisions (Lavalle et al., 2011). Its application can be found in different industries such as manufacturing, health care, energy and more. Enough studies have proven that the interpretation of big data can offer opportunities to firms (Diaz et al., 2015), but interpretation of Big data often requires the technological knowledge and platform to analyse the data (Lavalle et al., 2011). The lack of technological capabilities has caused firms to turn to technology service providers for support (Delen & Dermirkan, 2013). The interest for data analytics has been growing more and more as
companies see the added value to their products and services (Morabito, 2015). The growth has also caused an increase in competition for service providers (Sharda et al., 2014) and a high demand of analytics in every industry. More service providers are launching
partnerships within different industries to develop analytics solutions suited to the respective industries to answer this high demand for data analytics and the pressing
competition. In contrary to the increasing numbers of STA’s in data analytics there have not been written a lot about the implication of alliances for service providers in data analytics. Mainly the position of service providers has not been explored yet and this had let to the question of what the extent of forming such an alliance has on the service provider of data analytics.
Strategic Alliances and data analytics in general have been explored extensively separately in the past literatures but the study of alliances in data analytics specifically is still lacking due to this phenomenon of alliances has only been growing in the last decade. It is clear how vendors benefit from the inclusion of data analytics in their products and how this
potentially impacts their value proposition and competitive position in the market but it is however unknown how external service providers engaging in data analytics for vendors are benefitting from the cooperation as well.
This thesis aims to contribute to both the study of STA’s and data analytics by researching how the competitive position of service providers is influenced by engaging into a STA on data analytics.
By addressing the gaps in the current studies of Strategic technology alliances and Data Analytics, this thesis aims to find practical implications of the usage of alliances for service providers in their competitive position specifically in data analytics by stating the following research question:
To what extent do Strategic Technology Alliances influence the competitive
position of service providers in data analytics?
This thesis is drawing ideas from the strategic management literature on alliances. This includes theories revolving around the motives of alliances and reports on the competitive position of service providers in the last 5 years. The past literature and reports will form the basis of this research and will be discussed during the study.
This thesis is structured as follows: In the introduction above, the thesis topic and research question are introduced. This is followed by a literature review in the next chapter with relevant studies on the topic of STA’s, big data and data analytics. Then the thesis continues with the methodology of this research with an explanation on the selected firms included in this research. This will be followed by the results of the research and the discussion of it. Finally, the thesis will conclude with a conclusion, limitation, managerial implication and future research recommendation.
Literature review
In the following section the relevant literature will be discussed regarding STA’s and data analytics.
Strategic Alliances and Strategic Technology Alliance
It’s key to understand how the function, description and definition of alliances has changed over time hand how this has evolved in the definition particularly for Strategic Technology Alliances (STA). A review of the definition and description through the years of the concept of Strategic alliances is presented below.
The growth of alliances has been increasing in nearly every industry and has become one of the key drivers for superior growth (Booz-Allen & Hamilton, 1997). It is clear that engaging in an alliance can potentially bring success for parties involved as the numbers have been increasing in the last decades (Zineldin et al., 2015).
In the past, Strategic alliances were described as a distinct form to enter new market by using the alliance as a means to gain access to the market and the local infrastructure (Rhoades & Lush, 1997). The formation of alliances was seen as a relatively enduring
cooperation of firms with arrangements that involve the flow and linkages in using resources in jointly accomplishing the individual goals of each firm (Parkhe, 1991). The past
descriptions point outs possible aspects of Strategic alliances but are not always applicable to the current form of alliances. The description of Rhoades & Lush stresses the utilization of alliances for entering new market, this is potentially one of the benefits but is no longer the only focus of the formation. As some current alliances are formed for improving competitive positioning, sharing risk, economy of scope and/or other purposes. In addition, Strategic Alliances were stated by Parkhe (1991) as enduring which is not always the case. Despite the increasing numbers of alliances this does not always result in an everlasting partnership. Stability and sustainability of alliances is depending on the cooperation of the firms and their ability to adapt and respond to the changing internal and external environment (Zineldin et al., 2015). Today’s openness for collaborations exceeds more than just the exchange of ideas. Successful cooperation includes the high value each firm places on the contribution and the balanced arrangement that results in a valuable win-win output. Not many alliances are able to live up to these conditions as Zineldin et al. (2015) noted many studies
investigated the sustainability of alliances and over 50% of the alliances fail (Cartwright and Cooper, 1995). The term enduring is therefore not applicable in general when discussing alliances. This thesis will therefore focus on a more contemporary concept of Strategic alliances, as a collaboration with the focus on sharing. This refers to the concept of Elmuti & Kathawala (2001), the concept of Strategic Alliances has become widely used in business to refer to different types of agreements between two or more companies that pursue
collaborative strategic objectives. This thesis therefore defines Strategic Technology
Alliances as a partnership that aims specifically to achieve collaborative strategic objectives with regard to technological advancement by sharing knowledge and expertise
(Schoenmakers & Duysters, 2006). The focus of this thesis is merely placed on the interfirm sharing and the consequences of sharing on the service provider competitive position within a Strategic Technology Alliance.
Influence of Alliances on the competitive position
The choice of forming an alliance can mainly account for the firm’s willingness to overcome deficits in their own expertise and capability in order to gain competitive advantage
(Isoraite, 2009). By forming an alliance, firms improve their competitive position and entry to new markets, meanwhile the alliance offers critical skills wanted by the firms (Isoraite, 2009). The forming of alliances brings in the needed expertise and personnel to create the desired skillsets and capabilities but also brings potential risk with it (Das & Teng, 2001). These capabilities, skillsets and risk have an influence on the firms’ performance and operations.
Looking into the main goals and risks of alliances will expose the possible influence Strategic Technology Alliances are expected to have on the service providers’ performance. In the following a set of influences has been defined based on past research and review articles on Strategic Alliances in general. The main contribution for the defined advantages in this thesis comes from the articles of Elmuti & Kathawala (2001) and Soares (2007).
Market entry
Through the support of an alliance the firm will achieve the benefits of rapid entry while keeping the cost down due to the customer base of the partner firm or the credibility of having the alliance with a strong alliance partner (Solesvik & Westhead, 2010). Next to it the alliance can offer benefits such as economies of scope and scale in marketing and
distribution (Isoraite, 2009). Performance risk
In the literature of Strategic Alliances two main operational risks have been identified, Relational risk and Performance risk (Das & Teng, 2001). Relational risk is caused internally in contrast to performance risk which is caused externally. Relational risk relates to the act of self-interest of firms involved in an alliance and performance risk relates to the risk of the market the firms operates in. Despite the influence of relational risk on the performance of a firm in an alliance, this thesis will disregard the relational risk as this concerns alliances internal organization. The focus will be put at Performance risk as this concerns the competitive landscape in which the alliance operates in and the risk of service providers operating in such a competitive environment.
Performance risk can still occur despite having a satisfactory cooperation as this can be affected by outside factors. The external factors affecting the cooperation of the alliance relates to the performance risk; the probability of external factors influencing the
performance of alliances (Das & Teng, 2001). This risk is present in all types of strategies and is not alliance specific. This risk is influenced by factors such as a change in government policies, change in market demands, new competitors or just sheer bad luck. Firms may experience an increase of this risk when the market is unstable and when there are lot uncertainties, particularly when entering new markets or launching new products/services (Isoraite, 2009). The formation of STA’s can reduce the risk the firm faces or control this by jointly approaching the market or launching solutions together and dividing the risk faced together (Solesvik & Westhead, 2010).
Knowledge and expertise
Firms in general are only competent in some area and are lacking expertise in other areas. Alliance can therefore offer knowledge and expertise in areas each alliance firm is lacking of (Solesvik & Westhead, 2010). Due to the increased extended knowledge and capabilities by the formation of alliances, STA’s are expected to be able to address complex problems which they would not have individually (Tidd et al., 2005). This will increase the business
opportunities firms can be involved in (Solesvik & Westhead, 2010). The accessibility of knowledge and expertise increases the firm capability to innovate, this especially is the case in STA’s as these alliances are formed with a focus on the R&D endeavours and firms commit to jointly (Lin et al., 2012).
Competitive position
Having discussed the potential influences of forming alliances (Market entry, Risk, Knowledge and Expertise), it’s key to look into how this affect the competitive position. The dynamic of the influences and how this relates to the competitive position is presented in a conceptual model for this thesis in figure 1 showing a relationship of two firms (Firm A and Firm B). The dotted line shows an influential relationship and the solid lines represent the visible relationships.
Firms engaging in alliances can bring synergy into the business processes and leverages each other strengths and reputation (Ireland, Hitt & Vaidyanath, 2002). The competitive
positioning is improved by the extended resources, knowledge, market presence and
customer base (Parise & Casher, 2003). The extent however depends on the partner fit and if the activities are complementary to each other.
Big Data & Data Analytics by Service Providers
In the introduction of this research, the term data analytics was mentioned as the focus of this research. Data analytics in this thesis refers to the analytics of Big data. Big data is a term coined to describe a large set of data that is both structured and unstructured collected from the business operations on day to day basis (Etzion & Aragon-Correa, 2016). Despite the popular use of the term in the past decade, a concrete definition is yet to be received (De Mauro et al., 2015). One of the main acknowledged definition so far on what sets Big data apart from regular data is the three V’s of Laney (2001) of data management: Volume, velocity and Variety. The three V’s relates to: Volume as in the large amount of data in Terabytes, Velocity as in the data being created in or near real-time, Variety as in a variety in structured and unstructured data.
Big data itself is useless when considered alone, it’s potential value lays in the analyzation also known as Data Analytics, and the implementation of the information in decision making (Lavalle et al., 2011). As the current business environment evolves in a more technological integrated and data filled business, decision making becomes more and more complex (Delen & Dermikan, 2013). The pressing economy and competition require managers to make thoughtful decisions backed up by evidence and information. The lack of capabilities of firms to analyse and/or capture the data for these decisions has resulted in the use of
service providers in supporting firms with their data analysis (Banerjee & Williams, 2009). The concept of Data Analytics in this thesis follows the definition of Kwon et al. (2014) and Chen et al. (2012) as their broad definition include all the aspects that service providers are offering in their data analytics solutions. Kwon et al. and Chen et al. defined Data Analytics as technologies and techniques a company can use for analysing large amount of complex data for different ways of application in augmenting firm performance in a variety of dimensions. The process of big data analytics itself can be divided into five stages that form two main sub-processes. The two sub processes are the data management part and the data analytics part (Gandomi & Haider, 2015). Figure 2 (Gandomi & Haider, 2015) gives an
overview of the five different stages.
For the purpose of this research we will consider service providers offering data analytics as defined by Kwok et al. (2014) and Chen et al. (2012), with the inclusion of the whole Big data process defined by Gandomi & Haider (2015).
figure 2: Big data processes
Methodology
Research selection
There are different ways and methods in conducting research. It’s crucial to align the research question and goal of the study with the research strategy and the method of data collection at the start of the research (Saunders et al., 2009). The aim for this thesis is to conduct an exploratory research as the aim of this study is to uncover new insights and put the topic discussed in a scientific perspective (Robson, 2002). This is with regard to the fact that a study on the influence of STA on the competitive position of service providers in data analytics specific is a fairly unfamiliar territory in the current literature. Since this research is exploring the extent the influence of STA’s has on service providers that offer data analytics solution, a qualitative research will be more suitable as Gephart (2004) noted that a
qualitative approach offers a greater extent in in-depth information opposed to a
quantitative research collecting statistical data. The qualitative approach allows an in-depth examination of the extent the influences of STA’s have and helps to understand how this is reflected in service providers that compete in a high technology industry, therefore this research will be qualitative.
Service Providers selection
As discussed, this research will narrow the topic of STA’s to a study of service providers and their competitive position in data analytics. Due to a time limit the study will be reduced to 4 key providers in the data analytics service market that makes up an estimated one third of the Big Data Analytics services market following the market share estimates from the Nelson Hall report (2018) on the Global Business Data Analytics Service market. The selection of service providers for this research has been made based on the list of Global Business Data Analytics Service market by Nelson Hall (2018), found in table 1 on the next page, and the authors relationship with the firms. The first three firms of the chosen four vendors are Accenture, IBM and Capgemini. The three vendors are selected based on their rank as they make up the top three of the global market in data analytics (Nelson Hall, 2018) as seen in table 1. The top three vendors are purposely chosen due to their rank as this offers an extensive amount of already available information, making the investigation possible for this research. The fourth vendor, Atos, was chosen based on the author of this thesis current relationship with the firm, the author is an intern at the firm and this offers him access to information of Atos data analytics practice.
Table 1
The global Business Data Analytics (BDA) Service market 2017 based on the revenue closing year (CY) of 2017
Source: The Global Data Analytics Service Market. By Nelson Hall (2017)
An overview of the four chosen firms is given next, offering short background information on the firms and their data analytics practice.
Accenture
Accenture is a management consulting, technology services and outsourcing company (Accenture, 2018). Accenture provides big data and analytics services mostly through its Accenture Analytics (AA) practice, now renamed Accenture Applied
Intelligence (AI) practice (Nelson Hall, 2018). IBM
IBM has a great history in business intelligence and has built further on this with their analytics tools which resulted in their strong presence in the data analytics market (PAC, 2017). The core of IBM data analytics offering is IBM Watson, which was launched in 2014. Watson is an extensive offer from IBM consisting of cloud platform, analytics tools and cognitive functions (IBM, 2018).
Capgemini
Capgemini is a global firm in consulting, technology services and digital transformation (Capgemini, 2018). Capgemini started their data analytics and consulting services in 2010 under the global practice Business Information Management, this later evolved in 2015 in the global practice Insight & Data (I&D) (Capgemini, 2015). The evolution goes paired with the firms more business approach, leveraging their analytics consulting capabilities to Big data and analytics and improving their alignment with their other innovation unit (Nelson Hall, 2017).
Atos
Atos is an information technology services company that specializes in hi-tech transactional services, cyber security, data center, cloud solution, software, transformation, integrated systems and big data (Bloomberg, 2018). In 2015 the company set up a new business unit, Atos Digital offering services such as digital commerce, digital integration and data analytics.
In that same year the company set up a Center of Excellence of Atos Codex which was launched as an Atos brand in 2016 for advanced analytics, IoT and cognitive solutions (Atos, 2016). Atos Codex is an end-to-end analytics suite consisting of methodologies, consulting services, innovative capabilities, analytics platform and a set of vertical offerings. The Atos Codex Platform as a Service resulted from a joint investment with and for Siemens in 2014 but launched later in 2015 as an open platform (Nelson Hall, 2017).
Research Method selection
The qualitative research conducted for this thesis consists of two parts. The first part is an analysis of service providers in data analytics using vendor assessment and industry reports. This part of the research will uncover how vendors in data analytics services have changed in their competitive position over the past 5 years and offers information on how the
operations of the firm in data analytics influenced this and to what extent alliances are responsible for the change in competitive position.
The second part of the research consists of 6 in-depth semi structured interviews, conducted to collect data on what specific advantage from STA’s, acknowledged in the strategic alliance literature, have influenced the service providers in their competitive position. The interviews part of the research will be a case study of one STA due to the limited time and resources. The interviews are done so that it opens up the discussion for participants on how STA is reflected in their firms’ competitive advantage.
The two analyses together will offer insight in which advantages of Strategic Alliances influenced the competitive advantage of service providers and to what extent the competitive advantage of STA’s impact the competitive position of the service provider. Each research method of the two-part analysis is elaborated below with a description of the approach.
Industry vendor analysis
This part of the research is realized through selected reports from industry experts. The contribution can be found from two different analyst firms, Gartner and Nelson Hall. The Magic Quadrant reports from Gartner together with the collected information of firms’ activities in data analytics from Nelson Halls’ vendor reports will be analysed and grouped together based on year and firm. The results will be evaluated by reviewing the activities that a firm has undertaken in a year and if they correlate with the change in the firms’ competitive position. The reports chosen for this research are published in the last 5 years, from 2014 up to and including 2018, as this helps to frame a specific research timeline and supports an understanding on how the position of the firms has changed over the years. The next section explains the reports used and what kind of information it contains.
Gartner
First the Magic Quadrants reports from Gartner on Data analytics service vendors are one of the reports frequent used for this research. Gartner is a global research and advisory firm for the IT industry and they are members of the S&P 500 (Gartner, 2018). Gartner published every year or two Magic Quadrants, the reports are made to analyse markets where growth is high and provider differentiation is distinct (Gartner, 2018). The Magic Quadrants of Gartner offer in-depth analyses and compares vendors based on Gartner’s standard criteria and methodology (Gartner, 2018). Each report includes a Magic Quadrant graphic that depicts a market using a two-dimensional matrix that evaluates vendors based on their
“Completeness of Vision” and “Ability to Execute” (Gartner, 2018) as seen in figure 3 below. The “Completeness of Vision” relates to the firms’ strategic vision meaning their
understanding of the market, their strategy and innovative vision (Gartner, 2018). The “Ability to Execute” in contrast relates to the firms’ capacity to implement the services described in their strategic plans, meaning their market operations, their track record and overall viability (Gartner, 2018). Both of these matrixes consider a set of evaluation criteria and each of the criteria has an assigned weight based on the market the Magic Quadrant is reporting on. An in-depth explanation of the evaluation criteria for each of the matrix can be found in Appendix C (Ability to Execute) and Appendix D (Completeness in Vision).
The matrixes together provide a graphical positioning of four types of technology providers: Leaders, Challengers, Visonaries and Niche Players which can be seen in figure 3 below. Leaders in the Magic Quadrants are described as firms that execute well against their
current vision and are well positioned for tomorrow (Gartner, 2018). Visionaries in the Magic Quadrants are described as firms that understand where the market is going or have a vision for changing market rules, however these types of firms do not yet execute well (Gartner, 2018). Niche Players on the other hand is described as firms that focus successfully on a small segment, or are unfocused and do not out-innovate or outperform others (Gartner, 2018). The last type are the Challengers, these are firms that execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction (Gartner, 2018). An elaborate description of the four types as technology providers can be found in Appendix A.
figure 3: Magic Quadrant model
Source: Gartner Magic Quadrant. by Gartner (2018).
The position of vendors in a Magic Quadrant does not imply the profitability of the firm as the quadrant solely offers valuable information on how the firm is positioned compared to their competitors. The quadrant however does help investors in their decision making as one can decide what criteria is considered more important. For example, if you want to make a
strategic investment in a technology, a vendor's viability is crucial. You would therefore weigh a vendor's ability to execute more heavily than its completeness of vision, which means evaluating Challengers before Visionaries (Gartner, 2018). In contrast, if you could gain a competitive advantage by investing in an emerging technology, you would evaluate Visionaries before Challengers (Gartner, 2018). That being said, in a competitive landscape, such as the one of data analytics service vendors, a large market presence is favored and therefore the position as a Leader would be seen as an optimal position and the vendors in this thesis are expected to stay or grow into the position of Leader in the Magic Quadrants. The Magic Quadrants have been cited in different scientific articles (Hu et al., 2014; Mariscal et al., 2010; Chen et al., 2012; Liberatore & Luo, 2010) and are accepted industry wide. The Magic Quadrants specifically used for this thesis are the one on Global Business Data and Analytics Service Providers Worldwide (GBDASPW). The inclusion and exclusion criteria for vendors to be included in the Magic Quadrants are presented in Appendix B. The
weighted criterions assigned for the Quadrants used in this thesis are presented in table 2 and 3. The definition of the review criteria can be found in Appendix C for Ability to Execute and Appendix D for Completeness in Vision.
Table 2: Table 3:
Evaluation Criteria “Ability to Execute” Evaluation Criteria “Completeness in Vision” for the GBDASPW Magic Quadrant for the GBDASPW Magic Quadrant
Source: Gartner, 2018 Source: Gartner, 2018 Nelson Hall
The Key Vendor Assessments (KVA) from Nelson Hall are the other frequent reports used in this research. The Key Vendor Assessment reports from Nelson Hall provide information on the firms’ key activity within a year that impacted their data analytics operations. KVA is a program of Nelson Hall for over a decade and is acknowledged industry wide (NelsonHall, 2018). The Assessments provide in-depth information on IT services vendors. The KVA are meant for buyers to make informed vendor selection decisions and vendors themselves to maintain up to date on their competitors and service partners.
Case study interview analysis
The previous part of the research presents a general overview of how the activities firms engages in relate to their competitive position in data analytics. This part of the research zooms in on one specific STA and the activities the alliance engages in for a more in-depth
understanding of how STA and their competitive advantage influence the competitive
position of the service provider. The analyst reports in the previous part of the research does not specify how the advantage of alliances is reflected in the firm as they only uncover the activity that may have influenced the service providers’ competitive position. In order to understand how alliance advantages have influenced the firms involved, an in-depth
additional qualitative research is conducted with a semi-constructed interview method. Due to a limited time a case study is chosen as approach since focussing on one specific alliance will allow enough time to explore the influence of STA fully.
Atos and Siemens Case study selection motivation
The case study of this part of the research will be on the Strategic alliance of Atos and Siemens in Data Analytics. The choice for the case study was motivated by two reasons. First, the author of this thesis is currently interning at the Global Siemens Alliance team from Atos and has therefore been in the privileged position of experiencing a technology alliance on first hand basis. The internship has inspired the author of this thesis to approach the topic of STA’s on an academic level to further his understanding on corporate operations and strategic decision making. In addition, the authors position in Atos has offered him the accessibility to contact top level management relevant for this study. Also, the authors’ internal knowledge of the cooperation of the alliance made a deep dive into the subject possible, making interviews efficient and results more fruitful.
The alliance of Atos and Siemens is an extensive partnership as it consists of a joint venture, joint go-to-market approach and the firm’s management involvement in both firms’ boards. Atos, a French headquartered international IT service company in digital services, with Siemens, a German headquartered global company, the largest industrial manufacturing company in Europe, offers a strong European alliance. This European alliance in a landscape of North American dominated company makes it in addition interesting to investigate in, especially in the current global usage of data and the data usage/storage policy of non-European firms as non-non-European data management firms have been criticized for their protection of data (Diker Vanberg, 2018; Hall, 2018; van Eijk et al., 2017).
Atos & Siemens
Atos and Siemens launched their strategic alliance in a form of a contractual arrangement in 2011 for combining their IT solutions and services resources (Siemens, 2015). The aim of Siemens, a global industrial technology manufacturer, and Atos, a global IT provider, was to maximize the strengths of both firms in their products and solutions by combining the capabilities of engineering and hardware from Siemens with the IT services expertise of Atos (Siemens, 2015). The Alliance of Atos and Siemens offers and integrated “end-to-end
solutions” in which it builds, designs, operate and implement the solutions for their customers (Atos, 2018).
The organization of the collaboration of the two firm is set up in three ways; Siemens is a shareholder, client and alliance partner from Atos. Siemens is a shareholder with a 12,2% stake in the capital of Atos and a membership at the Atos board of directors. Atos at the same time is integrated into the Siemens one, the go-to-market framework of Siemens for large scale accounts (Atos, 2018). Next the firms have established a joint investment fund with an equal contribution of each firm making it therefore a STA. The fund is set up for developing joint capabilities which the alliance can jointly bring to the market. In addition, the alliance covers 15 areas of collaboration in which they offer joint solutions (Atos, 2018).
The governance of the alliance is set up in a way in which the senior management, middle management and operational management of both firms meet up frequently. In addition, there is local alignment at the management and sales levels in each country to ensure the collaboration at a local level. An overview of the relationship is presented in figure 4.
Figure 4: The basis of the Atos and Siemens Alliance
Source: Atos (2016)
Semi structured interview method
This part of the qualitative research was executed through interviews with high level
employees with a function related to the alliance of Atos and Siemens and/or data analytics. The interviewees come from both Atos and Siemens and are either leading projects in the alliance on data analytics or in a management position within the alliance and engage on several topics which data analytics is one of.
The interview follows a semi structured interview approach which gives the participants the opportunity to freely express their opinions and ideas well within the research topic (Louise Barriball & While, 1994). The use of semi structured interview aims to have a deeper
understanding for the discussed topic (King, 1994). The questions used are based on the topic of alliances with a focus on data analytics. A semi structured interview also allows additional and follow up questions to be asked, a flexible order of questions depending on how the conversation is going and the direction it is taking (Saunders & Lewis, 2012). The degree of these flexible questions regarding the topics may vary from interview to interview due to the focus on the discovery and exploration of the various aspect of the topic using the semi structure interview approach (Bryman & Bell, 2015). After the interviews with each participant a transcribed version of the recorded interview is made and offered to the corresponding candidate for their approval.
Proposition
The interview is structured along a set of propositions, developed based on the literature of Strategic Alliances. To develop the proposition, three advantages have been identified from the literature: Ease of market entry, Risk and Knowledge & expertise as discussed in the literature review. A conceptual model presented at the literature review (Figure 1) was used to clarify how the advantages is reflected in the competitive position of the firm.
Based on the conceptual model and the identified advantages, three propositions have been drawn in order to support the research. The propositions are stated next with the
elaboration for each of the proposition.
1. STA’s have a positive influence on the market entry of service providers in data analytics
2. STA’s have a negative influence on the risk of service providers in data analytics solutions
3. STA’s have a positive influence on the access to knowledge and expertise of service providers data analytics solutions
Proposition 1: STA’s have a positive influence on the market entry of service providers in data analytics
This proposition refers to the extent of increased accessibility of new market which is
expected due to the existence of the alliance partner presence, expertise and their customer base. This follows Solesvik & Westhead (2010) proposed benefit of alliances. The expected positive influence can for example be accounted to the customer base of the partner firm as in this case study Atos may benefit from the existing customer base of Siemens. This will offer new markets to the partner firm and entry in the market easier as each firm operate in different industries, geographies and can offer support in the entry of new markets.
Proposition 2: STA’s have a negative influence on the risk of service providers in data analytics solutions
This proposition refers to the influence of performance risk as this is expected to be minimized for their joint activity in data analytics. The expectancy of performance risk perceived by the alliance of Atos and Siemens is low since multiple studies on strategic alliances has shown a lowered performance risk due to the division of cost, time and the increase of efficiency (Das & Teng, 2001).
Proposition 3: STA’s have a positive influence on the access to knowledge and expertise of service providers data analytics solutions
This proposition refers to the positive influence alliances have on the firm accessible knowledge and expertise. This is expected to be positive in the case of STA’s due to their joint innovation, particular the case study as Atos and Siemens have a joint fund for collaboration and developing new products and services.
Interview structure
The interview has been split into four section, each with their corresponding subsections: 1. Introduction
2. The operation of the Alliance of Atos and Siemens in data analytics solutions 3. Influence of the Alliance of Atos and Siemens on the capabilities of the firm in
data analytics solutions a. Market entry b. Risk
c. Knowledge and expertise 4. Conclusion
a. Controlling and future research recommendation b. Word of thanks and closing the interview
The structure of the interview helps to give it direction and is done for efficiency purposes. In total 13 questions are prepared (Appendix E) and grouped in the corresponding section following the structure of the interview. In Appendix E the questions are presented with an overview in which one can see whether the questions apply to any of the stated proposition or not. In the following, each section is explained with their objectives and the expected questions.
Section 1: Introduction
The introduction is established as an introductory part of each interview. This helps the participant to introduce him/her self and his/her background with the firm before diving into the questions related to the literature and the stated proposition of this research.
Section 2: The operation of the Alliance of Atos and Siemens in data analytics solutions This section establishes the basic operation of the Alliance in data analytics. These questions relate to all the propositions since this considers the operation of the alliance in data
analytics in general. These questions are set up to help illustrate the participants ideas regarding the alliance on data analytics before diving into the identified advantages from the literature.
Section 3: Influence of the Alliance of Atos and Siemens on the capabilities of the firms in data analytics solutions
This section is oriented on answering the proposition established for this research. By stating findings from past literature and to elaborate on those findings helps the participants
understand the current literature on Alliances. After informing the participants on the three influences found in the Strategic Alliances literature, each participant is asked to voice his/her opinion on the advantages and how he/she thinks this is reflected in the alliance of Atos and Siemens in data analytics. As this is a semi structured interview, opportunity to ask follow up questions is available during the interview.
Section 4: Conclusion
This the final part of the interview and function as the last opportunity for participants to express relevant opinions that might not have been brought up yet or which have not been looked into before. In addition, a summary of the participants opinions are summed up and
checked with the participants as a control question. The interview is then concluded with a last question about future research recommendation and then the interview ends with a word of thanks.
Validity and Reliability
The interview quality is tested and conforms to the validity and reliability of the three criteria Saunders et al. (2007) stated in the book “Formulating the research design”. Saunders et al. stated reliability of the qualitative data to be high when the following three criteria are met:
1. The measure in this research yields the same result on other occasions
2. The outcome will be similar when the same measure is done by another observer. 3. There is transparency in how conclusions were drawn from the raw data
Participants
In total six employees, four from Atos and two from Siemens were selected for the interviews. The participant was selected using the snowball non-probability sampling method (Saunders and Lewis, 2012). This method helps identify subsequent participants by information received from initial participants (Saunders and Lewis, 2012). This method proved to be a helpful tool especially reaching participants that were less accessible through the use of this method the prospect was reached successfully.
The selected participants for the interviews are based on their function, function level and their personal experience with the alliance of the discussed firms. These criteria gave insight into the difference experiences from the alliance cooperation and insight in the partnership. The candidates and their information can be found at Appendix F.
Data analysis method
Each interview has been recorded, transcribed following Saunders et al. (2009) approach for data collection. The data collected through the interviews have been analyzed following the process described by Ryan & Bernard (2003) for data analysis. This approach consists of a four-steps of gradually analyzing collected data from interviews.
The first step was to identify central themes with their corresponding sub codes under which the data can be categorized. The identified themes sprung from a deductive and inductive approach by determining this through the past literature and the transcripts.
Next the themes and their sub codes go through a selection in which only the relevant themes and sub codes remain that are relevant to this research. This was followed by the rearrangement of the themes and codes in a logical hierarchical order. Finally, the selected themes and sub codes are connected with the different propositions for this research in a synthesis table to offer an overview of the results with regard to the propositions.
This analysis approach is based on selecting and connecting the themes and patterns to the propositions through subjective interpretations on the collected data (Neuendorf, 2002). The approach can be accounted to a content analysis approach in which the categorization and arrangement of data is done so to give a clear understanding of the data and to highlight relevant aspects of it (Neuendorf, 2002).
Results
Industry vendor analysis
The movements in competitive position of the selected service providers is traced by putting the Gartner Magic Quadrants from the period 2014 up and including 2018 next to each other (figure 5) and labelling the firms with a specific colour. The colours used for labeling are defined in table 4 with the corresponding firm. The results are presented in the next pages of this chapter together with the collected data from the vendor reports of each firm.
Figure 5. Gartner Magic Quadrant 2014 – 2018.
Source: Gartner (2014;2015;2017;2018) Table 4: Labelled firms of the Magic Quadrants
Firm Colours
Accenture Orange
Atos Green
Capgemini Yellow
Accenture
Accenture has been placed in the Leaders quadrant in every data analytics service providers Magic Quadrant report of Gartner dating from 2014 up until now.
In 2014 Accenture was acknowledged by Gartner for their strength in execution capabilities through their investments for data management scientist, analytics assets and innovation centres (Gartner, 2014). These investments helped Accenture gain ability to offer products and resources for information analysis and management that is significant broad in the market at that period of time (Gartner, 2014).
Their capabilities further increased with the launch of their Accenture Analytics Application Platform and their Accenture Insight Platform in 2015 (Accenture, 2015). The firm continues to build a broad portfolio in that same year by investing in innovation, academic alliances with Massachusetts Institute of Technology and the Stevens Institute of Technology, acquisitions (Schlumberger business consulting, Cloud Sherpas, FusionX and Agilex) and a collaborative business group with Amazon Web Services (Accenture, 2018).
In 2016 and 2017 Accenture continued to invest in their analytics platform together with a focus on partnerships, mainly launching partnerships with major vendors (SAP, Google and Apple). In that same year Accenture also made several small acquisitions. The position of Accenture in the Magic Quadrant however remained the same in 2017 compared to 2015 while other firms in the Leaders quadrant during 2015 have moved higher within the quadrant in 2017 (Gartner, 2015; 2017).
In 2018 the position of Accenture in the Leaders quadrant has improved towards a more completeness of vision. In that same year the firm has made public their achievement of four key acquisition related to customer experience (Accenture, 2018).
IBM
IBM has been placed in the Leaders quadrant since 2014 and has been recognized as a Leader up including now. The firm has a strong history in the data and analytics market (Forrester, 2017) and therefore has maintained their presence in the data analytics service market throughout the years.
In 2014 IBM was noted for their investment in upskilling their consultants through high levels of talent investment for data science, mobile, cloud and cognitive-oriented teams (Gartner, 2014). This together with IBM’s delivery centers and analytics centers offers customers a full spectrum of analytics services. There were however weaknesses defined in the analytics organization such as slow responsiveness and flexibility, this was due to their resource issues and unavailability of local staff in certain regions (Gartner, 2014).
In 2015 the resource issue was solved by IBM through reorganization and creating an additional IBM analytics business unit. In addition, the firm continues to invest in nine new Analytics Solution Centers and new partnerships for innovation purposes (Gartner, 2015). In 2016 and 2017 IBM made a notable acquisition of the Weather Company and started a global partnership with Salesforce which further marked their presence in the market (Gartner, 2017).
In 2018 IBM continued to invest in innovation by teaming up with universities on several project and strengthened their global strategic partnership with Salesforce (Gartner, 2018).
Capgemini
In 2014 Capgemini started in the Challengers quadrant of the Gartner Magic Quadrant. The recognition as a Challenger was due to the firm shown capabilities in Data Analytics but their lack of an innovative vision. Gartner (2014) noted their low global presence in their data analytics solutions at that time as a point of improvement. The firm since then has moved from Challengers to Visionaries (2015) and Leaders (2017 & 2018).
The firm biggest acquisition to date is IGATE, a global IT services company operating mainly in North America. The acquisition was completed in 2015 and has improved the firms’ profitability and their presence in North America. The acquisition has also influenced their data analytics operations as it has increased the number of clients in the I&D practice (Nelson Hall, 2017).
In the research scope of 2014-2018 Capgemini has expanded their number of partnerships and partnership agreement with existing partners (Nelson Hall 2018).
These expansions include:
• Cloudera, Capgemini expanded their partnership in 2014 as a reseller of Cloudera
products and training additional 500 personnel on Cloudera products by the end of 2015.
• Pivotal joint Business Data Lake, which aims to combine big data volumes from new sources with traditional internal client sources and make it easier to access data (Capgemini, 2013).
• Informatica and their participation in the Business Data Lake of Capgemini and Pivotal (Capgemini, 2015)
• SAS in 2015 and later in 2018 Amazon Web Services (AWS)
In addition, Capgemini launched several Applied Innovation Exchange (AIE) centers during the course of 2016 and 2017 which focus on innovation. The AIE centers continue to develop and in 2018 the centers have grown their self-service and platform automation capabilities with multiple cloud offerings, in addition to their acquisitions of Lyons Consulting Group, Itelios, and Idean in that same year (Nelson Hall, 2017).
Atos
Atos started as a Niche player in 2014 and slowly the firm has shifted its position to the Challengers’ position in 2018 (Gartner, 2014; 2018). The firm was recognized as a Niche player back in 2014 due to the lack of integration of their Business intelligence and information management with their consultancy offers. However, Atos’ Alliance with Siemens was recognized as a strength of the firm as this offers to be a bridge for IT and operational technology business analytics needs.
In 2015 Atos made improvement and moved from the Niche to the Visionary quadrant. Their Alliance with Siemens has expanded that year into their analytics and Internet of Things offers, especially in manufacturing which complements its existing solutions. This was part of their new defined initiatives during their alliance board meeting (Atos, 2015). In addition, Atos made two big acquisition: Bull, an IT firm with capabilities in Cloud, Cyber Security and Big data, at the beginning of the year and the IT outsourcing business from Xerox later that year to further expand their portfolio.
In 2017 Atos was placed again in the Visionary quadrant. In that same year Atos Codex was launched, an integrated offering of end-to-end analytics solutions in which they partner with different parties across the full IT value chain. The alliance with Siemens has been
mentioned in 2017 again as this has strengthened at the end of 2016 by the increased joint investment fund and their collaboration on the Atos Codex platform (Atos, 2016).
In 2018 Atos has moved to the Challengers quadrant. The firm acquired in that year a technology company, zData, and multiple healthcare companies (Anthelio Healthcare Solutions, Pursuit Healthcare advisors, Coduent’s Healthcare provider consulting and Breakaway Group businesses). In addition, the firm also launched new technology alliances with a strategic partner and a start-up (Google Cloud & Air-Lynx) (Gartner, 2018).
Case study interview results
The answers of the respondents have been transcribed (Appendix G) and each answer is grouped based on the themes and subthemes and the relevant quotes have been put into a synthesis table (Appendix H). The synthesis table gives an overview of the relevant answers by grouping the sort of answers together and presenting the citations.
The duration of the interviews lasted between 37 minutes to 1 hour and 26 minutes. 5 out of 6 of the interviews were mostly held in English with the exception of one which was held in Dutch. The Dutch interview was later translated to English for the transcription. All the interviews have been taped and saved for the validity of this research.
In the following section an overview of the results is presented following the interview structure presented in the methodology. The presentation of the results starts with section 2 as section 1 of the interview structure is the introduction and is not part of this research result.
Section 2 The joint approach of the Alliance of Atos and Siemens in data analytics solutions Section 2 is focused on the joint approach of the Alliance of Atos and Siemens regarding data analytics. Based on the literature and the way the Alliance was organized there was high expectations regarding this theme as the Alliance described itself as combining the strengths of the two firms in a joint business approach. However, this approach is not apparent in the joint data analytical solutions but rather in their collaboration of the go-to-market strategy which is strengthened by their combined sales force.
The solutions itself are developed individually by each firm, the development foundation is however shared in a form of a platform which both parties developed together. As
respondent 1 clarified this through the following: “If you talk about collaboration the only thing that has been collaboratively was the platform. Both the parties have decided not to share the analytical solutions which they have built on top of the platforms. The target was to enable both companies to create digital business.”
He points out that the main joint activity is the creation of business opportunities as he argues: “The main joint activity is creating a market for data analytics services. This is the real main activity. Other than that, is creating architecture and maintaining the architecture for a common platform. Everything else is done separately.”
However, all respondents acknowledge the success as Respondent 4 points out: “You win time as you are faster and putting resources together. You know having requirements from different domains from the Atos and Siemens domains really contribute to the robustness of the functionality of the applications we are developing jointly together. This helps us identify the common building blocks that could be used in various domains and jointly develop a general building block that can be used on different domains.” Which is further proven by
respondent 6 “I think the alliance has shown this idea, concept is very successful and I believe
without the alliance. Would some the solution be done without the alliance? Yes, because we have partners. But I believe half of it would not have been here without the alliance.”
Section 3 The influence of the Alliance of Atos and Siemens on the capabilities of the firms in data analytics solutions
The following section considers the results gathered for section 3, the influence of the alliance on the capabilities of the firm in data analytics solutions. This has been divided in the 3 advantages identified from the literature: Market entry, Risk and Knowledge & Expertise.
Market entry
The advantage “Market entry” considers the accessibility to new markets by the firms within the alliance and the influence the alliance has on this specific topic.
As expected based on the past literature there is a significant ease of entry in new markets as all respondent agreed on this as a benefit in general. There are three prevalent
contributing factors found in the answers of the respondent. This has been recognized as: customer base, reference and industry knowledge.
• Customer base
All the respondents contribute the ease of market entry to the broadened customer base of the partner firm. The finding is for example voiced by respondent 1 as follows: “There is definitely an ease of market entry. Atos has access to customers and markets that Siemens does not readily have access to. We have a long-standing collaboration of addressing customers with IT
operational or integration needs. Together with Atos in our alliance, this is also true for more analytically challenging customers and use cases”. The respondents acknowledge the customer base of each partner as an opportunity to reach a market that was not accessible before for the other partner. This was also highlighted by respondent 4 “We have a lot of customers on both sides where
each firm can extent their businesses in. We try to address them jointly, there are Siemens customers that Atos is interested in but this is also the other way around. We have a market development board on both side in each business unit where we tried to identify such customers and approach them jointly. I think we are quite successful in it “.
• Reference
Respondents also acknowledged a shift in identity and credibility of the firms due to the Alliance. The way the firms are perceived together as the alliance within markets is pointed out by respondent 5 perfectly by stating the following: “We have developed joint go to market in some areas were we jointly address
customers. Which is one clear benefit where typically Atos brings their IT expertise and Siemens brings the industry expertise. It really gives credibility to Atos and Siemens on the market in solutions gathering both IT and industry competence on the market”.
• Industry presence
Entering a new market can be met with difficulties as it is important to
understand the market needs in order to ensure a successful position. Both firms have knowledge of different markets and different domains, the respondents pointed out that the knowledge both firms possessed in different markets is reflected as a benefit received from the alliance as respondent 6 noted: “When Atos moved into a technology field, Siemens has definitely helped us there. Have we been helping Siemens in the same way? Not as much but we are starting to see this more and more now. Let's take for example the postal services, a lot of the OT in the postal service area are moving to the IT. We see this in many other areas. All the OT are moving under the umbrella of IT and we will be offering new markets to Siemens to enter because they don't have the relations, knowledge or the people and they can't talk the language of the market so to speak. That is where we can support them.”
Performance Risk
The advantage of shared performance risk had a couple of mixed responses as not all perceived risk and the division of it within the Alliance in the same way. Obviously as previously mentioned Atos and Siemens have a joint innovation fund through which the Alliance develops joint solutions together and for which the risk is shared but some respondents considered shared risk more than just the risk of innovation for the alliance. The answers of respondents regarding risk are divided in Financial risk and Market perception risk.
• Financial risk
Multiple respondents addressed the shared risk as a matter of shared risk on cost. This is prevalent in the case of the innovation fund as respondent 1 explained this as follows: “Well we definitely share innovation risk so this is what the innovation fund of the alliance is about. I can only talk about the innovation fund not about the alliance in general. I know the innovation fund is 180 million euro and there we share risk equally. Every euro is paid in half by each firm. Every cost made and lost in a project is shared by the firms.” The risk on innovation is therefore not increased but the decision on innovation project has rather become more complex as respondent 4 points out: “The decision is still on both side, I mean it only increases the complexity since you have to find a joint agreed decision. You have to discuss this and the
companies have different views, cultural background etc. So, the complexity is higher but not the risk”.
If one solely looks at the fund purely than indeed the fund and the alliance
innovation endeavors financed by the fund are shared, meaning also the risk, but if you look at it on project basis this is different as respondent 2 points out: “If you talk about shared risk in a financial context the shared risk is different. We (Atos) don’t have a regulation of joint financials in the deals we conduct together with Siemens. Meaning if Siemens suffers a financial loss in a project we as Atos can still credit the project as successful with a profit”. Next respondent 2 also pointed out the perceived growth potential risk which is not shared as he elaborates this as follows: “The risk is in the missed growth potential of your firm. In the end the shareholders are not concerned with the loss the firm makes but they are concerned and invested in the
firm for its growth potential”. This is significant as the firms work as two separate entities in the alliance and the loss of growth that a firm experiences in the alliances each of the firms has to compensate on its own. That being said the cost however is minimized as respondent 5 mentioned when asked about the possibility of the firms going on their own in creating the same solutions: “It would have been possible but it would take longer and this would have jeopardized the firm time to market and we would have concentrated on open source and cloud, meaning we would have to prioritize. This would have cost more and we had to do it all of it on our own.” • Market perception risk
As discussed at the Ease of market entry the relationship of the alliance has an influence on the market perception of the firm. This can give the firms credibility and open doors but at the same time this can also be a liability as respondent 2 pointed out “Siemens and Atos do share risk in the perception of the market. If Atos is preforming bad this will reflect on Siemens based on the relationship we have with Atos as an alliance, this is vice versa”.
Knowledge and expertise
The third advantage considers the interfirm sharing of the knowledge and expertise within each firm. The opinions regarding this topic are mixed. Some of the respondents considered the sharing to be the least that has taken shape within the Alliance. The answers regarding this topic are mainly explained with regard to the operation of the Alliance.
• Operations
Some respondents consider the interfirm sharing of knowledge and expertise limited within the Alliance. The respondents pointed out that sharing knowledge does not occur in the current joint solutions. Respondents see the interfirm sharing limited to a best practice as respondent 1 points out: “It doesn't really go very much beyond what you do on conferences and best practice exchanges between experts in the community anyway.” This has been elaborated by respondent 2 as a sharing of past experiences: “There is a different form of sharing within the alliance. You save each other from mistakes in the joint investment based on your firm previous experiences. There is an added value of the control aspect even though there is not an exchange of explicit information regarding the IP of both companies”. On the contrary others consider the interfirm sharing fruitful especially in new technology development. They considered the interfirm sharing rather apparent as they exemplified this with current joint research teams that work fully integrated. Respondent 3 pointed this out with the current integrated approach team: “Nowadays we develop our project in a fully integrated way with mixing the teams with joined competence development centers etc. Here obviously, you have the full effect of the synergy of expertise”. When asked about the influence of IP protection in the extent of sharing respondent 3 elaborated as “This is not relevant as the purpose of sharing is more about the combining the expertise of developing new R&D products”.
