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Bachelor’s Thesis – Faculty of Economics & Business
Specialization Business Administration
“Blockchain alliances, collaborations and consortiums in
financial industry – An insight into motives behind their
creation, their objectives and key success factors”
Evgeny Mironov Thesis supervisor:
11106298 Willem Dorresteijn
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Statement of Originality
This document is written by student Evgeny Mironov who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used to create it. The Faculty of Economics and Business is only responsible for the supervision of completion of the work, not for the contents.
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Table of Contents
Statement of Originality ... 1
Abstract ... 3
1. Introduction & Problem Definition ... 4
2. Literature Overview & Theoretical Framework... 6
2.1. Existing Literature Overview ... 6
2.2. Conceptual Framework ... 8
3. Research Method & Methodology ... 14
3.1. Research Design & Methodology ... 14
3.2. Data Collection ... 15
3.3. Risks & Threats ... 16
3.4. Data Analysis Methods ... 17
3.5. Possible Limitations ... 17
4. Data Collection & Analysis ... 18
4.1. Starting Point ... 19 4.2. Alliance Specifications ... 21 4.3. Post-formation ... 22 5. Discussion ... 23 6. Conclusion ... 25 Bibliography ... 27 Appendix ... 32 Coding ... 32 Interview links ... 34
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Abstract
Blockchain technology is an un-and-coming innovation expected to cause disruptions in various industries and change existing business practices in both B2C and B2B sectors. However, widespread integration of perpetually growing decentralized ledgers in day-to-day operations requires multiple competitors within an industry to come to a mutual agreement regarding the technical nuances, while developing specific guidelines. To assist in accomplishing that goal, certain institutions such as trade associations, cooperation networks, alliances and consortiums are established. The following study analyzes multiple factors related to inception, operation and specific characteristics of those bodies in the financial industry using a novel Extended Strategic Alliance Analysis Framework. This research unveils that the main driving force behind technology adoption in the financial industry is increased transaction security, outlines the desired characteristics of organizations created to guide technology adoption as well as giving insights into different points of view on these entities’ objectives.
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1. Introduction & Problem Definition
Blockchain technology has been an increasingly popular discussion topic over the last years, with an incredible amount of buzz surrounding it. Distributed consensus model, the framework behind blockchain, was even called “the most important invention since the Internet” by Marc Andreessen, one of Internet’s pioneers (as cited in Crosby et al., 2016). From almost-instant, anonymous, yet completely secure transactions to intelligent assets and self-executing contracts, blockchain has the potential to revolutionize almost every aspect of how modern business operates and have an astonishing impact on our lives (Wright & Filippi, 2015). But what is the blockchain?
In essence, blockchain is a series of “blocks” that is perpetually growing, with each block containing an encrypted record of the previous block along with a timestamp, designed to “record transactions between two parties … in a verifiable and permanent way” (Iansiti et al., 2017). Each transaction then adds another “block” to the never-ending chain of records, which is made public, allowing anyone to verify the correctness of these records. Each agent exchanging digital tokens (also called coins) has two sets of keys – a public one designed to be shared with the other participant and a private one, which is similar to a personal password. To perform a transaction, two participating sides exchange their public keys and the transaction is then stored using a hash (an encrypted digital signature), which contains the transaction details (Pilkington, 2016). However, despite the transactions being publicly available, the distinguishing feature of blockchain recordkeeping is that the public keys are not linked to a real-life identity, allowing for a completely new and never-seen-before layer of security and anonymity (Underwood, 2016).
Blockchain’s main feature, keeping a decentralized and publicly available ledger of all transactions completed, could be a cornerstone in creating a new paradigm, completely changing the rules of the game in many areas of our lives. This innovative technology, expected to be extremely disruptive, has a wide range of possible applications, ranging from increasing the speed and security of financial transactions while reducing transaction costs to protecting
5 intellectual property (IP) by keeping a permanent movement record since the IP’s inception (Swan, 2015). The possibility of maintaining end user’s privacy while simultaneously making transaction history transparent, along with the decentralized nature in which recordkeeping is performed, fuels the demand for its implementation amid the numerous scandals related to personal data leaks and increased government intervention in citizens’ private life (Zyskind & Nathan, 2015). Combined, these factors, along with a wide range of technology’s possible applications, provide businesses an opportunity to gain competitive advantage even in highly contested industries and make blockchain implementation a highly lucrative strategic target for numerous companies (D. Tapscott & A. Tapscott, 2016).
However, a widespread B2B application of blockchain-related initiatives requires a combined effort of multiple companies within an industry (Anjum et al., 2017). For these purposes, various alliances and networks such as Blockchain in Transport Alliance (BiTA), R3 financial institution consortium, Winding Tree Decentralized Travel and Enterprise Ethereum Alliance, are formed in various industries. With blockchain’s main features being an increased speed and convenience of transaction processing and a high level of security (Korpela et al., 2017), current technology application focus naturally turns to its application in payment and monetary transaction processing. The existing state of affairs led to formulation of the following research questions: What is the driving force behind the creation of organizational entities aimed to speed up the adoption of blockchain? What characteristics should they have and what objectives should be these bodies’ top priority? The insights gathered will be of use in outlining next steps to be taken by industry leaders to successfully lay a foundation for widespread blockchain technology application in day-to-day business operations.
The structure of the research is as follows: first of all, an overview of existing literature on the topic is presented, outlining gaps in the existing body of knowledge and defining the concepts to be used in later chapters. Following this, an extensive theoretical framework to be used later in analyzing findings is introduced. Afterwards, a description of methods used and research design adopted is outlined to familiarize the reader with the research approach chosen. After this, results are presented followed by a brief discussion. The study is then concluded with a
6 chapter providing a summary of findings, limitations encountered and suggestions for further research direction.
2. Literature Overview & Theoretical Framework
2.1. Existing Literature Overview
Conducting a literature overview on the topic of current blockchain real-life applications uncovered a significant amount of limitations and deficiencies. Stemming from new and groundbreaking nature of the technology, academic sources related to blockchain mostly come in form of either explanatory and descriptive research regarding the essence of technology (Deshpande et al., 2017; Korpela et al., 2017; Kupriyanov et al., 2017) or focusing on practical applications of blockchain, highlighting the usability of features like increased privacy of transactions and lower costs associated with it (Zyskind & Nathan, 2015; Pilkington, 2016; Mattila, 2016). In terms of revolutionizing and modernizing business processes worldwide, blockchain’s potential was already compared to that of double-entry bookkeeping, implying that the usability and possible benefits of a “World Wide Ledger of Value” are hard to overestimate (D. Tapscott & A. Tapscott, 2016). Side benefits of keeping track of financial transactions also include an eradication of double-spending problem where the transactions are processed twice due to a machine error because of how the ledger is designed – these errors are not possible when working with a distributed ledger due to the verification procedures that cross-check every transaction against the previous one, ensuring that the inputs were not previously transferred anywhere else (Pilkington, 2016).
Academic literature on the topic of blockchain also features an abundance of material containing expectations and predictions of how this innovation could reshape the business world as we know it (D. Tapscott & A. Tapscott, 2016; Lopez-Pintado et al., 2017). Additionally, while the applications discussed above mostly focus on financial solutions, existing literature also details the possibility of using decentralized ledgers for other purposes that are in demand in the
7 current shared economy-driven world like digital rights management (DRM) and preserving cultural heritage by keeping a permanent and never-ending record from the moment of blockchain’s inception (Huckle et al., 2016).
However, one of the lesser discussed topics in academic literature is the role of alliances, consortiums and other bodies existing for the purpose of bringing a common standard of blockchain application within a certain industry. Just like an existing “golden standard” of transaction syntax developed for faster and more secure interbank transfers, SWIFT, has a large organization, the Society for Worldwide Interbank Financial Telecommunication, behind it (Scott & Zachariadis, 2017), blockchain-fueled record keeping as a new industry standard should have a governing body supporting it. But is it really the case? Are these organizations necessary? Saloner (1990), examining the impact and growth of alliances aimed to establish Unix as an industry standard operating system, concluded that it is crucial to create and maintain implicit or formal partnerships between rivals in order to develop and sponsor common standards if a single accepted solution has not yet emerged or if an up-and-coming innovation provides significant advantages over an old way of doing things. Similar conclusions can be found in academic literature covering different industries – for example, Heinecke et al. (2004) examined AUTOSAR, a development partnership amongst top automotive manufacturers (including Ford, Toyota, BMW Groups and many others), stating that the development of industry-wide solutions allows participants to focus on innovation, rather than concerning themselves with adapting multiple components and process stages to different competing standards. This equally applies to other areas where using international standards is required – for example, implementation of International Standards for Neurological and Functional Classification of Spinal Cord Injuries would not have been possible without American Spinal Injury Association’s extensive work and International Medical Society of Paraplegia’s endorsement (Maynard et al., 1997). Combining these findings, it can be unequivocally stated that blockchain’s widespread application in financial transaction processing requires extensive work to be carried out by specialized organizations.
8 Combining all of the findings revealed during the literature review, it can be certainly stated that a gap in an existing academic literature has been identified and filling that gap with practical information supported by empirical findings to be analyzed using a suitable framework adapted for specific application in a real-life context would prove useful for increasing the existing body of knowledge on the topic.
2.2. Conceptual Framework
Existing models of intercompany cooperation provide multiple angles to analyze an existing stage of development of blockchain networks and pinpointing the desired characteristics of such. Multiple frameworks were developed with different areas of focus – trust between participants (Das & Teng, 2001), learning (Doz, 1996), partner fit (Brouthers et al., 1995) and interfirm diversity (Parkhe, 1991). However, to properly assess the current state of development of these cooperation units, a more general framework is required in order to avoid overlooking crucial insights. For these purposes, Strategic Alliance Formation (SAF) framework (Hynes, 1998), which is based on a previous strategy/structure/performance model by Chandler (1962), will be used as a foundation as it provides a complete overview of factors present and suits the overall research goal. The SAF model is presented in Figure 1.
9 The central assumption that has to be fulfilled to allow using this model is that the alliances to be analyzed must be characterized as “strategic”, i.e. that the goal, to be achieved by a conscious choice of companies to enter the alliance, is strategic in nature – just like developing a common standard for blockchain integration in the whole industry is indeed a strategic goal (D. Tapscott & A. Tapscott, 2016). However, in order to properly analyze each of these stages and capture the “big picture” while also paying attention to specific circumstances and nuances present, a more in-depth approach is required at each of the stages. Combining multiple frameworks and approaches to analyze complex data is essential in acquiring the full range of insights and causal relationships between variables (Croft, 2002). Thus, an expansion of SAF framework is required to fully dissect each of the model’s building blocks.
As such, exploring Antecedents and Motives (the first part of the framework later referred to as “Starting Point”) requires a more extensive and detailed structure. To break down all of the factors at play, an additional layer of the grand framework is suggested. The Four C’s of Strategic
Alliances (Brouthers et al., 1995), an analytic system designed to examine circumstances leading
to alliance formation, will be utilized to tackle the problem. It mentions four key factors leading to successful strategic alliance (SA) emergence: Compatible Goals, Cooperative Cultures,
Complimentary Skills and Commensurate Risk. A graphic representation of that framework is
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Figure 2: Four C’s of Strategic Alliances (Brouthers et al., 1995)
To properly analyze the Motives group, it is possible to refer back to the supporting theories presented by Hynes (1998). These include Cost Advantages, Decreased Risk, Learning,
Managing Industry Structure and Timing (Speed). This division represents a structured approach
that is required to properly assess the driving forces behind B2B networking. Inclusion of these is based on different schools of managerial thought represented by various approaches. Main contributions to this structure were brought by the following theories: resource-based with a focus on an inability of companies to be fully self-sufficient (Glaister, 1996), transaction cost theory that implies that companies enter alliances to reduce costs (Williamson, 1979) and the strategic behavior theory, which states that the firms enter these agreements only if these partnerships allow them to meet their strategic agreements (Kogut, 1988).
Following the evaluation of existing Antecedents and Motives, the next element of the overall framework concerns Alliance Type and Factors Influencing Success (this part will later be referred to as “Alliance Specifications”). Due to a need to thoroughly investigate all of the components, an additional layer will be deployed to determine a required Alliance Type.
11 Proposed solution for this is the Four Ways to Collaborate framework (Pisano & Verganti, 2009). This sub-framework would then be of assistance in finding a perfect solution regarding Alliance Type to be used – a visualization of it is available in Figure 3. Insights gathered to investigate the alliance type will be of theoretical nature with a focus on literature review, as different alliance types pursue different goals, as evident from their structure (Pisano & Verganti, 2009). For example, gathering new and innovative ideas (like T-shirt designs) is futile in a network that has artificial barriers to entry and a stable roster of participants. On the other hand, an industry leader cannot expect other network participants to be open about their latest developments in high-tech industries if the membership system of that alliance is open for anyone – the dangers associated with exposing your intellectual property to an unknown amount of possible competitors will surely dissuade other key players from sharing know-how inside that network (Stepandic, Liese & Trappey, 2015).
12 Figure 3: Four Ways to Collaborate (Pisano & Verganti, 2009)
Concluding the Alliance Specifications part of the framework, Factors Influencing Success, according to Hynes (1998), are categorized into four groups: Cultural, Partner Complementarity,
Perceived Loss and Gain, External Factors. However, for the purposes of this research, this part
will not be analyzed thoroughly as there is an existing need to gather data which goes beyond the access capabilities of author. The reason for this is the abundance of bias related to self-reporting when discussing monetary gains or losses, especially in publicly available sources (Fadnes et al., 2009). In order to avoid these errors, quantitative data should be gathered and studied (McMillan & Schumacher, 2010), however, quantifying a perception of possible future gains and losses while controlling for possible bias is almost impossible to properly execute given that no historical data to estimate these values from is available (Vogt, 2006). Further analysis of these factors would be an interesting development of the current study once reasonable approximations of losses and gains are available based on past performance of such joint ventures.
The Objectives of the strategic alliance are then broken down into four areas: Market,
Product, Resources, Skills, following an initial structure introduced by Hynes (1998). This part,
along with an overview of possible developments and further blockchain industry unions’ evolution, shapes the final part, which will be later referred to as “Post-formation Stage”. Following that part, to better reflect the steps that need to be taken in order to answer main research question, forecasting future developments is possible, especially regarding the future of these networks and organizations, based on the current state of affairs in the field, bundled in a part titled “Maintenance & Evolution”. This study does not attempt to forecast future developments, instead focusing on describing the desired characteristics of the alliances and exploring the causal relationships leading to an emergence of such entities. Unifying all of the proposed components, a combined modified framework incorporating all of the aforementioned elements is then introduced by the author. This extension of SAF will later be referred to as the
13 roadmap to guide the research process carried out in this study. A visual representation of the complete framework is shown in Figure 4.
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3. Research Method & Methodology
3.1. Research Design & Methodology
In order to perform an extensive analysis of the current stage of partnership networks’ and strategic alliances’ development and thus answer the research question at hand, a significant amount of data has to be collected and studied. The overall research design combines descriptive and exploratory elements as the study focuses on both assessing the visible characteristics of blockchain networks and looking into the root causes responsible for the current state of affairs. Exploratory design lays a suitable foundation for gathering and systematizing background information and is often used to generate hypotheses and develop understanding when exploring lesser-researched topics (Cooper, Schindler & Sun, 2006). Descriptive elements of this research are used mainly as a pre-cursor to exploratory inquiries, as well as allowing for a more general overview of the “big picture” to be constructed (Anastas, 1999).
Qualitative methodology is thus chosen, as in-depth inquiries into the nature of phenomena and analyzing participating social actors’ experiences are considered to be the best approach at answering a complex research question (Baxter & Jack, 2008). Furthermore, a range of academic sources suggests that gathering insights into the core rationale behind certain decisions and actions cannot be accurately measured using quantitative tools (Silverman, 1993; Bryman & Burgess, 1994; Feldman, 1995 as cited in Attride-Striling, 2001). Moreover, qualitative data suits the research design chosen best as it allows for unpredicted discoveries to happen throughout the research process (Zainal, 2007). Overall, the choice of methodology was dictated by how well it fits with the overall research design, research objectives and expected outcomes. Additionally, using quantitative methods would introduce the risk of overlooking or wrongfully dismissing insights that might seem insignificant when working with quantifiable data (Render & Stair, 2006).
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3.2. Data Collection
Selection of data collection methods was carried out according to the extent to which they fit the overall study goals and research design chosen. Arising from a qualitative methodology with a mixture of exploratory and descriptive objectives, data gathered should paint a comprehensive picture of the driving forces leading to an emergence of strategic blockchain alliances in the financial industry and the objectives these entities have. Despite various risks to reliability, generalizability and validity (specifically these related to self-reporting by interviewees and sampling bias when using purposeful sampling), the procedure of gathering insights from unstructured and semi-structured interviews remains the best data gathering technique available if direct observation of participants is deemed impossible for the purposes of the study (Polkinghorne, 2005). On top of this, according to Kvale (1996), a direct verbal report from a participant may help uncover events and causal relations that might not be easily observable (as cited in Alshenqeeti, 2014). However, due to access and cost limitations, performing these interviews with industry heads and other influential experts in the field directly was not possible for this study. Instead, an expert sample of 26 pre-existing interviews about partnerships, alliances, cooperation networks and consortiums was collected for further analysis. Sources for these interviews include academic journals, professional websites, transcripts of conference presentations and library catalogues. Interview sample includes 10 interviews with representatives of various Fintech companies (financial transaction processing companies, software developers and stock market technical support businesses), 9 interviews with heads of blockchain alliances (R3, Ethereum Enterprise Alliance, Wall Street Blockchain Alliance and others), 4 interviews with top managers of leading financial institutions (JP Morgan, Citibank, ANZ) and 3 interviews with blockchain experts of different backgrounds (cryptocurrency developer, journalist and an academic researcher from Blockchain Research Institute). The full list of interviewees, organizations they represent and classification is available in the Appendix.
It is then important to keep in mind that this data collection method is prone to induce risks to validity and reliability that stems from interviewer effects, such as subconsciously guiding the interviewee towards a desired answer (Hollway & Jefferson, 2000). Other potential threats include interviewer’s and interviewee’s faulty memory, misunderstandings between the two, an
16 interviewee’s desire to conceal limited knowledge by stating incorrect information and other risks that concern human nature (Roulston, 2010). Additional drawbacks of this method include interviewee’s desire to present themselves in a favorable light and exaggerate certain aspects of their behavior while concealing their shortcomings (Kajornboon, 2005). Finally, using secondary sources of information leads to a threat of losing control over data quality – for example, operating with data that was gathered with limitations that are not explicitly stated can lead to incorrect conclusions (Saunders, 2011).
3.3. Risks & Threats
In order to limit the risks associated with using secondary sources, a number of requirements for an interview to be considered suitable for the purposes of this study, while minimizing risks associated with the data collection method, are introduced. First of all, interviews chosen should be unstructured or semi structured in nature as these provide the best opportunities for uncovering unexpected findings (Polkinghorne, 2005). Secondly, a direct video or audio recording of an interview or availability of alternative sources to be used to verify the transcript presented in the original one, must be available in public access as this would allow to control for the note-taking process errors and bias (Hollway & Jefferson, 2000). In addition to this, only interviews with a clear indication of who the interviewer and the interviewee are, date of interview and other significant circumstances stated, are considered acceptable so as to further limit risks associated with source trustworthiness and recency of material (Saunders, 2011). Finally, as blockchain-based technologies’ development, testing and implementation stages are extensively dynamic and fast-paced (Pilkington, 2016), a recency limit of a year is imposed on materials gathered to avoid using outdated information in the study.
An important point to be considered is the possibility of multiple ethical issues arising during the interview and data collection process. Essential conditions for performing ethical research (which impose further restrictions on interviews selected), according to Denzin and Lincoln (2008), include participants’ full informed consent, their understanding of privacy protection mechanisms available and absence of harm (both physical and mental) towards the
17 interviewees. Controlling for ethical aspects is also available in a limited form via studying video or audio recordings of the interviews (Kvale, 1994).
3.4. Data Analysis Methods
Following the initial data acquisition, each section of the following chapter presents an additional step of the analysis. One of the biggest issues when working with multiple unstructured interviews scattered in time and performed by multiple interviewers with different goals is recognizing patterns and similarities in responses and quantifying data to be used at later stages (Attride-Striling, 2001). The best approach to handling this kind of data to meet the exploratory and descriptive study goals was described by Ong (2012), who supported an idea of transforming scattered pieces of data into structured batches of information to be analyzed later by assigning certain codes to find patterns. The method behind it, dubbed “Grounded Theory Method”, is useful in finding shared attitudes and reoccurring themes in multiple transcripts of social actors’ experiences (Ong, 2012). Another reason for choosing that approach is its fit with the research question at hand, as Grounded Theory Method involves “construction of theory and possible explanations through methodic gathering and analysis of data”, thus allowing us to draw generalized conclusions from observations (Martin & Turner, 1986). The same claim found support by Charmaz (2007). On top of this, in her book “Grounded Theory” she claims that using open and axial coding to structure and organize data with the purpose of recognizing repetitive occurrences is useful in limiting subjectivism when explaining the results. An overview of codes used for each stage of analysis is presented in part 4: “Data Collection & Analysis”.
3.5. Possible Limitations
Expected limitations include the following: an insufficient amount of academic knowledge on the topic, possible unwillingness of companies and their spokespeople to disclose their participation in industry-wide blockchain standardization programs and a low level of generalizability of the findings, as testing for potential applications on a broader scale (for
18 example, applying the outcomes to test the speed of diffusion and adoption of other disruptive technologies) will most likely not be possible due to their unique nature. Risks related to research methods chosen include low transferability due to small sample size (Welsh, 2002) and increased possibility of threats to the overall validity (Saunders, 2011). Further research on the topic can alleviate these limitations by using a mixture of qualitative and quantitative methods along with using exclusively primary data sources (Roulston, 2010).
The research undertaken will have a theory-oriented focus with the key purpose of contributing to the existing body of academic knowledge regarding the topic. The main goal of this study is obtaining insights suitable for application in real-life business scenarios as well as outlining possible directions for future research regarding blockchain alliances, collaboration networks, standalone organizations seeking to implement common standards within a certain industry and other similar entities. Moreover, this study can serve as a starting point in further analysis of these organizations as insights gathered will need to be confirmed via multiple research methods due to the low replicability associated with data collection methods used (Gubrium & Holstein, 2002).
4. Data Collection & Analysis
To answer research questions initially stated in the introduction, a certain amount of data collection and analysis should be carried out. The data collection and analysis process follows the structure that is reflected in ESAAF, with two of its three components (Starting Point and Post-formation) being consequent and drawing from the insights obtained while examining previous stages of the framework, while the third one (Alliance Specifications) requires academic sources backing to uncover the correct specifications. Proposed format also follows strategic networks’ lifecycle from inception to present moment with crucial practical applications surfacing at each of the stages proposed.
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4.1. Starting Point
Strategic Alliance formation begins when companies realize the need for cooperation within or outside of the industries they operate in (Hynes, 1998). As such, it is important to analyze the motives behind blockchain SA formation within financial industry. In order to select the initial set of open codes to be used further, a look back at the conceptual framework is needed. ESAAF suggests that the four main components that shape cooperation networks’ success are Cost Advantages, Decreased Risk, Learning, Managing Industry Structure and Timing. Open codes that reflect Cost Advantages are numerous and include such text strings as “lower transaction costs”, “decreased costs of payment processing”, “less money spent on clearing transactions”. An axial code assigned to this range of expressions is “Cost Advantages”. Learning is represented by quotes similar to “exchanging know-how”, “transfer of knowledge” and “mutual learning”. This selection of codes is grouped up in an axial code dubbed “Learning”. Managing Industry Structure implies that the interviewee mentioned changes in the established landscape of the industry, using phrases such as “new industry paradigm”, “changes in industry structure” and “a new business environment”. These codes were then reclassified as a “Managing Industry Structure” axial code. Decreased Risk implies that the interviewee mentioned various security-related attributes in their speech, such as “creating a risk-free market”, “double verification of transactions” and “enhanced cybersecurity”. These codes are then bundled together in a “Decreased Risk” axial code for further quantification. Last of the initially outlined motives, Timing (Speed), was then represented by open codes such as “faster transaction processing time”, “increasing payment clearing speed” and others phrases similar to it. Together, they make up the “Speed” axial code.
Codifying the interview transcripts (an overview of interviews along with the axial codes encountered is available in the Appendix) brought along a number of findings. First of all, it is important to note that half of the respondents (13 answers) explicitly mentioned increased security of transactions (and thus, decreased risks of fraudulent actions and cyberthreats) as one of the reasons for blockchain adaptation. Such an answer is equally popular among Fintech companies’ representatives, Blockchain alliances and other organizations’ representatives and respondents from other companies and institutions. This insight goes in line with existing theory,
20 supporting the findings of Zyskind & Nathan (2015). Second most popular reason for implementation of blockchain technology in financial applications (and thus, creating and participating in various alliances and consortiums, 8 answers) is the reduction of costs associated with payment processing. This discovery also gives an empirical proof to claims stated by Pilkington (2016), bringing the research outcomes even further in line with existing academic consensus on the topic. Other motives were not mentioned as frequently. The frequency goes as follows: Managing Industry Structure – 7 axial code mentions, transaction processing speed – 6 occurrences and Learning – 5 interviews mentioned knowledge sharing and acquisition as a motive for creating and entering these alliances.
However, upon analyzing the interviews, an unexpected discovery was made. Due to a high frequency of mentions in interviews, an additional axial code labeled “Convenience” was introduced. This code combined open codes such as “ease of use”, “simplification of processes” and “convenient solution”. A total of 7 interviews contained keywords related to convenience of blockchain-based transactions for end users or the financial organizations themselves. This finding also finds reflection in existing literature on the topic with some researchers claiming that convenience of transaction performing is as important as processing speed and security for all of the parties involved (Porath, 2017).
Data analysis for the Antecedents block of the Extended Strategic Alliance Analysis Framework followed a similar logic. The four parts – Compatible Goals, Cooperative Cultures,
Complimentary Skills, Commensurate Risks – are assigned axial codes that combine a wider array
of open codes represented by various phrases and text strings. Compatible Goals axial code consists of open codes such as “Common goal”, “Mutual benefit observed”, “Aligned objectives”. Cooperative Cultures axial code tries to capture a certain mindset and a set of values within an alliance that is supposed to be shared by all of the participating entities. An axial code for this success factor is compiled of open codes “Compatible environments”, “Similar values within the network” and “Culture fit” – just like William Zuo’s (interview #25) statement that his organization’s partnership network “… feels like a democracy of sorts … based on mutual trust”. Complimentary Skills axial code is a compilation of various open codes directly related to
21 technical knowledge, experience accumulated within the company and competencies developed. This axial code has multiple open codes associated with it – for example, “Competencies developed”, “Technical know-how” and “Experience acquired with blockchain and other disruptive high-tech innovations”. The last axial code, Commensurate Risks, is made up of open codes referring to possible risk-sharing within an organization. The collection of statements under this axial code is a direct reflection of how the risks associated with implementing a ground-breaking technology might be diversified when entering a strategic alliance as was previously formulated by Conybeare (1992). However, due to only observing one instance of interviewees directly talking about risk-sharing prospects of blockchain alliances, this axial code is only represented by a single open code “Difficulties embracing the new business environment”. Quantifying data available leads us to observe a few interesting characteristics that are not as obvious from the first glance. First of all, the aforementioned lack of discussion regarding risk sharing (1 mention) within these communities raises an array of questions to be discussed later in this study. Other factors were mentioned in approximately a quarter of interviews each (7 mentions for Compatible Goals, 6 instances of axial code detection for both Complimentary Skills and Cooperative Cultures). However, it is interesting to notice that the fixation on a single factor is different for each type of participant. For example, a staggering insight that 5 out of 6 (83%) of Cooperative Cultures success factor mention instances were detected in interviews with people representing various Alliances, Consortiums and Networks, despite them amounting to just 34.6% of respondents. This insight will be further scrutinized in the following chapters.
4.2. Alliance Specifications
Now that the driving forces behind SA creation have been established, it is then necessary to specify the attributes and key features of a suitable alliance. Existing literature mainly focuses on two key characteristics of alliances to describe and classify them. First characteristic is related to membership opportunities, classifying alliances as Closed Participation networks and Open Participation networks (Pisano & Verganti, 2009). Undeniably, if the alliance’s main purpose is introduction, monitoring, governing an implementation of a common standard or a single
22 solution throughout most, if not all, of the companies in the financial industry, then the members should be encouraged to join the organization, as introducing further restrictions can slow down the desired outcome even further (Camarinha-Matos et al., 2009).
The second characteristic concerns the governance structure and hierarchy (Pisano & Verganti, 2009). In a similar vein, the answer here is straightforward – if the goal of SA is unifying competitors for achieving a common solution to a problem, introducing a hierarchal structure within the network would discourage the competitors of network’s head from joining based on concerns that their interests will not be properly represented unless every participant has more or less equal standing within the network (Gulati & Singh, 1998).
According to Pisano & Verganti (2009), this choice of governance policy and membership rules would then classify blockchain-related alliances as Innovation Communities. Advantages of such approach include an immense influx of ideas, free flow of information and a broad range of possible solutions stemming from the varying sources of experience accumulated by these organizations. However, with a wide range of possible solutions to chose from, participants must be ready to spend a considerable amount of time screening, evaluating and testing multiple solutions, so the question of optimizing these processes becomes increasingly important for the success of this joint venture (Gulati & Singh, 1998).
4.3. Post-formation
Following the emergence of a network incorporating multiple unrelated entities within a particular industry, the next stage, called the Post-formation Stage, begins. The main goal at this stage is to gather more knowledge about the Objectives of the strategic alliance, as they will be paramount in future alliance development (Hynes, 1998). These, according to both the Extended Strategic Alliance Analysis Framework and the less complex Strategic Alliance Framework (Hynes, 1998), are divided into four distinct areas: Market, Product, Resources, Skills. To conduct a sufficient analysis, a procedure identical to the one used in part 4.1. Starting Point involving axial and open coding is performed.
23 A group of open codes combined in a single axial code denoted “Market Objectives” is used to quantify the degree of relative performance of this goal. Open codes used in this part include “Market development”, “Modernizing existing business practices in the industry” and “Improving the overall industry performance”. These goals were explicitly stated in 9 out of 26 interviews (34.6%) with over a half of mentions (5 out of 9, 55.5%) attributed to representatives of industry-wide blockchain initiative entities. “Product Objectives” refers to goals set for product performance. Smaller scale objective compared to the previous one, it mainly concerns statements that represent interviewee’s focus on product characteristics (for example, new banking service based on cryptocurrency transactions or funds processing using decentralized and publicly available ledger). Axial code “Product Objectives” is composed of open codes “Better banking product”, “New financial solution”, “Developing a new product/service” and “Innovative approach to a routine task”. This code appeared to be most frequently encountered throughout the interviews with 12 out of 26 (46.1%) of respondents mentioning these objectives, split evenly between interviewees from financial institutions, existing alliances and networks and other types of entities.
The remaining two types of objectives, “Resources” and “Skills”, shared the same amount of mentions (specified in 7 out of 26 interviews, 26.9%). “Resources” axial code combined open codes related to financial institutions’ acquisition of valuable assets, including intellectual property. “Skills”-related objectives focus on knowledge transfer and diffusion within strategic alliances, particularly these concerning the technical aspects of blockchain implementation in existing business processes, with the following open codes used: “Learning opportunities”, “Knowledge-sharing”, “Critical skills development” and “Experience gathering”.
5. Discussion
Findings presented in the previous chapter allow for extensive discussion regarding possible sources of the phenomena to take place.
The first finding is related to motives behind technology adoption and, subsequently, the appearance of organizations governing and guiding the process within an industry. By inquiring into driving forces responsible for creation of these entities within an industry, it also answers
24 the first part of the initially proposed research question. Existing research on the topic mostly suggests the speed of transaction processing and the reduced costs per transaction as the main driving forces behind blockchain implementation (Pilkington, 2016). However, according to the results of this study, the most important feature of decentralized ledger bookkeeping is the increased security and minimized risks of security breaches happening. This hypothesis was also reflected in academic sources (Zyskind & Nathan, 2015), however, no empirical research regarding the relative importance of these factors was previously conducted.
The second finding is related to the type and characteristics of organizations acting as
Innovation Communities with the purpose of establishing and popularizing a single common
standard of blockchain application in financial industry. This finding answers the second part of the research question stated in the introductory chapter of this study. According to a review of academic sources on the topic, these organizations should have a flat hierarchy and open membership in order to achieve goals set, while the participants should focus on developing tools, frameworks and mechanisms of appraising, evaluating and testing solutions suggested by other alliance members in order to effectively sort incoming ideas (Pisano & Verganti, 2009).
The final finding related to objectives set for cooperation and collaboration networks is two-fold. Firstly, it is important to remember that, according to results obtained, representatives of alliances and other unifying entities put an additional emphasis on how the disruptive potential of blockchain is able to shape the whole markets and industries, completely changing the “rules of the game” established long before. Contrarily, existing financial institutions and auxiliary service providers tend to think more in terms of the product their company is able to provide. That difference perfectly illustrates a distinction in approaching the same problem at hand by various entities based on their objectives. These outcomes give an in-depth answer to the final part of the research question posed in this study, thus fulfilling the research objective.
Study carried out adds to an existing body of knowledge on the topic in multiple ways. First of all, Extended Strategic Alliance Analysis Framework is a useful tool in constructing a complete “big picture” of the alliance from motives behind its inception to objectives it aims to achieve after the alliance is up and running. Based on Strategic Alliance Framework (Hynes,
25 1998), proposed framework slightly alters structure originally proposed by Hynes, adding extra layers to provide a more in-depth analysis of Motives & Antecedents, Alliance Specifications and the Post-formation stage.
Secondly, insights gathered upon the analysis of an extensive amount of qualitative data shed some light on the relative importance of reasons behind intercompany networking towards implementation of blockchain in day-to-day business operations and the objectives these entities set. This knowledge would be of use for managers working in organizations that attempt pilot blockchain projects and are currently contemplating what an attractive cooperation proposition is before addressing other businesses in the industry with a proposition of combining efforts.
Moreover, interesting findings about reasons that motivate companies to join these organizations can find practical applications for representatives of such networks and alliances looking to make their membership offers more lucrative and attractive to big players in the industry.
6. Conclusion
This study used a combination of descriptive and exploratory design characteristics with a qualitative methodology focus in order to properly assess the antecedents acting as forces driving the creation of intercompany networks and alliances related to blockchain implementation in the financial industry. A purposeful sample of 26 interviews with representatives of various financial institutions, innovative tech firms, alliances, networks and academic institutions was analyzed using the Grounded Theory method and codifying data with the purpose of quantifying scattered and massively different data. Multiple findings were unveiled in the process that have applications both for expanding the existing body of academic knowledge on the topic and for real-life application by top management of financial organizations.
While carrying out the study, a number of limitations was encountered. These have roots in both the data acquisition methods (using secondary sources limits the reliability while having interviews as the main data source negatively impacts the generalizability of results when applied
26 to other industries and disruptive technologies) and the limited amount of existing academic sources on the topic. Suggestions for further growth of body of knowledge related to blockchain-focused organizations include overcoming existing limitations by deploying different research methods as well as supplying this study with a greater body of empirical evidence from quantitative sources.
Blockchain’s journey to revolutionize an extremely extensive range of areas related to financial transactions, logistics and transportation, bookkeeping, digital rights management, keeping historical records and many others, has just begun. Anyone who has even a basic understanding of the technology can agree with certainty – we are witnessing an emergence of something that will one day be an integral part of our lives. Of course, no evolution is possible without a sustained effort of dedicated innovators, so the author urges you to not be a bystander during such an exciting time!
“The blockchain cannot be described as just a revolution. It is a tsunami-like phenomenon, slowly advancing and gradually enveloping everything along its way by the force of its progression”
27
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Appendix
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