NETWORK STRUCTURE AND ECOSYSTEM EVOLUTION: AN EXPLORATORY ANALYSIS OF DIGITAL PLATFORM COMPANIES’ FORAY INTO FINTECH

By

CARLOS L. PASCUAL, JR

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF BUSINESS AT THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF BUSINESS ADMINISTRATION

UNIVERSITY OF FLORIDA

2020

© 2020 Carlos L. Pascual, Jr.

To Nancy, my wife, my love, and my partner in life, whose unwavering support throughout the years has allowed me to pursue my dreams.

To Michael, Lauren, Kevin and Brian, my life’s inspirations. Your keep the fire burning inside me. A father could not ask for anything more.

You are the light of my life and why I strive to be the best person I can be.

ACKNOWLEDGMENTS

This has been an incredible journey. I thank my chair, Dr. Gwendolyn Lee, the Chester

C. Holloway Professor at the University of Florida Warrington College of Business,

Management. Her enthusiastic, unselfish, and unwavering support has been instrumental in getting me here today. The trust and confidence she placed in me had an energizing effect in the toughest of times. I cannot say enough about how she pushed and nudged me to challenge myself and my though process, moving beyond the practical blinders and opening my eyes to academia’s approach. Each conversation was a thought-provoking learning experience with

Gwen imparting her knowledge and helping me understand the evolutionary thought process as we worked through new concepts and literature. I am forever indebted, and I am here today because she took an interest in me and my work.

INTRODUCTION

Rapid pace of change in digital technologies, communications, and data management is causing severe systemic effects while challenging industries and the dominance of incumbent firms. Referred to as digital disruption, it is defined by Skog, et al (2018) as “…a type of environmental turbulence induced by digital innovation that leads to the erosion of boundaries and approaches that previously served as foundations for organizing the production and capturing of value” (Karimi & Walter 2015; Weill & Woerner 2015; Rauch et al. 2016; Skog et al. 2018). Industries and firms have been forever changed by new business models with digital moving into the physical world. Nowhere has it been truer than in financial services where the move has been fast and unrivaled. The disruption is spearheaded by the innovation-led rapid growth of the financial technology (FinTech) sector which is ushering in a new era for financial services. Nimble months old firms and large digital platform companies (e.g. Alibaba, Amazon and Alphabet) enter the FinTech space daily, modifying a highly regulated industry and challenging established 100 plus year-old firms. FinTech is an umbrella term, has many forms, and operates across the entire value chain and within each branch of financial services.

The disruption started with digital transformation, defined as the use of technology to generate a more effective and efficient value creation process (Reddy & Reinartz, 2017).

However, to be clear, FinTech is not just digitizing financial services. The industry has been digital for decades. In fact, the case can be made that the financial services industry was the first industry that became truly digital during the 1980’s (Arner et al., 2015).

In the current era of FinTech, firms inside and outside of the financial industry have begun to offer innovative products and services directly to consumers and businesses (Arner et

al., 2015), offering new technology solutions, and disintermediating incumbent firms (Lee,

2017). Industry boundaries are blurring forcing business model changes. The ability to conduct financial transactions on social networks and other social applications (i.e. Facebook, Twitter,

WhatsApp) have shown that platform-based business models can permeate even the most trust- based and regulated industries. The pace of innovation is accelerating and the speed with which platforms and network structures are evolving is what marks FinTech’s oversized impact.

Digital platform companies invest heavily in FinTech, especially where they can leverage their platforms, large captive customer bases, data troves and strong financial positions to provide financial services and products. The basic principles for doing business have changed.

The new players have refocused the customer narrative, reduced margins and changed the playing field conditions forever.

Why should firms take note? A quick look at what happened in the mobile phone industry can put things into perspective. Almost overnight, Apple and Alphabet’s platforms knocked out Nokia, Sony Ericsson, LG, Motorola, and Samsung. All the latter were well positioned and profitable. What ensued is a classic case study in the power of platforms, the value they create for their constituents, and the network structures of ecosystems. Rather than just building a product, the former companies created a new ecosystem with a two-sided market and used the hardware and software as an entry to provide platform services. The rest, as we say, is history (Van Alstyne et al., 2016).

Firms are required to make defining choices about options, strategies, and technologies

(Zeppini & van den Bergh, 2013). Those that do not understand the rules and strategy changes brought on by FinTech or cannot evolve platforms or develop competitive network structures

may not survive (Van Alstyne et al., 2016). As firms progress through their digital strategies, many are looking at what they may do to shorten time to market, take advantage of research and development activities in the FinTech ecosystem, and overcome barriers within their own firms.

While several platform-led network structure approaches have been attempted, none has been identified as the preferred approach.

Yet, not all platforms are equally successful. Why do some platforms thrive and others do not? Zhu and Iansiti (2019) posit that thriving platforms have network structures that leverage or mitigate each of five fundamental properties: (1) strength of network effects; (2) network clustering; (3) disintermediation; (4) multi-homing; and (5) network bridging. They advise managers to use these properties as structural guidelines in developing their ecosystem network structure. The way a firm structures its network can and does have an impact on its ability to generate network effects from its platforms, create and capture value, and grow. It is through the effective management of each of these that managers can protect their firms’ platforms and ecosystems from external threats.

In general, extant literature on FinTech, the ecosystems, and strategies to address the challenges and opportunities brought on by digital disruptions is scarce and in its nascent stages

(Shim & Shin, 2016) with limited theoretical grounding (Weiyi, 2018). The review reveals a need to examine how the network structure of an ecosystem can be constructed and managed for delivering FinTech-enabled financial services as a digital strategy of platform companies. The objective of this research was to use case study and data-driven visualization methodologies in an exploratory study to better understand the complex and fluid evolution of financial services and how their differing approaches have led to significantly different ecosystems, levels interconnectedness and platform-led network effects.

While Zhu and Iansiti’s (2019) five properties are essential, a sixth network property, coined “multi-nodal combination”, was identified and is submitted as an essential component and lever for ecosystem growth. It is theoretically rooted in the strategy principles and core processes articulated by Adner, Puranam, and Zhu (2019) in the context of digital technologies.

Multi-nodal combination harnesses the power of enhanced connectivity, data aggregation, and

“multi-nodal collaboration”. Multi-nodal collaboration is also introduced and developed by extending the processes of the fundamental properties from Zhu and Iansiti (2019). Multi-nodal combination enriches the extant literature in answering an important question as digital platforms embrace FinTech disruptions: What network structure should be used to construct and grow an ecosystem?

The theory is built from case study research complemented by network visualization techniques for the comparative analyses. Case based research was selected as it is interesting and impactful for the reader (Bartunek et al, 2006), is effective in helping to understand complex situations (Harrison et al., 2017) and the articles covering case-based research methodology are among the most cited works within the Academy of Management Journal (Eisenhardt &

Graebner, 2007). More importantly, case-based research provides insights that may not be achieved by other means (Rowley, 2002; Harrison et al., 2017). It is also pragmatic, flexible, and suitable to use for exploratory research especially in areas where existing theory may not address it appropriately (Eisenhardt, 1989; Harrison et al., 2017). The rapidly evolving nature of the network structures of FinTech ecosystems firms provides us with an opportune canvas from which to analyze.

Three case studies are used to review the evolution of network structure and compare how the structures and their evolutionary paths differ. Sampling is based on potential industry

impact and contrasting approaches. Thus, three influential platform-led BigTech firms’ financial services businesses – Alibaba (Ant Financial), Alphabet (Google Pay), Amazon (“Amazon financial services”) – have been selected in lieu of emerging FinTechs or incumbent financial services firms (e.g. JPMorgan Chase (e.g. hub and spoke network), VISA (two-sided), or PayPal) which have compelling network structures yet add additional industry variables which are beyond the scope of this work.

Why compare the three firms’ networks? The BigTech financial services arms have strategically constructed the network structures for their FinTech ecosystems, in response to digital disruption, in contrasting styles. Each approach is different with each of them leading to significantly different network structures. Which structure is the one that leverages multi-nodal combination and the other network properties best to create value? Each network structure has value based on business objective, yet the better value is gleaned from leveraging multi-nodal combination throughout the ecosystem. Suboptimal leveraging will limit value creation and ecosystem growth.

The research introduces and develops the network property multi-nodal combination as a key factor in network structure and ecosystem growth. It establishes an integrated network structure layout for the core ecosystems identifying the unique characteristics of the three firms’ network structures and generates comparative visual representations. The firms’ diverging evolutionary paths have led to distinct network structures that are visually described. I also demonstrate which firm’s network structure develops multi-nodal combination and is most efficient in managing and leveraging Zhu and Iansiti’s five fundamental properties.

This research is expected to have important implications and thus benefit practitioners and academia. Practitioners will benefit from the insights provided by the visualization of the major disruptors’ network structures and their evolutionary paths. FinTech academic literature, which is currently at the nascent stage of development, will also be augmented.

The paper is structured as follows. The first section contains the literature review and establishing the boundaries for the remainder of the discussion. The second introduces and develops multi-nodal combination and third addresses the study’s methodology, case selection, and data. The final four sections present the observations, conclusions and contributions, limitations, and future research.

LITERATURE

FinTech

FinTech, in its daily use, is an umbrella term. It has been said to be the application of technology to finance (Arner, 2015) and it has been referred to as a concept, a sector, an industry, a company (“a FinTech”), and a service platform. FinTech is not just digitizing financial services. The financial services industry has been digital for decades. In fact, a case can be made that the financial services industry was the first industry that became a truly digital industry during the 1980’s (Arner et al., 2015).

The origin of the term FinTech has not been well established. Some have put forth that it came circa 1972 in an academic paper published by a Manufacturers Hanover Trust vice president while others believe the phrase may have had its origin in an initiative started by

Citibank in the early 1990’s (Arner et al., 2015; Gimple et al., 2018). In either case, neither envisioned what we know as FinTech today. It has many forms, operates across the entire value

chain and within each branch of the financial services industry, and has a myriad of business models. Unfortunately, there has been little agreement on a common definition of FinTech and the term has been used ambiguously and inconsistently. To address this shortcoming and establish a common ground for academics and practitioners, Schueffel (2016) conducted the most comprehensive review of the term and proposed the following definition as a starting point:

“FinTech is a financial industry that applies technology to improve financial activities”. Even though Schueffel undertook a scientific approach to establish his definition, it still falls short. In my dissertation, the concept of FinTech refers to the use of technology and the new business models facilitated by technology in the provision of financial services (Havrychyk & Verdier,

2018). Its scope is the entire universe of services and products in the financial industry. Arner et al. (2015) posit that FinTech is comprised of five sectors: Finance and investment, operations and risk management, payments and infrastructure, data security and monetization, and customer interface.

With over 7,500 FinTech firms globally (Capgemini, 2018), the pace of innovation is accelerating. Investment into these firms continues to be in the tens of billions of dollars each year with over $45 billion invested in a period of 15 months (CBInsights, 2018). Not including the FinTechs which have exited or gone public, there are 33 unicorns (i.e. privately owned firms with valuations of over $1 billion) with a collective valuation of over $115 billion (CBInsights,

2018). These unicorns include diverse FinTech companies such as stripe (online payment infrastructure provider and global e-commerce enabler), SoFi (online personal finance company primarily focused on refinancing of student loans), paytm (Indian digital payments platform and wallet), Oscar (tech-enabled health insurance carrier), coinbase (crypto currency trading platform) and TransferWise (unique international peer-to-peer money transfer platform). These

new entrants are changing how customers are approached, engaged, and serviced. As examples, peer-to-peer lending companies such as Prosper, on the consumer side, and Yieldstreet, on the commercial side, are replacing traditional lenders like Bank of America and TDBank.

International money transfer giants Western Union and MoneyGram are seeing innovators

Transferwise and WorldRemit come in with vastly different business models at significantly lower cost and price levels. For instance, with the Transferwise platform, to send money abroad instead of making one international transfer, two local transfers are made, thus avoiding having to own and manage a true treasury function and carry its added costs. Incumbent insurance companies, AllState, Progressive, and GEICO, have Trov and Metromile to deal with. Within each subindustry and within each firm, there is a different business approach and technological angle.

To further complicate matters, tech giants (BigTech) are also disintermediating financial services firms, especially in mobile and on-line financial services where they can leverage their platforms, large captive customer bases, and strong financial positions to provide financial services and products. Social network platform and App companies are also getting into the game. Facebook comes out with Libra, and Tencent has WeChat financial services (e.g. wallet, lending). All these platform-based firms get to play by different sets of rules and regulations, all in the name of innovation.

Platforms

The open source approach of Web 2.0 and its businesses are structured for collaboration, co-development, content reuse, and information sharing (Carnabuci & Operti, 2013; Lang &

Arakji, 2010). These Web 2.0 open innovation models and collaborations emphasize value

creation as they exploit outside intellectual property and have complex business models, especially in platform economies (Lang & Arakji, 2010; Seminer, 2010). Platforms (1) are leading outcomes and beneficiaries of the Web 2.0 open innovation landscape; (2) have come into existence due to rapid changes in technology, internet access and connectivity (Dhar &

Stein, 2017); (3) are focused on enabling social and business interaction, and; (4) facilitate value creation and value exchange by its participants (Chaudary, 2015; Van Alstyne et al. 2016).

Platforms are a software-based business framework upon which products, services and business models can be built upon (Bridgwater, 2015). Referencing platforms, Chaudary (2015) states that “it is the ability of software to orchestrate people and resources, make intelligent decisions, and enable a connected global workforce to create value that is the real force driving disruption today”. In simpler terms, platforms facilitate the creation of value and the exchange or consumption of that value by its participants. The participants can be from two or more sides

(e.g. Alibaba, Uber, Airbnb) or complementors and users (e.g. Google Android) (Evans, 2003;

Rysman, 2009). Value must be created for both sides of the platform market (producers and consumers) and the platform owners must ensure the value they extract to be appropriate for each side, all while capturing and analyzing data from their interactions.

Digital platforms have been studied through the lens of Innovation Management Theory, and more specifically through digital innovation platform ecosystems (Adner et al, 2015;

Bouwer, 2017). In his work, Bouwer (2017) conceptualized a digital innovation platform framework which can be used by researchers to advance inquiry into this area, including open platform innovation. Increased levels of innovation arise from ideas and knowledge gained from outside the boundaries of the platform firm, giving the participants the ability to recombine, bundle, or substitute assets between the parties (Seminer, 2010). From a strategic management

perspective, delivering greater value at lower cost and price results in competitive advantages for platform firms (e.g. FinTechs, BigTechs) (McIntyre & Srinivasan, 2017). Further complementing this is research from several scholars which addresses how competitive advantages are obtained from a variety of value creation views of platforms. Among them are value co-creation from complementors of the networks (Adner & Kapoor, 2010); strategic coordination and interactions between platforms and networks (Cennamo & Santalo, 2013;

Kapoor & Lee, 2013); and platform leadership (Gawer & Henderson, 2007). Though their work dealt mostly with the hardware/software combination and feedback dynamics, Katz & Shapiro

(1994) also identified value creation and network effects from complementors. Complementors are individuals or firms that build goods or services (i.e. complements) on a platform which enhance the value of the network (McIntyre & Srinivasan, 2017).

Cusumano, Gawer and Yoffie (2019) categorize platforms into two general categories, transaction and innovation. Transaction platforms are mostly online marketplaces that play an intermediary role between two parties facilitating transactions like the exchange of goods, services and information (e.g. Amazon Marketplace, eBay). Innovation platforms provide a core technological infrastructure and facilitate the creation of complementary products and services by other entities. Examples of these include Amazon Web Services, Google Android and

Microsoft Windows. With both types of platforms, value is being created by parties outside of the platform firm, building support for the increased benefits coming from collaborative alliances and partnerships. Firms can leverage each platform as they build their business model, network and ecosystem. If they start off with a transaction platform, the firm can add an innovation platform to increase innovative products, services or features from third party firms. Innovation platform firms can add transaction platforms to establish distribution capabilities. The core idea

here is that firms do not necessarily need to stay with one platform approach or the other. In fact, having a hybrid approach – differing degrees of integration between different platforms within a firm – may be one of the best approaches if executed appropriately. Cusumano, Gawer & Yoffie

(2019) cite hybrid firms (e.g. Amazon, Alibaba, Facebook) as some of the most valuable companies in the world. These authors believe that hybrids are the next phase in the evolution of platform thinking since hybrid firms can leverage complementarities, technological integration, scale, strategy and scope.

Platforms use network effects to scale. Network effects are the engine behind platform ecosystems because the effects play out when products and services become more valuable for all constituents as more people use them (e.g. Facebook, Venmo) (Chaudary, 2015; McAfee &

Brynjolfsson, 2017). Network effects are self-reinforcing feedback loops with two general types of network effects having been identified, direct and indirect, as value creators (Cusumano,

Gawer & Yoffie, 2019; McIntyre & Srinivasan, 2017). Direct network effects, or same-sided network effects, arise when the benefit of network participation depends on the number of network users with whom they can interact (Eisenmann, Parker & Van Alstyne, 2006). Indirect network effects happen when opposite sides of a network can benefit from the size and characteristics of the other (e.g. eBay, uber, Airbnb) (Armstrong, 2006; Evans, 2003; Parker &

Van Alstyne, 2005; Rochet & Tirole, 2006). For example, the more buyers there are on a transaction platform, the more interesting it is for sellers to be on that platform, and vice-versa.

However, to drive strong network effects, companies must make the right strategic decisions in how they (1) structure and integrate their platforms; (2) leverage organizational capabilities and existing assets; (3) add products and services and; (4) build complementary businesses

(Cusumano, Gawer & Yoffie, 2019).

Network effects also help platforms develop their brands and generate loyalty.

Consumers, producers and complementors usually develop a greater affinity to a platform brand than to the entity or person on the other side of the network (McAfee & Brynjolfsson, 2017). For instance, with the Uber platform, the passenger is more entrenched with Uber than they are with the provider of transportation. The same can be said for the drivers, who are more loyal to Uber than to the passengers they pick up.

As platforms become ubiquitous, they capture most if not all the value because they reduce information asymmetry amongst the participants and unleash the power of network effects allowing platforms to leverage the interactions in the ecosystem (McAfee &

Brynjolfsson, 2017). Participants’ relationship with a platform can be viewed from the perspective of the “relationship quality” construct. The relationships developed by the participants on the network have a cumulative quality based on trust, satisfaction and commitment (Arcand et al., 2016; Brun, 2014; Palmatier, 2008). As the participants transact successfully and find their interactions on the platform valuable, they begin to develop a lasting relationship with the platform centered on the three core components of the construct (i.e. trust, commitment, satisfaction). As long as the platform continues to provide effective and efficient transactions with engaged participants, the relationships will continue to build, reinforcing the value of the platform, drawing new participants, and adding to its scale.

Platforms may change the scope of their businesses over time. They could move quickly into other businesses deploying hybrid platform strategies that could raise barriers to entry

(Cusumano, Gawer & Yoffie, 2019). Case in point is Google which moved from web browser into maps and other functions (e.g. auto driving cars, voice recognition) (Van Alstyne et al.

2016). In the FinTech, telecommunications giant Vodaphone launched M-Pesa in 2007 and it is

now one of the largest and most successful branchless banking FinTechs in developing markets

(Arner, 2015). BigTechs like Alibaba, Amazon, Google, and Facebook are formidable competitors of financial services firms and FinTechs alike. The BigTechs’ vast customer bases are ripe for monetization, as demonstrated by Alibaba through its subsidiary, Ant Financial.

FinTech platforms have benefitted from low costs of communication and operation, and the reduction of information asymmetry (Theis, 2016). In general terms, FinTech reduces operational costs and enhances the efficiency and availability of financial services, creating and unleashing value not seen before. As a result, FinTech platforms allow for a cheaper way to conduct transactions and an easier way to create value through complementors such as applications developed by external parties (Kabakova et al., 2016). However, Lee & Shin (2018) suggest that we must first analyze the FinTech ecosystem before we can understand the competitive and collaborative dynamics of these firms.

Ecosystems

An ecosystem is a community of interconnected heterogeneous actors with complementary competencies and participating in a value-creation process (Demil et al., 2018;

Moore, 1993, 1996). Jacobides et al. (2018) extended the definition to mean “…groups of firms that must deal with either unique or supermodular complementarities that are nongeneric, requiring the creation of a specific structure of relationships and alignment to create value and not fully hierarchically controlled”. In other words, an ecosystem is the alignment structure of a multilateral set of partners that need to interact for a focal value proposition to materialize with the ecosystem network structure being the alignment of the firms, links and activities (Adner,

2017). With the business environments quickly and continuously evolving, firms are faced with

immediate challenges on how to build, acquire and coordinate new capabilities. Ecosystems require a new way of thinking about business and provide managers with new opportunities on managing the trade-off between flexibility and commitment.

Because of its importance in business, “the business ecosystem is widely used as an important concept in establishing a strategy for competition or mutual cooperation between companies in the industry” (Li, 2009; Lee & Kim, 2017). While the theoretical literature on ecosystem development and evolution is progressing (Ahuja et al., 2012; Still et al., 2019), we know firms build ecosystems to create or extract more value (Jacobides et al., 2018). The ecosystem that a firm establishes results from the decisions of top management (Demil et al.,

2018) and includes the strategic position and role of the firm within the network structure. This is critical because the structural interdependencies, technological interactions, and the structure of flow interdependence between firms in an ecosystem have important impacts on firms’ innovative processes within the ecosystem (Ganco, Kapoor & Lee). Leaders must navigate their firms through the disruption impacting their industry. They must design and evolve the ecosystem-level network structures to increase innovation and value creation. They must also strategically determine the firms’ involvement and embeddedness in the ecosystems, or face marginalization.

When platform firms engage their participants to interact, they create a platform ecosystem. Jacobides et al. (2018) defined platform ecosystems based on how the participants are organized around the platform and within the network structure. This model is more in line with their hub and spoke example where developers generate application programming interfaces

(APIs) and apps to sit on the Android platform. However, this definition is too narrow as there are platforms that do not allow third parties to add to their platform (e.g. Uber, Lending Club).

Thus, for the purpose of my dissertation, the platform ecosystem is defined as the platform and its community of participants (i.e. consumers, producers, complementors) who interact to create value (Adner & Kapoor, 2010; Ceccagnoli et al., 2012; McIntyre & Srinivasan, 2017). Platforms ecosystems generate greater value and deliver competitive advantages for their firms. In 2018, seven of the ten largest firms in the world were platform-based firms.

In general, platforms and their ecosystems have four constituents (Van Alstyne et al., 2016) and three key priorities (Chaudary, 2015). The four constituents are: platform owners, platform providers, producers and consumers. And, the three key priorities are: (1) the platform must attract users and consumers to participate on the platform; (2) the platform must facilitate the interactions between the two constituents, and; (3) the platform must ensure that it matches the right producers with the right consumers. As proposed by Dhar and Stein (2017), platforms are deemed to be complete platforms when (1) they are open and allow easy access for participants, starting the process from which to create a network effect; (2) they implement key business and operational processes, further solidifying the network effects that increase in value as participation increases, and; (3) they implement business processes automatically, using technology which also gathers and retains data for future manipulation, analysis and use.

By adding two key concepts from Kabakova et al. (2016) to Dhar and Stein’s (2017) platform definition, we can further strengthen it. In their grounded theory-based study of Russian

FinTech Platforms, Kabakova and her co-authors found that FinTech platforms needed to be dynamic and rely on independent participants of the ecosystem. The dynamism allows for business strategy to be flexible in a rapidly changing environment and affords firms the opportunity to collaborate outside their boundaries. Platform assets are the community and the resources the members of the community provide (Van Alstyne et al., 2016) with control of the

user interface and digital experience being a key component (McAfee & Brynjolfsson, 2017).

Platform owners determine what information is seen and shared, how transactions are processed, and the data that is generated and collected for future use. The data accumulation can add significant value as the platform owners and providers leverage analytics, artificial intelligence

(AI), machine learning, modelling and other monetization activities allowing the platform ecosystem to grow in scale and in the value created (Cusumano, Gawer & Yoffie, 2019; McAfee

& Brynjolfsson, 2017).

Value creation is a driving factor in the ideation and creation of FinTech platforms and applications. Continuous development and incremental exploratory initiatives are encouraged to ensure that multiple avenues are tested before winners, although sometimes temporary, are identified and expanded (Ashta & Biot-Paquet, 2018). This is because platforms have an ecosystem-based view (EBV) of value creation which contrasts with the traditional resource- based view (RBV) of value creation. In RBV, control of resources is an important source of competitive advantage where in the EBV the resources are controlled by the platform community and value is created by the platform users and the platform’s ability to optimize the matching of the two sides and the flow of value (Chaudary, 2015). Value is created by technology through cost reductions (absolute and marginal), revenue increases from greater accessibility, analytics and targeting initiatives, and risk distribution throughout the platform community.

The EBV of value creation is also consistent with Gunther McGrath’s (2010) view of a discovery driven approach for business model construct. McGrath, like EBV value creation, sees that experimentation and learning are required in discovering and exploiting new business models. The evolution of FinTech firms, business models, ecosystems, and network structures are emblematic of this test-and-learn approach.

The business ecosystem concept has directed attention to new models of value creation, capture and appropriation with partner alignment as a critical strategic challenge (Adner, 2017).

Business ecosystem concepts share principles that are grounded in network structures. The three core principles drawn from prior work and cited by Lee and Kim (2017) are: “1) many companies form a network, (2) these companies are interconnected and interdependent, and (3) for companies to survive, not only must they compete but they must also cooperate mutually for coevolution” (Iansiti & Levien 2004; Moore 1993; Peltoniemi 2006).

The network design of ecosystems and their evolutions are becoming critical in understanding what drives innovation, creates customer value and determines success in platform firms. Kumar and his co-authors (2015) propose that strategists must develop a network-centric approach in today’s ecosystem-based world to ensure ecosystem perpetuity. They believe managers should focus on strengthening participants in an ecosystem while contributing to the prosperity and defense of the ecosystem. Ganco, Kapoor and Lee (2019) find that as technological interdependencies within an ecosystem increase, innovation of the participant firms increases as well. While they do identify that the positioning of a firm within the ecosystem and the types and flows of interdependencies matter for the scale of increased innovation, the more interdependence that exists, the more firms will benefit from innovation.

Agarwal (2017), in her dissertation on interfirm dependence and firm performance also identified that “(1) a firm’s interdependence with other actors in the ecosystem matters both for its performance and the sustainability of its superior performance; and (2) a manager’s understanding of these interdependencies can have significant implications on firm performance and the choice of governance structures”.

FinTech firms develop technologies and design services in a valuable customer centric manner (Gomber et al., 2018). Intent on continuous innovation, value creation and capture, these firms evolve their network structures by facilitating integration through the establishment of new linkages and integrations to build market share, create barriers to entry and provide a continuous feed of new data for accumulation. They create interdependencies between firms to simplify the collection, analysis, and application of data thus generating virtuous cycles, or feedback loops, that strengthen the interconnection between the firms. Ecosystem participant firms can then develop innovative solutions to be introduced to the broader ecosystem with ongoing collaboration. The expansion and evolution of the network structure and ecosystem becomes logical and mutually reinforcing.

Network Properties

The network structures that enable digital platforms to fight off competition and grow profits start with what Zhu and Iansiti (2019) refer to as the five fundamental properties of networks. It is the effective management of these network properties that allow platforms to develop lasting competitive advantages and continually grow profits while fighting off competitive threats.

Strength of Network Effects: The strength of network effects can alter a platform’s ability to create, capture and sustain value (Zhu & Iansiti, 2019). When strong network effects are present, the value of the platform rises sharply with participants. When weak network effects are present, there are mitigating factors that influence the strength of the network effects. Video game consoles are examples of weak network effects where value is driven by the popularity of certain games on each platform and not the platform itself (Zhu & Iansiti, 2019). The authors also

postulate that the strength of network effects can vary across time. Just because a platform benefits from strong network effects for an extended period does not mean that it will continue to do so. A case in point is Windows, which dominated during the last decade of the 20th century only to see its network effects diminish, barriers to entry fall and its dominance falter as cross- operating-system internet-based apps took hold over the next two decades. However, strong network effects by themselves are not enough to exert dominance over a market (Cusumano,

Gawer, & Yoffie, 2019).

Network Clustering: Zhu, Iansiti, Li and Valavi (2019) found that network structure has a direct impact on a platform’s ability to sustain its scale. Their research identified that network fragmentation into local geographic clusters isolates the clusters and opens the business to new challengers (Zhu et al., 2019). Using Airbnb and Uber, we can see the vulnerabilities that exist in a fragmented network ecosystem as demonstrated in the examples using visualization methodology below. Airbnb’s global interconnected network is dependent on consumers and providers residing in different geographies. Uber also has a global network. However, for the consumer and the transportation driver it is important to be in the same geographic area. If not, it does not matter how many participants are on each side of the network since there is limited utility. Uber ran into these issues in markets such as China and Southeast Asia where they ended up selling to Didi Chuxing and Grab, respectively. They are also facing extensive competition and issues in Russia (Yandex.taxi) and Brazil (99).

Zhu & Iansiti (2019)

Disintermediation: Disintermediation occurs when members bypass a platform and connect directly (Zhu & Iansiti, 2019). A prime example of what happens to platforms that suffer this fate is Homejoy. Homejoy, a popular home service provider platform, connected homeowners with service personnel for work around the home. It was unable to prevent homeowners and service providers from connecting directly after the first service calls. It closed within five years since its business model depended solely on capturing value from the brokering of the transactions between the two parties.

Several approaches with varying effectiveness levels have been taken to address the risk of disintermediation. The most effective is possibly Alibaba’s with their Taobao e-commerce platform. They provided complementary services and technology to each side of the platform, sometimes at no cost, to entice the participants to stay on the platform and within the ecosystem.

Vulnerability to Multi-homing: Multi-homing is present when participants on a platform connect to multiple platforms offering similar services (e.g. ridesharing and food delivery) (Zhu &

Iansiti, 2019). In ridesharing, passengers and drivers can sign on to Uber and Lyft with virtually no incremental costs. Similar issues exist for restaurant delivery services with restaurants and consumers practicing multi-homing. Attempts at curtailing multi-homing have been shown to be effective to varying degrees though they sometimes generate unintended consequences. Those that have been successful in curtailing multi-homing usually have some sort of market power

(e.g. Amazon) or their platform ecosystem can sustain such controls as exclusive agreements

(e.g. video game consoles and game publishers).

Network Bridging: Successful platforms use the data they have gathered to further expand and diversify into new lines of business. These platforms also connect with multiple networks for

additional expansion and data gathering. By leveraging synergies, firms that have succeeded in one industry vertical often diversify into different lines of business and improve their economics

(Zhu & Iansiti, 2019). This is a fundamental reason why Amazon, Google and Alibaba have expanded horizontally and vertically into many markets. When platform owners connect with other networks, they can build important synergies. Alibaba successfully bridged its payment platform, Alipay, with its e-commerce platforms Taobao and Tmall, providing a much-needed service to both buyers and sellers and fostering trust between them. They then took the information from merchant sales and started offering loans to small businesses on the e- commerce platforms.

MULTI-NODAL COMBINATION

Conceptual Activities – Introduction

While the five properties are essential, there is an additional property which must be considered when designing and evolving the network structures of digital platform firms’ ecosystem(s). With the understanding that (1) strongly interconnected network structures are more defensible (Zhu et al., 2019); (2) increasing the number and concentration of firms and the likelihood of new interactions and combinations in an ecosystem increases the overall value creation and system value through direct and indirect network externalities (Adner, 2017; Van

Alstyne, Parker, & Choudary, 2016), and (3) growth, competitive advantage and value capture depend on the interaction between platform and network (Zhu & Iansiti, 2019), we propose multi-nodal combination as a sixth property. We coin the term multi-nodal combination to mean the management of connectivity, data aggregation and multi-nodal collaboration – three main activities for managing an ecosystem’s network structure. We posit that the coalescing of these

three activities yields a network structural property that may exponentially accelerate the growth of the network, the ecosystem, and its sustainability.

Multi-nodal combination takes its basis from network bridging, extending beyond the conventional notion of entry into new industries and segments. The concept of multi-nodal combination is theoretically rooted in the strategy principles for technological transitions articulated by Adner, Puranam, and Zhu (2019). We develop the three main activities of multi- nodal combination by introducing “multi-nodal collaboration” and extending the processes of the fundamental properties from Zhu and Iansiti (2019)—digital transformation, connectivity, and data aggregation.

Connectivity – refers to the activities that link the firms (nodes) for commercial or other value creation or capture purposes.

Data Aggregation – refers to the activities that provide the ability to combine previously disjoint data from multiple sources.

Multi-nodal Collaboration – refers to the interactions amongst the connected nodes that are the lynchpin of multi-nodal combination. Multi-nodal collaboration are concerted efforts by connected firms to productively work together and add value to each other, the network, and the ecosystem. Multi-nodal collaboration has two activities: engagement and reciprocity between firms. The two activities can be used by digital platform firms to orchestrate their ecosystem network structures.

Engagement – The ability of focal firms to engage and mobilize connected firms, more than just the connections that have been forged, is what drives value creation (Storbacka, 2019;

Storbacka et al., 2016). Using natural ecosystems as a base of comparison, Chang and West

(2006) see interaction and engagement as one of the four essences of digital ecosystems. They posit that the technology supporting digital ecosystems provides for “a cross-disciplinary interaction and engagement for productivity, prosperity and growth”. Integrated firms leverage platforms by participating in a robust interconnected ecosystem that facilitates collaborative work and the adoption of new technologies. Ganco, Kapoor and Lee (2019) add that innovation search, important for innovation in firms, is dependent on the nature of connections in ecosystems.

Brodie et al. (2019) systematically developed and defined engagement “as a dynamic and iterative process that reflects actors’ (firms’) dispositions to invest resources in their interactions with other connected actors (firms) in a service system”. Connectedness is a critical property of engagement (Brodie et al. 2019). Connectedness among firms in a network and ecosystem affects the desire of firms to engage in new ideas and create value (Chandler & Lusch, 2015:

Storbacka, 2019). Engagement also addresses the increasing connectivity among firms and includes the emergence of new types of organizations such as platform businesses. Engagement reflects multiple firm interactions, interdependencies (Vargo and Lusch, 2016) and reciprocities within networks and at various levels of aggregation. “From a network perspective, actors

(firms) are inseparable from their actions and connections” (Brodie et al., 2019). Thus, the ability to engage and mobilize connected firms is a strategic priority in structuring and managing networks and ecosystems, including understanding how new linkages can be created (Storbacka,

2019).

Reciprocity – This activity enables the evolution of cooperation (Nowak & Roch, 2006) with exchanges that are mutually beneficial for connected firms and foster a collaborative

environment. Network structure can influence the evolution of network reciprocity, in which those cluster of cooperators outperform those that choose not to reciprocate (Nowak, 2006).

Conceptual Development

Platform ecosystems are powerful tools. Appropriately structured and implemented, they leverage the network properties in creating value for all constituents. Platforms incorporated as part of an ecosystem can significantly change how and how much value is created and how that value is captured and allocated within the ecosystem. As such, platform-based firms have become pillars within global societies and economies (e.g. Alphabet/Google, Facebook, Alibaba,

Amazon, Flipkart). Leaders of established and emergent firms seek to complement their ecosystem by aligning within the network structures of the best digital platform firms. Firms that participate in network structures with multi-nodal combination have the focal firm leading connectivity push and have long-standing and significant impacts on the network structures and ecosystems. The configuration or structure of the network can have many evolutionary trajectories and each network structure is different, yet we submit that the right network design and appropriate governance will determine the platform ecosystems structured for growth.

In general, networks interact and depend on other networks. While ecosystems are more elaborate models of collaboration, with multi-nodal collaboration, connected firms (i.e. subsidiaries, partners) are working together towards a unified purpose – improvement of every firms’ business – with the encouragement of the orchestrators of the ecosystem (i.e. focal digital platform firm). The refinement and competitiveness of ecosystems has been accelerated by data.

Previously inaccessible data has become available with network structure playing a key role in ensuring continued data aggregation and analytics.

Multi-nodal combination is enabled by the ecosystem orchestrators and takes its basis from network bridging. However, it differs from both network clustering and network bridging.

It is different from network clustering in that network clustering focuses on the connectivity of firms that are on opposite sides of a platform while multi-nodal combination leverages firms on the “same side” of a platform, as the focal point are the firms (i.e. nodes) within the provider side of the ecosystem. A look at JPMorgan provides us with a good example of network clustering.

JPMorgan established relationships with Roostify (self-service mortgages), iCapital (alternative investments), Global PayEx (invoicing and payments for companies) and Motif (IPO issuers), all of which are platforms to enhance services to different end users. Each of these firms becomes part of a cluster, of which none of the firms will interact or connect with each other without the encouragement and engagement of JPMorgan. In network bridging, the focal firm diversifies and moves into new markets by adding connections to other nodes (e.g. firms, platforms) which afford it the opportunity to expand the products and services that the focal firm offers and capture data. As an example, JPMorgan’s InstaMed acquisition allowed it to acquire a platform to expand into medical payments. It should be noted that market entry by itself does not necessarily guarantee that the focal firm need not worry about new or existing niche competitors.

Uber serves as another example. As we discussed earlier, Uber is adversely impacted by network clustering at a geographic level. With its current platform strategy and business model, to benefit from multi-nodal combination, Uber would have to establish relationships with other provider firms which could provide it with access to new data, insights, and customers. For instance, Uber could partner with scooter rental companies such as Lime, Bolt and Bird whereby the companies could collaborate and establish a value proposition for those riders that have had too much to drink, it has gotten too late or inclement weather has led the riders to feel

uncomfortable riding a scooter, to take an Uber home. The scooter company could inform their customer that it might be best to take another form of transportation home and provide an offer from Uber. Similar types linkages with firms in different industries could be made where new customers and data are fed into each companies’ data trove and analyzed for consumer trends and behaviors.

Multi-nodal combination is an attribute of purposefully structured ecosystems and goes further than network bridging with digital platforms benefiting from ease of connectivity.

However, the connectivity of the nodes is not just with the focal firm as in network bridging.

Firms that participate in network structures with multi-nodal combination have the focal firm leading connectivity push and have long-standing and significant impacts on the network structures and ecosystems. The focal firm encourages each of the nodes to also connect and engage with each other.

Referencing the JPMorgan example, JPMorgan could have encouraged InstaMed to establish linkages and engage with other members of its ecosystem (i.e. Bill.com) that may have benefitted from synergies. In contrast, Ant Financial who established connections with mynt

(Philippines), bkash (Bangladesh), KakaoPay (South Korea), and Emtek (Indonesia) took a different approach. After establishing the primary connections, Ant Financial then had the “new to network” firms develop linkages into Alipay. Each of these firms brings in different strengths, assets, and opportunities. For instance, bkash can leverage the QR functionality from Ant

Financial and Alipay with mynt benefitting from an increase in the services it offers such as microloans.

As firms connect, the network density strengthens and increases. The “shift from connectivity-on-demand to connectivity-by-default” has resulted in a significant change in network density and value. The number of connected points increases exponentially as the connectivity of platforms manifests itself in the many-to-many connections, the size of the network, and the density of the network. The greater the density, the greater the value created.

The connectivity is “always on”, for the most part, following Metcalfe’s law (Adner, Puranam, &

Zhu, 2019; Metcalfe, 2013). The between-firm connections need not be just related extensions such as the addition of a risk engine platform to a crowd lending platform. Connections should also be made in unrelated areas of business. While the benefit may not be immediately clear, by connecting two unrelated areas of business such as ecommerce and investment platforms, the participants on the ecommerce platform are exposed to the products for investing excess financial resources. Collaborative dynamics are accelerated as new connections yield opportunities for greater connections. Initial indirect connections act as temporary bridges for nodes not directly connected. The relationships can then be expanded with direct links that provide an opportunity for new data and interactions to be added to the ecosystem. An example would be if Ant Financial encouraged two of its ecosystem partners, Danke Apartments, a

Chinese start-up that is using a dorm-like approach to urban housing co-living and, YUM China, a fast food conglomerate, to link into each other. The benefits are obvious and accrue to each party.

With enhanced or extreme connectivity, firms can generate significant interactions amongst the connected nodes. The heightened connectivity and data aggregation can generate incremental insights across the network allowing for a reinforcing network effect which is what drives multi-nodal combination. New opportunities arise and become evident for firm managers,

allowing them to create and capture new and enduring value. Supporting the benefits that accrue to connected firms, Zhu et al. (2019) found that a firm’s profitability increases with interconnectivity of their network structures. Agarwal (2017), focused on the connectedness of complementor firms (i.e. apps for iOS and Android) with the primary platform as well as with other complementors in that platform-based ecosystem, concentrating on the extent of interaction, integration, and complexity for each firm. In her study, Agarwal found that firms’ interdependence with other players in an ecosystem is important for success and sustainability, and that higher connectedness levels were associated with greater likelihood of commercial success.

In connecting with each other, the effort for firms is not connectivity for the sake of connectivity, but to build engaged and reciprocal interactions amongst the firms. Engagement has been a priority in marketing and service research. The conceptual domain shifts from customer to actor engagement (referred to as “engagement” hereafter) when the former becomes a subset of the latter (Brodie et al., 2019). Actors’ definition of engagement includes collections of humans such as organizations (e.g. firms).

Referencing engagement, Hollebeek et al. (2018) state that “ecosystem actors (firms) do not operate in isolation but are part of broader networks” that will affect the needs, objectives, and expression of engagement. Engagement is subject to each firm’s dispositions and behaviors

(Brodie et al., 2019) with each linked firm being active and working together, thus allowing and inspiring opportunities for new innovations. Through their interactive engagement behaviors, engaged firms invest resources directly and indirectly with and towards other ecosystem actors

(Alexander et al., 2018; Jaakkola & Alexander, 2014; Storbacka et al., 2016). High levels of reciprocity help drive data sharing and accelerate data aggregation, in addition to connectivity.

This multi-nodal collaboration, with an established data acquisition strategy, leads firms to quickly accumulate increasing levels of data from the network. Extraneous and disjointed data that may never have been associated under alternative network structures is made available by the collaborating linked firms. Data accumulates quickly as firms share information related to customers, transactions, etc. The effective management and manipulation of the newly collected data results in increased value creation and capture within the ecosystem. The orchestrators may also facilitate AI and analytics capabilities, foster dissemination of data and insights, and coordinate the connectivity and expansion of the ecosystem.

The focal firm is key in expanding Matt Turck’s (2016) concept of “data network effects”. Different from network effects which are based on the benefits accruing from increased levels of users of a platform, the data network effects are produced from the aggregated data.

With each node and linkage that contributes data, firms can build insights and learnings making it increasingly harder to replicate as the value is built at the software and data level. This is effected through the deployment of AI and analytics to make sense of non-evident connections from the numerous types of data in this reciprocal and connectivity-by-choice network ecosystem. Novel solutions, products and innovative ideas are brought to light along with improvements in customer experience and firm diversification (Cusumano, Gawer & Yoffie,

2018). Firms within the network progressively improve in delivering upon the end-user’s expectations. The focal firm’s engagement, encouragement and facilitation of connectivity and collaboration amongst the firms is also in line with Ganco, Kapoor and Lee’s (2019) and

Agarwal’s (2017) findings on the benefits accruing to interdependent and connected participant firms within ecosystems in relation to innovation search and commercial success, respectively.

Multi-nodal combination also affords firms the opportunity to address some fundamental challenges to network structures of platform-based ecosystems (e.g. reducing the impact of niche platform competitors, building barriers to entry) that may not be addressed with Zhu and Iansiti’s five properties. Multi-nodal combination strengthens an ecosystem’s network structure against niche or differentiated competition and allows firms to decide on how to build its network structure. For instance, by analyzing data on their transaction platform, Amazon was able to enter new verticals to counter the impact of specialized platform firms (Cusumano, Gawer &

Yoffie, 2018). Similar to platforms with huge numbers of complementors that have linkages only to the platforms (Cusumano, Gawer & Yoffie, 2018), ecosystems that have a large number of interrelated and engaged firms reciprocating can build barriers to entry as the unique ecosystem network structure delivers scale and scope which becomes difficult to replicate. Competitive advantages that result from multi-nodal combination become more entrenched along with an increase in switching costs for the firms within the ecosystem.

Section Summary

By collectively exploiting each of the three main activities of multi-nodal combination, digital platform companies have strategically expanded vertically and horizontally.

Configuration and system orderliness of the nodes and linkages foster accelerated collaborative dynamics, facilitated engagement, and multiple integrations across the ecosystem, further strengthening each node. As nodal connections mutually reinforce each other there is an increase in value and the defensiveness of the ecosystem.

The digital platform firms nurture the network structure and ecosystem growth by leveraging the degree of integration and synergies amongst the connected firms. Orchestrators

can establish and evolve network structures such that platforms, participants, and the ecosystem tie into each other resulting in an intertwined network that is reliant on each connected node.

Aggregation of data is accelerated as nodes connect and engage. Whether the nodes are subsidiaries of the orchestrator or partners of the orchestrator, the benefits from the economic transactions and data gleaned from the overarching ecosystem can be aggregated, analyzed, and fed back to each party. The network structure benefits and facilitates inputs into decision making, targeted actions and new initiatives. Thus, each party has an interest in continuing to increase participation in the ecosystem to create more connections and gain more data and insights. The self-enhancing networks within the ecosystem grow at ever increasing rates and can have powerful effects on value with the nodal ties generating significant combinatorial expansion and adding to the growth of the ecosystem.

Thus, we introduce and posit that multi-nodal combination is (1) a vital network property which significantly contributes to value creation within ecosystems; (2) critical in the establishment of network structures, and; (3) core for the ongoing viability of the ecosystems and its firms. Establishing and maintaining strong interdependencies between the firms allow them to accumulate and retain disparate and seemingly unrelated data points for future commercial use by members of the ecosystem. Limiting or not fostering an increasingly interconnected and interdependent network strategy may limit value creation for all constituents and lead to a less optimized network. Growth, supported by sustainability and superior performance, of the network ecosystem structure should be the objective in the evolutionary network structure of each platform-led ecosystem. Harnessing the power of multi-nodal combination, firms within platform-based ecosystems can enhance their business’s growth through new solutions, services,

and other value-add initiatives that are gleaned from the amalgamation and manipulation of the data.

METHODOLOGY, CASE SELECTION AND DATA

To analyze and explain differences in network structures and ecosystem evolution, we build our theory from case study research. We use a multiple-case study design and develop three case studies on the evolution of network structure. We compare how the structure and its evolutionary path differ among the ecosystems created by three BigTech firms that are FinTech disruptors—Alibaba (Ant Financial), Amazon (financial services) and Alphabet (Google Pay).

The empirical setting for the study is the financial services industry, and, more specifically, how

BigTech platform companies have entered and disrupted this industry.

Case study approach

Case based research is interesting and impactful (Bartunek et al., 2006), is effective for understanding complex situations (Harrison et al., 2017) and the articles covering the case-based research methodology are among the most cited works within the Academy of Management

Journal (Eisenhardt & Graebner, 2007). Importantly, case-based research provides insights that may not be achieved by other means (Rowley, 2002; Harrison et al., 2017). It is pragmatic, flexible, and appropriate for exploratory research, especially where existing theory may not aptly address the topic (Eisenhardt, 1989; Harrison et al., 2017).

Building theory from case studies involves using one or more cases from which we develop new theory inductively. It is conducted from rich empirical qualitative data and is characterized by pattern recognition of the relationships and the logical arguments (Eisenhardt &

Graebner, 2007). This approach is likely to produce theory that is accurate, interesting, and

testable. Case study research is appropriate when researchers are trying to answer “how” and

“why” rather than the “how often”, “how many” or the relative importance of constructs

(Rowley, 2002; Eisenhardt & Graebner, 2007; Edmondson & McManus, 2007). Case studies are also appropriate when the researchers cannot manipulate behaviors or events (Yin, 1994;

Rowley, 2002) but they are not appropriate to test theory. In a comparative case study, the objective is to develop an understanding of the issue in a real-life setting for multiple cases and compare the organizations in a systematic way. The cases should be carefully selected based on the research purpose such that they can produce contrasting results (Rowley, 2002; Harrison et al., 2017). Case studies do have their disadvantages, however. The disadvantages include little generalizability, potential for lack of rigor, difficult to conduct, and overwhelming data collection (Yin, 1984).

For this research, the case study method is appropriate. It opens a venue to properly analyze and develop theory using the ever changing FinTech environment as a point of departure.

Industry and Case Selection

Industry Selection

The rapidly evolving nature of the FinTech ecosystem provides an opportune real-life canvas for an exploratory analysis. Financial services have experienced an outsized disruptive impact from non-financial firms in the form of FinTech. New platform-based products and services from FinTech startups and existing BigTech platform firms have disrupted firms and ecosystems. The sheer size of investment and number of firms entering the space is challenging even for the most digitally advanced financial services firms. New entrants have refocused the

customer narrative, reduced margins and changed the playing field conditions. As firms enter and others adapt, business models and networks have become core in understanding how to structure ecosystems for growth.

Case Selection

To study the network evolution and develop a comparative exploratory analysis of the structural differences amongst the ecosystems, three large and distinct digital platform-based firms with FinTech subsidiaries were purposefully selected – Ant Financial, Google Pay, and

Amazon (financial services). All three are businesses of large and influential global digital platform firms that have entered financial services and are actively building FinTech ecosystems.

They are adapting to the competitive challenges daily, formulating strategic responses to disruption and creating value. Each BigTech’s approach is different, resulting in distinct network structures, and each BigTech’s network has value based on business objective. However, the better structure will deliver growth by gleaning more value from leveraging or mitigating the full complement of network properties across the ecosystem. Sub-optimally addressing the properties within the structure, especially multi-nodal combination, will limit value creation and may lead to a less optimized network structure and ecosystem.

Amazon does not have a formally defined subsidiary for financial services though it provides products and services such as Amazon Pay, Amazon Cash, Amazon Protect, Amazon

Reload and Amazon Lending. Amazon may also have mortgages in the planning stages

(American Banker, 2018). Amazon’s financial services have substantial size and scale as evidenced by Amazon Pay’s over 300 million users. Amazon’s overall strategy with their

FinTech foray has focused on supporting its primary objective of increasing customer

participation on its main Amazon platform and ecosystem (CBInsights, 2019). It has built up a treasure trove of customer data which is being leveraged in the provision and targeting of the

Amazon FinTech products and services.

Ant Financial was created in 2014 and includes Alipay, which was founded in 2004. It is the largest FinTech by market capitalization and has a presence in 54 geographical areas across multiple continents. It prides itself on being a technology driven open ecosystem that supports over 1 billion consumers globally and works with other financial institutions with a “glocal”

(global / local) approach. Ant Financial considers itself as a TechFin, a term coined by them, and not a FinTech. Not encumbered with any of the legacy infrastructure issues, Ant Financial appears to be developing a “self-feeding” ecosystem which connects firms across business lines and markets.

Google Pay is Alphabet’s foray into the FinTech space. It was initially established as a virtual wallet (i.e. a place to store and use consumers’ credit cards with an Android phone) and has evolved into a platform ecosystem across select markets. Google Pay is the combination of several fragmented offerings previously provided by Google (e.g. Android Pay, Google Wallet) and is the first step in the building of its FinTech ecosystem (Business Insider, 2018). It has also built links with key processors like Adyen, stripe and Vantiv to simplify future integrations.

Google is also expected to be launching a checking account in 2021. Google understands that the connections among its products are important. Google also has an enviable data warehouse on customer insights (e.g. consumer research, shopping and purchase behaviors, demographics, habits, etc.), preferences, and interests.

Data Visualization

Critical ecosystem knowledge can provide firm managers with insights to address strategic business issues and can be gleaned from network visualization techniques and the comparison of various representations of the data (Basole, et al. 2015; Iyer, 2016). However, one must be careful as complex ecosystems sometimes provide overwhelming information which can be misinterpreted if the visualization technique is not applied appropriately.

To develop an integrated ecosystem layout of each firm, we use a data-driven visualization methodology. This type of methodology allows for the visualization and comparison of each firm’s ecosystem. Adopting the approach taken by Basole and his co-authors

(2013, 2015, 2018) and Ahuja et al. (2012), an exploratory analysis using a bottom-up approach is the most appropriate for comparing the network structures and deducing insights on the firms’ strategies and objectives for network structure of the ecosystems. The software that we use for social network analysis is UCINET and it can be accessed at: http://www.analytictech.com/archive/ucinet.htm. Professor Steve Borgatti, who is the Paul

Chellgren Endowed Chair at the University of Kentucky in the Management Department of the

Gatton College of Business and Economics, wrote the software (Borgatti et al., 2002).

Data Visualization is a multi-step process – boundary specification, network construction, visualization and sense making (Basole et al., 2015, 2018). It is used to develop an integrated layout for the core ecosystem. Integrated with UCINET is the NetDraw program for drawing diagrams of social networks.

Boundary specification encompasses the creation of the data structure by defining the network architecture’s nodes, relationships between the nodes, and an analysis timeframe (Ahuja

et al., 2012; Basole et al., 2015, 2018). As the collection of primary data for research on business networks and ecosystems is expensive (Basole et al., 2015), the database is constructed from datasets that were created from information collected during an extensive review of the academic, quasi-academic, practitioner literature and news media articles available on the three firms for the period 2012 – 2019. Data was accessed through internet search engines, websites, and blogs and is vast and publicly available. The start date of 2012 was selected as this is one of the inflection points in FinTech. According to S&P Global Market Intelligence (2018) and

CBInsights (2018), FinTech startups almost doubled from the prior year and kept growing for the next few years.

In network construction, a taxonomy of the ties is created. Nodes are directly connected to their first order relationship. In other words, they have a direct relationship with the focal firm

[i.e. Amazon -financial services, Ant Financial, Google Pay). By considering each possible combination of nodes beyond the three primary firms, second order relationships are tabulated for relationship, if any.

Visualization will be conducted using an interactive animated approach for visualizing multiple views including the incorporation of temporal data (Basole et al., 2015). The temporal data will allow us to see changes in the network structure and ecosystem evolution resulting from adaptive behavior to digital disruption.

OBSERVATIONS

Ant Financial

We observed multi-nodal combination in Ant Financial (Table 1). Ant Financial strives for global collaboration through its platform-based business strategy, expanding vertically and

horizontally via local, regional, and global partnerships, acquisitions, and equity positions. This affords Ant Financial the opportunity to invest in an open fintech ecosystem in markets outside its primary market. The global collaboration aims to increase Ant Financial’s ability to influence its partners while amassing massive amounts of data in almost every aspect (Alibaba and its

Fintech Ecosystem, 2019; Xie, 2018). Ant Financial’s approach results in intertwined, interconnected and increasingly interdependent networks, platforms, and ecosystem where it gains synergies and accumulates data that deliver solutions and seamless experiences for its customers and partners.

While its network structure has Zhu and Iansiti’s (2019) five properties, it is the deployment and execution of multi-nodal combination that further strengthens and protects Ant

Financial’s ecosystem. Its network structure leverages the core activities of multi-nodal combination—connectivity, data aggregation, and multi-nodal collaboration (i.e. engagement and reciprocity)—that we theorized earlier. Its strategic approach commences with a core service/product platform in each geographical area that it enters to aggregate data, connect, and integrate into the ecosystem. Its entry mode tends to be alliances via investments or partnerships where it links its technology into the local partners and provides expertise. The alliances usually target businesses in digital wallets, money transfer or mobile payments, collaborating with alliance partners such as mynt (Philippines), bKash (Bangladesh) and Kakao Pay (Korea). Its objective for the collaboration is to better understand the market, gather data and build platforms for the mobile wallets business. As connectivity, engagement, and reciprocity increase, new data become aggregated and leveraged through advanced analytics and AI. This provides Ant

Financial and firms within the ecosystem with flexibility to build new solutions, customized products, and/or personalized services enhancing multi-nodal collaboration. Customers and

partners are tied further into the network, its platforms and ecosystem, at times at no cost. Such was the case with Telenor Microfinance Bank (TMB) where cross-border remittance service was introduced by TMB using Alipay’s technology. Alipay provided the services and forewent transaction fees for the first year (Morris, 2019).

With connectivity across markets, data aggregate quickly, analytics are deployed and multi-nodal combination is facilitated. The better the insights, the greater the value, and thus the easier it is to foster reciprocity and build global collaboration. New partners and markets are added with targeted products/services (e.g. wealth management, remittance services, QR and bar code compatibility) introduced and creating value for the ecosystem.

Ant Financial’s customers are benefitting from the network structure approach and helping to build multi-nodal combination. Over 80% of the customers have two or more services that are offered within the ecosystem demonstrating the success of the network structure and making it more enticing for participant firms to connect within and across markets. The power of multi-nodal combination is captured with self-propagating data aggregation accelerating from the further intertwining of the firms.

In Table 1 we demonstrate how some of Ant Financial’s activities leverage or mitigate each of Zhu and Iansiti’s five fundamental properties of networks and the sixth property that we propose, multi-nodal combination. First, we observe that not every description of Ant Financial’s activities, as shown in the column of description in Table 1, matches all six properties. We do not expect a matrix with every cell marked with an X. What we do expect and see is that Ant

Financial’s activities show a pattern of trying to address as many of the properties as possible to ensure that they are harnessing the power of each or mitigating the challenges.

Second, we observe that, with multi-nodal combination, the two descriptions where X’s are missing are shortfalls, indicating that the core activities required for multi-nodal combination are not being satisfied. For instance, if we look at building network components and the use and utility descriptions, a wealth management option, which is rather basic and easy to establish, is missing in many of the markets outside of China. In some markets, such as the Philippines and

Korea, more robust product lines and additional firms must also be engaged and brought into the network structure. When reviewing Zhu and Iansiti’s five fundamental properties, we also see gaps. Now, as stated earlier, we do not expect that each description will be able to fulfill the requirements of all properties. For instance, using the payments subindustry will not directly address Ant Financial’s vulnerability to multi-homing. However, building use and utility services and product lines across markets will help address network clustering concerns.

In general, this means that in building its network structure, Ant Financial still has potential opportunities that have not been realized. As we will demonstrate in Figures 1 – 6 below, while Ant Financial has made progress, there are opportunities to harness the power of multi-nodal combination by encouraging firms to connect and engage within the network, strengthening their competitive advantage.

Table 1 – Ant Financial Addresses Network Properties & Captures Power of Multi-nodal

Combination

Description Strength of Clustering or Risk of Vulnerability Network Multi-nodal Network Fragmentation Disintermediation to Multi- Bridging combination Effects homing

Ant Financial Ecosytem builder - diverse partnership strategies, breadth and scale of partners, data X X X X X X accumulation Use Payments sub-industry (e.g. digital wallets, cross-border transactions) as a primary tool to X X X X X build ecosystem Build network components that work with internal/external firms and provide seamless X X X X experience (e.g. Yu'e Bao and Ant Fortune)

Data Aggregation - Advanced Analytics drive new products, services, personalization and tools X X X X X X Global collaboration - connectivity across markets and industries X X X X X X Builds network connectivity, reciprocity and multinodal collaboration - Provide value-added technology,services to partners and customers X X X X X free of charge. Add marketing services and tools. Build use and utility - Create and provide diverse value-add service and product lines across X X X X multiple interlocking markets Cross-sell proprietary and partner prodcuts. Interlocking platform business. X X X X X

Visualization

Ant Financial has built an extensive network structure that is comprised of diverse sets of partnerships and customers. It works to generate traffic through its network and actively supports its network structure with complementary and value-added services and tools from its and its partners’ platforms. Ant Financial attempts to gain synergies from transactions, payments, and cross-selling, all while creating more touch points between network firms and customers, collecting data, and developing more ecosystem use cases (Sengupta et al., 2019). This includes providing multi-industry and cross-industry solutions, and mixing cross-platform with vertical industry connectivity, collaboration, and engagement. Take Ant Fortune, Ant Financial’s wealth management platform powered by artificial intelligence, as an example. Just within Ant Fortune, they have over 100 asset management partnerships comprised of third parties and related

companies, tying in a diverse set of customers and being able to capture and share data and insights based on product promotion and customer needs and preferences.

Ant Financial also provides value-added tech services (e.g. online risk management, fraud prevention, etc.) to third-party financial institutions further increasing the switching costs

(making it more expensive) for these third parties to pull away from the ecosystem (Alibaba and its FinTech Ecosystem, 2019). By providing complementary and, at times, complimentary services, Ant Financial gets firms to use its technology stack and services, which in turn lock in the firms to also use services that are compatible with that technology stack. As more services are built on the technology infrastructure, firms become more dependent on Ant Financial and its technology. High switching costs make it more difficult and expensive for the firm to move to another technology since each service would have to be rebuilt on a different technological platform as it would no longer be compatible with the one provided by Ant Financial. A good example of Ant Financial’s building of services for their partners is their collaboration with

Hoperun Information Technology. Ant Financial built a new “banking product dubbed

Distributed Core Banking Platform (DCBP) [that] helps financial firms move their business models to customer-oriented models and away from transaction-oriented models. The new banking platform is also designed to help financial firms contend with digital challenges such as distributed development, financial product management and accounting liquidation” (Ant

Financial Launches Banking as a Service Platform, 2019).

Ant Financial builds connections within and beyond each local market and has a global partner strategy that facilitates collaboration, creates and fosters increased engaged interactions, and enables constant learning (Fasnacht, 2019). All are key ingredients for the core activities of multi-nodal combination. As the orchestrator, they develop the strategic partnerships and

alliances using digital technology to make links, provide operational experience, knowledge, product, and technology, and share customers and data with partner firms. We should note that

Alipay is Ant Financial’s internally developed subsidiary and is the entity which has been used to establish beachheads in several markets and whose technology and knowhow are widely used with its payments partners in the geographic markets outside of China.

In Figures 1–6, we observe Ant Financial’s network structure strategy and how that approach has led to a leveraging of multi-nodal combination. In Figure 1, we start with Ant

Financial and its key subsidiary, Alipay.

Figure 1 – Ant Financial and Alipay

We then add Ant Financial’s acquisitions and joint ventures in Figure 2. The acquisitions

(Ele.me, HelloPay, World First and EyeVerify) are presented in the top left quadrant, with Ant

Financial’s joint ventures presented in the bottom right quadrant.

Figure 2 – Ant Financial Acquisitions and Joint Ventures

In Figure 3, we provide a visualization of investments (one-way directional arrows represent capital flowing from Ant Financial) and strategic partnerships (two-way directional arrows). The visualization demonstrates how Ant Financial starts to evolve its network by building initial relationships in new and existing geographic markets and businesses.

Figure 3 – Ant Financial Strategic Relationships

We then overlay Alipay’s relationships, including investments and partnerships, along with the relationships that exist between other firms within the network structure (Figure 4). With each incremental linkage, a more connected, engaged, and collaborative network is created. Data aggregation, analytics and AI are used to build, tailor, and target each node’s products and services. We demonstrate how, with each partnership added, Ant Financial provides opportunities for firms in its network to connect with one another. The essence of multi-nodal combination is the connections that are possible because all nodes within the network can be linked and engaged.

Figure 4 – Ant Financial and Alipay Strategic Relationships

In Figure 5, we use the representation of Figure 4 and highlight (in blue) the connections that are the essence of multi-nodal combination. The links are possible because Ant Financial engages and encourages Alipay and the firms in the network. Alipay, being the lead subsidiary, is the most advanced in linking with the firms in Ant Financial’s network. However, both are encouraging and facilitating increasing levels of connectivity amongst the firms. This hyperconnectivity and interdependency foster the development of other key activities (i.e. multi- nodal collaboration, data aggregation) which trigger and capture the power of multi-nodal combination. The firms mutually reinforce one another and obtain data flows from multiple sources resulting in massive amounts of information ranging from logistics data to behavioral data. The firms use this to offer innovative solutions in diverse markets and sectors, create value

(Fasnacht, 2019) and grow.

Figure 5 – Multi-nodal Combination Linkages with Alipay

While Ant Financial today collaborates with over 200 firms in its technology-driven ecosystem, it still has opportunity to foster the activities for multi-nodal combination. We see that Alipay is leading the multi-nodal combination efforts, connecting with most of the entities.

We can also see how there are other entities linking to each other – Zomato and bkash, wirecard and vanguard, yum and ele.me. In Figure 6, we highlight (in blue) these links that have formed between firms in the ecosystem and that do not involve Ant Financial or Alipay. This is the essence of multi-nodal combination – connectivity, engagement, reciprocity, and data aggregation – beyond just the focal firm and its subsidiary. In the near future, new direct connections between Paytm, Kakao Pay, TnG, and mynt are expected as Ant Financial supports the firms in leveraging and integrating with technology and knowhow provided through itself and via Alipay. Each of the firms will then be able to use new data with advanced analytics to develop new solutions and expand into new areas.

Figure 6 – Multi-nodal Combination Linkages without Alipay

Google Pay

By contrast, we did not observe multi-nodal combination in much of Google’s network structure (Table 2). One would believe that Google, which is powerful and possibly possesses more data than any other entity globally, could leverage the network properties and multi-nodal combination to make the Google Pay network an unrivaled FinTech ecosystem. Unfortunately,

Google has historically suffered from fragmentation and this has been evident within the Google

Pay ecosystem. The fragmentation challenges have limited its ability to drive connectivity and multi-nodal collaboration throughout its network structure (Table 2). This leaves it vulnerable to risks of the network properties while also limiting its ability to capitalize on them.

Google’s approach to FinTech started off with its global presence and extensive data trove from its core business. It gleaned insights to expand horizontally and launch various

unconnected payments services (e.g. Android Pay, Google Wallet). This fragmented approach led to the incompatibility of two operating systems (i.e. Android for mobile and Chrome OS for laptops) and having payments products that were not unified under a cohesive strategy. This was recently addressed with the digital payments unified under the Google Pay brand. This allows for retail purchases and P2P transfers in one app (Knight, 2018) and is being used to draw in and engage users within a cohesive and convenient brand-wide payment ecosystem (Toplin, 2018).

Table 2 – Google Pay’s Challenges in Addressing the Five Properties and Capturing the

Power of Multi-nodal Combination

Google Pay, like Google, has not been able to harness the power of multi-nodal combination. It has also struggled in addressing Zhu and Iansiti’s five properties of networks.

This is because firms within the network are not connecting, engaging, and reciprocating. Using the parent firm to demonstrate Google Pay’s strategy, in a study on Google’s app ecosystem, van

Angeren et al., (2013) found that 73% of the firms in the ecosystem only connected with Google and not with any other firm. The lack of connectivity prevents any of the other activities required for multi-nodal combination from taking place. An example of missed opportunities of

connectivity and engagement is exhibited in a recent deal Google made with Olo, a food delivery business. Google Pay, a logical service addition, was not incorporated as part of the deal and thus the firm missed out an opportunity to tie in an additional platform into the Google Pay ecosystem.

Google Pay’s consolidation did address some of the fragmentation challenges and has now become the lead product for Google in FinTech. Google Pay is being integrated into other platforms (e.g., Play Store, Google Assistant) and it is also leveraging proprietary hardware (e.g.,

Pixel products) to tie customers to the ecosystem. However, the strategic approach of how

Google connects to its platforms also hinders multi-nodal combination. Google appears to follow

Apple’s playbook, where the focus is on tying customers into its hardware and software ecosystem, which alienates other firms. It also does not help that Google first ties into its proprietary Pixel ecosystem before making integration available for outside firms (Cipriani,

2019). This is visible in Table 2 where we show the absence of driving strong network effects, not mitigating fragmentation, and missing out on multi-nodal combination. A more inclusive approach, one like Ant Financial’s, could foster increased connectivity and engagement. This would allow Google Pay to leverage the trove of data captured and deploy its analytics capabilities to target and draw in more firms and consumers to its ecosystem.

Google Pay is now engaging and collaborating with FinTech startups and incumbent financial services firms to shore up its ecosystem offerings. It appears to understand the power of multi-nodal combination as evidenced by their efforts in India. In this geographic market, they have brought together and connected payments, the retail value chain, job search and skill development into the Google Pay ecosystem (Baxi, 2019; Subramaniam, 2019). They are looking to continue to build an integrated network structure in the country by making future

investments in insurance, brokerage, and retail tech (Bansal, 2019). This drives the key activities of multi-nodal combination, even if only in one market.

Google Pay could be a platform with great reach and significant influence across all financial services. While, it has started building its ecosystems in the US and India, it could expand quickly into other regions (Green, 2019) as evidenced by the e-money license it obtained in Lithuania at the end of 2018. However, it will not achieve its potential without harnessing and effectively addressing multi-nodal combination. It must find a way to facilitate and engage the firms in its network structure such that connectivity and multi-nodal collaboration become prevalent.

Amazon (Financial Services)

Amazon “Financial Services” (AFS) also has challenges in addressing Zhu and Iansiti’s five properties and, more importantly, in capturing the power of multi-nodal combination (Table

3). In AFS we observed limited, much of it insular, multi-nodal combination. AFS’ products and services have been built to strengthen the proprietary Amazon ecosystem (CBInsights, 2019) also leaving it open to clustering (Table 3). The focus has been on the vertical elements before opening them to more participants (e.g., Amazon Allowance, Cash, Lending, Reload).

Connectivity is limited as most services are proprietary and restricted to a few markets, though interest has been shown for establishing an emerging market presence (e.g. India, Mexico). The lack of connectivity does not allow for multi-nodal combination and leaves AFS vulnerable to most of the network properties’ risks such as multi-homing and disintermediation. As demonstrated in Table 3, this network structure approach makes AFS vulnerable to

fragmentation, risk of disintermediation and multi-homing while failing to fully capitalize on strengthening network effects and network bridging.

Table 3 – Amazon “Financial Services” has Challenges in Addressing the Five Properties and Capturing the Power of Multi-nodal Combination

In practice, Amazon has relied on internal product development rather than engaging in partnerships, acquisitions, or investments to broaden its financial services presence with new products and services (CBInsights, 2019). Its prioritization has further limited connectivity and data aggregation, key activities required for multi-nodal combination. AFS’ network structure is a virtual closed loop where there are integrations with other Amazon applications (e.g., Alexa) but limited engagement outside, again restricting the activities required for multi-nodal combination. Because AFS builds its financial products internally and limits them to the proprietary elements of the Amazon ecosystem, the power of multi-nodal combination cannot be captured nor can the other properties of networks be leveraged for the benefit of AFS and its ecosystem.

As indicated earlier, AFS’ has made few investments. But when they have invested, the investments have been concentrated in one market (i.e. India). Its ability to broadly leverage multi-nodal combination is negligible without nodal expansion. Recently, AFS started to adjust its strategy making new FinTech investments and securing partnerships (e.g., WorldPay,

Greenlight Financial) that may help foster multi-nodal combination. With WorldPay, Amazon

Pay gains acceptance outside of the Amazon ecosystem, thus allowing for increased connectivity, engagement, and data aggregation from non-Amazon merchants. Amazon also recently invested in ToneTag, a contactless payments product provider, and added partnerships with Swiggy (food delivery) and BookMyShow (ticketing) to expand its payments reach in India.

Adding functionality to Amazon Pay makes it a more viable product. More importantly, the nodal expansion that comes with it affords new connectivity, engagement, and reciprocity possibilities along with new data. In a continuation of its nodal expansion in India, AFS added an insurance firm (Acko) to its ecosystem through a joint investment with MasterCard.

Amazon Lending and Amazon Protect have also been opened to partnerships by AFS, allowing for the key activities required for multi-nodal combination to begin take place. AFS must now encourage these new firms to connect, engage, and reciprocate. Assisting with data aggregation and providing advanced analytics to the firms will help build multi-nodal combination and stimulate value creation within the network as new insights are garnered and leveraged. However, the end users are still within the Amazon ecosystem, and thus the benefits of multi-nodal combination will be limited until this aspect of AFS’ strategy is completely modified.

CONCLUSIONS AND CONTRIBUTIONS

As digital platforms embrace FinTech disruptions, an important question must be addressed: What network structure should be used to construct and grow an ecosystem? Zhu &

Iansiti (2019) posit five fundamental properties that explain why some platforms thrive and others do not. We submit that there is a sixth fundamental property which harnesses the power of enhanced connectivity, data aggregation, and multi-nodal collaboration. We coin the term multi- nodal combination to mean the management of three activities (connectivity, aggregation, and multi-nodal collaboration) in the network structures of FinTech ecosystems. This property is theoretically rooted in the strategy principles articulated by Adner, Puranam, and Zhu (2019) in the context of digital technologies. We also introduce Multi-nodal collaboration to be the management of two activities between firms: engagement and reciprocity. We theorize how multi-nodal combination is important to the growth of digital platform firms as they design the network structures of FinTech ecosystems.

We use case study methodology to analyze three large digital platform firms – Ant

Financial, Google Pay, and Amazon (financial services) and support it with a visualization approach. Our case studies suggest that only Ant Financial demonstrates increased leveraging of multi-nodal combination, though none of the three have fully captured its power. While all three have made initial strides into leveraging multi-nodal combination, our visualization of Ant

Financial’s network structure demonstrates that it is the most advanced.

Understanding ecosystem network structures is of paramount importance for academic research and managerial practice. Our exploratory research develops theory and contributes to the literature on network properties, observing and introducing a new network property, multi-

nodal combination, and a core activity, multi-nodal collaboration. This property is important in the development and growth of platform-led network structures as firms link with each other, leveraging connectivity, data aggregation, engagement and reciprocity in the identification, development, targeting, and delivery of products and services. This is all done with the aid of advanced analytics, artificial intelligence and machine learning utilizing the troves of aggregated data along with the sharing of technology and strategic insights. With our visualization approach, we demonstrate how multi-nodal combination can help firms better understand how they might approach developing the network structures of their ecosystems. Specifically, our research suggests that firms could build interconnected networks through a diverse set of partnership strategies (e.g. investments, JV’s, partnerships) where all firms can manage the three core activities of multi-nodal combination to generate growth, establish barriers to entry and capitalize from the insights derived from diverse data captured across different sources.

Our research also suggests that Zhu and Iansiti’s five fundamental properties are not uniformly observed across the three cases. Ant Financial appears to be the most efficient in managing and leveraging the five properties, with Google Pay suffering from fragmentation and

AFS being adversely impacted by fragmentation, risk of disintermediation and multi-homing.

Ant is a strong ecosystem builder leveraging its technology stack and know-how with a structured and diverse partnership strategy. They look to generate traffic, accumulate data, create multiple partner and customer touch points, provide value added services on its technology stack, and develop utility within its network. Each of these leverage or mitigate Zhu and Iansiti’s properties. In contrast, Google built several fragmented ecosystems within payments (e.g.

Android Pay, Google Wallet). They have now consolidated most under the Google Pay brand and ecosystem. Google Pay has commenced to build a diverse network structure in India but it

still focuses on leveraging both Google software and hardware, continuing to expose itself to multi-homing, fragmentation, and risk of disintermediation. Amazon Pay has expanded beyond the greater Amazon ecosystem but is still highly dependent on it. The core focus continues to be on providing services and solutions for Amazon. Investments in FinTech continue to be limited and concentrated within a couple of geographic markets. Collectively, all these leave Amazon

Pay vulnerable to fragmentation, risk of disintermediation and multihoming.

Limitations

Exploratory research and case studies come with limitations and, as such, so does this study. As with all case studies, while all efforts were made to ensure the accuracy of the data including all firms and links, we are dependent on publicly available information. And while our information search was extensive, it may not have captured all the data. We are also limited by our sample of three FinTech businesses of BigTech platform-led firms. We may have not been able to fully observe the extent of multi-nodal combination in effect at each of the FinTech firms.

Including the parent firms of Alibaba, Alphabet, and Amazon may afford unique insights into the power of multi-nodal combination. In addition, our research was focused on three large FinTech firms. As such, we will not have captured all the nuances within this sub-industry nor within the greater financial services industry that may be evident from analyzing smaller firms. The

FinTech empirical context and the case study approach also limit the generalizability of our theory to other firms and industries. These limitations offer various opportunities for future research that we present below.

Future Research

One avenue of future research is extending multi-nodal combination beyond FinTech and into other industries and segments within the platform-based ecosystems. This may allow for a better understanding of the evolution of network structures and how the power of multi-nodal combination impacts firms within those networks. Another avenue lies within network structure and ecosystem research. More specifically, within the areas of value creation, capture and governance. We posit that the management of three core activities are critical in the successful deployment of the multi-nodal combination as the sixth network property. To do so, focal platform firms must focus on the interconnectivity amongst participant firms. What are the appropriate governance structures required to manage such network? How should firms connect, engage, reciprocate, and collaborate? Who will harness the data trove and provide the analytics?

And so on. An additional stream of future research may also support current work within strategy. How can firms manage the competitive advantages gained from building network structures that develop multi-nodal combination? Are there optimal approaches to balance the six properties of networks in the creation, capture and allocation of value among participant firms in the ecosystem? Our contribution may foster a continual and mutually beneficial relationship between academic research and managerial practice.

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BIOGRAPHICAL SKETCH

Carlos Pascual is the Chief Revenue Officer and co-founder of bTLAR. He is recognized as an accomplished senior global business executive and entrepreneur known for building and leading profitable, high-growth businesses and reinventing underperforming units on four continents. Prior to bTLAR, Carlos spent much of his career at American Express where he led the creation and growth of American Express’ Global Network Services business, most recently having been the General Manager and Head of American Express’ International Insurance business. Carlos’ board memberships include American Express Ltd. where he was an Officer and Board Member and American Express Insurance Agency of Puerto Rico where he was also its President. He has a Doctor of Business Administration from the University of Florida and BA in Economics and an MBA from Florida International University. His research interests are in strategic management, FinTech, Networks and platform-based markets and businesses.