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United by the purpose:

Impact of the purpose on the collaboration between banks and fintechs Master Thesis

Author: Yelena Kim Student number: 13196359 Date: 20/06/2022

Qualification: MSc in Business Administration – Digital Business Track Institution: Amsterdam Business School, University of Amsterdam Supervisor: Nazanin (Fatemeh) Masihkhah

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Statement of Originality

This document is written by Yelena Kim who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in this text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision and completion of the work, not for the contents.

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Abstract

The increasing concern in both new entrants to the banking industry as well as higher interest in the topics of sustainability and CSR initiatives led to increasing pressure on incumbent banks to modify their business activities and value creation process through collaborations with fintechs. This study examines the relationship between the purpose-driven orientation of the banks and its effect on partnerships between banks and fintechs. The purpose of the bank is measured by its ESG score.

Using a panel of 747 collaborations from banks in the Netherlands, France, Germany, and Switzerland, I provide the evidence that in line with stakeholder maximization and strategic alliances theories, purpose-driven banks are more likely to be involved in partnerships with fintechs and have a higher number of bank-fintech collaborations they participate in. Overall, the findings suggest that purpose has a positive influence on collaboration.

Key words: purpose, fintech, ESG, CSR, strategic alliances, banks

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Table of Contents

1. Introduction ... 6

1.1 Motivation and the background ... 6

1.2 Research Question ... 9

1.3 Structure of the thesis ... 10

2. Literature review and hypothesis construct ... 12

2.1 Purpose ... 12

2.2 Collaborations, partnerships, M&As ... 14

2.3 Linking purpose theories to collaboration theories ... 16

3. Methodology ... 20

3.1 Data Collection ... 20

3.2 Variables ... 22

3.2.1 Dependent Variable: Collaboration with fintechs and new partnerships ... 22

3.2.2 Explanatory Variable: Purpose ... 22

3.2.3 Control Variables ... 23

3.3 Methods used ... 26

3.3.1 Probit panel regression ... 26

3.3.2 Panel Count Model ... 27

4. Results ... 28

4.1 Final Dataset ... 28

4.2 Descriptive Statistics ... 29

4.3 Empirical results ... 31

4.3.1 Testing Hypothesis 1 ... 31

4.3.2 Testing Hypothesis 2 ... 32

5. Discussion... 36

5.1 Literature discussion ... 36

5.2 Theoretical and practical implications ... 39

6. Conclusion ... 41

6.1 Final remarks ... 41

6.2 Limitations ... 42

6.3 Further research ... 43

References ... 44

Appendix ... 51

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Acknowledgements

I would like to express my gratitude and appreciation to my supervisor, Nazanin

Masihkhah, who gave me a support and guided me through every stage of the writing process. I would also like to extend my sincere thanks to my dad, mom, and sister without whom my Master’s degree would not be possible at all for their unconditional support and belief in me.

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1. Introduction

1.1 Motivation and the background

In nowadays transforming economy, banks are constantly facing new challenges and customer expectations requiring them to revolutionize the way they operate. In recent years, digitalization impacted many industries by introducing innovative ideas and implementing new ways of working (Autio et.al., 2018). Logically, incumbent organizations are concerned about losing market share due to disruption from other banks, fintech firms, or non-banking organizations since the boundaries of markets and industries are blurring. On top of that, decreased trust in the traditional banking system after the financial crisis of 2008 made customers even more responsive to the performance and actions of the incumbent banks (Uslaner, 2010). Overall, with the rise of technologies, globalization, and easy access to information, customers are more aware of global challenges, which in turn impacts their consumer behaviour towards more sustainable businesses.

Consumer readiness to pay a premium for items created by firms that operate in a sustainable manner has increased in the last decade (Kum et al., 2016). Consequently, shifting to a new business model is crucial not just to increase the performance and regain customers' trust but simply to survive.

According to Volberda et al. (2018), the business model reflects elements of the business, the connection between them, the value creation process, and how it impacts the competitive strategy.

Accordingly in the banking literature, the attention on the banking business model increased from 2 articles in 2002 to 25 articles in 2020 (Hanafizadeh & Marjaie, 2021). As an explanation, according to Hanafizadeh and Marjaje (2019), the increased scholarly attention is due to the aforementioned financial crisis and the rise of fintechs. Wilson et al. (2010, p. 154) argue that "the scale, scope, governance, performance, and the safety and soundness of financial institutions" in terms of the business model need to be reconsidered. One of the responses to those concerns is a purpose-driven business model, where the purpose is incorporated into the value creation of the business. It has become increasingly clear that clarity of purpose is vital for a company's survival (Kim et al., 2021).

Even though there is no agreed-upon definition of higher purpose in the literature, there are common

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features that show it is a contributing aim that is tied to the firm's day-to-day operations but goes beyond profit maximization. The "why" of culture, the aims toward which cultural ideas, values, and rituals are oriented, is provided by an organizational higher purpose (Bunderson & Thakor, 2021).

According to Thakor and Quinn (2020), Corporate Social Responsibility (CSR) and purpose are closely related, however, CSR goes broader than purpose since purpose can include topics and activities related to CSR but not vice versa. For the sake of simplicity, CSR is treated as a proxy for the purpose in this paper. The increasing significance that businesses place on CSR has been extensively documented, indicating a definite move toward sustainability. According to a survey conducted by Blasco and King (2017), 93 percent of the world's largest firms by revenue publish information about their CSR initiatives in their annual financial reports. This obviously demonstrates that CSR awareness is already well-established in the corporate sector. However, because it might be difficult to comprehend how and what CSR should be in practice and how it should be applied on a firm level, a framework that tries to quantify the CSR activities of corporate entities is advantageous.

Environmental, social, and government (ESG) metrics offer a complete framework for categorizing and conceptualizing CSR actions for better understanding for investors, businesses, and scholars, where “E” stands for the environmental impact, “S” for the social dimension and “G” for governance variable (Deng et al., 2013)

As a response to shifted consumer preferences and increasing focus on corporate sustainability, the emergence of new companies was inevitable. One of the biggest examples of purpose-driven companies and sustainability-oriented organizations are fintechs and start-ups.

Overall, a start-up is considered a driver of sustainability and transformation in the industry it operates (Palmie et al., 2021). The topic of sustainability and purpose in fintechs is usually prominent from the beginning of their foundation since the main idea behind them is to help to relieve the pains of the consumers (Hammadi & Nobanee, 2019). In recent years multinational non-banking tech firms and different startups innovated new applications that are more user-friendly and customer-centric, hence forcing banks to reassess their current market positions and become more open to market

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interactions including collaborations, partnerships, and mergers with fintech startups (Kohtamaki et.al., 2019). It is usual practice to believe that both startups and incumbent organizations tend to benefit from the collaboration.On one hand, startups have the ability to be more agile, innovative, technologically advanced, and sustainability oriented. On the other hand, incumbent organizations have the experience and expertise in the industry.

There are several ways organizations can collaborate. One of the ways that has been studied by several authors in business and management studies specifically is the topic of sustainability and Mergers and Acquisitions (Aktas, De Bodt, & Cousin, 2011; Bettinazzi & Zollo, 2017; Gomes &

Marsat, 2018; Gomes, 2019; Goergen & Renneboog, 2004). Most of that literature has looked at the relationship between corporate sustainability and CSR activities of acquirers and targets and traditionally dependent variables in M&A studies like post-merger and post-acquisition performance (Aktas, De Bodt, & Cousin, 2011; Deng, Kang, & Low, 2013; Bettinazzi & Zollo, 2017), price premiums (Gomes & Marsat, 2018), uncertainty (Gomes, 2019), and shareholders value (Goergen &

Renneboog, 2004). Despite a wide range of coverage of these two topics, very few authors focused on the influence of intangibles like purpose on the likelihood of those M&As, partnerships, and collaborations happening in the first place. Because purpose-driven banks are more concerned about their customers and society overall, it is more likely that they tend to pursue partnerships with fintechs to better fit the customers' needs in a sustainable way.

Based on the existing academic research I see a clear gap in the connection between purpose and its impact on the collaboration between fintechs and banks. This research provides an insight into how higher corporate purpose influences the collaboration between banks and fintech companies and contributes to the existing literature in both directions: a purpose-driven business model in the banking industry as well as strategic innovation and alliance literature that cover topics of interactions of the company with other companies. There is no research including both variables in one study, thus, it brings relevant academic implications. Table 1 presents the current research in the related fields of collaboration and purpose that can overview the relationship between them.

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Authors Focus Relevance Jensen, 2010 Value maximization and

stakeholders’ theory

The management’s considerations about all stakeholders involved impact the company’s choices and decisions

Deng et al., 2013 Corporate social responsibility and stakeholder value maximization

Being purpose-driven increases trust between stakeholders and lowers transaction costs

The market perceives the deal in a positive way in case of the high CSR engagements from the acquirer because it is

recognized as the maximization of stakeholder value

Gomes, 2019 CSR influence on M&A transactions

There is a higher chance of the deal happening between two companies if both have a strong orientation towards

environmental and social goals.

Vastola and Russo, 2020

Effects of M&A on acquirer’s and target’s

sustainability

If both the target and the acquirer share a similar vision toward corporate social responsibility and activities related to

that, the success of the merger or acquisition is higher

Vallaster, 2005;

Kautonen et al., 2020

Startups’ roles.

Sustainability in SME

The start-ups are purpose-driven and have high CSR standards

Das and Kumar, 2006 Strategic Alliances and learning dynamics

The choice of the partner will be positively influenced by mutual environmental, social and governmental capabilities.

Das and Teng, 1998 Strategic Alliances and confidence in partner

cooperation

The more trust there is, the higher the confidence is in the collaboration or alliance.

Gulati et al., 2009;

Heimeriks and Duysters, 2006

Strategic Alliances’

experiences and gains from it

Learning more about an alliance partner can affect decisions about joining and learning more about new partnerships. As a

result, learning-about not only relates to advancing a particular partnership but also to whether a company

establishes new ones Table 1. Overview of the important empirical studies on the related concepts

1.2 Research Question

This thesis focuses on incumbent banks and fintech companies, the collaboration between banks and startups and the role of the corporate purpose in this collaboration. The corporate purpose in a business model is quite novel in academic literature. Mainly, the studies written before were exploratory and qualitative (Bunderson & Thakor, 2021; Thakor and Quinn, 2020). Hence, this paper

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aimed to quantify the aforementioned concept and the subsequent partnership decision. This research aimed to identify how a purpose incorporated in a business model in the banking sector and strategic alliances can enable traditional banks and financial service providers. Thus, the following research question was drawn.

RQ: How purpose will affect the collaboration between fintech and banks?

By answering this research question, several substantial implications can be drawn from this paper for banks’ managers, fintech entrepreneurs and academics.

On the one hand, this thesis contributes to the literature by providing theoretical development on the topics of strategic alliances and innovations and purpose-driven business models in the context of the banking industry through the analysis of the effect of purpose and sustainability orientation on the partnership. This analysis would be connected to both strategic alliances literature, which has a range of different types of interactions between firms, as well as the financial industry specifically related to the innovations and the sustainability research. This thesis provides the method of how to empirically test the effect of the purpose on partnership.

On the other hand, the management of banks and fintechs can get an insight into what can cause a partnership to happen in the first place and the benefits of having a purpose incorporated in the business model in case of willingness to form an alliance. This paper provides an insight for a fintech entrepreneurs who are willing to form a partnership with a bank into which banks have a higher probability of collaboration. From the perspective of the bank, it would be useful to assess the purpose and CSR initiatives it has and whether it has an influence on the partnership.

1.3 Structure of the thesis

The rest of the thesis is organized in the following way. First, major theoretical findings on the topics of purpose and its main components, on the topics of the collaboration, partnership, or M&A activities and theories that might explain the relationship between purpose and collaboration are presented. Based on those the main hypotheses are derived. Then, in the third section methodology, sample data, research design and data collection process are presented. The main

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findings and empirical results from constructed models are reflected and analyzed in the fifth section.

Next, in the discussion part, contributions of the results, the connections between findings and literature, and further theoretical and practical implications are reported. Finally, a conclusion shows the main takeaways and the overview, followed by the possible limitations and future research suggestions.

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2. Literature review and hypothesis construct

In this chapter, the current state of academic literature and theoretical framework is discussed and developed. Moreover, the connections between the main variables of the research are drawn and based on those connections the possible hypotheses are stated.

2.1 Purpose

Though there is an existing research interest in purpose-driven companies and an increasing surge in the topic of organizational purpose overall, there is no clear definition of purpose within the organization. The possible theory that could be behind the purpose-driven companies is the stakeholder maximization theory developed by Freeman (1984). According to this view, the organization behaves in the best interests of not only shareholders, but all stakeholders involved (Jensen, 2010). Thus, management must consider all stakeholders while making choices in order to achieve this. When a company takes only profit maximization into its consideration, other stakeholders may take actions, such as governmental penalties or strikes from employees, that can potentially hurt this goal (Ruf et al., 2001). As a result, working in the interests of various stakeholders rather than just shareholders may boost shareholder value in return. The possible explanation lies in the support the firm gets from stakeholders that increases the level of trust and lowers the transaction costs among all parties involved (Deng et al., 2013).

There are two groups of authors focusing on the meaning of the purpose. The first one argues that it does not have to constitute social concern and focuses mainly on the product or service of the company itself and the values behind it (Gartenberg et al., 2019). Similarly, Thakor and Quinn (2020) argue that CSR and purpose are closely related, however, CSR goes broader than purpose since the latter can include topics and activities related to CSR but not vice versa. CSR activities can include improvement of labour practices, improvement of environmental actions, reducing CO2 footprint, fairtrade, participating in charities and volunteering in the community projects. Only the first three initiatives could be possible purpose propositions while the other three are only related to CSR practices (Thakor & Quinn, 2020). The second group, on contrary, fully connects purpose to the

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concept of CSR by considering purpose as an assessment of the entirety of the companies' actions towards environmental and social aspects (Ahsen and Gauch, 2021). Evidently, this group supports the stakeholder maximization theory since it involves taking into consideration companies' stakeholders. However, this combination of CSR and purpose is criticized by some authors claiming that CSR is related to business ethos, yet not its higher purpose (Pontefract, 2017). These controversial ideas might come from the fact that purpose is still considered to be a pretty novel and conceptual topic with no defined limits and borders. However, most scholars mention that behind the purpose there is an idea of achieving something more than profit maximization (Mayer, 2016; Porter and Kramer, 2011; Thakor and Quinn, 2013; World Economic Forum, 2020; Steen, 2015; Gartenberg et al., 2016; Hurst, 2014). Almost the same definition relies upon CSR and ESG as the metrics of CSR concepts that can explain why these authors connect concepts of CSR and purpose as the synonymous ones. Thus, this thesis will focus on the idea of a purpose centered on social and environmental impact connected with CSR initiatives.

Purposewas empirically proven to enhance customer loyalty and the relationship between companies and stakeholders among different industries in the study by Collins and Saliba (2020).

This research also highlighted that the customers would prefer companies that are purpose-driven or have CSR initiatives that they actively pursue. Same reports were produced by Edelmann (2012), Porter Novelli (2019) and Villela et al. (2019), all proving the positive impact of purpose on consumer loyalty. This relationship potentially emerged because of the increased attention towards sustainability from the consumers and the influence of it on the consumers’ choices and preferences.

However, according to Manas-Viniegra et al. (2020), it is not the case if the company's credibility is relatively low because of the prior unpleasant incidents. Another group of researchers like Nga Wai Chan (2015) and Bunderson and Thakor (2021) studied the impact of purpose on employee satisfaction, commitment to work and trust in the leadership, showing the positive impact on all variables. According to the report written by Kim et al. (2021), there are positive correlations between purpose-driven strategies and better financial performance and market valuations, as well as

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correlations with higher levels of customer loyalty and retention. Similarly, higher corporate purpose and purpose-driven strategy can lead to the following benefits: brand value that customers trust and that is built around purpose; improved KPI metrics and decreased compliance risk because of the transparency, and the ability to hire the best talent since a lot of the workforce nowadays care about the image and sustainability of a company they work for (McIntyre & Skan, 2020). Based on most of the research in the field of purpose-led companies, it has a positive effect on the performance of the companies both tangible, which includes the stock price and financial performance, and intangible, which includes customer and employee loyalty.

Even though there is high interest in the topic of purpose, and it covered a decent range of topics, there is not much academic literature on purpose-driven banking and to what extent it is integrated within the activities of the banks and how it might influence the collaboration between banks and fintech companies. The only comprehensive study of the banking business model in the last 5 years was written by Hanafizadeh and Marjaie (2021) where they came up with a framework conformed by four types of business models. However, it was not related to the topic of the higher purpose of banks, as it was not the focus of their research.

2.2 Collaborations, partnerships, M&As

While it is common knowledge in the business field that one of the ways to capture the digital capabilities of the other company is to be engaged in merger or acquisition activity, the research by Borah & Tellis (2014) proves to find other ways of interaction between companies to form alliances for joint product development or innovation opportunities. In that sense, collaboration with fintech companies can give incumbent banks a flexible opportunity to enter the digitally based business models in the banking and financial industry and realize the benefits of digital transformation.

According to Brandl and Hornuf (2019), the majority of interactions between fintechs and banks are product-based collaborations. Hence, this thesis will focus on any type of interaction including M&A, product-based collaboration, and joint ventures.

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In order to deal with intense competition from tech companies and fintechs, the understanding of the business model is of crucial importance for banks since it is a source of competitive advantage when applied and implemented effectively and appropriately. (Volberda et al., 2018). Business model innovation is directly connected to collaborations, partnerships, and M&A activities since some scholars suggest that it has a vital role in the innovation strategy of the company (Papa et al., 2018).

According to Scott et al. (2017) banks have traditionally produced financial innovations to boost their profitability and have embraced digital services as a new source of growth (Barrett et al. 2015). Beck et al. (2016) claim that financial innovations are linked to bank expansion due to those innovations.

Moreover, new IT-enabled service models and digital servitization are likely to improve the financial performance of incumbent banking organizations similar to what happened due to the recent change of century-old business models in the computer equipment and software industry that moved from traditional to SaaS business models (Kohtamäki et al., 2020). Similarly, Sjödin et al. (2019) analyze how relational governance for the supply of advanced services can improve a firm's financial performance in a study of 50 Swedish advanced service providers. They recognized the necessity to employ a variety of relational governance measures in order to achieve greater financial results. These processes of introducing innovations and addressing new business models involving technologies are based on the Digital Transformation process. According to several authors (Volberda et.al., 2018), digital transformation must reflect three of the following elements: adoption of new technologies, new forms of value creation, and organizational changes. In one way or another, each of these elements can be reached by a bank through collaboration, partnership, or a merger with a fintech.

In the field of strategic management, there are two types of learning in the alliances: about the partner and from the partner (Khanna et al., 1998; Inkpen and Tsang, 2007). Due to the scope of this thesis, this paper focuses on the former one. The research of Das and Kumar (2007) argues that the selection of the partner phase is the main constituent of the learning-about process and in case of the positive knowledge about that partner, it increases the likelihood of entering into a partnership agreement. The work of Doz (1996) centres on the five dimensions (process, environment, skills,

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goals, and task) of this learning-about phase, meaning that all those dimensions are taken into consideration before engaging in the alliance. Several authors (Gulati et al., 2009; Heimeriks and Duysters, 2006) propose that learning more about an alliance partner can affect decisions about joining and learning more about new partnerships. As a result, learning-about not only relates to advancing a particular partnership but also to whether a company establishes new ones. It is worth mentioning that it goes both ways in the case of this thesis: from banks to fintechs and from fintechs to banks. Both parties are involved in the process of learning about each other's goals and capabilities.

In addition to that, Jo and Kim (2019), claimed that the prior experience in acquisitions in the IT industry relates to the higher attempts of acquisitions in the future. It potentially means that if a bank was involved in one partnership with a fintech, there will be more partnerships formed in the future.

Overall, fintechs and banks may partner for a variety of reasons. Fintechs can acquire access to a larger customer base, receive a superior understanding of how to cope with financial regulations, and develop their own digital offerings by forming an agreement with a well-established financial player. Some fintechs form a partnership with a bank to gain access to a banking license, which is sometimes too difficult and costly for a fintech startup to obtain (Klus et al. 2019). In contrast, banks can capture a competitive advantage by cooperating with fintechs that are creating or have already developed a better way to provide financial services. In some cases, investment in fintech companies can grant banks access to use certain applications or licenses which can exclude competitors from the same access. In addition to that, purpose-driven banks are more likely to have a vision toward digital transformation because they have to develop digital capabilities to satisfy current market demands (Kim et al., 2019).

2.3 Linking purpose theories to collaboration theories

Most of the research in the field of intersection between CSR and M&A focuses on the impact of the sustainable orientation of the acquirer on the overall performance after the M&A activities.

According to Bettinazzi and Zolo (2017), there is a positive influence of factors like orientation towards employees, local communities, customers, and suppliers on the overall performance after

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acquisition. In addition to that, research written by Deng et al. (2013) proves that the higher the level of sustainability of the acquirer, the higher the returns after the acquisition announcement, stock returns, long-term performance and those firms are less likely to fail in the post-acquisition process.

Moreover, according to Vastola and Russo (2020), if both the target and the acquirer share a similar vision toward corporate social responsibility and activities related to that, the success of the merger or acquisition is higher. If the success is perceived to be higher, then there is a higher likelihood of the partnership happening due to the increased confidence in the success of the deal. Another group of researchers place the focus on the extra advantages of CSR and sustainability towards M&A deals.

For instance, Gomes (2019) claims that there is a higher chance of the deal happening between two companies if both have a strong orientation towards environmental and social goals. Yet, there is evidence from Waddock and Graves (2006) that the negative effect of acquisition can impact the once high level of sustainability of the target company because of the lower level of sustainability of the acquirer. Similarly, there is evidence that the market perceives the deal in a positive way in case of the high CSR engagements from the acquirer because it is recognized as the maximization of stakeholder value (Deng et al., 2013). It is in line with the stakeholder maximization theory previously described that presumably can influence the decision of purpose-driven banks to participate in the collaboration with a fintech.

According to Rode and Vallaster (2005) and Kautonen et al. (2020), a start-up formulates the purpose that is usually linked to the reason for the founding of that startup itself. Thus, it is assumed that the start-ups are purpose-driven and have high CSR standards.

Uniting both theories on collaborations and theories on alliances, the main insights concerning the banking sector were the following. Purpose-driven banks are closer to fintechs in terms of CSR and purpose orientation and according to the strategic alliance's theory mentioned in Das and Kumar (2006), the choice of the partner will be positively influenced by mutual environmental, social and governmental capabilities. Moreover, based on the stakeholder maximization theory, if a company pursues CSR-related initiatives, there is a higher trust between all the stakeholders of the firm (Jansen,

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2010), including potential partners of that firm, fintechs or startups, and according to Das and Teng (1998), the more trust there is, the higher the confidence is in the collaboration or alliance. This higher confidence, in return, can influence the decision to participate in the partnership from both perspectives: a fintech' one and a bank' one. In addition to that, purpose-driven banks, based on stakeholders' theory, are willing to serve the interests of customers in the best way possible and one of these ways is to collaborate with fintech that offers superior digital capabilities. Based on the existing literature the following hypothesis is constructed.

Hypothesis 1: Purpose-driven banks are more likely to establish alliances with fin-techs.

Several authors like Gulati et al. (2009) and Heimeriks and Duysters (2006) propose that learning more about an alliance partner can affect decisions about joining and learning more about new partnerships. As a result, the process of the learning-about not only relates to advancing a particular partnership but also to whether a company establishes new ones. In addition, the trust from the other stakeholders including potential partners increases in case of the high corporate purpose and CSR initiatives (Jansen, 2010). The more sustainable and purpose driven the bank is, the more potential partners it may attract. Thus, the following hypothesis is drawn.

Hypothesis 2: There is a positive relationship between purpose-driven banks and the number of partnerships between those banks with fintechs.

In order to measure the relationship between the purpose and the partnership, the researcher drew two hypotheses because the researcher wanted to measure this relationship in two different ways. First, whether there is a higher probability of at least 1 partnership between a fintech and a bank in a particular year. The second one is how many more partnerships/interactions do purpose vs non-purpose banks have.

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Figure 1. Conceptual model of the variables used in this research.

Purpose-driven/Traditional bank

Probability of collaboration with Fin-tech

Number of Collaborations

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3. Methodology

In this chapter, the data collection process, independent, dependent, and control variables, and sample used in this research are described. Moreover, the research design, as well as the methods, are stated. Taking into consideration the nature of the research question and previous studies, this paper focused on the quantitative method. Even though a qualitative analysis would be equally appropriate, the researcher was interested in the statistical difference between traditional and purpose- driven banks in terms of collaboration with fintechs.

3.1 Data Collection

To answer the research question, the paper was divided into two parts. First, the researcher collected the data and identified how to qualify a bank to be considered as purpose-driven. To construct the whole dataset the combined method of data collection was used. Collaboration with fin- techs was hand-collected from official banks' websites, annual reports, press releases, and FactSet database concerning the partnerships, alliances, M&A, or minority stake purchases. Data combined from different sources was necessary since there is no existing database that adequately assesses all the formed partnerships, product-based collaborations, joint ventures, and minority stake purchases.

It happens because of the focus of the databases mainly on M&A activities and not partnerships or product-based collaborations. The advantage of this combined method is precision since it applies a more holistic approach to data collection; the main disadvantage, however, is time spent on searching through all these data sources to find information about collaborations. Other variables were collected from Orbis and FactSet databases and this process was relatively straightforward. The second part of this research was testing the existing hypothesis with STATA version 17 using Panel data Probit regressions and Panel data count models, Negative Binomial regression specifically.

The chosen sample initially consisted of panel data of the 150 largest banks by assets in the countries of Western Europe in the period from 2013 to 2020. However, due to the missing values across those 150 banks, the final sample consisted of 101. According to Haddad and Hornuf (2019), there is evidence of a higher number of fintechs formations in well-developed economies where

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venture capital is easily accessible. That is why the focus is on countries with developed economies such as the Netherlands, France, Germany, and Switzerland. The data on the largest banks by assets was retrieved from ORBIS and FactSet databases with the latest available year with the largest number of observations, which was fiscal year of 2020. Table 2 and Figure 2 give an overview of the banks used in the sample. The full list of the banks can be found in the Appendix A.

Figure 3. Distribution of banks by the country of origin.

Switzerland Germany

BANK-NOW AG DEUTSCHE BANK AG

BANQUE CANTONALE VAUDOISE COMMERZBANK AG

CREDIT SUISSE AG UNICREDIT BANK AG

RAIFFEISEN SCHWEIZ GENOSSENSCHAFT DEUTSCHE KREDITBANK AG (DKB)

UBS SWITZERLAND AG SANTANDER CONSUMER BANK AG

VALIANT BANK AG REWE-ZENTRALFINANZ EG

France Netherlands

BNP PARIBAS ING BANK NV

SOCIETE GENERALE ABN AMRO BANK NV

CREDIT AGRICOLE CORPORATE DE VOLKSBANK N.V.

NATIXIS SA NIBC BANK NV

LA BANQUE POSTALE AEGON BANK NV

HSBC CONTINENTAL EUROPE TRIODOS BANK NV

Table 2. Largest banks by assets in the Netherlands, France, Germany, and Switzerland, which were used in this research

15

22

6

58

Netherlands Germany Switzerland France

Number of banks

Distribution of banks by the country

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3.2 Variables

3.2.1 Dependent Variable: Collaboration with fintechs and new partnerships

Merger and acquisition, as well as a product-based collaboration and a partnership were considered. The collaboration was coded as 1 if the bank made a partnership/ collaboration/ majority stake/ minority stake/ merger/ acquisition with a fintech in a particular year and 0 if the bank did not.

In addition to that, the variable Number of new collaborations which was used to identify the number of collaborations the bank was involved in any given year was analyzed as well. For the collaboration with fintechs, the researcher ran an extensive search using the official websites of the identified banks to find press releases, Crunchbase database to identify fintechs that formed partnerships with banks and FactSet, particularly a section for the global news monitoring. There are several requirements for this search. First, banks must be located in one of the 4 countries identified earlier, yet fintech can be anywhere in the world. Secondly, the collaboration between banks and fintechs must be announced in the last 8 years between the fiscal year 2013 and 2020.

3.2.2 Explanatory Variable: Purpose

In this paper, the purpose is measured by CSR performance that is proxied by Factset's Truvalue Labs ESG score identified by SASB (Sustainability Accounting Standards Board) through the FactSet database. Different ESG scores as proxies of CSR performance were used in academic literature before (Deng et al., 2013). For instance, Gomes (2019) used the Thomas Reuters ASSET4 database as the measure of CSR initiatives. These scores provide comprehensive information on the environmental, social and governance dimensions of the company. The researcher used Truvalue Labs over the others since it covered 120,000 public and private companies across the globe and different industries including banking. Large quantities of unstructured data are analyzed and sorted by deploying machine learning algorithms into 26 categories outlined by the SASB materiality framework to create Truevalue ESG scores (Factset, 2022). SASB categories include Environment, Social capital, Leadership and Governance, Human Capital and Business Model Innovation. The most of information comes from 100,000 data sources in thirteen different languages. They include

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company-provided materials, analysts' perspectives, advocacy groups, and government and law regulators as reported by independent media. Because of the diversity of data sources in terms of the different languages, the researcher assumes that scores for all the countries represented in the sample get a full scope of the analysis. The scale of the score lines in 5 different levels: laggard, below average, average, above average and leader, each given score of 1, 2, 3, 4 and 5, respectively. For the sake of the analysis scores from 1 to 3 were recorded in 0 and 4 and 5 to 1. The ESG score quantifies the sustainability of CSR initiatives and allows to run a regression model to get statistical results on the relationship of the variables.

Figure 2. 5 dimensions of FactSet Truevalue Labs ESG Score based on SASB categories

3.2.3 Control Variables

Previous studies in the banking literature suggest incorporating the control variables for the increased validation of the research (Peng et al. 2017). Control variables in banking literature include the financial indicators, such as equity ratio, natural log of total assets, return on average assets, and banks' features like the age of the bank, whether it is publicly listed or not. Control variables in banking literature are usually used to control for the size of the bank, the financial capital structure, organizational structure, management decisions, and age (Peng et al., 2017).

Leadership and Governance

Business Model Innovation Human

Capital

Environment

Social Capital

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Thus, the natural logarithm of total assets is used to control for the size of banks presented in the dataset, usually, scholars use a natural logarithm to normalize the distribution and to achieve a constant variance that allows comparing different banks in one dataset. With the big size in terms of total assets, firms are more likely to pay higher price premiums to acquire another company (Moeller et al., 2004). It can be also connected to the partnerships where the fintechs might be forced to enter the alliance for a big monetary incentive. Moreover, big banks collaborate with fintechs more often than smaller ones (Hornuf et el., 2020). Based on the literature, this control variable is included in the model to fix the effect of the bank’s size on the probability of collaboration.

The equity ratio is used for identifying the capital structure of the firm. In the case of a small equity ratio, it means that the most of assets were acquired through the debt and indicates that there is higher pressure in terms of profit results since there is a substantial amount of debt obligations to fulfil that can in return influence the interaction between purpose and collaboration variable in both directions. On one hand, focusing on profit maximization instead of stakeholder maximization can drive the purpose of the company downwards. On the other hand, willingness to collaborate with fintechs might increase since they are a source of innovations and new revenue streams.

Organizational structure is reflected in whether a bank is publicly listed or not. Companies are usually more pressured regarding reporting their activities in the case of the former, which can potentially have an influence on the variables. In order to check the efficiency of the management team and their decisions, in banking literature (Peng et al., 2017), it is common to use Return on Average Assets. It reflects the performance outcome that is directly connected to the potential decision about the collaboration, which is one of the dependent variables. The researcher uses them in order to make sure that the independent variable has a significant effect while control variables are held on a constant level. The data on the financial performance and indicators were derived from the ORBIS database and Crunchbase database.

According to Hofmann and Gavin (1998), for the sake of the interpretation of the results in a meaningful way all the continuous variables used in a regression analysis must be centered around

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their mean instead of the zero. Consequently, equity ratio, natural log of total assets, natural log of banks’ age and ROAA were centered by subtracting the mean of each of these variables from the initial value of individual observations. The data points were not centered before the logarithmic transformation since some of the values were negative and according to logarithmic rules, it is not possible to transform negative values.

Table 3 provides the variables that are going to be used in this research.

Variable Explanation Data Source

Collaboration (d) Binary variable coded as 1 or 0:

1 in case of at least the partnership/collaboration/M&A

Press releases, annual statements, FactSet database, Crunchbase database, official websites, and news

Number of new collaborations

Number of collaborations/alliances/partnerships each year for a given bank

Press releases, annual statements, FactSet database, Crunchbase database, official websites, and news

Purpose (d) Binary variable coded as 1 or 0:

1 in case of the purpose-driven business model

Truevalue Labs ESG score from FactSet database

Equity Ratio Ratio of the equity in the bank FactSet and Orbis databases

Total Assets Natural Log of the total assets to control for the bank’s size

Orbis database

Listed (d) Binary variable coded as 1 or 0: 1 in case of the publicly listed

FactSet and Orbis databases

ROAA Ratio of the return on average assets of the bank in the year

FactSet and Orbis databases

Age Natural logarithm of age of the bank to control for the age FactSet database, official websites

Table 3. Overview of the variables used in this research. d indicates the dummy variable

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3.3 Methods used

3.3.1 Probit panel regression

All the gathered data is presented in the form of the panel dataset for the 2013-2020 years to assess the number of bank-fintech collaborations in those years. Similar to the paper of Hornuf et al.

(2020), the researcher used the Probit panel regression analysis with random effects to identify the probability of collaboration with fintech for purpose-driven and traditional banks. Overall, probit regression does not estimate fixed effects, that is why model with random effects was used.

The probability of at least 1 partnership between fintechs and bank (i) in a year (t) is based on the cumulative normal probability function:

𝑃 (partnership = 1|𝑥1, 𝑥2, 𝑥3, 𝑥4, … 𝑥𝑘) = 𝐺 (𝛼 + 𝛽1Purpose𝑖,𝑡 + 𝛽2Assets𝑖,𝑡 + 𝛽3Listed𝑖,𝑡 + 𝛽4𝑅𝑂A𝑖,𝑡 + 𝛽5Age𝑖,𝑡 + 𝛽6Loantoasset,𝑡 + 𝛿𝑖 + 𝜃𝑡 + 𝜗𝑖,𝑡)

with 𝐺 (𝛼 + 𝑋 𝛽) = 𝛷 (𝛼 + 𝑋 𝛽) where 𝛷 (.) is the Cumulative Density Function (CDF) of a normal distribution. To measure the effect of the individual variables, the researcher cannot use the coefficients of the Probit regression itself because it is difficult to intuitively interpret them since the data is transformed similar to the log transformation in logistic regression. That is why average marginal effects’ coefficients must be used to get meaningful results (Mullahy, 2017). Marginal effects of the model reflect that the one-unit change in the independent variable's coefficients will increase the probability of the dependent variable by the value of that coefficient. The Probit model was chosen over logit for the sake of simplicity in interpreting coefficients since Probit deals with probability, while logit with odds. However, both models should not lead to significantly different results.

In addition, since the dataset is relatively small, this paper utilized the bootstrapping technique, as in the work of Hornuf et al. (2020). Even though, it is nearly impossible to get a 100%

accurate confidence interval, the produced bootstrapped standard errors are considered to be more asymptotically more accurate than the same errors in the identical sample under the assumption of

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normality (Davison & Hinkley, 1997). It is a method to control and check for the robustness and the stability of results and it is used in this model.

3.3.2 Panel Count Model

To test another hypothesis on whether the number of partnerships between fintechs and banks correlates with purpose-driven orientation the researcher used the Panel Count model where the Number of Partnerships is a count dependent variable. Because of the nature of the dependent variable, which takes the form of a nonnegative integer from 0 to the maximum number of partnerships, this variable was treated as a count one. In econometrics literature, there are 2 commonly used types of models that fit this type of data: Poisson regression and Negative Binomial regression, which is used in the case of the overdispersion in the data. The former one assumes the Poisson distribution of datapoints where the mean and the variance are equal. If it is not the case, the Negative binomial distribution applies, allowing the variance to be greater than the mean due to the wider shape of the distribution. Outcomes of the count models are log transformed and the coefficients produced by this method do not have an intuitive interpretation. The predicted difference in the log of the number of the dependent variable calculates for a one-unit difference in the predictor (Martin, 2021). That is why the Incidence Rate Ratio (IRR) applies to interpret the results of Poisson and Negative binomial regressions. Using exponentiation allows the researcher to transform the coefficient into IRR. It can be interpreted as the rate of change in the dependent variable for a one unit increase in the independent variable with control of other independent variables.

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4. Results

In this chapter, the final dataset, descriptive statistics, and correlation matrix are provided.

In addition, the results of regression analyses and statistical significance are reported.

4.1 Final Dataset

The whole process started with the cleaning the data. The panel dataset of 101 banks across 4 countries in the span of 8 years from 2013 to 2020, initially consisted of 808 observations. However due to the missing values of several financial indicators, the dataset was cleaned, and some observations were eliminated. The final dataset consisted of 747 observations. After that, I recoded the variable Purpose from a scale on 1 to 5 to a dichotomous one. The purpose variable in the sample has a maximum value of 4, not 5. Prior to the recoding, the Probit regression was run using purpose on the original scale from 1 to 4 and collaboration as an independent and dependent variable, respectively. The only statistically significant results were going from any score of 1, 2, and 3 to the score 4. Based on that, there was no reason not to recode the purpose as a binary variable for the easier interpretation of results. To check the normality of the data, tests for it were run to identify the skewness and kurtosis. There was no significant deviation from the normal distribution, so it was assumed that the data was normally distributed that would not cause any additional problems in the further analysis.

Out of 101 banks, only 61 collaborated or formed a partnership with fintechs. In total, those 61 banks formed 697 collaborations/alliances/acquisitions from 2013 to 2020. The distribution of collaborations and partnerships by years and countries are presented in table 4. Additionally, out of those 101 banks, only 21 were considered purpose driven.

2013 2014 2015 2016 2017 2018 2019 2020 Total

Switzerland 1 1 1 2 4 6 10 10 35

Germany 1 2 7 6 13 21 28 32 110

France 13 13 29 41 59 72 78 109 414

Netherlands 6 8 15 14 21 25 22 27 138

Total 21 24 52 63 97 124 138 178 697

Table 4. Distribution of partnerships across 4 countries from 2013 to 2020.

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4.2 Descriptive Statistics

To get a better understanding of the dataset Table 5 below shows the descriptive statistics of the dependent, independent, and control variables. There are in total 747 observations across 101 banks in 8 years. The independent variable, Purpose, is on average equal to 0.221. Even though it is a categorical variable, it clearly identifies that there are more traditional banks than purpose-driven banks in the sample. Indeed, the purpose-driven banks accounted only for 21 banks out of 101.

A similar situation happened with the Collaboration variable. In total, only 61 banks collaborated with a fintech some years between 2013 and 2020. Out of those 61 banks and thus 488 observations, 61 multiplied by 8 years, only 283 instances had at least 1 partnership with a fintech.

That means that even if the bank collaborated with a fintech at some point between those years, not every year out of those 8 carried a collaboration or a partnership.

The next dependent variable, the number of partnerships formed each year, the average number was 0.933 with a minimum value of 0 and the maximum value of 8. A total number of formed alliances/partnerships/collaborations in a span of 8 years was equal to 697 deals proving that on average across the banks that took a part in partnerships there were 11.43 partnerships, 697 divided by 61, formed during those 8 years sampled.

Variable Obs Mean Median SD Min Max

Dependent Variable

Collaboration (d) 747 0.379 0 0.485 0 1

Number of new collab 747 0.933 0 1.445 0 8

Independent Variable

Purpose (d) 747 0.221 0 0.415 0 1

Control Variable

ROAA 747 -0.025 0.0032 0.515 -11.306 1.934

Listed (d) 747 0.094 0 0.292 0 1

Ln (total assets) 747 15.117 15.350 3.636 2.86 21.84

Ln (age) 747 3.572 3.710 1.122 0 5.65

Equity ratio 747 0.157 .0743 0.224 0 1

Table 5. Summary statistics of the variables used in this research with mean, median, standard deviation, minimum value, and maximum values. d indicates dummy variable.

Moving on to the control variables, the following can be observed. First, banks’ size measured as the natural logarithm of total assets has a wide range from 2.86 to 21.84 with almost equal mean

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and median. It means that the distribution of instances in terms of total assets is symmetric and has almost zero skewness. Second, banks’ leverage is measured as equity ratio, which is shareholders’

funds as a percentage of the total assets. The mean is equal to 15.7% while the median is at around 7.43%. Compared to the mean, the median is relatively small, which provides evidence that the data is skewed to the right and half of the instances are smaller than 7.43%. It means that the most of assets were acquired through the debt and indicates that there is higher pressure in terms of profit results since there is a substantial amount of debt obligations to fulfil that can in return influence the interaction between purpose and collaboration variable in both directions. The third control variable, banks’ age, is symmetrical and has zero skewness with a minimum value equal to 0 and a maximum of 5.65. In terms of normal years, the average number of years that banks operate is equal to 60.57 years. Fourth, out of 101 banks, 92 were private and 9 were publicly listed. Even though the data was unbalanced, the researcher still kelp this variable to check on the significance since it can have a potential future implication, where the data will be more balanced. The final control variable, Return on Average Assets does not show a positive mean and is equal to -0.025, while the median is at 0.0032. It shows that half of the banks are not successful in earning profit using their assets.

Table 6 depicts the correlation matrix providing all the correlation coefficients between variables used in this research. According to Pallant (2005), the correlation between independent variables should not exceed 0.5 or -0.5 since it is a sign of moderate collinearity. If correlation coefficients between independent variables exceed 0.7 or -0.7, then it proves the presence of the high collinearity. High correlation coefficients are identified between collaboration and the number of new collaborations, which makes sense for the nature of those variables. However, since both of them are dependent variables and will be used in different analyses, it is not a problem. The correlation matrix does not show any more issues of collinearity except the ones described above, thus all remaining variables were included in the subsequent analysis.

Variables (1) (2) (3) (4) (5) (6) (7) (8)

Collaboration (1) 1.0000

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Number of new collab (2)

0.8276 1.0000

Purpose (3) 0.3891 0.4047 1.0000

ROAA (4) 0.0386 0.0326 0.0288 1.0000

Listed (5) 0.0708 0.1390 (0.0716) 0.0178 1.0000

Ln (total assets) (6) 0.2990 0.2946 0.2146 0.1611 0.3915 1.0000

Ln (age) (7) 0.1424 0.1353 0.0997 0.0303 0.3649 0.3337 1.0000

Equity ratio (8) 0.0579 0.0458 0.0298 0.0799 0.0186 0.1979 (0.0263) 1.0000 Table 6. Correlation Matrix. Parentheses indicate negative values.

4.3 Empirical results

4.3.1 Testing Hypothesis 1: Purpose-driven banks are more likely to establish alliances with fintechs. Estimating the marginal effects of the purpose on the probability of at least one partnership between fintechs and bank (i) in a year (y)

This study used Probit regression for panel data using STATA version 17 with the deployment of the xtprobit command. The researcher was trying to examine whether purpose-driven orientation within the bank has an influence on the bank–fintech partnership. This method was chosen because it provides the marginal effects of the independent variables, so it is possible to identify the statistical difference between groups.

Table 7 provides the results of the random-effects panel probit regression to test the probability of at least one interaction between the bank and a fintech happening based on the full sample. The coefficients show the average marginal effects with bootstrapped standard errors in parentheses. Model 1 includes only control variables. Model 2 provides information on both control and independent variables. The full model was found to be statistically significant with a chi-squared value equal to 36.89 (n = 747), p = 0.000, showing that the model was able to distinguish between banks that formed the collaboration with a fintech versus those which did not.

It was found that the coefficient of Purpose is positive and significant, proving that having a purpose in the strategic focus of the bank increases the probability of partnership with a fintech by 37.98% at p<0.01 level of significance, standard error of 0.098 and 95% confidence interval from 0.187 to 0.572.

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The first model’s chi-squared was equal to 18.65 with the marginal effects’ coefficient for ln (total assets) of 0.051 at the significance level at p<0.01, equity ratio with the coefficient of 0.44 at p<0.01, and ln (age) with the coefficient of 0.11 at p<0.01. All these control variables were found statistically significant.

The final model with both control and independent variables is significant with chi-squared equal to 36.89 at p = 0.00 level of significance. Moreover, statistically significant variables include purpose, the natural logarithm of assets and equity ratio with marginal effects of 0.041 and -0.029 at p<0.01 respectively. Overall, the results show that the model was able to distinguish between purpose-driven and traditional banks that formed the partnership with the bank. Consequently, it can be claimed that my findings support my Hypothesis 1: Purpose-driven banks are more likely to establish alliances with fintechs. It means that if the bank is purpose-driven, then the probability of at least one partnership with fintech increases by 37.98%. The hypothesis is accepted.

Model 1(control variables) Model 2

ln(age) 0.11***(0.037) 0.077*(0.04)

ln(assets) 0.051***(0.014) 0.041***(0.013)

listed -0.261(0.139) -0.137(0.112)

ROAA -0.539(0.549) -0.502(0.346)

Equity Ratio 0.44***(0.155) 0.431***(0.139)

Purpose (d) 0.3798***(0.098)

Number of observations 747 747

Number of banks 101 101

Log likelihood -346.67 -336.63

Prob > chi2 0.002 0.000

Chi-squared 18.65 36.89

Table 7. Results of panel data analysis with the use of random effects Probit regression measuring the probability of whether at least 1 partnership between a bank and a fintech happens (partnership = 0) or not (partnership = 0). *** significance level of p<0.01, **

significance level of p<0.05, * significance level of p<0.1. The coefficients reflect the average marginal effects with bootstrapped standard error reflected in parentheses. d indicates the dummy variable.

4.3.2 Testing Hypothesis 2: There is a positive relationship between purpose-driven banks and the number of partnerships between those banks with fintechs. Estimating the Number of the partnerships between the fintechs and a bank (x) in the year (y)

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This paper uses the Panel Count model since the dependent variable of this model is a count variable with non-negative integers and the data contained multiple instances per bank over 8 years.

After running Poisson regression, it was clear that this method cannot be used since the goodness-of- fit test statistics were at the level of p<0.05 with a large value of chi-square equal which are the indicators that the data might suffer from overdispersion. That is why the study deployed the random- effect negative binomial regression method for the panel dataset as it allowed overdispersion with the usage of STATA version 17 using the xtbreg function. The random effect was chosen after running the Hausman test to identify which is more appropriate.

Table 8 provides the results of the negative binomial regression to test whether purpose correlates with the number of fintechs with which a bank collaborates based on the full sample. The Incident Rate Ratios report the effect of the independent variables on the count variable. Again, as in the previous hypotheses, Model 1 includes only control variables. Model 2 provides information on both control and independent variables. The full model was found to be statistically significant with a chi-squared value equal to 47.02 (n = 747), p = 0.000, showing that the model was able to distinguish the number of collaborations between purpose-driven banks and not.

It was found that the coefficient of Purpose is positive and significant, proving that having a purpose in the strategic focus of the bank increases the number of partnerships with fintechs by 4.9 partnerships for purpose-driven banks in comparison with those without the purpose at p<0.01 level of significance, standard error of 1.83 and 95% confidence interval from 2.36 to 10.204.

The first model’s chi-square value was equal to 33.4 with an IRR for ln (age) of 1.92 at the significance level at p<0.01, ln (total assets) with the IRR of 1.387, equity ratio with the IRR of 13.726 at p<0.01, listed with the IRR of 0.119 at p<0.01, and ROAA with the IRR of 0.045 at p<0.01.

All of these control variables were found statistically significant.

The final model with both control and independent variables is significant with chi-squared equal to 47.02 at the level of significance at p = 0.00. Moreover, as in the model 1, all of the control variables used in this analysis were found to be statistically significant at a different level of

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significance from p<0.01 for ln (age), ln (assets), and equity ratio to listed and ROAA at the p- value<0.05. Meaning that the big, publicly listed, old banks with a higher Equity Ratio and Return on Assets form partnerships with fintechs more often. If the bank is listed, then the count increases by approximately 0.23 partnerships. If the bank is big in terms of total assets, the count of partnerships with fintech increases by merely 1.3 partnerships. Overall, the results show that the model was able to distinguish between purpose-driven and traditional banks in terms of the number of formed partnerships with fintechs. Consequently, it can be claimed that these findings support Hypothesis 2:

there is a positive relationship between purpose-driven banks and the number of partnerships between those banks with fintechs. It means that the purpose has a significant positive impact on the count of partnerships. The hypothesis is accepted.

Model 1 (control variables) Model 2

ln(age) 1.919***(0.342) 1.52***(0.24)

ln(assets) 1.387***(0.104) 1.333***(0.089)

listed 0.119***(0.102) 0.234**(0.149)

ROAA 0.045***(0.059) 0.054**(0.077)

Equity Ratio 13.726***(9.581) 14.052***(9.872)

Purpose (d) 4.912***(1.832)

Number of observations 747 747

Number of banks 101 101

Log likelihood -814.701 -806.706

Prob > chi2 0.000 0.000

Chi-squared 33.40 47.02

Table 8. Results of panel data analysis with the use of random-effects negative binomial regression measuring the number of partnerships of the bank in a particular year. *** significance level of p<0.01, ** significance level of p<0.05. The coefficients reflect the average marginal effects with bootstrapped standard error reflected in parentheses. d indicates dummy variable.

Both proposed hypotheses were accepted according to the above-mentioned tests. A further discussion of the results and the elaborated reflection on the existing literature is expected in the subsequent “Discussion” section.

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Figure 3. Conceptual model of the variables used in this research. *** p<0.01.

Purpose-driven/Traditional bank

Probability of collaboration with Fin-tech

Number of Collaborations H1. 0.3798***

H2. 4.912***

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