Abstract
Purpose
This work aims to explore the effects of (equity and non-equity) strategic alliances between banks and FinTechs on FinTechs' online visibility.
Design/methodology/approach
For a sample of 124 Italian FinTechs, the authors measured online visibility through their website ranking (Google PageRank) and website traffic (Google Trends). Consistent to the historical depth of these measures, the authors separately investigated the effect of equity and non-equity (contractual) agreements on online visibility by means of ordinal logistic regressions and diff-in-diff analysis.
Findings
Strategic alliances with banks enhance FinTechs' online visibility. Although both equity and contractual agreements positively influence the popularity of FinTechs' website achieved through the activity of internal and external online content creators (websites ranking), only equity agreements are effective in attracting Internet users (website traffic).
Practical implications
When deciding to interact with banks, FinTechs' managers should consider that equity agreements may be a powerful strategic choice for enlarging the customer base and boosting visibility of FinTechs.
Social implications
Fostering strategic alliances between banks and FinTechs contributes to FinTechs' growth, generating virtuous mechanisms of innovation, financial inclusion and better allocative efficiency of the financial system.
Originality/value
This work expands marketing knowledge and literature regarding online visibility determinants, by investigating the benefits of strategic alliances and cooperation in the market, while providing an empirical strategy replicable by future marketing studies.
Keywords
Citation
Cosma, S. and Pennetta, D. (2024), "Enhancing online visibility through strategic alliances: the case of bank-FinTech relationships", International Journal of Bank Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJBM-02-2023-0071
Publisher
:Emerald Publishing Limited
Copyright © 2020, Stefano Cosma and Daniela Pennetta
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Technological advancement is strongly supporting the digitalization of the financial industry and new technologies and innovations are increasingly applied to every aspect of the production and distribution of financial services, moving towards online and digitalized value generation (Niemand et al., 2021). The digital revolution of financial services is primarily driven by financial start-ups, i.e. FinTech firms (Elsaid, 2021).
FinTechs are internet-based companies that bring to the market digital financial services through the implementation of new technologies and information technology (IT) solutions. FinTechs only serve customers through their websites (Laidroo et al., 2021) and online visibility is a strategic asset for their growth. Often in direct competition with traditional financial institutions, FinTech firms widen the range of financial services at individuals' and organizations' disposal, giving them the opportunity to access convenient, personalized and innovative solutions even when they belong to traditionally excluded or underserved customer segments (Salampasis and Mention, 2018).
Although innovative financial solutions have several benefits for customers, FinTech firms face a number of challenges when trying to expand their market presence in the financial services industry, where the level of competition is increasing strongly (Elsaid, 2021). Here, traditional financial institutions, in particular banks, still serve the largest share of the market, and competition among FinTech firms is very high as well. Furthermore, FinTechs are usually young financial firms and their ability to survive may be threatened by lack of reputation, limited financial resources, limited experience and, above all, lack of a sufficiently large customer base (Nguyen et al., 2022; Carbó-Valverde et al., 2022; Felländer et al., 2018).
In this challenging context, enhancing online visibility is of the utmost importance for FinTechs to attract a greater number of prospective customers and increase sales and profitability. Also, greater online visibility might allow FinTech firms to deal with the growing competition in the financial market by attracting the attention of web users more effectively (Lee et al., 2015).
In this respect, establishing strategic alliances with incumbents, particularly banks, may be a feasible way for FinTechs to enhance their online visibility (Harasim, 2021), thanks to the possibility of exploiting reputational spill-overs and the access to wider customer base, distribution channels and media coverage (Enriques and Ringe, 2020; Klus et al., 2019; Bömer and Maxin, 2018). This is somewhat common in FinTechs' expectation when strategically cooperate with traditional financial institutions. For example, the equity crowdfunding platform BacktoWork24, that allows private and professional investors to fund start-ups, small-medium enterprises (SMEs) and real estate projects, decided to join the Intesa Sanpaolo Banking Group in order to support the development of its business plan and attract more investors to the platform and thus increasing the available financial resources for companies as stated by the co-founder and chief executive officer (CEO) of BacktoWork24 [1]. The same reasons are behind the partnership between Sella Banking Group and Moneyfarm, a FinTech company offering innovative advisory and investment services. The partnership was an important step for growth, gaining the opportunity to offer their innovative investment solution to new market segments [2].
Strategic alliances with banks are expected to strengthen FinTechs' ability to attract potential customers, reputation and market presence and, thus, their online visibility. This is the focus of the current study, which aims to empirically test the causal effect between bank-FinTech relationships and FinTechs' online visibility, while exploring whether equity and contractual agreements have similar effects.
The study is motivated by some gaps that we identified in the extant literature.
Starting from literature on online visibility determinants (Pant and Pant, 2018; Wang and Xu, 2017; Molla-Descals et al., 2014; Nikolaeva, 2005; Drèze and Zufryden, 2004), for the first time we test if strategic alliances have any role in the enhancement of firms' online visibility. We study online visibility within the financial industry, which is underexplored by this literature stream (Cioppi et al., 2019), and specifically concerning FinTech literature, we offer the first empirical evidence of the possibility to enhance FinTechs' online visibility through relationships with banks, an issue that has only been proposed in the literature from a purely theoretical point of view.
In this paper, we consider two aspects of online visibility, i.e. website ranking and website traffic (Wang and Xu, 2017; Serrano-Cinca et al., 2010). The proxy used for website ranking is Google PageRank, which measures the quality of FinTechs' website through the number of references (or links) to it, giving more weight to links from high-traffic websites (Garofalakis et al., 2002; Murphy and Scharl, 2007). From FinTechs' perspective, Google PageRank represents the degree of accessibility and popularity of their website achieved through internal advertising and outreach efforts, but also through the attention that external online content creators (partners, blogs, online magazines, newspapers, etc.) pay to FinTechs' business and activities. As a measure of website traffic, we employed Google Trends data, that measures how many searches have been made for a specific keyword (FinTechs' name) relative to the total number of searches over time (Ding and Hou, 2015) and that have the potential to reveal Internet users' interest (Sulong et al., 2022; Liu et al., 2021; Vosen and Schmidt, 2011).
By analysing a sample of 124 Italian FinTech companies, we empirically demonstrate that strategic alliances with banks enhance FinTechs' online visibility, measured through website ranking and traffic. However, we identified a significant difference between equity and non-equity (contractual) agreements. Although both types of strategic alliance positively influence FinTechs' websites ranking, we found that only equity agreements are effective in attracting users to FinTechs' websites, thus increasing website traffic.
Our results are really important for FinTechs' managers. If they wish to maximize the accessibility and popularity of their websites by increasing their networks and external links, they can opt for both equity and non-equity partnerships, but the path through equity deals, although more difficult and longer, ensures stable growth in FinTechs' web traffic and attractiveness.
From a theoretical standpoint, our work expands marketing knowledge and literature regarding online visibility determinants, by investigating the benefits of strategic alliances and cooperation in the market. In this vein, this work also offers an empirical strategy replicable by future research on online visibility studies and other marketing topics. The evidence that equity and contractual agreements have different effects on website ranking and website traffic open up a new and important theoretical stream. Indeed, the different reactions and behaviours emerging between online content creators and Internet users deserve further investigation by future studies and research. Finally, this work contributes to FinTech literature, expanding current studies on the development paths of FinTechs start-ups, highlighting the positive role of the stakeholder network and contributing to the debate on the nature and benefits of bank-FinTech relationships.
The remainder of the paper is structured as follows. In Section 2 we present the literature review and develop our hypotheses. Section 3 is dedicated to the description of samples, data and methods. Results of the analysis and robustness checks are described and discussed in Sections 4 and 5, respectively. Section 6 concludes the paper, also highlighting implications, limitations and directions for future research.
2. Literature review and hypotheses development
In this Section, we review the main contributions belonging to the online visibility and FinTech literature strands, we identify literature gaps and introduce our hypotheses. More specifically, after delving into the concept of “online visibility” and its centrality in the Internet adoption process (Section 2.1), we then discuss the importance of online visibility enhancing in the specific case of FinTech firms (Section 2.2). In line with this, we particularly stress the channels through which strategic alliances with banks may improve FinTechs' online visibility. Following the identification of literature gaps in Section 2.3, we conclude suggesting our hypotheses in Section 2.4 and 2.5.
2.1 The concept of online visibility
The literature defines firms' online visibility as “the extent to which a user is likely to come across a reference to a company’s Web site in his or her online […] environment” (Drèze and Zufryden, 2004). Online visibility is considered as the middle (and crucial) stage of the Internet adoption process, which follows a company’s decision to create an online presence and precedes the build-up of their online reputation (Cioppi et al., 2019).
Online presence, i.e. firms' decision to present themselves on the web (De Bakker and Hellsten, 2013), is a necessary but not a sufficient condition for their success in the online environment. Potentially, any organization can access the web, thus the mere decision to be present online is not enough to differentiate from competitors (Raguseo et al., 2017). As also underlined by Smithson et al. (2011), competitive advantage depends on how Internet presence is managed and exploited, making it effective in maximizing online visibility. An effective online presence is essential for organizations to achieve higher search engine rankings, to increase Internet traffic to their websites and, finally, to boost sales and gain market shares (Chua et al., 2009; Murphy and Scharl, 2007).
Indeed, once firms manage to intensify their online visibility and web traffic flow, their approach in designing and providing information and services in the online context will determine their overall online reputation (Rodríguez-Díaz et al., 2018; Charest and Bouffard, 2015) and ability to attract more investors, clients, suppliers or employees and to gain long-term price, cost and performance advantages (Diana-Jens and Ruibal, 2015; Reuber and Fischer, 2011).
Naturally, even if online visibility is not enough to be fully successful in the online environment, it certainly represents the first step in the achievement of a competitive advantage (Smithson et al., 2011). In the extant literature, there is empirical evidence on the association between online visibility and business performance measures. Vaughan (2004), for example, finds positive correlations between revenues and online visibility, measured through the number of inlinks to company websites for IT companies. Similarly, Vaughan and Romero-Frías (2010), focussing on the banking industry (top 50 banks in the world), find positive associations between online visibility and net income in addition to revenues. Wang and Xu (2017), Wang and Vaughan (2014) and Vaughan and Yang (2013), analysing samples of firms operating in several industries, show that web visibility positively affects short- and long-term business performance measures, including revenues, profits and Tobin’s Q. Similar results are also provided by Melo et al. (2017) for the tourism industry, by demonstrating that greater visibility on an infomediary website positively influences business profitability.
2.2 FinTech and online visibility: the role of bank-FinTech relationships
FinTechs are predominantly in start-up stage, suffering from increasing competition and limited financial resources, experience and, above all, customer base (Carbó-Valverde et al., 2022; Felländer et al., 2018). In this context, the enhancement of online visibility may be fundamental for FinTech firms' success. Indeed, FinTechs are similar in some respects to dot-com companies: they adopt a virtual business approach and only sell financial services through their websites (Sakas et al., 2023; Serrano-Cinca et al., 2010; Razi et al., 2004), so the enhancement of online visibility is crucial and necessary (Drèze and Zufryden, 2004). By attracting more users towards their websites, FinTechs may have the opportunity to attract and retain a greater number of prospective customers (Cioppi et al., 2019), with benefits in terms of sales, time to break-even and business scaling-up. Also, the higher the level of their website’s popularity, the better its positioning in search engines (Gonzalez and Palacios, 2004). This may allow FinTechs to better deal with the growing competition in attracting the attention of web surfers, who are faced with an ever-increasing number of website alternatives and tend to exhibit a low level of patience while surfing the Internet (Drèze and Zufryden, 2004).
Many authors have theorized that FinTechs may enhance their visibility through collaborations with incumbents, in particular banks (Enriques and Ringe, 2020; Bömer and Maxin, 2018). According to the resource-based view (RBV) of the firm (Grant, 1991; Rumelt, 1997), strategic alliances play a positive role in firm’s growth (Powell et al., 1996), as they represent a way to overcome internal constraints by gathering the necessary resources from external strategic partners (Baum et al., 2000; Henderson and Cockburn, 1994). In this perspective, strategic alliances, motivated by complementary know-how and competencies, integrate firms' resources and capabilities (Frenken, 2000; Marion and Fixson, 2014; Piva et al., 2012).
Furthermore, the literature identifies some mechanisms underlying the causal effect between bank-FinTech strategic alliances and FinTechs' visibility. Banks provide a wide range of financial services (advisory, credit, payment and investment services, etc.) to different types of customers, who interact with banks in the role of entrepreneurs, investors, borrowers or information acquirers. So, banks are diversified businesses and have a huge number of stakeholders. From this perspective, strategic alliances with banks may allow FinTechs to access to banks' relational networks and stakeholders (Klus et al., 2019; Milanov and Shepherd, 2013).
The establishment of bank-FinTech relationships is usually announced to stakeholders in news reports, either through the websites of the two parties to the agreement or through third-party newspapers and online magazines. As stakeholders, including potential customers, read about a firm of interest in the news, they might be persuaded to visit the corporate website to gather more information, with a positive effect on online visibility and web traffic (Pant and Pant, 2018; Nikolaeva, 2005). In addition, when banks agree to advertise or distribute FinTechs' financial products and services, the latter gain access to wider online distribution channels and, consequently, increase their online visibility (Raguseo et al. (2017) highlight a similar mechanism for small hotels which decide to offer rooms through online travel agencies (OTAs), such as Booking.com and Expedia).
2.3 Literature gaps
Overall, the extant literature on online visibility, on strategic alliances and FinTech exhibits some literature gaps, which are addressed and, as far as possible, filled by this study. When studying the determinants of firms' online visibility, researchers have proposed and tested several drivers, including country, industry, firm, product and buyer characteristics (Pant and Pant, 2018; Wang and Xu, 2017; Molla-Descals et al., 2014; Nikolaeva, 2005). To date, the literature totally lacks of empirical contributions exploring the role of strategic alliances in enhancing firms' online visibility. At the same time, and from a managerial standpoint, the study of the effects of strategic alliances paid major attention to aspects related to financial and operational performance, competitive advantage, knowledge and resources acquisition, customer satisfaction and innovation (Kohtamäki et al., 2018), leaving aside potential benefits for alliances' participants in terms of visibility. By focussing on strategic alliances between banks and FinTech firms, this study provides the first empirical evidence on the issue. The choice to focus on bank-FinTech relationships stems from three considerations. Firstly, FinTechs are internet-based service companies and results of our study concerning strategic alliances with incumbents might be interesting for other companies operating in other industries. Secondly, and specifically concerning FinTech literature, the possibility to enhance FinTechs' online visibility through relationships with banks has been proposed in the literature from a purely theoretical point of view, without offering empirical demonstrations on the issue. Thirdly, as concern online visibility, the majority of scientific contributions mainly focus on the tourism and industrial sectors, while the financial sector has received less attention (Cioppi et al., 2019).
2.4 Hypotheses development: the effects of bank-FinTech relationships on FinTechs' online visibility
With the aim of filling the gaps identified in Section 2.3, and considering the line of reasoning of Sections 2.1 and 2.2, we suggest the following hypothesis:
Strategic alliances with banks positively affect FinTechs' online visibility.
Online visibility is often measured through firms' website ranking and website traffic (Rossi et al., 2018; Pant and Pant, 2018; Chua et al., 2009; Murphy and Scharl, 2007). Compared with other online visibility measures based on surveys (Smithson et al., 2011; Drèze and Zufryden, 2004), website ranking and website traffic are simple, objective and easy to collect measures (Wang and Vaughan, 2014). They reflect two different perspectives of online visibility (Wang and Xu, 2017; Serrano-Cinca et al., 2010). From the FinTechs' point of view, website ranking represents the positioning of its website during searches, so that the higher the ranking, the greater the online visibility and the probability that an Internet surfer will land on their website, which consequently gains credibility and reputation (Serrano-Cinca et al., 2010; Murphy and Scharl, 2007). Search engines use proprietary algorithms to rank web pages, and are usually based on inlink counts, i.e. direct references or external links to a website that reveal it to potential visitors (Wang and Xu, 2017). Inlinks are extremely valuable for companies' online visibility and are considered equivalent to online word-of-mouth (WOM) referrals (Liu et al., 2021). On the other hand, web traffic measures FinTechs' ability in attracting visitors (i.e. prospective customers) to their websites, with a potential positive effect on sales and performance (Serrano-Cinca et al., 2010). Therefore, website ranking represents the degree of accessibility and popularity of FinTechs' website achieved through internal advertising and outreach efforts, but also through the attention that external online content creators (partners, blogs, online magazines, newspapers, etc.) pay to FinTechs' business and activities. Web traffic, instead, reveals Internet users' interest (Sulong et al., 2022; Liu et al., 2021; Vosen and Schmidt, 2011). To better address the role of bank-FinTech relationships in newcomers' online visibility, following the above line of reasoning we further divide H1 into two sub-hypotheses:
Strategic alliances with banks positively affect FinTechs' website ranking.
Strategic alliances with banks positively affect FinTechs' website traffic.
The measures of website ranking and traffic used in this paper are based on data provided by Google, as explained in Section 3.1. Given the different nature and frequency of the measures of online visibility, the testing of H1a and H1b will follow two different empirical strategies, as explained in Sections 3.2 and 3.3 for website ranking and website traffic, respectively.
2.5 Hypotheses development: the need to consider the type of bank-FinTech relationships
The literature on strategic alliances also suggests that there are major differences between equity and non-equity (contractual) agreements (Das and Teng, 2000; Yoshino and Rangan, 1995). The types of alliance are shown to have different effects on growth, innovation and performance of startups participating in the agreement (Cacciolatti et al., 2020; Hagedoorn et al., 2018; Plummer et al., 2016). Therefore, when testing the hypotheses introduced above, it would be feasible to observe differences between equity and contractual agreements in terms of magnitude and timing of the effects on FinTechs' online visibility. This may be the result of the different degrees of interaction and integration between the parties, to which different levels of complexity in resource-sharing and synergies achievement may correspond (Todeva and Knoke, 2005; Williamson, 1991). Consequently, external stakeholders, including existing and potential customers, may have a different appreciation and understanding of the logic underlying equity and contractual agreements (Kale et al., 2001). To consider this aspect, we explore the issue by considering equity and contractual agreements separately and we introduce the following hypotheses:
Equity agreements with banks positively affect FinTechs' online visibility.
Contractual agreements with banks positively affect FinTechs' online visibility.
As further detailed in Sections 3.2 and 3.3, these two hypotheses are simultaneously tested with H1a and H1b, by including in the empirical models both explanatory variables concerning equity and contractual agreements with banks.
3. Sample, data and methods
3.1 Sample and online visibility measures
To test our hypotheses, we analysed a sample of 124 Italian FinTechs providing payment, financing, investment and insurance services to consumers and businesses. These were identified through the “Italian FinTech ecosystem report” produced by E&Y (2020). In this paper we use three measures of FinTechs' online visibility: one is a search engine ranking and two are measures of website traffic.
To test our research hypotheses, we implemented Google_PageRank as the measure of FinTechs' website ranking. Google lists search results based on the PageRank algorithm (Garofalakis et al., 2002), which measures the quality of a website through the number of references (or links) to it, giving more weight to links from high-traffic websites, and assigns a score from 0 to 10 (Murphy and Scharl, 2007). Accordingly, the ordinal categorical variable Google_PageRank assumes a value between 0 and 10, so that the higher the score, the higher a website’s position. Unlike previous studies in the literature, we were unable to use data provided by Alexa (Wang and Xu, 2017; Piñeiro-Chousa et al., 2021), a subsidiary of Amazon.com, since it discontinued the service on 1st May 2022 (https://www.alexa.com/).
With regard to website traffic measures, like Liu et al. (2021) and Molla-Descals et al. (2014) we measured FinTechs' website traffic through Google Trends. Launched at the beginning of 2004, the search volume index provided by Google Trends measures how many searches have been made for a specific keyword relative to the total number of searches over time (Ding and Hou, 2015). As highlighted in the literature, tools like Google Trends have the potential to reveal consumers' interests and to forecast consumption and firms' performance (Sulong et al., 2022; Liu et al., 2021; Vosen and Schmidt, 2011). Therefore, we introduce the following measures of FinTechs' website traffic: (1) G_Trends, which measures the search interest of Google users in each FinTech in the sample (we used the FinTech’s name as keyword). Google Trends data range from 0 to 100, which respectively represent the lowest and the highest search volume of a specific keyword in a certain time frame, and are usually provided at weekly or monthly intervals. Therefore, we computed the yearly average of the Google Trends search volume index for each FinTech. To study the development of FinTechs' web traffic over time, we also introduce the variable (2) G_Trends_Growth, a web traffic growth indicator which measures the increase or decrease in the Google Trends rating compared to the year in which it first records a score different from zero (considered as base year). In order to obtain reliable data from Google Trends, we searched for FinTechs' names with topic, time and geographical filters applied, thus considering only information regarding searches in the “Finance” field, starting from the year in which FinTech firms were established and limited to the Italian context. Definitions of online visibility measures and all other variables used in this paper are provided in Table 1.
Naturally, the three measures of online visibility differ in terms of frequency. Unlike G_Trend and G_Trend_Growth, which supply data on a yearly basis, Google_PageRank lacks historical depth (information on websites' ranking was retrieved from Google in December 2022). While the former suits panel data analysis, the latter only allows cross-sectional analysis. For this reason, we describe the methodology applied for FinTechs' website ranking and website traffic separately.
3.2 Modelling FinTechs' website ranking: methods and independent and control variables
To investigate whether strategic alliances with banks, in the form of both equity and contractual agreement, affect FinTechs' website ranking, we model the Google_PageRank ordinal logistic variables by representing the probability that the
Model 1.
Here
In line with the extant literature, we also include several control variables. Following the empirical evidence provided by Pant and Pant (2018), we include control variables capturing FinTechs' efforts to engage and attract more users through the inclusion of blogs, forums or links to online social media, such as Facebook, Twitter and LinkedIn (also known as information prosociality), which are shown to have a positive impact on firms' website visibility. Social media platforms and blogs are widely used by FinTech firms (Abdillah and Mukti, 2021) and are of utmost importance for collecting customers' opinions, judgements and viewpoints, which are useful in improving and suitably complete the range of financial services in offer, in improving communication on collaborations, figures, events and activities (Franco-Riquelme and Rubalcaba, 2021) and marketing efforts and in increasing website visibility and e-reputation (Moccia et al., 2021; Garzaro et al., 2021). Therefore, we include the following control variables: (1) Blog, a binary variable that takes the value of 1 when the FinTech supports experience and knowledge-sharing between current and new customers through the inclusion of a blog or a forum on their website, and (2) Social, a binary variable that takes the value of 1 when the FinTech’s website includes external links to various social media platforms, facilitating engagement with them.
According to the study by Wang and Xu (2017), business-to business (B2B) and business-to-consumer (B2C) firms are different in terms of number, risks, experience and switching behaviour of customers. This also means that B2B and B2C firms are faced with different search and purchasing behaviour on the part of customers, with different implications for online visibility. For this reason, we also include the categorical variable Customer, characterized by three different levels: “Business”, if the FinTech offers financial services to business customers, “Consumer”, if the FinTech offers financial services to consumer customers and “All” (base level), if customers are both businesses and consumers.
In the FinTech industry, online visibility may also depend on the specialization or diversification strategy adopted by FinTechs. This choice influences the number and size of competitors, as well as the concentration and growth opportunity of the market, which are also important drivers of value creation in the digital environment (Varadarajan and Yadav, 2002). Therefore, we also control for the type of financial services offered by FinTech firms through the variable Product, which has the following levels: “Payments” (base level), “Financing”, “Investments”, “Insurance” and “Diversified” (Tanda and Schena, 2019), where the last level refers to FinTech firms operating in more than one segment.
Finally, following the empirical evidence provided by Molla-Descals et al. (2014), we include Age as control variable, i.e. FinTechs' years of business activity. Firm age has been associated to stronger brand identity, more experience and resources (Steinfield et al., 2005) and, above all, more inlinks to their website from external sources (Kannan and Govindan, 2011).
When studying the determinants of online visibility, some authors, including Pant and Pant (2018) and Wang and Xu (2017), consider firm size among the control variables. However, the close link between size and visibility may arise from a strong correlation rather than a causal relationship. Indeed, some contributions in the literature actually use firms' size as a proxy for their visibility (see for example Januarti et al., 2019; Rand and Tarp, 2012; Hackston and Milne, 1996). In line with this stream, our sample shows a correlation between FinTechs' size (i.e. logarithm of total assets) and their online visibility ranging between 0.3 and 0.5, depending on the measure of online visibility considered. For this reason, in order to avoid biased results, we decided to not include FinTechs' size as control variable, but rather to implement it in our robustness checks and treat it as an alternative measure of online visibility.
Descriptive statistics of the variables in our cross-sectional dataset are shown in Table 2, while the correlation matrix is displayed in Table 3.
3.3 Modelling FinTechs' website traffic: a diff-in-diff approach
To study the effect of equity and contractual agreements on FinTechs' website traffic, we exploit the longitudinal nature of our dependent variables (i.e. G_Trends and G_Trends_Growth) and use the diff-in-diff (DID) estimation method. More specifically, the DID method allows us to estimate the effects of strategic alliances with banks on website traffic by comparing FinTechs which have these alliances (treated) and those which do not (control). The DID method requires a completely random selection between treatment and control group, otherwise the estimations can be largely biased. To mitigate endogeneity problems caused by selection bias, we applied the propensity score matching (PSM) technique before implementing DID, so as to ensure accurate results regarding the effects of equity and contractual agreements on FinTechs' website traffic.
3.3.1 Propensity score matching (PSM)
PSM computes the probability of a FinTech to be involved in a strategic alliance as follows:
PSM was carried out following strict criteria:
The PSM was applied separately for equity agreements (27 treated FinTechs) and contractual agreements (36 treated FinTechs), in order to study the effect of a single version of the treatment (consistency) as requested by the assumptions underlying the DID method;
To enhance the comparability of treated and control units, PSM is carried out year by year, thus avoiding that active treated FinTechs are matched with control units that have not yet been established;
We consider as matching variables (
The 1:2 proportion is applied, thus for each treated FinTech we matched two control units. The matching is without replacement, so that each control unit can be used as a match only once;
All triplets of FinTech firms matched in a specific year are excluded from the PSM of the following years.
Table 4 provides a yearly overview of the number of treated and control FinTechs, as well as the cumulative final number of FinTech firms belonging to two separate panel datasets: one focused on equity agreements (81 FinTechs observed during 2009–2021 for a total of 442 records) and the other focused on contractual agreements (108 FinTechs observed during 2013–2021 for a total of 560 records).
We handle the balance test of PSM as shown in Table 5. Overall, results indicate that on average matching estimations are reliable: after the matching, the reduction of absolute value of standard deviation of covariates is strong, in particular for ROA and efficiency, and the absolute value is less than the threshold of 20 (Rosenbaum and Rubin, 1985). Furthermore, treated and control FinTech firms are highly comparable, as shown in Table 6. Indeed, if we consider the 5% significance level as threshold, the treated and control samples are not statistically different from each other, excluding a possible selection bias.
3.3.2 DID baseline models
The width of the time window considered in our analysis is
Model 2a.
Model 2b.
4. Results
4.1 Website ranking
Table 7 provides the results of Model 1, in which we study the determinants of FinTechs' website ranking as a proxy of their efforts to improve website popularity and online visibility. In all specifications, the two main assumptions of ordinal logistic regression are satisfied: there is no evidence of multicollinearity problems, as suggested by the low value of the variance inflation factor (VIF) reported in Table 3 (VIF <5 for each independent variable), and the proportional odds assumption holds, as indicated by the Brant test (Brant, 1990) reported at the bottom of Table 7 (p-value >0.05). Furthermore, reverse causality should not be a concern for the analysis. Indeed, as explained in Section 3.2, Google_PageRank refers to December 2022, while the independent variables Equity_Agr and Partner_Agr concern strategic alliances established before 2022, in particular between 2009–2021 for equity agreements and between 2013–2021 for contractual agreements. Hence, there is no reason to assume that our measure of website ranking influences the establishment of strategic alliances between banks and FinTechs.
Both equity and contractual agreements are strategic in increasing external links to the website, which actually improve online visibility. The full model (specification 5 of Table 7) reveals that FinTech firms with at least one shareholder-bank show, ceteris paribus, 1.027 points higher log odds of gaining a Google ranking position than FinTechs that do not receive capital infusions from banks, an effect significant at the 5% level. For FinTech firms that establish contractual agreements with banks, the log odds are equal to 0.689, significant at the 10% level.
In line with previous studies, we also provide empirical evidence that FinTechs' information prosociality, i.e. their efforts to engage and attract more users through the inclusion of blogs, forums or links to online social media, positively influences firms' website visibility. This holds in particular for links to online social media (Social), which shows the largest positive and significant log odds in all specifications of Table 7.
As we expected, the choice to focus on the business segment is associated with a lower probability of gaining Google ranking positions (with log odds of almost −0.8): therefore, targeting a broader and more diverse customer base is the key to greater online visibility.
Similarly, FinTechs which diversify their offerings (i.e. which operate in multiple product segments) are able to exploit greater growth opportunities and target a larger market share, with a positive effect on online visibility (specifications 2 and 5, Table 7). Finally, there is no evidence (specification 5 Table 6) that the age of FinTech firms have effects on probability of gaining ranking positions.
4.2 Website traffic
In Tables 8 (Model 2a) and 9 (Model 2b) we report the baseline results of DID analysis, which also include firm fixed effects, time fixed effects and the Post*Age interaction as controls.
It can be seen that FinTech firms with at least one shareholder-bank show, on average, a higher level of website traffic, in terms of both volume and growth. Indeed, for all the time intervals considered in the analysis, coefficient of the interaction Post*Bank_Equity is statistically significant at the 1 and 5% level for G_Trends and G_Trends_Growth, respectively (Table 8). Interestingly, the coefficients increase as the time window considered lengthens, showing that the differential benefits arising from equity agreements with banks increase over time. Contractual agreements and partnerships, on the other hand, do not contribute to an improvement in website traffic (Table 9).
4.2.1 Exogeneity assumption
The DID approach requires strict exogeneity, that is the establishment of equity or contractual agreements (treatment) must not be determined by the level of FinTechs' website traffic (outcome) at baseline. At this stage, it cannot be excluded that banks decide to establish strategic alliances with FinTech firms that already reached a strong online visibility. To address this possible violation of the exogeneity assumption, we carried out Granger causality test, a statistical test usually employed for determining whether a variable
In our specific case, we tested if Bank_Equity and Bank_Partner Granger cause our measures of website traffic, i.e. G_Trends and G_Trends_Growth. Results of the tests are reported in Table 10. By considering the typical statistical significance threshold of 5%, we concluded that exogeneity assumption is satisfied in our DID analysis.
4.2.2 Parallel trend assumption (PTA)
The DID method is robust only when the parallel trend assumption (PTA) is satisfied, i.e. the assumption that without strategic alliances with banks, treated and control FinTech firms would have followed the same time paths. As in Bronzini et al. (2020), to verify this hypothesis, we first visually test the similarity of web traffic trends for treated and control firms before treatment. The results are plotted in Figure 1.
As you can see from the plots, pre-treatment trends of G_Trends and G_Trends_Growth seem to be similar for treated and control FinTechs, for both equity and contractual agreements. To further investigate the issue, we first performed a Wald pre-test for the PTA, as proposed by Callaway and Sant’Anna (2021). The results of Wald pre-test (p-values >0.05), included in Figure 1 as well, confirm that PTA holds in our DID setting. Secondly, following other studies in extant literature, we further addressed PTA by adding the interactors of Pre, a dummy variable equal to 1 for the years prior to the treatment, and the treatments, i.e. Bank_Equity and Bank_Partner (Zhang, 2022; Cao et al., 2022). If the added interactors are insignificant, the DID model setting satisfies the PTA. Results of this additional test are provided in Tables 11 and 12.
As you can see, the coefficients of DID effects persist compared to the baseline model, while the pre-DID effects are insignificant, demonstrating that model satisfies the PTA and that our fundamental estimations are reliable.
4.2.3 Stable unit treatment values assumption (SUTVA)
The stable unit treatment assumption (SUTVA) is satisfied under two conditions. First, SUTVA holds when there no different forms or versions of the treatment (consistency), which is the case of our study: we test the effect of equity and contractual agreements on online visibility separately and in two different specifications, so that there are not different versions or changes of the treatment over time. Second, spillover effects are not allowed, thus a FinTech’s potential outcome should not be affected by other FinTechs' exposure to the treatment. As concern this point, we do not exclude potential spillover effects between FinTech firms providing similar financial services. Indeed, there is the possibility that Internet surfers, once they become aware of a strategic alliance involving a particular FinTech, become interested not only in the financial services offered by this one, but are also eager to explore similar and alternative financial services offered by other FinTechs. For this reason, we carried out an additional analysis by following an empirical strategy similar to Sinclair et al. (2012). In particular, we control the presence of possible spillover effects by including a control variable that, for a specific FinTech in a particular year, considers if other sampled FinTechs offering similar financial services (i.e. “Payment”, “Financing”, “Investment” or “Diversified”) are involved in an equity or a contractual agreement. We named these control variables “Spillover_Equity_Agr” and “Spillover_Partner_Agr”. Results of this additional analysis are provided in Tables 13 and 14.
As you can see, the analysis excludes the existence of spillover effects among FinTech firms. Indeed, the coefficients of Spillover_Equity_Agr and Spillover_Partner_Agr are never statistically significant. Hence, the baseline models satisfy the SUTVA and provide reliable results.
4.3 Overview of results
Overall, although with some differences between equity and contractual arrangements, strategic alliances with banks appear to have a positive effect on FinTechs' online visibility, measured through their website ranking and traffic. Therefore, our results support Hypotheses 1a and 1b. Strategic alliances based on equity investments improve the accessibility and popularity of FinTechs' website (ranking), while also effectively attracting and retaining new and old customers, thus recording an increase in website traffic volumes. This evidence supports H2, in which we stated that equity agreements with banks positively affect FinTechs' online visibility. Contractual agreements, on the other hand, are strategic in improving FinTechs' ranking, but are not effective in attracting a greater number of website users. For this reason, H3 is only partially supported.
5. Robustness tests
To test the robustness of DID analysis, we first focus on the circumstance that strategic alliances in the sample are established in different years that is the treatment occurs with different timing for treated FinTechs. In this regard, it is worth noting that the 75% of bank-FinTech relationships considered are established over the period 2017–2020, percentage that remains high at 48% even when considering a shorter time period from 2019 to 2021. The concentration of strategic alliances in a short time window makes the average difference in treatment timing almost irrelevant, reducing problems arising from a possible non-comparability between early-treated and late-treated FinTech firms. Despite this, there could be still specificities characterizing the different cohort (year) of treated FinTechs, thus we perform a robustness test controlling for the year of establishment of strategic alliances through the interactors Post*Cohort_Equity_Agr and Post*Cohort_Partner_Agr, added to the baseline Model 2a and Model 2b, respectively. Cohort_Equity_Agr and Cohort_Partner_Agr are factor variables indicating the year of establishment of strategic alliances. The aim is to check if main findings concerning the effects of equity and contractual agreements of FinTechs' website traffic persist even when cleansed from cohort specificities. The results are reported in Tables 15 and 16 and confirm our fundamental estimations.
Also, since the results of DID estimations may be influenced by the length of the time intervals considered, we re-run Models 2a and 2b for the time window (−1,4). In this way, we check whether the positive difference between treated and control FinTech firms in terms of website traffic persists even comparing the year before the establishment of strategic alliances with the following years. As Tables 17 and 18 shows, the main results of Models 2a and 2b hold.
In addition, we test the robustness of our results by using an alternative measure of online visibility, i.e. FinTechs' size. As stated in Section 3.2, we decided to implement FinTechs' size as a proxy of online visibility because the close link between the two may arise from a strong correlation rather than a causal relationship. Therefore, we re-run Models 1, 2a and 2b with Size (i.e. logarithm of total assets) as dependent variable. The results of this robustness check (Tables 19 and 20) are very similar to the baseline results commented on in Section 4.
Finally, we also carried out a robustness test for the variable Social, which is the variable with the largest and positive effect on website visibility among the explanatory variables capturing FinTech’s information prosociality. In particular, we re-run Model 1 introduced in Section 3.2 by replacing the variable Social with the variable N_Social, which represent the number of social medias to which FinTechs provide external links on their websites. Results of this robustness test are provided in Table 21 and confirm the effectiveness of FinTechs' information prosociality in enhancing website visibility.
6. Conclusions, implications and limitations
In the last decade, the financial industry has witnessed an increase in the level of competition, due to the entrance of FinTech firms into the market. Thanks to technological innovations and a favourable regulatory framework, FinTechs can develop effective and innovative financial service delivery processes and can address a broader customer base, which includes excluded and underserved market segments (Salampasis and Mention, 2018). Although FinTech firms are gaining importance worldwide, many of them struggle to survive: they are new in the financial market, so they typically lack reputation and cannot rely on a large customer base, which is crucial for profitability and growth (Carbó-Valverde et al., 2022; Felländer et al., 2018). For these reasons, online visibility is of the utmost importance for FinTechs. It is the first step in reaching a greater number of potential customers, building reputation and, overall, ensuring a less risky development path (Enriques and Ringe, 2020; Bömer and Maxin, 2018). The current study aims to empirically test the causal effect between bank-FinTech relationships and FinTechs' online visibility, while exploring whether equity and contractual agreements have similar effects.
The general conclusion supported by our analysis is that cooperation with banks enhances FinTechs' online visibility, measured through website ranking and traffic. Therefore, relationships with banks are strategic in enabling FinTechs to enlarge their presence in the digital environment and attract a greater number of prospective customers, with potential benefits in terms of sales, time to break-even and business scaling-up. This also is an important conclusion for marketing literature on online visibility because we point out the relevance of strategic alliances on firms' online visibility and the positive role of cooperation policies.
However, we identified an important difference between equity and non-equity (contractual) agreements. Although both types of strategic alliance positively influence the volume and relevance of direct external links to FinTechs' websites (measured through Google PageRank), we found that only equity agreements are effective in increasing interest in FinTechs' activities and attracting users to FinTechs' websites, thus increasing website traffic (measured through Google Trends). This difference is a valuable insight for the formulation of managerial implications and opens up new avenues of investigation from a theoretical perspective.
Bearing in mind that website ranking measures the degree of accessibility and popularity of FinTechs' website achieved through internal and external online content creation, while website traffic reveals Internet users' interest (Sulong et al., 2022; Liu et al., 2021; Vosen and Schmidt, 2011), FinTechs' managers should consider that equity agreements are a powerful strategic choice for enlarging a FinTech’s customer base and boosting interest in its brand. Equity agreements with banks allow FinTechs to benefit from reputational spill-overs and are a strong quality signal for customers, promoting FinTechs' trustworthiness and customers' propensity to take a look at FinTechs' financial products and services.
The creation of commercial partnerships and contractual agreements helps to expand the access network and the dissemination of links but does not originate positive signalling mechanisms. It is feasible that the missing link between a website’s ranking and traffic improvement stems from customers' difficulty in understanding the high professional standards and soundness which FinTech firms must demonstrate to be selected as partners by banks, given the high reputational risk associated to these agreements.
Furthermore, we advise FinTechs' managers to support the creation of online communities, allowing a variety of customers to share experience and knowledge through blogs, forums and social media platforms. As our analysis reveals, prosocial behaviour is repaid in stakeholder attention, leading to greater online visibility.
In a theoretical perspective, our work contributes to the extant literature. First, it expands marketing knowledge and literature regarding online visibility determinants investigating the benefits of strategic alliances and cooperation in the market. This is a new and very important theoretical stream as it links online visibility to strategies and organizational behavioural studies.
Another important theoretical stream is opened up by the evidence that equity and contractual agreements have different effects on website ranking and website traffic; the former captures the communication activities of internal and external online content creators following the establishment of strategic alliances (in terms of links and news dissemination), while the latter captures their effects on the interest and searches carried out by Internet users. Thus, the different reactions and behaviours emerging between online content creators and Internet users deserve further investigation by future studies and researches.
Turning to the financial field, we study online visibility within the financial industry, which is underexplored in the literature (Cioppi et al., 2019), by also providing an empirical strategy to future research which is replicable for online visibility studies and other marketing topics. Concerning FinTech literature, the empirical demonstration of the possibility to enhance FinTechs' online visibility through strategic alliances with banks expands current studies on the development paths of FinTechs in the early stages of the life cycle, highlighting the positive role of the stakeholder network and contributes to the debate on the nature and benefit of the relationship between FinTechs and incumbents, in particular banks.
From a societal point of view, the development of FinTech firms has important implications in terms of financial innovation, financial inclusion and efficiency of the financial system and, therefore, in resource allocation and development of the economic system. Authorities and policymakers should aim to foster strategic alliances between FinTechs and banks, contributing to increasing FinTechs' online visibility and development, while generating virtuous innovation mechanisms for traditional financial intermediaries and for the financial system in general.
Despite its valuable insights and implications, this study has some limitations, which open up future research directions. In particular, we used Google data to measure online visibility, that, if on one side it allows to carry out quantitative analysis on a sufficiently large sample of FinTechs, on the other it does not consider the online behaviour of Internet surfers, which is important in assessing the effectiveness of online presence. Basing on a case-study approach, future research may investigate if strategic alliances with banks have some effect on customers' behaviour while navigating FinTechs' website, by considering, for example, the number and which pages they have visited, the time of usage and the actual purchase of financial services. Furthermore, future research may include FinTechs' advertising efforts and other forms of driving traffics among explanatory variables, which are not included in this study because of the difficulties in sourcing data. Regarding the generalizability of the results, it is important to underline that this study focuses on the Italian market, thus future research could expand the sample and provide cross-country evidence on the topic. Finally, our work can be extended by studying the association between FinTechs' online visibility and their performance, while considering a possible mediator role for strategic alliances with banks.
Figures
Definition of variables in the datasets
| Variable | Description |
|---|---|
| Online visibility measures | |
| Google_PageRank | Ordinal categorical variable assuming a value ranging between 0 and 10 |
| G_Trends | Yearly average of Google Trends |
| G_Trends_Growth | Yearly Google Trends growth indicator. It assumes the value of 1 until the FinTech firstly register a positive yearly average Google Trends score (base year). In subsequent years, it measures the increase or decrease compared to the base year |
| Bank-FinTech relationships | |
| Equity_Agr | Binary variable equal 1 if the FinTech has had at least a shareholder bank |
| Partner_Agr | Binary variable equal 1 if the FinTech has previously established a contractual agreement with at least one bank |
| Bank | Binary variable equal 1 for treated FinTechs (with equity or contractual agreements with at least a bank) |
| FinTechs' features | |
| Blog | Binary variable equal 1 when the FinTech includes a blog/forum on their website |
| Social | Binary variable equal 1 when the FinTech includes external link to social media platforms |
| Customer | Categorical variable with three levels: “Business”, “Consumer” and “All” |
| Product | Categorical variable with five levels: “Payments”, “Financing”, “Investments”, “Insurance” and “Diversified” |
| Age | FinTechs' years of business activity |
| ROA (*) | The ratio of EBIT to total assets multiplied by 100 |
| Revenues (*) | Logarithm of revenues |
| Efficiency (*) | The ratio of EBIT to operating costs |
| Leverage (*) | The ratio of debt to total assets |
| Intangibles (*) | Incidence of intangibles on total assets |
| Size (*) | Logarithm of total assets |
Note(s): (*) Variables ONLY employed for propensity score matching (PSM)
Source(s): Table was created by the authors
Summary statistics of variables implemented in Model 1 (cross-sectional sample of 124 FinTechs)
| Binary and discrete variables | |||||
|---|---|---|---|---|---|
| Mean | Median | Standard deviation | Minimum | Maximum | |
| Google_PageRank | 2.758 | 3 | 1.212 | 0 | 7 |
| Equity_Agr | 0.226 | 0 | 0.42 | 0 | 1 |
| Partner_Agr | 0.298 | 0 | 0.459 | 0 | 1 |
| Blog | 0.436 | 0 | 0.498 | 0 | 1 |
| Social | 0.734 | 1 | 0.444 | 0 | 1 |
| Age | 5 | 5 | 2.922 | 1 | 20 |
| Categorical variables (% of cases in the sample) | |
|---|---|
| Customers | Business 33.1%, Consumer 20.2%, All 46.7% |
| Products | Payments 13.7%, Financing 48.4%, Investments 27.4%, Insurance 6.5%, Diversified 4% |
Source(s): Table was created by the authors
Correlation matrix of the variables implemented in Model 1
| VIF | Google_ PageRank | Equity_Agr | Partner_Agr | Blog | Social | Customer | Product | Age | |
|---|---|---|---|---|---|---|---|---|---|
| Google_PageRank | 1 | ||||||||
| Equity_Agr | 1.108 | 0.316 *** | 1 | ||||||
| Partner_Agr | 1.089 | 0.233 *** | 0.238 *** | 1 | |||||
| Blog | 1.373 | 0.311 *** | 0.07 | −0.004 | 1 | ||||
| Social | 1.283 | 0.469 *** | 0.151 * | 0.153 * | 0.419 *** | 1 | |||
| Customer | 1.416 | −0.052 | 0.011 | −0.117 | −0.013 | 0.123 | 1 | ||
| Product | 1.651 | 0.048 | 0.003 | 0.031 | 0.071 | −0.004 | 0.12 | 1 | |
| Age | 1.07 | 0.168 * | 0.166 * | 0.079 | −0.011 | 0.169 ** | −0.025 | −0.13 | 1 |
Note(s): Significance levels: *, **, *** for 10%, 5% and 1% respectively
Source(s): Table was created by the authors
Yearly overview of panel datasets' construction
| Year | N. of treated FinTechs | N. of control FinTechs | Total n. of FinTechs | Cumulative n. of FinTechs |
|---|---|---|---|---|
| Equity agreements | ||||
| 2009 | 1 | 2 | 3 | 3 |
| 2013 | 1 | 2 | 3 | 6 |
| 2014 | 1 | 2 | 3 | 9 |
| 2015 | 2 | 4 | 6 | 15 |
| 2016 | 3 | 6 | 9 | 24 |
| 2017 | 4 | 8 | 12 | 36 |
| 2018 | 4 | 8 | 12 | 48 |
| 2019 | 6 | 12 | 18 | 66 |
| 2020 | 5 | 10 | 15 | 81 (*) |
| Contractual agreements | ||||
| 2013 | 1 | 2 | 3 | 3 |
| 2015 | 3 | 6 | 9 | 12 |
| 2016 | 4 | 8 | 12 | 24 |
| 2017 | 2 | 4 | 6 | 30 |
| 2018 | 7 | 14 | 21 | 51 |
| 2019 | 9 | 18 | 27 | 78 |
| 2020 | 6 | 12 | 18 | 96 |
| 2021 | 4 | 8 | 12 | 108 (**) |
Note(s): PSM is performed every year in which an equity or contractual agreement is established. For each year, treated FinTech are matched with two control units, which are excluded from the PSM of the following year. Matching variables are ROA, revenues, efficiency, leverage, intangibles, size, age, customer segment and product offered to the market
(*) Final number of FinTechs in the panel dataset focused on equity agreement, with a total of 442 records
(**) Final number of FinTechs in the panel dataset focused on contractual agreement, with a total of 560 records
Source(s): Table was created by the authors
Balance test of variables before and after PSM
| Group | Balance test | ||||
|---|---|---|---|---|---|
| Variable | Before/After matching | Treated | Control | Avg bias | Reduction Avg bias (%) |
| Equity agreements | |||||
| ROA | Before | −20.214 | −18.505 | 80.958 | 77.9% |
| After | −20.214 | −14.442 | 18.674 | ||
| Revenues | Before | 4.396 | 4.106 | 1.983 | 13.4% |
| After | 4.396 | 3.987 | 1.718 | ||
| Efficiency | Before | −0.652 | −39.186 | 1367.042 | 99.9% |
| After | −0.652 | −0.328 | 1.762 | ||
| Leverage | Before | 0.397 | 0.527 | 0.785 | 58.6% |
| After | 0.397 | 0.462 | 0.325 | ||
| Intangibles | Before | 0.353 | 0.288 | 0.617 | 60.1% |
| After | 0.353 | 0.355 | 0.246 | ||
| Size | Before | 5.810 | 5.550 | 94.620 | 99.2% |
| After | 5.810 | 5.482 | 0.790 | ||
| Age | Before | 1.539 | 3.083 | 3.032 | 55.9% |
| After | 1.539 | 1.294 | 1.336 | ||
| Customer | Before | 1.989 | 1.863 | 1.179 | 17.9% |
| After | 1.989 | 2.012 | 0.968 | ||
| Product | Before | 1.806 | 1.827 | 0.771 | 27.8% |
| After | 1.806 | 1.759 | 0.557 | ||
| Contractual agreements | |||||
| ROA | Before | −18.728 | −24.036 | 83.988 | 77% |
| After | −18.728 | −19.870 | 19.313 | ||
| Revenues | Before | 3.430 | 4.350 | 1.972 | 6% |
| After | 3.430 | 3.720 | 1.854 | ||
| Efficiency | Before | −1.022 | −59.770 | 1360.272 | 99.9% |
| After | −1.022 | −0.429 | 2.009 | ||
| Leverage | Before | 0.405 | 0.572 | 0.829 | 34.6% |
| After | 0.405 | 0.495 | 0.542 | ||
| Intangibles | Before | 0.264 | 0.349 | 1.177 | 3.7% |
| After | 0.264 | 0.374 | 1.134 | ||
| Size | Before | 5.403 | 5.571 | 0.847 | 7.3% |
| After | 5.403 | 5.365 | 0.785 | ||
| Age | Before | 1.863 | 3.204 | 2.944 | 29.5% |
| After | 1.863 | 2.068 | 2.075 | ||
| Customer | Before | 1.843 | 1.991 | 1.169 | 15.1% |
| After | 1.843 | 1.935 | 0.993 | ||
| Product | Before | 1.568 | 1.743 | 0.773 | 3.6% |
| After | 1.568 | 1.748 | 0.745 | ||
Note(s): Results for equity agreements represents the average value of the statistics computed through PSM in the years 2009 and from 2013 to 2020. Results for contractual agreements represent the average value of the statistics computed through PSM in the years 2013 and from 2015 to 2021
Source(s): Table was created by the authors
Summary statistics of pre-treatment characteristics of treated and control FinTech firms
| Treated (1) | Control (2) | t-Test (1–2) | |
|---|---|---|---|
| Equity agreements | |||
| ROE (%) | −49.322 | −22.584 | −1.304 |
| ROA (%) | −16.541 | −10.701 | −0.869 |
| Revenues (*1000 euro) | 407 | 119 | 1.287 |
| Efficiency | −0.449 | −0.334 | −0.582 |
| Leverage | 0.472 | 0.394 | 1.103 |
| Intangibles | 0.372 | 0.338 | 0.478 |
| Age | 1.348 | 0.779 | 1.397 |
| Size (*1000 euro) | 1,347 | 442 | 1.817 |
| Fisher exact test | |||
| Customer | 0.948 | ||
| Product | 0.419 | ||
| Contractual agreements | |||
| ROE (%) | −53.281 | −46.777 | −0.304 |
| ROA (%) | −24.306 | −17.019 | −0.872 |
| Revenues (*1000 euro) | 197 | 739 | −1.125 |
| Efficiency | −0.825 | −5.616 | 0.901 |
| Leverage | 0.499 | 0.518 | −0.219 |
| Intangibles | 0.267 | 0.277 | −0.228 |
| Age | 1.556 | 1.604 | −0.107 |
| Size (*1000 euro) | 1,509 | 1,283 | 0.257 |
| Fisher exact test | |||
| Customer | 0.525 | ||
| Product | 0.423 | ||
Note(s): This Table displays the average values of FinTechs' characteristics in the years before the formation of strategic alliances with banks. For continuous variables we tested differences between treated and control units through a two-sample t-Test, while for categorical variables, i.e. Customer and Product, we carried out a Fisher Exact Test. All differences are not statistically significant at the 5% level
Source(s): Table was created by the authors
Determinants of website ranking: results of Model 1 when implementing Google_PageRank as dependent variable
| Dependent variable: Google_PageRank | |||||
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| Equity_Agr | 1.516*** | 1.16*** | 1.027** | ||
| (0.43) | (0.439) | (0.434) | |||
| Partner_Agr | 1.054*** | 0.846** | 0.689* | ||
| (0.382) | (0.386) | (0.388) | |||
| Blog | 0.824* | 1.041** | 0.917** | ||
| (0.443) | (0.434) | (0.444) | |||
| Social | 1.894*** | 1.68*** | 1.769*** | ||
| (0.596) | (0.57) | (0.605) | |||
| Customer: Business | −0.84* | −0.811* | −0.83* | ||
| (0.428) | (0.438) | (0.425) | |||
| Customer: Consumer | −0.652 | −0.502 | −0.586 | ||
| (0.588) | (0.602) | (0.58) | |||
| Product: Diversified | 1.437* | 1.393 | 1.282* | ||
| (0.754) | (0.85) | (0.709) | |||
| Product: Financing | 0.027 | −0.273 | −0.081 | ||
| (0.563) | (0.588) | (0.571) | |||
| Product: Insurance | 0.594 | 0.381 | 0.544 | ||
| (0.657) | (0.748) | (0.683) | |||
| Product: Investments | 0.054 | −0.224 | −0.025 | ||
| (0.642) | (0.631) | (0.646) | |||
| Age | 0.087 | 0.11* | 0.088 | ||
| (0.061) | (0.066) | (0.06) | |||
| N° observations | 124 | 124 | 124 | 124 | 124 |
| LR test | 14.018*** | 56.003*** | 8.467*** | 53.59*** | 59.182*** |
| Cox and Snell’s R2 | 0.107 | 0.363 | 0.066 | 0.351 | 0.38 |
| Nagelkerke’s R2 | 0.112 | 0.38 | 0.069 | 0.367 | 0.397 |
| AIC | 387.289 | 363.303 | 392.84 | 365.716 | 362.124 |
| Residual deviance | 371.289 | 329.303 | 376.84 | 331.716 | 326.124 |
| Brant test (p-value) | 0.24 | 0.52 | 0.99 | 0.94 | 0.3 |
Note(s): Ordinal logistic regressions for the dependent variable Google_PageRank. Log odds ratio is displayed. Standard errors are clustered at firm level. For the definition of all variables see Table 1. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Diff-in-diff baseline results: effect of equity agreements with banks on FinTechs' website traffic (Model 2a) for different time intervals
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Equity | 0.346*** | 0.533*** | 0.589*** | 0.564*** | 0.586*** |
| (0.132) | (0.153) | (0.176) | (0.171) | (0.171) | |
| Post*Age | −0.083** | −0.089* | −0.131** | −0.144** | −0.149** |
| (0.042) | (0.054) | (0.060) | (0.059) | (0.058) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.055 | 0.092 | 0.102 | 0.092 | 0.091 |
| F-stat | 4.989*** | 11.99*** | 16.336*** | 16.478*** | 17.471*** |
| Treated pre-treatment average (*): 13.270 | |||||
| Control pre-treatment average (*): 8.838 | |||||
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Equity | 0.109** | 0.219** | 0.321** | 0.368** | 0.451** |
| (0.047) | (0.087) | (0.134) | (0.166) | (0.221) | |
| Post*Age | −0.017* | −0.023 | −0.045 | −0.059* | −0.073 |
| (0.010) | (0.019) | (0.028) | (0.036) | (0.045) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.054 | 0.059 | 0.043 | 0.033 | 0.021 |
| F-stat | 4.916*** | 7.529*** | 6.564*** | 5.577*** | 3.681** |
| Treated pre-treatment average (*): 5.228 | |||||
| Control pre-treatment average (*): 0.759 | |||||
Note(s): The coefficient of the interaction Post*Bank_Equity (β3) measures the effect of equity agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
(*) Pre-treatment average of the dependent variable for treated and control FinTech firms
Source(s): Table was created by the authors
Diff-in-diff baseline results: effect of contractual agreements with banks on FinTechs' website traffic (Model 2b) for different time intervals
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Partner | 0.054 | 0.125 | 0.228 | 0.210 | 0.232 |
| (0.099) | (0.124) | (0.175) | (0.149) | (0.155) | |
| Post*Age | −0.034 | −0.051 | −0.062 | −0.063 | −0.065 |
| (0.036) | (0.037) | (0.041) | (0.041) | (0.041) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 367 | 447 | 502 | 534 | 558 |
| R2 | 0.011 | 0.024 | 0.036 | 0.028 | 0.025 |
| F-stat | 1.138 | 3.596** | 6.348*** | 5.294*** | 5.144*** |
| Treated pre-treatment average (*): 11.671 | |||||
| Control pre-treatment average (*): 4.080 | |||||
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Partner | −0.004 | 0.015 | 0.062 | 0.031 | 0.038 |
| (0.027) | (0.042) | (0.075) | (0.091) | (0.123) | |
| Post*Age | −0.011 | −0.018 | −0.023 | −0.023 | −0.024 |
| (0.010) | (0.012) | (0.015) | (0.014) | (0.015) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 367 | 447 | 502 | 534 | 558 |
| R2 | 0.007 | 0.023 | 0.026 | 0.006 | 0.004 |
| F-stat | 0.954 | 2.788* | 4.589** | 1.048 | 0.819 |
| Treated pre-treatment average (*): 2.974 | |||||
| Control pre-treatment average (*): 1.888 | |||||
Note(s): The coefficient of the interaction Post*Bank_Partner (β3) measures the effect of contractual agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1% respectively
(*) Pre-treatment average of the dependent variable for treated and control FinTech firms
Source(s): Table was created by the authors
Granger causality results for diff-in-diff analysis
| Bank_Equity Granger causes G_Trends | Bank_Equity Granger causes G_Trends_Growth | |||||
|---|---|---|---|---|---|---|
| Time intervals | 1 lag | 3 lags | 5 lags | 1 lag | 3 lags | 5 lags |
| Panel A – Equity agreements | ||||||
| −4 to 0 | 0.863 | 0.889 | 0.498 | 0.053 | 0.111 | 0.124 |
| −4 to 1 | 0.643 | 0.882 | 0.329 | 0.094 | 0.198 | 0.273 |
| −4 to 2 | 0.595 | 0.859 | 0.463 | 0.126 | 0.439 | 0.485 |
| −4 to 3 | 0.635 | 0.839 | 0.509 | 0.148 | 0.533 | 0.318 |
| −4 to 4 | 0.603 | 0.850 | 0.511 | 0.187 | 0.616 | 0.804 |
| Bank_Partner Granger causes G_Trends | Bank_Partner Granger causes G_Trends_Growth | |||||
|---|---|---|---|---|---|---|
| Time intervals | 1 lag | 3 lags | 5 lags | 1 lag | 3 lags | 5 lags |
| Panel B – Contractual agreements | ||||||
| −4 to 0 | 0.517 | 0.866 | 0.826 | 0.244 | 0.510 | 0.661 |
| −4 to 1 | 0.539 | 0.799 | 0.395 | 0.276 | 0.436 | 0.458 |
| −4 to 2 | 0.500 | 0.798 | 0.804 | 0.227 | 0.425 | 0.563 |
| −4 to 3 | 0.579 | 0.865 | 0.919 | 0.449 | 0.543 | 0.632 |
| −4 to 4 | 0.573 | 0.863 | 0.929 | 0.508 | 0.663 | 0.740 |
Note(s): Null hypothesis of “no Granger Causality” between dependent and independent variable. The table reports the p-values for Granger Causality tests with Bank_Equity (Panel A) and Bank_Partner (Panel B) for all time intervals considered in the baseline diff-in-diff analysis and considering 1, 3 and 5 lags
Source(s): Table was created by the authors
Effect of equity agreements: test of parallel trend assumption (PTA)
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Equity | 0.621** | 0.822** | 0.845*** | 0.836*** | 0.843*** |
| (0.313) | (0.323) | (0.319) | (0.324) | (0.324) | |
| Pre*Bank_Equity | 0.296 | 0.312 | 0.278 | 0.295 | 0.279 |
| (0.264) | (0.273) | (0.274) | (0.279) | (0.279) | |
| Post*Age | −0.084** | −0.090* | −0.132** | −0.146** | −0.150*** |
| (0.042) | (0.054) | (0.060) | (0.059) | (0.058) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.063 | 0.097 | 0.105 | 0.095 | 0.093 |
| F-stat | 3.860** | 8.530*** | 11.240*** | 11.354*** | 11.967*** |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Equity | 0.226** | 0.356*** | 0.422*** | 0.442*** | 0.479** |
| (0.089) | (0.135) | (0.153) | (0.164) | (0.180) | |
| Pre*Bank_Equity | 0.126 | 0.149 | 0.109 | 0.081 | 0.030 |
| (0.086) | (0.098) | (0.100) | (0.112) | (0.136) | |
| Post*Age | −0.017* | −0.023 | −0.046* | −0.060* | −0.073 |
| (0.010) | (0.019) | (0.028) | (0.035) | (0.045) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.074 | 0.065 | 0.044 | 0.033 | 0.021 |
| F-stat | 4.551*** | 5.523*** | 4.454*** | 3.737** | 2.449* |
Note(s): Test of PTA through the additional interaction term Pre*Bank_Equity. The coefficient of the interaction Pre*Bank_Equity (β3) measures the effect of equity agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Effect of contractual agreements: test of parallel trend assumption (PTA)
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Partner | 0.336 | 0.401 | 0.486 | 0.467 | 0.482 |
| (0.281) | (0.262) | (0.371) | (0.381) | (0.374) | |
| Pre*Bank_Partner | 0.340 | 0.327 | 0.304 | 0.302 | 0.293 |
| (0.223) | (0.223) | (0.226) | (0.227) | (0.229) | |
| Post*Age | −0.037 | −0.054 | −0.065 | −0.065 | −0.068 |
| (0.037) | (0.038) | (0.042) | (0.042) | (0.042) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 367 | 447 | 502 | 534 | 558 |
| R2 | 0.023 | 0.032 | 0.039 | 0.030 | 0.027 |
| F-stat | 1.970 | 3.639** | 5.238*** | 4.377*** | 4.107*** |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Partner | 0.084 | 0.100 | 0.133 | 0.101 | 0.102 |
| (0.086) | (0.086) | (0.092) | (0.106) | (0.122) | |
| Pre*Bank_Partner | 0.106 | 0.101 | 0.084 | 0.082 | 0.076 |
| (0.088) | (0.088) | (0.090) | (0.094) | (0.100) | |
| Post*Age | −0.012 | −0.019 | −0.024 | −0.024 | −0.025* |
| (0.011) | (0.013) | (0.015) | (0.015) | (0.015) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 367 | 447 | 502 | 534 | 558 |
| R2 | 0.019 | 0.023 | 0.026 | 0.006 | 0.004 |
| F-stat | 1.618 | 2.668** | 3.510** | 0.813 | 0.613 |
Note(s): Test of PTA through the additional interaction term Pre*Bank_Partner. The coefficient of the interaction Pre*Bank_Partner (β3) measures the effect of contractual agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Effect of equity agreements: test of SUTVA assumption (spillover effects)
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Equity | 0.370*** | 0.548*** | 0.592*** | 0.565*** | 0.589*** |
| (0.130) | (0.153) | (0.176) | (0.171) | (0.171) | |
| Post*Age | −0.090** | −0.095* | −0.132** | −0.145** | −0.150*** |
| (0.043) | (0.054) | (0.060) | (0.060) | (0.058) | |
| Spillover_Equity_Agr | 0.086 | 0.100 | 0.026 | 0.009 | 0.035 |
| (0.059) | (0.069) | (0.057) | (0.061) | (0.057) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.063 | 0.099 | 0.102 | 0.092 | 0.091 |
| F-stat | 3.815** | 8.742*** | 10.908*** | 10.959*** | 11.717*** |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Equity | 0.113*** | 0.223** | 0.314** | 0.359** | 0.450** |
| (0.037) | (0.087) | (0.131) | (0.161) | (0.220) | |
| Post*Age | −0.018* | −0.025 | −0.043 | −0.057* | −0.073 |
| (0.011) | (0.019) | (0.026) | (0.034) | (0.045) | |
| Spillover_Equity_Agr | 0.014 | 0.032 | −0.058 | −0.075 | −0.007 |
| (0.020) | (0.023) | (0.052) | (0.063) | (0.029) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.057 | 0.063 | 0.048 | 0.037 | 0.021 |
| F-stat | 3.438** | 5.327*** | 4.799*** | 4.183*** | 2.449* |
Note(s): Test of spillover effects of equity agreement (SUTVA assumption) through the additional control variable Spillover_Equity_Agr, a binary variable which, for a specific FinTech observed in a specific year, is equal to 1 when other FinTechs operating in the same market segment are involved in an equity agreement in the same year. The coefficient of the interaction Post*Bank_Equity (β3) measures the effect of equity agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Effect of contractual agreements: test of SUTVA assumption (spillover effects)
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Partner | 0.132 | 0.172 | 0.592 | 0.251 | 0.274 |
| (0.156) | (0.135) | (0.152) | (0.155) | (0.171) | |
| Post*Age | −0.033 | −0.051 | −0.061 | −0.061 | −0.064 |
| (0.053) | (0.037) | (0.041) | (0.042) | (0.042) | |
| Spillover_Partner_Agr | 0.048 | 0.024 | 0.022 | 0.066 | 0.085 |
| (0.054) | (0.048) | (0.048) | (0.051) | (0.053) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 367 | 447 | 502 | 534 | 558 |
| R2 | 0.013 | 0.025 | 0.036 | 0.029 | 0.029 |
| F-stat | 1.078 | 2.776** | 4.778*** | 4.271*** | 4.316*** |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Partner | 0.008 | 0.018 | 0.064 | 0.039 | 0.043 |
| (0.047) | (0.042) | (0.06) | (0.091) | (0.125) | |
| Post*Age | −0.010 | −0.018 | −0.023 | −0.022 | −0.024 |
| (0.008) | (0.012) | (0.015) | (0.015) | (0.015) | |
| Spillover_Partner_Agr | 0.022 | 0.004 | −0.003 | 0.053 | 0.017 |
| (0.029) | (0.018) | (0.018) | (0.035) | (0.024) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 367 | 447 | 502 | 534 | 558 |
| R2 | 0.009 | 0.017 | 0.024 | 0.008 | 0.004 |
| F-stat | 0.832 | 1.876 | 3.095** | 1.095 | 0.582 |
Note(s): Test of spillover effects of contractual agreement (SUTVA assumption) through the additional control variable Spillover_Partner_Agr, a binary variable which, for a specific FinTech observed in a specific year, is equal to 1 when other FinTechs operating in the same market segment are involved in a contractual agreement in the same year. The coefficient of the interaction Post*Bank_Partner (β3) measures the effect of contractual agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Robustness check: controlling for the year of establishment (cohort) of equity agreements
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Equity | 0.363*** | 0.542*** | 0.599*** | 0.558*** | 0.577*** |
| (0.131) | (0.156) | (0.189) | (0.184) | (0.185) | |
| Post*Age | −0.100** | −0.103* | −0.144** | −0.150** | −0.154** |
| (0.048) | (0.060) | (0.069) | (0.067) | (0.066) | |
| Post*Cohort_Equity_Agr | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.074 | 0.106 | 0.116 | 0.108 | 0.107 |
| F-stat | 1.311 | 2.737*** | 3.677*** | 3.864*** | 4.118*** |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Equity | 0.114** | 0.221** | 0.331** | 0.379** | 0.473*** |
| (0.049) | (0.088) | (0.139) | (0.172) | (0.180) | |
| Post*Age | −0.019 | −0.026 | −0.048 | −0.062 | −0.082 |
| (0.013) | (0.021) | (0.032) | (0.040) | (0.053) | |
| Post*Cohort_Equity_Agr | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.083 | 0.097 | 0.072 | 0.058 | 0.035 |
| F-stat | 1.476 | 2.463*** | 2.175** | 1.952** | 1.250 |
Note(s): Robustness check controlling for the year of establishment of equity agreements through the interaction Post*Cohort_Equity_Agr, where Cohort_Equity_Agr is a factor variable indicating the year of establishment of equity agreements. The coefficient of the interaction Post*Bank_Equity (β3) measures the effect of equity agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Robustness check: controlling for the year of establishment (cohort) of contractual agreements
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Partner | 0.151 | 0.187 | 0.284 | 0.259 | 0.277*** |
| (0.146) | (0.130) | (0.175) | (0.158) | (0.176) | |
| Post*Age | −0.040 | −0.054 | −0.065 | −0.063 | −0.066 |
| (0.036) | (0.038) | (0.042) | (0.042) | (0.042) | |
| Post*Year_Partner_Agr | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.045 | 0.051 | 0.061 | 0.058 | 0.050 |
| F-stat | 1.290 | 1.963** | 2.767*** | 2.800*** | 2.554*** |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Partner | 0.002 | 0.013 | 0.059 | 0.017 | 0.016 |
| (0.035) | (0.042) | (0.059) | (0.091) | (0.125) | |
| Post*Age | −0.012 | −0.019 | −0.023 | −0.018 | −0.017 |
| (0.012) | (0.013) | (0.016) | (0.015) | (0.016) | |
| Post*Year_Partner_Agr | Yes | Yes | Yes | Yes | Yes |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.011 | 0.027 | 0.041 | 0.031 | 0.035 |
| F-stat | 0.300 | 1.001 | 1.809* | 1.465 | 1.745* |
Note(s): Robustness check controlling for the year of establishment of contractual agreements through the interaction Post*Cohort_Partner_Agr, where Cohort_Partner_Agr is a factor variable indicating the year of establishment of contractual agreements. The coefficient of the interaction Post*Bank_Partner (β3) measures the effect of contractual agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4 to −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Diff-in-diff robustness check: effect of equity agreements with banks on FinTechs' website traffic (Model 2a) for the time window (−1, 4)
| Time intervals | −1 to 0 | −1 to 1 | −1 to 2 | −1 to 3 | −1 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Equity | 0.100 | 0.244* | 0.278* | 0.271* | 0.306** |
| (0.129) | (0.133) | (0.156) | (0.159) | (0.119) | |
| Post*Age | −0.054 | −0.053 | −0.088** | −0.098** | −0.104*** |
| (0.034) | (0.037) | (0.041) | (0.041) | (0.040) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 156 | 225 | 277 | 315 | 341 |
| R2 | 0.038 | 0.036 | 0.037 | 0.032 | 0.033 |
| F-stat | 1.494 | 2.617* | 3.721** | 3.811** | 4.328** |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Equity | 0.042 | 0.142* | 0.270* | 0.358 | 0.489 |
| (0.042) | (0.087) | (0.160) | (0.235) | (0.337) | |
| Post*Age | −0.017* | −0.025 | −0.053 | −0.072 | −0.092 |
| (0.010) | (0.020) | (0.036) | (0.049) | (0.065) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 156 | 225 | 277 | 315 | 341 |
| R2 | 0.034 | 0.028 | 0.026 | 0.024 | 0.017 |
| F-stat | 1.327 | 2.008 | 2.558* | 2.811* | 2.188 |
Note(s): The coefficient of the interaction Post*Bank_Equity (β3) measures the effect of equity agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Diff-in-diff robustness check: effect of contractual agreements with banks on FinTechs' website traffic (Model 2b) for the time window (−1, 4)
| Time intervals | −1 to 0 | −1 to 1 | −1 to 2 | −1 to 3 | −1 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: G_Trends | |||||
| Post*Bank_Partner | 0.004 | 0.059 | 0.143 | 0.135 | 0.164 |
| (0.113) | (0.101) | (0.109) | (0.114) | (0.120) | |
| Post*Age | −0.036 | −0.044** | −0.050** | −0.048** | −0.050** |
| (0.024) | (0.022) | (0.022) | (0.022) | (0.022) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 207 | 287 | 342 | 374 | 398 |
| R2 | 0.035 | 0.031 | 0.029 | 0.018 | 0.015 |
| F-stat | 1.748 | 2.809* | 3.401** | 2.331* | 2.104 |
| Panel B – Dependent variable: G_Trends_Growth | |||||
| Post*Bank_Partner | −0.038 | −0.031 | 0.004 | −0.018 | −0.005 |
| (0.028) | (0.035) | (0.046) | (0.068) | (0.089) | |
| Post * Age | −0.013*** | −0.017*** | −0.018*** | −0.014* | −0.012 |
| (0.005) | (0.006) | (0.006) | (0.008) | (0.009) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 207 | 287 | 342 | 374 | 398 |
| R2 | 0.086 | 0.034 | 0.018 | 0.002 | 0.001 |
| F-stat | 4.564** | 3.109** | 2.118 | 0.229 | 0.117 |
Note(s): The coefficient of the interaction Post*Bank_Partner (β3) measures the effect of contractual agreements with banks on the website traffic of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Diff-in-diff robustness check: effect of equity and contractual agreements with banks on FinTechs' Size, implemented as an alternative measure of website traffic. Estimations for the time window (−4, 4)
| Time intervals | −4 to 0 | −4 to 1 | −4 to 2 | −4 to 3 | −4 to 4 |
|---|---|---|---|---|---|
| Panel A – Dependent variable: size | |||||
| Post*Bank_Equity | 0.338*** | 0.291*** | 0.300*** | 0.320*** | 0.315*** |
| (0.109) | (0.108) | (0.108) | (0.112) | (0.114) | |
| Post*Age | −0.102*** | −0.094*** | −0.094*** | −0.100*** | −0.102*** |
| (0.020) | (0.022) | (0.021) | (0.022) | (0.023) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 257 | 326 | 378 | 416 | 442 |
| R2 | 0.097 | 0.090 | 0.094 | 0.092 | 0.081 |
| F-stat | 9.268*** | 11.833*** | 14.922*** | 16.602*** | 15.469*** |
| Panel B – Dependent variable: size | |||||
| Post*Bank_Partner | 0.150 | 0.164 | 0.204 | 0.207 | 0.228 |
| (0.106) | (0.129) | (0.140) | (0.141) | (0.145) | |
| Post*Age | −0.036** | −0.040** | −0.042** | −0.044** | −0.046** |
| (0.018) | (0.018) | (0.019) | (0.020) | (0.021) | |
| Firm fixed effects | Yes | Yes | Yes | Yes | Yes |
| Year fixed effects | Yes | Yes | Yes | Yes | Yes |
| N° observations | 367 | 447 | 502 | 534 | 558 |
| R2 | 0.028 | 0.033 | 0.037 | 0.035 | 0.037 |
| F-stat | 3.711** | 5.735*** | 7.502*** | 7.642*** | 8.397*** |
Note(s): The coefficient of the interactions Post*Bank_Equity and Post*Bank_Partner (β3) measures, respectively, the effect of equity agreements and contractual with banks on the Size of treated FinTech firms compared to control units and can only be estimated when considering also post-treatment periods. All the specifications include firm fixed effects, time fixed effects and the interaction Post*Age. The columns show the results of separate panel regressions for each time interval, with the dummy Post equal to 1 in the years when we want to evaluate the effect of strategic alliances with banks and 0 in pre-treatment period (−4, −1). Standard errors are clustered at firm level. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Robustness check: Model 1 implementing Size as an alternative measure of online visibility
| Dependent variable: Size | |||||
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| Intercept | −0.001 | −1.232*** | 0.088 | −1.129*** | −1.265*** |
| (0.098) | (0.297) | (0.112) | (0.344) | (0.288) | |
| Equity_Agr | 1.218*** | 0.977*** | 0.909*** | ||
| (0.194) | (0.176) | (0.166) | |||
| Partner_Agr | 0.624*** | 0.46** | 0.286* | ||
| (0.202) | (0.177) | (0.149) | |||
| Blog | −0.161 | −0.058 | −0.13 | ||
| (0.201) | (0.213) | (0.199) | |||
| Social | 0.751*** | 0.693*** | 0.695*** | ||
| (0.194) | (0.2) | (0.193) | |||
| Customer: business | 0.187 | 0.219 | 0.204 | ||
| (0.17) | (0.193) | (0.167) | |||
| Customer: consumer | 0.303 | 0.423* | 0.351* | ||
| (0.202) | (0.228) | (0.197) | |||
| Product: diversified | 1.211** | 1.325** | 1.147** | ||
| (0.478) | (0.601) | (0.466) | |||
| Product: financing | 0.233 | 0.073 | 0.205 | ||
| (0.244) | (0.286) | (0.233) | |||
| Product: insurance | 0.881*** | 0.793* | 0.874*** | ||
| (0.316) | (0.425) | (0.314) | |||
| Product: investments | 0.349 | 0.124 | 0.324 | ||
| (0.274) | (0.308) | (0.266) | |||
| Age | 0.073*** | 0.09*** | 0.073*** | ||
| (0.026) | (0.025) | (0.027) | |||
| N° observations | 124 | 124 | 124 | 124 | 124 |
| F-stat | 36.27*** | 9.185*** | 9.476*** | 6.125*** | 8.739*** |
| Adjusted R2 | 0.223 | 0.4 | 0.064 | 0.294 | 0.409 |
Note(s): Linear regressions for the dependent variable Size. Standard errors are clustered at firm level. For the definition of all variables see Table 1. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Robustness check: Model 1 implementing N_Social as an alternative measure of information prosociality
| Dependent variable: Google_PageRank | |||||
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| Equity_Agr | 1.516**** | 1.236*** | 1.096** | ||
| (0.43) | (0.442) | (0.439) | |||
| Partner_Agr | 1.054*** | 0.829** | 0.646* | ||
| (0.382) | (0.387) | (0.383) | |||
| Blog | 0.878** | 1.105** | 0.964** | ||
| (0.433) | (0.424) | (0.439) | |||
| N_Social | 0.473*** | 0.41*** | 0.442*** | ||
| (0.131) | (0.123) | (0.133) | |||
| Customer: business | −0.641 | −0.635 | −0.65 | ||
| (0.421) | (0.429) | (0.423) | |||
| Customer: consumer | −0.343 | −0.236 | −0.301 | ||
| (0.551) | (0.588) | (0.55) | |||
| Product: diversified | 0.965 | 0.972 | 0.864 | ||
| (0.828) | (0.9) | (0.763) | |||
| Product: financing | −0.000 | −0.313 | −0.099 | ||
| (0.566) | (0.606) | (0.574) | |||
| Product: insurance | 0.695 | 0.456 | 0.649 | ||
| (0.672) | (0.771) | (0.684) | |||
| Product: investments | −0.214 | −0.483 | −0.265 | ||
| (0.617) | (0.6) | (0.615) | |||
| Age | 0.091 | 0.118 | 0.093 | ||
| (0.071) | (0.076) | (0.068) | |||
| N° observations | 124 | 124 | 124 | 124 | 124 |
| LR test | 14.018*** | 59.008*** | 8.467*** | 55.478*** | 61.757*** |
| Cox and Snell’s R2 | 0.107 | 0.379 | 0.066 | 0.361 | 0.392 |
| Nagelkerke’s R2 | 0.112 | 0.396 | 0.069 | 0.378 | 0.411 |
| AIC | 387.289 | 360.299 | 392.84 | 363.829 | 359.55 |
| Residual deviance | 371.289 | 326.299 | 376.84 | 329.829 | 323.55 |
| Brant test (p-value) | 0.24 | 0.73 | 0.99 | 0.83 | 0.43 |
Note(s): Ordinal logistic regressions for the dependent variable Google_PageRank. Log odds ratios are displayed. Standard errors are clustered at firm level. For the definition of all variables see Table 1. Significance levels: *, **, *** for 10%, 5% and 1%, respectively
Source(s): Table was created by the authors
Notes
For example, if we want to evaluate the effect in the time window (−4,2), then the variable
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