Switching Behavior Phenomenon Analysis
SME’s switching behavior from traditional financial institutions to Fintech financing
Max Lindner – 10621423, University of Amsterdam, 2017/2018 Qualification: Msc in Business administration – Digital Business
Date of submission: 22.06.2018 Draft: Final
Statement of Originality
This document is written by Student Max Lindner who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of Contents
1. Introduction ... 5
2. Literature review ... 7
2.1 SMEs and their long-standing struggle for credit ... 8
2.2 Disintermediation shift and the underbanked ... 10
2.3 Service provider switching ... 12
2.4 Switching behavior - for online business models ... 14
2.5 Switching costs ... 17
2.6 Switching costs and their impact on relational outcomes ... 20
2.7 The specific switching phenomenon in online financing ... ……22
2.8 Alternative (Fintech) financing options for SMEs ... 25
2.9 SMEs’ switching behavior to Fintech financing marketplaces ... 28
2.10 The push–pull–mooring (PPM) framework ... 29
2.11 Objectives ... 30 2.12 Propositions ... 31 3. Research design ... 31 4. Methodology ... 34 4.1 Research instruments ... 34 4.2 Sample ... 34 4.3 Procedures ... 35 5. Results ... 38 5.1 Findings ... 38
5.2 SME switching behavior modeling: push, pull and mooring factors ... 39
5.2.1 Pull factors ... 39 5.2.2 Push factors ... 43 5.2.3 Mooring factors ... 46 5.3 Summary ... 53 6. Discussion ... 54 6.1 Key findings ... 54 6.2 Discussion points ... 56
6.2.1 SMEs’ access to financing ... 56
6.2.2 Trust and confidence ... 57
6.2.3 SMEs’ risk, value creation and banks’ inefficiency in serving SMEs ... 59
6.2.4 Specialization versus product breadth ... 61
6.2.5 Regulatory disadvantage versus advantage ... 62
6.2.6 Credit scoring history, judgement and quality ... 65
6.2.7 Information asymmetry and uncertainty ... 68
6.2.8 SMEs’ switching intentions versus actual behavior – strength of PPM variables ... 70
6.3 Strengths and limitations ... 73
6.4 Practical implications and points of future research ... 74
7. Conclusion ... 76
8. References ... 79
Appendices ... 85
Appendix 1: Interview protocol - structure of the interviews ... 85
Appendix 2: Overview of preliminary codes ... 87
Appendix 3: Open, axial and selective coding - switching behavior ... 88
ABSTRACT
This study investigates the specific switching behavior phenomenon of small and medium-sized enterprises (SMEs) changing service providers from traditional financial institutions towards Fintech financing. Here, a time frame after the last financial crisis in 2007-2009 is adopted to scrutinize disruptive effects. Importantly, conducting in-depth interviews with twelve industry experts, from a broad array of backgrounds, yields an integrative account of the issues SMEs face while switching. Here, primary information was gathered through adopting an interpretivist multi-perspective research approach. Following, an Interpretative Phenomenological Analysis (IPA) research strategy proved to be relevant in interpreting industry experts’ views, and to explore their subjective experiences on the specific switching behavior phenomenon. Accordingly, relevant theory was generated within an abductive grounded theory-lite data analysis. Essentially, experts’ perspectives on relational, procedural and relational switching costs are interpreted and then illustrated on the basis of a push, pull and mooring (PPM) migration framework. My findings show that current financing practices entail positive, negative and intervening issues for SMEs. Specifically, this study delivers practical evidence that SMEs face great relational switching costs, and that SME switching behavior is restricted by their own personal and situational factors. Thus, SMEs show signs of mistrust against alternative financing measures and may still trust banks and have
confidence in their brand although being disappointed by banks beforehand. Importantly, my results also suggest that Fintechs may still depend on banks. For practitioners and academia, this study adds value to the contemporary stream of knowledge on the intervening obstacles (mooring variables) that moderate positive and negative aspects around changing online service providers. To sum up, this study has great implications on the successful retention and/or acquisition of SMEs in financing.
1. Introduction
Ever since the beginning of the economic crisis in 2007-2009 the financial industry has changed drastically. Not only has there been continued distrust and antagonism against traditional financial institutions, such as banks, but also a large fraction of customers
frequently continue to feel “unbanked” or respectively “underbanked”. In fact, a recent report by the Federal Reserve System (2017) indicated that 26% of U.S. population is currently being underserved by banks. Essentially, the banking environment faces a significant amount of disruptive innovations. Here, a lot of attention is currently aimed at financial technological inventions, which promise to increase trust in the system (Angel & McCabe, 2015). The most prominent example of the recent hype is the cryptocurrency known as Bitcoin, which was created in response to losses made during the financial crisis (Kewell & Ward, 2017).
Arguably, the most influential innovations of the past decade have been so-called Fintechs, commonly standing for financial technology companies. Schueffel (2016) defined Fintech as “a new financial industry that applies technology to improve financial activities” (p. 45). Hence, one can assume that companies acting in this field are direct, or respectively indirect, contestants to established routines in order to improve financial offerings. As such, the Fintech company Kreditech (Kreditech, n.d.) stated that “2 billion adults worldwide are underbanked in financial services” and the company’s mission is to increase the financial freedom of the underbanked through the use of technology. Essentially, the underbanked are non-prime individuals with little or no credit history, and thus excluded from mainstream access to credit at fair and sustainable conditions.
Accordingly, Fintech business models reformulate the practices in which purchaser’s store, save, borrow, invest, move, spend, and protect financial products and service (Lee & Shin, 2018). The World Fintech Report 2017 (Capgemini, Linkedin & Efma, 2016) showed that already more than half of global customers indicate that they work with at least one
non-
traditional company for banking, insurance, payments or investment management.
Innovations in financial technology have been changing the way financing works since the beginning of financial institutions. However, new entrants caused a dramatic impact to the financial sector after the last global financial crisis. Fintech start-ups focus on the welfare of customers by linking finance with new technologies (Arner, Barberis & Buckley, 2017).
Among those enterprises that draw the most scrutiny today are Fintechs specializing on crowdfunding. In detail, crowdfunding is made up of crowdlending, peer-to-peer lending and equity financing. Ultimately, I will investigate the reasons and issues involved in small and medium-sized enterprises (SMEs) switching from traditional financial institutions towards Fintech lending, and/or equity financing.
In general, I focused on a time frame after the global economic crisis hit in 2008. Here, the so-called Fintech Revolution has been disrupting the established financial services and products by combining finance with technology (Economist, 2015). Although most of the Fintech business models are quite novel, SMEs can already obtain funding from online financing companies (Tsai & Peng, 2017). While bypassing banking intermediaries, Fintech companies have been transforming traditional value chain financing. Lin (2015) described this phenomenon of purposefully avoiding banks as financial disintermediation. Here, traditional banking intermediaries are also replaced by private equity firms which possess more significant influence in capital markets than they did in earlier generations. Thus, SMEs can successfully utilize the prospect benefits which alternative financing offers, and the Fintech revolution could in theory increase financial access of underserved SMEs, and thereby lead to financial inclusion.
In general, research conducted into Fintech is popular, but still in its early stages due to its recent evolvement. However, most of research is targeted at enhancing knowledge on financial technology, and how the Fintech revolution is disrupting the financial sector. In
addition, there is little empirical evidence on the benefits or disadvantages which Fintech may offer for SMEs. However, the known issues of specific SMEs switching behavior to Fintech business models are either vague, too obvious, or backed up by little empirical proof.
Hence, an Interpretive Phenomenon Analysis (IPA) is justified which delivers empirical evidence on SMEs’ switching intentions and behavior. Following, an IPA is conducted to interpret the SME switching phenomenon rigorously, and to explore the subjective experiences (Biggerstaff & Thompson, 2008). Explicitly, experts’ perspectives on the social understandings which stakeholders attach to SME switching behavior are interpreted. Thus, this study specifically investigates the social phenomenon of switching behavior, and will therefore yield cutting edge information on the issues related to SMEs changing service providers from traditional financial institutions to Fintech.
Here, the objective is to find out what issues SMEs are facing in the financing industry, and contingently what those mean for SMEs switching behavior. This master thesis addresses a specific practical gap in the literature by conducting expert interviews within interpretive qualitative research techniques, and by answering the following research question: What issues do SMEs face when switching from traditional financial institutions to Fintech? Furthermore, I will add two sub questions to my research question, namely 1) what are the issues faced by SMEs in the financing industry, and 2) what switching behavior do SMEs exemplify when considering Fintech financing. In practice, both topics will be scrutinized in the literature review and consequently iterated upon in the results and discussion section.
2. Literature Review
This chapter provides a comprehensive overview of the literature on the recent disruptive innovations in the financial sector, and a detailed description on the specific switching behavior phenomenon for SMEs. Firstly, current issues for SMEs occurring in the financing industry are outlined. Here, characteristics of SMEs and their ongoing struggle to
receive finance are scrutinized. Following, my literature review elaborates on financial disintermediation and explains why SMEs may feel unbanked and underserved by the traditional financial market. Consequently, a thorough analysis of online service provider switching, switching behavior, (relational) switching costs, and online banking specific switching behavior is conducted.
Following, a review of alternative financing methods, respectively non-bank services, outlines effects and outcomes of the last financial crisis. Finally, the ultimate data
representation framework, push-pull-mooring, will be introduced before stating the research objectives and propositions.
2.1 SMEs and their long-standing struggle for credit
Representing the majority of enterprises worldwide, SMEs account for roughly two-third of all employment. The Organization of Economic Coordination and Development (OECD) (2005) defined small and medium-sized enterprises (SMEs) as “non-subsidiary, independent firms which employ fewer than a given number of employees” (p. 17). Here, the specific company size differs among countries from 250 to fewer than 500 employees. Still, in the European Union, the most common limit characterizing an SME is less than 250 employees. However, recent trends in organizational design and progress in technology allow companies to reduce their number of employees. Thus, it may be possible that companies employ less than a specified number of staff, but received large funding and expertise already, and thus should be classified as big enterprises. According to Frantz, Dugan, Hinchberger, Maseth, Al Sharfa, and Al-Jaroodi (2017), SMEs exemplified the following three characteristics: being small in nature, both in size and in terms of budget, being single, and being local. The single characteristic refers to having a single or a small group of owners and selling only a few products.
have less access to formal sources of external finance than big corporations (Beck, & Demirguc-Kunt, 2006). In detail, Frantz et al. (2017) and Ahmed, Beck, McDaniel, and Schropp, (2015) named the following weaknesses which SMEs generally depict. Firstly, SMEs lack quality consciousness. In detail, this evolves by being single- or few-minded. Thus, due to their small size, SMEs may suffer from substandard record keeping, unclear planning activities, and also from lacking a long run strategic focus (Frantz et al., 2017).
Secondly, a weakness of SMEs can be weak financial strength due to the insufficient knowledge on financing options, or overreliance on internal financing. On the one hand, this weakness arises from inferior amounts of assets which can be used for collateral, but also through lenders not being able to access third-party information regarding credit profiles and histories. On the other hand, SMEs are characterized by financial illiteracy and tend to lack connections in the financial system (Ahmed et al., 2015). Hence, SMEs may incur higher costs of financing due to their inferior knowledge on financing options.
Thirdly, the lack of professionalism and work routines may cause problems for SMEs. Here, weak management, and importantly, poor orientation can be characterized as
weaknesses. In addition, SMEs face shortages of skilled workers and also high turnover of key personnel (Frantz et al., 2017). Lastly, all of those factors are limiting the ability of SMEs to maintain a competitive advantage and thus entail high risk for the venture. Although competitive advantages in technology may be initially created, these are very hard to sustain when key personnel are being headhunted by large corporations.
In addition, the wake of the financial crisis in 2008 has left a large funding gap for the years to come. Thus, it became even more difficult for small and medium-sized companies to receive funding due to the relatively larger risk associated with granting SME credit (Ahmed et al., 2015). Naturally, the consequence of the last financial crisis induced less credit being available for both borrowers and lenders, causing a credit squeeze. Wehinger (2014) stated
that SMEs were most affected by the financial crisis. This problem can be further explained by the rising reluctance of established financial institutions to undertake business in high-risk and relatively low-yield SME loans (Ryan, O'Toole, & McCann, 2014).
Conclusively, Casey and Toole (2014) delivered empirical evidence that during credit crunches, SMEs are inclined to apply for non-bank adopt alternative financing. In detail, rejected loan applicants are 9% more likely to submit alternative financing applications while borrowers, who do not apply due to high loan fees, are 18% more likely to use non-bank services (Casey & Toole, 2014).
2.2 Disintermediation shift and the underbanked
Due to inherent mistrust and dissatisfaction with some services banks and traditional financial institutions provide, a large fraction of both developed and underdeveloped
economies remains unbanked. In fact, the Federal Reserve System (2017) reported that seven percent of adults are unbanked while 19% are underbanked, here defined as “having a
depository account but also using at least one alternative financial service in the prior year” (p. 31). Angel and McCabe (2015) stated that those numbers could be explained by poverty and unbanked individuals’ concern that creditors will take away their money. In detail, Schwarcz (2013) outlined three market failures of the financial sector and the subsequent causes for underserved customers. Namely, information failure takes root in asymmetric information and a lack of transparency being available in financing. In general, the public sees bankers as only being interested in selling their products while neglecting clients’ interests and needs. Essentially, banks may also be unable to serve SMEs with the
appropriate advice and treatment due to their small size (Ryan et al., 2014). Hence, clients cannot be sure they are obtaining objective advice and the open market may have better alternatives. Secondly, agency failure is being caused by discontent between principals and their agents. Lastly, banks show responsibility failure when a company is able to materialize
a part of the costs of undertaking a risky action (Schwarcz, 2013). As a result, Fintech companies are developing products which have the potential to benefit a significant number of underbanked customers (Illman, 2017).
Due to inherent complexity of financing and widespread financial illiteracy, van Raaij (2017) found that financial advice is imperative for most customers. Here, intermediaries work as middlemen between two parties, and are assumed to reduce the complexity and risk involved (Lin, 2015). Importantly, intermediaries should serve in the customer’s best interest and grant cheap prices for individuals looking for ways to finance their ventures. However, some financial intermediaries are found to put too much emphasis on fee formation, and in fact, may therefore lead to riskier investments and less efficient transactions. In addition, as described above, the client base of banks lacks trust in financial institutions and also does not understand the financial offerings without effective advice.
Ever since the emergence of the Internet and electronic commerce, communication between parties involved in a transaction has become more effective. On this basis, Chircu and Kauffman (1999) already proposed strategies and tactics for Internet middlemen and described a dis-re-intermediation cycle in the financial sector. In detail, disintermediation happens when an established middleman is ejected out of a market niche. However,
intermediation means that parties are carrying out business indirectly, and reintermediation entails a reestablishment of a once pushed out party as an intermediary (Chircu & Kauffman, 1999).
Now, the financial system shows disintermediation outcomes since organizations and individuals bypass the traditional banking system. Tsai and Peng (2017) demonstrated that online financing, granted by Fintechs via crowdfunding, show fundamental characteristics of disintermediation. Here, traditional links between banks and individuals are broken up, essentially creating a shadow-banking sector. In addition, Yip and Bocken (2018) stated that
financial technology startups and also the Blockchain technology push out traditional institutions, which threatens traditional banking activities. In practice, disintermediation can have multiple ways in which it takes effect. For example, Langley and Leyshon (2017) showed that organizations may raise capital through crowdfunding after they were rejected by traditional financing channels.
Thus, crowdfunding, or respectively crowdlending, is shown to perform
re-intermediation strategies. Similarly, Lin (2015) outlined that pure disre-intermediation rarely exists, but rather Fintech has been increasing intermediation through substitution. Here, Fintechs mostly substitute established middlemen without wiping out the actual need for the specific purpose. Additionally, layering in this case can be understood as the deception of pushing out middlemen and thereby creating new layers and forms of intermediation, while actually, the process still requires third parties. For example, Apple pay added another layer of intermediation instead of eliminating a link of the largely intermediates payment process.
Hence, intermediaries prevail and the basics of financing will remain the same due to the interconnectedness of finance and its consumers. Therefore, this situation brings
opportunities and threats to the financial sector. In essence, the emergence of Fintech
business models actually assisted banks by paving the way for digitization (Kotarba, 2016). 2.3 Service provider switching
In the age of mass consumption, scholars and marketing practitioners have been dedicating significant attention to customer switching behaviors. As a result of megatrends in technology, and the emergence of the information age, consumers are becoming increasingly informed about alternate competitive offerings. Here, switching a service provider has critical effects on the respective organizations. Hence, it is very important to understand why
consumers change service providers. Essentially, Bansal, Taylor & St. James (2005) summarized variables that may impact switching as: quality, customer satisfaction, value,
perceived costs of switching, attitudes towards switching, attractiveness of substitutes, trust, commitment, social influence, and the disposition to seek variety.
In essence, effective retention of customers is decisive if the company is planning to stay profitable and grow in the long term. In practice, it is in the company’s interest to retain customers due to multiple reasons. Essentially, retaining customers is cheaper than acquiring new ones. Furthermore, loyal customers are less price sensitive, engage in multiple purchases and are also indirectly creating value through positive word of mouth (WOM) (Hsieh, Hsieh, Chiu, & Feng, 2012). Given the possible adverse effects of customer switching behavior, practitioners and academics have been comprehensively investigating factors which may consumers to switch.
In the following section, I will outline factors which may affect the customer adoption, respectively switching behavior. Importantly, customer satisfaction has earned persistent attention in explaining consumer retention and switching behavior. Szymanski and Henard (2001) demonstrated that dissatisfied customers are unlikely to repurchase, and thus unnecessarily occupy resources and managements’ time. Here, performance and expectation disconfirmation play a significant role in customer satisfaction. Thus, companies essentially induce switching behavior if they overpromise and under-deliver when servicing clients.
Importantly, Wieringa and Verhoef (2007) found relationship quality, switching costs, degree of consumption and contracts, and attractiveness of switching to be significant
determinants of dis- or continuing repurchase. Here, relationship quality had the biggest positive impact influence on switching, followed by switching cost and then attractiveness of switching. On the other hand, the number of contracts had a stronger negative effect than usage. Therefore, Wieringa and Verhoef’s (2007) findings demonstrated that switching is mainly influenced by relationship quality. In detail, relationship quality is influenced by both social and economic relationship with the supplier. Here, economic characteristics which
affect loyalty account for monetary value of the relationship, including recognized price-value proportions, such as economic benefits and costs (Bolton & Lemon, 1999). On the other hand, relationship quality is also influenced by social characteristics such as trust, exemplified through honest and compassionate behavior, and affective relationships with suppliers (Verhoef, 2003).
2.4 Switching behavior - for online business models
Due to macroeconomic megatrends, such as globalization and the increasing prevalence of online shopping activities, competition between service providers is
accelerating. Here, online services usually face multiple competitive options which may offer comparable functions and thus significant substitutability of their products to users (Hsieh et al., 2012). In detail, web-based offerings are characterized by lowered search expense, reduced entry barriers and declined individuality of service (Vatanasombut, Igbaria, Stylianou & Rodgers, 2008). Information technology (IT) is readily available, and after a customer has embraced a service, they can conveniently upgrade, drop out, or alternatively switch to competitor (Ye, Seo, Desouza, Sangareddy, & Jha, 2008). Thus, clients may discontinue using a system which is already in place if they become aware of an alternative. As a result, the rise of digital services not only forces users to choose amongst similar and substitutable web-based offerings, but also decreases companies’ capability to retain customers.
Importantly, Wu, Chen, Chen, and Cheng (2014) stated that users face environmental complexity and unpredictability when shopping online. Here, uncertainty emerges from inherent information asymmetry and also from individual assumptions. When shopping online, users face asymmetry in the market because online business models usually have more information readily available than the end user. In detail, this causes two transactional dangers, namely adverse selection and a moral hazard (Wu et al., 2014). Adverse selection
generally refers to a situation in which sellers own information that buyers do not have, or also contrarily, buyers possessing knowledge on respective product characteristics. In detail, adverse selection emanates due to information overload, as users are unlikely to process all the information present. As such, they may choose a relatively worse product. Other
transactional danger, moral hazard, describes the fact that the online provider does not carry the risk of third party entities providing their goods on their website (Wu et al., 2014). Hence, although online business models greatly widen users’ access to a variety of products and deliver information, end users are still bound by their rationality and opportunism.
Chen and Hitt (2002) developed and exercised a framework for measuring the drivers of customer retention for online business models, determined by customer switching and attrition. Naturally, an internet enabled business would prefer higher acquisitions rates while facing lower switching and attrition. Here, they track firm level characteristics along five dimensions. The first dimension is ease of use, which is characterized by functionality, simplicity of account opening and transactions, consistency of design and navigation, adherence to user interaction and integration of data. Secondly, client confidence is a notion of quality and is generally measured by a website’s transparency, depth, breadth and
availability of services and privacy policies (Chen & Hitt, 2002). Thirdly, the online
resources dimension accounts for the broadness in offerings which is generally measured by availability and function abilities. Fourthly, relationship services are corresponding to the degree of personalization of services which may include advice but also loyalty incentives. Lastly, overall cost looks at the cost of a usual basket of services offered and takes minimum balance requirements and administrative costs into account.
Chen and Hitt (2002) demonstrated that, on the one hand, website quality, resources, and website usage are strong predictors of switching behavior. On the other hand, ease of use, change in usage and multiple competitors tend to be less influential for switching while
demographic effects of customers are minimal. Correspondingly, Chen & Hitt (2002) found clients’ usage and changes of usage to be strong predictors of switching and attribution, highlighting the need to monitor system usage fluctuations. In contrast, minimum requirements, frequency of website usage, and use of multiple alternatives are good predictors of consumer behavior. Furthermore, firm specific attributes such as the breadth and quality of the products and services being offered limit switching and also reduce customer satisfaction erosion. In short, the authors identified little influence of consumer demographics on switching, but that users’ consumption and the implicated system quality are related to reduced switching behavior.
Similarly, Ye et al.’s (2008) results also demonstrated that user satisfaction and magnitude of use of the current product are negatively related to switching behavior.
Furthermore, the authors illustrated that the corresponding superiority in service and security of the alternative service stimulates switching behavior. Here, current user satisfaction appeared to be the most influential predictor of switching (Ye et al., 2008). Still, user
experience did not restrain the relative advantage. In contrast, perceived ease of use is found to have a significant and positive effect while Chen and Hitt (2002) estimated a negative and inefficient effect. Nevertheless, anticipated ease of use of the alternative is a strong stimulator of service even when the user is satisfied with the current service and has been consuming the incumbent service for a long time. Summarizing, the prospects’ own usage and their
perception of the alternative online service plays a significant role in substituting a service. In practice, however, customer satisfaction and service quality are not enough to manage retention (Burnham, Frels, & Mahajan, 2003). Instead, organizations have to acknowledge divergent motivations and factors that drive repeating choice behavior. One of these would be switching costs. Accordingly, Burnham et al. (2003) studied switching costs which are defined as “onetime costs that customers associate with the process of switching
from one provider to another” (Burnham et al., p. 110). 2.5 Switching costs
In general, switching costs act as a barrier that retains clients in service relationships. Accordingly, Jones, Mothersbaugh, and Beatty (2002) defined switching costs as “the
perceived economic and psychological costs associated with changing from one alternative to another” (p. 441). From a theoretical angle, switching costs help to understand and predict customer retention. However, in practice, controlling a customer’s awareness of switching costs allow companies to manage and foster intent to stay.
Jones et al. (2012) differentiated between three categories of switching costs, namely, continuity, learning, and sunk costs. In detail, continuity costs exhibit implicated lost
privilege benefits and uncertainty costs of possible lower performance when switching to another provider. Furthermore, learning costs can be differentiated between “pre-switching search and evaluation costs”, “post-switching behavioral and cognitive costs” and “setup costs” (Jones et al., 2012, p. 442), which all relate to the time, effort and expense of
switching. Lastly, sunk costs comprise of non-economic costs incurred by prior investment in building and maintaining a relationship with a company.
Following, Burnham et al. (2003) described three types of switching cost, namely, procedural switching costs, financial switching cost, and relational switching cost. Firstly, procedural switching costs are associated with spending time and effort on finding a new brand or provider. This involves an economic risk and induced learning, evaluation, and setup costs when switching. Secondly, financial switching costs are associated with losing financially quantifiable belongings, such as benefit-loss (Burnham et al., 2003). In practice, this could have implications of losing reward points or paying money to break a contract. Thirdly, relational switching costs involve psychological or emotional irritation when losing brand identification and the breaking of bonds with employees. Importantly, interpersonal
ties between users and staff heighten consumers feelings of trust and safety. In turn, those further increase switching cost due as clients redeem their investment in the relationship with the company (Jones et al, 2002).
In addition, Blut, Evanschitzky, Backhaus, Rudd and Marck (2016), classified Jones et al. (2002) continuity, learning, and sunk costs according to Burnham and collague’s (2003) framework. Accordingly, uncertainty costs, pre-switching search and evaluation costs, post-switching behavioral and cognitive costs, and setup costs are referred to as procedural switching costs. Further, sunk costs and lost performance costs belong to the financial switching costs (Blut et al., 2016). However, relational switching costs seem to be underrepresented in in Jones et al. (2002) “sunk costs” (p. 443). Thus, Blut et al. (2016), added brand relationship loss costs and personal relationship loss costs to Burnham et al.’s (2003) relational switching cost type.
Specifically, Burnham et al. (2003) suggested that the importance of procedural switching costs is heightened when customers acknowledge product complexity or disparateness in service. However, as described above, within web-based offerings this is limited. Still, companies can increase procedural switching costs by making it more difficult to switch to competitors, educating their clients about product feature variety, or by bundling services and thus facilitating broader product use. Similarly, financial switching costs are greater when bundling products and services, amplifying product complexity, and stimulating extensive product use. Here, customers may be less likely to switch if they are part of
intangible value creation, such as loyalty programs.
Correspondingly, Burnham et al. (2003) empirically analyzed those switching costs in two samples and found significant results that relational switching costs show the strongest association with intent to stay (0.3) while procedural switching costs account for 20% of intent to stay. In detail, financial switching costs account for the least intent to stay with a
standardized coefficient of 0.15. Still, satisfaction accounted for 30% of customers intent to stay. However, the study did not prove that switching costs moderate the effect between satisfaction and intent to stay.
Blut, Frennea, Mittal, and Mothersbaugh (2015) replicated Burnham and colleague’s (2003) bivariate study and found a relation between switching costs and repurchase intention and/ or behavior. In short, Blut et al. (2015), conducted a meta-analysis and showed that relational switching costs have the strongest relation with repurchase intentions and behavior. Further, they demonstrated that procedural and relational switching costs diminish the
relation amid satisfaction and repurchase intentions behavior, while financial switching costs actually enhance it. In detail, the authors drew data from 133,734 customers and 233 effect sizes, with a dataset containing 153 empirical articles and 178 independent samples.
Here, the authors confirmed Burnham et al.’s (2003) findings that relational switching costs have the biggest impact, followed by procedural, and then financial switching costs. However, when conducting a multivariate analysis, differences emerge. Firstly, the overall effect, referring to repurchase behavior plus intention, of relational switching costs was positive and vigorous (Blut et al., 2015). In addition, the overall impact of procedural switching costs was zero, and the total effect of financial costs was small and negative.
In effect, those results have several implications for managers and also practitioners. Essentially, decision-makers do best by acknowledging how distinctive switching costs are complementary to satisfaction in affecting intent to stay for customers. Still, depending on the company’s focal point of either intent to buy or repurchase behavior, financial, procedural, and relational switching costs must be managed individually. In practice, raising switching costs might lead to customer’s increased intent to stay and repurchase behavior. However, this may also undermine the relation among consumer happiness and repurchase (Blut et al., 2015). Hence, finding the appropriate balance between exploiting repurchase by putting up
switching barriers, and providing memorable customer experience, is difficult but essential for managing consumer retention. Thus, repurchase intentions and switching behavior may be promoted successfully if adopting a context-specific approach.
2.6 Switching costs and their impact on relational outcomes
As outlined above, relational switching costs have the most vigorous impact on repurchase behavior and intent to stay (Blut et al., 2015; Blut et al., 2016; Burnham et al., 2003; Wieringa & Verhoef, 2007). In detail, relational switching costs imply the destruction of identification and sentimental relationship with both the organization and the staff with whom the client had interaction in the past.
In addition, when referring to business to business (B2B) ties, relational switching costs are the most substantial. Here, Blut et al. (2016) demonstrated that relational costs influence a consumer’s share-of-wallet, cross-buying behavior, and actual switching behavior. In contrast, procedural switching costs only impact share-of-wallet, and financial switching costs only influence cross-buying behavior.
Evanschitzky et al. (2012) defined share-of-wallet as “percentage of the value of purchases by a customer at the retailer to the total value of purchases at all other retailers used by the customer” (p. 629). Thus, share-of-wallet indicates how users split up their shopping among competing services. In detail, while switching behavior accounts, making an explicit purchase from a specific provider, share-of-wallet measures the amount of
consumption on the basis of the consumers accessible budget. In practice, this means that a switching behavior variable differentiates only amidst consumption and non-consumption. However, share-of-wallet indicates a level of loyalty on the comparative spending amount (Blut et al., 2016). Cross-buying, on the other hand, is defined as “the degree to which customers purchase products or services from a set of related or unrelated categories of the company” (Reinartz & Kumar, 2003, p. 81). Essentially, cross-buying behavior can be
explained by relational and financial switching costs. Here, this could implicate reduced costs when buying goods from the same supplier or cross-buying could be stimulated by the lower exposure related to purchasing from a known source (Blut et al., 2016).
In essence, cross-buying behavior relates to Jones et al.’s (2002) uncertainty costs. Here, the switching costs may be greater when it is uncertain and risky to gauge service quality among untested alternatives. As such, there are psychological and uncertainty costs related to discontinuing a service. Interestingly, due to the lower risk involved, a trustworthy provider has thus a greater chance to cross-sell a broad variety of products in various
categories to the customer. Hence, a provider with an impactful brand and good user relationships is therefore more likely to stimulate a trustful relationship with its consumers (Blut et al., 2016).
Here, relationships with service providers tend to be intangible and heterogeneous. Hence, switching costs are identified as ways for holding customers in relationships where their actual happiness with the supplier does not matter (Jones, Reynolds, Motherbaugh & Betty, 2007). As outlined by Burnham et al. (2003), raising switching costs, or at least customer’s perception, is an effective way to pin down users in their service relationship. However, this strategic move may also cause negative responses from customers even if they foster retention.
Correspondingly, Jones et al. (2007) differentiated between positive, meaning social and lost benefits, and negative, meaning procedural, switching costs and proposed a
framework to understand the ways in which switching costs impact relational outcomes. As such, a way to influence relational outcomes could be clients’ retention and word of mouth. Here, the authors yielded an advanced view on switching costs taking into account emotions and behavioral intentions. As a result, Jones et al. (2007) demonstrated that procedural switching cause negative word of mouth (WOM) in the form of calculative commitment,
which basically implicates that clients stay because practical measures forced them. On the other hand, affective commitment, which implies that users stay deliberately, stimulates the development of social bonds and value-added benefits. Essentially, affective commitment is demonstrated to increase switching costs by itself and also to reduce lost benefits. Hereby, users think more positively of companies, even if they encounter a failure (Blut et al, 2016). In addition, companies can utilize direct value from creating long-term relationships with clients, whereby users tend to be less price sensitive, engage in more sales, and also from indirect value through positive WOM (Hsieh et al., 2012).
Subsequently, affective commitments are seen as positive switching costs which foster retention and repurchase decisions. Summarizing, Jones et al. (2007) showed that the most relevant customer relationships were those characterized by low levels of calculative commitment, and greater levels of affective commitment.
2.7 The specific switching phenomenon in online financing
In the specific case of online banking, Vatanasombut et al. (2008) stated that the trend of distinction and speed-to-market induced an expanded use of online services. However, online banking providers are similar in their appearance and set of offerings. Therefore, for users it is difficult to detect differences and thus may switch effortlessly.
Here, banks tend to do business in monopolistic structures where they developed long-term relationships with customers. Nevertheless, due to their consolidated status, monopolistic organizations tend to be less focused on product and service quality, and also, they do not appear to be customer oriented (Wieringa & Verhoef, 2007). However,
monopolistic companies gain from relational norms, (forced) commitment and trust. Jones et al. (2002) stated that due to the standardized nature of banks, perceptions of setup costs, pre-switching search costs and evaluation costs are relatively low. However, the authors did not find those variables to be significantly related to repurchase intention.
In short, Ryan et al. (2014) findings demonstrated that great bank market power is associated with adverse effects on SMEs financing. Hence, SMEs are less likely to receive firm investment and thus face financing constraints due to monopolistic bank structures.
Still, due to increased competition and transparency gained through the internet, banks are forced to offer higher incentives to stimulate switching, or respectively, customer retention. In effect, Vatanasombut et al. (2008) demonstrated that people who joined a service in the first-place due to incentives are more likely to keep on searching for an increase in value elsewhere. Thus, such users are less likely to commit to long-run contractual agreements. However, in the banking sphere customers tend to be forced to commit to ongoing relationships (Ye et al., 2008).
Importantly, due to the relatively high switching costs involved with changing a banking service, customers may tend to feel locked in. In response, Jones et al. (2007) stated that trapped users may become dissatisfied, passive, antagonistic, hostile, and may commit to relational outcomes that have serious negative long-term consequences for the restrictive company, such as resignation, negative WOM, or obstruction.
However, financing products are inherently more complex and also require a significant amount of learning and becoming used to certain banking procedures. Here, clients are frequently not only bound to banks and their employees for a significant amount of time, but also trust in their offerings have ample implications (Vatanasombut et al., 2008). Here, trust was defined as “willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (Mayer, David & Schoorman, 1995, p. 712).
Accordingly, Bart, Shankar, Sultan, and Urban (2005) described dominant drivers of online trust in finance to be security, absence of errors, services, taxes, search, good brand
strength and advice. Ultimately, the authors provided evidence that brand strength heightens online trust and retention for an entity, hrough the reduced information searching cost the advice of their brand ambassadors.
Vatanasombut et al. (2008) investigated the role of trust as a predictor of service continuation in the specific case of online banking and found that trust is a more influential predictor of intent to stay. The authors found significant proof that trust has a positive effect on committing to a relationship with an online financing provider. In detail, trust is found to be dependent on shared value, communication, and perceived security. Furthermore, the switching cost relating to terminating a relationship was found to have a negative effect on switching. However, empowering users had a positive effect on committing to a relationship. Nevertheless, the benefit of the relationship and shared value had no significant effect on committing to a relationship (Vatanasombut et al., 2008). In addition, cost savings were reported to be a main reason of inducing switching behavior, while online banking satisfaction was correlated to the implicated communication, shared value, and empowerment.
Still, Bart et al. (2005) stated that banking products and services are relatively less dependent on trust due to the fact that financing requires frequent attention from clients. Hence, clients regularly check their ongoing activities and thus should be more informed about processes.
Importantly, the disruptive nature of Fintechs’ business models stems from their unique value propositions and customer journey. Since Fintechs operate in a highly competitive environment, financial technology companies communicate heightened value proposition to acquire and retain customers (Lee & Shin, 2018). Thus, customer relationships are paramount to convince consumers and eventually lead them to switch from traditional institutions. Kotarba (2016) demonstrated that dominant Fintechs are utilizing all components
of the Customer Relationship Management (CRM) value chain in order to enhance
intelligence on the customer’s needs. In order to better understand the clients’ needs, Fintech companies show agility in technology and concentrate on a social dialog with the customers. 2.8 Alternative (Fintech) financing options for SMEs
Small and medium-sized enterprises have long been profoundly reliant on loans from established banking institutions. However, retail banks have been showing a significant shift of lending to individual consumers rather than SMEs (Langley & Leyshon, 2017). Still, the financial crisis has exposed the implicit weaknesses of the traditional financing system and demonstrated that SMEs should diversify their range of financing tools and thus look for alternative ways to finance (Wehinger, 2014).
In effect, new business and service models emerged in the financial sector that promise convenience and new opportunities to customers. Compelled by technological progress and a great need for change, Fintech enterprises dare to test the status quo by utilizing technology to derive value to the customer in an alternate way (Maier, 2016).
Langley and Leyshon (2017) differentiated between three main fields of Fintechs. Firstly, are new ways of risk calculations, that allow to accounting for creditworthiness through extending the sources of information outside the limits of traditional institutions. For example, Fintechs use diverse metrics to determine solvency by gauging trust in the
borrower. The second field is new payment systems which include cryptocurrencies and blockchain-based disbursement systems. Here, the most widely known entity is Bitcoin, created by a pseudonym Satoshi Nakamoto in 2008, which they defined as a “system for electronic transactions without relying on trust” (p. 12). Finally, new forms of financing, which can be split up into equity and peer-to-peer lending (Langley & Leyshon, 2017).
Crowdfunding materialized simultaneously in developed economies as a direct response to the global financial crisis. Belleflamme, Lambert and Schwienbacher (2014)
defined crowdfunding as involving “an open call, mostly through the Internet, for the provision of financial resources either in the form of donation or in exchange for the future product or some form of reward to support initiatives for specific purposes” (p. 588). Crowdfunding enables individual investors to pool their investments and uses collective execution through platforms to appraise and obtain financing for new projects (Bruton, Khavul, Siegel & Wright, 2015). Although, crowdfunding platforms primarily funded artistic and entertainment projects in the beginning, they also moved toward structured loans and equity financing for entrepreneurs. Hence, this concept has been spreading very fast in a brief time period. Thus, crowdfunding has the disruptive potential to unleash the democratization of capital for social, cultural and economic enterprises (Langley & Leyshon, 2017).
Crowdfunding encompasses four different types of activities. Namely, donation crowdfunding represents symbolic pledges and gifts of individuals similar to charity while rewards crowdfunding entails payments from consumers as a way of pre-ordering or co-creating products (Langley & Leyshon, 2017). The most prominent entity of these types of crowdfunding is Kickstarter, which so far founded roughly 145,000 projects with the help of over 15 million backers (Kickstarter, n.d.). Third, equity crowdfunding comprises an
investment of individuals that are granted a share of another company in exchange, for example through Crowdcube (n.d.). This type of crowdfunding is very similar to those investments made in venture capital and capital markets (Maier, 2016). Last, in peer-to-peer (P2P) lending, or also known as debt crowdfunding, enterprises request loans in order to receive funding for projects, rather than for equity, rewards, or donations (Tsai & Peng, 2017). An example of P2P lending is Auxmoney (n.d.).
Following the global financial crisis, both debt and equity crowdfunding gained increasing applicability due to the extraordinarily low interest rates. Here, it is very important to make a clear distinction between crowdfunding and peer-to-peer (P2P) lending, or also,
crowdlending. Maier (2016) defined crowdlending as “the direct financing of credits by a group of consumers” (p. 143). Instead of asking for equity or donations the investor only finances specific loan appeals. Crowdlending may be considered to be the most relevant subtype of crowdfunding and is growing faster than equity crowdfunding (Bruton et al., 2015). In comparison to other forms of crowdfunding, P2P lending necessitates financial returns through a formal credit agreement in exchange for amounts granted by the large number of unrelated individuals. In addition, crowdlending platforms are usually not the consolidator of funds, but rather, they facilitate the transfer of loans directly from one entity to another (Bruton et al., 2015). Maier (2016) stated that crowdlending platforms replace traditional financial institutions by bridging prospect lenders and promising borrowers in an honest and direct way of external financing. Although crowdlending platforms could only be realized through technological progress, those are in fact not a technical innovation, but in rather, depict an efficient alternative for traditional financial brokers (Lehner 2013).
However, the most typical types of crowdfunding only operate on top of the traditional financing mechanisms, by facilitating payments and working as trusted third parties. This means SMEs may still depending on traditional financial institutions when engaging in crowdfunding. Nevertheless, there are many more ways in which Fintechs are changing the financial markets. Importantly, Fintechs are reshaping supply chain financing and trade credit by providing external financing online through short-term loans, which used to be done exclusively by banks (Tsai & Peng, 2017; Wehinger, 2014).
Lin (2015) stated that financial innovation leads to substitution of old-fashioned institutions without eradicating the demand for those services. Hence, new businesses models are developed on Fintech progress. In effect, other non-bank institutions also gain more importance, such as private equity firms, which prompt larger impact on the markets and thus on SMEs. The definition of Fintech provided earlier by Schueffel (2016) is universal enough
to mention incremental as well as disruptive financial services, while evidently rejecting the old-fashioned paper-based banking services. Importantly, banks also show signs of moving into the Fintech sphere by providing similar innovative services, such as New10 (n.d.), which is a venture from the bank ABN-AMBRO.
2.9 SMEs’ switching behavior to Fintech financing marketplaces
The switching behavior of SMEs from banks to non-traditional institutions is significantly unexplored. Maier (2016) found that crowdfunding platforms should chiefly advertise their functional performance, such as convenience and process transparency in order to induce switching behavior from SMEs. While convenience in practice means the speed, flexibility, and availability of service, transparency here relates to process clarity and predictability. Essentially, Fintech companies revolutionized the transparency aspect of finance. Within offline banking, customers had to apply for a loan and trust in the bank agent to satisfy SMEs wants. Instead, Fintech companies rely less on personal contact and the company provides automatic information about quotes and loan requirements. Thus,
borrowers are aware of the duration and prices or collateral in the consideration stage (Maier, 2016). Hence, transparency may be believed to succeed the relationship with the bank.
However, in line with Wu et al. (2014), uncertainty for SMEs arises from switching to Fintech because of information asymmetry and their human behavioral assumptions. Here, the environmental complexity and the unpredictability of Fintech marketplace financing may inhibit SME switching behavior. In practice, SMEs cannot process all the available
information. Namely, SMEs are bounded to their inferior knowledge of what those marketplaces may offer and also their continuance of business in the future may not be certain. Furthermore, offering third party loans and equity may implicate moral hazards and adverse selection. Here, Fintechs do not bear similar risks of a financing provider for not delivering the agreed-upon goods to the consumers after a repurchase has taken place (Wu et
al., 2014). In addition, the ongoing and long-lasting relationships of SMEs with banks may infer great relational and procedural switching costs.
Lastly, it must be noted that Fintechs are only serving a small part of the value chain. In line with Chen and Hit (2002), traditional financial institutions offer a wide range of products. In turn, this may limit switching behavior of SMEs as they have to commit time and resources to look for additional products elsewhere. However, not being dependent on up-selling enables non-traditional companies to specialize on offering one service. In practice, this may force SMEs to be clients of multiple Fintechs for distinct purposes. 2.10 The push–pull–mooring (PPM) framework
Essentially, the push-pull-mooring (PPM) framework is utilized to construct and present my results. Importantly, the PPM model has been a dominant paradigm in human migration composition, outlining why individuals switch from one place to another for a certain period of time (Bansal et al., 2005). Here, the PPM scheme is developed on grounds of push–pull models which have the goal to explain population migrations.
Importantly, Bansal et al. (2005) stated that PPM is superior to any other framework in modeling switching behavior. Consequently, the authors examined the appropriateness of the PPM model as a general guideline in acknowledging users’ service provider switching behaviors. Here, Bansal et al. (2005) demonstrated that push, pull and mooring variables have significant direct and moderating effects on switching intentions.
Correspondingly, Hsieh et al. (2012) confirmed that the PPM model is a relevant scheme for understanding the wrestling forces that impact users’ intent to stay and switching to online service substitutes. In detail, push effects are negative factors that urge individuals to distance themselves from an original point. Accordingly, in changing service providers, push drivers are satisfaction, quality, value, trust, commitment, and price perceptions. Here,
Jones et al. (2007) accounted for reduced levels of affective commitment as push variables, which then lead to switching intentions, ultimately impacting switching behavior.
In contrast, pull effects are positive factors that draw attention to a particular target (Hsieh et al., 2012). Similarly, pull factors are attributes of the place or point, and not related to the individual. Bansal et al. (2005) showed the attractiveness of the competing alternative to be a pull switching behavior variable. Further, Hsieh et al. (2012) named enjoyment, relative usefulness and relative ease of use as pull factors. Here, Ye et al. (2008) related pull effects in the financing sphere as being measured by “I would probably be happy with the products and services of another bank” (p. 2127).
On the other hand, mooring effects are contextual, personal, or social factors which have both direct impact on switching and can also moderate the repercussions of push and pull effects (Hsieh et al., 2012). In practice, this means that even if pull and push factors are forceful, an individual still may not switch. Hence, mooring effects are person specific, situational and contextual constraints which prevent or also induce switching behavior. Bansal et al. (2005) identified “unfavorable attitude towards switching”, “unfavorable subjective norms”, “high switching costs”, “infrequent prior switching behavior” and “low variety seeking” (p. 101) as mooring effects in the service switching conceptualization. Hsieh et al. (2012) summarized switching cost and past experience as significant mooring variables. To sum up, mooring effects can be seen as intervening obstacles which supplement the traditional push-pull paradigm and can lead to either switching facilitation or inhibition. 2.11 Objectives
The objectives are as follows: firstly, the literature review expands the understanding on the push and pull implications which financial technology (Fintech) entails for SMEs. Secondly, primary insight is generated on the specific connotations of the SME switching phenomenon. Thirdly, I assess issues occurring for SMEs in financing when switching from
traditional financial institutions, and adjacently, how mooring factors intervene in SMEs’ switching behavior. To practitioners, this study aims to provide concrete evidence on which factors and issues motivate SMEs to switch to Fintech services.
Q: What issues do SMEs face when switching from traditional financial institutions to Fintech? 2.12 Propositions
Before answering the research question, four propositions are made. The initial
proposition is that SMEs know about financing alternatives and may consider to switch from traditional financial institutions to Fintech financing models. Secondly, on the basis of positive externalities and inclusive factors which Fintech financing entails, SMEs may be ‘pulled’ towards Fintech. Thirdly, on the basis of negative issues revolving around traditional financial institutions, SMEs may be ‘pushed’ away from traditional financial institutions. Lastly, I propose that SMEs’ actual switching behavior may be inhibited by mooring factors, which consequently may play intervening roles in push and pull factors.
3. Research Design
The purpose of the research is exploratory. Hence, I followed an Interpretivism philosophy in order to answer the research question. Importantly, interpreting insights from industry experts allows for the examination of push, pull, and mooring factors on switching behavior. The nature of reality (ontology) of the interpretivist philosophy is socially
constructed, up to change, and dealing with multiple views. Interpretivism proposes that the researcher needs to acknowledge differences between decision makers in their roles as social actors (Saunders, Lewis & Thornhill, 2009). The epistemological implication is focusing on the social phenomenon of switching, and aiming to understand individuals’ subjective meanings that motivate them to act. In turn, focusing on the details of the situation which SMEs are in, and the reality behind this context, constituted knowledge on SME switching behavior. Here, I followed an abduction approach which entails a combination of inductive
and deductive reasoning in drafting interview questions and analyzing data.
The research design is characterized by a mono method qualitative study. Qualitative research allows for the study of participants’ meanings and the relationships between
different perspectives. Here, the qualitative research is related to the interpretive philosophy since I interpreted the subjective and socially constructed connotations, expressed by industry experts (Saunders et al., 2012).
Importantly, I adopted an Interpretative Phenomenological Analysis (IPA) research strategy in order to interpret industry experts’ insights, and to explore their subjective experiences on the switching behavior phenomenon. Essentially, following IPA allowed me to rigorously interpret the way in which respondents construct meanings and social
cognitions on a certain phenomenon (Biggerstaff & Thompson, 2008). As such, I explored how individuals ascribe meaning to their experiences within their interactions in the
financing environment. Here, it was especially important to capture how a given individual, in the SME financing context, makes sense of the switching behavior phenomenon (Lawkin & Thompson, 2012). In practice, “through careful and explicit interpretative methodology, it becomes possible to access an individual’s cognitive inner world” (Biggerstaff & Thompson, 2008, p. 215). Hence, utilizing an IPA approach made it possible to address the research problem, and thus interpret the switching behavior phenomenon of SMEs from traditional financial institutions to Fintech in a logical and effective way.
The time horizon of this study is cross-sectional since interviews were conducted over a short period of time. In practice, the phenomenon was studied at a particular time frame.
Ultimately, the unit of research for data collection and data analysis is the SME financing industry. Lawkin and Thompson (2012) stated that IPA fits research questions in which topics matter to the respondents, and where participants are selected purposively, in order to yield a valuable perspective on the topic at hand. Hence, samples in IPA are
frequently relatively homogeneous where respondents have some understanding of the topic being investigated. In addition, IPA asks for open research questions and exploratory settings (Lawkin & Thompson, 2012).
As the research strategy was exploratory, this study aimed to provide an in-depth analysis of the present Fintech financing dynamics. Hence, it was essential to select complementary respondents as well as contradicting perspectives in order to enable literal and theoretical replication.
Here, interpreting and examining multiple perspectives from industry experts had several advantages. For example, previously overlooked topics may reveal increased significance if demonstrated within multiple lenses, or if they reveal significant conflicts between layers. Also, exercising a multiple-perspective approach allowed to show an integrative picture on the current situation of the switching behavior phenomenon. I hand-picked respondents from the entire spectrum in an effort to represent every stakeholder. This choice was appropriate to my research question since it ensured maximum variation of perspectives and thus, practical relevance.
Primarily, I attained access to industry experts through my employment as a working student at a private equity firm. The company has a broad network which spans throughout Fintech, banks, consultants, and also to SMEs. In addition, I utilized my own network to connect and approach companies/ individuals through LinkedIn, E-mail and phone calls. Following, I describe the characteristics of the selected respondents. Specifically, Fintech cases represented a new type of alternative financing. Some of those were formerly active as bankers and can thus deliver comparative information on both banks and Fintech. Further, bankers’ active in the Fintech VC sphere delivered an integrative perspective on the phenomenon. In addition, consultants and academics were appropriate to deliver an external perspective. Importantly, SMEs communicated their individual needs and characteristics.
4. Methodology 4.1 Research instruments
The data source is qualitative data from interviews from semi-structured and in-depth interviews. Here, a semi-open structure was essential to research participants’ meanings and understand relationships between different perspectives.
The research was accompanied by non-probability sampling. Thus, it was not possible to specify the probability whether a case was included in the sample, and I selected samples based on subjective judgement (Saunders et al., 2012). Hence, I hoped for the best quality of data sources by conducting expert interviews.
In order to capture a variety of stakeholders, the data collection involved eleven interviews with twelve respondents. Namely, I conducted a pre-test of the interview protocol with an SME who is currently looking for financing; another SME interview followed later. Consequently, I asked topic-related questions to five respondents who were active as banking executives. Here, two had switched sides and started their own Fintech ventures. In contrast, one is active as a consultant, and two of the bankers are now active as VCs for a German bank. Furthermore, I interviewed two representatives from Fintech equity financing, and one from consumer lending, who delivered an opposing perspective. Also, one respondent is active in bank consulting while another one is working as an academic.
Interviews lasted around 45 minutes and were recorded and transcribed. An outline of the interview structure can be found in Appendix 1. Probing answers allowed interviewees to explain their responses, which was essential to understand the meanings that participants attribute to the switching phenomenon (Saunders et al., 2009). Leech (2002) emphasized to ask the easy questions first before reaching sensitive topics.
4.2 Sample
those industry experts represent eleven companies from the Netherlands and Germany. Essentially, respondents were anonymized to protect sensitive data and in order to capture more detailed insider information. However, I conducted an interview with two persons at the same time, which represent a venture capital firm. An overview is presented below.
Table 1 Overview of the interviews
Background of respondent Function of respondent Interview date
SME Business development 27.03.2018
Fintech – SME lending
• Professional investors Founding Partner Ex-banker
04.04.2018 Fintech – SME lending
• Professional investors Founder Ex-banker 10.04.2018 Academic Board member Fintech Expert Professor Senior Executive 13.04.2018 Financial Services Consultant
Public sector/ Policy making
Senior Adviser Ex-banker
18.04.2018
Consultant Senior Director 25.04.2018
Fintech – Consumer lending • Both retail and professional
investors Director Investments Chief of Staff 30.04.2018 SME CEO 04.05.2018 Venture Capitalist (VC) Fintech – SME marketplace • Professional investors
CEO Ex-banker
07.05.2018
Venture Capitalist
Fintech – SME marketplace • Professional investors
Business & Venture Development Ex-banker
07.05.2018
Fintech - Equity crowdfunding • Both retail and professional
investors
Member of the Advisory Board Founding Partner
Founder
15.05.2018
Fintech - Equity crowdfunding • Both retail and professional
investors
Regional Manager 16.05.2018
4.3 Procedures
The interviews were voice recorded (if agreed) and then transcribed. In addition, face gestures and other important psychological observations were noted during the interview. Later, I pivoted from hand-written notes to taking notes on a computer to maximize effectiveness.
After transcribing most of the interviews, I used the computer software program QRS International NVivo 12 to analyze and code the data. Essentially, this qualitative research software made sense of data in a structured and thematic way. Here, this program did not only yield a good overview of the information but also helped me to classify patterns in a digital and convenient way.
Importantly, during the data collection and analysis I utilized an abductive research approach that combined deduction and induction. According to Saunders et al. (2012), abduction involves “the collection of data to explore a phenomenon, identify themes and explain patterns, to generate a new – or modify an existing – theory which is subsequently tested” (p. 665). In practice, this means that I will not only develop interpretations based on data, but also examine existing theories on the switching behavior phenomenon during my research.
Here, I utilized a grounded theory-lite technique in data analysis. Importantly, Pidgeon and Henwood (1997) distinguished between full grounded theory (GT), which requires theoretical sampling, and grounded theory-lite, which only uses the techniques of GT for the emergence of concepts and to interpret the relationship between developed concepts. Hence, I did not employ a full GT research strategy but rather adapted it to draft theories around the switching behavior phenomenon for SMEs. Accordingly, Suddaby (2006) described GT as research strategy which focuses on the interpretive process. Here, this
systematic process of collecting, analyzing, and comparing of data complements a multi-perspective approach.
Importantly, Saunders et al. (2012) illustrated underpinning coding as a process of constant comparison. Accordingly, switching behavior data was categorized on the basis of items being compared with others, and also contrasting codes with each. Here, information gathered during the expert interviews was examined on the basis of similarities and
